Adobe Inc.

États‑Unis d’Amérique

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Type PI
        Brevet 6 329
        Marque 542
Juridiction
        États-Unis 6 492
        Europe 162
        International 126
        Canada 91
Propriétaire / Filiale
[Owner] Adobe Inc. 6 666
Adobe Systems Incorporated 205
Date
Nouveautés (dernières 4 semaines) 37
2024 mars (MACJ) 24
2024 février 33
2024 janvier 30
2023 décembre 32
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Classe IPC
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques 559
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales 554
G06N 3/08 - Méthodes d'apprentissage 468
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet 466
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques 394
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Classe NICE
09 - Appareils et instruments scientifiques et électriques 400
42 - Services scientifiques, technologiques et industriels, recherche et conception 265
35 - Publicité; Affaires commerciales 100
16 - Papier, carton et produits en ces matières 91
41 - Éducation, divertissements, activités sportives et culturelles 67
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Statut
En Instance 653
Enregistré / En vigueur 6 218
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1.

ZERO-SHOT ENTITY-AWARE NEAREST NEIGHBORS RETRIEVAL

      
Numéro d'application 17934690
Statut En instance
Date de dépôt 2022-09-23
Date de la première publication 2024-03-28
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Tambi, Ritiz
  • Kale, Ajinkya Gorakhnath

Abrégé

Systems and methods for query processing are described. Embodiments of the present disclosure identify a target phrase in an original query, wherein the target phrase comprises a phrase to be replaced in the original query; replace the target phrase with a mask token to obtain a modified query; generate an alternative query based on the modified query using a masked language model (MLM), wherein the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase; and retrieve a search result based on the alternative query.

Classes IPC  ?

  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
  • G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement

2.

SYSTEMS AND METHODS FOR COLLABORATIVE AGREEMENT SIGNING

      
Numéro d'application 18168677
Statut En instance
Date de dépôt 2023-02-14
Date de la première publication 2024-03-28
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Stratton, Norman A.
  • Munsey, Jacob
  • Nguyen, Thu Hien
  • Eppert, Polai Av

Abrégé

Systems and methods for collaborative document signing are described. According to one aspects, a method for collaborative document signing includes initiating a live communication session including a user, identifying a source document for an agreement using an agreement signing interface of the live communication session, assigning the user as a signer of the agreement using the agreement signing interface, and generating the agreement. In some cases, the agreement includes the source document. The method further includes obtaining a signature for the agreement from the user and generating a signed agreement including the signature.

Classes IPC  ?

  • G06F 40/186 - Gabarits
  • G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
  • G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
  • G06F 21/31 - Authentification de l’utilisateur
  • G06F 40/174 - Remplissage de formulaires; Fusion

3.

GRAPHICAL, INCREMENTAL ATTRIBUTION MODEL BASED ON CONDITIONAL INTENSITY

      
Numéro d'application 17948914
Statut En instance
Date de dépôt 2022-09-20
Date de la première publication 2024-03-28
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Snyder, Jr., James William
  • Arava, Sai Kumar
  • Meisami, Amirhossein
  • Tao, Jun

Abrégé

Methods and systems are provided for facilitating generation and utilization of causal-based models. In embodiments described herein, a set of events comprising touchpoints resulting in a conversion are obtained. A direct attribution indicating credit for an event contribution to the conversion is determined. An adjusted attribution for the event based on the direct attribution for the event augmented with an indirect attribution for the event is determined. The indirect attribution can be identified based on the event causing a subsequent event of the set of events to result in the conversion. Thereafter, the adjusted attribution for the event is provided to indicate an extent of credit assigned to the event for causing the corresponding conversion.

Classes IPC  ?

  • G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds

4.

IMAGE AND SEMANTIC BASED TABLE RECOGNITION

      
Numéro d'application 17947737
Statut En instance
Date de dépôt 2022-09-19
Date de la première publication 2024-03-28
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Gu, Jiuxiang
  • Morariu, Vlad
  • Sun, Tong
  • Kuen, Jason Wen Yong
  • Nenkova, Ani

Abrégé

In various examples, a table recognition model receives an image of a table and generates, using a first encoder of the table recognition machine learning model, an image feature vector including features extracted from the image of the table; generates, using a first decoder of the table recognition machine learning model and the image feature vector, a set of coordinates within the image representing rows and columns associated with the table, and generates, using a second decoder of the table recognition machine learning model and the image feature vector, a set of bounding boxes and semantic features associated with cells the table, then determines, using a third decoder of the table recognition machine learning model, a table structure associated with the table using the image feature vector, the set of coordinates, the set of bounding boxes, and the semantic features.

Classes IPC  ?

  • G06V 30/412 - Analyse de mise en page de documents structurés avec des lignes imprimées ou des zones de saisie, p.ex. de formulaires ou de tableaux d’entreprise
  • G06V 30/262 - Techniques de post-traitement, p.ex. correction des résultats de la reconnaissance utilisant l’analyse contextuelle, p.ex. le contexte lexical, syntaxique ou sémantique
  • G06V 30/414 - Extraction de la structure géométrique, p.ex. arborescence; Découpage en blocs, p.ex. boîtes englobantes pour les éléments graphiques ou textuels

5.

UTILIZING TREND SETTER BEHAVIOR TO PREDICT ITEM DEMAND AND DISTRIBUTE RELATED DIGITAL CONTENT ACROSS DIGITAL PLATFORMS

      
Numéro d'application 17934485
Statut En instance
Date de dépôt 2022-09-22
Date de la première publication 2024-03-28
Propriétaire Adobe Inc. (USA)
Inventeur(s) Saad, Michele

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer-readable media that distribute item-based digital content across digital platforms using trend setting participants of those digital platforms. For instance, in one or more embodiments, the disclosed systems generate affinity metrics for digital items from a catalog of digital items with respect to a plurality of trend setting participants of a plurality of digital platforms using attributes of digital posts by the plurality of trend setting participants on the plurality of digital platforms and corresponding attributes of the digital items. The disclosed systems further determine predicted demand metrics for the digital items on the plurality of digital platforms using the affinity metrics. Using the predicted demand metrics, the disclosed systems distribute digital content related to the digital items for display on a plurality of client devices via the plurality of digital platforms.

Classes IPC  ?

  • G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail

6.

SYSTEMS AND METHODS FOR JOINT DOCUMENT SIGNING

      
Numéro d'application 18168665
Statut En instance
Date de dépôt 2023-02-14
Date de la première publication 2024-03-28
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Stratton, Norman A.
  • Munsey, Jacob Scott
  • Pilla, Krishna Kishore
  • Shillingford, Justin Gerald
  • Weider, James William
  • Eppert, Polai Av
  • Nguyen, Thu Hien
  • Hershon, Sharon
  • Kuo, Christine

Abrégé

Systems and methods for joint document signing are described. According to one aspect, a method for joint document signing includes establishing a live communication session including a plurality of users. In some cases, the plurality of users correspond to a set of signers of a document. The method further includes initiating a signing process during the live communication session, receiving a signature for the document from each of the plurality of users during the live communication session based on the signing process, and generating a signed document including the signature from each of the plurality of users.

Classes IPC  ?

  • G06F 40/166 - Traitement de texte Édition, p.ex. insertion ou suppression
  • G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
  • H04L 65/1069 - Gestion de session Établissement ou terminaison d'une session

7.

3D MODELING USER INTERFACES BY INTRODUCING IMPROVED COORDINATES FOR TRIQUAD CAGES

      
Numéro d'application 17947035
Statut En instance
Date de dépôt 2022-09-16
Date de la première publication 2024-03-28
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Thiery, Jean
  • Boubekeur, Tamy

Abrégé

A modeling system displays a three-dimensional (3D) space including a 3D object including a plurality of points and a cage model of the 3D object including a first configuration of vertices and quad faces. Each of the plurality of points is located at a respective initial location. The modeling system generates cage coordinates for the cage model including a vertex coordinate for each vertex of the cage model and four quad coordinates for each quad face of the cage model corresponding to each corner vertex of the quad. The modeling system deforms, responsive to receiving a request, the cage model to change the first configuration of vertices to a second configuration. The modeling system generates, based on the cage coordinates, the first configuration of vertices, and the second configuration of vertices, an updated 3D object by determining a subsequent location for each of the plurality of points.

Classes IPC  ?

  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation
  • G06T 17/10 - Description de volumes, p.ex. de cylindres, de cubes ou utilisant la GSC [géométrie solide constructive]

8.

GENERATIVE PROMPT EXPANSION FOR IMAGE GENERATION

      
Numéro d'application 17933595
Statut En instance
Date de dépôt 2022-09-20
Date de la première publication 2024-03-21
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Tambi, Ritiz
  • Kale, Ajinkya Gorakhnath

Abrégé

Systems and methods for query processing are described. Embodiments of the present disclosure identify an original query; generate a plurality of expanded queries by generating a plurality of additional phrases based on the original query using a causal language model (CLM) and augmenting the original query with each of the plurality of additional phrases, respectively; and provide a plurality of images in response to the original query, wherein the plurality of images are associated with the plurality of expanded queries, respectively.

Classes IPC  ?

  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques

9.

POSE RECOMMENDATION AND REAL-TIME GUIDANCE FOR USER-GENERATED CONTENT

      
Numéro d'application 17946202
Statut En instance
Date de dépôt 2022-09-16
Date de la première publication 2024-03-21
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Singhal, Gourav
  • Das, Tridib
  • Gupta, Sourabh

Abrégé

In some embodiments, techniques for producing user-generated content are provided. For example, a process may involve sending a product identifier; receiving a first candidate image that is associated with the product identifier; determining that a similarity between a user structure and a target structure satisfies a threshold condition, wherein the user structure characterizes a figure of a user in a first input image and the target structure is based on a pose guide associated with the first candidate image; and capturing, based on the determining, the first input image.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
  • G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
  • G06V 40/20 - Mouvements ou comportement, p.ex. reconnaissance des gestes
  • H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance

10.

FACILITATING GENERATION AND PRESENTATION OF ADVANCED INSIGHTS

      
Numéro d'application 18484674
Statut En instance
Date de dépôt 2023-10-11
Date de la première publication 2024-03-21
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Rony, Md Main Uddin
  • Du, Fan
  • Burhanuddin, Iftikhar Ahamath
  • Rossi, Ryan
  • Chhaya, Niyati Himanshu
  • Koh, Eunyee

Abrégé

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

Classes IPC  ?

  • G06F 40/106 - Affichage de la mise en page des documents; Prévisualisation
  • G06F 40/40 - Traitement ou traduction du langage naturel

11.

DELIVERY-RELATED SEARCH AND ANALYTICS

      
Numéro d'application 17948997
Statut En instance
Date de dépôt 2022-09-20
Date de la première publication 2024-03-21
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Oribio, Ronald Eduardo
  • Burke, Jr., Robert William
  • Saad, Michele
  • Mejia, Irgelkha

Abrégé

A search system employs arrival times with associated confidence scores as search facets for identifying items. The search system identifies a plurality of items based on search input. An arrival time and associated confidence score are determined for each item from the plurality of items. Search results are provided for the plurality of items in response to the search input. The search results are provided based at least in part on the arrival times and associated confidence scores for the plurality of items.

Classes IPC  ?

  • G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail

12.

DETECTING AND RECOVERING PATTERNS IN DIGITAL RASTER IMAGES

      
Numéro d'application 17932478
Statut En instance
Date de dépôt 2022-09-15
Date de la première publication 2024-03-21
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Beri, Tarun
  • Agarwal, Vineet
  • Fisher, Matthew

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed for automatically detecting and reconstructing patterns in digital images. The disclosed system determines structurally similar pixels of a digital image by comparing neighborhood descriptors that include the structural context for neighborhoods of the pixels. In response to identify structurally similar pixels of a digital image, the disclosed system utilizes non-maximum suppression to reduce the set of structurally similar pixels to collinear pixels within the digital image. Additionally, the disclosed system determines whether a group of structurally similar pixels define the boundaries of a pattern cell that forms a rectangular grid pattern within the digital image. The disclosed system also modifies a boundary of a detected pattern cell to include a human-perceived pattern object via a sliding window corresponding to the pattern cell.

Classes IPC  ?

  • G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
  • G06T 9/20 - Codage des contours, p.ex. utilisant la détection des contours
  • G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p.ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]; Caractéristiques régionales saillantes

13.

Automated Digital Tool Identification from a Rasterized Image

      
Numéro d'application 18511899
Statut En instance
Date de dépôt 2023-11-16
Date de la première publication 2024-03-21
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Fisher, Matthew David
  • Batra, Vineet
  • Sardar, Mrinalini
  • Phogat, Ankit

Abrégé

A visual lens system is described that identifies, automatically and without user intervention, digital tool parameters for achieving a visual appearance of an image region in raster image data. To do so, the visual lens system processes raster image data using a tool region detection network trained to output a mask indicating whether the digital tool is useable to achieve a visual appearance of each pixel in the raster image data. The mask is then processed by a tool parameter estimation network trained to generate a probability distribution indicating an estimation of discrete parameter configurations applicable to the digital tool to achieve the visual appearance. The visual lens system generates an image tool description for the parameter configuration and incorporates the image tool description into an interactive image for the raster image data. The image tool description enables transfer of the digital tool parameter configuration to different image data.

Classes IPC  ?

  • G06T 11/40 - Remplissage d'une surface plane par addition d'attributs de surface, p.ex. de couleur ou de texture
  • G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport utilisant des icônes
  • G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
  • G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
  • G06F 18/2411 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur la proximité d’une surface de décision, p.ex. machines à vecteurs de support
  • G06F 18/40 - Dispositions logicielles spécialement adaptées à la reconnaissance des formes, p.ex. interfaces utilisateur ou boîtes à outils à cet effet
  • G06T 7/11 - Découpage basé sur les zones
  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte

14.

EXPLORATION OF LARGE-SCALE DATA SETS

      
Numéro d'application 17932742
Statut En instance
Date de dépôt 2022-09-16
Date de la première publication 2024-03-21
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Kelkar, Sachin Madhav
  • Kale, Ajinkya Gorakhnath
  • Ghouas, Alvin
  • Faieta, Baldo Antonio

Abrégé

Systems and methods for image exploration are provided. One aspect of the systems and methods includes identifying a set of images; reducing the set of images to obtain a representative set of images that is distributed throughout the set of images by removing a neighbor image based on a proximity of the neighbor image to an image of the representative set of images; arranging the representative set of images in a grid structure using a self-sorting map (SSM) algorithm; and displaying a portion of the representative set of images based on the grid structure.

Classes IPC  ?

  • G06F 16/54 - Navigation; Visualisation à cet effet
  • G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une forme; Localisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
  • G06V 10/772 - Détermination de motifs de référence représentatifs, p.ex. motifs de valeurs moyennes ou déformants; Génération de dictionnaires

15.

Text-based color palette searches utilizing text-to-color models

      
Numéro d'application 18051417
Numéro de brevet 11934452
Statut Délivré - en vigueur
Date de dépôt 2022-10-31
Date de la première publication 2024-03-19
Date d'octroi 2024-03-19
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Rohit
  • Mehadi, Syed Mohd
  • Vinay, Vishwa
  • Echevarria Vallespi, Jose Ignacio

Abrégé

The present disclosure relates to systems that perform text-based palette searches that convert a text query into a color distribution and utilize the color distribution to identify relevant color palettes. More specifically, the disclosed systems receive a textual color palette search query and convert, utilizing a text-to-color model, the textual color palette search query into a color distribution. The disclosed systems determine, utilizing a palette scoring model, distance metrics between the color distribution and a plurality of color palettes in a color database by: identifying swatch matches between colors of the color distribution and unmatched swatches of the plurality of color palettes and determining distances between the colors of the color distribution and matched swatches of the plurality of color palettes. The disclosed systems return one or more color palettes of the plurality of color palettes in response to the textual color palette search query based on the distance metrics.

Classes IPC  ?

  • G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques
  • G06F 16/538 - Présentation des résultats des requêtes
  • G06F 40/40 - Traitement ou traduction du langage naturel
  • G06T 11/00 - Génération d'images bidimensionnelles [2D]

16.

CUSTOMIZING DIGITAL CONTENT TUTORIALS BASED ON TOOL PROFICIENCY

      
Numéro d'application 17930154
Statut En instance
Date de dépôt 2022-09-07
Date de la première publication 2024-03-14
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Gupta, Subham
  • Chandrashekar, Padmassri
  • Murarka, Ankur

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed for customizing digital content tutorials for a user within a digital editing application based on user experience with editing tools. The disclosed system determines proficiency levels for a plurality of different portions of a digital content tutorial corresponding to a digital content editing task. The disclosed system generates tool proficiency scores associated with the user in a digital editing application in connection with the portions of the digital content tutorial. Specifically, the disclosed system generates the tool proficiency scores based on usage of tools corresponding to the portions. Additionally, the disclosed system generates a mapping for the user based on the tool proficiency scores associated with the user and the proficiency levels of the portions of the digital content tutorial. The disclosed system provides a customized digital content tutorial for display at a client device according to the mapping.

Classes IPC  ?

  • G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte

17.

RECONSTRUCTING LINEAR GRADIENTS

      
Numéro d'application 17901583
Statut En instance
Date de dépôt 2022-09-01
Date de la première publication 2024-03-14
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Chakraborty, Souymodip
  • Batra, Vineet
  • Lukác, Michal
  • Fisher, Matthew David
  • Phogat, Ankit

Abrégé

Embodiments are disclosed for reconstructing linear gradients from an input image that can be applied to another image. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a raster image, the raster image including a representation of a linear color gradient. The disclosed systems and methods further comprise determining a vector representing a direction of the linear color gradient. The disclosed systems and methods further comprise analyzing pixel points along the direction of the linear color gradient to compute color stops of the linear color gradient. The disclosed systems and methods further comprise generating an output color gradient vector with the computed color stops of the linear color gradient, the output color gradient vector to be applied to a vector graphic.

Classes IPC  ?

  • G06T 3/60 - Rotation d'une image entière ou d'une partie d'image
  • G06T 5/20 - Amélioration ou restauration d'image en utilisant des opérateurs locaux

18.

SYSTEMS AND METHODS FOR EVENT PROCESSING

      
Numéro d'application 17931778
Statut En instance
Date de dépôt 2022-09-13
Date de la première publication 2024-03-14
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Kucera, John Thomas
  • Nawathe, Sandeep

Abrégé

Systems and methods for event processing are provided. One aspect of the systems and methods includes receiving an event corresponding to an interaction of a user with a digital content channel; identifying a rule state for a segmentation rule that assigns users to a segment; assigning the user to the segment by evaluating the segmentation rule based on the rule state and the event from the digital content channel; updating the rule state; and providing customized content to the user based on the assignment of the user to the segment.

Classes IPC  ?

  • H04L 47/762 - Contrôle d'admission; Allocation des ressources en utilisant l'allocation dynamique des ressources, p.ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
  • H04L 47/70 - Contrôle d'admission; Allocation des ressources

19.

MULTIDIMENTIONAL IMAGE EDITING FROM AN INPUT IMAGE

      
Numéro d'application 17942101
Statut En instance
Date de dépôt 2022-09-09
Date de la première publication 2024-03-14
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Park, Taesung
  • Zhang, Richard
  • Schechtman, Elya

Abrégé

Various disclosed embodiments are directed to changing parameters of an input image or multidimensional representation of the input image based on a user request to change such parameters. An input image is first received. A multidimensional image that represents the input image in multiple dimensions is generated via a model. A request to change at least a first parameter to a second parameter is received via user input at a user device. Such request is a request to edit or generate the multidimensional image in some way. For instance, the request may be to change the light source position or camera position from a first set of coordinates to a second set of coordinates.

Classes IPC  ?

  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
  • G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p.ex. interaction avec des règles ou des cadrans
  • G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

20.

ATTENTION AWARE MULTI-MODAL MODEL FOR CONTENT UNDERSTANDING

      
Numéro d'application 17944502
Statut En instance
Date de dépôt 2022-09-14
Date de la première publication 2024-03-14
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Kumar, Yaman
  • Ahlawat, Vaibhav
  • Zhang, Ruiyi
  • Aggarwal, Milan
  • Palwe, Ganesh Karbhari
  • Krishnamurthy, Balaji
  • Khurana, Varun

Abrégé

A content analysis system provides content understanding for a content item using an attention aware multi-modal model. Given a content item, feature extractors extract features from content components of the content item in which the content components comprise multiple modalities. A cross-modal attention encoder of the attention aware multi-modal model generates an embedding of the content item using features extracted from the content components. A decoder of the attention aware multi-modal model generates an action-reason statement using the embedding of the content item from the cross-modal attention encoder.

Classes IPC  ?

  • G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
  • G06F 16/55 - Groupement; Classement

21.

CUSTOM ATTRIBUTES FOR SEARCH

      
Numéro d'application 17903295
Statut En instance
Date de dépôt 2022-09-06
Date de la première publication 2024-03-07
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Oribio, Ronald Eduardo
  • Burke, Jr., Robert William
  • Saad, Michele
  • Mejia, Irgelkha

Abrégé

A search system generates custom attributes for use as search facets. User input associated with an image of a target item available on a listing platform is received. The image is analyzed to determine an attribute of the target item as a custom attribute. A value for the custom attribute is determined for each of a number of other items available on the listing platform that are of the same item type as the target item. Search results are provided based at least in part on the values of the custom attribute for the other items.

Classes IPC  ?

  • G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail

22.

CONTENT-SPECIFIC-PRESET EDITS FOR DIGITAL IMAGES

      
Numéro d'application 18504821
Statut En instance
Date de dépôt 2023-11-08
Date de la première publication 2024-03-07
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Gupta, Subham
  • Sil, Arnab
  • ., Anuradha

Abrégé

The present disclosure describes systems, non-transitory computer-readable media, and methods for generating object-specific-preset edits to be later applied to other digital images depicting a same object type or applying a previously generated object-specific-preset edit to an object of the same object type within a target digital image. For example, in some cases, the disclosed systems generate an object-specific-preset edit by determining a region of a particular localized edit in an edited digital image, identifying an edited object corresponding to the localized edit, and storing in a digital-image-editing document an object tag for the edited object and instructions for the localized edit. In certain implementations, the disclosed systems further apply such an object-specific-preset edit to a target object in a target digital image by determining transformed-positioning parameters for a localized edit from the object-specific-preset edit to the target object.

Classes IPC  ?

  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
  • G06N 20/00 - Apprentissage automatique
  • G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles

23.

RECONSTRUCTING CONCENTRIC RADIAL GRADIENTS

      
Numéro d'application 17823574
Statut En instance
Date de dépôt 2022-08-31
Date de la première publication 2024-03-07
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Lukac, Michal
  • Chakraborty, Souymodip
  • Fisher, Matthew David
  • Batra, Vineet
  • Phogat, Ankit

Abrégé

Systems and methods for image processing are described. Embodiments of the present disclosure receive a raster image depicting a radial color gradient; compute an origin point of the radial color gradient based on an orthogonality measure between a color gradient vector at a point in the raster image and a relative position vector between the point and the origin point; construct a vector graphics representation of the radial color gradient based on the origin point; and generate a vector graphics image depicting the radial color gradient based on the vector graphics representation.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]

24.

Electronic shopping cart prediction and caching of electronic shopping cart computations

      
Numéro d'application 17903360
Numéro de brevet 11935085
Statut Délivré - en vigueur
Date de dépôt 2022-09-06
Date de la première publication 2024-03-07
Date d'octroi 2024-03-19
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Saad, Michele
  • Miniailo, Igor

Abrégé

Embodiments provide systems, methods, and computer storage media for prediction and computation of electronic shopping carts. In an example embodiment, for each interaction between an e-shopper and an e-commerce application, one or more predicted electronic shopping carts that represent a combination of items the e-shopper is likely to purchase are generated based on current items in the e-shopper's electronic shopping cart and recent interactions with the e-shopper. For some or all of the predicted electronic shopping carts (e.g., those with top predicted confidence levels), corresponding shopping cart computations (e.g., identifying application promotions, determining a price total for the items in the predicted shopping cart) are executed and cached prior to the e-shopping adding the predicted items. As such, a page configured to visualize the predicted electronic shopping cart with a value retrieved from the cached shopping cart computations (e.g., price total for the predicted electronic shopping cart) is generated.

Classes IPC  ?

  • G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
  • G06Q 30/0207 - Remises ou incitations, p.ex. coupons ou rabais
  • G06Q 30/0601 - Commerce électronique [e-commerce]

25.

DETERMINING VIDEO PROVENANCE UTILIZING DEEP LEARNING

      
Numéro d'application 17822573
Statut En instance
Date de dépôt 2022-08-26
Date de la première publication 2024-02-29
Propriétaire
  • Adobe Inc. (USA)
  • University of Surrey (Royaume‑Uni)
Inventeur(s)
  • Black, Alexander
  • Bui, Van Tu
  • Collomosse, John
  • Jenni, Simon
  • Swaminathan, Viswanathan

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.

Classes IPC  ?

  • H04N 21/434 - Désassemblage d'un flux multiplexé, p.ex. démultiplexage de flux audio et vidéo, extraction de données additionnelles d'un flux vidéo; Remultiplexage de flux multiplexés; Extraction ou traitement de SI; Désassemblage d'un flux élémentaire mis en paquets
  • G06F 16/732 - Formulation de requêtes
  • G06F 16/78 - Recherche de données caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
  • H04N 21/84 - Génération ou traitement de données de description, p.ex. descripteurs de contenu
  • H04N 21/845 - Structuration du contenu, p.ex. décomposition du contenu en segments temporels

26.

AUTOMATIC DETECTION AND REMOVAL OF TYPOGRAPHIC RIVERS IN ELECTRONIC DOCUMENTS

      
Numéro d'application 17894058
Statut En instance
Date de dépôt 2022-08-23
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Ashish
  • Jain, Arushi

Abrégé

Embodiments are disclosed for removing typographic rivers from electronic documents. The method may include receiving an electronic document including a plurality of words for automatic typographic correction. A typographic river is identified in the electronic document, the typographic river including a plurality of nodes, each node including an empty glyph. A candidate adjustment that removes the first node of the plurality of nodes is identified and the candidate adjustment is applied to the electronic document.

Classes IPC  ?

  • G06F 40/109 - Maniement des polices de caractères; Typographie cinétique ou temporelle
  • G06F 40/166 - Traitement de texte Édition, p.ex. insertion ou suppression

27.

IMAGE COMPRESSION PERFORMANCE OPTIMIZATION FOR IMAGE COMPRESSION

      
Numéro d'application 17895758
Statut En instance
Date de dépôt 2022-08-25
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Wang, Haoliang
  • Petrangeli, Stefano
  • Swaminathan, Viswanathan

Abrégé

The context-aware optimization method includes training a context model by determining whether to split each node in the context by identifying a first subset of virtual context to evaluate by identifying a second subset of virtual contexts to evaluate and obtaining an encoding cost of splitting of the context model for each virtual context in the second subset and identifying the first subset of virtual contexts to evaluate by selecting a predetermined number of virtual contexts from the second subset based on the encoding cost such that the predetermined number of virtual contexts with lowest encoding cost are selected. The modified tree-traversal method includes encoding a mask or performing a speculative-based method. The modified entropy coding method includes representing data into an array of bits, using multiple coders to process each bit in the array and combining the output from the multiple coders into a data range.

Classes IPC  ?

  • G06T 9/40 - Codage sous forme arborescente, p.ex. à quatre branches, à huit branches
  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image

28.

GENERATION USING DEPTH-CONDITIONED AUTOENCODER

      
Numéro d'application 17896574
Statut En instance
Date de dépôt 2022-08-26
Date de la première publication 2024-02-29
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Park, Taesung
  • Paris, Sylvain
  • Zhang, Richard
  • Shechtman, Elya

Abrégé

An image processing system uses a depth-conditioned autoencoder to generate a modified image from an input image such that the modified image maintains an overall structure from the input image while modifying textural features. An encoder of the depth-conditioned autoencoder extracts a structure latent code from an input image and depth information for the input image. A generator of the depth-conditioned autoencoder generates a modified image using the structure latent code and a texture latent code. The modified image generated by the depth-conditioned autoencoder includes the structural features from the input image while incorporating textural features of the texture latent code. In some aspects, the autoencoder is depth-conditioned during training by augmenting training images with depth information. The autoencoder is trained to preserve the depth information when generating images.

Classes IPC  ?

  • G06T 7/40 - Analyse de la texture
  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction

29.

GENERATING SYMMETRICAL REPEAT EDITS FOR IMAGES

      
Numéro d'application 17896798
Statut En instance
Date de dépôt 2022-08-26
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Nellutla, Aditya
  • Kumar, Harish

Abrégé

Embodiments are disclosed for identifying and generating symmetrical repeat edits to similar objects in an image. A selection of a first object and an edit to the first object in an image is received. The image is searched for a plurality of candidate objects that have a similar shape to the first object and the plurality of candidate objects are filtered to include one or more objects that are symmetrical with the first object. A symmetric object is selected from the plurality of candidate objects. An axis of symmetry is computed between the symmetric object and the first object. The edit is applied to the symmetric object and to the first object.

Classes IPC  ?

  • G06T 7/68 - Analyse des attributs géométriques de la symétrie
  • G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image
  • G06T 3/60 - Rotation d'une image entière ou d'une partie d'image
  • G06T 7/11 - Découpage basé sur les zones
  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06V 10/20 - Prétraitement de l’image

30.

MACHINE LEARNING CONTEXT BASED CONFIDENCE CALIBRATION

      
Numéro d'application 17822029
Statut En instance
Date de dépôt 2022-08-24
Date de la première publication 2024-02-29
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Patel, Parth Shailesh
  • Mehra, Ashutosh

Abrégé

Systems and methods for machine learning context based confidence calibration are disclosed. In one embodiment, a processing logic may obtain an image frame; generate, with a first machine learning model, a confidence score, a bounding box, and an instance embedding corresponding to an object instance inferred from the image frame; and compute, with a second machine learning model, a calibrated confidence score for the object instance based on the instance embedding, the confidence score, and the bounding box.

Classes IPC  ?

  • G06N 20/00 - Apprentissage automatique
  • G06N 5/04 - Modèles d’inférence ou de raisonnement

31.

GENERATING IMAGE EDITING PRESETS BASED ON EDITING INTENT EXTRACTED FROM A DIGITAL QUERY

      
Numéro d'application 17823429
Statut En instance
Date de dépôt 2022-08-30
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Sil, Arnab
  • Srivastava, Akash
  • Meghana, Gorantla

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer readable media that recommend editing presets based on editing intent. For instance, in one or more embodiments, the disclosed systems receive, from a client device, a user query corresponding to a digital image to be edited. The disclosed systems extract, from the user query, an editing intent for editing the digital image. Further, the disclosed systems determine an editing preset that corresponds to the editing intent based on an editing state of an edited digital image associated with the editing preset. The disclosed systems generate a recommendation for the editing preset for provision to the client device.

Classes IPC  ?

  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
  • G06F 16/432 - Formulation de requêtes

32.

DIFFUSION MODEL IMAGE GENERATION

      
Numéro d'application 17823582
Statut En instance
Date de dépôt 2022-08-31
Date de la première publication 2024-02-29
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Jandial, Surgan
  • Ramesh, Siddarth
  • Deshmukh, Shripad Vilasrao
  • Krishnamurthy, Balaji

Abrégé

Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.

Classes IPC  ?

  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques

33.

TRANSFORM AWARE BLEND OBJECT GENERATION

      
Numéro d'application 17894965
Statut En instance
Date de dépôt 2022-08-24
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Nellutla, Aditya
  • Kumar, Apurva

Abrégé

Embodiments are disclosed for blending complex objects. The method may include identifying a first complex object and a second complex object. A first primary object associated with the first complex object and a first sequence of geometric repeat operations are determined. A second primary object associated with the second complex object and second sequence of geometric repeat operations are also determined. A blending operation is applied to the first primary object and the second primary object to generate one or more intermediate primary objects. One or more intermediate complex objects are generated from the one or more intermediate primary objects.

Classes IPC  ?

  • G06T 15/50 - Effets de lumière
  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image

34.

Visual Reordering Of Partial Vector Objects

      
Numéro d'application 17896342
Statut En instance
Date de dépôt 2022-08-26
Date de la première publication 2024-02-29
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Kumar, Harish
  • Dhanuka, Praveen Kumar

Abrégé

In implementations of systems for visual reordering of partial vector objects, a computing device implements an order system to receive input data describing a region specified relative to a group of vector objects that includes a portion of a first vector object and a portion of second vector object. A visual order as between the portion of the first vector object and the portion of the second vector object within the region is determined. The order system computes a modified visual order as between the portion of the first vector object and the portion of the second vector object within the region based on the visual order. The order system generates the group of vector objects for display in a user interface using a render surface and a sentinel value to render pixels within the region in the modified visual order.

Classes IPC  ?

  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte

35.

LANGUAGE MODEL WITH EXTERNAL KNOWLEDGE BASE

      
Numéro d'application 17897419
Statut En instance
Date de dépôt 2022-08-29
Date de la première publication 2024-02-29
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Bhatia, Sumit
  • Kaur, Jivat Neet
  • Bansal, Rachit
  • Aggarwal, Milan
  • Krishnamurthy, Balaji

Abrégé

The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Classes IPC  ?

  • H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
  • G06F 40/295 - Reconnaissance de noms propres
  • G06N 5/02 - Représentation de la connaissance; Représentation symbolique

36.

REGULARIZING TARGETS IN MODEL DISTILLATION UTILIZING PAST STATE KNOWLEDGE TO IMPROVE TEACHER-STUDENT MACHINE LEARNING MODELS

      
Numéro d'application 17818506
Statut En instance
Date de dépôt 2022-08-09
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jandial, Surgan
  • Puri, Nikaash
  • Krishnamurthy, Balaji

Abrégé

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that regularize learning targets for a student network by leveraging past state outputs of the student network with outputs of a teacher network to determine a retrospective knowledge distillation loss. For example, the disclosed systems utilize past outputs from a past state of a student network with outputs of a teacher network to compose student-regularized teacher outputs that regularize training targets by making the training targets similar to student outputs while preserving semantics from the teacher training targets. Additionally, the disclosed systems utilize the student-regularized teacher outputs with student outputs of the present states to generate retrospective knowledge distillation losses. Then, in one or more implementations, the disclosed systems compound the retrospective knowledge distillation losses with other losses of the student network outputs determined on the main training tasks to learn parameters of the student networks.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

37.

RESOLUTION INDEPENDENT 3-D VECTORIZATION FOR GRAPHIC DESIGNS

      
Numéro d'application 17889168
Statut En instance
Date de dépôt 2022-08-16
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Phogat, Ankit
  • Sun, Xin
  • Batra, Vineet
  • Dhingra, Sumit
  • Carr, Nathan A.
  • Hasan, Milos

Abrégé

Embodiments are disclosed for performing 3-D vectorization. The method includes obtaining a three-dimensional rendered image and a camera position. The method further includes obtaining a triangle mesh representing the three-dimensional rendered image. The method further involves creating a reduced triangle mesh by removing one or more triangles from the triangle mesh. The method further involves subdividing each triangle of the reduced triangle mesh into one or more subdivided triangles. The method further involves performing a mapping of each pixel of the three-dimensional rendered image to the reduced triangle mesh. The method further involves assigning a color value to each vertex of the reduced triangle mesh. The method further involves sorting each triangle of the reduced triangle mesh using a depth value of each triangle. The method further involves generating a two-dimensional triangle mesh using the sorted triangles of the reduced triangle mesh.

Classes IPC  ?

  • G06T 15/10 - Effets géométriques
  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation
  • G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles

38.

Location Operation Conflict Resolution

      
Numéro d'application 17891643
Statut En instance
Date de dépôt 2022-08-19
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Arushi
  • Dhanuka, Praveen Kumar

Abrégé

Location operation conflict resolution techniques are described. In these techniques, a likely user's intent is inferred by a digital image editing system to prioritize anchor points that are to be a subject of a location operation. In an example in which multiple anchor points qualify for location operations at a same time, these techniques are usable to resolve conflicts between the anchor points based on an assigned priority. In an implementation, the priority is based on selection input location with respect to an object.

Classes IPC  ?

  • G06V 10/24 - Alignement, centrage, détection de l’orientation ou correction de l’image
  • G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une forme; Localisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance

39.

DETERMINING FEATURE CONTRIBUTIONS TO DATA METRICS UTILIZING A CAUSAL DEPENDENCY MODEL

      
Numéro d'application 18492551
Statut En instance
Date de dépôt 2023-10-23
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Goel, Pulkit
  • Poddar, Naman
  • Sinha, Gaurav
  • Chauhan, Ayush
  • Maiti, Aurghya

Abrégé

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.

Classes IPC  ?

40.

TEXT CO-EDITING IN A CONFLICT-FREE REPLICATED DATA TYPE ENVIRONMENT

      
Numéro d'application 17890203
Statut En instance
Date de dépôt 2022-08-17
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Beri, Tarun
  • Pandey, Kush

Abrégé

Embodiments are disclosed for managing text co-editing in a conflict-free replicated data type (CRDT) environment. A method of co-editing management includes detecting a burst operation to be performed on a sequential data structure being edited by one or more client devices. A segment of the sequential data structure associated with the burst operation is determined based on a logical index associated with the burst operation. A tree structure associated with the segment is generated, where a root node of the tree structure corresponds to the burst operation. A global index for the root node of the tree structure is determined and an update corresponding to the burst operation, including the root node and the global index, is sent to the one or more client devices.

Classes IPC  ?

  • G06F 16/23 - Mise à jour
  • G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage

41.

Generating and Propagating Personal Masking Edits

      
Numéro d'application 17890461
Statut En instance
Date de dépôt 2022-08-18
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Gupta, Subham
  • Sil, Arnab

Abrégé

In implementations of systems for generating and propagating personal masking edits, a computing device implements a mask system to detect a face of a person depicted in a digital image displayed in a user interface of an application for editing digital content. The mask system determines an identifier for the person based on an identifier for the face. Edit data is received describing properties of an editing operation and a type of mask used to modify a particular portion of the person depicted in the digital image. The mask system edits an additional digital image identified based on the identifier of the person using the type of mask and the properties of the editing operation to modify the particular portion of the person as depicted in the additional digital image.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
  • G06T 7/11 - Découpage basé sur les zones
  • G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion

42.

DEFORMABLE NEURAL RADIANCE FIELD FOR EDITING FACIAL POSE AND FACIAL EXPRESSION IN NEURAL 3D SCENES

      
Numéro d'application 17892097
Statut En instance
Date de dépôt 2022-08-21
Date de la première publication 2024-02-22
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Shu, Zhixin
  • Xu, Zexiang
  • Athar, Shahrukh
  • Sunkavalli, Kalyan
  • Shechtman, Elya

Abrégé

A scene modeling system receives a video including a plurality of frames corresponding to views of an object and a request to display an editable three-dimensional (3D) scene that corresponds to a particular frame of the plurality of frames. The scene modeling system applies a scene representation model to the particular frame, and includes a deformation model configured to generate, for each pixel of the particular frame based on a pose and an expression of the object, a deformation point using a 3D morphable model (3DMM) guided deformation field. The scene representation model includes a color model configured to determine, for the deformation point, color and volume density values. The scene modeling system receives a modification to one or more of the pose or the expression of the object including a modification to a location of the deformation point and renders an updated video based on the received modification.

Classes IPC  ?

  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
  • G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie

43.

Content analytics as part of content creation

      
Numéro d'application 18133725
Numéro de brevet 11907508
Statut Délivré - en vigueur
Date de dépôt 2023-04-12
Date de la première publication 2024-02-20
Date d'octroi 2024-02-20
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Kumar, Yaman
  • Singh, Somesh
  • George, William Brandon
  • Liu, Timothy Chia-Chi
  • Basetty, Suman
  • Prasoon, Pranjal
  • Puri, Nikaash
  • Naware, Mihir
  • Corlan, Mihai
  • Butikofer, Joshua Marshall
  • Chauhan, Abhinav
  • Singh, Kumar Mrityunjay
  • O'Reilly, James Patrick
  • Chung, Hyman
  • Dest, Lauren
  • Goudie-Nice, Clinton Hansen
  • Pack, Brandon John
  • Krishnamurthy, Balaji
  • Jain, Kunal Kumar
  • Klimetschek, Alexander
  • Rozen, Matthew William

Abrégé

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Classes IPC  ?

  • G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
  • G06F 18/2415 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p.ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
  • G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
  • G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles
  • G06F 40/166 - Traitement de texte Édition, p.ex. insertion ou suppression
  • G06F 40/151 - Transformation

44.

HTML element based rendering supporting interactive objects

      
Numéro d'application 18165522
Numéro de brevet 11907646
Statut Délivré - en vigueur
Date de dépôt 2023-02-07
Date de la première publication 2024-02-20
Date d'octroi 2024-02-20
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Garg, Tarun
  • Shotts, Kerri
  • Vikram, Aditya

Abrégé

A method includes receiving a user event associated with content of an add-on for a web application displayed on a first user interface. The add-on is a non-native application executed using a hypertext markup language (HTML) element. The method further includes passing the user event to a document object model of the web application using a blank native element. The blank native element links the add-on to the document object model. The method further includes processing the user event using an HTML element renderer. The method further includes displaying updated content associated with the add-on based on the processed user event.

Classes IPC  ?

  • G06F 40/143 - Balisage, p.ex. utilisation du langage SGML ou de définitions de type de document
  • G06F 40/154 - Transformation en arborescence pour documents en configuration arborescente ou balisés, p.ex. langages XSLT, XSL-FO ou feuilles de style
  • G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
  • G06F 16/958 - Organisation ou gestion de contenu de sites Web, p.ex. publication, conservation de pages ou liens automatiques

45.

Generating Blend Objects from Objects with Pattern Fills

      
Numéro d'application 17887735
Statut En instance
Date de dépôt 2022-08-15
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Kumar, Apurva
  • Sharma, Paranjay

Abrégé

In implementations of systems for generating blend objects from objects with pattern fills, a computing device implements a blend system to generate a source master texture using a first pattern fill of a source object and a destination master texture using a second pattern fill of the a destination object. First colors are sampled from the source master texture and second colors are sampled from the destination master texture. The blend system determines a blended pattern fill for the first pattern fill and the second pattern fill by combining the first colors and the second colors. The blend system generates an intermediate blend object for the source object and the destination object for display in a user interface based on the blended pattern fill.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06T 11/40 - Remplissage d'une surface plane par addition d'attributs de surface, p.ex. de couleur ou de texture
  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline

46.

MODIFYING PARAMETRIC CONTINUITY OF DIGITAL IMAGE CONTENT IN PIECEWISE PARAMETRIC PATCH DEFORMATIONS

      
Numéro d'application 18152981
Statut En instance
Date de dépôt 2023-01-11
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s) Peterson, John

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed for modifying parametric continuity between portions of a digital image in piecewise parametric patch deformations. For example, the disclosed system determine parametric patches in a parametric quilt corresponding to a digital image in response to a request to deform the digital image. The disclosed system divides the digital image into a plurality of separate portions along edges of the parametric patches, each parametric patch comprising a separate set of control points. The disclosed system generates sets of interactive handles for each anchor control point in the parametric patch corresponding to metadata flags that determine parametric continuities between portions of the digital image. Additionally, in response to a user input, the disclosed system modifies the parametric continuity at a portion of the digital image corresponding to an anchor control point by modifying a metadata flag for the control point.

Classes IPC  ?

  • G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image
  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte

47.

FILLING DIGITAL DOCUMENTS USING MULTI-TIERED USER IDENTITY MODELS BASED ON DECENTRALIZED IDENTITY

      
Numéro d'application 17819540
Statut En instance
Date de dépôt 2022-08-12
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • He, Songlin
  • Sun, Tong
  • Lipka, Nedim
  • Wigington, Curtis
  • Jain, Rajiv
  • Roy, Anindo

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer readable media that fill in digital documents using user identity models of client devices. For instance, in one or more embodiments, the disclosed systems receive a digital document comprising a digital fillable field. The disclosed systems further retrieve, for a client device associated with the digital document, a decentralized identity credential comprising a user attribute established under a decentralized identity framework. Using the user attribute of the decentralized identity credential, the disclosed systems modify the digital document by filling in the digital fillable field.

Classes IPC  ?

  • H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
  • G06F 21/31 - Authentification de l’utilisateur
  • G06F 40/174 - Remplissage de formulaires; Fusion

48.

GENERATING SNAPPING GUIDE LINES FROM OBJECTS IN A DESIGNATED REGION

      
Numéro d'application 17887322
Statut En instance
Date de dépôt 2022-08-12
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Pal, Shivi
  • Dhanuka, Praveen Kumar
  • Jain, Arushi

Abrégé

Embodiments are disclosed for generating snapping guide lines from objects in a selected region to an object or drawing tool by a digital design system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a first selection of an object from a plurality of objects within a drawing displayed in a graphical user interface (GUI). The disclosed systems and methods further comprise receiving a second selection of a region of interest. The disclosed systems and methods further comprise identifying one or more objects in the region of interest. The disclosed systems and methods further comprise, in response to an input indicating a moving operation of the selected object, generating guide lines from objects in the region of interest to the selected object. The disclosed systems and methods further comprise performing the moving operation of the selected object based on alignment with the generated guide lines.

Classes IPC  ?

  • G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
  • G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels

49.

Digital Object Animation Authoring Interfaces

      
Numéro d'application 17887815
Statut En instance
Date de dépôt 2022-08-15
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Ma, Jiaju
  • Wei, Li-Yi
  • Rubaiat Habib, Kazi

Abrégé

An animation system configured for generating an animation scene that includes at least one animation stylization effect applied to one or more three-dimensional digital objects is described. The animation system includes an interface having a timeline portion and a node graph portion. The timeline portion represents various animation stylization effects as clips arranged chronologically relative to a timeline and the node graph portion includes a node cluster for each clip, where individual node clusters are made up of an animate node, an action node, and an effect node. Input at the timeline portion modifying at least one parameter of the animation scene propagates to the node graph portion, and vice versa. The animation system thus presents dual representations of an animation scene in a manner that enables complex animation customizations while organizing animation effects in a simplified, chronological manner.

Classes IPC  ?

  • G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels

50.

SPOKEN QUERY PROCESSING FOR IMAGE SEARCH

      
Numéro d'application 17887959
Statut En instance
Date de dépôt 2022-08-15
Date de la première publication 2024-02-15
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Ajay
  • Tagra, Sanjeev
  • Soni, Sachin
  • Rozich, Ryan
  • Puri, Nikaash
  • Roeder, Jonathan

Abrégé

An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.

Classes IPC  ?

  • G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G10L 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p.ex. modèles de langage
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
  • G06F 40/30 - Analyse sémantique
  • G06F 3/16 - Entrée acoustique; Sortie acoustique
  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques

51.

GENERATING ITERATIVE INPAINTING DIGITAL IMAGES VIA NEURAL NETWORK BASED PERCEPTUAL ARTIFACT SEGMENTATIONS

      
Numéro d'application 17815418
Statut En instance
Date de dépôt 2022-07-27
Date de la première publication 2024-02-08
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Amirghodsi, Sohrab
  • Zhang, Lingzhi
  • Lin, Zhe
  • Shechtman, Elya
  • Zhou, Yuqian
  • Barnes, Connelly

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.

Classes IPC  ?

  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/11 - Découpage basé sur les zones

52.

Corrective Lighting for Video Inpainting

      
Numéro d'application 18375187
Statut En instance
Date de dépôt 2023-09-29
Date de la première publication 2024-02-08
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Wang, Oliver
  • Nelson, John
  • Oxholm, Geoffrey
  • Shechtman, Elya

Abrégé

One or more processing devices access a scene depicting a reference object that includes an annotation identifying a target region to be modified in one or more video frames. The one or more processing devices determine that a target pixel corresponds to a sub-region within the target region that includes hallucinated content. The one or more processing devices determine gradient constraints using gradient values of neighboring pixels in the hallucinated content, the neighboring pixels being adjacent to the target pixel and corresponding to four cardinal directions. The one or more processing devices update color data of the target pixel subject to the determined gradient constraints.

Classes IPC  ?

  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/269 - Analyse du mouvement utilisant des procédés basé sur le gradient

53.

MACHINE LEARNING MODELING FOR PROTECTION AGAINST ONLINE DISCLOSURE OF SENSITIVE DATA

      
Numéro d'application 18489399
Statut En instance
Date de dépôt 2023-10-18
Date de la première publication 2024-02-08
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Mejia, Irgelkha
  • Oribio, Ronald
  • Burke, Robert
  • Saad, Michele

Abrégé

Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.

Classes IPC  ?

  • G06Q 50/26 - Services gouvernementaux ou services publics
  • G06F 16/48 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
  • G06F 40/40 - Traitement ou traduction du langage naturel
  • G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
  • G06N 3/08 - Méthodes d'apprentissage
  • G06Q 10/0635 - Analyse des risques liés aux activités d’entreprises ou d’organisations
  • G06Q 10/10 - Bureautique; Gestion du temps
  • G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme

54.

SYSTEMS AND METHODS FOR MESH GENERATION

      
Numéro d'application 17816813
Statut En instance
Date de dépôt 2022-08-02
Date de la première publication 2024-02-08
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Yang, Jimei
  • Yao, Chun-Han
  • Aksit, Duygu Ceylan
  • Zhou, Yi

Abrégé

Systems and methods for mesh generation are described. One aspect of the systems and methods includes receiving an image depicting a visible portion of a body; generating an intermediate mesh representing the body based on the image; generating visibility features indicating whether parts of the body are visible based on the image; generating parameters for a morphable model of the body based on the intermediate mesh and the visibility features; and generating an output mesh representing the body based on the parameters for the morphable model, wherein the output mesh includes a non-visible portion of the body that is not depicted by the image.

Classes IPC  ?

  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation

55.

UTILIZING MACHINE LEARNING MODELS FOR PATCH RETRIEVAL AND DEFORMATION IN COMPLETING THREE-DIMENSIONAL DIGITAL SHAPES

      
Numéro d'application 17817776
Statut En instance
Date de dépôt 2022-08-05
Date de la première publication 2024-02-08
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Chaudhuri, Siddhartha
  • Sun, Bo
  • Kim, Vladimir
  • Aigerman, Noam

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed that utilizes machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes. In particular, in one or more implementations the disclosed systems utilize a machine learning model to predict a coarse completion shape from an incomplete 3D digital shape. The disclosed systems sample coarse 3D patches from the coarse 3D digital shape and learn a shape distance function to retrieve detailed 3D shape patches in the input shape. Moreover, the disclosed systems learn a deformation for each retrieved patch and blending weights to integrate the retrieved patches into a continuous surface.

Classes IPC  ?

  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation
  • G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une forme; Localisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
  • G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires

56.

DEBIASING IMAGE TO IMAGE TRANSLATION MODELS

      
Numéro d'application 17880120
Statut En instance
Date de dépôt 2022-08-03
Date de la première publication 2024-02-08
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Tanjim, Md Mehrab
  • Singh, Krishna Kumar
  • Kafle, Kushal
  • Sinha, Ritwik

Abrégé

A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

Classes IPC  ?

  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image

57.

Miscellaneous Design

      
Numéro d'application 018981946
Statut En instance
Date de dépôt 2024-02-02
Propriétaire Adobe Inc. (USA)
Classes de Nice  ?
  • 09 - Appareils et instruments scientifiques et électriques
  • 42 - Services scientifiques, technologiques et industriels, recherche et conception

Produits et services

Scientific, research, navigation, surveying, photographic, cinematographic, audiovisual, optical, weighing, measuring, signalling, detecting, testing, inspecting, life-saving and teaching apparatus and instruments; apparatus and instruments for conducting, switching, transforming, accumulating, regulating or controlling the distribution or use of electricity; apparatus and instruments for recording, transmitting, reproducing or processing sound, images or data; recorded and downloadable media, computer software, blank digital or analogue recording and storage media; mechanisms for coinoperated apparatus; cash registers, calculating devices; computers and computer peripheral devices; diving suits, divers; masks, ear plugs for divers, nose clips for divers and swimmers, gloves for divers, breathing apparatus for underwater swimming; fire-extinguishing apparatus; computer software; computer software programs; computer software platforms; computer software development tools; downloadable computer software; application software; interactive computer software; computer programs [downloadable software]; computer search engine software; apps; recorded computer software; software applications; mobile software applications; applications for smartphones and tablets; electronic publications; downloadable publications; Computer application software for streaming audio-visual media content via the internet; Software for processing images, graphics, audio, video and text; Computer databases; Electronic databases; Interactive database software; Database and file management software; Computer software for creating searchable databases of information and data; System software; Computer systems; Data processing systems; System and system support software, and firmware; Computer operating system software; Document management system software; Workflow management system software; Computer software downloadable from global computer networks; Computer software applications, downloadable; Computer software for database management; Computer software for business purposes; Computer software for wireless network communications; Computer software for application and database integration; Computer software for scanning images and documents; Downloadable computer software for the transmission of data and information; Downloadable computer software for the management of data; Computer software for use as an application programming interface (API); Downloadable computer software for use as an application programming interface (API); Downloadable digital files authenticated by nonfungible tokens [NFTs]; earphones; headphones; sunglasses; eyewear; laptop cases; mobile phone cases; mobile phone holders; DVDs; CDs; MP3 players; smart watches; smart bands; smart jewellery; spectacle cases and sunglasses; graphic art software; software for generating virtual images; Downloadable computer programs and downloadable computer software in relation to artificial intelligence models for content generation and management; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer programs and downloadable computer software for voice and speech recognition; downloadable computer programs and downloadable computer software for creating and generating text; parts and fittings for all the aforesaid goods. Scientific and technological services and research and design relating thereto; industrial analysis, industrial research and industrial design services; quality control and authentication services; design and development of computer hardware and software; Technological research and development; Safety testing services relating to computer software; Certification of safety standards relating to computer software; Computer software research; Computer software integration; Software development services; Hosting services, software as a service, and rental of software; Development of interactive multimedia software; Providing online, non-downloadable software; Software as a service (SAAS) in relation to artificial intelligence models for content generation and management; Software as a service (SAAS) services using artificial intelligence models for content generation and management, namely, image generation from user prompts, video generation from user prompts and image editing; application service provider featuring application programming interface (API) software; Software as a service (SAAS) providing online non-downloadable software for natural language processing, generation, understanding and analysis; research and development services in the field of artificial intelligence; research, design and development of computer programs and software; Computer systems development; Computer system integration services; Development of computer systems; Development of computer platforms; Design and development of data storage systems; Design and development of data entry systems; Design and development of data processing systems; Design and development of electronic data security systems; Development of systems for the transmission of data; Cloud computing services; Cloud storage services for electronic files and data; Maintaining databases; Database design; Database design and development; data security services; design and development of virtual reality hardware and software; application service provider (ASP) services featuring software for use in virtual currency, digital currency, cryptocurrency, and digital asset exchange and transactions; information, advisory and consultancy services relating to the aforesaid.

58.

STYLIZED MOTION EFFECTS

      
Numéro d'application 17814940
Statut En instance
Date de dépôt 2022-07-26
Date de la première publication 2024-02-01
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Aggarwal, Pranav Vineet
  • Ghouas, Alvin
  • Kale, Ajinkya Gorakhnath

Abrégé

Systems and methods for image processing are described. Embodiments of the present disclosure receive a first image depicting a scene and a second image that includes a style; segment the first image to obtain a first segment and a second segment, wherein the first segment has a shape of an object in the scene; apply a style transfer network to the first segment and the second image to obtain a first image part, wherein the first image part has the shape of the object and the style from the second image; combine the first image part with a second image part corresponding to the second segment to obtain a combined image; and apply a lenticular effect to the combined image to obtain an output image.

Classes IPC  ?

  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
  • G06T 7/11 - Découpage basé sur les zones
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan

59.

ADAPTING GENERATIVE NEURAL NETWORKS USING A CROSS DOMAIN TRANSLATION NETWORK

      
Numéro d'application 17815451
Statut En instance
Date de dépôt 2022-07-27
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Li, Yijun
  • Kolkin, Nicholas
  • Lu, Jingwan
  • Shechtman, Elya

Abrégé

The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.

Classes IPC  ?

  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
  • G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p.ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]; Caractéristiques régionales saillantes

60.

Systems for Efficiently Generating Blend Objects

      
Numéro d'application 17873848
Statut En instance
Date de dépôt 2022-07-26
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Kumar, Harish
  • Kumar, Apurva

Abrégé

In implementations of systems for efficiently generating blend objects, a computing device implements a blending system to assign unique shape identifiers to objects included in an input render tree. The blending system generates a shape mask based on the unique shape identifiers. A color of a pixel of a blend object is computed based on particular objects of the objects that contribute to the blend object using the shape mask. The blending system generates the blend object for display in a user interface based on the color of the pixel.

Classes IPC  ?

  • G06T 15/50 - Effets de lumière
  • G06T 15/00 - Rendu d'images tridimensionnelles [3D]
  • G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie

61.

RESOLVING GARMENT COLLISIONS USING NEURAL NETWORKS

      
Numéro d'application 17875081
Statut En instance
Date de dépôt 2022-07-27
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Zhou, Yi
  • Wang, Yangtuanfeng
  • Sun, Xin
  • Tan, Qingyang
  • Ceylan Aksit, Duygu

Abrégé

Embodiments are disclosed for using machine learning models to perform three-dimensional garment deformation due to character body motion with collision handling. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input, the input including character body shape parameters and character body pose parameters defining a character body, and garment parameters. The disclosed systems and methods further comprise generating, by a first neural network, a first set of garment vertices defining deformations of a garment with the character body based on the input. The disclosed systems and methods further comprise determining, by a second neural network, that the first set of garment vertices includes a second set of garment vertices penetrating the character body. The disclosed systems and methods further comprise modifying, by a third neural network, each garment vertex in the second set of garment vertices to positions outside the character body.

Classes IPC  ?

  • G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels

62.

REINFORCEMENT LEARNING-BASED TECHNIQUES FOR TRAINING A NATURAL MEDIA AGENT

      
Numéro d'application 18479486
Statut En instance
Date de dépôt 2023-10-02
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Brandt, Jonathan
  • Fang, Chen
  • Kim, Byungmoon
  • Jia, Biao

Abrégé

Some embodiments involve a reinforcement learning based framework for training a natural media agent to learn a rendering policy without human supervision or labeled datasets. The reinforcement learning based framework feeds the natural media agent a training dataset to implicitly learn the rendering policy by exploring a canvas and minimizing a loss function. Once trained, the natural media agent can be applied to any reference image to generate a series (or sequence) of continuous-valued primitive graphic actions, e.g., sequence of painting strokes, that when rendered by a synthetic rendering environment on a canvas, reproduce an identical or transformed version of the reference image subject to limitations of an action space and the learned rendering policy.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G09G 5/37 - Dispositions ou circuits de commande de l'affichage communs à l'affichage utilisant des tubes à rayons cathodiques et à l'affichage utilisant d'autres moyens de visualisation caractérisés par l'affichage de dessins graphiques individuels en utilisant une mémoire à mappage binaire - Détails concernant le traitement de dessins graphiques
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

63.

GENERATING IMPROVED PANOPTIC SEGMENTED DIGITAL IMAGES BASED ON PANOPTIC SEGMENTATION NEURAL NETWORKS THAT UTILIZE EXEMPLAR UNKNOWN OBJECT CLASSES

      
Numéro d'application 18487453
Statut En instance
Date de dépôt 2023-10-16
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Hwang, Jaedong
  • Oh, Seoung Wug
  • Lee, Joon-Young

Abrégé

This disclosure describes one or more implementations of a panoptic segmentation system that generates panoptic segmented digital images that classify both known and unknown instances of digital images. For example, the panoptic segmentation system builds and utilizes a panoptic segmentation neural network to discover, cluster, and segment new unknown object subclasses for previously unknown object instances. In addition, the panoptic segmentation system can determine additional unknown object instances from additional digital images. Moreover, in some implementations, the panoptic segmentation system utilizes the newly generated unknown object subclasses to refine and tune the panoptic segmentation neural network to improve the detection of unknown object instances in input digital images.

Classes IPC  ?

  • G06T 7/11 - Découpage basé sur les zones
  • G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
  • G06F 18/2413 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence

64.

CONTRASTIVE CAPTIONING FOR IMAGE GROUPS

      
Numéro d'application 18487183
Statut En instance
Date de dépôt 2023-10-16
Date de la première publication 2024-02-01
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Tran, Quan Hung
  • Mai, Long Thanh
  • Lin, Zhe
  • Li, Zhuowan

Abrégé

A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

Classes IPC  ?

  • G06V 20/30 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p.ex. des photos ou des vidéos issus des réseaux sociaux
  • G06F 16/55 - Groupement; Classement
  • G06F 16/535 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes
  • G06F 40/205 - Analyse syntaxique
  • G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
  • G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

65.

LONG-TAIL COLOR PREDICTION

      
Numéro d'application 17814921
Statut En instance
Date de dépôt 2022-07-26
Date de la première publication 2024-02-01
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Chen, Qiuyu
  • Tran, Quan Hung
  • Kafle, Kushal
  • Bui, Trung Huu
  • Dernoncourt, Franck
  • Chang, Walter W.

Abrégé

Systems and methods for color prediction are described. Embodiments of the present disclosure receive an image that includes an object including a color, generate a color vector based on the image using a color classification network, where the color vector includes a color value corresponding to each of a set of colors, generate a bias vector by comparing the color vector to teach of a set of center vectors, where each of the set of center vectors corresponds to a color of the set of colors, and generate an unbiased color vector based on the color vector and the bias vector, where the unbiased color vector indicates the color of the object.

Classes IPC  ?

  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”

66.

GENERATING NEURAL NETWORK BASED PERCEPTUAL ARTIFACT SEGMENTATIONS IN MODIFIED PORTIONS OF A DIGITAL IMAGE

      
Numéro d'application 17815409
Statut En instance
Date de dépôt 2022-07-27
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Amirghodsi, Sohrab
  • Zhang, Lingzhi
  • Lin, Zhe
  • Shechtman, Elya
  • Zhou, Yuqian
  • Barnes, Connelly

Abrégé

Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.

Classes IPC  ?

  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan

67.

OPEN-DOMAIN TRENDING HASHTAG RECOMMENDATIONS

      
Numéro d'application 17877469
Statut En instance
Date de dépôt 2022-07-29
Date de la première publication 2024-02-01
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Sarkhel, Somdeb
  • Chen, Xiang
  • Swaminathan, Viswanathan
  • Mehta, Swapneel
  • Mitra, Saayan
  • Rossi, Ryan
  • Guo, Han
  • Aminian, Ali
  • Garg, Kshitiz

Abrégé

Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.

Classes IPC  ?

  • G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme

68.

Systems and methods for facial image generation

      
Numéro d'application 17813987
Numéro de brevet 11941727
Statut Délivré - en vigueur
Date de dépôt 2022-07-21
Date de la première publication 2024-02-01
Date d'octroi 2024-03-26
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Motiian, Saeid
  • Lin, Wei-An
  • Ghadar, Shabnam

Abrégé

Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions

69.

THE LETTER A

      
Numéro de série 98384361
Statut En instance
Date de dépôt 2024-01-31
Propriétaire Adobe Inc. ()
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Downloadable computer software for word processing, exchanging documents, archiving documents, and creating, editing, annotating, indexing, laying out, printing, scanning, organizing, managing, sharing, signing, encrypting, converting, and providing access control of documents; downloadable computer software for collaborative authoring, editing, and electronic signing of electronic and multimedia documents; downloadable computer software for creating, viewing, editing, sharing, scanning, signing, and distributing Portable Document Format (PDF) files; downloadable computer software for page recognition and rendering for use in viewing, printing, navigating, editing, annotating and indexing electronic documents, filling in and submitting forms on-line, and transferring electronic documents via a local or global communications network; downloadable computer software for use in creating, viewing, manipulating, distributing, printing, storing, transferring and retrieving computer aided graphics, text documents, fonts, multimedia applications, digital movies, video images, audio recordings, animation and still images; downloadable augmented reality software for creating immersive content; downloadable computer software for aggregating, editing, animating, displaying images, photos, and other multimedia content into an augmented reality experience; downloadable augmented reality software for use in mobile devices for integrating electronic data with real world environments for the purpose of creating immersive content; downloadable software in the nature of a mobile application for use in creating, designing, editing, importing, exporting, processing, transmitting, publishing and sharing images, text, fonts, logos, collages, flyers, posters, banners, video, video stories, audio recordings, animation, graphics, digital slideshows, web pages, photographs, photo galleries, and multimedia content; downloadable computer programs using artificial intelligence for machine learning, deep learning, statistical learning, data mining, contextual prediction, personalization, business predictive analytics, business predictive modeling and business intelligence; downloadable computer software using artificial intelligence for machine learning, deep learning, statistical learning, data mining, contextual prediction, personalization, business predictive analytics, business predictive modeling and business intelligence; downloadable computer graphics software; downloadable software for creating, processing, editing, manipulating, and designing images, graphics, and text; downloadable computer aided design software for creating, processing, editing, manipulating, and designing images, graphics, and text; downloadable software for digitizing photographic or cartoon images; downloadable generation and modification software for textures, particularly procedural textures, namely, downloadable software for generating and modifying procedural textures; downloadable software for the 3D modeling and creation of computer generated graphics; downloadable computer software for designing animated films; downloadable software for creating, processing, editing, manipulating and designing digital animations, digital lighting, digital netting, 2D and 3D images, graphics, text, audio, video, multimedia works, models, textures, videos, movies, video games, and digital images, and industrial designs, 3D space layout, fashion and clothing design, architectural and interior design layouts; downloadable computer software for use in graphic design, desktop publishing, electronic publishing, printing, artistic and technical drawing, creating fonts and typefaces, and special graphical and textual effects; downloadable computer software for displaying clip art, and typefaces; downloadable computer software for viewing, managing, indexing, storing, transferring, and exchanging digital photographs, interactive documents and works, and text documents; downloadable computer software for creating greeting cards, calendars, books, documents, automated PDF slide shows, and web photo galleries and albums; downloadable software in the nature of a mobile application for creating, viewing, manipulating, editing, managing, indexing, cataloguing, sorting, organizing, storing, transferring, synchronizing, printing, and exchanging digital photographs, digital and graphic images, data, text, audio, video, multimedia and interactive documents and works, text documents, and recorded information; downloadable software in the nature of a mobile application for transferring digital photographs, digital and graphic images, data, text, audio, video, multimedia and interactive documents and works, text documents, and recorded information for use over computer networks, wireless networks and global communication networks; downloadable software in the nature of a mobile application for creating greeting cards, calendars, books, documents, automated PDF slide shows, and web photo galleries and albums; downloadable computer software for creating fonts; downloadable computer software for displaying recorded typeface; type designs recorded as latent images in machine readable electronic data storage media; downloadable computer software for organizing fonts; downloadable website development software; downloadable computer software, namely, software development tools for the creation of mobile internet applications and client interfaces; downloadable computer software for use in task management, schedule management, business management, document management, business planning, human resource allocation, workforce collaboration, financial resource allocation, and workflow tracking in the field of business process management

70.

THE LETTER A

      
Numéro de série 98384530
Statut En instance
Date de dépôt 2024-01-31
Propriétaire Adobe Inc. ()
Classes de Nice  ? 41 - Éducation, divertissements, activités sportives et culturelles

Produits et services

Educational and training services, namely, classroom training, online training, web based training, and video training in the fields of computer software, cloud computing, desktop publishing, digital publishing, electronic publishing, graphic design, marketing, advertising, analytics, e-commerce, digital asset management, data management, business management, business process management, business document and forms creation, and automation of business document and forms processing and workflow; educational services, namely, arranging professional workshops and training courses, conducting classes, seminars, conferences, and workshops in the fields of computer software, cloud computing, desktop publishing, digital publishing, electronic publishing, graphic design, marketing, advertising, analytics, e-commerce, digital asset management, data management, business management, business process management, business document and forms creation, and automation of business document and forms processing and workflow; educational and training sessions in the field of organization and business matters relating to creative professionals

71.

THE LETTER A

      
Numéro de série 98385205
Statut En instance
Date de dépôt 2024-01-31
Propriétaire Adobe Inc. ()
Classes de Nice  ? 42 - Services scientifiques, technologiques et industriels, recherche et conception

Produits et services

Computer services, namely, monitoring the websites of others to improve the scalability and performance of the websites of others; application service provider (ASP), namely, featuring software for managing and optimizing website performance and the effectiveness of online marketing campaigns; application service provider (ASP), namely, featuring software for managing, collecting, integrating, and sharing a wide variety of data and information regarding website visitor behavior in the field of online business optimization; application service provider (ASP), namely, featuring software for tracking web site activity and managing, monitoring, tracking and optimizing the performance and effectiveness of websites, and online marketing campaigns; application service provider (ASP), namely, featuring web analytics software; providing online, non downloadable software for managing, collecting, integrating, and sharing a wide variety of data and information from various sources; hosting of digital photographs, digital and graphical images, and digital content on the internet; providing temporary use of non downloadable software for creating, viewing, manipulating, editing, printing, and exchanging digital photographs, digital and graphic images; computer services, namely, creating an online community for registered users to view images, create, share, store, upload and download digital images, showcase their skills, post artwork and photographs, get feedback from their peers, form virtual communities, engage in social networking, collect preference data, and improve their skills; computer services, namely, hosting online web facilities for others for organizing and conducting online meetings, gatherings, and interactive discussions; computer services, namely, software as a service (SaaS), platform as a service (PaaS), computer software design, computer software consultation, and cloud hosting provider services, featuring software for digital advertising and marketing, analytics, e-commerce and customer experience management; computer consulting services in the field of design, implementation and use of computer e commerce software systems for others; software as a service (SaaS), namely, providing software for creating, forecasting, executing, managing, monitoring, tracking, testing and optimizing the performance and effectiveness of digital advertising and marketing campaigns across multiple channels, including websites, social networks, online video, mobile websites, mobile applications, mobile devices, TV, email, display and search advertising; software as a service (SaaS), namely, providing software for digital asset management and web content management; software as a service (SaaS), namely, providing software for use in customer journey and customer experience management; software as a service (SaaS), namely, providing software for online audience measurement, segmentation and insight; software as a service (SaaS), namely, providing software in the field of web analytics that collects, manages, integrates, analyzes, monitors, and tracks the activity of website visitors for the purpose of enhancing online experiences and optimizing the performance and effectiveness of both online and offline advertising and marketing campaigns and websites; software as a service (SaaS), namely, providing marketing automation software, omnichannel marketing software, lead generation software and software for account based marketing; software as a service (SaaS), namely, providing computer software to allow users to perform electronic business transactions in the field of e-commerce; providing online, non downloadable software for providing data driven TV advertising services; platform as a service (PAAS) featuring computer software platforms for data warehousing and management, data collection, data analytics, reporting, segmentation, visualization and presentation of data; platform as a service (PAAS) featuring computer software platforms for retrieving, tracking, analyzing, displaying, visualizing, optimizing, testing, measuring and managing customer data; platform as a service (PAAS) featuring computer software platforms for customer analysis; platform as a service (PAAS) featuring computer software platforms, namely, a demand side platform (DSP) for the planning, buying, measurement, and optimization of advertising; software as a service (SaaS) featuring software for a framework of intelligent data science based services using artificial intelligence (AI) and machine learning for improving digital experiences in the fields of customer experience management and the delivery of marketing and advertising; providing online non downloadable software using data science, namely, artificial intelligence (AI), machine learning, deep learning, statistical learning and data mining, for contextual prediction, personalization, predictive analytics, predictive modeling, visualization, and business intelligence; application service provider (ASP) services featuring application programming interface (API) software for building, creating and developing digital advertising and marketing, analytics, e commerce and customer experience management applications and integrations; application service provider (ASP), namely, hosting, managing, developing, analyzing, and maintaining applications, software, and web sites of others in the fields of advertising, marketing, analytics, e commerce and customer experience management; application service provider (ASP) services featuring computer software for use in creating, designing, building, publishing, and managing websites; application service provider (ASP), namely, providing software and software developer tools used to create and deliver video, multimedia, advertising, marketing and promotional content via the Internet, computer networks, other telecommunications networks, and mobile communications devices; software as a service (SaaS), namely, providing software for the creation, management, delivery, publishing and distribution of mobile, web, and digital advertising content, across multiple channels; software as a service (SaaS), namely, providing software for managing, collecting, integrating, reporting, analyzing, visualizing, indexing, filtering and sharing a wide variety of data and information regarding website visitor behavior in the field of online business optimization; software as a service (SaaS), namely, providing software for use in search marketing management, namely, software that manages, collects, integrates, analyzes, reports, and tracks internet search results and a wide variety of data and information relating to user activity on web sites; data mining services; computer services, namely, providing technical information and consulting services in the fields of computer software and cloud computing; providing technical support services in the nature of troubleshooting in the fields of computer software and cloud computing; technical support services in setting up ecommerce stores, namely, troubleshooting the implementation and use of computer e commerce software systems and the design of homepages and websites; research, design and development of computer software; maintenance, updating, and rental of computer software for creating, viewing, manipulating, editing, printing, and exchanging digital photographs, digital and graphic images; computer programming; providing temporary use of on-line non-downloadable computer graphics software; providing temporary use of on-line non-downloadable software for creating, processing, editing, manipulating, and designing images, graphics, and text; providing temporary use of on-line non-downloadable computer aided design software for creating, processing, editing, manipulating, and designing images, graphics, and text; providing temporary use of on-line non-downloadable software for digitizing photographic or cartoon images; providing temporary use of on-line non-downloadable generation and modification software for textures, particularly procedural textures, namely, downloadable software for generating and modifying procedural textures; providing temporary use of on-line non-downloadable software for the 3D modeling and creation of computer generated graphics; providing temporary use of on-line non-downloadable computer software for designing animated films; providing temporary use of on-line non-downloadable software for creating, processing, editing, manipulating and designing digital animations, digital lighting, digital netting, 2D and 3D images, graphics, text, audio, video, multimedia works, models, textures, videos, movies, video games, and digital images, and industrial designs, 3D space layout, fashion and clothing design, architectural and interior design layouts; providing temporary use of on-line non-downloadable software for use in graphic design, desktop publishing, electronic publishing, printing, artistic and technical drawing, creating fonts and typefaces, and special graphical and textual effects; providing temporary use of on-line non-downloadable software for providing and designing clip art, and typefaces; providing temporary use of on-line non-downloadable computer software for viewing, managing, indexing, storing, transferring, and exchanging digital photographs, interactive documents and works, and text documents; providing temporary use of on-line non-downloadable computer software for creating greeting cards, calendars, books, documents, automated PDF slide shows, and web photo galleries and albums; rental of computer software for image modeling, editing, and creation of computer generated graphics, document and image digitization, and providing design services; computer software consultancy; design, programming and development of software; hosting a website that allows users to create, design, edit, import, export, process, transmit, publish, and share images, text, fonts, logos, collages, flyers, posters, banners, video, video stories, audio recordings, animation, graphics, digital slideshows, web pages, photographs, photo galleries, and multimedia content; application service provider (ASP) services featuring computer software for use in creating, designing, editing, importing, exporting, processing, transmitting, publishing and sharing images, text, fonts, collages, video, video stories, audio recordings, animation, graphics, digital slideshows, web pages, photographs, photo galleries, and multimedia content; providing temporary use of online non downloadable software using data science, namely, artificial intelligence (AI), machine learning, deep learning, statistical learning and data mining, for contextual prediction, personalization, predictive analytics, predictive modeling, visualization, and business intelligence; software as a service (SaaS) services featuring artificial intelligence (AI) and machine learning driven tools for processing large volumes of data; application service provider (ASP) services featuring application programming interface (API) software for artificial intelligence (AI), machine learning, deep learning, statistical learning and data mining to enable contextual prediction, personalization, predictive analytics, predictive modeling and business intelligence services; scientific and technical research, namely, scientific and product research in the fields of artificial intelligence (AI), machine learning and deep learning; Software as a service (SAAS) services using artificial intelligence models for content generation and management, namely, image generation from user prompts, video generation from user prompts and image editing; Application service provider featuring application programming interface (API) software; Online non-downloadable computer software for using artificial intelligence models for content generation and management for use in the fields of desktop publishing, digital and electronic publishing, printing, imaging, graphics and typesetting; providing temporary use of non-downloadable software for viewing, manipulating, editing, digital photographs, digital and graphic images; computer software services, namely, providing temporary use of on line non downloadable software for task management, schedule management, business management, document management, business planning, human resource allocation, workforce collaboration, financial resource allocation, and workflow tracking in the field of business process management; providing temporary use of on-line non-downloadable website development software; providing temporary use of on-line non-downloadable software development tools for developing computer software, mobile internet applications, and client interfaces

72.

Identifying templates based on fonts

      
Numéro d'application 17977730
Numéro de brevet 11886809
Statut Délivré - en vigueur
Date de dépôt 2022-10-31
Date de la première publication 2024-01-30
Date d'octroi 2024-01-30
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jindal, Nipun
  • Khanna, Anand
  • Brdiczka, Oliver

Abrégé

In implementations of systems for identifying templates based on fonts, a computing device implements an identification system to receive input data describing a selection of a font included in a collection of fonts. The identification system generates an embedding that represents the font in a latent space using a machine learning model trained on training data to generate embeddings for digital templates in the latent space based on intent phrases associated with the digital templates and embeddings for fonts in the latent space based on intent phrases associated with the fonts. A digital template included in a collection of digital templates is identified based on the embedding that represents the font and an embedding that represents the digital template in the latent space. The identification system generates an indication of the digital template for display in a user interface.

Classes IPC  ?

  • G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
  • G06F 40/186 - Gabarits
  • G06N 3/08 - Méthodes d'apprentissage
  • G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
  • G06F 40/109 - Maniement des polices de caractères; Typographie cinétique ou temporelle
  • G06F 40/30 - Analyse sémantique

73.

Assistive digital form authoring

      
Numéro d'application 18153595
Numéro de brevet 11886803
Statut Délivré - en vigueur
Date de dépôt 2023-01-12
Date de la première publication 2024-01-30
Date d'octroi 2024-01-30
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Arneh
  • Taneja, Salil
  • Mangla, Puneet
  • Ahuja, Gaurav

Abrégé

In implementations of systems for assistive digital form authoring, a computing device implements an authoring system to receive input data describing a search input associated with a digital form. The authoring system generates an input embedding vector that represents the search input in a latent space using a machine learning model trained on training data to generate embedding vectors in the latent space. A candidate embedding vector included in a group of candidate embedding vectors is identified based on a distance between the input embedding vector and the candidate embedding vector in the latent space. The authoring system generates an indication of a search output associated with the digital form for display in a user interface based on the candidate embedding vector.

Classes IPC  ?

  • G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
  • G06F 40/174 - Remplissage de formulaires; Fusion
  • G06F 40/40 - Traitement ou traduction du langage naturel

74.

CR CONTENT CREDENTIALS

      
Numéro d'application 230718300
Statut En instance
Date de dépôt 2024-01-26
Propriétaire Adobe Inc. (USA)
Classes de Nice  ?
  • 09 - Appareils et instruments scientifiques et électriques
  • 35 - Publicité; Affaires commerciales
  • 41 - Éducation, divertissements, activités sportives et culturelles
  • 42 - Services scientifiques, technologiques et industriels, recherche et conception

Produits et services

(1) Downloadable software for use by creators, artists, authors, and owners of digital works to demonstrate the authenticity and provenance of their works; downloadable software for authenticating digital images; downloadable image and multimedia files containing artwork, video, and photographs. (1) Promoting the digital works of others by means of providing digital content authentication tools and providing information relating thereto; promoting public interest and awareness of content authenticity and provenance. (2) Providing online non-downloadable software for use by creators, artists, authors, and owners of digital works to demonstrate the authenticity and provenance of their works; authentication in the field of digital images, artwork, videos, and photographs; providing a website featuring technology that enables users to demonstrate the authenticity and provenance of their works; providing online non-downloadable image and multimedia files containing artwork, video, and photographs; providing a website featuring technology that enables users to facilitate the authentication of digital artwork, images, and photographs; development of voluntary standards for authentication of digital works featuring an online protocol for authenticating works of others for the reproduction and use of said material in digital formats and providing information relating thereto.

75.

CR

      
Numéro d'application 230718600
Statut En instance
Date de dépôt 2024-01-26
Propriétaire Adobe Inc. (USA)
Classes de Nice  ?
  • 09 - Appareils et instruments scientifiques et électriques
  • 35 - Publicité; Affaires commerciales
  • 41 - Éducation, divertissements, activités sportives et culturelles
  • 42 - Services scientifiques, technologiques et industriels, recherche et conception

Produits et services

(1) Downloadable software for use by creators, artists, authors, and owners of digital works to demonstrate the authenticity and provenance of their works; downloadable software for authenticating digital images; downloadable image and multimedia files containing artwork, video, and photographs. (1) Promoting the digital works of others by means of providing digital content authentication tools and providing information relating thereto; promoting public interest and awareness of content authenticity and provenance. (2) Providing online non-downloadable software for use by creators, artists, authors, and owners of digital works to demonstrate the authenticity and provenance of their works; authentication in the field of digital images, artwork, videos, and photographs; providing a website featuring technology that enables users to demonstrate the authenticity and provenance of their works; providing online non-downloadable image and multimedia files containing artwork, video, and photographs; providing a website featuring technology that enables users to facilitate the authentication of digital artwork, images, and photographs; development of voluntary standards for authentication of digital works featuring an online protocol for authenticating works of others for the reproduction and use of said material in digital formats and providing information relating thereto.

76.

A

      
Numéro d'application 230718000
Statut En instance
Date de dépôt 2024-01-26
Propriétaire Adobe Inc. (USA)
Classes de Nice  ? 42 - Services scientifiques, technologiques et industriels, recherche et conception

Produits et services

(1) Software as a service (SAAS) services featuring software using artificial intelligence models for editing, generating, and management of digital content being images generated from user prompts, videos generated from user prompts, in the field of image editing; (2) Application service provider offering application programming interface (API) software for image and text editing

77.

SYSTEMS AND METHODS FOR CONTENT CUSTOMIZATION

      
Numéro d'application 17813622
Statut En instance
Date de dépôt 2022-07-20
Date de la première publication 2024-01-25
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Sinha, Atanu R.
  • Maiti, Aurghya
  • Ganesh, Atishay
  • Myana, Saili
  • Chopra, Harshita
  • Kapoor, Sarthak
  • Mahapatra, Saurabh

Abrégé

Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.

Classes IPC  ?

  • H04N 21/2668 - Création d'un canal pour un groupe dédié d'utilisateurs finaux, p.ex. en insérant des publicités ciblées dans un flux vidéo en fonction des profils des utilisateurs finaux
  • H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p.ex. authentification des utilisateurs finaux ou des dispositifs clients ou

78.

EXPERIMENTALLY VALIDATING CAUSAL GRAPHS

      
Numéro d'application 17814394
Statut En instance
Date de dépôt 2022-07-22
Date de la première publication 2024-01-25
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Porwal, Vibhor
  • Sinha, Gaurav

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer-readable media that verify causal graphs utilizing nodes from corresponding Markov equivalence classes. For instance, in one or more embodiments, the disclosed systems receive a causal graph to be validated and a Markov equivalence class that corresponds to the causal graph. Additionally, the disclosed systems determine an intervention set using the causal graph, the intervention set comprising nodes from the Markov equivalence class. Using a plurality of interventions on the nodes of the intervention set, the disclosed systems determine whether the causal graph is valid.

Classes IPC  ?

  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
  • G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques

79.

WIRE SEGMENTATION FOR IMAGES USING MACHINE LEARNING

      
Numéro d'application 17870496
Statut En instance
Date de dépôt 2022-07-21
Date de la première publication 2024-01-25
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Chiu, Mang Tik
  • Barnes, Connelly
  • Wei, Zijun
  • Lin, Zhe
  • Zhou, Yuqian
  • Zhang, Xuaner
  • Amirghodsi, Sohrab
  • Kainz, Florian
  • Shechtman, Elya

Abrégé

Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.

Classes IPC  ?

  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 5/30 - Erosion ou dilatation, p.ex. amincissement
  • G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image

80.

Automatic Item Placement Recommendations Based on Entity Similarity

      
Numéro d'application 18478856
Statut En instance
Date de dépôt 2023-09-29
Date de la première publication 2024-01-25
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Chen, Xiang
  • Swaminathan, Viswanathan
  • Sarkhel, Somdeb

Abrégé

Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.

Classes IPC  ?

  • G06Q 30/0251 - Publicités ciblées
  • G06F 16/28 - Bases de données caractérisées par leurs modèles, p.ex. des modèles relationnels ou objet
  • G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
  • G06Q 30/0241 - Publicités

81.

CONFIDENCE EVALUATION MODEL FOR STRUCTURE PREDICTION TASKS

      
Numéro d'application 17815448
Statut En instance
Date de dépôt 2022-07-27
Date de la première publication 2024-01-25
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Tensmeyer, Christopher
  • Barmpalios, Nikolaos
  • Madapoosi Ravi, Sruthi
  • Deshpande, Ruchi
  • Manjunatha, Varun
  • Bangalore Naresh, Smitha
  • Mathur, Priyank
  • Sido, Oghenetegiri

Abrégé

Techniques for training for and determining a confidence of an output of a machine learning model are disclosed. Such techniques include, in some embodiments, receiving, from the machine learning model configured to receive information associated with a data object, information associated with a predicted structure for the data object; encoding, using a second machine learning model, the information associated with the predicted structure for the data object to produce encoded input channels; evaluating, using the second machine learning model, the information associated with the data object with the encoded input channels; and based on the evaluating, determining, using the second machine learning model, a probability of correctness of the predicted structure for the data object.

Classes IPC  ?

  • G06N 20/20 - Techniques d’ensemble en apprentissage automatique
  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

82.

Systems for Spatially Coherent UV Packing

      
Numéro d'application 17872595
Statut En instance
Date de dépôt 2022-07-25
Date de la première publication 2024-01-25
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Bishev, Artem
  • El Hajjar, Jean-François

Abrégé

In implementations of systems for spatially coherent UV packing, a computing device implements a packing system to identify pairs of boundary vertices of different two-dimensional islands included in a set of two-dimensional islands. A first boundary vertex and a second boundary vertex of the pairs of boundary vertices both correspond to a same three-dimensional coordinate of a three-dimensional mesh. The packing system determines transformations for two-dimensional islands included in the set of two-dimensional islands based on distances between the first boundary vertex and the second boundary vertex of the pairs of boundary vertices. A three-dimensional object is generated for display in a user interface based on the transformations and the three-dimensional mesh.

Classes IPC  ?

  • G06T 7/00 - Analyse d'image
  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation

83.

MULTIMODAL INTENT DISCOVERY SYSTEM

      
Numéro d'application 17811963
Statut En instance
Date de dépôt 2022-07-12
Date de la première publication 2024-01-18
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Maharana, Adyasha
  • Tran, Quan Hung
  • Yoon, Seunghyun
  • Dernoncourt, Franck
  • Bui, Trung Huu
  • Chang, Walter W.

Abrégé

Systems and methods for intent discovery and video summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video, encode the video to obtain a sequence of video encodings, encode the transcript to obtain a sequence of text encodings, apply a visual gate to the sequence of text encodings based on the sequence of video encodings to obtain gated text encodings, and generate an intent label for the transcript based on the gated text encodings.

Classes IPC  ?

  • G06F 16/738 - Présentation des résultats des requêtes
  • G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p.ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
  • G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu

84.

Vector Object Jitter Application

      
Numéro d'application 17862956
Statut En instance
Date de dépôt 2022-07-12
Date de la première publication 2024-01-18
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Agrawal, Reena
  • Eisley, Jr., William C.
  • Guglani, Rohit Kumar
  • George, Paul A
  • Tayal, Gourav
  • Sinha, Deep

Abrégé

Jitter application techniques are described for vector objects as implemented by a vector object jitter application system. In an implementation, the vector object jitter application system receives an input defining a stroke to be drawn on a user interface. A vector object is then generated representing the stroke and having a variable width determined by applying jitter to the stroke. The vector object having the variable width is displayed in the user interface as the input is received.

Classes IPC  ?

  • G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles
  • G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
  • G06T 5/00 - Amélioration ou restauration d'image

85.

UNIVERSAL STYLE TRANSFER USING MULTl-SCALE FEATURE TRANSFORM AND USER CONTROLS

      
Numéro d'application 18474588
Statut En instance
Date de dépôt 2023-09-26
Date de la première publication 2024-01-18
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Li, Yijun
  • Mironica, Ionut

Abrégé

Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.

Classes IPC  ?

  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/50 - Récupération de la profondeur ou de la forme
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

86.

OBJECT-AGNOSTIC IMAGE REPRESENTATION

      
Numéro d'application 17812596
Statut En instance
Date de dépôt 2022-07-14
Date de la première publication 2024-01-18
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Kelkar, Sachin
  • Kale, Ajinkya Gorakhnath
  • Harikumar, Midhun

Abrégé

Systems and methods for image processing, and specifically for generating object-agnostic image representations, are described. Embodiments of the present disclosure receive a training image including a foreground object and a background, remove the foreground object from the training image to obtain a modified training image, inpaint a portion of the modified training image corresponding to the foreground object to obtain an inpainted training image, encode the training image and the inpainted training image using a machine learning model to obtain an encoded training image and an encoded inpainted training image, and update parameters of the machine learning model based on the encoded training image and the encoded inpainted training image.

Classes IPC  ?

  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan
  • G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
  • G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
  • G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
  • G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques

87.

NODE GRAPH OPTIMIZATION USING DIFFERENTIABLE PROXIES

      
Numéro d'application 17864901
Statut En instance
Date de dépôt 2022-07-14
Date de la première publication 2024-01-18
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Deschaintre, Valentin
  • Hu, Yiwei
  • Guerrero, Paul
  • Hasan, Milos

Abrégé

Embodiments are disclosed for optimizing a material graph for replicating a material of the target image. Embodiments include receiving a target image and a material graph to be optimized for replicating a material of the target image. Embodiments include identifying a non-differentiable node of the material graph, the non-differentiable node including a set of input parameters. Embodiments include selecting a differentiable proxy from a library of the selected differentiable proxy is trained to replicate an output of the identified non-differentiable node. Embodiments include generating an optimized input parameters for the identified non-differentiable node using the corresponding trained neural network and the target image. Embodiments include replacing the set of input parameters of the non-differentiable node of the material graph with the optimized input parameters. Embodiments include generating an output material by the material graph to represent the target image using the optimized input parameters for the non-differentiable node.

Classes IPC  ?

  • G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
  • G06T 15/04 - Mappage de texture
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

88.

EDITING PORTRAIT VIDEOS

      
Numéro d'application 17810651
Statut En instance
Date de dépôt 2022-07-05
Date de la première publication 2024-01-11
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Oh, Seoungwug
  • Lee, Joon-Young
  • Lu, Jingwan
  • Seo, Kwanggyoon

Abrégé

Systems and methods for video processing are described. Embodiments of the present disclosure identify an image that depicts an expression of a face; encode the image to obtain a latent code representing the image; edit the latent code to obtain an edited latent code that represents the face with a target attribute that is different from an original attribute of the face and with an edited expression that is different from the expression of the face; modify the edited latent code to obtain a modified latent code that represents the face with the target attribute and a modified expression, wherein a difference between the expression and the modified expression is less than a difference between the expression and the edited expression; and generate a modified image based on the modified latent code, wherein the modified image depicts the face with the target attribute and the modified expression.

Classes IPC  ?

  • G11B 27/036 - Montage par insertion
  • G06V 20/40 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans le contenu vidéo
  • G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
  • G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images
  • G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances

89.

CONTEXT-BASED REVIEW TRANSLATION

      
Numéro d'application 17857416
Statut En instance
Date de dépôt 2022-07-05
Date de la première publication 2024-01-11
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Singhal, Gourav
  • Jindal, Amol

Abrégé

A translation system provides machine translations of review texts on item pages using context from the item pages outside of the review text being translated. Given review text from an item page, context for machine translating the review text is determined from the item page. In some aspects, one or more keywords are determined based on text, images, and/or videos on the item page. The one or more keywords are used as context by the machine translator to translate the review text from a first language to a second language to provide translated review text, which can be presented on the item page.

Classes IPC  ?

  • G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image
  • G06V 20/62 - Texte, p.ex. plaques d’immatriculation, textes superposés ou légendes des images de télévision
  • G06V 20/40 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans le contenu vidéo
  • G06F 40/279 - Reconnaissance d’entités textuelles
  • G06F 40/58 - Utilisation de traduction automatisée, p.ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel

90.

Spacing guides for objects in perspective views

      
Numéro d'application 17858262
Numéro de brevet 11935207
Statut Délivré - en vigueur
Date de dépôt 2022-07-06
Date de la première publication 2024-01-11
Date d'octroi 2024-03-19
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Jain, Ashish
  • Jain, Arushi

Abrégé

In implementations of systems for generating spacing guides for objects in perspective views, a computing device implements a guide system to determine groups of line segments of perspective bounding boxes of objects displayed in a user interface of a digital content editing application. Interaction data is received describing a user interaction with a particular object of the objects displayed in the user interface. The guide system identifies a particular group of the groups of line segments based on a line segment of a perspective bounding box of the particular object. An indication of a guide is generated for display in the user interface based on the line segment and a first line segment included in the particular group.

Classes IPC  ?

  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
  • G06T 15/20 - Calcul de perspectives

91.

POINT-BASED NEURAL RADIANCE FIELD FOR THREE DIMENSIONAL SCENE REPRESENTATION

      
Numéro d'application 17861199
Statut En instance
Date de dépôt 2022-07-09
Date de la première publication 2024-01-11
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Xu, Zexiang
  • Shu, Zhixin
  • Bi, Sai
  • Xu, Qiangeng
  • Sunkavalli, Kalyan
  • Philip, Julien

Abrégé

A scene modeling system receives a plurality of input two-dimensional (2D) images corresponding to a plurality of views of an object and a request to display a three-dimensional (3D) scene that includes the object. The scene modeling system generates an output 2D image for a view of the 3D scene by applying a scene representation model to the input 2D images. The scene representation model includes a point cloud generation model configured to generate, based on the input 2D images, a neural point cloud representing the 3D scene. The scene representation model includes a neural point volume rendering model configured to determine, for each pixel of the output image and using the neural point cloud and a volume rendering process, a color value. The scene modeling system transmits, responsive to the request, the output 2D image. Each pixel of the output image includes the respective determined color value.

Classes IPC  ?

92.

MULTICHANNEL CONTENT RECOMMENDATION SYSTEM

      
Numéro d'application 17862258
Statut En instance
Date de dépôt 2022-07-11
Date de la première publication 2024-01-11
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Vaddamanu, Praneetha
  • Jain, Nihal
  • Maheshwari, Paridhi
  • Kulkarni, Kuldeep
  • Vinay, Vishwa
  • Srinivasan, Balaji Vasan
  • Chhaya, Niyati
  • Agrawal, Harshit
  • Mahapatra, Prabhat
  • Saha, Rizurekh

Abrégé

Embodiments are disclosed for multichannel content recommendation. The method may include receiving an input collection comprising a plurality of images. The method may include extracting a set of feature channels from each of the images. The method may include generating, by a trained machine learning model, an intent channel of the input collection from the set of feature channels. The method may include retrieving, from a content library, a plurality of search result images that include a channel that matches the intent channel. The method may include generating a recommended set of images based on the intent channel and the set of feature channels.

Classes IPC  ?

  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques
  • G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
  • G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 20/30 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p.ex. des photos ou des vidéos issus des réseaux sociaux
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06F 16/535 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes

93.

Articulated part extraction from sprite sheets using machine learning

      
Numéro d'application 17829120
Numéro de brevet 11875442
Statut Délivré - en vigueur
Date de dépôt 2022-05-31
Date de la première publication 2024-01-04
Date d'octroi 2024-01-16
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Fisher, Matthew David
  • Xu, Zhan
  • Zhou, Yang
  • Aneja, Deepali
  • Kalogerakis, Evangelos

Abrégé

Embodiments are disclosed for articulated part extraction using images of animated characters from sprite sheets by a digital design system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including a plurality of images depicting an animated character in different poses. The disclosed systems and methods further comprise, for each pair of images in the plurality of images, determining, by a first machine learning model, pixel correspondences between pixels of the pair of images, and determining, by a second machine learning model, pixel clusters representing the animated character, each pixel cluster corresponding to a different structural segment of the animated character. The disclosed systems and methods further comprise selecting a subset of clusters that reconstructs the different poses of the animated character. The disclosed systems and methods further comprise creating a rigged animated character based on the selected subset of clusters.

Classes IPC  ?

  • G06T 13/80 - Animation bidimensionnelle [2D], p.ex. utilisant des motifs graphiques programmables
  • G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p.ex. de visages similaires sur les réseaux sociaux
  • G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
  • G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source

94.

HIERARCHICAL TOPIC MODEL WITH AN INTERPRETABLE TOPIC HIERARCHY

      
Numéro d'application 17853141
Statut En instance
Date de dépôt 2022-06-29
Date de la première publication 2024-01-04
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Anand, Tanay
  • Bhatia, Sumit
  • Shahid, Simra
  • Srikanth, Nikitha
  • Puri, Nikaash

Abrégé

Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

Classes IPC  ?

95.

EXTRACTION OF HIGH-VALUE SEQUENTIAL PATTERNS USING REINFORCEMENT LEARNING TECHNIQUES

      
Numéro d'application 17855085
Statut En instance
Date de dépôt 2022-06-30
Date de la première publication 2024-01-04
Propriétaire
  • Adobe Inc. (USA)
  • Delhi Technological University (Inde)
Inventeur(s)
  • Anand, Tanay
  • Gupta, Piyush
  • Badjatiya, Pinkesh
  • Puri, Nikaash
  • Subramanian, Jayakumar
  • Krishnamurthy, Balaji
  • Singla, Chirag
  • Bansal, Rachit
  • Parihar, Anil Singh

Abrégé

In some embodiments, techniques for extracting high-value sequential patterns are provided. For example, a process may involve training a machine learning model to learn a state-action map that contains high-utility sequential patterns; extracting at least one high-utility sequential pattern from the trained machine learning model; and causing a user interface of a computing environment to be modified based on information from the at least one high-utility sequential pattern.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

96.

SCENE-BASED EDIT SUGGESTIONS FOR VIDEOS

      
Numéro d'application 17856180
Statut En instance
Date de dépôt 2022-07-01
Date de la première publication 2024-01-04
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Murarka, Ankur
  • Agarwal, Sneha

Abrégé

Embodiments are disclosed for determining scene-based editing recommendations for video content. A method of determining scene-based editing recommendations for video content includes receiving an input video comprising video content, dividing the input video into the plurality of scenes based on the video content, identifying a representative frame for each scene, determining a plurality of editing settings for each representative frame, determining editing settings for each scene based on an effectiveness score, and generating an output video using the input video and the editing settings for each scene.

Classes IPC  ?

  • G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p.ex. de signaux audio, vidéo
  • G06V 20/40 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans le contenu vidéo

97.

MACHINE LEARNING BASED CONTROLLABLE ANIMATION OF STILL IMAGES

      
Numéro d'application 17856362
Statut En instance
Date de dépôt 2022-07-01
Date de la première publication 2024-01-04
Propriétaire ADOBE INC. (USA)
Inventeur(s)
  • Kulkarni, Kuldeep
  • Mahapatra, Aniruddha

Abrégé

Systems and methods for machine learning based controllable animation of still images is provided. In one embodiment, a still image including a fluid element is obtained. Using a flow refinement machine learning model, a refined dense optical flow is generated for the still image based on a selection mask that includes the fluid element and a dense optical flow generated from a motion hint that indicates a direction of animation. The refined dense optical flow indicates a pattern of apparent motion for the at least one fluid element. Thereafter, a plurality of video frames is generated by projecting a plurality of pixels of the still image using the refined dense optical flow.

Classes IPC  ?

  • G06T 13/80 - Animation bidimensionnelle [2D], p.ex. utilisant des motifs graphiques programmables
  • G06T 7/215 - Découpage basé sur le mouvement
  • G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image

98.

TRANSFERRING HAIRSTYLES BETWEEN PORTRAIT IMAGES UTILIZING DEEP LATENT REPRESENTATIONS

      
Numéro d'application 18467397
Statut En instance
Date de dépôt 2023-09-14
Date de la première publication 2024-01-04
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Chakrabarty, Saikat
  • Kumar, Sunil

Abrégé

The disclosure describes one or more embodiments of systems, methods, and non-transitory computer-readable media that generate a transferred hairstyle image that depicts a person from a source image having a hairstyle from a target image. For example, the disclosed systems utilize a face-generative neural network to project the source and target images into latent vectors. In addition, in some embodiments, the disclosed systems quantify (or identify) activation values that control hair features for the projected latent vectors of the target and source image. Furthermore, in some instances, the disclosed systems selectively combine (e.g., via splicing) the projected latent vectors of the target and source image to generate a hairstyle-transfer latent vector by using the quantified activation values. Then, in one or more embodiments, the disclosed systems generate a transferred hairstyle image that depicts the person from the source image having the hairstyle from the target image by synthesizing the hairstyle-transfer latent vector.

Classes IPC  ?

  • G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
  • G06N 3/08 - Méthodes d'apprentissage
  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions

99.

RETRIEVING DIGITAL IMAGES IN RESPONSE TO SEARCH QUERIES FOR SEARCH-DRIVEN IMAGE EDITING

      
Numéro d'application 17809781
Statut En instance
Date de dépôt 2022-06-29
Date de la première publication 2024-01-04
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Zhang, Zhifei
  • Lin, Zhe
  • Ding, Zhihong
  • Cohen, Scott
  • Prasad, Darshan

Abrégé

The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.

Classes IPC  ?

  • G06F 16/538 - Présentation des résultats des requêtes
  • G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques
  • G06T 7/11 - Découpage basé sur les zones
  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu

100.

APPLYING OBJECT-AWARE STYLE TRANSFER TO DIGITAL IMAGES

      
Numéro d'application 17810392
Statut En instance
Date de dépôt 2022-07-01
Date de la première publication 2024-01-04
Propriétaire Adobe Inc. (USA)
Inventeur(s)
  • Zhang, Zhifei
  • Lin, Zhe
  • Cohen, Scott
  • Prasad, Darshan
  • Ding, Zhihong

Abrégé

The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Classes IPC  ?

  • G06T 11/40 - Remplissage d'une surface plane par addition d'attributs de surface, p.ex. de couleur ou de texture
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/12 - Découpage basé sur les bords
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