A system and method for machine learning architecture for prospective resource allocations are described. The method may include: receiving data records representing historical resource allocations from a user account associated with a first identifier to a resource account associated with a second identifier; deriving input features based on the data records; computing, using a trained neural network architecture, a predicted resource allocation amount and a predicted resource allocation date for the predicted resource allocation amount based on the derived input features; determining, using the trained neural network architecture, a first selection score associated with the predicted resource allocation amount and a second selection score associated with the predicted resource allocation date; and when the first or second selection score is above a minimum threshold, causing to display, at a display device, the associated resource allocation amount or date corresponding to the second identifier.
A hybrid computer architecture a process providing flexible computing resources across a combination of on-premise computing resources and cloud-based computing resources.
Methods, systems, and techniques for using an actor model payment processing engine to process payments. A payment instruction is received. An event corresponding to the payment instruction is stored in an event journal. The payment processing engine, which is event-sourced and actor-based, performs the payment instruction. Performing the payment instruction involves transitioning the engine through one or more states in response to the payment instruction, and may involve performing actions with non-event sourced and event sourced actors in both stateless and stateful environments.
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
4.
SYSTEMS AND METHODS FOR TOKEN-BASED BROWSER EXTENSION FRAMEWORK
A computer-implemented system and method for orchestrating at least two extensions installed on a browser and for authenticating a user are disclosed. An example method for orchestration includes: receiving, by an extension orchestrator, from a browser launched on a user device, a request from a first extension manager associated with a first extension installed on the browser, the request comprising a first extension ID for the first extension and a second extension ID for a second extension installed on the browser; retrieving, based on the first and second extension IDs, a first extension configuration for the first extension and a second extension configuration for the second extension from a metadata database; and routing a response to the first extension manager, the response comprising the first and second extension configurations and an extension ranking.
An electronic payment device and methods of its operation are disclosed. The payment device has a secure element for storing payment tokens, each associated with a payment card; an input interface that enables a user to select from among the payment cards; a display interface; and a processor. In response to a user selection of one of the payment cards by way of the input interface, a descriptor of the selected payment card is displayed by way of the display interface; and an unconsumed one of the payment tokens associated with the selected payment card is activated to prepare the payment card device for effecting payment using the selected payment card, thereby consuming the payment token. The payment device also includes a wireless communication interface for receiving additional payment tokens, thereby replenishing the payment tokens.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
A method for deploying a containerized application from a central application management hub to a plurality of cloud environments, the method comprising the steps of: receiving a containerized application suitable for deployment; receiving an environment file designating a first environment and a second environment of the plurality of cloud environments; consulting a routing table to determine a first network path associated with the first environment and a second network path associated with the second environment; generating packets of the containerized application; and sending the packets on the first network path and the second network path; wherein the containerized application is received by a respective operators of the first environment and the second environment for subsequent deployment.
Methods, systems, and techniques for agricultural greenhouse gas estimation. Farm data in the form of at least one of revenue generated by a farm, crop information for one or more crops grown on the farm, and land use/farm practice data for land used on the farm to grow the one or more crops is obtained. An emissions estimate is determined based on the obtained data and caused to be displayed to the user via a graphical user interface. A user may be a person responsible for managing multiple farms. That user may be presented with aggregate emissions-related information for all farms, including projected future emissions under various scenarios, and may also iteratively experiment with different farm data values in order to attempt to reduce projected emissions or increase data quality/emissions estimate accuracy.
A method on applying user data for providing services to a user from a platform of services, the method comprising the steps of: obtaining user profile data pertaining to the user of a network system of an institution; comparing the user profile data to a plurality of different potential life stages in order to determine a selected life stage; identifying one or more services from the platform of services based on the selected life stage; identifying the one or more services to the user via a user interface of a user device; receiving a request from the user through the user device for access to the one or more services; and updating contents of the user profile to include additional profile content related to activity of the user with the one or more services.
A method for training a neural network utilizing Long Short-Term Memory (LSTM) to model a computer application log as a natural language sequence comprises feeding a training set of application log files to a log file parser, generating, by the log file parser, a set of X application log clusters, where X is a whole number, feeding the whole number X to an untrained LSTM neural network as a hyperparameter representing a number of classes, and training the untrained LSTM neural network using the training set of log files and the hyperparameter X to obtain a trained LSTM neural network.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
A procurement system allows a user to provide a request for goods or services. The request is processed to determine its complexity and, for high complexity cases, select an appropriate procurement professional using a trained classifier to handle the procurement request.
Computer systems, apparatuses, processors, and non-transitory computer-readable storage devices configured for executing a method for generating proactive advisor recommendation using artificial intelligence. The method has the steps of: partitioning a plurality of clients using a clustering model based on data of the plurality of clients for clustering the plurality of clients into a plurality of client clusters; classifying the clients of at least a first client cluster of the plurality of client clusters into a plurality of client classifications by using one or more random-forest classifiers; and generating financial recommendations for the clients of at least a first client classification of the plurality of client classifications.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06Q 40/02 - Banking, e.g. interest calculation or account maintenance
12.
SYSTEMS AND METHODS FOR FACILITATING PROACTIVE RECRUITMENT
Methods, systems, and techniques for facilitating proactive recruitment are disclosed, comprising: receiving a user annotation of a candidate profile stored in a database, the user annotation provided by a user; based on at least the received user annotation, determining a sentiment of the user with respect to a candidate associated with the candidate profile; and when the sentiment of the user is determined to be positive, scheduling a notification to be sent to the user in response to a trigger event.
Methods, systems, and techniques for data mapping. Company identifiers and an electronic commerce transaction history, such as an online banking transaction history, of a user are retrieved from one or more data repositories. The electronic commerce transaction history includes purchases made from one or more companies identified by the company identifiers. Data mapping is then performed to associate the company identifiers with the purchases represented in the electronic commerce transaction history to identify the companies represented by the company identifiers from which the user made purchases. The company identifiers are then caused to be displayed on a graphical user interface as suggestions to the user as investment suggestions.
A data source is monitored. During the monitoring, an arrival at the data source of each of one or more sets of one or more features is detected. In response to detecting the arrival at the data source of at least a first set of one or more features of the one or more sets of one or more features, data is extracted from the first set of one or more features, data for at least a second set of one or more features of the one or more sets of one or more features is estimated, wherein the second set of one or more features has not yet arrived at the data source, and, based on the extracted data and the estimated data, a data quality metric is predicted.
A method for monitoring a network service based on a correlation including network traffic metrics experienced by the network service and infrastructure operational metrics of the network service, the method comprising the steps of: obtaining periodic data including the network traffic metrics, the infrastructure operational metrics, and social media metrics, the social media metrics including content associated with one or more services provided by the network service; storing the network traffic metrics, the infrastructure operational metrics, and social media metrics in a storage for use as historical data representing a predefined period of time; providing a correlation defining a relationship between metrics content of the periodic data; receiving the periodic data during operation of the network service and using the correlation to process the received periodic data to determine an output representing an infrastructure operational metric; comparing the infrastructure operational metric to a predefined operational constraint; generating an alert notification when the infrastructure operational metric contradicts the predefined operational constraint; and sending at least one of the infrastructure operational metric and the alert notification to a support system for subsequent processing.
Methods, systems, and techniques for facilitating client authentication are disclosed, comprising: receiving an identifier of a client; retrieving client information based on the identifier of the client; assessing a plurality of risk indicators for the client from the client information; determining a risk level for the client based on the plurality of risk indicators; and outputting the risk level for display on a user device.
The present disclosure describes an artificial intelligence approach to digital content recommendation where the recommendation mechanics differ based on the amount of information available. In one aspect, a user is identified as an above-threshold user who has consumed at least a threshold number of digital artifacts or a below-threshold user who has consumed fewer digital artifacts and different recommendation engines are used for above-threshold users and below-threshold users. In another aspect, users are bifurcated into low-data users and high-data users. For high-data users, digital artifacts are directly selected, and for low-data users, digital artifacts are indirectly selected by first selecting a digital artifact property criteria and then selecting digital artifacts that satisfy the selected digital artifact property criteria. In another aspect, digital artifacts are selected according to a common recommendation engine, wherein a quantity of digital artifacts consumed by the user is an input to the common recommendation engine.
A method of generating data on cryptocurrencies is described. Using one or more computer processors, a request to display a benchmark index relating to the cryptocurrencies is received. In response to receiving the request, for each of the cryptocurrencies, a market capitalization value and a price of the cryptocurrency over time are determined. Based on the market capitalization values and the prices over time, the benchmark index is generated and then displayed. In addition, based on the total value of one or more cryptocurrencies over a past period of time, the future price of the one or more cryptocurrencies over the future period of time may be predicted.
A method of making cryptographic key metadata available to key owners while protecting the integrity of the cryptographic key metadata comprises extracting key metadata from a metadata storage on a key data storage system. The metadata storage is logically isolated from a sensitive cryptographic data storage on the key data storage system. The method further comprises transmitting, by unidirectional communication, the extracted key metadata to a user-accessible metadata database that is separate and distinct from the metadata storage on the key data storage system. The method identifies, from the user-accessible metadata database, user-specific metadata for at least one cryptographic key associated with an authorized user associated with the at least one cryptographic key, and communicates the identified user-specific metadata to the authorized user.
In one aspect, a system for managing data processes in a network of computing resources is configured to: receive, from an instructor device, a parent request for execution of at least one parent data process executable by a plurality of computing resources at least one computing resource; generate at least one child request for execution of at least one corresponding child data process for routing to at least one corresponding destination device, each of the at least one child data process for executing at least a portion of the at least one parent data process, and each of the at least one child request including a respective destination key derived from at least one instructor key; and route each of the at least one child request to the at least one corresponding destination device. The at least one child request can be obtained by a supervisor server via the routing.
H04L 45/302 - Route determination based on requested QoS
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers.
A system and method for auto-populating an electronic transaction process is provided. The system comprises at least one processor, and a memory storing instructions which when executed by the at least one processor configure the processor to obtain a scanned payee identifier from an optical character recognition scan of a digital bill document, compare the scanned payee identifier with a set of stored payee identifiers to obtain at least one first identifier match, determine a score for each of the at least one identifier match, and select the stored payee identifier associated with a highest score. The method comprises obtaining a scanned payee identifier from an optical character recognition scan of a digital bill document, comparing the scanned payee identifier with a set of stored payee identifiers to obtain at least one first identifier match, determining a score for each of the at least one identifier match, and selecting the stored payee identifier associated with a highest score.
Systems 100, 1000, methods, and machine-interpretable programming or other instruction products for the management of data transmission by multiple networked computing resources 106, 1106. In particular, the disclosure relates to the synchronization of related requests for transmitting data using distributed network resources.
Methods, systems, and techniques for event notification. An event, such as a payment event that represents a payment transaction having been initiated, completed, or that the transaction is in progress, results in an event engine sending an upstream message to one or more servers. The one or more servers receive the upstream message, which is in a first format. The one or more servers convert the upstream message into a downstream message that is in a second format, such as an ISO 20022 format, and the downstream message is subsequently consumed by an event consumer. The event consumer may consume the downstream message in real-time relative to when the event occurs. Undelivered upstream or downstream messages may be stored in a dead letter channel repository for attempted redelivery.
Embodiments generally relate to the field of reward processing, and more particularly, systems, methods, and computer readable media for digital reward processing utilizing distributed ledger technology. Distributed ledger technology is utilized wherein distributed ledgers are stored on a plurality of node computing devices, the distributed ledgers including sequential entries that are cryptographically linked to one another.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 20/06 - Private payment circuits, e.g. involving electronic currency used only among participants of a common payment scheme
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
G06Q 20/10 - Payment architectures specially adapted for home banking systems
G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
G06Q 30/0226 - Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
H04L 9/06 - Arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
26.
DEVELOPMENT AND IMPLEMENTATION OF CONTAINERIZED APPLICATIONS
A method for developing a containerized application using a pipeline platform consisting of a plurality of stages with associated development tools, the method comprising the steps of: receiving application parameters and a check-in code for the containerized application; generating a configuration file based on the application parameters, the configuration file containing configuration content including insert code; embedding the insert code into the check-in code; dynamically provisioning an opinionated pipeline based on contents of the configuration file, the opinionated pipeline including the plurality of stages with the associated development tools; setting up one or more control gates in one or more of the plurality of stages; receiving customized code for the containerized application, the customized code representing modifications of the insert code; and packaging the containerized application to include code contents of the check-in code, the customized code, and the insert code; wherein the containerized application is submitted for deployment to one or more environment platforms upon satisfying the one or more control gates or the containerized application is restricted from the subsequent deployment based on failure of the one or more control gates.
Methods, systems, and techniques for performing automatic source code generation for use in a data transformation process. A computer obtains a data file comprising data transformation rules. Using those rules, the computer automatically generates computer source code for use in a data transformation process to transform source data into target data. The source data may, for example, be raw data from a data lake, and the computer source code may be Scala computer code for execution within an Apache Spark™ framework. The data lake may execute the computer source code to transform the raw data stored in the data lake into the target data, and the target data may then be stored in the data warehouse.
Systems, methods, and computer-readable media for coordinating processing of data by multiple networked computing resources include monitoring data associated with a plurality of networked computing resources, and coordinating the routing of data processing segments to the networked computing resources.
G06Q 40/04 - Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
H04L 47/283 - Flow control; Congestion control in relation to timing considerations in response to processing delays, e.g. caused by jitter or round trip time [RTT]
H04L 67/62 - Establishing a time schedule for servicing the requests
29.
MAPPING NETWORK CONNECTIONS BY TCP/IP DATA AGGREGATION
A method for mapping network connections among a plurality of servers comprises invoking inbuilt OS-native utilities on the servers to identify TCP/IP connections on the servers, parsing the TCP/IP connections into a common representation format, and using the common representation format to map dependencies in the network by differentiating the TCP/IP connections into inbound TCP/IP connections and outbound TCP/IP connections. Local scripts may be used to invoke the inbuilt OS-native utilities and parse the TCP/IP connections into the common representation format.
Systems 100, 1000, methods, and machine-interpretable programming or other instruction products for the management of data processing by multiple networked computing resources 106, 1106. In particular, the disclosure relates to the synchronization of related requests for processing of data using distributed network resources.
H04L 47/283 - Flow control; Congestion control in relation to timing considerations in response to processing delays, e.g. caused by jitter or round trip time [RTT]
H04L 67/62 - Establishing a time schedule for servicing the requests
H04L 67/63 - Routing a service request depending on the request content or context
31.
DETECTING NETWORK ANOMALIES BY CORRELATING MULTIPLE INFORMATION SOURCES
A method for detecting network anomalies comprises monitoring a network that provides public-facing application services and monitoring at least one external public Internet platform outside of the network to obtain volumetric problem report data about the application services. The external public Internet platform is nonspecific to the application services. Responsive to the volumetric problem report data from the external public Internet platform(s) exceeding a threshold, at least one internal network event logging tool is queried for alerts, and from the alerts, at least one anomaly associated with the volumetric problem report data is identified and an anomaly report about the at least one anomaly is generated. Responsive to generating the anomaly report, it may be determined whether the at least one anomaly has a known remediation, and if so, the known remediation may be initiated automatically. Network administrator(s) may also be automatically notified.
Systems, devices, methods, and non-transient machine-interpretable programming and/or other instruction products for the generation, transfer, storage, and other processing of secure data sets used in electronic payment transactions, including particularly the secure creation, administration, manipulation, processing, and storage of electronic data useful in processing of pre-funded, pre-paid, and/or otherwise pre-authorized payment transactions. Devices and methods in accordance with the disclosure can be used to create pre-funded payment token data sets, the token data sets comprising secure data items or records representing negotiable monetary or other economic value, and to share them between network communication devices such as smart phones, home or business desktop computers, etc., for use in purchases and other transactions.
G06Q 20/28 - Pre-payment schemes, i.e. "pay before"
G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
G06Q 20/34 - Payment architectures, schemes or protocols characterised by the use of specific devices using cards, e.g. integrated circuit [IC] cards or magnetic cards
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
Systems, methods and data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions and other secure data processes. Aspects of such systems include trusted platforms by which networked communication devices and merchant systems may registered as trusted entities. Information associated with particular payment means, such as accounts or payment tokens, can be stored on device(s) secure data sets known as virtual or electronic wallets, or in the form of secure payment tokens. Among other improvements, the invention enables the use of multiple payment accounts to fund purchases and other electronic transactions.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/12 - Payment architectures specially adapted for electronic shopping systems
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
A system for detecting phishing events is provided. A data receiver is configured to receive datasets representative of web traffic associated with access to or on-going usage of an application hosted on a server of a production environment by a user. A machine learning engine is configured to generate a score based at least on the datasets representative of the web traffic indicative of whether the user is a malicious user or a non-malicious user. A routing modification engine is configured to route downstream web traffic associated with access to or on-going usage of the application by the user if the score is greater than a threshold to a server of a sandbox environment that is configured to emulate a graphic user interface of the production environment.
A de-coupled computing infrastructure is described that is adapted to provide domain specific contextual engines based on conversational flow. The computing infrastructure further includes, in some embodiments, a mechanism for directing conversational flow in respect of a backend natural language processing engine. The computing infrastructure is adapted to control or manage conversational flows using a plurality of natural language processing agents.
A method for operating a neural network using an encoder-based model to provide a time series forecast, the method comprising: down sampling a time series dataset to generate an initial input having a first scale resolution, such that the first scale resolution is less than a scale resolution of the time series dataset; processing as a first iteration, using the model, the initial input to generate a first output; upsampling by an upsampling function the first output to generate a second input having a second scale resolution, the second scale resolution being higher than the first scale resolution, such that the second input is based on the first output; and processing as a second iteration, using the model, the second input to generate a second output; wherein the second output represents a time series forecast of the time series dataset.
A process for time-series forecasting is described that decouples stationary conditional distribution modeling from non-stationary dynamic modeling. The forecasting can be applied to non-stationary time-series.
A system for reinforcement learning in a dynamic resource environment includes at least one memory and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine.
A method for preparing a trained complete selective classifier can be applied to a trained complete selective classifier having an existing trained selection mechanism. The trained selective classifier is modified to disregard the existing trained selection mechanism and use, as a basis for an alternate selection mechanism, at least one classification prediction value, for example the predictive entropy or the maximum predictive class logit. Optionally, before modifying the trained selective classifier, the method commences with an untrained selective classifier, which may be trained with a modified loss function to obtain the trained selective classifier. The modified loss function has at least one added term, relative to an original loss function, and the at least one added term decreases entropy.
A computer implemented system for controlling access to data associated with an entity includes a data storage device having a computer memory, and one or more processors. The one or more processors are configured for: storing a secret key associated with the entity in a computer memory associated with the entity; upon receiving entity data, storing the entity data in the computer memory; and upon receiving an access grant signal, enabling communication of information relating to the entity data.
H04L 9/30 - Public key, i.e. encryption algorithm being computationally infeasible to invert and users' encryption keys not requiring secrecy
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
41.
SYSTEM AND METHOD FOR MULTI-OBJECTIVE REINFORCEMENT LEARNING WITH GRADIENT MODULATION
Systems are methods are provided for processing multiple input objectives by a reinforcement learning agent. The method may include: instantiating a reinforcement learning agent that maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating task requests; receiving a plurality of input data representing a plurality of user objectives associated with a task request and a plurality of weights; generating a plurality of preferences based on the plurality of user objectives and the plurality of weights; computing a plurality of loss values; computing a plurality of first gradients based on the plurality of loss values; for a plurality of pairs of references, computing a plurality of similarity metrics; computing an updated gradient based on the first gradients and the plurality of similarity metrics; and updating the reinforcement learning neural network based on the updated gradient.
Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.
Systems, methods, and devices for communication are described. A communication system includes a first communication device configured to communicate with a backup device and a destination; and a first tapping device for monitoring messages sent over a first communication link between the first communication device and the backup device. The first communication device includes at least one processor configured to: before sending a first message destined for the destination, send a backup message corresponding to the first message over the communication link for backup at the backup device; and upon confirmation of a tap copy of the backup message from the first tapping device, send the first message to the destination.
Systems and methods for generating access entitlements to networked computing resources. Systems may be configured to: receive an input data set representing an entitlement request associated with a user identifier; generate an entitlement prediction associated with the user identifier based on an entitlement model and at least one hierarchical level, the entitlement model defining a cluster representation of entitlement similarity, and wherein the entitlement prediction is based on one or more similarity relationships corresponding to the at least one hierarchical level; and transmit a signal representing the entitlement prediction for granting downstream access to a networked computing resource.
Systems are methods are provided for processing multiple input objectives by a reinforcement learning agent. The method may include: instantiating a reinforcement learning agent that maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating task requests; receiving a plurality of input data representing a plurality of user objectives associated with a task request; generating, based on the reinforcement learning neural network and the plurality of input data, an action output for generating a signal for communicating the task request; computing a reward based on the action output and the plurality of input data; and updating the reinforcement learning neural network based on the reward.
A system and method for handling crypto-asset transactions includes: receiving from a payment processing system an electronic transaction request including: a payment token corresponding to a payment identifier associated with the customer account, and a transaction amount in a fiat currency; determining current price data corresponding to a first crypto-asset and a second crypto-asset associated with the customer account; associating the electronic transaction request with at least one data processing task for executing at least one crypto-asset transaction; and when at least one crypto-asset confidence condition is satisfied based on the current price data of at least one of the first crypto-asset or the second crypto-asset, generating signals for providing, via the payment processing system, an indication that the electronic transaction request is authorized without waiting for confirmation of execution of the at least one crypto-asset transaction in the respective distributed ledger.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
Systems, methods, and machine-executable data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions and other secure data processes. Aspects of such systems include trusted platforms by which networked communication devices and merchant systems may be registered as trusted entities. Information associated with particular payment means, such as accounts or payment tokens, can be stored on device(s) secure data sets known as virtual or electronic wallets, or in the form of secure payment tokens. Common application programming interfaces executed by devices may facilitate push and pull processes between electronic wallets and other secure data stores. Users may thereby initiate and complete electronic transactions directly from within applications on trusted devices.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06Q 20/12 - Payment architectures specially adapted for electronic shopping systems
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
48.
MULTI-MODAL ARTIFICAL NEURAL NETWORK AND A SELF-SUPERVISED LEARNING METHOD FOR TRAINING SAME
A multi-modal artificial neural network and a self-supervised learning method for training that network. The learning method involves processing, using a first modality simple Siamese network, a pair of first modality augmented views of an input; processing, using a second modality simple Siamese network, a pair of second modality augmented views of the input; determining at least one cross-modal loss between the first and second modality simple Siamese networks; determining a total loss from: (i) first and second modality losses respectively determined during the processing using the first and second modality simple Siamese networks; and (ii) the at least one cross-modal loss; and training the first and second modality simple Siamese networks based on the total loss. The trained network may be used to analyze multi-modal content such as video content that has an audio track. A Multi-Modal Multi-Head Network (M3HN) may also be trained to process modality-specific and modality-agnostic representations.
A computing system configured to generate empathy-based machine-learning outputs, which, for example, can include notifications, automatic service delivery, payments, among others. The system receives as inputs a first set of data sets representative of historical behaviour through tracked interactions, a second set of data sets representative of circumstantial knowledge (e.g., environmental factors, such as weather), and a set of empathy model weights from one or more machine learning models that are configured to model one or more empathy consideration components (e.g., curiosity, preconceptions, inspirations, direct experiences, listened experiences, imagination, among others). Corresponding methods and non-transitory computer readable media are contemplated.
System and method for facilitating management of a time-varying resource pool are provided. The system includes a processor and a memory coupled to the processor. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a time-series data set including data entries associated with one or more consumed resources; identify one or more recurring resource allocations based on recurring data entries of the time-series data set; identify additional resource allocations based on irregularly-timed data entries of the time-series data set; determine a forecasted resource pool value based on a combination of the identified recurring resource allocations and the additional resource allocations; and upon detection of a trigger condition, generate data for display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value.
A method is provided for training a selective network that includes a selection node for selecting whether to make a prediction. During training, the selection node is reparameterized as a differentiable function of learnable parameters acting on noise from a base distribution. The differentiable function approximates a sampling from a categorical distribution.
A computer system and method for populating electronic payment credentials is provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises receiving a browser extension activation input, sending a payment details request message to a financial institution system, receiving payment details from the financial institution system following authentication at a mobile device, and populating a payment form on the browser using the payment details. Dynamic credentials are provided by the financial institution system and combined with pre-populated tokenized credentials during automatic entry into the payment form.
Systems and methods for monitoring suspicious communication network traffic. The methods include obtaining data associated with a sequence of communication events transmitted via the communication network and determining an entropy approximation measure associated at least one event attribute for the sequence of communication events. The method includes generating a threat prediction value based on an anomaly classification model and the entropy approximation measure. The anomaly classification model is trained based on prior sequences of communication events to identify a non-outlier anomaly range associated with the at least one event attribute. The threat prediction value is generated based on classification of the entropy approximation measure relative to the non-outlier anomaly range associated with the at least one attribute for identifying a potential threat. The method includes transmitting a signal for communicating that the sequence is a potential threat within the communication network.
An artificial neural network for data imbalanced regression and a method for training that network. A regression dataset is obtained that includes multiple pairs that respectively are made up of inputs and corresponding targets. The inputs are represented in a feature space and the targets are represented in a label space of continuous values. Label space similarities between the targets as represented in the label space are determined, and analogously feature space similarities between the inputs as represented in the feature space are determined. A loss may then be determined based on differences between rankings of the label space similarities and corresponding feature space similarities. That loss may be used to train an artificial neural network.
Systems 100; devices 110, 120, 130, 150, 160; methods 2400, 2500; and machine-executable programming structures stored in persistent (i.e., non-transitory), computer-readable media 604, 606, 618, 126, 139 for the rapid and secure negotiation, authorization, execution, and confirmation of multi-party data processes, including payment transactions conducted between purchasers 190 having electronic access to bank accounts and other sources of payment, merchants operating e- and/or m-commerce transaction systems 132, 134, 136, and banks and other financial institutions 120 capable of electronically communicating with both.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06Q 30/0226 - Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
G06Q 20/12 - Payment architectures specially adapted for electronic shopping systems
Systems, methods, and non-transient machine-interpretable data representing executable instruction sets and/or other products for the processing of data for the 5 secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of payment transactions and other secure data processes. In various aspects and embodiments the disclosure provides secure means for the authorization of sensitive and other data processes subject to controlled access. Such processes include, for example the creation, administration, 10 authorization, virtualization, storage, and other manipulation or processing of electronic data representing characteristics of, instructions for, and information associated with consumer, business, and other payment accounts, and other forms of secure payment elements, such as payment tokens; and data useful in processing transactions using such accounts and elements. Information associated with 15 particular payment means, such as accounts or payment tokens, can be stored, for example, in a data set, usually secure, sometimes referred to as a virtual or electronic wallet, or a secure payment token.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
Methods, systems, and techniques for facilitating identification of electronic data exfiltration. A message transmission log and screenshot metadata are obtained. A screenshot corresponding to the screenshot metadata is matched to a sent electronic message, such as an email, having a file attachment represented in the message transmission log to generate an event. The screenshot metadata indicates that the screenshot was captured prior to when the message transmission log indicates the electronic message was sent. An anomaly score is determined for the sent electronic message is determined by applying unsupervised machine learning, such as by applying an isolation forest, to score the sent electronic message relative to a baseline. The anomaly score meeting or exceeding an anomaly threshold is treated as potentially being indicative of electronic data exfiltration.
There is provided a computer system and method for orchestrating user interface, the method include: obtaining a first data set representative of intercepted data communication messages between a user interface of a user and a merchant hosting server; obtaining a second data set representing an instruction set for loading visual elements on the user interface provided from the merchant hosting server; analyzing the first data set to obtain one or more user-specific characteristics; determining if the user-specific characteristics associated with the user satisfy a trigger condition associated with a current resource offering; and responsive to a positive determination: injecting, into the instruction set for loading the visual elements on the user interface provided from the merchant hosting server, code corresponding to an interactive visual element corresponding to the current resource offering.
Methods, systems, and techniques for detecting a cybersecurity breach. The cybersecurity breach may be a synthetic account or an account having been subjected to an account takeover. Electronic account data representative of accounts is obtained in which a first group of the accounts includes accounts flagged as being associated with the breach, and a second group of the accounts includes a remainder of the accounts. The computer system generates from the account data nodes representing the accounts and edges based on account metadata that connect the nodes. The computer system determines, such as by applying a link analysis method to the nodes and edges, a ranking of the accounts of at least part of the second group indicative of a likelihood that those accounts are also associated with the cybersecurity breach. That ranking may be used to identify which of those accounts is also identified with the cybersecurity breach.
A virtual agent can implement a chatbot to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
A system and method for auto-populating an electronic transaction process is provided. The system comprises at least one processor, and a memory storing instructions which when executed by the at least one processor configure the processor to obtain a scanned payee identifier from an optical character recognition scan of a digital bill document, compare the scanned payee identifier with a set of stored payee identifiers to obtain at least one first identifier match, determine a score for each of the at least one identifier match, and select the stored payee identifier associated with a highest score. The method comprises obtaining a scanned payee identifier from an optical character recognition scan of a digital bill document, comparing the scanned payee identifier with a set of stored payee identifiers to obtain at least one first identifier match, determining a score for each of the at least one identifier match, and selecting the stored payee identifier associated with a highest score.
Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D/3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service).
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
G06N 3/04 - Architecture, e.g. interconnection topology
G10L 17/00 - Speaker identification or verification
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 40/40 - Spoof detection, e.g. liveness detection
64.
SYSTEM AND METHOD FOR SEQUENTIAL DATA PROCESS MODELLING
A system for machine learning architecture for prospective resource allocations. The system may include a processor and a memory. The memory may store processor-executable instructions that, when executed, configure the processor to: receive a sequence of data records representing historical resource allocations from a user associated with a first identifier to another user associated with a second identifier; derive record features based on the sequence of data records representing the historical resource allocations for identifying irregular record features; determine a prospective resource allocation associated with the first identifier and the second identifier based on a neural network model and the derived record features; determine, based on the neural network model, a selection score associated with the prospective resource allocation; and when the selection score is above a minimum threshold, cause to display, at a display device, the prospective resource allocation corresponding to the second identifier.
A computer-implemented system and method for detecting a boundary in an image are provided. The system includes at least one processor and memory in communication with said at least one processor, wherein the memory stores instructions, when executed at said at least one processor, cause said system to: receive or access a first image comprising a first polygon structure; generate, using a data model representing a neural network, a second image based on the first image by splitting the first polygon structure in the first image, wherein the second image comprises a first portion and a second portion partitioned by a line across the first polygon structure; and generate, based on the second image, a geo-image comprising corresponding spatial-reference information for one or more pixels in the geo-image, the geo-image comprising one of the first portion and the second portion in the second image.
Disclosed are systems, methods, and devices for computing an action for an automated agent. A neural network configured for deep multi-task learning is provided. Each of a subset of layers of the neural network is connected with a respective gating unit configured for dynamically activating or deactivating the respective layer of the neural network. The method includes: receiving, via a communication interface, input data associated with a task type; selecting, from a plurality of layers of a neural network, a subset of layers based on at least the task type; dynamically activating, based on the input data, at least one layer of the subset of layers; and generating an action signal based on a forward pass of the neural network using the dynamically activated at least one layer of the neural network.
A computer-implemented system and method for training a neural network with enforced monotonicity are disclosed. An example system includes at least one processor and memory in communication with said at least one processor, wherein the memory stores instructions for providing a data model representing a neural network for predicting an outcome based on input data, the instructions when executed at said at least one processor causes said system to: receive a feature data as input data; predict an outcome based on the input data using the neural network; compute a loss function based on the predicted outcome and an expected outcome associated with the input data, the loss function being dependent on a monotonicity penalty Ω computed based on a set of training data including the feature data and on a set of random data; and update weights of the neural network based on the loss function.
A computer-implemented system and method for estimating a Cumulative Distribution Function (CDF) are provided. The method includes: receive input data representing a volume V of a target space indicating a future target event; compute, using the trained neural network, an estimation of a first flux through a boundary of the volume V; compute, using the trained neural network, an estimation of a second flux through a boundary of a volume W of a base space based on the estimation of the first flux through the boundary of the volume V; generate, using the trained neural network, an estimation of a CDF for the volume V based on the second flux through the boundary of the volume W; compute a probability for the future target event based on the estimated CDF for the volume V; and generate a control command based on the probability for the future target event.
An insurance recommendation engine receives customer data and using trained models recommends one or more insurance products that are suitable for the customer. The recommendation engine also provides an explanation as to why the particular products have been recommended. The recommendation models are incorporated into a system that can improves the customer's experience.
An automated machine learning approach and toolkit is developed for evaluating the causal impact of an event. This approach includes data generation, optimal model selection, model stability evaluation and model explanation. An example approach includes: generating predictive output data of physical geospatial objects is proposed whereby a first data set representative of geospatial event-based data and a second data set representative of the characteristics of the physical geospatial objects are spatially joined together and utilized to generate a causal graph data model that is then provided for at least one of a trained regression machine learning model, a trained causal machine learning model, and a trained similarity machine learning model to generate the predictive output data representative of event-adjusted characteristics of the physical geospatial objects.
Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. The system includes a communication interface, a processor, memory, and software code stored in the memory. The software code, when executed, causes the system to: instantiate an automated agent that maintains the reinforcement learning neural network; receive current state data of a resource for a first task; receive historical state metrics of the resource computed based on a plurality of historical tasks; compute normalized state data based on the current state data; and provide the historical state metrics and the normalized state data to the reinforcement learning neural network of said automated agent for training.
Systems, devices, and methods for training an automated agent are disclosed. Multiple automated agents are instantiated, each of the automated agents configured to train over a plurality of training cycles. For each resource, a dedicated portion of a memory device to store state data for the respective resource is allocated. The method includes receiving a request for state data for a particular resource from a subset of the automated agents; for each of the training cycles for the subset of the plurality of automated agents, storing updated state data for the particular resource in the dedicated portion of the memory device allocated to the particular resource; and transmitting an address of the dedicated portion of the memory device for the particular resource to the subset of the automated agents, to facilitate asynchronous reading of the stored state data for the particular resource during each training cycle.
Salient features are extracted from a training data set. The training data set includes, for each of a subset of known legitimate websites and a subset of known phishing websites, Uniform Resource Locators (URLs) and Hypertext Markup Language (HTML) information. The salient features are fed to a machine learning engine, a classifier engine to identify potential phishing websites is generated by applying the machine learning engine to the salient features, and parameters of the classifier engine are tuned. This enables identification of potential phishing websites by parsing a target website into URL information and HTML information, and identifying predetermined URL features and predetermined HTML features. A prediction as to whether the target website is a phishing website or a legitimate website, based on the predetermined URL features and the predetermined HTML features, is received from the classifier engine.
A marketplace for trading bonds on the block chain includes a bond token smart contract that tokenizes the bond for buying/selling using a stablecoin. Each bond generates a corresponding marketplace smart contract. A whitelist smart contract is used to provide permissions for trading bonds on the block chain.
A method for generating a user profile based on a comparison to peer group data, the user being a member of an institution, the method comprising the steps of: obtaining user profile data pertaining to a user of a network service of the institution; accessing group profile data associated with the user; comparing the user profile data to the group profile data to generate comparative data; generating a user profile for presentation on a user interface, the user profile including the comparative data; sending the user profile to the user; receiving a request from the user for a product of institution; and updating the user profile to include information pertaining to the product.
A method is provided for dynamically visualizing an impact field based on weighted ESG. A portfolio is received, which includes a plurality of assets according to a first configuration, each asset having an associated quantum variable. A raw ESG score is retrieved for each of the assets. A weighted ESG score is determined for each asset by multiplying the raw ESG score by the quantum variable. A first composite ESG score is formed by summing the weighted ESG scores for the assets in the first configuration of the portfolio. This is then visually represented by rendering and displaying an impact field having a gradient variable reflective of the first composite ESG score. A recommendation is made for at least one asset in the first configuration. The configuration is changed, another composite ESG score is determined, and the impact field is updated accordingly.
Systems, devices, and methods for automated generation of resource task requests are disclosed. A reinforcement learning neural network having an output layer with a plurality of policy heads is maintained. At least one reward is provided to the reinforcement learning neural network, the at least one reward corresponding to at least one prior resource task request generated based on outputs of the reinforcement learning neural network. State data are provided to the reinforcement learning neural network, the state data reflective of a current state of an environment in which resource task requests are made. A plurality of outputs is obtained, each from a corresponding policy head, the plurality of outputs including a first output defining a quantity of a resource and a second output defining a cost of the resource. A resource task request signal is generated based on the plurality of outputs from the plurality of policy heads.
Methods, systems, and techniques for right-sizing resource requests for applications in a dynamically scalable computing environment. In one aspect, a method comprises monitoring resource usage of at least one computer resource by an application executing on a computer system, and monitoring resource requests for the computer resource(s) associated with the application. The method further comprises determining, for the computer resource(s), a resource usage upper bound associated with the application, testing the resource usage upper bound against at least one threshold, determining, from the testing, a resource request adjustment, and dynamically applying the resource request adjustment to the resource requests for the computer resource(s) associated with the application.
A method is provided for tracking funds in a real estate transaction using a real estate transaction portal. Through an interface of a real estate transaction portal, a request is accepted from a pre-registered buyer to transfer funds to a pre-registered beneficiary, the funds being in settlement of at least a portion of a real estate transaction. A corresponding payment request is initiated through a digital payment channel. On receipt of a first automated message through the payment channel, the first automated message is decoded as a confirmation of the initiation of the payment request. In real time, a graphical status indicator is displayed to the pre-registered buyer and the pre-registered beneficiary showing the initiation. On receipt of a second automated message through the payment channel, the second automated message is decoded as a completion of the payment request and the graphical status indicator is accordingly updated in real time.
There is described a method of determining whether a fraud claim initiated by a client is legitimate. The method is performed by one or more processors. A fraud claim is received from the client. The fraud claim is in respect of a potentially fraudulent transaction associated with the client. Client data associated with the client is retrieved. The client data includes data relating to historical financial transactions associated with the client. Based on the data relating to the historical financial transactions associated with the client, and based on one or more parameters of the potentially fraudulent transaction, a fraud score associated with the fraud claim is determined. Based on the fraud score, a determination is made as to whether the fraud claim is legitimate.
Systems, devices, and methods for training an automated agent are disclosed. An automated agent is instantiated. The automated agent includes a reinforcement learning neural network that is trained over a plurality training cycles and provides a policy for generating resource task requests. A learning condition that is expected to impede training of the automated agent during a given training cycle of the plurality of training cycles is detected. In response to the detecting, a disable signal is generated to disable training of the automated agent for at least the given training cycle.
A method for managing data processes in a network of computing resources includes: receiving at least one child request being routed from an intermediary device to at least one corresponding destination device, the at least one child request requesting execution of at least one corresponding child data process, each of the at least one child data process for executing at least a portion of the at least one parent data process from an instructor device, and each of the at least one child request including a destination key derived at least in part from the at least one instructor key; storing the at least one child request in at least one storage device; modifying the at least one child request upon receiving a child request modification signal; and generating signals for communicating the child requests to one or more requesting devices.
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. The system includes a communication interface, a processor, memory, and software code stored in the memory. The software code, when executed, causes the system to: instantiate an automated agent for communicating resource task requests; receive a current feature data structure related to a resource of the resource task requests; maintain a plurality of historical feature data structures related to said resource for a plurality of prior time steps; compute normalized feature data using the current feature data structure and the plurality of historical feature data structures; compute supplemented state data appended with the normalized feature data; and transmit said supplemented state data to the reinforcement learning neural network to train said automated agent.
A computer system and computer-implemented method for duplicating an application state are provided, the method including: recording one or more point-in-time characteristics generated by prior user inputs at one or more user interface elements, the one or more point-in-time characteristics associated with a first application state of a first application instance; transferring the one or more point-in-time characteristics to the provisioned memory resources for generating the second application state; generating a second application instance based on the one or more point-in-time characteristics; configuring the second application state based on the one or more point-in-time characteristics to duplicate the first application state of the first application instance; and storing the prior user inputs in a journal, wherein the journal is configured to enable reproduction of a state of a plurality of modified states of the first application instance.
G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
G06F 9/451 - Execution arrangements for user interfaces
G06F 8/38 - Creation or generation of source code for implementing user interfaces
H04L 67/1095 - Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
85.
SYSTEM AND METHOD FOR COMPOSITE CRYPTOGRAPHIC TRANSACTIONS
A composite cryptographic data structure is described, and corresponding methods, systems, and computer readable media. The composite cryptographic data structure is instantiated based on an underlying set of cryptographic tokens (e.g., blockchain/distributed ledger tokens) that, in some embodiments, are transferrable through on-chain transactions established on one or more distributed ledger networks. Identity validation, in some embodiments, may occur at one of composite cryptographic data structure instantiation or composite cryptographic data structure redemption, or both, through the use of a whitelist or a blacklist data structure.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
86.
SYSTEM AND METHOD FOR RISK SENSITIVE REINFORCEMENT LEARNING ARCHITECTURE
A computer-implemented system and method for training an auomated agent are disclosed. An example system includes: a communication interface; at least one processor; memory in communication with said at least one processor; software code stored in said memory, which when executed causes said system to: instantiate an automated agent that maintains a reinforcement learning neural network and generates, according to outputs of said reinforcement learning neural network, signals for communicating task requests; receive a plurality of states and a plurality of actions for the automated agent; initialize a learning table Q for the automated agent based on the plurality of states and the plurality of actions; compute a plurality of updated learning tables based on the initialized learning table Q using a utility function, the utility function comprising a monotonically increasing concave function; and generate an averaged learning table Q′ based on the plurality of updated learning tables.
A technical validation mechanism is described that includes the use of facial feature recognition and tokenization technology operating in combination with machine learning models can be used such that specific facial or auditory characteristics of how an originating script is effectuated can be used to train the machine learning models, which can then be used to validate a video or a particular dynamically generated passphrase by comparing overlapping phonemes or phoneme transitions between the originating script and the dynamically generated passphrase.
G06Q 20/10 - Payment architectures specially adapted for home banking systems
G10L 15/25 - Speech recognition using non-acoustical features using position of the lips, movement of the lips or face analysis
G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
G06V 40/40 - Spoof detection, e.g. liveness detection
88.
METHOD FOR ANOMALY DETECTION IN CLUSTERED DATA STRUCTURES
A method for generating visual representations of financial interests includes: receiving an input data set including one or more data structures storing data fields and data values representative of financial interests; extracting, from the input data, one or more extracted features from the funds, the extracted features collectively indicative of a distance between different funds; generating one or more clusters of funds, based on the extracted features of the funds; determining, based on identified differences between one or more funds relative to at least one other fund in a corresponding cluster of funds, one or more fund anomalies based on the one or more extracted features; generating one or more adjustment recommendations based on the one or more fund anomalies, the one or more adjustment recommendations representing control instruction sets for automatically modifying characteristics of the corresponding fund.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
89.
SYSTEM AND METHOD FOR MULTI-USER SESSION FOR COORDINATED ELECTRONIC TRANSACTIONS
Systems, methods, and computer readable media are directed in various embodiments for providing multiuser sessions for coordinated electronic transactions. A technical solution is directed to coordinating the electronic transactions across a plurality of instances, where the underlying users of the instances can include at least two users. Access to sensitive information can be restricted using a trusted execution environment and access can be given in accordance with the coordinated electronic transactions.
Systems, methods, and machine-executable data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions. Aspects of such methods, systems, and data structures include providing at an electronic device, an output indicating that a dynamically-configured electronic token is in a transaction-ready state, where the dynamically-configured electronic token is associated with a plurality of loyalty accounts; in response to one or more signals providing information regarding a location of the electronic device, obtaining token data associated with a loyalty account of the plurality of loyalty accounts corresponding to the location of the electronic device; and via a data communication interface, route a token, generated from the token data, for processing at a transaction processing system.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
G06Q 20/10 - Payment architectures specially adapted for home banking systems
G06Q 20/12 - Payment architectures specially adapted for electronic shopping systems
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
91.
SYSTEM AND METHOD FOR CONTINUOUS DYNAMICS MODEL FROM IRREGULAR TIME-SERIES DATA
A system for machine learning architecture for time series data prediction. The system may be configured to: maintain a data set representing a neural network having a plurality of weights; obtain time series data associated with a data query; generate, using the neural network and based on the time series data, a predicted value based on a sampled realization of the time series data and a normalizing flow model, the normalizing flow model based on a latent continuous-time stochastic process having a stationary marginal distribution and bounded variance; and generate a signal providing an indication of the predicted value associated with the data query.
A computer system and method for predicting an output for an input are provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises at least one of estimating a posterior for a plurality of inputs and associated outputs, or providing a point estimate without sampling. The method also comprises predicting the output for a new observation input.
A system and method for adversarial vulnerability testing of machine learning models is proposed that receives as an input, a representation of a non-differentiable machine learning model, transforms the input model into a smoothed model and conducts an adversarial search against the smoothed model to generate an output data value representative of a potential vulnerability to adversarial examples. Variant embodiments are also proposed, directed to noise injection, hyperparameter control, and exhaustive/sampling-based searches in an effort to balance computational efficiency and accuracy in practical implementation. Flagged vulnerabilities can be used to have models re-validated, re-trained, or removed from use due to an increased cybersecurity risk profile.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06N 3/04 - Architecture, e.g. interconnection topology
A computer implemented system for anonymous electronic verification of location credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's location credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
The methods and systems are directed to computational approaches for training and using machine learning algorithms to predict the conditional marginal distributions of the position of agents at flexible evaluation horizons and can enables more efficient path planning. These methods model agent movement by training a deep neural network to predict the position of an agent through time. A neural ordinary differential equation (neural ODE) that represents this neural network can be used to determine the log-likelihood of the agent's position as it moves in time.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
96.
SYSTEM AND METHOD FOR PROBABILISTIC FORECASTING USING MACHINE LEARNING WITH A REJECT OPTION
A computer-implemented system and method for training a machine learning model are disclosed, the method includes: maintaining a data set representing a neural network having a plurality of weights; receiving input data comprising a plurality of time series data sets ending with timestamp t−1; generating, using the neural network and based on the input data, a probabilistic forecast distribution prediction at timestamp t and a selection value associated with the probabilistic forecast distribution prediction at timestamp t; computing a loss function based on the selection value; and updating at least one of the plurality of weights of the neural network based on the loss function.
In one aspect, a system for managing data processes in a network of computing resources is configured to: receive, from an instructor device, a parent request for execution of at least one parent data process executable by a plurality of computing resources at least one computing resource; generate at least one child request for execution of at least one corresponding child data process for routing to at least one corresponding destination device, each of the at least one child data process for executing at least a portion of the at least one parent data process, and each of the at least one child request including a respective destination key derived from at least one instructor key; and route each of the at least one child request to the at least one corresponding destination device. The at least one child request can be obtained by a supervisor server via the routing.
H04L 45/302 - Route determination based on requested QoS
G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
Systems and methods for secure communication of data packets are described using a communications gateway and protocol. One or more payment generator devices utilize trusted execution environments to store identity attestation parameters which are then utilized during registration and/or validation of device identity at the gateway for secure transmission of secure data, including, for example, payment data.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
A computational approach is proposed herein for controlling a user interface for rendering of interactive graphical control elements representing offers and coupons that are inserted into a computational payment process. In particular, the offers and coupons can interact with stored payment information resident (or tokens thereof) on a digital wallet data structure. The approach can be implemented as a computing system, a computing method operable on a computing system, or a computer program product affixed in the form of a non-transitory computer readable medium storing machine-interpretable instructions.
Systems and methods for establishing data linkages are described in various embodiments. A system architecture is described which provides a data processing orchestrator device or service which securely interoperates with data sets at various points in time associated with a set of interactions a user may have with computer systems. The data sets are obtained from different data repositories, and are combined together for analysis such that a first data set representing intents (e.g., web search/browse history) can be combined together with a second data set representing outcomes (e.g., purchase transaction history, web site shopping carts).
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
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