Visa International Service Association

Theodore David Harris

Patrick L Faith

Yue Li

Tatiana Korolevskaya

Craig O'Connell

Scott Edington

Phillip Kumnick

Edward Katzin

Jennifer A Schulz

Kevin Paul Siegel

Ayman A Hammad

John F Sheets

Thomas Purves

Igor V Karpenko

Saurav Chatterjee

Diane C Salmon

Michael Kenji Mori

Stacy Sweeney Pourfallah

Peter Ciurea

Tenni Theurer

Terry Angelos

Julian Hua

Shipra Jha

Shaw Li

Prakash Hariramani

Abhinav Shrivastava

Gregory Kenneth Storey

Andrew Beck

Miroslav Gavrilov

Sebastian Badea

Susan French

Vanita Pandey

Amit Bhargava

Ben Pfisterer

Victoria Graham

Todd Sawyer

Mark Andrew Carlson

Mark Allen Nelsen

Zhongxiao Jiang

Mark Nelson

Sander Idelson

Nick Giannaris

Craig O'connell

A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.

Blockchain security system

Methods for controlling genetic learning for predictive models using predefined strategies may include, for each of a plurality of agents, selecting a type of predictive model. A strategy may be selected from predefined strategies. Candidate genomes may be generated and may include a plurality of genes. Each gene may be associated with a feature of the agent predictive model. A fit of each candidate genome to the agent strategy may be determined. A candidate genome may be selected based on the fit. For each of a plurality of epochs, a plurality of training iterations may be performed for each agent. A fitness of each agent predictive model may be determined. A subset of agents with a highest fitness may be determined. For each agent of the subset, at least one new agent may be generated. The genomes of the new agents may be merged with some genomes of the subset.

Method, system, and computer program product for controlling genetic learning for predictive models using predefined strategies

Client devices can send access request messages to resource management computers to request access to a resource. A data security hub can provide centralized routing between different client devices, resource management computers, and authentication data processing servers. The data security hub can reduce the risk of sensitive authentication information from leaking (e.g., due to a breach) by limiting the amount or types of authentication information distributed to the data processing servers. The data security hub can limited the authentication information being distributed based on its sensitivity, the trust level of the client device, and the security level of the requested resource. The data security hub can also evaluate the client devices and data processing servers to identify security breaches and can cancel or reroute access requests accordingly. Thus, the data security hub can maintain resource security while better preserving the privacy of the client device's authentication information.

Data security hub

Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Dynamic claims submission system

Described herein are a system and techniques for enabling access control utilizing one or more blockchains associated with a user. A blockchain provider can manage one or more blockchains specifically associated with a an entity, where each blockchain may be associated with a differing sensitivity level. The entity may be a person or a machine such as an IOT (Internet of Things) device. The blockchain provider can manage access control policies associated with each blockchain such that access to the data of the blockchain may be allowed or restricted to requesting entities according to those access control policies.

Portable reputation brokering using linked blockchains and shared events

A method includes collecting, by a first AI application of a first AI computer, data to include in a dataset. The first AI application of the first AI computer can include an offer for the dataset into an offering platform. The first AI application of the first AI computer can then receive a response to the offer from a second AI application of a second AI computer. The first AI application of the first AI computer can determine whether or not to provide the dataset to the second AI application of the second AI computer. The first AI application of the first AI computer can, based on the determination, provide the dataset to the second AI application of the second AI computer.

AI to AI communication

Embodiments of the invention are directed to methods and devices for predicting interactions. One embodiment is directed to a method comprising receiving, by one or more computers, interaction data for a plurality of known interactions between resource providers and users, and creating a topological graph based on the plurality of known interactions. The method may further comprise determining, by the one or more computers, a plurality of communities to form a predictive model, and receiving a request for a prediction. In addition, the method may comprise applying the request to the predictive model, by the one or more computers, by identifying a community in the plurality of communities corresponding to the request, determining a node within the identified community, and providing information regarding the node as the requested prediction.

Machine learning and prediction using graph communities

A disclosed method includes a) receiving by a server computer network data comprising a plurality of transaction data for a plurality of transactions. Each transaction data comprises a plurality of data elements with data values. At least one of the plurality of data elements comprises a user identifier for a user. The server computer can then b) generate one or more graphs comprising a plurality of communities based on the network data. The server computer can c) determine fuzzy values for at least some of the data values for each transaction of the plurality of transactions. For each user. the server computer can d) determine fuzzy values for communities within the plurality of communities. The server computer can then e) generate a model using the fuzzy values obtained in steps c) and d), and at least some of the data values.

Privacy-preserving graph compression with automated fuzzy variable detection

A system and method are described which attempt to connect merchants with phone numbers that are known to be associated with fraudulent users.

Artificial intelligence enhanced transaction suspension

Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.

System and method to use past computer executable instructions to evaluate proposed computer executable instructions

A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

Hyper-graph learner for natural language comprehension

A method includes receiving, by a server computer, data of a communication device; training, by the server computer, a neural network model based on the data of the communication device and communication device metadata from one or more additional communication devices, to generate a machine learning model configured to determine, based on a metadata associated with an application, a security value related to an indication of a security threat; and transmitting the machine learning model to the communication device. The communication device can use the machine learning model to determine the security value, by inputting the metadata associated with the application, as a vectorized data into the machine learning model. The communication device can determine whether to run or install the application based upon the security value.

Offline security value determination system and method

A method is disclosed and includes receiving, by a first communication device, a first local authentication model, the first local authentication model being derived from a master authentication model at a remote server computer, and receiving a request to perform an interaction with a second communication device, the interaction being performed in an offline manner. The method may further include applying, by the first communication device, the first local authentication model to the interaction to determine a first authentication result and determining whether or not to allow the interaction to proceed based upon the first authentication result. The method may also include updating the first local authentication model using the master authentication model when the first communication device is online.

Offline authorization of interactions and controlled tasks

Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Graph model build and scoring engine

A method for the use of electronic transactional tokens includes: generating a plurality of transactional tokens including a first token; associating each of the transactional tokens with a plurality of users, the first token associated with a first user; monitoring usage of the transactional tokens in a plurality of transactions in a local region or network; and in response to receiving transactional data for the first transaction, updating the first token from a first state to a second state (e.g., to provide a benefit when the token is applied in the local region or network). The tokens may be generated and monitored using a token processing system, which uses transaction data received by a transaction handler that is handling transaction processing for the transactions.

Growing community of inventors

San Francisco, CA, United States of America

Theodore David Harris