Patents by Inventor Tatiana Korolevskaya

Tatiana Korolevskaya has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210142333
    Abstract: 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.
    Type: Application
    Filed: May 16, 2017
    Publication date: May 13, 2021
    Inventors: Theodore Harris, Yue Li, Craig O'Connell, Tatiana Korolevskaya
  • Publication number: 20210112068
    Abstract: A method is disclosed. A user device may determine, prior to joining a mesh network comprising a plurality of user devices, a first data security value. The user device may then communicate with the plurality of user devices to join the mesh network. The user device may receive a plurality of additional data security values from a plurality of proximate user devices. The user device may then determine an updated data security value based at least upon evaluating the plurality of additional data security values associated with the plurality of proximate user devices. The user device may allow or not allow the user device to perform an interaction based at least upon the updated data security value.
    Type: Application
    Filed: May 15, 2018
    Publication date: April 15, 2021
    Inventors: Theodore D. Harris, Yue Li, Tatiana Korolevskaya
  • Publication number: 20210090136
    Abstract: 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.
    Type: Application
    Filed: September 18, 2020
    Publication date: March 25, 2021
    Inventors: Yue Li, Theodore Harris, Tatiana Korolevskaya
  • Publication number: 20210073283
    Abstract: 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.
    Type: Application
    Filed: November 19, 2020
    Publication date: March 11, 2021
    Inventors: Theodore D. Harris, Craig O'Connell, Terry Angelos, Tatiana Korolevskaya, Yue Li, Todd Sawyer
  • Publication number: 20210042421
    Abstract: 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.
    Type: Application
    Filed: August 5, 2019
    Publication date: February 11, 2021
    Inventors: Yue Li, Theodore Harris, Tatiana Korolevskaya
  • Publication number: 20210027182
    Abstract: A series of algorithms can be applied to an automated machine learning model building process in order to reduce complexity and improve model performance. In addition, the settings and parameters for implementing the automated machine learning model building process can be tuned to improve performance of future models. The model building process can also be monitored to ensure that the current build is based on new information compared to previously builds.
    Type: Application
    Filed: March 21, 2018
    Publication date: January 28, 2021
    Inventors: Theodore Harris, Yue Li, Tatiana Korolevskaya
  • Patent number: 10872298
    Abstract: 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.
    Type: Grant
    Filed: July 11, 2017
    Date of Patent: December 22, 2020
    Assignee: Visa International Service Association
    Inventors: Theodore D. Harris, Craig O'Connell, Terry Angelos, Tatiana Korolevskaya, Yue Li, Todd Sawyer
  • Publication number: 20200387364
    Abstract: Systems and methods are provided for transcompiling non-distributed source code for a nondistributed software program into a distributed software package for implementation on a distributed computing system. A transcompiler can identify loops within non-distributed source code written in a data-driven language. The transcompiler can generate MapReduce jobs using mapper keys based on grouping indicators associated with each of the loops. The MapReduce jobs can be linked together based on input-output connections of the loops in the non-distributed source code. Then, the transcompiler can generate a distributed software package including the generated MapReduce jobs to implement the same functionality as the non-distributed source code on the distributed computing system, thereby improving the speed of execution over very large datasets. The distributed software package can be optimized using machine learning searching algorithms.
    Type: Application
    Filed: August 11, 2017
    Publication date: December 10, 2020
    Inventors: Craig O'Connell, Theodore Harris, Yue Li, Tatiana Korolevskaya
  • Publication number: 20200118028
    Abstract: A method including collecting, by a communication device comprising a machine learning model obtained at least in part from a server computer, metadata associated with an application. The communication device can then embed the metadata to form vectorized data. The communication device can input the vectorized data into the machine learning model to obtain a security value. The communication device can determine whether to run or install the application based upon the security value.
    Type: Application
    Filed: October 10, 2019
    Publication date: April 16, 2020
    Inventors: Theodore Harris, Yue Li, Tatiana Korolevskaya, Craig O'Connell
  • Publication number: 20200097817
    Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.
    Type: Application
    Filed: September 20, 2019
    Publication date: March 26, 2020
    Inventors: Theodore Harris, Tatiana Korolevskaya, Yue Li
  • Publication number: 20190362263
    Abstract: 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.
    Type: Application
    Filed: July 11, 2017
    Publication date: November 28, 2019
    Inventors: Theodore D. Harris, Craig O'Connell, Terry Angelos, Tatiana Korolevskaya, Yue Li, Todd Sawyer
  • Publication number: 20190188218
    Abstract: 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 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.
    Type: Application
    Filed: December 18, 2018
    Publication date: June 20, 2019
    Inventors: Theodore D. Harris, Tatiana Korolevskaya, Yue Li
  • Publication number: 20190005407
    Abstract: 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.
    Type: Application
    Filed: June 30, 2017
    Publication date: January 3, 2019
    Inventors: Theodore D. Harris, Yue Li, Tatiana Korolevskaya, Craig O'Connell
  • Publication number: 20180330258
    Abstract: Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.
    Type: Application
    Filed: May 9, 2017
    Publication date: November 15, 2018
    Inventors: Theodore D. Harris, Craig O'Connell, Yue Li, Tatiana Korolevskaya