Patents by Inventor Saar Yalov

Saar Yalov 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: 20240386492
    Abstract: Systems and cache-based methods for explaining tree-based models using interventional Shapley values are disclosed. With this technology, interventional Shapley values are used to compute attribution values from a leaf-wise approach within tree-based machine learning models. Reference traversal tables and test traversal tables are created and stored for each leaf of a decision tree. Based on the created tables, a subset of traversal permutations and respective subset size are determined on a tree by tree, leaf by leaf and feature by feature basis. For each of the nodes in a traversal path to each of the leaves, partial attribution values are generated, and an attribution for the node is adjusted based on the generated partial attribution values and a multiplier indicated in the reference traversal tables. An output explanation of a score can advantageously be obtained with reduced computational complexity and runtime.
    Type: Application
    Filed: May 17, 2024
    Publication date: November 21, 2024
    Inventors: Geoffrey Michael Ward, Saar Yalov, Zijian Wu, Sean Javad Kamkar
  • Publication number: 20240303553
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Application
    Filed: April 29, 2024
    Publication date: September 12, 2024
    Inventors: David Sheehan, Siavash Yasani, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Patent number: 11972338
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Grant
    Filed: May 2, 2023
    Date of Patent: April 30, 2024
    Assignee: ZestFinance, Inc.
    Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Publication number: 20230359944
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Application
    Filed: May 2, 2023
    Publication date: November 9, 2023
    Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik