Patents by Inventor Kaycee Kuan-Cheng Lai

Kaycee Kuan-Cheng Lai 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).

  • Executing services across multiple trusted domains for data analysis
    Patent number: 12380199
    Abstract: Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.
    Type: Grant
    Filed: June 1, 2022
    Date of Patent: August 5, 2025
    Assignee: Promethium, Inc.
    Inventors: Shuo Yang, Xicheng Chang, Himangshu Das, Azary Smotrich, Puneet Gupta, Kaycee Kuan-Cheng Lai
  • Modifying data pipeline based on services executing across multiple trusted domains
    Patent number: 12314373
    Abstract: Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.
    Type: Grant
    Filed: June 1, 2022
    Date of Patent: May 27, 2025
    Assignee: Promethium, Inc.
    Inventors: Shuo Yang, Xicheng Chang, Himangshu Das, Azary Smotrich, Puneet Gupta, Kaycee Kuan-Cheng Lai
  • Querying heterogeneous data sources using machine learning based language model
    Patent number: 12271374
    Abstract: An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.
    Type: Grant
    Filed: April 23, 2024
    Date of Patent: April 8, 2025
    Assignee: Promethium, Inc.
    Inventors: Kaycee Kuan-Cheng Lai, Aleksey Vinokurov, Ravikanth Kasamsetty
  • EXECUTING SERVICES ACROSS MULTIPLE TRUSTED DOMAINS FOR DATA ANALYSIS
    Publication number: 20220382791
    Abstract: Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.
    Type: Application
    Filed: June 1, 2022
    Publication date: December 1, 2022
    Inventors: Shuo Yang, Xicheng Chang, Himangshu Das, Azary Smotrich, Puneet Gupta, Kaycee Kuan-Cheng Lai
  • MODIFYING DATA PIPELINE BASED ON SERVICES EXECUTING ACROSS MULTIPLE TRUSTED DOMAINS
    Publication number: 20220382852
    Abstract: Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.
    Type: Application
    Filed: June 1, 2022
    Publication date: December 1, 2022
    Inventors: Shuo Yang, Xicheng Chang, Himangshu Das, Azary Smotrich, Puneet Gupta, Kaycee Kuan-Cheng Lai