Patents by Inventor Martin R. Linenweber

Martin R. Linenweber 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).

  • Search analysis and retrieval via machine learning embeddings
    Patent number: 12169512
    Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.
    Type: Grant
    Filed: October 21, 2022
    Date of Patent: December 17, 2024
    Assignee: UnitedHealth Group Incorporated
    Inventors: Laura D. Hamilton, Vinit Garg, Ayush Tomar, Martin R. Linenweber, Preet Kamal S. Bawa, David Armbrust, Rupesh Kartha, Lun Yu
  • SEARCH ANALYSIS AND RETRIEVAL VIA MACHINE LEARNING EMBEDDINGS
    Publication number: 20230409614
    Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.
    Type: Application
    Filed: October 21, 2022
    Publication date: December 21, 2023
    Inventors: Laura D. Hamilton, Vinit Garg, Ayush Tomar, Martin R. Linenweber, Preet Kamal S. Bawa, David Armbrust, Rupesh Kartha, Lun Yu