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US20240013064A1 MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS 审中-公开

  • 专利标题： MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS
  • 申请号： US17811229
    申请日： 2022-07-07
  • 公开(公告)号： US20240013064A1
    公开(公告)日： 2024-01-11
  • 发明人: Paul J. Godden , Erik A. Nystrom , Gregory J. Boss
  • 申请人： Optum, Inc.
  • 申请人地址： US MN Minnetonka
  • 专利权人： Optum, Inc.
  • 当前专利权人： Optum, Inc.
  • 当前专利权人地址： US MN Minnetonka
  • 主分类号： G06N3/12
  • IPC分类号： G06N3/12
MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS
摘要：
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. Certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.
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