公开(公告)号：US11669558B2

公开(公告)日：2023-06-06

申请号：US16368798

申请日：2019-03-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yan Wang ,  Ye Wu ,  Houdong Hu ,  Surendra Ulabala ,  Vishal Thakkar ,  Arun Sacheti

IPC: G06N3/04 ,  G06N5/02 ,  G06N3/045 ,  G06F16/33 ,  G06F16/245 ,  G06F16/248 ,  G06V20/62 ,  G06F18/2413 ,  G06F17/16

CPC classification number: G06F16/3347 ,  G06F16/245 ,  G06F16/248 ,  G06F18/2413 ,  G06N3/04 ,  G06N3/045 ,  G06N5/02 ,  G06V20/62 ,  G06F17/16

Abstract: A computer-implemented technique generates a dense embedding vector that provides a distributed representation of input text. The technique includes: generating an input term-frequency (TF) vector of dimension g that includes frequency information relating to frequency of occurrence of terms in an instance of input text; using a TF-modifying component to modify the term-specific frequency information in the input TF vector by respective machine-trained weighting factors, to produce an intermediate vector of dimension g; using a projection component to project the intermediate vector of dimension g into an embedding vector of dimension k, where k is less than g. Both the TF-modifying component and the projection component use respective machine-trained neural networks. An application performs any of a retrieval-based function, a recognition-based function, a recommendation-based function, a classification-based function, etc. based on the embedding vector.

公开(公告)号：US20190180146A1

公开(公告)日：2019-06-13

申请号：US15840823

申请日：2017-12-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  FNU Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F17/30

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.

公开(公告)号：US10607118B2

公开(公告)日：2020-03-31

申请号：US15840823

申请日：2017-12-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  FNU Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F16/532 ,  G06F16/583 ,  G06K9/00

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.

公开(公告)号：US10997468B2

公开(公告)日：2021-05-04

申请号：US16799528

申请日：2020-02-24

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  Fnu Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F16/532 ,  G06F16/583 ,  G06K9/00

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.