Invention Grant
- Patent Title: Cooperatively training and/or using separate input and subsequent content neural networks for information retrieval
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Application No.: US15476280Application Date: 2017-03-31
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Publication No.: US11188824B2Publication Date: 2021-11-30
- Inventor: Brian Strope , Yun-hsuan Sung , Matthew Henderson , Rami Al-Rfou' , Raymond Kurzweil
- Applicant: Google Inc.
- Applicant Address: US CA Mountain View
- Assignee: Google Inc.
- Current Assignee: Google Inc.
- Current Assignee Address: US CA Mountain View
- Agency: Middleton Reutlinger
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N5/04 ; G06F16/00 ; G06N3/04 ; G06F16/335

Abstract:
Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.
Public/Granted literature
Information query