- Patent Title: Distributed in-browser deep learning for predictive pre-fetching
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Application No.: US15941305Application Date: 2018-03-30
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Publication No.: US10795965B2Publication Date: 2020-10-06
- Inventor: Nitin Pasumarthy
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Hickman Palermo Becker Bingham LLP
- Main IPC: G06F15/16
- IPC: G06F15/16 ; G06F16/957 ; G06N3/08 ; G06Q50/00 ; G06F21/62 ; G06N3/04

Abstract:
Techniques for distributed processing and pre-fetching content using an in-browser neural network model are disclosed herein. In some embodiments, a server transmits a neural network model to a client device, where the neural network model is stored a persistent store of a browser on the client device, and, during a networking session in which the browser on the client device is accessing a page of an online service, the client device predicts at least one link from a plurality of links on the page using the stored neural network model. The client device then fetches content associated with the predicted link(s) from a server of the online service prior to any selection of the predicted link(s) during the networking session.
Public/Granted literature
- US20190303504A1 DISTRIBUTED IN-BROWSER DEEP LEARNING FOR PREDICTIVE PRE-FETCHING Public/Granted day:2019-10-03
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