Machine learning model to preload search results
Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, u...
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creator | Baecke, Paul Sheldon, David Pradhan, Malik Mehdi Hill, Daniel Novielli, Nathan Lyndersay, Sean Zhong, Yan Mehta, Dheeraj |
description | Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate. |
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As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. 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Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDDwTUzOyMxLVchJTSzKy8xLV8jNT0nNUSjJVygoSs3JT0xRKAbKJGcoFKUWl-aUFPMwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUkvjQYEMDc1MTS0MjJyNjYtQAAFwrK20</recordid><startdate>20200825</startdate><enddate>20200825</enddate><creator>Baecke, Paul</creator><creator>Sheldon, David</creator><creator>Pradhan, Malik Mehdi</creator><creator>Hill, Daniel</creator><creator>Novielli, Nathan</creator><creator>Lyndersay, Sean</creator><creator>Zhong, Yan</creator><creator>Mehta, Dheeraj</creator><scope>EVB</scope></search><sort><creationdate>20200825</creationdate><title>Machine learning model to preload search results</title><author>Baecke, Paul ; Sheldon, David ; Pradhan, Malik Mehdi ; Hill, Daniel ; Novielli, Nathan ; Lyndersay, Sean ; Zhong, Yan ; Mehta, Dheeraj</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US10754912B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Baecke, Paul</creatorcontrib><creatorcontrib>Sheldon, David</creatorcontrib><creatorcontrib>Pradhan, Malik Mehdi</creatorcontrib><creatorcontrib>Hill, Daniel</creatorcontrib><creatorcontrib>Novielli, Nathan</creatorcontrib><creatorcontrib>Lyndersay, Sean</creatorcontrib><creatorcontrib>Zhong, Yan</creatorcontrib><creatorcontrib>Mehta, Dheeraj</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Baecke, Paul</au><au>Sheldon, David</au><au>Pradhan, Malik Mehdi</au><au>Hill, Daniel</au><au>Novielli, Nathan</au><au>Lyndersay, Sean</au><au>Zhong, Yan</au><au>Mehta, Dheeraj</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Machine learning model to preload search results</title><date>2020-08-25</date><risdate>2020</risdate><abstract>Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Machine learning model to preload search results |
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