Patents by Inventor Shihao Ji
Shihao Ji has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11714977Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: GrantFiled: December 17, 2021Date of Patent: August 1, 2023Assignee: Intel CorporationInventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Patent number: 11449918Abstract: The makeup scheme recommendation method provided by embodiments of the present disclosure includes: acquiring makeup parameters of a user, the makeup parameters including at least one of an environment parameter, a body parameter, and a makeup time parameter; searching for a target makeup scheme matching the makeup parameters from a makeup scheme pool; optimizing the target makeup scheme in accordance with cosmetic information about the user, and generating a recommended makeup scheme, the cosmetic information being used to indicate cosmetics owned by the user; and providing the recommended makeup scheme to the user.Type: GrantFiled: March 11, 2019Date of Patent: September 20, 2022Assignee: Beijing BOE Technology Development Co., Ltd.Inventors: Shihao Ji, Xin Li
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Publication number: 20220108093Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: ApplicationFiled: December 17, 2021Publication date: April 7, 2022Applicant: Intel CorporationInventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Patent number: 11250532Abstract: Embodiments of the present disclosure relate to a computer-implemented method. The method may include receiving identification information of a vehicle and identification information of a parking space to bind the vehicle with the parking space and generate binding information, receiving a time when the vehicle enters the parking space and a time when the vehicle exits the parking space, calculating a parking fee of the vehicle according to the time when the vehicle enters the parking space and the time when the vehicle exits the parking space, determining identification information of a shopping cart that corresponds to the vehicle according to the identification information of the vehicle, the binding information of the vehicle and the parking space, and a correspondence between the parking space and the shopping cart, and sending the parking fee of the vehicle to the shopping cart that corresponds to the identification information of the shopping cart.Type: GrantFiled: January 2, 2019Date of Patent: February 15, 2022Assignee: BOE TECHNOLOGY GROUP CO., LTD.Inventors: Shihao Ji, Xin Li, Hui Rao, Zhiguo Zhang
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Patent number: 11232273Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: GrantFiled: October 12, 2020Date of Patent: January 25, 2022Assignee: Intel CorporationInventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Publication number: 20210027029Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: ApplicationFiled: October 12, 2020Publication date: January 28, 2021Applicant: Intel CorporationInventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Patent number: 10867142Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: GrantFiled: June 29, 2016Date of Patent: December 15, 2020Assignee: Intel CorporationInventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Publication number: 20200357090Abstract: Embodiments of the present disclosure relate to a computer-implemented method. The method may include receiving identification information of a vehicle and identification information of a parking space to bind the vehicle with the parking space and generate binding information, receiving a time when the vehicle enters the parking space and a time when the vehicle exits the parking space, calculating a parking fee of the vehicle according to the time when the vehicle enters the parking space and the time when the vehicle exits the parking space, determining identification information of a shopping cart that corresponds to the vehicle according to the identification information of the vehicle, the binding information of the vehicle and the parking space, and a correspondence between the parking space and the shopping cart, and sending the parking fee of the vehicle to the shopping cart that corresponds to the identification information of the shopping cart.Type: ApplicationFiled: January 2, 2019Publication date: November 12, 2020Inventors: Shihao JI, Xin LI, Hui RAO, Zhiguo ZHANG
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Publication number: 20200226657Abstract: The makeup scheme recommendation method provided by embodiments of the present disclosure includes: acquiring makeup parameters of a user, the makeup parameters including at least one of an environment parameter, a body parameter, and a makeup time parameter; searching for a target makeup scheme matching the makeup parameters from a makeup scheme pool; optimizing the target makeup scheme in accordance with cosmetic information about the user, and generating a recommended makeup scheme, the cosmetic information being used to indicate cosmetics owned by the user; and providing the recommended makeup scheme to the user.Type: ApplicationFiled: March 11, 2019Publication date: July 16, 2020Inventors: Shihao JI, Xin LI
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Publication number: 20190130148Abstract: Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.Type: ApplicationFiled: June 29, 2016Publication date: May 2, 2019Inventors: Gautham Chinya, Shihao Ji, Arnab Paul
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Patent number: 9910481Abstract: In an embodiment, a processor a plurality of cores to independently execute instructions, the cores including a plurality of counters to store performance information, and a power controller coupled to the plurality of cores, the power controller having a logic to receive performance information from at least some of the plurality of counters, determine a number of cores to be active and a performance state for the number of cores for a next operation interval, based at least in part on the performance information and model information, and cause the number of cores to be active during the next operation interval, the performance information associated with execution of a workload on one or more of the plurality of cores. Other embodiments are described and claimed.Type: GrantFiled: February 13, 2015Date of Patent: March 6, 2018Assignee: Intel CorporationInventors: Victor W. Lee, Daehyun Kim, Yuxin Bai, Shihao Ji, Sheng Li, Dhiraj D. Kalamkar, Naveen K. Mellempudi
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Patent number: 9594838Abstract: Methods, systems, and computer-readable media for query simplification are provided. A search engine executed by a server receives a query. In response, the search engine determines whether the query is a long or hard query. For long or hard queries, the search engine drops one or more terms based on search engine logs. The search engine may utilize statistical models like machine translation, condition random fields, or max entropy, to identify the terms that should be dropped. The search engine obtains search results for the simplified query and transmits the results to a user that provided the query.Type: GrantFiled: March 14, 2013Date of Patent: March 14, 2017Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Ye-Yi Wang, Xiaodong He, Xiaolong Li, Shihao Ji, Bin Zhang
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Publication number: 20160239065Abstract: In an embodiment, a processor a plurality of cores to independently execute instructions, the cores including a plurality of counters to store performance information, and a power controller coupled to the plurality of cores, the power controller having a logic to receive performance information from at least some of the plurality of counters, determine a number of cores to be active and a performance state for the number of cores for a next operation interval, based at least in part on the performance information and model information, and cause the number of cores to be active during the next operation interval, the performance information associated with execution of a workload on one or more of the plurality of cores. Other embodiments are described and claimed.Type: ApplicationFiled: February 13, 2015Publication date: August 18, 2016Inventors: VICTOR W. LEE, DAEHYUN KIM, YUXIN BAI, SHIHAO JI, SHENG LI, DHIRAJ D. KALAMKAR, NAVEEN K. MELLEMPUDI
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Patent number: 8886641Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.Type: GrantFiled: October 15, 2009Date of Patent: November 11, 2014Assignee: Yahoo! Inc.Inventors: Anlei Dong, Yi Chang, Ruiqiang Zhang, Zhaohui Zheng, Gilad Avraham Mishne, Jing Bai, Karolina Barbara Buchner, Ciya Liao, Shihao Ji, Gilbert Leung, Georges-Eric Albert Marie Robert Dupret, Ling Liu
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Publication number: 20140279995Abstract: Methods, systems, and computer-readable media for query simplification are provided. A search engine executed by a server receives a query. In response, the search engine determines whether the query is a long or hard query. For long or hard queries, the search engine drops one or more terms based on search engine logs. The search engine may utilize statistical models like machine translation, condition random fields, or max entropy, to identify the terms that should be dropped. The search engine obtains search results for the simplified query and transmits the results to a user that provided the query.Type: ApplicationFiled: March 14, 2013Publication date: September 18, 2014Applicant: MICROSOFT CORPORATIONInventors: Ye-Yi Wang, Xiaodong He, Xiaolong Li, Shihao Ji, Bin Zhang
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Publication number: 20110093459Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.Type: ApplicationFiled: October 15, 2009Publication date: April 21, 2011Applicant: YAHOO! INC.Inventors: Anlei Dong, Yi Chang, Ruiqiang Zhang, Zhaohui Zheng, Gilad Avraham Mishne, Jing Bai, Karolina Barbara Buchner, Ciya Liao, Shihao Ji, Gilbert Leung, Georges-Eric Albert Marie Robert Dupret, Ling Liu
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Publication number: 20110029517Abstract: To estimate, or predict, the relevance of items, or documents, in a set of search results, relevance information is extracted from user click data, and relational information among the documents as manifested by an aggregation of user clicks is determined from the click data. A supervised approach uses judgment information, such as human judgment information, as part of the training data used to generate a relevance predictor model, which minimizes the inherent noisiness of the click data collected from a commercial search engine.Type: ApplicationFiled: July 31, 2009Publication date: February 3, 2011Inventors: Shihao Ji, Anlei Dong, Ciya Liao, Yi Chang, Zhaohui Zheng, Olivier Chapelle, Gordon Guo-Zheng Sun, Hongyuan Zha