Patents by Inventor Guven Burc Arpat
Guven Burc Arpat 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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Publication number: 20200286000Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.Type: ApplicationFiled: May 27, 2020Publication date: September 10, 2020Applicant: Facebook, Inc.Inventors: Guven Burc ARPAT, Saiyad SHAH, Srikant Ramakrishna AYYAR
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Patent number: 10733527Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.Type: GrantFiled: December 28, 2015Date of Patent: August 4, 2020Assignee: Facebook, Inc.Inventors: Miaoqing Fang, Guven Burc Arpat
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Patent number: 10706367Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.Type: GrantFiled: September 10, 2013Date of Patent: July 7, 2020Assignee: FACEBOOK, INC.Inventors: Guven Burc Arpat, Saiyad Shah, Srikant Ramakrishna Ayyar
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Patent number: 10679147Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.Type: GrantFiled: August 31, 2017Date of Patent: June 9, 2020Assignee: Facebook, Inc.Inventors: Guven Burc Arpat, Saiyad Shah, Srikant Ramakrishna Ayyar
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Patent number: 10394953Abstract: Some embodiments include a method of detecting memes, as “key terms,” in a chatter aggregation in a social networking system. The method can include aggregating user-generated content objects within the social networking system into the chatter aggregation according to a set of filters. A meme analysis engine can define a target group within the chatter aggregation to compare against a background group. The meme analysis engine can extract key terms from textual content of the target group. The meme analysis engine can determine a relevancy rank of a term in the key terms based on an accounting of the term in the textual content of the target group and a linguistic relevance score of the term according to a linguistic model.Type: GrantFiled: July 17, 2015Date of Patent: August 27, 2019Assignee: Facebook, Inc.Inventors: Satyavarta Satyavarta, Guven Burc Arpat, Mui Thu Tran
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Patent number: 10057349Abstract: Some embodiments include a stream consolidation engine in a social networking system. The stream consolidation engine can receive two or more input data streams (e.g., an activity record data stream and an application service output stream) from the social networking system. The stream consolidation engine can merge an activity record from the activity record data stream with at least a data record from the input data streams (e.g., from the activity record data stream or the application service output stream) to create a conglomerate data record. The stream consolidation engine can supplement the conglomerate data record with asynchronous information from a data storage or derivative data computed based on content in or referenced by the conglomerate data record. The stream consolidation engine can publish the conglomerate data record in a consolidated data stream. The consolidated data stream can be substantially synchronous to at least one of the input data streams.Type: GrantFiled: November 12, 2015Date of Patent: August 21, 2018Assignee: Facebook, Inc.Inventors: Neil A. Kodner, Jason Sundram, Guven Burc Arpat
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Publication number: 20180012146Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.Type: ApplicationFiled: August 31, 2017Publication date: January 11, 2018Inventors: Guven Burc Arpat, Saiyad Shah, Srikant Ramakrishna Ayyar
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Publication number: 20170337518Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a first score generated by a first scoring algorithm that determines a degree to which a resume is matched to a job pipeline of an organization. A second score generated by a second scoring algorithm that determines a degree to which the resume is matched to the job pipeline is determined. The first score and the second score are processed to generate an aggregate score.Type: ApplicationFiled: May 23, 2016Publication date: November 23, 2017Inventors: Miaoqing Fang, Guven Burc Arpat, Jesse William Czelusta
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Publication number: 20170286865Abstract: Systems, methods, and non-transitory computer readable media are configured to determine scores regarding suitability of connections of a user for employment with an organization with which the user is employed based on a first machine learning model. Job titles for which the connections are suited are determined based on a second machine learning model. A user interface for presenting in real time information relating to the connections and associated job titles determined for the connections is generated.Type: ApplicationFiled: April 5, 2016Publication date: October 5, 2017Inventors: Miaoqing Fang, Guven Burc Arpat, Brendan Michael Viscomi, Shuye Wu, Varun Singh, Shuo Shen, Anthony Victor Paves
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Publication number: 20170185911Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.Type: ApplicationFiled: December 28, 2015Publication date: June 29, 2017Inventors: Miaoqing Fang, Guven Burc Arpat
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Publication number: 20170142200Abstract: Some embodiments include a stream consolidation engine in a social networking system. The stream consolidation engine can receive two or more input data streams (e.g., an activity record data stream and an application service output stream) from the social networking system. The stream consolidation engine can merge an activity record from the activity record data stream with at least a data record from the input data streams (e.g., from the activity record data stream or the application service output stream) to create a conglomerate data record. The stream consolidation engine can supplement the conglomerate data record with asynchronous information from a data storage or derivative data computed based on content in or referenced by the conglomerate data record. The stream consolidation engine can publish the conglomerate data record in a consolidated data stream. The consolidated data stream can be substantially synchronous to at least one of the input data streams.Type: ApplicationFiled: November 12, 2015Publication date: May 18, 2017Inventors: Neil A. Kodner, Jason Sundram, Guven Burc Arpat
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Publication number: 20170017638Abstract: Some embodiments include a method of detecting memes, as “key terms,” in a chatter aggregation in a social networking system. The method can include aggregating user-generated content objects within the social networking system into the chatter aggregation according to a set of filters. A meme analysis engine can define a target group within the chatter aggregation to compare against a background group. The meme analysis engine can extract key terms from textual content of the target group. The meme analysis engine can determine a relevancy rank of a term in the key terms based on an accounting of the term in the textual content of the target group and a linguistic relevance score of the term according to a linguistic model.Type: ApplicationFiled: July 17, 2015Publication date: January 19, 2017Inventors: Satyavarta Satyavarta, Guven Burc Arpat, Mui Thu Tran
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Publication number: 20160358086Abstract: Some embodiments include a method of performing a content analysis study around a central theme utilizing a concept study system. The concept study system can generate a classifier machine corresponding to the content analysis study based on a super topic taxonomy including one or more concept identifiers. The concept study system can process a content object, associated with a user activity in a social networking system, through the classifier machine to determine whether to assign the user activity to the content analysis study. The concept study system can aggregate at least an attribute derived from the user activity in a study-specific data container associated with the content analysis study and compute a statistical or analytical insight based on aggregated attributes in the study-specific data container.Type: ApplicationFiled: June 5, 2015Publication date: December 8, 2016Inventors: Jason Sundram, Neil A. Kodner, Mui Thu Tran, Guangdeng Liao, Justin Thomas Palumbo, Guven Burc Arpat, Amit Bahl
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Publication number: 20160140609Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.Type: ApplicationFiled: November 14, 2014Publication date: May 19, 2016Inventors: Deniz Demir, Michael Desmond Pinkowish, Liang He, Yingsheng Gao, Islam Farid Hamed AbdelRahman, Alexandra Louise Krakaris, Ajoy Joseph Frank, Reid Steven Gershbein, Srikant Ramakrishna Ayyar, Guven Burc Arpat, Michael Lee Develin, Michael Nicholas Hudack, Maxim Sokolov, Wenrui Zhao, Jonathan Shottan, Aaron Ted Glemann, Ksenia Timonina
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Publication number: 20160140620Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.Type: ApplicationFiled: November 14, 2014Publication date: May 19, 2016Inventors: Michael Desmond Pinkowish, Deniz Demir, Alexandra Louise Krakaris, Liang He, Yingsheng Gao, Islam Farid Hamed AbdelRahman, Ajoy Joseph Frank, Reid Steven Gershbein, Srikant Ramakrishna Ayyar, Guven Burc Arpat, Michael Lee Develin, Michael Nicholas Hudack, Maxim Sokolov, Jonathan Shottan, Wenrui Zhao
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Publication number: 20160140605Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.Type: ApplicationFiled: November 14, 2014Publication date: May 19, 2016Inventors: Michael Lee Develin, Guven Burc Arpat, Srikant Ramakrishna Ayyar
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Publication number: 20150074020Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.Type: ApplicationFiled: September 10, 2013Publication date: March 12, 2015Applicant: Facebook, Inc.Inventors: Guven Burc Arpat, Saiyad Shah, Srikant Ramakrishna Ayyar