Patents by Inventor Belle Tseng
Belle Tseng 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: 11575632Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: GrantFiled: December 30, 2019Date of Patent: February 7, 2023Assignee: YAHOO ASSETS LLCInventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Publication number: 20220273227Abstract: Embodiments of the present disclosure relate systems and methods for detecting cognitive decline of a subject using passively obtained data from at least one mobile device. In an exemplary embodiment, a computer-implemented method comprises receiving passively obtained data from at least one mobile device. The method further comprises generating digital biomarker data from the passively obtained data. The method further comprises analyzing the digital biomarker data to determine whether the subject is exhibiting signs of cognitive decline.Type: ApplicationFiled: July 9, 2020Publication date: September 1, 2022Inventors: Richard Jia Chuan CHEN, Luca FOSCHINI, Filip Aleksandar JANKOVIC, Hyun Joon JUNG, Lampros KOURTIS, Vera MALJKOVIC, Nicole Lee MARINSEK, Melissa Anna Maria PUGH, Jie SHEN, Alessio SIGNORINI, Han Hee SONG, Marc Orlando SUNGA, Andrew Daniel TRISTER, Belle TSENG, Roy YAARI
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Publication number: 20200137012Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: ApplicationFiled: December 30, 2019Publication date: April 30, 2020Inventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Patent number: 10523610Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: GrantFiled: May 7, 2018Date of Patent: December 31, 2019Assignee: Oath Inc.Inventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Patent number: 10298526Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: GrantFiled: September 12, 2016Date of Patent: May 21, 2019Assignee: OATH INC.Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue
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Publication number: 20180255012Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: ApplicationFiled: May 7, 2018Publication date: September 6, 2018Inventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Patent number: 9967218Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: GrantFiled: October 26, 2011Date of Patent: May 8, 2018Assignee: Oath Inc.Inventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Publication number: 20170046046Abstract: Methods and systems for presenting content such as articles based on utility are provided. In one embodiment, a plurality of articles are determined, each article in the plurality of articles including article content and a corresponding preview icon, the preview icon defining a link to the corresponding article content when presented. For each article in the plurality of articles, a user experience utility value is determined. And for each article in the plurality of articles, an economic utility value is also determined. A ranked order of the articles is determined based upon each article's user experience utility value and economic utility value.Type: ApplicationFiled: October 21, 2016Publication date: February 16, 2017Inventors: Howard Scott Roy, Belle Tseng, Pradheep Elango, Bee-Chung Chen, Jayavel Shanmugasundaram, Raghu Ramakrishnan, Andrei Z. Broder, Deepak Agarwal, Todd Beaupre, Nitin Motgi, John Tomlin
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Publication number: 20170012912Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: ApplicationFiled: September 12, 2016Publication date: January 12, 2017Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue
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Patent number: 9519682Abstract: Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.Type: GrantFiled: May 26, 2011Date of Patent: December 13, 2016Assignee: Yahoo! Inc.Inventors: Jay Pujara, Vishwanath Tumkur Ramarao, Xiaopeng Xi, Martin Zinkevich, Anirban Dasgupta, Belle Tseng, Wei Chu, Jyh-Shin Gareth Shue
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Patent number: 9442881Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: GrantFiled: August 31, 2011Date of Patent: September 13, 2016Assignee: Yahoo! Inc.Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue
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Patent number: 9177207Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.Type: GrantFiled: January 16, 2015Date of Patent: November 3, 2015Assignee: Zynga Inc.Inventors: Lyndon Kennedy, Roelof van Zwol, Nicolas Torzec, Belle Tseng
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Publication number: 20150131900Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.Type: ApplicationFiled: January 16, 2015Publication date: May 14, 2015Inventors: Lyndon Kennedy, Roelof van Zwol, Nicolas Torzec, Belle Tseng
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Patent number: 8955044Abstract: A method of generating a time managed challenge-response test is presented. The method identifies a geometric shape having a volume and generates an entry object of the time managed challenge-response test. The entry object is overlaid onto the geometric shape, such that the entry object is distributed over a surface of the geometric shape, and a portion of the entry object is hidden at any point in time. The geometric shape is rotated, which reveals the portion of the entry object that is hidden. A display region on a display is identified for rendering the geometric shape and the geometric shape is presented in the display region of the display.Type: GrantFiled: October 4, 2010Date of Patent: February 10, 2015Assignee: Yahoo! Inc.Inventors: Kunal Punera, Shanmugasundaram Ravikumar, Anirban Dasgupta, Belle Tseng, Hung-Kuo (James) Chu
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Patent number: 8938116Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model.Type: GrantFiled: December 8, 2011Date of Patent: January 20, 2015Assignee: Yahoo! Inc.Inventors: Lyndon Kennedy, Roelof van Zwol, Nicolas Torzec, Belle Tseng
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Patent number: 8935194Abstract: Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device.Type: GrantFiled: February 14, 2013Date of Patent: January 13, 2015Assignee: Yahoo! Inc.Inventors: Anirban Dasgupta, Liang Zhang, Maxim Gurevich, Achint Oommen Thomas, Belle Tseng
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Patent number: 8832101Abstract: Various users' navigational behaviors relative to search results presented by a search engine are monitored. URLs that are visited and revised queries that are submitted after the submission of an original query are placed within a trail that begins with the original query. These trails are grouped based on the original queries with which they begin. For each trail group, a set of URLs that frequently occur in that group's trails, and a set of revised queries that frequently occur in that group's trails, are determined. These frequently occurring elements are mapped to the original queries with which all the trails in the corresponding trail group begin. In response to subsequent submissions of the same original query, the search engine ensures that URLs and revised queries that are mapped to the original query are prominently displayed on the search results pages that are initially returned in response to those submissions.Type: GrantFiled: February 18, 2010Date of Patent: September 9, 2014Assignee: Yahoo! Inc.Inventors: David Ciemiewicz, Ya Zhang, Belle Tseng, Jean-Marc Langlois
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Publication number: 20130148880Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model.Type: ApplicationFiled: December 8, 2011Publication date: June 13, 2013Applicant: Yahoo! Inc.Inventors: Lyndon Kennedy, Roelof van Zwol, Nicolas Torzec, Belle Tseng
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Publication number: 20130111005Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.Type: ApplicationFiled: October 26, 2011Publication date: May 2, 2013Applicant: Yahoo!, Inc.Inventors: Wei Chu, Martin Zinkevich, Lihong Li, Achint Oommen Thomas, Belle Tseng
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Publication number: 20130097005Abstract: Techniques for providing group discounts are described. A group discount package is configured by associating a plurality of different items with the package, associating a discount price with each item, and associating a threshold value with at least one item. One or more actions that have corresponding threshold values may also be associated with the package. The group discount package may be offered by enabling users to request to purchase items associated with the package. Each user may request to purchase one or more of the items associated with the package at the associated discount price. Furthermore, the users may be enabled to perform any actions associated with the package. A deal with the package is confirmed when each associated threshold value is met.Type: ApplicationFiled: October 12, 2011Publication date: April 18, 2013Applicant: YAHOO! INC.Inventors: Jie Yang, Liang Zhang, Belle Tseng