Patents by Inventor Saleema Amershi
Saleema Amershi 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: 20240135098Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: December 1, 2023Publication date: April 25, 2024Inventors: Patrice Y. SIMARD, David G. GRANGIER, Leon BOTTOU, Saleema A. AMERSHI
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Patent number: 11023677Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: July 13, 2016Date of Patent: June 1, 2021Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Aparna Lakshmiratan, Saleema A. Amershi
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Publication number: 20200349596Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.Type: ApplicationFiled: July 20, 2020Publication date: November 5, 2020Applicant: Microsoft Technology Licensing, LLCInventors: Jaime B. Teevan, Saleema Amershi, Shamsi Tamara Iqbal, Daniel John Liebling, Semiha Ece Kamar Eden, Kristina N. Toutanova, Robert Warren Gruen, Darren Francis Gehring, Pallavi Choudhury, Ann Paradiso, Anthony Lee Carbary
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Patent number: 10755296Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.Type: GrantFiled: June 29, 2016Date of Patent: August 25, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Jaime B. Teevan, Saleema Amershi, Shamsi Tamara Iqbal, Daniel John Liebling, Semiha Ece Kamar Eden, Kristina N. Toutanova, Robert Warren Gruen, Darren Francis Gehring, Pallavi Choudhury, Ann Paradiso, Anthony Lee Carbary
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Patent number: 10460256Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.Type: GrantFiled: August 9, 2016Date of Patent: October 29, 2019Assignee: Microsot Technology Licensing, LLCInventors: Saleema A. Amershi, Bongshin Lee, Jina Suh, Jason Douglas Williams, Donghao Ren
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Patent number: 10372815Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: August 6, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David G. Grangier, Leon Bottou, Saleema A. Amershi
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Publication number: 20190213252Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: March 19, 2019Publication date: July 11, 2019Inventors: PATRICE Y. SIMARD, DAVID G. GRANGIER, LEON BOTTOU, SALEEMA A. AMERSHI
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Patent number: 10068185Abstract: Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.Type: GrantFiled: December 7, 2014Date of Patent: September 4, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Saleema Amershi, Michael J. Brooks, Bongshin Lee, Steven M. Drucker, Patrice Y. Simard, Jin A. Suh, Ashish Kapoor
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Publication number: 20180046935Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.Type: ApplicationFiled: August 9, 2016Publication date: February 15, 2018Inventors: SALEEMA A. AMERSHI, BONGSHIN LEE, JINA SUH, JASON DOUGLAS WILLIAMS, DONGHAO REN
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Patent number: 9886669Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.Type: GrantFiled: February 26, 2014Date of Patent: February 6, 2018Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Saleema A. Amershi, Steven M. Drucker, Bongshin Lee, Patrice Yvon Rene Simard, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez, Denis X. Charles, David G. Grangier, David Maxwell Chickering
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Patent number: 9766922Abstract: Embodiments of the invention relate to generating automated web task procedures from an analysis of web history logs. One aspect of the invention concerns a method that comprises identifying sequences of related web actions from a web log, grouping each set of similar web actions into an action class, and mapping the sequences of related web actions into sequences of action classes. The method further clusters each group of similar sequences of action classes into a cluster, wherein relationships among the action classes in the cluster are represented by a state machine, and generates automated web task procedures from the state machine.Type: GrantFiled: September 16, 2013Date of Patent: September 19, 2017Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Saleema A. Amershi, Tessa A. Lau, Jalal U. Mahmud, Jeffrey W. Nichols
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Publication number: 20170103407Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.Type: ApplicationFiled: June 29, 2016Publication date: April 13, 2017Inventors: Jaime B. Teevan, Saleema Amershi, Shamsi Tamara Iqbal, Daniel John Liebling, Semiha Ece Kamar Eden, Kristina N. Toutanova, Robert Warren Gruen, Darren Francis Gehring, Pallavi Choudhury, Ann Paradiso, Anthony Lee Carbary
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Publication number: 20170103359Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user such as the creator of the content item or an administrator. The microtasks can be assigned to one or more workers including the creator of the content item. When a determination is made that an assigned worker is available to complete a microtask (e.g., when the worker is waiting in line, has just closed an application or file, or has just completed a phone call, etc.), the assigned microtask is presented to the worker for completion.Type: ApplicationFiled: October 12, 2015Publication date: April 13, 2017Inventors: Jaime Teevan, Shamsi Tamara Iqbal, Curtis von Veh, Daniel Liebling, Semiha Ece Kamar Eden, Andres Monroy-Hernandez, Pallavi Choudhury, Kristina Toutanova, Saleema Amershi
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Publication number: 20170039486Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: July 13, 2016Publication date: February 9, 2017Inventors: Patrice Y. SIMARD, David Max CHICKERING, David G. GRANGIER, Aparna LAKSHMIRATAN, Saleema A. AMERSHI
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Patent number: 9489373Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: November 8, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Denis X. Charles, Leon Bottou, Saleema A. Amershi, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez
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Patent number: 9430460Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: August 30, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Aparna Lakshmiratan, Saleema A. Amershi
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Publication number: 20160162803Abstract: Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.Type: ApplicationFiled: December 7, 2014Publication date: June 9, 2016Inventors: Saleema Amershi, Michael J. Brooks, Bongshin Lee, Steven M. Drucker, Patrice Y. Simard, Jin A. Suh, Ashish Kapoor
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Patent number: 9122995Abstract: The described implementations relate to data classification. One implementation includes identifying one or more likely classifications for an incoming data item using an algorithm. The implementation can also include providing the one or more identified classifications to a user. A selection of an individual identified classification for the incoming data item can be received from the user. The algorithm can be refined to reflect the selection by the user.Type: GrantFiled: March 15, 2011Date of Patent: September 1, 2015Assignee: Microsoft Technology Licensing, LLCInventors: Bongshin Lee, Ashish Kapoor, Ratul Mahajan, Blaine S. Christian, Saleema Amershi
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Publication number: 20150242761Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.Type: ApplicationFiled: February 26, 2014Publication date: August 27, 2015Applicant: MICROSOFT CORPORATIONInventors: SALEEMA A. AMERSHI, STEVEN M. DRUCKER, BONGSHIN LEE, PATRICE YVON RENE SIMARD, APARNA LAKSHMIRATAN, CARLOS GARCIA JURADO SUAREZ, DENIS X. CHARLES, DAVID G. GRANGIER, DAVID MAXWELL CHICKERING
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Publication number: 20150019463Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: November 8, 2013Publication date: January 15, 2015Applicant: Microsoft CorporationInventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, DAVID G. GRANGIER, APARNA LAKSHMIRATAN, SALEEMA A. AMERSHI