Patents by Inventor Sarah Lynch
Sarah Lynch 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: 20220128291Abstract: A system is described herein that includes a smart or an artificial intelligence (AI) refrigerator for use in retail, a server, and a computing device associated with a customer. The server utilizes one or more computer vision or artificial intelligence means to control the AI refrigerator.Type: ApplicationFiled: October 7, 2021Publication date: April 28, 2022Applicant: Mama Gaia Smart Fridge Corporation dba Mama GaiaInventors: Sarah Lynch, Giovanni Sudiro
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Patent number: 11200452Abstract: A computer-implemented method according to one embodiment includes identifying a first classifier training data element and a second classifier training data element, calculating a similarity metric between the first classifier training data element and the second classifier training data element, and determining a classification for the first classifier training data element and the second classifier training data element, utilizing the similarity metric between the first classifier training data element and the second classifier training data element.Type: GrantFiled: January 30, 2018Date of Patent: December 14, 2021Assignee: International Business Machines CorporationInventors: Stefan Van Der Stockt, Sihang B. Fang, Manali Jairam Chanchlani, Rahul P. Akolkar, Sarah Lynch, Kristi Farinelli
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Patent number: 11003705Abstract: A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to produce a physical action or transformation of a physical hardware device.Type: GrantFiled: February 26, 2018Date of Patent: May 11, 2021Assignee: International Business Machines CorporationInventors: Stefan A. Van Der Stockt, Sihang B. Fang, Sarah Lynch, Joseph L. Sharpe, III, Rahul P. Akolkar, Brian E. Bissell, Manali J. Chanchlani
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Patent number: 10740544Abstract: Embodiments provide a computer implemented method in a data processing system including a processor and memory storing instructions, which are executed by the processor to cause the processor to implement the method for providing an annotation policy for annotating a corpus including a plurality of electronic documents. The method includes: annotating an occurrence of a first term with a class in an electronic document; recommending a new annotation policy based on at least one annotation for the occurrence of first term; and storing the new annotation policy in a storage device.Type: GrantFiled: July 11, 2018Date of Patent: August 11, 2020Assignee: International Business Machines CorporationInventors: Sarah Lynch, Kristi Farinelli, Rahul P. Akolkar, Alexander Block, Joseph L. Sharpe, III, Stefan Van Der Stockt
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Patent number: 10565189Abstract: A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to product a physical action or transformation of a physical hardware device.Type: GrantFiled: February 26, 2018Date of Patent: February 18, 2020Assignee: International Business Machines CorporationInventors: Stefan A. Van Der Stockt, Sihang B. Fang, Sarah Lynch, Joseph L. Sharpe, III, Rahul P. Akolkar, Brian E. Bissell, Manali J. Chanchlani
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Publication number: 20200019599Abstract: Embodiments provide a computer implemented method in a data processing system including a processor and memory storing instructions, which are executed by the processor to cause the processor to implement the method for providing an annotation policy for annotating a corpus including a plurality of electronic documents. The method includes: annotating an occurrence of a first term with a class in an electronic document; recommending a new annotation policy based on at least one annotation for the occurrence of first term; and storing the new annotation policy in a storage device.Type: ApplicationFiled: July 11, 2018Publication date: January 16, 2020Inventors: Sarah Lynch, Kristi Farinelli, Rahul P. Akolkar, Alexander Block, Joseph L. Sharpe, III, Stefan Van Der Stockt
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Publication number: 20190266281Abstract: A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to product a physical action or transformation of a physical hardware device.Type: ApplicationFiled: February 26, 2018Publication date: August 29, 2019Applicant: International Business Machines CorporationInventors: Stefan A. Van Der Stockt, Sihang B. Fang, Sarah Lynch, Joseph L. Sharpe, III, Rahul P. Akolkar, Brian E. Bissell, Manali J. Chanchlani
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Publication number: 20190266270Abstract: A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to product a physical action or transformation of a physical hardware device.Type: ApplicationFiled: February 26, 2018Publication date: August 29, 2019Applicant: International Business Machines CorporationInventors: Stefan A. Van Der Stockt, Sihang B. Fang, Sarah Lynch, Joseph L. Sharpe, III, Rahul P. Akolkar, Brian E. Bissell, Manali J. Chanchlani
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Publication number: 20190236409Abstract: A computer-implemented method according to one embodiment includes identifying a first classifier training data element and a second classifier training data element, calculating a similarity metric between the first classifier training data element and the second classifier training data element, and determining a classification for the first classifier training data element and the second classifier training data element, utilizing the similarity metric between the first classifier training data element and the second classifier training data element.Type: ApplicationFiled: January 30, 2018Publication date: August 1, 2019Inventors: Stefan Van Der Stockt, Sihang B. Fang, Manali Jairam Chanchlani, Rahul P. Akolkar, Sarah Lynch, Kristi Farinelli