Patents by Inventor Ehsan Hosseini Asl
Ehsan Hosseini Asl 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: 11934952Abstract: Embodiments described herein provide natural language processing (NLP) systems and methods that utilize energy-based models (EBMs) to compute an exponentially-weighted energy-like term in the loss function to train an NLP classifier. Specifically, noise contrastive estimation (NCE) procedures are applied together with the EBM-based loss objectives for training the NLPs.Type: GrantFiled: December 16, 2020Date of Patent: March 19, 2024Assignee: Salesforce, Inc.Inventors: Tianxing He, Ehsan Hosseini-Asl, Bryan McCann, Caiming Xiong
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Publication number: 20240078389Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.Type: ApplicationFiled: November 9, 2023Publication date: March 7, 2024Inventors: Ehsan Hosseini-Asl, Wenhao Liu
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Patent number: 11853706Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.Type: GrantFiled: September 8, 2021Date of Patent: December 26, 2023Assignee: salesforce.com, inc.Inventors: Ehsan Hosseini-Asl, Wenhao Liu
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Patent number: 11676022Abstract: A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.Type: GrantFiled: August 30, 2021Date of Patent: June 13, 2023Assignee: salesforce.com, inc.Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Publication number: 20220366145Abstract: Sentiment analysis is a task in natural language processing. The embodiments are directed to using a generative language model to extract an aspect term, aspect category and their corresponding polarities. The generative language model may be trained as a single, joint, and multi-task model. The single-task generative language model determines a term polarity from the aspect term in the sentence or a category polarity from an aspect category in the sentence. The joint-task generative language model determines both the aspect term and the term polarity or the aspect category and the category polarity. The multi-task generative language model determines the aspect term, term polarity, aspect category and category polarity of the sentence.Type: ApplicationFiled: September 8, 2021Publication date: November 17, 2022Inventors: Ehsan Hosseini-Asl, Wenhao Liu
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Publication number: 20220058348Abstract: Embodiments described herein provide natural language processing (NLP) systems and methods that utilize energy-based models (EBMs) to compute an exponentially-weighted energy-like term in the loss function to train an NLP classifier. Specifically, noise contrastive estimation (NCE) procedures are applied together with the EBM-based loss objectives for training the NLPs.Type: ApplicationFiled: December 16, 2020Publication date: February 24, 2022Inventors: Tianxing He, Ehsan Hosseini-Asl, Bryan McCann, Caiming Xiong
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Publication number: 20210389736Abstract: A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.Type: ApplicationFiled: August 30, 2021Publication date: December 16, 2021Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Patent number: 11106182Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.Type: GrantFiled: August 3, 2018Date of Patent: August 31, 2021Assignee: salesforce.com, inc.Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Patent number: 10963685Abstract: Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.Type: GrantFiled: November 1, 2019Date of Patent: March 30, 2021Assignee: DST Technologies, Inc.Inventor: Ehsan Hosseini Asl
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Patent number: 10783875Abstract: A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.Type: GrantFiled: July 3, 2018Date of Patent: September 22, 2020Assignee: salesforce.com, inc.Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Publication number: 20200065573Abstract: Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.Type: ApplicationFiled: November 1, 2019Publication date: February 27, 2020Inventor: Ehsan Hosseini Asl
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Patent number: 10515265Abstract: Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.Type: GrantFiled: December 15, 2017Date of Patent: December 24, 2019Assignee: Captricity, Inc.Inventor: Ehsan Hosseini Asl
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Publication number: 20190295530Abstract: A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.Type: ApplicationFiled: July 3, 2018Publication date: September 26, 2019Applicant: salesforce.com, inc.Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Publication number: 20190286073Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.Type: ApplicationFiled: August 3, 2018Publication date: September 19, 2019Inventors: Ehsan Hosseini-Asl, Caiming Xiong, Yingbo Zhou, Richard Socher
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Publication number: 20180181805Abstract: Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.Type: ApplicationFiled: December 15, 2017Publication date: June 28, 2018Inventor: Ehsan Hosseini Asl
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Publication number: 20170076152Abstract: A shred is digital data that includes an image of a portion of a document, such as a field of a form. Optical Character Recognition (OCR) is traditionally used to convert images of text into textual content. However, OCR engines are often not sufficiently capable to convert images of handwritten text into textual content. In a disclosed technique, a library of shreds is created where each shred is manually associated with a character string that represents the textual content of the shred. A computer extracts visual features of a new shred that includes an image of a handwritten text. Based on the visual features, and without performing OCR, the computer identifies a shred from the library of shreds that is visually similar to the new shred, and determines that the character string associated with the library shred accurately represents the textual content of the new shred.Type: ApplicationFiled: September 13, 2016Publication date: March 16, 2017Inventors: Ehsan Hosseini Asl, Angshuman Guha