Patents by Inventor Rahul Goel
Rahul Goel 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: 20240153499Abstract: Multi-modal natural language processing systems are provided. Some systems are context-aware systems that use multi-modal data to improve the accuracy of natural language understanding as it is applied to spoken language input. Machine learning architectures are provided that jointly model spoken language input (“utterances”) and information displayed on a visual display (“on-screen information”). Such machine learning architectures can improve upon, and solve problems inherent in, existing spoken language understanding systems that operate in multi-modal contexts.Type: ApplicationFiled: December 7, 2023Publication date: May 9, 2024Inventors: Angeliki Metallinou, Rahul Goel, Vishal Ishwar
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Patent number: 11915506Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.Type: GrantFiled: September 7, 2021Date of Patent: February 27, 2024Assignee: TATA CONSULTANCY SERVICES LIMITEDInventors: Indira Priyadarsini Muthukrishnan, Subramanian Kuppuswami, Chandan Singh, Uma Mundoli Narayanan, Rajkumar Pallikuth, Rahul Kanna Rajarathinam, Parvatharaj Sundaresan Balasubramanian, Ishan Verma, Tushar Goel, Lipika Dey
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Patent number: 11842727Abstract: Multi-modal natural language processing systems are provided. Some systems are context-aware systems that use multi-modal data to improve the accuracy of natural language understanding as it is applied to spoken language input. Machine learning architectures are provided that jointly model spoken language input (“utterances”) and information displayed on a visual display (“on-screen information”). Such machine learning architectures can improve upon, and solve problems inherent in, existing spoken language understanding systems that operate in multi-modal contexts.Type: GrantFiled: April 18, 2022Date of Patent: December 12, 2023Assignee: Amazon Technologies, Inc.Inventors: Angeliki Metallinou, Rahul Goel, Vishal Ishwar
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Publication number: 20230289538Abstract: Systems and methods for generating code-switched semantic parsing training data and training of semantic parsers. In some examples, a processing system may be configured to use a trained first language model to translate a first single-language text sequence and first parsing data into a second code-switched text sequence and associated second parsing data, and to generate a second training example based on the second code-switched text sequence and the second parsing data. In some examples, the processing system may be further configured to generate a training set from two or more of these second training examples, and to use the training set to train a semantic parser to semantically parse code-switched utterances.Type: ApplicationFiled: November 4, 2022Publication date: September 14, 2023Inventors: Rahul Goel, Shyam Upadhyay, Anmol Agarwal
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Publication number: 20220246139Abstract: Multi-modal natural language processing systems are provided. Some systems are context-aware systems that use multi-modal data to improve the accuracy of natural language understanding as it is applied to spoken language input. Machine learning architectures are provided that jointly model spoken language input (“utterances”) and information displayed on a visual display (“on-screen information”). Such machine learning architectures can improve upon, and solve problems inherent in, existing spoken language understanding systems that operate in multi-modal contexts.Type: ApplicationFiled: April 18, 2022Publication date: August 4, 2022Inventors: Angeliki Metallinou, Rahul Goel, Vishal Ishwar
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Patent number: 11227585Abstract: Methods and systems for determining an intent of an utterance using contextual information associated with a requesting device are described herein. Voice activated electronic devices may, in some embodiments, be capable of displaying content using a display screen. Entity data representing the content rendered by the display screen may describe entities having similar attributes as an identified intent from natural language understanding processing. Natural language understanding processing may attempt to resolve one or more declared slots for a particular intent and may generate an initial list of intent hypotheses ranked to indicate which are most likely to correspond to the utterance. The entity data may be compared with the declared slots for the intent hypotheses, and the list of intent hypothesis may be re-ranked to account for matching slots from the contextual metadata. The top ranked intent hypothesis after re-ranking may then be selected as the utterance's intent.Type: GrantFiled: March 11, 2020Date of Patent: January 18, 2022Assignee: Amazon Technologies, Inc.Inventors: Alexandra R. Shapiro, Melanie Chie Bomke Gens, Spyridon Matsoukas, Kellen Gillespie, Rahul Goel
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Patent number: 11194973Abstract: A system that can engage in a dialog with a user may select a system response to a user input based on how the system estimates a user may respond to a potential system response. Models may be trained to evaluate a potential system response in view of various available data including dialog history, entity data, etc. Each model may score the potential system response for various qualitative aspects such as whether the response is likely to be comprehensible, on-topic, interesting, likely to lead to the dialog continuing, etc. Such scores may be combined to other scores such as whether the potential response is coherent or engaging. The models may be trained using previous dialog/chatbot evaluation data. At runtime the scores may be used to select a system response to a user input as part of the dialog.Type: GrantFiled: March 25, 2019Date of Patent: December 7, 2021Assignee: Amazon Technologies, Inc.Inventors: Rahul Goel, Chandra Prakash Khatri, Tagyoung Chung, Raefer Christopher Gabriel, Anushree Venkatesh, Behnam Hedayatnia, Sanghyun Yi
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Patent number: 10872601Abstract: A natural language understanding (NLU) system that uses a reduced dimensionality of word embedding features to configure compressed NLU models that use reduced computing resources for NLU tasks. A modified NLU model may include a compressed vocabulary data structure of word embedding data vectors that include a set of values corresponding to a reduced dimensionality of the original word embedding features, resulting in a smaller sized vocabulary data structure and reduced size of the vocabulary data structure. Further components of the modified NLU model perform matrix operations to expand the dimensionality of the reduced word embedding data vectors up to the expected dimensionality of later layers of the NLU model. Additional training and reweighting can adjust for potential loses in performance resulting from reductions in the word embedding features. Thus the modified NLU model can achieve similar performance to an original NLU model with reductions in use of computing resources.Type: GrantFiled: September 27, 2018Date of Patent: December 22, 2020Assignee: Amazon Technologies, Inc.Inventors: Anish Acharya, Angeliki Metallinou, Rahul Goel, Inderjit Dhillon
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Publication number: 20200279555Abstract: Methods and systems for determining an intent of an utterance using contextual information associated with a requesting device are described herein. Voice activated electronic devices may, in some embodiments, be capable of displaying content using a display screen. Entity data representing the content rendered by the display screen may describe entities having similar attributes as an identified intent from natural language understanding processing. Natural language understanding processing may attempt to resolve one or more declared slots for a particular intent and may generate an initial list of intent hypotheses ranked to indicate which are most likely to correspond to the utterance. The entity data may be compared with the declared slots for the intent hypotheses, and the list of intent hypothesis may be re-ranked to account for matching slots from the contextual metadata. The top ranked intent hypothesis after re-ranking may then be selected as the utterance's intent.Type: ApplicationFiled: March 11, 2020Publication date: September 3, 2020Inventors: Alexandra R. Shapiro, Melanie Chie Bomke Gens, Spyridon Matsoukas, Kellen Gillespie, Rahul Goel
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Publication number: 20200251098Abstract: Multi-modal natural language processing systems are provided. Some systems are context-aware systems that use multi-modal data to improve the accuracy of natural language understanding as it is applied to spoken language input. Machine learning architectures are provided that jointly model spoken language input (“utterances”) and information displayed on a visual display (“on-screen information”). Such machine learning architectures can improve upon, and solve problems inherent in, existing spoken language understanding systems that operate in multi-modal contexts.Type: ApplicationFiled: December 20, 2019Publication date: August 6, 2020Inventors: Angeliki Metallinou, Rahul Goel, Vishal Ishwar
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Patent number: 10600406Abstract: Methods and systems for determining an intent of an utterance using contextual information associated with a requesting device are described herein. Voice activated electronic devices may, in some embodiments, be capable of displaying content using a display screen. Entity data representing the content rendered by the display screen may describe entities having similar attributes as an identified intent from natural language understanding processing. Natural language understanding processing may attempt to resolve one or more declared slots for a particular intent and may generate an initial list of intent hypotheses ranked to indicate which are most likely to correspond to the utterance. The entity data may be compared with the declared slots for the intent hypotheses, and the list of intent hypothesis may be re-ranked to account for matching slots from the contextual metadata. The top ranked intent hypothesis after re-ranking may then be selected as the utterance's intent.Type: GrantFiled: March 20, 2017Date of Patent: March 24, 2020Assignee: Amazon Technologies, Inc.Inventors: Alexandra R. Shapiro, Melanie Chie Bomke Gens, Spyridon Matsoukas, Kellen Gillespie, Rahul Goel
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Patent number: 10515625Abstract: Multi-modal natural language processing systems are provided. Some systems are context-aware systems that use multi-modal data to improve the accuracy of natural language understanding as it is applied to spoken language input. Machine learning architectures are provided that jointly model spoken language input (“utterances”) and information displayed on a visual display (“on-screen information”). Such machine learning architectures can improve upon, and solve problems inherent in, existing spoken language understanding systems that operate in multi-modal contexts.Type: GrantFiled: November 30, 2017Date of Patent: December 24, 2019Assignee: Amazon Technologies, Inc.Inventors: Angeliki Metallinou, Rahul Goel, Vishal Ishwar