Patents by Inventor Angeliki Metallinou
Angeliki Metallinou 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: 11908468Abstract: A system that is capable of resolving anaphora using timing data received by a local device. A local device outputs audio representing a list of entries. The audio may represent synthesized speech of the list of entries. A user can interrupt the device to select an entry in the list, such as by saying “that one.” The local device can determine an offset time representing the time between when audio playback began and when the user interrupted. The local device sends the offset time and audio data representing the utterance to a speech processing system which can then use the offset time and stored data to identify which entry on the list was most recently output by the local device when the user interrupted. The system can then resolve anaphora to match that entry and can perform additional processing based on the referred to item.Type: GrantFiled: December 4, 2020Date of Patent: February 20, 2024Assignee: Amazon Technologies, Inc.Inventors: Prakash Krishnan, Arindam Mandal, Siddhartha Reddy Jonnalagadda, Nikko Strom, Ariya Rastrow, Ying Shi, David Chi-Wai Tang, Nishtha Gupta, Aaron Challenner, Bonan Zheng, Angeliki Metallinou, Vincent Auvray, Minmin Shen
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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: 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: 11393454Abstract: A dialog generator receives data corresponding to desired dialog, such as application programming interface (API) information and sample dialog. A first model corresponding to an agent simulator and a second model corresponding to a user simulator take turns creating a plurality of dialog outlines of the desired dialog. The dialog generator may determine that one or more additional APIs are relevant to the dialog and may create further dialog outlines related thereto. The dialog outlines are converted to natural dialog to generate the dialog.Type: GrantFiled: December 13, 2018Date of Patent: July 19, 2022Assignee: Amazon Technologies, Inc.Inventors: Anish Acharya, Angeliki Metallinou, Tagyoung Chung, Shachi Paul, Shubhra Chandra, Chien-wei Lin, Dilek Hakkani-Tur, Arindam Mandal
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Publication number: 20220093101Abstract: A system that is capable of resolving anaphora using timing data received by a local device. A local device outputs audio representing a list of entries. The audio may represent synthesized speech of the list of entries. A user can interrupt the device to select an entry in the list, such as by saying “that one.” The local device can determine an offset time representing the time between when audio playback began and when the user interrupted. The local device sends the offset time and audio data representing the utterance to a speech processing system which can then use the offset time and stored data to identify which entry on the list was most recently output by the local device when the user interrupted. The system can then resolve anaphora to match that entry and can perform additional processing based on the referred to item.Type: ApplicationFiled: December 4, 2020Publication date: March 24, 2022Inventors: Prakash Krishnan, Arindam Mandal, Siddhartha Reddy Jonnalagadda, Nikko Strom, Ariya Rastrow, Ying Shi, David Chi-Wai Tang, Nishtha Gupta, Aaron Challenner, Bonan Zheng, Angeliki Metallinou, Vincent Auvray, Minmin Shen
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Publication number: 20220093094Abstract: A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.Type: ApplicationFiled: December 4, 2020Publication date: March 24, 2022Inventors: Prakash Krishnan, Arindam Mandal, Siddhartha Reddy Jonnalagadda, Nikko Strom, Ariya Rastrow, Shiv Naga Prasad Vitaladevuni, Angeliki Metallinou, Vincent Auvray, Minmin Shen, Josey Diego Sandoval, Rohit Prasad, Thomas Taylor, Amotz Maimon
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Patent number: 11200885Abstract: A dialog manager receives text data corresponding to a dialog with a user. Entities represented in the text data are identified. Context data relating to the dialog is maintained, which may include prior dialog, prior API calls, user profile information, or other data. Using the text data and the context data, an N-best list of one or more dialog models is selected to process the text data. After processing the text data, the outputs of the N-best models are ranked and a top-scoring output is selected. The top-scoring output may be an API call and/or an audio prompt.Type: GrantFiled: December 13, 2018Date of Patent: December 14, 2021Assignee: Amazon Technologies, Inc.Inventors: Arindam Mandal, Nikko Strom, Angeliki Metallinou, Tagyoung Chung, Dilek Hakkani-Tur, Suranjit Adhikari, Sridhar Yadav Manoharan, Ankita De, Qing Liu, Raefer Christopher Gabriel, Rohit Prasad
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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: 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: 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
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Patent number: 10304444Abstract: A system capable of performing natural language understanding (NLU) without the concept of a domain that influences NLU results. The present system uses a hierarchical organizations of intents/commands and entity types, and trained models associated with those hierarchies, so that commands and entity types may be determined for incoming text queries without necessarily determining a domain for the incoming text. The system thus operates in a domain agnostic manner, in a departure from multi-domain architecture NLU processing where a system determines NLU results for multiple domains simultaneously and then ranks them to determine which to select as the result.Type: GrantFiled: June 29, 2016Date of Patent: May 28, 2019Assignee: Amazon Technologies, Inc.Inventors: Lambert Mathias, Thomas Kollar, Arindam Mandal, Angeliki Metallinou
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Publication number: 20170278514Abstract: A system capable of performing natural language understanding (NLU) without the concept of a domain that influences NLU results. The present system uses a hierarchical organizations of intents/commands and entity types, and trained models associated with those hierarchies, so that commands and entity types may be determined for incoming text queries without necessarily determining a domain for the incoming text. The system thus operates in a domain agnostic manner, in a departure from multi-domain architecture NLU processing where a system determines NLU results for multiple domains simultaneously and then ranks them to determine which to select as the result.Type: ApplicationFiled: June 29, 2016Publication date: September 28, 2017Inventors: Lambert Mathias, Thomas Kollar, Arindam Mandal, Angeliki Metallinou