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公开(公告)号：USD978223S1
公开(公告)日：2023-02-14
申请号：US29785401
申请日：2021-05-25
Applicant: Amazon Technologies, Inc.
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公开(公告)号：US11574637B1
公开(公告)日：2023-02-07
申请号：US17014042
申请日：2020-09-08
Applicant: Amazon Technologies, Inc.
Inventor： Anoop Kumar , Anil K Ramakrishna , Sriram Venkatapathy , Rahul Gupta , Sankaranarayanan Ananthakrishnan , Premkumar Natarajan
Abstract: Techniques for using a federated learning framework to update machine learning models for spoken language understanding (SLU) system are described. The system determines which labeled data is needed to update the models based on the models generating an undesired response to an input. The system identifies users to solicit labeled data from, and sends a request to a user device to speak an input. The device generates labeled data using the spoken input, and updates the on-device models using the spoken input and the labeled data. The updated model data is provided to the system to enable the system to update the system-level (global) models.
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公开(公告)号：US11551695B1
公开(公告)日：2023-01-10
申请号：US15931455
申请日：2020-05-13
Applicant: Amazon Technologies, Inc.
Inventor： Vivek Govindan , Varun Sembium Varadarajan , Christian Egon Berkhoff Dossow , Himalay Mohanlal Joriwal , Sai Madhuri Bhavirisetty , Abhinav Kumar , Orestis Lykouropoulos , Akshay Nalwaya , Rahul Gupta , Sravan Babu Bodapati , Liangwei Guo , Julian E. S. Salazar , Yibin Wang , K P N V D S Siva Rama , Calvin Xuan Li , Mohit Narendra Gupta , Asem Rustum , Katrin Kirchhoff , Pu Zhao
Abstract: A transcription service may receive a request from a developer to build a custom speech-to-text model for a specific domain of speech. The custom speech-to-text model for the specific domain may replace a general speech-to-text model or add to a set of one or more speech-to-text models available for transcribing speech. The transcription service may receive a training data and instructions representing tasks. The transcription service may determine respective schedules for executing the instructions based at least in part on dependencies between the tasks. The transcription service may execute the instructions according to the respective schedules to train a speech-to-text model for a specific domain using the training data set. The transcription service may deploy the trained speech-to-text model as part of a network-accessible service for an end user to convert audio in the specific domain into texts.
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公开(公告)号：US11507752B1
公开(公告)日：2022-11-22
申请号：US17002862
申请日：2020-08-26
Applicant: Amazon Technologies, Inc.
Inventor： Pavel Bhowmik , Melanie C B Gens , Sachin Midha , Rahul Gupta , Sriram Venkatapathy , Xinhong Zhang , Anoop Kumar , Pooja Sanjay Sonawane , Samuel Harry Ingbar
Abstract: Techniques for evaluating a natural language understanding (NLU) component and determining an action to resolve an issue processing a user input are described. The system determines which component is invoked by a baseline NLU component is processing the user input, and which component is invoked by an updated NLU component. Based on that information, the system selects the action to resolve the updated NLU component generating an undesired response to the user input.
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公开(公告)号：US11081104B1
公开(公告)日：2021-08-03
申请号：US15838917
申请日：2017-12-12
Applicant: Amazon Technologies, Inc.
Inventor： Chengwei Su , Sankaranarayanan Ananthakrishnan , Spyridon Matsoukas , Shirin Saleem , Rahul Gupta , Kavya Ravikumar , John Will Crimmins , Kelly James Vanee , John Pelak , Melanie Chie Bomke Gens
IPC: G10L15/18 , G10L15/22 , G10L15/06 , G10L15/183 , H04L29/08 , G10L15/32 , G06K9/00 , H04W4/02 , G10L15/26 , G06F16/31 , G06F40/295
Abstract: A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.
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Patent Agency Ranking
公开(公告)号：US11043205B1
公开(公告)日：2021-06-22
申请号：US15838974
申请日：2017-12-12
Applicant: Amazon Technologies, Inc.
Inventor： Chengwei Su , Sankaranarayanan Ananthakrishnan , Spyridon Matsoukas , Rahul Gupta , Kelly James Vanee
IPC: G10L15/22 , G10L15/18 , G10L15/06 , G10L15/16 , G10L15/183 , G06N3/02 , G06N20/00 , G06F16/31 , G06F40/295
Abstract: A natural language processing system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing. The individual component scores may be weighted by weights trained to optimize the overall scores relative to each other. Each domain of the system may be configured with a separate component that determines the overall score with respect to the domain. Natural language hypotheses can be ranked using the overall score either within a specific domain or on a cross-domain basis.
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公开(公告)号：US12112752B1
公开(公告)日：2024-10-08
申请号：US17688279
申请日：2022-03-07
Applicant: Amazon Technologies, Inc.
Inventor： Rahul Gupta , Jwala Dhamala , Apurv Verma , Qingwen Ye , Mayur Himmatbhai Dabhi , Srinivasan Rengarajan Veeravanallur , Spyridon Matsoukas , Melanie C B Gens , Seyed Omid Razavi , Avni Khatri , Premkumar Natarajan
CPC classification number: G10L15/22 , G10L15/01 , G10L15/063 , G10L15/08 , G10L2015/0631 , G10L2015/223
Abstract: Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.
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公开(公告)号：US11978438B1
公开(公告)日：2024-05-07
申请号：US17215383
申请日：2021-03-29
Applicant: Amazon Technologies, Inc.
Inventor： Anil K. Ramakrishna , Rahul Gupta , Yuval Merhav , Zefei Li , Heather Brooke Spetalnick
CPC classification number: G10L15/1815 , G06N20/00 , G10L15/063 , G10L25/27
Abstract: Techniques for updating a machine learning (ML) model are described. A device or system may receive input data corresponding to a natural or non-natural language (e.g., gesture) input. Using a first ML model, the device or system may determine the input data corresponds to a data category of a plurality of data categories. Based on the data category, the device or system may select a ML training type from among a plurality of ML training types. Using the input data, the device or system may perform the selected ML training type with respect to a runtime ML model to generate an updated ML model.
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公开(公告)号：US11869490B1
公开(公告)日：2024-01-09
申请号：US16993482
申请日：2020-08-14
Applicant: Amazon Technologies, Inc.
Inventor： Rahul Gupta , Jwala Dhamala , Melanie C B Gens , Sachin Midha , Jennifer Yuen , Dewan Muhammed Ibtesham , Wael Hamza , Xinhong Zhang , Md Humayun Arafat
IPC: G10L15/183 , G10L15/06 , G06N3/08 , G06N20/00
CPC classification number: G10L15/183 , G06N3/08 , G06N20/00 , G10L15/063
Abstract: Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.
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公开(公告)号：USD966398S1
公开(公告)日：2022-10-11
申请号：US29757909
申请日：2020-11-10
Applicant: Amazon Technologies, Inc.
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