Patents by Inventor Ramkarthik Kalyanasundaram
Ramkarthik Kalyanasundaram 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: 20240096326Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: December 1, 2023Publication date: March 21, 2024Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11848018Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: GrantFiled: May 31, 2022Date of Patent: December 19, 2023Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11545147Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: GrantFiled: May 2, 2019Date of Patent: January 3, 2023Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20220293101Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: May 31, 2022Publication date: September 15, 2022Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11361768Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: GrantFiled: July 21, 2020Date of Patent: June 14, 2022Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20200349946Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: July 21, 2020Publication date: November 5, 2020Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20190304459Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: ApplicationFiled: May 2, 2019Publication date: October 3, 2019Inventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 10311872Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: GrantFiled: July 25, 2017Date of Patent: June 4, 2019Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20190035390Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: ApplicationFiled: July 25, 2017Publication date: January 31, 2019Inventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan