Patents by Inventor Noam M. Shazeer
Noam M. Shazeer 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: 20240403639Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating candidate output sequences using language model neural networks. In particular, an auto-regressive language model neural network is used to generate a candidate output sequence. The same auto-regressive language model neural network is used to evaluate the candidate output sequence to determine rating scores for each of one or more criteria. The rating score(s) are then used to determine whether to provide the candidate output sequence.Type: ApplicationFiled: August 8, 2024Publication date: December 5, 2024Inventors: Daniel De Freitas Adiwardana, Noam M. Shazeer
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Patent number: 12148421Abstract: As part of a dialog session between a user and an automated assistant, implementations can receive a stream of audio data that captures a spoken utterance including an assistant query, determine, based on processing the stream of audio data, a set of assistant outputs that are each predicted to be responsive to the assistant query, process, using large language model (LLM) output(s), the assistant outputs and context of the dialog session to generate a set of modified assistant outputs, and cause given modified assistant output, from among the set of modified assistant outputs, to be provided for presentation to the user in response to the spoken utterance. In some implementations, the LLM output(s) can be generated in an offline manner for subsequent use in an online manner. In additional or alternative implementations, the LLM output(s) can be generated in an online manner when the spoken utterance is received.Type: GrantFiled: November 22, 2021Date of Patent: November 19, 2024Assignee: GOOGLE LLCInventors: Martin Baeuml, Thushan Amarasiriwardena, Roberto Pieraccini, Vikram Sridar, Daniel De Freitas Adiwardana, Noam M. Shazeer, Quoc Le
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Patent number: 12142034Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel-color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel-color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel-color channel pair at the particular generation order position using the probability distribution.Type: GrantFiled: November 8, 2023Date of Patent: November 12, 2024Assignee: Google LLCInventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Niki J. Parmar, Ashish Teku Vaswani
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Patent number: 12100391Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing an utterance, and the input acoustic sequence comprising a respective acoustic feature representation at each of a first number of time steps; processing the input acoustic sequence using a first neural network to convert the input acoustic sequence into an alternative representation for the input acoustic sequence; processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings; and generating a sequence of substrings that represent a transcription of the utterance.Type: GrantFiled: October 7, 2021Date of Patent: September 24, 2024Assignee: Google LLCInventors: William Chan, Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Noam M. Shazeer
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Patent number: 12093829Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more switch layers.Type: GrantFiled: July 7, 2023Date of Patent: September 17, 2024Assignee: Google LLCInventors: William Bradley Fedus, Barret Zoph, Noam M. Shazeer
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Patent number: 12086713Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating candidate output sequences using language model neural networks. In particular, an auto-regressive language model neural network is used to generate a candidate output sequence. The same auto-regressive language model neural network is used to evaluate the candidate output sequence to determine rating scores for each of one or more criteria. The rating score(s) are then used to determine whether to provide the candidate output sequence.Type: GrantFiled: July 28, 2022Date of Patent: September 10, 2024Assignee: Google LLCInventors: Daniel De Freitas Adiwardana, Noam M. Shazeer
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Patent number: 12067476Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.Type: GrantFiled: September 8, 2023Date of Patent: August 20, 2024Assignee: Google LLCInventors: Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Stanislaw Maziarz
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Publication number: 20240256859Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.Type: ApplicationFiled: January 4, 2024Publication date: August 1, 2024Inventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Etienne Pot, Mohammad Saleh, Ben David Goodrich, Peter J. Liu, Ryan Sepassi
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Publication number: 20240220796Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.Type: ApplicationFiled: January 4, 2024Publication date: July 4, 2024Inventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Etienne Pot, Mohammad Saleh, Ben David Goodrich, Peter J. Liu, Ryan Sepassi
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Publication number: 20240211751Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.Type: ApplicationFiled: January 4, 2024Publication date: June 27, 2024Inventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Etienne Pot, Mohammad Saleh, Ben David Goodrich, Peter J. Liu, Ryan Sepassi
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Publication number: 20240211752Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.Type: ApplicationFiled: January 4, 2024Publication date: June 27, 2024Inventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Etienne Pot, Mohammad Saleh, Ben David Goodrich, Peter J. Liu, Ryan Sepassi
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Patent number: 12014276Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model using a deterministic data pipeline. One of the methods may include receiving a first request to generate a deterministic training dataset: transforming raw training examples obtained from the raw data source into pre-processed training examples; assigning a unique index to each pre-processed training example; and caching the pre-processed training examples into the cache directory specified in the received first request; receiving a second request to use the deterministic training dataset to train a machine learning model, the second request specifying a start index; and in response to receiving the second request: reading, from the cache directory, the pre-processed training examples that have indices beginning from the start index; and providing the read training examples in an order of the assigned indices for use in training the machine learning model.Type: GrantFiled: July 7, 2023Date of Patent: June 18, 2024Assignee: Google LLCInventors: Gaurav Mishra, Adam Joseph Roberts, Maarten Paul Bosma, Noam M. Shazeer, Jr.
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Publication number: 20240193926Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel-color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel-color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel-color channel pair at the particular generation order position using the probability distribution.Type: ApplicationFiled: November 8, 2023Publication date: June 13, 2024Inventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Niki J. Parmar, Ashish Teku Vaswani
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Publication number: 20240144006Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.Type: ApplicationFiled: January 8, 2024Publication date: May 2, 2024Inventors: Noam M. Shazeer, Aidan Nicholas Gomez, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Llion Owen Jones, Niki J. Parmar, Illia Polosukhin, Ashish Teku Vaswani
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Patent number: 11954594Abstract: This document generally describes a neural network training system, including one or more computers, that trains a recurrent neural network (RNN) to receive an input, e.g., an input sequence, and to generate a sequence of outputs from the input sequence. In some implementations, training can include, for each position after an initial position in a training target sequence, selecting a preceding output of the RNN to provide as input to the RNN at the position, including determining whether to select as the preceding output (i) a true output in a preceding position in the output order or (ii) a value derived from an output of the RNN for the preceding position in an output order generated in accordance with current values of the parameters of the recurrent neural network.Type: GrantFiled: May 10, 2021Date of Patent: April 9, 2024Assignee: Google LLCInventors: Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam M. Shazeer
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Patent number: 11893483Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.Type: GrantFiled: August 7, 2020Date of Patent: February 6, 2024Assignee: Google LLCInventors: Noam M. Shazeer, Aidan Nicholas Gomez, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Llion Owen Jones, Niki J. Parmar, Illia Polosukhin, Ashish Teku Vaswani
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Patent number: 11886998Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.Type: GrantFiled: January 13, 2023Date of Patent: January 30, 2024Assignee: Google LLCInventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Etienne Pot, Mohammad Saleh, Ben Goodrich, Peter J. Liu, Ryan Sepassi
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Publication number: 20230419079Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.Type: ApplicationFiled: September 8, 2023Publication date: December 28, 2023Inventors: Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Stanislaw Maziarz
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Patent number: 11816114Abstract: The present disclosure includes systems and techniques relating to ranking search results of a search query. In general, the subject matter described in this specification can be embodied in a computer-implemented method that includes determining a measure of relevance for a document result within a context of a search query for which the document result is returned, the determining being based on a first number in relation to a second number, the first number corresponding to longer views of the document result, and the second number corresponding to at least shorter views of the document result; and outputting the measure of relevance to a ranking engine for ranking of search results, including the document result, for a new search corresponding to the search query. The subject matter described in this specification can also be embodied in various corresponding computer program products, apparatus and systems.Type: GrantFiled: November 23, 2021Date of Patent: November 14, 2023Assignee: Google LLCInventors: Hyung-Jin Kim, Simon Tong, Noam M. Shazeer, Michelangelo Diligenti
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Patent number: 11816884Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel—color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel—color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel—color channel pair at the particular generation order position using the probability distribution.Type: GrantFiled: July 18, 2022Date of Patent: November 14, 2023Assignee: Google LLCInventors: Noam M. Shazeer, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Niki J. Parmar, Ashish Teku Vaswani