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公开(公告)号：US20240366148A1
公开(公告)日：2024-11-07
申请号：US18773046
申请日：2024-07-15
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US11475236B2
公开(公告)日：2022-10-18
申请号：US16880456
申请日：2020-05-21
Applicant: Google LLC
Inventor： Aren Jansen , Ryan Michael Rifkin , Daniel Ellis
Abstract: A computing system can include an embedding model and a clustering model. The computing system input each of the plurality of inputs into the embedding model and receiving respective embeddings for the plurality of inputs as outputs of the embedding model. The computing system can input the respective embeddings for the plurality of inputs into the clustering model and receiving respective cluster assignments for the plurality of inputs as outputs of the clustering model. The computing system can evaluate a clustering loss function that evaluates a first average, across the plurality of inputs, of a respective first entropy of each respective probability distribution; and a second entropy of a second average of the probability distributions for the plurality of inputs. The computing system can modify parameter(s) of one or both of the clustering model and the embedding model based on the clustering loss function.
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Patent Agency Ranking
公开(公告)号：US12070323B2
公开(公告)日：2024-08-27
申请号：US17045318
申请日：2018-05-04
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
CPC classification number: A61B5/4803 , A61B5/7264 , A61B5/7275 , G06N20/00 , G10L25/63 , G10L25/66
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US20200372295A1
公开(公告)日：2020-11-26
申请号：US16880456
申请日：2020-05-21
Applicant: Google LLC
Inventor： Aren Jansen , Ryan Michael Rifkin , Daniel Ellis
Abstract: A computing system can include an embedding model and a clustering model. The computing system input each of the plurality of inputs into the embedding model and receiving respective embeddings for the plurality of inputs as outputs of the embedding model. The computing system can input the respective embeddings for the plurality of inputs into the clustering model and receiving respective cluster assignments for the plurality of inputs as outputs of the clustering model. The computing system can evaluate a clustering loss function that evaluates a first average, across the plurality of inputs, of a respective first entropy of each respective probability distribution; and a second entropy of a second average of the probability distributions for the plurality of inputs. The computing system can modify parameter(s) of one or both of the clustering model and the embedding model based on the clustering loss function.
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公开(公告)号：US11335328B2
公开(公告)日：2022-05-17
申请号：US16758564
申请日：2018-10-26
Applicant: Google LLC
Inventor： Aren Jansen , Manoj Plakal , Richard Channing Moore , Shawn Hershey , Ratheet Pandya , Ryan Rifkin , Jiayang Liu , Daniel Ellis
Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.
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公开(公告)号：US20210361227A1
公开(公告)日：2021-11-25
申请号：US17045318
申请日：2018-05-04
Applicant: Google LLC
Inventor： Katherine Chou , Michael Dwight Howell , Kasumi Widner , Ryan Rifkin , Henry George Wei , Daniel Ellis , Alvin Rajkomar , Aren Jansen , David Michael Parish , Michael Philip Brenner
Abstract: The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.
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公开(公告)号：US20200349921A1
公开(公告)日：2020-11-05
申请号：US16758564
申请日：2018-10-26
Applicant: Google LLC
Inventor： Aren Jansen , Manoj Plakal , Richard Channing Moore , Shawn Hershey , Ratheet Pandya , Ryan Rifkin , Jiayang Liu , Daniel Ellis
Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.
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