Patents by Inventor Ajay KANNAN
Ajay KANNAN 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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Patent number: 11847532Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.Type: GrantFiled: February 11, 2021Date of Patent: December 19, 2023Assignee: Freenome Holdings, Inc.Inventors: Adam Drake, Daniel Delubac, Katherine Niehaus, Eric Ariazi, Imran Haque, Tzu-Yu Liu, Nathan Wan, Ajay Kannan, Brandon White
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Patent number: 11808839Abstract: Various arrangements for performing an initial setup process of a sleep tracking device are presented. User input may be received that requests a sleep tracking setup process be performed. In response to the user input, a detection process may be performed based on data received from the radar sensor to determine whether a user is present and static. In response to the detection process determining that the user is present and static, a consistency analysis may be performed over a time period to assess a duration of time that the user is present and static. Based on the consistency analysis, sleep tracking may be activated such that when the user is detected in bed via the radar sensor, the user's sleep is tracked.Type: GrantFiled: August 11, 2020Date of Patent: November 7, 2023Assignee: Google LLCInventors: Dongeek Shin, Michael Dixon, Desmond Chik, Ajay Kannan, Andrew William Goldenson
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Patent number: 11754676Abstract: Various devices, systems and methods for performing targeted sleep tracking are presented herein. Multiple digital radar data streams from multiple antennas may be received. A direction optimization process may be performed to determine a first weighting and a second weighting. The first weighting may be applied to a first digital radar data stream. The second weighting may be applied to a second digital radar data stream. The weighted first digital radar data stream and the weighted second digital radar data stream may be combined to create a first directionally-targeted radar data stream. A sleep analysis can be performed based on the first directionally-targeted radar data stream. Sleep data may be output for a user based on the performed sleep analysis.Type: GrantFiled: August 11, 2020Date of Patent: September 12, 2023Assignee: Google LLCInventors: Dongeek Shin, Michael Dixon, Ajay Kannan, Jeffrey Yu, Ashton Udall, Ken Mixter, Reena Lee, David Janssens, Andrew William Goldenson
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Patent number: 11681953Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.Type: GrantFiled: April 15, 2019Date of Patent: June 20, 2023Assignee: Freenome Holdings, Inc.Inventors: Adam Drake, Daniel Delubac, Katherine Niehaus, Eric Ariazi, Imran Haque, Tzu-Yu Liu, Nathan Wan, Ajay Kannan, Brandon White
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Publication number: 20220047210Abstract: Various arrangements for performing an initial setup process of a sleep tracking device are presented. User input may be received that requests a sleep tracking setup process be performed. In response to the user input, a detection process may be performed based on data received from the radar sensor to determine whether a user is present and static. In response to the detection process determining that the user is present and static, a consistency analysis may be performed over a time period to assess a duration of time that the user is present and static. Based on the consistency analysis, sleep tracking may be activated such that when the user is detected in bed via the radar sensor, the user's sleep is tracked.Type: ApplicationFiled: August 11, 2020Publication date: February 17, 2022Applicant: Google LLCInventors: Dongeek Shin, Michael Dixon, Desmond Chik, Ajay Kannan, Andrew William Goldenson
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Publication number: 20220050175Abstract: Various devices, systems and methods for performing targeted sleep tracking are presented herein. Multiple digital radar data streams from multiple antennas may be received. A direction optimization process may be performed to determine a first weighting and a second weighting. The first weighting may be applied to a first digital radar data stream. The second weighting may be applied to a second digital radar data stream. The weighted first digital radar data stream and the weighted second digital radar data stream may be combined to create a first directionally-targeted radar data stream. A sleep analysis can be performed based on the first directionally-targeted radar data stream. Sleep data may be output for a user based on the performed sleep analysis.Type: ApplicationFiled: August 11, 2020Publication date: February 17, 2022Applicant: Google LLCInventors: Dongeek Shin, Michael Dixon, Ajay Kannan, Jeffrey Yu, Ashton Udall, Ken Mixter, Reena Lee, David Janssens, Andrew William Goldenson
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Publication number: 20210210205Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.Type: ApplicationFiled: February 11, 2021Publication date: July 8, 2021Inventors: Adam Drake, Daniel Delubac, Katherine Niehaus, Eric Ariazi, Imran Haque, Tzu-Yu Liu, Nathan Wan, Ajay Kannan, Brandon White
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Publication number: 20210174958Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.Type: ApplicationFiled: April 15, 2019Publication date: June 10, 2021Inventors: Adam Drake, Daniel Delubac, Katherine Niehaus, Eric Ariazi, Imran Haque, Tzu-Yu Liu, Nathan Wan, Ajay Kannan, Brandon White
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Patent number: 10796686Abstract: Described herein are embodiments of a fully-convolutional attention-based neural text-to-speech (TTS) system, which various embodiments may generally be referred to as Deep Voice 3. Embodiments of Deep Voice 3 match state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. Deep Voice 3 embodiments were scaled to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, common error modes of attention-based speech synthesis networks were identified and mitigated, and several different waveform synthesis methods were compared. Also presented are embodiments that describe how to scale inference to ten million queries per day on one single-GPU server.Type: GrantFiled: August 8, 2018Date of Patent: October 6, 2020Assignee: Baidu USA LLCInventors: Sercan O. Arik, Wei Ping, Kainan Peng, Sharan Narang, Ajay Kannan, Andrew Gibiansky, Jonathan Raiman, John Miller
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Patent number: 10657955Abstract: Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.Type: GrantFiled: January 30, 2018Date of Patent: May 19, 2020Assignee: Baidu USA LLCInventors: Eric Battenberg, Rewon Child, Adam Coates, Christopher Fougner, Yashesh Gaur, Jiaji Huang, Heewoo Jun, Ajay Kannan, Markus Kliegl, Atul Kumar, Hairong Liu, Vinay Rao, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu
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Publication number: 20190122651Abstract: Described herein are embodiments of a fully-convolutional attention-based neural text-to-speech (TTS) system, which various embodiments may generally be referred to as Deep Voice 3. Embodiments of Deep Voice 3 match state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. Deep Voice 3 embodiments were scaled to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, common error modes of attention-based speech synthesis networks were identified and mitigated, and several different waveform synthesis methods were compared. Also presented are embodiments that describe how to scale inference to ten million queries per day on one single-GPU server.Type: ApplicationFiled: August 8, 2018Publication date: April 25, 2019Applicant: Baidu USA LLCInventors: Sercan O. ARIK, Wei PING, Kainan PENG, Sharan NARANG, Ajay KANNAN, Andrew GIBIANSKY, Jonathan RAIMAN, John MILLER
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Publication number: 20180247643Abstract: Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.Type: ApplicationFiled: January 30, 2018Publication date: August 30, 2018Applicant: Baidu USA LLCInventors: Eric BATTENBERG, Rewon CHILD, Adam COATES, Christopher FOUGNER, Yashesh GAUR, Jiaji HUANG, Heewoo JUN, Ajay KANNAN, Markus KLIEGL, Atul KUMAR, Hairong LIU, Vinay RAO, Sanjeev SATHEESH, David SEETAPUN, Anuroop SRIRAM, Zhenyao ZHU