Patents by Inventor Satish Srinivasan BHAT
Satish Srinivasan BHAT 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: 11811794Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.Type: GrantFiled: May 12, 2021Date of Patent: November 7, 2023Assignee: Sharecare AI, Inc.Inventors: Gabriel Gabra Zaccak, William Hartman, Andrés Rodriguez Esmeral, Devin Daniel Reich, Marina Titova, Brett Robert Redinger, Philip Joseph Dow, Satish Srinivasan Bhat, Walter Adolf De Brouwer, Scott Michael Kirk
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Patent number: 11755709Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: February 21, 2022Date of Patent: September 12, 2023Assignee: SHARECARE AI, INC.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20220337418Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic biometric identifiers. The method includes generating a scannable code upon receiving a success nonce from a registration server. The registration server can access a user identifier and a hash of at least a signature using the success nonce. The signature can be generated based at least in part upon a biometric identifier of a user. The method includes recreating the hash of the signature stored by the registration server. The method includes generating the scannable code by encrypting the success nonce and the recreated hash. The biometric identifier of the user is generated by feeding a plurality of non-deterministic biometric inputs to a trained machine learning model producing a plurality of feature vectors. The method includes projecting the plurality of feature vectors onto a surface of a unit hyper-sphere and computing a characteristic identity vector representing the user.Type: ApplicationFiled: November 15, 2021Publication date: October 20, 2022Applicant: Sharecare AI, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20220269771Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: May 2, 2022Publication date: August 25, 2022Applicant: SHARECARE AI, INC.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20220179943Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: February 21, 2022Publication date: June 9, 2022Applicant: SHARECARE AI, INC.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Patent number: 11321447Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: May 3, 2022Assignee: SHARECARE AI, INC.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20220068463Abstract: The technology disclosed relates to a system and method for detecting when to diverge to a tangential conversation from a linearly structured steps of a cognitive behavioral therapy (CBT) session in a series of sessions for a mental health disorder. The system includes logic to determine when to merge the tangential conversation back to flow of linearly structured steps in the therapy session. The system can bookmark the last completed step in the flow of linearly structured steps of the CBT therapy session before starting the tangential conversation. The system can calculate a return to CBT session score after each succeeding utterance from the patient in the tangential conversation. The system resumes flow of the linearly structured steps of the CBT session upon detecting that the return to CBT session score has exceeded a threshold.Type: ApplicationFiled: August 28, 2020Publication date: March 3, 2022Applicant: doc.ai, Inc.Inventors: Eric William DOLAN, Joel Thomas KAARDAL, Jeremy Fine HARTMAN, Satish Srinivasan BHAT, Srivatsa Akshay SHARMA, Walter Adolf DE BROUWER
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Publication number: 20220068462Abstract: The technology disclosed relates to a system and method for remembering content received from a patient for future use and understood by computerized natural language processing during a chatbot therapy session. The system includes a graph of entities and relationships in an artificial memory graph data structure. The entity nodes in the graph represent entities that are related to a patient root node representing the patient. The entity nodes are connected to patient root node by one or more relationship edges that have named roles. The entity nodes and/or relationship edges can be slotted for at least a distinguishing name, a patient sentiment towards the entity and an entity state.Type: ApplicationFiled: August 28, 2020Publication date: March 3, 2022Applicant: doc.ai, Inc.Inventors: Eric William DOLAN, Joel Thomas KAARDAL, Jeremy Fine HARTMAN, Satish Srinivasan BHAT, Srivatsa Akshay SHARMA, Walter Adolf DE BROUWER
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Patent number: 11256801Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: February 22, 2022Assignee: doc.ai, Inc.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20210360010Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.Type: ApplicationFiled: May 12, 2021Publication date: November 18, 2021Applicant: Sharecare AI, Inc.Inventors: Gabriel Gabra ZACCAK, William HARTMAN, Andrés Rodriguez ESMERAL, Devin Daniel REICH, Marina TITOVA, Brett Robert REDINGER, Philip Joseph DOW, Satish Srinivasan BHAT, Walter Adolf DE BROUWER, Scott Michael KIRK
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Patent number: 11177960Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: GrantFiled: April 20, 2021Date of Patent: November 16, 2021Assignee: Sharecare AI, Inc.Inventors: Axel Sly, Srivatsa Akshay Sharma, Brett Robert Redinger, Devin Daniel Reich, Geert Trooskens, Meelis Lootus, Young Jin Lee, Ricardo Lopez Arredondo, Frederick Franklin Kautz, IV, Satish Srinivasan Bhat, Scott Michael Kirk, Walter Adolf De Brouwer, Kartik Thakore
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Publication number: 20210326422Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20210328801Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE
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Publication number: 20210326433Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.Type: ApplicationFiled: April 20, 2021Publication date: October 21, 2021Applicant: doc.ai, Inc.Inventors: Axel SLY, Srivatsa Akshay SHARMA, Brett Robert REDINGER, Devin Daniel REICH, Geert TROOSKENS, Meelis LOOTUS, Young Jin LEE, Ricardo Lopez ARREDONDO, Frederick Franklin KAUTZ, IV, Satish Srinivasan BHAT, Scott Michael KIRK, Walter Adolf DE BROUWER, Kartik THAKORE