Patents by Inventor Kartik THAKORE

Kartik THAKORE 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).

  • Patent number: 11915802
    Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.
    Type: Grant
    Filed: August 4, 2020
    Date of Patent: February 27, 2024
    Assignee: SHARECARE AI, INC.
    Inventors: Brett Robert Redinger, Kartik Thakore, Sandra Ann R Steyaert, Walter Adolf De Brouwer, Srivatsa Akshay Sharma, Lijing Guo
  • Patent number: 11853891
    Abstract: Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.
    Type: Grant
    Filed: March 11, 2020
    Date of Patent: December 26, 2023
    Assignee: SHARECARE AI, INC.
    Inventors: Walter Adolf De Brouwer, Srivatsa Akshay Sharma, Neerajshyam Rangan Kashyap, Kartik Thakore, Philip Joseph Dow
  • Patent number: 11755709
    Abstract: 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: Grant
    Filed: February 21, 2022
    Date of Patent: September 12, 2023
    Assignee: 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
  • Patent number: 11481688
    Abstract: The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions.
    Type: Grant
    Filed: November 10, 2021
    Date of Patent: October 25, 2022
    Assignee: Hammer of the Gods Inc.
    Inventors: Kartik Thakore, Srivatsa Akshay Sharma, Walter Adolf De Brouwer, Geert Trooskens, Meelis Lootus, Sam Leroux, Holly Ly
  • Publication number: 20220337418
    Abstract: 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: Application
    Filed: November 15, 2021
    Publication date: October 20, 2022
    Applicant: 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
  • Patent number: 11455163
    Abstract: The technology disclosed relates to systems and methods for deploying cross-platform applications to tiny ML hardware. The system provides tools to maintain definitions of a first, a second and a third declarative instruction. The system provides tools to maintain compiled bytecode for a procedure block and a tiny ML model that runs on the tiny ML hardware. The compiled bytecode further includes a manifest of one or more capabilities, one or more procedure blocks and at least one output supported by the tiny ML model. The system provides a loader that connects to an instance of the tiny ML hardware. The loader includes logic to verify that the instance of the ML hardware supports the one or more capabilities, the one or more procedure blocks and at least one output specified in the manifest. The loader loads the bytecode and verifies integrity of the load.
    Type: Grant
    Filed: November 10, 2021
    Date of Patent: September 27, 2022
    Assignee: Hammer of the Gods Inc.
    Inventors: Kartik Thakore, Srivatsa Akshay Sharma, Walter Adolf De Brouwer, Geert Trooskens, Meelis Lootus, Sam Leroux, Holly Ly
  • Publication number: 20220269771
    Abstract: 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: Application
    Filed: May 2, 2022
    Publication date: August 25, 2022
    Applicant: 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
  • Publication number: 20220179943
    Abstract: 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: Application
    Filed: February 21, 2022
    Publication date: June 9, 2022
    Applicant: 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
  • Publication number: 20220147341
    Abstract: The technology disclosed relates to systems and methods for deploying cross-platform applications to tiny ML hardware. The system provides tools to maintain definitions of a first, a second and a third declarative instruction. The system provides tools to maintain compiled bytecode for a procedure block and a tiny ML model that runs on the tiny ML hardware. The compiled bytecode further includes a manifest of one or more capabilities, one or more procedure blocks and at least one output supported by the tiny ML model. The system provides a loader that connects to an instance of the tiny ML hardware. The loader includes logic to verify that the instance of the ML hardware supports the one or more capabilities, the one or more procedure blocks and at least one output specified in the manifest. The loader loads the bytecode and verifies integrity of the load.
    Type: Application
    Filed: November 10, 2021
    Publication date: May 12, 2022
    Applicant: Hammer of the Gods Inc., dba HOT-G
    Inventors: Kartik THAKORE, Srivatsa Akshay SHARMA, Walter Adolf DE BROUWER, Geert TROOSKENS, Meelis LOOTUS, Sam LEROUX, Holly LY
  • Publication number: 20220147874
    Abstract: The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions.
    Type: Application
    Filed: November 10, 2021
    Publication date: May 12, 2022
    Applicant: Hammer of the Gods Inc., dba HOT-G
    Inventors: Kartik THAKORE, Srivatsa Akshay SHARMA, Walter Adolf DE BROUWER, Geert TROOSKENS, Meelis LOOTUS, Sam LEROUX, Holly LY
  • Patent number: 11321447
    Abstract: 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: Grant
    Filed: April 20, 2021
    Date of Patent: May 3, 2022
    Assignee: 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
  • Patent number: 11256801
    Abstract: 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: Grant
    Filed: April 20, 2021
    Date of Patent: February 22, 2022
    Assignee: 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
  • Patent number: 11177960
    Abstract: 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: Grant
    Filed: April 20, 2021
    Date of Patent: November 16, 2021
    Assignee: 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
  • Publication number: 20210328801
    Abstract: 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: Application
    Filed: April 20, 2021
    Publication date: October 21, 2021
    Applicant: 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
  • Publication number: 20210326422
    Abstract: 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: Application
    Filed: April 20, 2021
    Publication date: October 21, 2021
    Applicant: 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
  • Publication number: 20210326433
    Abstract: 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: Application
    Filed: April 20, 2021
    Publication date: October 21, 2021
    Applicant: 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
  • Publication number: 20210249139
    Abstract: The technology disclosed relates to a system and method of drug adherence. The system includes an optical character recognition engine configured to process at least one image that depicts data characterizing medication-under-analysis and generate text identifying at least a name of the medication-under-analysis. The system comprises a name entity recognition engine to attribute the name of the medication-under-analysis to at least one family of medication. The system comprises a data augmenter engine configured to supplement the attributed medication name with a plurality of multiomics channels and generate an augmented set of channels. The system includes runtime logic to select a drug-specific adverse event mapper from a plurality of drug-specific adverse event mappers.
    Type: Application
    Filed: February 11, 2021
    Publication date: August 12, 2021
    Applicant: doc.ai, Inc.
    Inventors: Kartik THAKORE, Srivatsa Akshay SHARMA, Scott Michael KIRK, Joel Thomas KAARDAL, Axel SLY, Walter Adolf DE BROUWER
  • Publication number: 20210043285
    Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.
    Type: Application
    Filed: August 4, 2020
    Publication date: February 11, 2021
    Applicant: doc.ai, Inc.
    Inventors: Brett Robert REDINGER, Kartik THAKORE, Sandra Ann R STEYAERT, Walter Adolf DE BROUWER, Srivatsa Akshay SHARMA, Lijing GUO
  • Publication number: 20200293887
    Abstract: Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.
    Type: Application
    Filed: March 11, 2020
    Publication date: September 17, 2020
    Applicant: doc.ai, Inc.
    Inventors: Walter Adolf DE BROUWER, Srivatsa Akshay SHARMA, Neerajshyam Rangan KASHYAP, Kartik THAKORE, Philip Joseph DOW