Patents by Inventor Samir AWASTHI

Samir AWASTHI 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: 12266107
    Abstract: Described herein are systems and methods for responding to user input using an agent orchestrator. A system may include a cardiac catheter, a user interface, and a computing device configured to receive, from the user interface, a first user input, receive, from the catheter, the procedure data, determine, using an agent orchestrator, a first agent selection datum, wherein determining the first agent selection datum includes generating the first agent selection datum as a function of the first user input using a trained agent selection machine learning model, using a first agent corresponding to the first agent selection datum, determine a first agent output, wherein determining the first agent output includes inputting into the first agent the procedure data, and receiving, as an output from the first agent, the first agent output, and display, using the user interface, the first agent output.
    Type: Grant
    Filed: August 20, 2024
    Date of Patent: April 1, 2025
    Assignee: Anumana, Inc.
    Inventors: Suthirth Vaidya, Rakesh Barve, Abhijith Chunduru, Murali Aravamudan, Animesh Agarwal, Samir Awasthi, Maulik Nanavaty, Sai Saketh Chennamsetty, Arjun Puranik, Harish Kumar B V
  • Publication number: 20250046461
    Abstract: An apparatus for determining a patient survival profile using artificial intelligence-enabled electrocardiogram (ECG), the apparatus includes a processor and a memory containing instructions configuring the processor to receive a plurality of patient profiles, wherein each patient profile includes ECG data, define a plurality of cohort labels, wherein defining the plurality of cohort labels includes generating a condition score for each patient profile of the plurality of patient profiles and defining the plurality of cohort labels as a function of the condition score, assign the plurality of cohort labels to the plurality of patient profiles, generate condition training data by correlating the plurality of patient profiles with a plurality of condition identifiers, generate a condition evaluation model and a machine-learning algorithm using the condition training data, determine a patient survival profile for a user-inputted patient profile using the condition evaluation model, and display the patient surviv
    Type: Application
    Filed: August 3, 2023
    Publication date: February 6, 2025
    Applicant: Anumana, Inc.
    Inventor: Samir Awasthi
  • Publication number: 20250045458
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a de-identification method including receiving a text sequence; providing the text sequence to a plurality of entity tagging models, each of the plurality of entity tagging models being trained to tag one or more portions of the text sequence having a corresponding entity type; tagging one or more entities in the text sequence using the plurality of entity tagging models; and obfuscating each entity among the one or more tagged entities by replacing the entity with a surrogate, the surrogate being selected based on one or more attributes of the entity and maintaining characteristics similar to the entity being replaced.
    Type: Application
    Filed: August 15, 2024
    Publication date: February 6, 2025
    Applicant: nference, Inc.
    Inventors: Murali Aravamudan, Karthik Murugadoss, Sankar Ardhanari, Ajit Rajasekharan, Venkataramanan Soundararajan, Samir Awasthi, Tyler Wagner, Shamim Naqvi, Akash Anand, Rakesh Barve
  • Patent number: 12205691
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Grant
    Filed: October 26, 2023
    Date of Patent: January 21, 2025
    Assignee: nference, Inc.
    Inventors: Murali Aravamudan, Karthik Murugadoss, Sankar Ardhanari, Ajit Rajasekharan, Akash Anand, Rakesh Barve, Venkataramanan Soundararajan, Samir Awasthi, Tyler Wagner, Shamim Naqvi
  • Publication number: 20240363247
    Abstract: An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.
    Type: Application
    Filed: April 26, 2024
    Publication date: October 31, 2024
    Applicant: Mayo Foundation for Medical Education and Research
    Inventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy, Joerg Herrmann, Yash Gupta, John Rincón-Hekking, Ashim Prasad, Rakesh Barve, Samir Awasthi
  • Publication number: 20240274301
    Abstract: An apparatus for identifying clusters based on augmented data sets, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive one or more vitality data sets, retrieve auxiliary information for the one or more vitality data sets, generate one or more augmented data sets as a function of the auxiliary information and the one or more vitality data sets, identify at least one cluster based on the one or more augmented data sets and provide the at least one cluster to a cluster analysis platform, wherein the cluster analysis platform is configured to generate a similarity datum as a function of the at least one cluster and the one or more augmented data sets.
    Type: Application
    Filed: February 9, 2024
    Publication date: August 15, 2024
    Applicant: nference, Inc.
    Inventors: Samir Awasthi, John Rincon-Hekking
  • Publication number: 20240062860
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Application
    Filed: October 26, 2023
    Publication date: February 22, 2024
    Applicant: Nference, Inc.
    Inventors: Murali ARAVAMUDAN, Karthik MURUGADOSS, Sankar ARDHANARI, Ajit RAJASEKHARAN, Akash ANAND, Rakesh BARVE, Venkataramanan SOUNDARARAJAN, Samir AWASTHI, Tyler WAGNER, Shamim NAQVI
  • Patent number: 11848082
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Grant
    Filed: September 27, 2022
    Date of Patent: December 19, 2023
    Assignee: Nference, Inc.
    Inventors: Murali Aravamudan, Karthik Murugadoss, Sankar Ardhanari, Ajit Rajasekharan, Akash Anand, Rakesh Barve, Venkataramanan Soundararajan, Samir Awasthi, Tyler Wagner, Shamim Naqvi
  • Publication number: 20230044294
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Application
    Filed: September 27, 2022
    Publication date: February 9, 2023
    Inventors: Murali ARAVAMUDAN, Karthik MURUGADOSS, Sankar ARDHANARI, Ajit RAJASEKHARAN, Akash ANAND, Rakesh BARVE, Venkataramanan SOUNDARARAJAN, Samir AWASTHI, Tyler WAGNER, Shamim NAQVI
  • Patent number: 11545242
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Grant
    Filed: June 22, 2020
    Date of Patent: January 3, 2023
    Assignee: NFERENCE, INC.
    Inventors: Murali Aravamudan, Karthik Murugadoss, Sankar Ardhanari, Ajit Rajasekharan, Akash Anand, Rakesh Barve, Venkataramanan Soundararajan, Samir Awasthi, Tyler Wagner, Shamim Naqvi
  • Publication number: 20220189634
    Abstract: Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.
    Type: Application
    Filed: October 13, 2021
    Publication date: June 16, 2022
    Inventors: Tyler Wagner, Samir Awasthi Awasthi, Venkataramanan Soundararajan, Murali Aravamudan, Corinne Carpenter, Katherine Carlson, Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Surai Kapa, Francisco Lopez-Jimenez, Hilary M. Dubrock
  • Publication number: 20200402625
    Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 24, 2020
    Inventors: Murali ARAVAMUDAN, Karthik MURUGADOSS, Sankar ARDHANARI, Ajit RAJASEKHARAN, Akash ANAND, Rakesh BARVE, Venkataramanan SOUNDARARAJAN, Samir AWASTHI, Tyler WAGNER, Shamim NAQVI