Veveo, Inc.

Nference, Inc.

Lucent Technologies Inc.

Winphoria Networks, Inc.

Anumana, Inc.

At&t Corp.

Adeia Guides Inc.

Motorola Corporation

Rovi Guides, Inc.

Avaya Technology LLC

Winphoria Netwroks, Inc.

Murali Aravamudan

Ajit Rajasekharan

Rakesh Barve

Sashikumar Venkataraman

Kajamalai Gopalaswamy Ramakrishnan

Pankaj Garg

Shamim A Naqvi

Vineet Agarwal

Kumar K Vishwanathan

Venkataramanan Soundararajan

Sankar Ardhanari

Ganesh Ramamoorthy

Rangamani Sundar

Girish S Welling

Ahmed Nizam Mohaideen Pathurudeen

K China Srinivasulu Reddy

Arjun Puranik

Pranav Rajanala

Tyler Wagner

Sudhir Raman Ahuja

Keyur Faldu

Apurv Anand

Karthik Murugadoss

Abhijit Satchidanand Savarkar

Daren Gill

Manish Mohan Sharma

Mayank Gupta

James Robert Ensor

Suthirth Vaidya

Prakash R Iyer

Akash Anand

Prakash Iyer

Abhijith Chunduru

Animesh Agarwal

Sai Saketh Chennamsetty

Andreas Constantine Papanicolaou

Samir Awasthi

Rahul Agrawal

Soumyadip Majumder

Mangesh Dattatray Pujari

Corinne Carpenter

Katherine Carlson

Samuel J Asirvatham

Aravind Srinivasan

Ashim Prasad

Gurudutt Pai

Melwin Babu

Maulik Nanavaty

Girish Welling

Roy Philip Weber

Paul A Friedman

Venkatesh Krishnaswamy

Hosagrahar Visvesvaraya Jagadish

Peter H Stuntebeck

Ram S Ramamurthy

Suraj Kapa

Robert F Henrick

Uddeshya Upadhyay

Antonio DeSimone

Hong-Yi Tzeng

Francisco Lopez-Jimenez

Ashok K Kuthyar

Ajit V Rajasekharan

Depankar Neogi

Ravi Krishna

Deepak Anand

Satyanarayanan Ramaswamy

Todd Andrew Snide

Kajamalai G Ramakrishnan

Hilary M Dubrock

Praveen Anand

William Gibson

Harish Kumar B V

Itzhak Zachi Attia

Michiel Jm Niesen

Venkataraman Soundarajan

Keyur Vallabhbhai Faldu

Colin Pawlowski

Michael A Champa

Aiveliagaram Venkatakrishnan

Jeyendran Balakrishnan

Enrique Garcia-Rivera

Ashwin Murali

Gregory James Xikes

Sundar Rangamani

Apparatus and method for object pose estimation are disclosed. 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 sets of echo data, wherein the plurality of sets of echo data is configured for generation of a plurality of echo depth maps, segment the plurality of echo depth maps, determine a depth datum related to pixels of an object of interest as a function of the plurality of segmented echo depth maps, generate a three dimensional (3D) point cloud related to the object of interest as a function of the depth datum and generate a pose datum of the object of interest as a function of the 3D point cloud.

Apparatus and method for object pose estimation in a medical image

An apparatus and method for prediction of pulmonary vein reconnection is disclosed. The apparatus includes an electrocardiogram device, 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 generate ablation evaluation training data, train an ablation evaluation machine-learning model using the ablation evaluation training data, receive, from the electrocardiogram device, the electrocardiogram data, and generate, using the ablation evaluation machine-learning model, ablation evaluation data of the patient, wherein generating the ablation evaluation data of the patient includes inputting, into the ablation evaluation machine-learning model, the electrocardiogram data and receiving as output, from the ablation evaluation machine-learning model, the ablation evaluation data of the patient.

Apparatus and methods for prediction of repeat ablation efficacy

Apparatus for attribute detection in anatomical data and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive reference anatomy data and reference metadata, extract anatomic features from the received reference anatomy data and reference metadata, group the received reference anatomy data and reference metadata into a plurality of cohorts with one or more similar groups of anatomic features as a function of the extracted anatomic features, receive query anatomy data and query metadata, label the received query anatomy data and query metadata as a function of the plurality of cohorts, and detect at least an attribute as a function of the label.

Apparatus and methods for attribute detection in anatomy data

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.

Noninvasive methods for detection of pulmonary hypertension

A system for visualization of vectorcardiograms for ablation procedures, the system 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 an input matrix having a plurality of electrocardiogram signals associated with a plurality of time variables, transform the plurality of electrocardiogram signals into a cardiac vector as a function of the input matrix, and determine at least one ablative reaction as a function of the cardiac vector, wherein determining the at least one ablative reaction includes generating a graphical visualization of an X-Y plot, wherein cardiac deviations are plotted along a vertical axis of the X-Y plot and time variables are plotted along a horizontal axis of the X-Y plot.

System and method for visualization of vectorcardiograms for ablation procedures

Techniques for adaptively improving the performance of a locked machine learning program have been disclosed. In one particular embodiment, the techniques may be realized as a method for enabling a first party to provide a trained machine learning model to a second party, the method comprising receiving the trained machine learning model from the first party, the trained machine learning model being associated with one or more policies defining permissible operations; and constraining the second party to operate the trained machine learning model in a manner that is consistent with said one or more policies.

Systems and methods for adaptively improving the performance of locked machine learning programs

Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Systems and methods for diagnosing a health condition based on patient time series data

The present disclosure provides user-interface methods and systems for submitting search requests to search engines and presenting search results therefrom customized using content preferences learned about a user, comprising sending query information to at least two search engines, including a query identifying desired content, and user information, including context information describing the environment in which the query information is being sent, and a user signature representing content preferences learned about the user; receiving at least one set of a search result and auxiliary information from the at least one search engine in response to sending the query information, including information describing attributes of the search result that led to the search result being chosen by the at least one search engine; ordering the at least one search result based at least in part on the auxiliary information; and presenting the ordered search results to the user.

Method of and system for conducting personalized federated search and presentation of results therefrom

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.

System and method for responding to a user input using an agent orchestrator

A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user's navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.

User interface methods and systems for selecting and presenting content

Described herein is an apparatus and method for training a machine learning model. An apparatus may include a computing device configured to receive a corpus of data containing entries corresponding to a plurality of subjects; identify a plurality of entries within the corpus, corresponding to a first subject of the plurality of subjects and representing medical history of the first subject; determine a plurality of temporal attributes of the plurality of entries; generate a plurality of tokens as a function of the plurality of entries; generate a chronological data structure segment ordering one or more of the plurality of entries and the plurality of tokens, as a function of the plurality of temporal attributes; and train a multimodal machine learning model on a training dataset including the chronological data structure segment.

Apparatus and method for training a machine learning model

An apparatus method for leveraging a repository of images containing implant devices in a human body are disclosed. 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 sets of historical subject data, classify the plurality of sets of historical subject data into one or more implant cohorts, generate implant training data using the plurality of sets of classified historical subject data in the one or more implant cohorts, train an implant machine-learning model using the implant training data, receive an inquiry datum from a user, wherein the inquiry datum includes current subject data and generate an output datum as a function of the inquiry datum using the trained implant machine-learning model.

Apparatus and method for leveraging a repository of images containing implant devices in a human body

An apparatus and method for locating a position of an electrode on an organ model. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an organ model configured to digitally represent an organ, receive a set of sensor data from at least a sensor including an ultrasound sensor, determine an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein determining the electrode position includes determining a model position within the organ model as a function of the set of sensor data and determining the electrode position within the model position of the organ model as a function of the set of sensor data and add a visual marker onto the electrode position in the model position of the organ model.

Apparatus and method for locating a position of an electrode on an organ model

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.

Systems and methods for computing with private healthcare data

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input; generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model; and using a conversational interface displayed using the user interface, display the first generative model output to the user.

Configuring a generative machine learning model using a syntactic interface

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Andover, MA, United States of America

Murali Aravamudan