Sharecare Ai, Inc.

Hammer of the Gods Inc.

Doc.ai, Inc.

Kartik Thakore

Walter Adolf De Brouwer

Srivatsa Akshay Sharma

Geert Trooskens

Meelis Lootus

Brett Robert Redinger

Devin Daniel Reich

Satish Srinivasan Bhat

Scott Michael Kirk

Axel Sly

Ricardo Lopez Arredondo

Frederick Franklin Kautz, Iv

Young Jin Lee

Sam Leroux

Holly Ly

Philip Joseph Dow

Neerajshyam Rangan Kashyap

Sandra Ann R Steyaert

Lijing Guo

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.

Accelerated processing of genomic data and streamlined visualization of genomic insights

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.

System and method with federated learning model for medical research applications

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.

Artificial intelligence-based generation of anthropomorphic signatures and use thereof

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. The method includes executing the bytecode on the tiny ML hardware to process the declarative instructions without recompilation of the bytecode to adapt to alternative instances of the tiny ML hardware, each running its alternative VM layer.

Systems and methods for preparing cross-platform machine learning applications

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.

Systems and methods for preparing and deploying cross-platform applications

Systems and methods for generating and using anthropomorphic signatures to authenticate users

Systems and methods to verify identity of an authenticated user using a digital health passport

Growing community of inventors

Santa Clara, CA, United States of America

Kartik Thakore