Sharecare Ai, Inc.

Doc.ai, Inc.

Brett Robert Redinger

Walter Adolf De Brouwer

Srivatsa Akshay Sharma

Kartik Thakore

Devin Daniel Reich

Satish Srinivasan Bhat

Scott Michael Kirk

Geert Trooskens

Meelis Lootus

Axel Sly

Frederick Franklin Kautz, Iv

Young Jin Lee

Ricardo Lopez Arredondo

Gabriel Gabra Zaccak

Marina Titova

Philip Joseph Dow

Andrés Rodriguez Esmeral

William Hartman

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

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. The system comprises a differential privacy estimator configured to estimate a privacy guarantee for each of the executed training cycles in dependence on the modified training parameters.

Privacy interface for data loss prevention via artificial intelligence models

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

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

Oakland, CA, United States of America

Brett Robert Redinger