Healthy.io Ltd

Sharecare Ai, Inc.

Scanadu, Inc.

Hammer of the Gods Inc.

Other

Doc.ai, Inc.

Sound Patrol Inc.

Walter Adolf De Brouwer

Martin Zizi

Bernard Burg

Srivatsa Akshay Sharma

Anthony Smart

Kartik Thakore

Aaron Alexander Rowe

Geert Trooskens

Devin Daniel Reich

Brett Robert Redinger

Meelis Lootus

Brandon D Woolsey

Scott Michael Kirk

Satish Srinivasan Bhat

Wenyi Zhao

Whitney Morgan McGowan

Ricardo Lopez Arredondo

Axel Sly

Young Jin Lee

Frederick Franklin Kautz, Iv

Babak Aghazadeh

Jie Cui

Sheena Ruby Menezes

Jaime Tenedorio

Eron Anthony Villarreal

Gabriel Gabra Zaccak

Marina Titova

Ivo Clarysse

Sam Leroux

Holly Ly

Philip Joseph Dow

Scott Thomas

Yves A Behar

Tamay Aykut

Piotr Przybyszewski

Chethan R Sarabu

Alexander Cristoff

Andrés Rodríguez Esmeral

Avita Sirimitr

Andrés Rodriguez Esmeral

William Hartman

Neerajshyam Rangan Kashyap

Apurv Mishra

Samia De Brouwer

Sandra Ann R Steyaert

Lijing Guo

Eunseog Youn

Brian G La Plume

Maxim Akhterov

The technology disclosed relates to a provenance system for tracing repurposing of targeted artificial intelligence (AI) systems. A training logic is configured to train an AI system on a training dataset that satisfies a target artist configuration by requiring that at least some training samples in the training dataset are sourced from a target artist, and to generate a trained version of the AI system (trained AI system). The target artist configuration characterizes a work of the target artist. The trained AI system is configured to construct an output that satisfies that target artist configuration. A deployment logic is configured to make available the trained AI system via a blockchain network. A provenance logic is configured to provide access to the trained AI system, and to validate that new outputs generated by repurposing the trained AI system satisfy the target artist configuration.

Provenance for repurposing of artist-specific assets using non-fungible tokens (NFTs)

Color quantification of chemical test pads and titration of analytes can be performed under different lighting conditions. In one embodiment, the lighting condition is estimated under which a digital image is captured and utilized to select a set of reference colors from which the quantified color is compared to determine the titration. In another embodiment, a plurality of comparisons are made with different lighting conditions with the result having the highest confidence level being selected to determine the titration.

Quantifying color changes in chemical test pads induced by different concentrations of analytes under different lighting conditions

A reagent test paddle includes a contamination detection medium, a reference color bar, at least one chemical test medium, and a unique identifier. The contamination detection medium includes a reagent that changes color in the presence or when exposed to a hostile or inhospitable environment. Each chemical test medium includes a regent that is responsive to a respective analyte in a biological sample. The reference color bar includes reference color samples of different colors. The unique identifier, like a serial number, identifies the particular paddle and its chemical test medium so it can be uniquely and anonymously associated with a user. A method includes capturing and interpreting digital images of a biologically unexposed and subsequently exposed reagent test paddle at various delay times within an automatically calibrated environment; locating the paddle in a plurality of digital images, extracting the reference color bar and locating the contamination detection medium and chemical test medium in each digital image. Color changes of the chemical test medium and contamination medium are detected at various delay times after sample exposure. To determine validity of test results, the method further compares the detected colors of the contamination detection medium with predetermined colors expected for no contamination and contamination.

Method, apparatus and system for detecting and determining comprised reagent pads by quantifying color changes induced by exposure to a hostile environment

System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.

Image-based system and method for predicting physiological parameters

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 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

Method, apparatus and system for detecting and determining compromised reagent pads by quantifying color changes induced by exposure to a hostile environment

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

The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Systems and methods for virtual clinical trials

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

Systems, methods, and apparatus are disclosed aggregating at least two of vital signs data, diagnostic test results, medical data, and user parameters into a single medical record. In one example, a method may include periodically receiving vital signs data and/or diagnostic test results performed on a user; periodically receiving medical data in response to a medical examination of the user performed by a health care practitioner; receiving user parameters about the user; aggregating the vital signs data and/or diagnostic test results, the medical data, and the user parameters into a single medical record; associating the single personal medial record with the user; and storing the single personal medial record into a database of personal medical records. Life expectancy can be calculated. Data aggregation can be performed over time for comparison in real time to medical standard values allowing the user to set personal goals towards greater life expectancy.

Systems, methods, and apparatus for personal medical record keeping

Growing community of inventors

Los Altos Hills, CA, United States of America

Walter Adolf De Brouwer