Pulselight Holdings, Inc.

21ct, Inc.

Jonathan William Mugan

Jimmie Goode

Laura Hitt

Yuan Qu

Russ Gregory

Mallika Thanky

Thayne Richard Coffman

Eric John McDermid

A recurrent neural network (RNN) method implemented on a computer system is used to produce summaries of unstructured text generated by multiple networks of individuals interacting over time by encoding the unstructured text into intermediate representations and decoding the intermediate representations into summaries of each network. Parameter data for the RNN is obtained by using multiple different versions of the same source texts to train the computer system. The method and computer system can be used to identify which of the networks match a query by determining which network generates the query with low or lowest cost.

Method and system for analyzing entities

A method for generating synthetic training records for use in training a model to predict low-incidence events. A synthetic training record is generated from a minority-class training record by substituting a different value for a feature in the minority-class training record, where the probability of the different value occurring in the minority-class training record exceeds a probability threshold. Also disclosed are a non-transitory storage medium comprising minority-class training records and synthetic training records and a method of training a machine-leaning model using training records augmented with synthetic training records. An exemplary synthetic training records is a synthetic medical record for use in training a model to predict drug overdoses.

Feature prediction for minority class data augmentation

A system of machine learning ('ML') models for making actionable predictions regarding low-incidence events, including a generative ML model that produces synthetic minority-class records to form an augmented training data set, a predictive ML model that has been trained on the augmented training data set, a certainty ML model that produces a certainty estimate, and an explanatory model that produces an explanation. A method for producing actionable predictions of a low-incidence event by applying ML models to imbalanced class data by producing a prediction by a predictive ML model that has been trained on a data set comprising synthetic minority-class data records produced by a generative ML model, and producing a certainty estimate and an explanation. At least one of the certainty estimate or explanation determines an effective or appropriate response to the prediction. The low-incidence event may comprise risk of opioid use disorder.

Model ecosystem

Feature prediction

A computer-implemented method of creating synthetic training data records for machine learning, comprising a source computer system operatively connected to a separate target computer system, where the target system comprises real data records, the contents of which are not known to the source computer system. The source computer system comprises the generator component of a generative adversarial network (GAN) and the target computer system comprises a discriminator component of the GAN. The source computer system generates one or more synthetic training records using a computational process that does not reveal contents of the real data records. Also disclosed is a method of training a machine-learning model using the one or more synthetic training records.

Minimal trust data sharing

Disclosed is a method for fusing interaction data, such as intelligence data, comprising, embodying collections of interaction data from different interaction data sources in interaction graphs, defining a plurality of mappings of identifiers to entities, associating each mapping with a fused interaction graph, and identifying an optimal mapping by evaluation of compatibility of identifier attributes, mutual information across interaction data sources, and/or fit with one or more behavior models. Edges in the fused graph can be collapsed. Also claimed are a computer system and a computer-readable medium for fusing interaction data.

Growing community of inventors

Buda, TX, United States of America

Jonathan William Mugan