Cigna Intellectual Property, Inc.

David Fogarty

Jing Lin

Chit Ming Yip

Yee Wah Eva Lee

Xiao Xiao

Yifei Luo

Man Hin Wong

Sourav Maharana

Man Tat Lam

Robert E Chudzik

Ho Fai Yau

Chun Ho Chan

Nicholas F Nett

Margaret A Shaw

Stephanie C Swain

Mahendra Bisht

Wanyu Liao

Yogendra D Bhosrekar

Hei Fung

A computerized method of automatic distributed communication includes training a first and second machine learning models with historical feature vector inputs to generate a likelihood output and a mean count output, respectively. For each entity in a set, the method includes processing a likelihood feature vector input with the first machine learning model to generate a likelihood output indicative of a likelihood that the entity will have an avoidable negative health event within a specified first time period, and processing a mean count feature vector input with the second machine learning model to generate a mean count output indicative of an expected number of avoidable negative health events that the entity will have within a specified second time period. The method includes automatically distributing structured campaign data to at least a subset of entities in the set according to the likelihood output or the mean count output.

Machine learning systems for automated database element processing and prediction output generation

A computer system includes memory hardware configured to store a machine learning model, historical feature vector inputs, and computer-executable instructions, and processor hardware configured to execute the instructions. The instructions include training a first machine learning model with the historical feature vector inputs to generate a title score output, and training a second machine learning model with the historical feature vector inputs to generate a background score output. For each entity in a set, the instructions include processing a title feature vector input with the first machine learning model, and processing a background feature vector with a second machine learning model, to generate a tittle score output and a background score output each indicative of a likelihood that the entity is a decision entity. The instructions include automatically distributing structured campaign data to the entity based on the title score output and the background score output.

Systems and methods for constructing sets of synthetic data. A single data record is identified from a first set of data. The first set of data comprises a first plurality of data records, each of the data records including multiple items of data describing an entity. Using pattern recognition, the single data record is processed to identify a group of records from within the first set that have corresponding characteristics equivalent to the single data record. The identified group of records comprises a target set of variables and the group of records from the first set that are not identified comprises a control set of variables. The target set of variables and the control set of variables are processed, using probability estimation and optimization constraints, to determine a score for each of the records in the first set. The score describes how similar each of the records in the first set is to the single data record. The records associated with a percentage of the highest scores are identified. The data associated with the single data record is replaced with data associated with the identified records identified, item-by-item.

System and method for building predictive model for synthesizing data

System and method for synthesizing data

A single data record is identified from a first set of data. The first set of data comprises a first plurality of data records, each of the data records including multiple items of data describing an entity. Using pattern recognition, the single data record is processed to identify a group of records from within the first set that have corresponding characteristics equivalent to the single data record. A score for each of the records in the first set is determined. The score describes how similar each of the records in the first set is to the single data record.

Embodiments of the invention involve receiving a first set of data describing one or more first observations and a second set of data describing one or more second observations. The first set of data comprises at least two types of data and the second set of data comprises at least two types of data. At least one of the two types of data in the first data set are common with at least one of the two types of data in the second data set. The common types of data comprise common data to the first and second sets of data. The types of data that are not common comprise exclusive data for each of the first and second sets of data. A first multiple regression model is developed for the first data set. The common data for the first data set are set as independent variables and the exclusive data for the first data set are set as dependent variables. A second multiple regression model is developed for the second data set. The common data for the second data set are set as independent variables and the exclusive data for the second data set are set as dependent variables. Prediction results of the first and second multiple regression models are received. Based on the prediction results, at least some of the one or more first observations and the one or more second observations are classified as reasonable observations, which are well-predicted observations. At least some of the one or more first observations and the one or more second observations are classified as outlier observations, which are not classified as well-predicted observations. The outlier observations are removed. The reasonable observations are assigned into intervals for each of the types of data. Based on the assignment, the observations are merged to create a third data set.

System and method for combining data sets

Growing community of inventors

Old Greenwich, CT, United States of America

David Fogarty