The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 30, 2018
Filed:
Feb. 20, 2015
Cigna Intellectual Property, Inc., Philadelphia, PA (US);
Jing Lin, Tseung Kwan O, HK;
David Fogarty, Old Greenwich, CT (US);
Chit Ming Yip, Kwun Tong, HK;
Wanyu Liao, Kowloon, HK;
Cigna Intellectual Property, Inc., Wilmington, DE (US);
Abstract
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.