Patent Document ID: 10083403
Application ID: 14788764
Patent Flag: 1

Claim One:
1. A computer implemented data driven classification and data quality checking method for improving accuracy and quality of received data, the method comprising the steps of: coupling an interface application and an associative memory software to a computer system, the interface application in communication with the associative memory software, and the computer system comprising a computer having a processor device, an operating system, and storage devices comprising a computer memory and a persistent storage, wherein the interface application is executed by the processor device to receive data, and wherein the associative memory software is executed by the processor device to identify a plurality of associations in the received data; using the associative memory software to build a data driven associative memory model comprising a predictive model that enables a machine learning data quality checker for receiving data, the data driven associative memory model executed by the processor device of the computer and stored by the computer in the storage devices; categorizing one or more fields of the received data with the data driven associative memory model executed by the processor device of the computer; analyzing the received data with the data driven associative memory model executed by the processor device of the computer; calculating, with the data driven associative memory model executed by the processor device of the computer, a data quality rating metric associated with the received data, by comparing the received data with a pool of neighboring data in the category of field of the received data; accepting and adding the received data to the pool of neighboring data by a machine learning data quality checker, if the data quality rating metric is greater than or equal to a data quality rating metric threshold, the machine learning data quality checker enabled by the data driven associative memory model executed by the processor device of the computer, and coupled to the interface application; generating and communicating with the machine learning data quality checker, via the interface application, an alert of a potential error in the received data in the category of field of the received data, if the data quality rating metric is less than the data quality rating metric threshold, the alert generated and communicated by the machine learning data quality checker, via the interface application, and executed by the processor device of the computer; using a domain vocabulary processing tool to create a domain vocabulary for input into an associative memory of the associative memory software, the domain vocabulary comprising knowledge domain specific vocabulary for input into a control set tool, the domain vocabulary processing tool executed by the processor device of the computer and in communication with the interface application, the domain vocabulary processing tool comprising a text editing program having a text editing storage file, and further using the domain vocabulary processing tool to extract relevant domain-specific terms and domain-specific phrases, and their respective variant terms and variant phrases, from a remaining free text data or a remaining source text, which have been parsed, and further using the domain vocabulary processing tool to normalize domain-specific terms and the domain-specific phrases, and their respective variant terms and variant phrases, into canonical designations and corresponding variants; and forming a control set with the control set tool and a control set process executed by the processor device of the computer and stored by the computer in the storage devices, the control set in communication with the interface application and coupled to an associative memory of the associative memory software, wherein the control set utilizes associations within the associative memory to generate a diverse data set, wherein the diverse data set comprises a plurality of predefined data from a plurality of relevant categories generated by the associative memory and a plurality of nonassociated data not associated with the plurality of relevant categories, the diverse data set thereby defining a canonical phrase data set representative of canonical phrases associated with the associative memory, and wherein new data sets are classified by the control set, the control set tool comprising control set process instructions to perform scoring in the associative memory on a plurality of records of the received data, to reduce a total number of the plurality of records required, wherein the computer implemented data driven classification and data quality checking method provides an improved accuracy of the received data and of classified or scored records, provides an improved quality of the received data, and establishes a correctness of classifications of the predictive model.