A method for improving accuracy and quality of received data is provided. The method provides a computer implemented data driven classification and data quality checking system. The method uses the associative memory software to build a data driven associative memory model that enables a machine learning data quality checker for receiving data. The method categorizes one or more fields of received data, analyzes the received data, and calculates a data quality rating metric, by comparing the received data with a pool of neighboring data in the category of field of the received data. The method accepts and adds the received data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold, and generates and communicates an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for improving accuracy and quality of received data, the method comprising the steps of: providing a computer implemented data driven classification and data quality checking system having an interface application in communication with an associative memory software, operating on one or more computers; using the associative memory software to build a data driven associative memory model that enables a machine learning data quality checker for receiving data; categorizing one or more fields of received data with the data driven associative memory model; analyzing the received data with the data driven associative memory model; calculating, with the data driven associative memory model, 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; and 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. 2. The method of claim 1 wherein the step of using the machine learning data quality checker to generate and communicate the alert further comprises providing by the data driven associative memory model, via the interface application, reference samples of the pool of neighboring data and suggestions of a more relevant category of field of the received data. 3. The method of claim 1 further comprising the step of using the data driven associative memory model to normalize the data quality rating metric to a star rating system having star ratings comprising a star number range of stars numbering from one star to five stars, the star rating of one star representing a least association of the received data with the category of field of the received data, and the star rating of five stars representing a most association of the received data with the category of field of the received data. 4. The method of claim 1 further comprising the steps of: using the data driven associative memory model comprising a predictive model to predict a model predicted classification for a classification of an instance of the received data and to make predictive model decisions; calculating a prediction quality rating metric with the predictive model to include measurements of a strength and an assurance that establish a correctness of the model predicted classification; and generating and communicating with the machine learning data quality checker, via the interface application, an alert of a classification potential error in the classification of the instance, if the correctness of the model predicted classification is not established. 5. The method of claim 4 further comprising the steps of: using the predictive model to normalize the prediction quality rating metric, associated with the model predicted classification, to a star rating system having star ratings of one or more stars and a star rating accuracy level for each star rating; determining a prediction quality rating metric threshold based on an accuracy level required for the predictive model; accepting and applying the model predicted classification, if the prediction quality rating metric is greater than or equal to the prediction quality rating metric threshold; and rejecting the model predicted classification, if the prediction quality rating metric is less than the prediction quality rating metric threshold. 6. The method of claim 1 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a domain vocabulary processing tool in communication with the interface application and coupled to an associative memory of the associative memory software, the domain vocabulary processing tool configured 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 configured to normalize the domain-specific terms and the domain-specific phrases, and their respective variant terms and variant phrases, into canonical designations and corresponding variants for input into the associative memory. 7. The method of claim 6 wherein prior to extracting relevant domain-specific terms and domain-specific phrases, and their respective variant terms and variant phrases, the domain vocabulary processing tool is further configured to eliminate definite articles and indefinite articles from a free text data or a source text, and to eliminate conjunctions, punctuations, words, a plurality of predefined nonessential terms, spaces, and characters from the free text data or the source text. 8. The method of claim 1 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a control set formed by a control set tool and a control set process, and in communication with the interface application and coupled to an associative memory of the associative memory software, the control set comprising a diverse data set and configured to classify new data sets. 9. The method of claim 8 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, defining a canonical phrase data set representative of canonical phrases associated with the associative memory. 10. The method of claim 1 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a cluster-based batch classification tool in communication with the interface application and coupled to an associative memory of the associative memory software, the cluster-based batch classification tool configured to receive a set of matching records that are similarly grouped in a plurality of clusters, and further configured to enable batch classifying and batch evaluating of the set of matching records in the plurality of clusters simultaneously, providing a consistent classification for the set of matching records. 11. The method of claim 10 wherein the cluster-based batch classification tool is further configured to refine any records needing further refinement and to return similar records sorted in descending order of similarity, allowing for additional batch classifying and batch evaluating. 12. The method of claim 1 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a computer system having one or more computers coupled to and enabling operation of the interface application and the associative memory software. 13. A method for establishing a correctness of classifications of a predictive model, the method comprising the steps of: providing a computer implemented data driven classification and data quality checking system having an interface application in communication with an associative memory software, operating on one or more computers; using the associative memory software to build a predictive model to predict a model predicted classification for a classification of an instance of the received data and to make predictive model decisions; calculating a prediction quality rating metric with the predictive model to include measurements of a strength and an assurance that establish a correctness of the model predicted classification; and generating and communicating with the machine learning data quality checker, via the interface application, an alert of a classification potential error in the classification of the instance, if the correctness of the model predicted classification is not established. 14. The method of claim 13 further comprising the steps of: using the predictive model to normalize the prediction quality rating metric, associated with the model predicted classification, to a star rating system having star ratings of one or more stars and a star rating accuracy level for each star rating; determining a prediction quality rating metric threshold based on an accuracy level required for the predictive model; accepting and applying the model predicted classification, if the prediction quality rating metric is greater than or equal to the prediction quality rating metric threshold; rejecting the model predicted classification, if the prediction quality rating metric is less than the prediction quality rating metric threshold; and segmenting the predictive model decisions based on the star rating system into accepted predictive model decisions and rejected predictive model decisions. 15. The method of claim 14 further comprising the steps of: determining a star rating threshold based on the accuracy level required for the predictive model; accepting the model predicted classification or using information derived from the predictive model, if the star rating is greater than or equal to the star rating threshold; and rejecting the model predicted classification, or rejecting or deferring the information derived from the predictive model, if the star rating is less than the star rating threshold. 16. The method of claim 13 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing one or more of: a domain vocabulary processing tool in communication with the interface application and coupled to an associative memory of the associative memory software; a control set formed by a control set tool and a control set process and in communication with the interface application and coupled to the associative memory of the associative memory software; a cluster-based batch classification tool in communication with the interface application and coupled to an associative memory of the associative memory software; and a computer system having one or more computers coupled to and enabling operation of the interface application and the associative memory software. 17. A computer implemented data driven classification and data quality checking method comprising the steps of: providing a computer implemented data driven classification and data quality checking system comprising: a computer system having one or more computers coupled to and enabling operation of an interface application coupled to an associative memory software, the interface application enabled to receive data, the associative memory software incorporating an associative memory and a machine learning algorithm enabled to identify patterns of similarities in the received data; a domain vocabulary processing tool in communication with the interface application and coupled to the associative memory of the associative memory software, the domain vocabulary processing tool configured 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 configured to normalize the domain-specific terms and the domain-specific phrases, and their respective variant terms and variant phrases, into canonical designations and corresponding variants for input into the associative memory; a control set in communication with the interface application and coupled to the associative memory of the associative memory software, the control set comprising a diverse data set and configured to classify new data sets, the diverse data set comprising 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 relevant categories, defining a canonical phrase data set representative of canonical phrases associated with the associative memory; building a data driven associative memory model comprising a predictive model, learned using the associative memory software, the data driven associative memory model enabling a machine learning data quality checker for receiving data; categorizing one or more fields of received data, with the data driven associative memory model; analyzing the received data, with the data driven associative memory model; calculating, with the data driven associative memory model, a data quality rating metric associated with the received data, as a function of 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 the machine learning data quality checker, if the data quality rating metric is greater than or equal to a data quality rating metric threshold; 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; predicting a model predicted classification for a classification of an instance of the received data, with the predictive model; calculating, with the predictive model, a prediction quality rating metric to include measurements of a strength and an assurance that establish a correctness of the model predicted classification; accepting and applying the model predicted classification, by the machine learning data quality checker, if the correctness of the model predicted classification is established, and if the prediction quality rating metric is greater than or equal to a prediction quality rating metric threshold; and generating and communicating with the machine learning data quality checker, via the interface application, an alert of a classification potential error in the classification of the instance, if the correctness of the model predicted classification is not established, or if the prediction quality rating metric is less than the prediction quality rating metric threshold. 18. The method of claim 17 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a star rating system comprising star ratings of one or more stars, the data driven associative memory model normalizing the data quality rating metric to the star rating system, and the predictive model normalizing the prediction quality rating metric to the star rating system, the star rating system further comprising a star rating accuracy level calculated for each star rating, and a star rating threshold based on an accuracy level required by a process. 19. The method of claim 18 further comprising the step of using the star rating system to segment predictive model decisions by the predictive model into segmented predictive model decisions comprising accepted predictive model decisions and rejected predictive model decisions. 20. The method of claim 17 wherein the step of providing the computer implemented data driven classification and data quality checking system further comprises providing a cluster-based batch classification tool in communication with the interface application and coupled to an associative memory of the associative memory software, the cluster-based batch classification tool configured to receive a set of matching records that are similarly grouped in a plurality of clusters, and further configured to enable batch classifying and batch evaluating of the set of matching records in the plurality of clusters simultaneously, providing a consistent classification for the set of matching records.