Back to results
US 11,892,989 B2Granted
System and method for predictive structuring of electronic data
AssigneeBank of America Corporation
InventorsDeepak Jain, Bhakti Sanjay Gavhane, Simranjeet Singh Sandhu, Aditya Sharma
Life of this patent
Estimating the term…
Abstract
Embodiments of the invention are directed to a system, method, or computer program product for an approach to predictive structuring of electronic data. The system uses data mining and data clustering techniques using classification models to organize feature column groups comprising feature columns. The system identifies and flags feature column groups and/or feature columns based on regulatory data standards provided by regulatory bodies. Thereafter, data objects are imported into the system and prediction algorithms are implemented to characterize the feature columns containing the data objects.
Claims15
A system for organizing data objects, the system comprising:
- a memory device with computer-readable program code stored thereon;
- a communication device;
- a processing device operatively coupled to the memory device and the communication device, wherein the processing device is configured to execute the computer-readable program code to:
- receive entity data tables, wherein the entity data tables comprise data objects stored in a memory device of an entity system;
- receive regulatory standard documentation comprising data handling guidelines identified from keywords in text of the regulatory standard documentation, wherein the regulatory standard documentation is a pdf file;
- mine metadata from the entity data tables and mine the data handling guidelines from the regulatory standard documentation;
- perform clustering on mined data using a K-means algorithm to determine a common grouping of the mined data to prepare for identification of a feature column group, wherein the mined data comprises the mined data handling guidelines and the mined metadata, and wherein the feature column group comprises one or more feature columns determined by the K-means algorithm to relate to other feature columns within the feature column group based on the metadata and data handling guidelines of the data objects within each of the feature columns;
- import unknown data objects to a cell of a column;
- identify the feature column of the column using a machine learning model trained using training data objects with known feature columns and known feature columns groups;
- identify the feature column group based on data type and sensitivity;
- and
- improve a confidence interval of the identified feature column of the feature column group by subjecting the data objects to the machine learning model multiple times.
9 more elements
A computer program product for organizing data objects, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:
- receiving entity data tables, wherein the entity data tables comprise data objects stored in a memory device of an entity system;
- receiving regulatory standard documentation comprising data handling guidelines in text identified from keywords in text of the regulatory standard documentation, wherein the regulatory standard documentation is a pdf file;
- mining metadata from the entity data tables and mining the data handling guidelines from the regulatory standard documentation;
- performing clustering on mined data using a K-means algorithm to determine a common grouping of the mined data to prepare for identification of a feature column group, wherein the mined data comprises the mined data handling guidelines and the mined metadata, and wherein the feature column group comprises one or more feature columns determined by the K-means algorithm to relate to other feature columns within the feature column group based on the metadata and data handling guidelines of the data objects within each of the feature columns;
- importing unknown data objects to a cell of a column;
- identifying the feature column of the column using a machine learning model trained using training data objects with known feature columns and known feature columns groups;
- identifying the feature column group based on data type and sensitivity;
- and
- improving a confidence interval of the identified feature column of the feature column group by subjecting the data objects to the machine learning model multiple times.
6 more elements
A computer-implemented method for organizing data objects, the method comprising:
- providing a computing system comprising a computer processing device and a non-transitory computer readable medium, where the non-transitory computer readable medium comprises configured computer program instruction code, such that when said computer program instruction code is operated by said computer processing device, said computer processing device performs the following operations:
- receiving entity data tables, wherein the entity data tables comprise data objects stored in a memory device of an entity system;
- receiving regulatory standard documentation comprising data handling guidelines in text identified from keywords in text of the regulatory standard documentation, wherein the regulatory standard documentation is a pdf file;
- mining metadata from the entity data tables and mining the data handling guidelines from the regulatory standard documentation;
- performing clustering on mined data using a K-means algorithm to determine a common grouping of the mined data to prepare for identification of a feature column group, wherein the mined data comprises the mined data handling guidelines and the mined metadata, and wherein the feature column group comprises one or more feature columns determined by the K-means algorithm to relate to other feature columns within the feature column group based on the metadata and data handling guidelines of the data objects within each of the feature columns;
- importing unknown data objects to a cell of a column;
- identifying the feature column of the column using a machine learning model trained using training data objects with known feature columns and known feature columns groups;
- identifying the feature column group based on data type and sensitivity;
- and
- improving a confidence interval of the identified feature column of the feature column group by subjecting the data objects to the machine learning model multiple times.
7 more elements
Dependencies are inferred from the claim text.