Patent Issued for Model-based data transformation (USPTO 11416720): Teachers Insurance and Annuity Association of America

2022 SEP 06 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Agrawal, Pankaj (Concord, NC, US), Hintze, John V. (Charlotte, NC, US), Hursh, Daniel R. (Dayton, OH, US), Miller, Jr., Edward J. (Iola, KS, US), Pecoraro, Alexis S. (Huntersville, NC, US), Rauscher, James G. (Erie, CO, US), filed on December 3, 2019, was published online on August 16, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11416720 is assigned to Teachers Insurance and Annuity Association of America (New York, New York, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “A financial institution may exchange data with its existing and/or potential clients, including institutional clients, e.g., for performing client enrollment, onboarding, reporting, as well as various financial account management operations and/or financial transactions.

“Examples of financial institutions include, but are not limited to, banks, building societies, credit unions, trust companies, mortgage loan companies, insurance companies, investment banks, underwriters, brokerage firms, etc. Examples of financial accounts include, but are not limited to, checking accounts, savings accounts, loan accounts, revolving credit accounts, investment accounts, brokerage accounts, retirement accounts, annuity accounts, etc.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “Described herein are methods and systems of model-based data transformation.

“Various business processes may involve batch and/or interactive data import operations. For example, a financial institution may implement a batch process and/or an interactive process for customer enrollment or onboarding process, which may involve extracting the necessary information from one or more third-party data structures (e.g., employee information files generated by the employer). “Data structure” herein shell be interpreted as a data file, a memory data structure, or a data stream.

“However, the layouts, field nomenclature, field formatting and/or encoding rules of the third-party owned source data structures utilized in batch and/or interactive data import operations may be different from the layouts, field nomenclature, field formatting and/or encoding rules of the target data structures that are utilized by the financial institution, thus necessitating various data transformation operations to be performed on the data extracted from the source data structures.

“Systems and methods of the present disclosure employ graph models representing the source and target data structures for specifying the data transformations to be performed by batch data import operations. In an illustrative example, a pair of the graph models representing the source and target data structures may be utilized for specifying the data transformations that need to be performed in order to populate the target data structure by the data extracted from the source data structure. Nodes of a graph model represent data types, data objects that include multiple data items, or individual data items; edges of the graph model represent relationships between the data elements represented by nodes. In particular, terminal nodes of a graph model (also referred to as “attribute nodes” herein) represent data items of the type specified by higher level nodes (also referred to as “section nodes” herein), which are ultimately grouped into groupings specified by the upper level nodes (also referred to as “root nodes” herein).

“Data transformation operations that need to be performed on the data extracted from the source data structure in order to populate the target data structure may be specified by a transformation map that associates, directly or via a transformation node, each attribute node of the target data structure with one or more nodes of the source data structure. A computer system implementing the model-based data transformation methods described herein may iterate over the attributes of the target data structure, and for every attribute node may identify either a corresponding directly mapped attribute of the source data structure or a transformation node associating the target attribute node with one or more source attribute nodes. Then the computer system may extract the values of the identified one or more source attributes and either directly copy the extracted source attribute value to the target attribute or perform the identified transformation on the extracted source attribute values and store the transformation result to the target attribute, as described in more detail herein below. The methods described herein may be implemented in the batch and/or interactive mode.”

The claims supplied by the inventors are:

“1. A method, comprising: receiving, by a computer system, a source graph model describing a source data structure, wherein the source graph model comprises a source root node associated with a plurality of source section nodes, wherein each source section node of the plurality of source section nodes is associated with at least one source attribute node; receiving a target graph model describing a target data structure, wherein the target graph model comprises a target root node associated with a plurality of target section nodes, wherein each target section node of the plurality of target section nodes is associated with at least one target attribute node; receiving a transformation map associating a target attribute node of the target data structure with one or more source attribute nodes of the source data structure; extracting, from the source data structure, one or more source data items associated with the one or more source attribute nodes; generating, by applying a transformation specified by the transformation map to the one or more source data items, a target data item associated with the target attribute node; and storing the target data item in the target data structure.

“2. The method of claim 1, wherein the target data structure is one of: a data file, a memory data structure, or a data stream.

“3. The method of claim 1, further comprising: utilizing the target data structure for performing a business process.

“4. The method of claim 1, wherein the transformation is specified by an executable script.

“5. The method of claim 1, wherein the transformation is specified by a mapping data structure.

“6. The method of claim 1, wherein each source attribute node is associated with a property specifying an index of a corresponding source data item in the source data structure.

“7. The method of claim 1, wherein each source attribute node is associated with a property specifying a position of a corresponding source data item in the source data structure.

“8. The method of claim 1, further comprising: responsive to determining that the target data item is invalid, traversing the transformation map to identify at least one source attribute node associated with an invalid source value.

“9. The method of claim 1, further comprising: responsive to determining that the transformation map specifies a direct mapping between a target attribute node and a source attribute node, associating a value of the source attribute node with the target attribute node.

“10. A system, comprising: a memory; and a processing device operatively coupled to the memory, wherein the processing device is configured to: receive a source graph model describing a source data structure, wherein the source graph model comprises a source root node associated with a plurality of source section nodes, wherein each source section node of the plurality of source section nodes is associated with at least one source attribute node; receive a target graph model describing a target data structure, wherein the target graph model comprises a target root node associated with a plurality of target section nodes, wherein each target section node of the plurality of target section nodes is associated with at least one target attribute node; receive a transformation map associating a target attribute node of the target data structure with one or more source attribute nodes of the source data structure; extract, from the source data structure, one or more source data items associated with the one or more source attribute nodes; generate, by applying a transformation specified by the transformation map to the one or more source data items, a target data item associated with the target attribute node; and store the target data item in the target data structure.

“11. The system of claim 10, wherein the target data structure is one of: a data file, a memory data structure, or a data stream.

“12. The system of claim 10, wherein the processing device is further configured to: utilize the target data structure for performing a business process.

“13. The system of claim 10, wherein the transformation is specified by at least one of: an executable script or a mapping data structure.

“14. The system of claim 10, wherein each source attribute node is associated with a property specifying one of: an index of a corresponding source data item in the source data structure or a position of the corresponding source data item in the source data structure.

“15. The system of claim 10, wherein the processing device is further configured to: responsive to determining that the target data item is invalid, traverse the transformation map to identify at least one source attribute node associated with an invalid source value.

“16. A non-transitory computer-readable storage medium comprising executable instructions which, when executed by a computer system, cause the computer system to: receive a source graph model describing a source data structure, wherein the source graph model comprises a source root node associated with a plurality of source section nodes, wherein each source section node of the plurality of source section nodes is associated with at least one source attribute node; receive a target graph model describing a target data structure, wherein the target graph model comprises a target root node associated with a plurality of target section nodes, wherein each target section node of the plurality of target section nodes is associated with at least one target attribute node; receive a transformation map associating a target attribute node of the target data structure with one or more source attribute nodes of the source data structure; extract, from the source data structure, one or more source data items associated with the one or more source attribute nodes; generate, by applying a transformation specified by the transformation map to the one or more source data items, a target data item associated with the target attribute node; and store the target data item in the target data structure.

“17. The non-transitory computer-readable storage medium of claim 16, wherein the target data structure is one of: a data file, a memory data structure, or a data stream.

“18. The non-transitory computer-readable storage medium of claim 16, further comprising executable instructions causing the computer system to: utilize the target data structure for performing a business process.

“19. The non-transitory computer-readable storage medium of claim 16, wherein the transformation is specified by at least one of: an executable script or a mapping data structure.

“20. The non-transitory computer-readable storage medium of claim 16, wherein each source attribute node is associated with a property specifying one of: an index of a corresponding source data item in the source data structure or a position of the corresponding source data item in the source data structure.”

URL and more information on this patent, see: Agrawal, Pankaj. Model-based data transformation. U.S. Patent Number 11416720, filed December 3, 2019, and published online on August 16, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11416720.PN.&OS=PN/11416720RS=PN/11416720

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