US 12,461,990 B2
Model-based data transformation
Edward J. Miller, Jr., Iola, KS (US); Daniel R. Hursh, Dayton, OH (US); Alexis S. Pecoraro, Huntersville, NC (US); Pankaj Agrawal, Concord, NC (US); James G. Rauscher, Erie, CO (US); and John V. Hintze, Charlotte, NC (US)
Assigned to Teachers Insurance and Annuity Association of America, New York, NY (US)
Filed by Teachers Insurance and Annuity Association of America, New York, NY (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,632.
Application 17/846,632 is a continuation of application No. 16/701,988, filed on Dec. 3, 2019, granted, now 11,416,720.
Prior Publication US 2023/0010427 A1, Jan. 12, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/213 (2023.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01)
CPC G06F 18/213 (2023.01) [G06F 18/217 (2023.01); G06F 18/29 (2023.01)] 17 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a computer system, a source model describing a source data structure, wherein the source model references 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 of a plurality of source attribute nodes, wherein each source attribute node of the plurality of source attribute nodes is associated with a respective property specifying a position of a corresponding source data item in the source data structure;
receiving a target model describing a target data structure, wherein the target model references 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; and
generating, by applying a specified transformation to one or more source data items associated with the at least one source attribute node, a target data item associated with the at least one target attribute node.