US 12,423,067 B1
Enforcing quality procedures in validated systems through data integration and synchronization using an object-oriented data model
Erez Kaminski, Arlington, MA (US); and Jan Pöschko, Vienna (AT)
Assigned to KETRYX CORPORATION, Somerville, MA (US)
Filed by KETRYX CORPORATION, Somerville, MA (US)
Filed on Nov. 25, 2024, as Appl. No. 18/959,601.
Int. Cl. G06F 8/35 (2018.01); G06F 8/77 (2018.01); G06Q 30/018 (2023.01)
CPC G06F 8/35 (2013.01) [G06F 8/77 (2013.01); G06Q 30/018 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for computational enforcement of quality procedures of a validated system, comprising:
(a) receiving, via one or more processors, data from a plurality of remote systems associated with the validated system,
wherein the received data comprises information related to at least one of requirements, specifications, tests, risks, anomalies, complaints, change requests, corrective and preventive actions, dependencies, or other configuration items associated with the validated system;
(b) integrating, via the one or more processors, the received data into an object-oriented data model for managing quality procedures and change management in a development and release of the validated system,
wherein the integration includes:
(1) parsing, via the one or more processors, the received data using a plurality of parsers each configured for a respective remote system of the plurality of remote systems:
(2) mapping, via the one or more processors, the parsed data to corresponding items within the object-oriented data model; and
(3) synchronizing, via the one or more processors, changes between the plurality of remote systems and the object-oriented data model by polling the plurality of remote systems through respective application programming interfaces to programmatically detect changes;
(c) dynamically updating, via the one or more processors, the received data from the plurality of remote systems by interacting with the object-oriented data model to identify one or more discrepancies between expected relationships among objects within the object-oriented data model using an artificial intelligence model trained to identify one or more discrepancies between the objects within the object-oriented data model by determining whether each object within the object-oriented data model is properly associated with corresponding (i) tests, (ii) requirements, or (iii) specifications; and
(d) generating, via the one or more processors, one or more artifacts, wherein the one or more artifacts are automatically generated based on the integration of the received data, the synchronization of the changes, and h updating of the received data from the plurality of remote systems.