US 11,720,826 B2
Feedback loop learning between artificial intelligence systems
Jinho Hwang, Ossining, NY (US); Larisa Shwartz, Greenwich, CT (US); Hagen Völzer, Zurich (CH); Michael Elton Nidd, Zurich (CH); and Rodrigo Otavio Castrillon, Campinas (BR)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 24, 2019, as Appl. No. 16/521,185.
Prior Publication US 2021/0027136 A1, Jan. 28, 2021
Int. Cl. G06N 5/02 (2023.01); G06N 20/20 (2019.01); G06N 3/082 (2023.01); G06F 9/54 (2006.01); G06F 18/2413 (2023.01); G06N 3/044 (2023.01)
CPC G06N 20/20 (2019.01) [G06F 9/541 (2013.01); G06F 9/542 (2013.01); G06F 18/2413 (2023.01); G06N 3/044 (2023.01); G06N 3/082 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a data analytics component that, based on an analysis of a microservices mesh, constructs a dependency mapping graph for artificial intelligence components of the microservices mesh, wherein the nodes of dependency mapping graph represent the artificial intelligence components, and edges of the dependency mapping graph represent respective dependencies between the artificial intelligence components;
a monitoring component that identifies, in real time, a data pattern associated with data generated during execution of the artificial intelligence components of the microservices mesh in a runtime environment of an artificial intelligence system;
a machine learning component that employs machine learning to:
determine, in real time, a deviation of the data pattern from one or more historical data patterns associated with one or more prior executions of the artificial intelligence components in the runtime environment of the artificial intelligence system, wherein the deviation indicates a need for a modification of at least one corresponding artificial intelligence component, in a development environment of the artificial intelligence system, that corresponds to at least one artificial intelligence component of the artificial intelligence components, and wherein the deviation comprises a new class of data representative of a new use case for the artificial intelligence components, and
determine, based on the dependency mapping graph, a need for another modification of at least one other artificial intelligence component that depends on the at least one corresponding artificial intelligence component based on the modification of at least one corresponding artificial intelligence component; and
a development component that implements the modification of the at least one corresponding artificial intelligence component and the other modification of the at least one other artificial intelligence component in the development environment of the artificial intelligence system.