Patent application EP20177522A (EP), filed 2020-05-29
Publication number: EP3916646A1
Title: ADAPTIVE MACHINE LEARNING SYSTEM FOR AN EDGE DEVICE
Abstract: An adaptive machine learning system (1) for an edge device comprises at least one sensor (2), at least one input compensation module (3) and an evaluation module (4). The sensor (2) is designed to capture input data. The input compensation module (3) is designed to modify the input data such that edge device specific artifacts of the input data are compensated. The evaluation module (4) is trained to process the modified input data and to generate output data as a result.
IPC codes: G06N  20/    00            A I
Claims: number: 1
text: Adaptive machine learning system (1) for an edge device, comprising at least one sensor (2), at least one input compensation module (3) and an evaluation module (4),
wherein the sensor (2) is designed to capture input data,
wherein the input compensation module (3) is designed to modify the input data such that edge device specific artifacts of the input data are compensated,
wherein the evaluation module (4) is trained to process the modified input data and to generate output data as a result.
number: 2
text: The system (1) as claimed in claim 1,
wherein the input compensation module (3) is designed to modify the input data such that artifacts of the input data occurring due to aging of the edge device are compensated.
number: 3
text: The system (1) as claimed in one of the preceding claims,
wherein the input compensation module (3) is designed to modify the input data such that artifacts of the input data occurring due variations of environmental parameters are compensated.
number: 4
text: The system (1) as claimed in one of the preceding claims,
wherein the input compensation module (3) is designed to modify the input data such that artifacts of the input data occurring due to limited resources of the edge device are compensated.
number: 5
text: The system (1) as claimed in one of the preceding claims,
wherein the input compensation module (3) is designed to modify the input data such that sensor specific artifacts of the input data are compensated.
number: 6
text: Controlling system (5) comprising the adaptive machine learning system (1) as claimed in one of the preceding claims, at least one output compensation module (6) and at least one actuator (7),
wherein the output compensation module (6) is designed to modify the output data such that actuator specific requirements are fulfilled,
wherein the actuator (7) is designed to be controllable according to the modified output data.
number: 7
text: An edge device comprising the adaptive machine learning system (1) as claimed in one of the claims 1 to 5.
number: 8
text: An adaptive machine learning based method (8) comprising the following steps:
number: 9
text: The method (8) as claimed in claim 8 comprising the following further steps:
Office action: search_report, dated 2021-12-01
Prior art cited: ref_id: US2017372232A1
ref_type: patent
severity: background
ref_id: US5554273A
ref_type: patent
severity: background
ref_id: - MATSUI KANAE ET AL: "Error correction method considering fog and edge computing environment", 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS), IEEE, 6 May 2019 (2019-05-06), pages 517 - 521, XP033585549, DOI: 10.1109/ICPHYS.2019.8780317
ref_type: npl
severity: background
ref_id: - ZEBA IDREES ET AL: "Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development", SENSORS, vol. 18, no. 9, 10 September 2018 (2018-09-10), pages 3021, XP055748872, DOI: 10.3390/s18093021
ref_type: npl
severity: background
ref_id: US5554273
ref_type: patent
severity: background
ref_id: US2017372232
ref_type: patent
severity: background
ref_id: - MATSUI KANAE ET AL, "Error correction method considering fog and edge computing environment", 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS), IEEE, (20190506), doi:10.1109/ICPHYS.2019.8780317, pages 517 - 521, XP033585549 [I] 1-9 * page  517  - page  521, column  l, paragraph  1 *
ref_type: npl
severity: background
ref_id: - ZEBA IDREES ET AL, "Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development", SENSORS, (20180910), vol. 18, no. 9, doi:10.3390/s18093021, page 3021, XP055748872 [A] 1-9 * page  1  - page  11 *
ref_type: npl
severity: background
Final disposition: pending (2021-12-01)