Techniques for adaptive and context-aware automated service composition for machine learning (ML)
US11556862B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 4, 2020 |
| Grant date | Jan 17, 2023 |
| Priority date | — |
| Expiry date | Mar 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.