Generating model insights by progressive partitioning of log data across a set of performance indicators
US11501114B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 2, 2019 |
| Grant date | Nov 15, 2022 |
| Priority date | — |
| Expiry date | Jul 7, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The generating of actionable recommendations for tuning model metrics of an Artificial Intelligence (AI) system includes partitioning a key performance indicator (KPI) range associated with a target system into a plurality of buckets. Log data including at least one KPI of the target system and one or more AI model metrics is partitioned and distributed across the plurality of buckets. For each bucket, an aggregate value of the one or more AI model metrics across the log data is computed and weighted according to the volume of log data in that bucket. A correlation factor between the aggregate value and a representative KPI value for each bucket is determined. A model tuning recommendation to increase ranking of the AI model metrics according to the determined correlation factor is provided to an output device and/or to the AI system for updating the one or more AI model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.