System and method to generate discretized interpretable features in machine learning model
US12039725B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 28, 2022 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Feb 12, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30068
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method for processing values of a subset of numeric features to provide interpretability of the results of a machine learning model, by determining an extent of the contribution of the subset of features towards a predicted class by performing: (i) receiving the thermal image, (ii) obtaining a region of interest in the thermal image of the subject, (iii) extracting a plurality of numeric features associated with the region of interest of the thermal image, (iv) predicting a class, (v) estimate an extent of contribution of the subset of numeric features towards the decision of the first machine learning prediction model (M) and (vi) generate a report that includes a generated discrete values that determines the extent of contribution of the subset of numeric features towards the predicted class.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.