Method of controlling for undesired factors in machine learning models
US10282789B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 19, 2016 |
| Grant date | May 7, 2019 |
| Priority date | — |
| Expiry date | Jun 9, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0207
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyzes of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.