Machine-learned validation framework
US11238376B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 7, 2021 |
| Grant date | Feb 1, 2022 |
| Priority date | — |
| Expiry date | Apr 7, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/23
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and a method are disclosed herein for machine-learned detection of outliers within payload requests. An entity management system uses machine learning to cluster data characterizing requests from entities to route payloads, and determines one or more data clusters that are outliers. The system receives a request to route a payload to a destination, and applies a supervised machine learning model to size and type information indicated by the payload. The supervised machine learning model applies a label to the payload data (e.g., indicating that the payload routing request is an outlier). This outlier detection may drive a validation process to address detected outliers. The system may receive an indication to perform a validation function and transmit the payload to a validation destination. The system may leverage payload data and feedback received from an entity to optimize machine learning techniques to the entity.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.