Methods and systems for detecting disparate incidents in processed data using a plurality of machine learning models
US10997494B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 31, 2020 |
| Grant date | May 4, 2021 |
| Priority date | — |
| Expiry date | Dec 31, 2040 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA63F2300/5586
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for detecting disparate incidents in processed data using a plurality of machine learning models. For example, the system may receive native asset data. The system may extract telemetry data from the native asset data. The system may input the first feature input into a first machine learning model, wherein the first machine learning model is trained to detect known incidents of a first type in a first set of labeled telemetry data. The system may then detect a first incident based on a first output from the first machine learning model, wherein the first incident is a first event in an asset related to the user's behavior.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.