Adaptive object modeling and differential data ingestion for machine learning
US11238366B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 10, 2018 |
| Grant date | Feb 1, 2022 |
| Priority date | — |
| Expiry date | Mar 14, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/20
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A machine learning (ML)-based technique for user behavior analysis that detects when users deviate from expected behavior. A ML model is trained using training data derived from activity data from a first set of users. The model is refined in a computationally-efficient manner by identifying a second set of users that constitute a “watch list.” At a given time, a differential data ingestion operation is then performed to incorporate data for the second set of users into the training data, while also pruning at least a portion of the data set corresponding to data associated with any user included in the first set but not in the second set. These operations update the training data used for the machine learning. The machine learning model is then refined based on the updated training data that incorporates the activity data ingested from the users identified in the watch list.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.