Model management system for improving training data through machine learning deployment
US12340286B2 · kind B2 · utility
Assignee
Inventors
- Daniel Bibireata
- Andrew Yan-Tak Ng
- Pingyang He
- Zeqi Qiu
- Camilo Iral
- Mingrui Zhang
- Aldrin Leal
- Junjie Guan
- Ramesh Sampath
- Dillon Laird
- Yu Qing Zhou
- Juan Camilo Fernancez
- Camilo Zapata
- Sebastian Rodriguez
- Cristobal Silva
- Sanjay Bodhu
- Mark William Sabini
- Leela Seshu Reddy Cheedepudi
- Kai Yang
- Yan Liu
- Whit Blodgett
- Ankur Rawat
- Francisco Matias Cuenca-Acuna
- Quinn Killough
Key dates
| Filing date | Sep 9, 2021 |
| Grant date | Jun 24, 2025 |
| Priority date | — |
| Expiry date | Jan 28, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20081
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.