Detecting organization sensitive data in images via customized deep learning classifier
US12326957B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 17, 2022 |
| Grant date | Jun 10, 2025 |
| Priority date | — |
| Expiry date | Oct 17, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed is a method of building a customized deep learning (DL) stack classifier to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents, including distributing a trained feature map extractor stack, with stored parameters, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks and to save non invertible feature maps derived from the images, and ground truth labels for the image. Also included is receiving organization-specific examples including the non-invertible feature maps extracted from the organization-sensitive documents and the ground truth labels and using the received organization-specific examples to update a customer-specific DL stack classifier. Further included is sending the customer-specific DL stack classifier to the organization.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.