Customized deep learning classifier for detecting organization sensitive data in images on premises
US11475158B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 26, 2021 |
| Grant date | Oct 18, 2022 |
| Priority date | — |
| Expiry date | Jul 26, 2041 |
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 to an organization, under the organization's control, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks, without the organization forwarding images of organization-sensitive training examples, 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 generate 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.