Limiting identity space for voice biometric authentication
US12380892B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 3, 2022 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Apr 2, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L17/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed are systems and methods including computing-processes executing machine-learning architectures extract vectors representing disparate types of data and output predicted identities of users accessing computing services, without express identity assertions, and across multiple computing services, analyzing data from multiple modalities, for various user devices, and agnostic to architectures hosting the disparate computing service. The system invokes the identification operations of the machine-learning architecture, which extracts biometric embeddings from biometric data and context embeddings representing all or most of the types of metadata features analyzed by the system. The context embeddings help identify a subset of potentially matching identities of possible users, which limits the number of biometric-prints the system compares against an inbound biometric embedding for authentication. The types of extracted features originate from multiple modalities, including metadata from data communications, audio signals, and images. In this way, the embodiments apply a multi-modality machine-learning architecture.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.