NEC Multi-Modal Machine Learning Encoding Device Patent Application
Summary
The USPTO published patent application US20260099722A1 filed by NEC Corporation on December 10, 2025, and made available on April 9, 2026. The application covers machine learning devices and methods that train encoding models for multiple sensor data types and use adversarial training to improve estimation accuracy. CPC classifications G06N 3/094 and G06N 3/045 indicate AI and neural network innovations. This is a publication of a patent application, not a granted patent.
What changed
The USPTO published NEC Corporation's patent application covering machine learning devices that train encoding models for multiple sensor data types and use adversarial training techniques. The invention processes first and second sensor data through separate encoding models, with an estimation model using the encoded outputs and adversarial models improving cross-modal estimation.
For compliance officers, this document represents a patent application publication rather than an enforceable regulatory action. No compliance obligations or deadlines are created. However, organizations developing similar multi-modal AI or sensor fusion technologies should review the application's claims to assess potential patent landscape implications for their own R&D and product development activities.
Archived snapshot
Apr 18, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
MACHINE LEARNING DEVICE, ESTIMATION SYSTEM, TRAINING METHOD, AND RECORDING MEDIUM
Application US20260099722A1 Kind: A1 Apr 09, 2026
Assignee
NEC Corporation
Inventors
Kenichiro FUKUSHI, Hiroshi KAJITANI, Fumiyuki NIHEY, Chenhui HUANG, Kazuki IHARA, Zhenwei WANG, Yoshitaka NOZAKI, Kentaro NAKAHARA
Abstract
A machine learning device that trains a first encoding model for encoding first sensor data into first code, a second encoding model for encoding second sensor data into second code, and an estimation model for making estimation using the first code and the second code such that an estimation result from the estimation model conforms to correct answer data, trains a first adversarial estimation model that outputs an estimated value of the second code in response to the input of the first code such that the estimated value of the second code estimated by the first adversarial estimation model conforms to the second code outputted from the second encoding model, and trains the first encoding model such that the estimated value of the second code estimated by the first adversarial estimation model does not conform to the second code outputted from the second encoding model.
CPC Classifications
G06N 3/094 G06N 3/045
Filing Date
2025-12-10
Application No.
19414659
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