Method and apparatus for adapting machine learning to changes in user interest
Summary
USPTO granted patent US12596937B2 to AT&T Intellectual Property I, L.P. covering methods for adapting machine learning systems to changes in user interest over multiple cycles. The patent discloses assigning interest measures to inputs, tracking performance levels, and dynamically adjusting intelligence levels as new user inputs are received. The patent contains 20 claims and is classified under CPC groups G06N 5/04 and G06N 20/00.
What changed
USPTO issued Patent No. US12596937B2 to AT&T Intellectual Property I, L.P. on April 7, 2026. The patent covers methods and apparatus for adapting machine learning to changes in user interest by assigning interest measures to inputs, calculating performance levels, and determining intelligence levels based on products of interest measures and performance levels. The system reduces interest measures for prior inputs based on cycle elapsed time and assigns new interest measures to subsequent inputs.
For technology companies and software developers working on machine learning user adaptation systems, this patent establishes intellectual property rights that may require licensing or design-around considerations. The patent's broad claim scope covering interest measure assignment, performance level generation, and dynamic intelligence determination could affect development of similar personalization and adaptive learning technologies.
What to do next
- Monitor for potential licensing opportunities
- Review patent claims for freedom-to-operate analysis
Archived snapshot
Apr 7, 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.
Method and apparatus for adapting machine learning to changes in user interest
Grant US12596937B2 Kind: B2 Apr 07, 2026
Assignee
AT&T Intellectual Property I, L.P.
Inventors
Min-Hsuan Chen
Abstract
Aspects of the subject disclosure may include, for example, assigning a first interest measure associated with a first input to a learning machine at a first cycle, determining a first intelligence level according to a first product of the first interest measure and a first performance level based on the first input, and responsive to receiving a subsequent input at a subsequent cycle, reducing the first interest measure associated with the first input at the first cycle of the learning machine according to a total number of cycles that have occurred since the first cycle, assigning a new interest measure to the subsequent input at the subsequent cycle, generating a subsequent performance level according to the subsequent input, and determining a subsequent intelligence level according to a second product of the subsequent interest level and a subsequent performance level. Other embodiments are disclosed.
CPC Classifications
G06N 5/04 G06N 20/00
Filing Date
2021-03-30
Application No.
17217810
Claims
20
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