Learning Machine Interest Decay for Shifting User Preferences

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Solution Overview

Problem

Existing machine learning systems struggle to adapt effectively to changes in user interest, often failing to retain intelligence levels while quickly responding to new input patterns.

Innovation Solution

A learning machine that assigns an interest measure to inputs and uses an exponential decay model to degrade this measure over subsequent cycles, allowing it to retain intelligence levels while adapting to changes in user interest.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the learning machine uses traditional machine learning methods, then it can process inputs, but it fails to adapt effectively to changes in user interest and retain intelligence levels

Engineering Contradiction:

Improveadaptability to changes in user interestVSAvoidretention of intelligence levels

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent applies dynamics by making the interest measure decay over time according to an exponential decay model. The interest measure is not static but dynamically adjusts based on the number of cycles elapsed since the input was received, allowing the system to adapt to changing user interests while maintaining a structured approach to intelligence retention

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of interest measure over time by applying an exponential decay function. The interest measure parameter evolves from its initial value based on the elapsed cycles, transforming how the system weights and responds to inputs as time progresses, thereby enabling adaptation to user interest changes

Inventive Principle:

Principle #35Parameter changes

2Speed

If the learning machine responds quickly to new input patterns, then it enhances adaptability, but it may lose retention of original intelligence levels

Engineering Contradiction:

Improveresponse speed to new input patternsVSAvoidretention of intelligence levels

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent implements periodic action by evaluating and updating the interest measure at each cycle. This periodic reassessment allows the system to systematically respond to new inputs while maintaining a rhythm of adaptation that balances responsiveness with intelligence retention

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system uses feedback by continuously monitoring the interest measure and adjusting its response based on the degraded interest value. This feedback mechanism ensures that the system responds appropriately to new patterns while being guided by the accumulated intelligence from previous cycles

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12596937B2Method and apparatus for adapting machine learning to changes in user interest

Publication Date: 2026.04.07 AT&T INTELLECTUAL PROPERTY I L P

AI summary

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.