Recommendation Feature Exposure for Explainable User Feedback

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Solution Overview

Problem

Existing action selection models in user devices lack transparency, are prone to errors, and are not easily tunable to individual user preferences, often making unsatisfactory recommendations due to reliance on unexplained features and limited user feedback.

Innovation Solution

Implement a system that provides users with natural language descriptions of features used for action selection, allows for intuitive feedback through dual interaction modes, and enables model retraining based on user feedback, enhancing model robustness and adaptability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If an action selection model is used to automatically recommend actions, then user convenience is improved, but the lack of transparency and explainability causes user dissatisfaction

Engineering Contradiction:

Improveuser convenienceVSAvoidtransparency of recommendation logic

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system provides feedback to users by displaying explanations for why certain actions are recommended, based on analyzed features and user data. This feedback loop allows users to understand the reasoning behind recommendations while maintaining automated operation convenience.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary explanation layer between the action selection model and the user. This intermediary translates complex model decisions into understandable feature-based explanations, bridging the gap between automated recommendation and user comprehension.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If the action selection model relies on multiple features for recommendation, then recommendation accuracy is improved, but the complexity of the system increases

Engineering Contradiction:

Improverecommendation accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the recommendation system into distinct components: feature analysis module, user data module, and action selection module. Each component handles specific aspects independently, reducing overall system complexity while maintaining multi-feature recommendation accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts which features are analyzed and displayed based on user preferences and context. By changing parameters selectively rather than processing all features uniformly, the system maintains accuracy while reducing complexity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the system displays all available actions, then user choice is improved, but irrelevant actions clutter the interface

Engineering Contradiction:

Improveuser choiceVSAvoidinterface clutter

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies partial action by displaying only a subset of available actions - specifically those most relevant to the user based on feature analysis and historical data. This partial display approach maintains user choice for important actions while eliminating interface clutter from irrelevant options.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system applies different quality levels to different actions in the interface - highlighted or prominent display for high-relevance actions, and subdued or hidden display for low-relevance actions. This local differentiation maintains versatility while reducing overall clutter.

Inventive Principle:

Principle #3Local quality

4Measurement precision

If the model is retrained frequently based on user feedback, then model accuracy is improved, but computational resource consumption increases

Engineering Contradiction:

Improvemodel accuracyVSAvoidcomputational resource requirements

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements periodic retraining based on accumulated feedback thresholds rather than continuous retraining. The model is retrained periodically when sufficient user feedback is collected, maintaining accuracy while reducing computational resource consumption compared to frequent retraining.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system performs preliminary analysis of feedback to determine whether retraining is necessary before actually retraining. This preliminary filtering action reduces unnecessary retraining operations, conserving computational resources while maintaining model accuracy when needed.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12474938B2Feature exposure for model recommendations and feedback

Publication Date: 2025.11.18 GOOGLE LLC

AI summary

Systems, methods and apparatus for providing user feedback to an action selection model. In an aspect, a method includes displaying interaction elements for recommendations selected by a selection model. Each interaction element may be selected by one of a first interaction mode or a second interaction mode. Selection by the first interaction mode indicates an acceptance of the recommendation described the interaction element. Selection by the second interaction mode causes the user device to display the decision data that caused the selection model to select the recommendation described by the interaction element. In some implementations, the recommendations are actions that a user device may perform. In other implementations, each recommendation may be one of an action that the user device may perform or content that a user may consume.