User Activeness Clustering for Personalized Device Setting Recommendations

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Solution Overview

Problem

Existing technologies fail to provide personalized device setting recommendations for users, as they are based on expert-defined presets rather than individual user preferences, leading to incorrect or irrelevant suggestions due to infrequent or unexplainable device setting changes.

Innovation Solution

Collect device usage log data to identify user activeness in setting changes, cluster users into groups based on consistency and explainability, and generate customized recommendations for valid user groups.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If preset or predefined picture modes are provided based on expert definitions, then device settings can be easily configured, but the recommendations may be incorrect or irrelevant for individual users

Engineering Contradiction:

Improveease of device setting configurationVSAvoidaccuracy of device setting recommendations

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system segments users into different clusters based on their device usage patterns and activeness metrics. By dividing the user base into distinct groups with similar behaviors, the system can provide tailored recommendations for each segment rather than applying a one-size-fits-all approach, thereby improving recommendation accuracy while maintaining ease of operation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the parameters used for recommendation from static expert-defined presets to dynamic parameters derived from actual user behavior data. By analyzing device usage log data and computing activeness metrics, the system adapts recommendations based on observed user patterns, improving reliability while preserving user-friendly configuration.

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If device setting changes are collected from all users, then more data is available for recommendations, but unexplainable or infrequent changes reduce recommendation quality

Engineering Contradiction:

Improveamount of device usage dataVSAvoidquality of recommendation data

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system applies different quality standards to different users based on their individual activeness metrics. Rather than uniformly treating all data points, it evaluates each user's device setting changes locally against their established patterns, explaining the quality of data contribution for each user and filtering out unexplainable changes on a per-user basis.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system implements feedback mechanisms where user device setting changes are continuously monitored and evaluated against cluster patterns. Unexplainable changes trigger re-evaluation of user activeness metrics and potential re-clustering, allowing the system to adapt to genuine user preferences while filtering out erroneous or anomalous data points.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If users are clustered into groups based on device setting activeness, then personalized recommendations can be provided, but the complexity of data processing increases

Engineering Contradiction:

Improvepersonalization of device settingsVSAvoidcomplexity of user data processing

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-defining activeness metrics and clustering criteria before processing user data. By establishing the framework for evaluation and grouping in advance, the system reduces the complexity of real-time processing while still enabling personalized recommendations through systematic user segmentation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables users to effectively serve themselves by allowing their device usage patterns to automatically generate their own cluster assignments and personalized recommendations. The clustering process leverages the inherent structure in user behavior data, reducing the need for complex external processing while achieving personalization.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12389071B2Generation of device setting recommendations based on user activeness in configuring multimedia or other user device settings

Publication Date: 2025.08.12 SAMSUNG ELECTRONICS CO LTD

AI summary

A method includes obtaining device usage log data associated with multiple user devices. The device usage log data is related to device settings associated with the user devices, where each device setting associated with the user devices is adjustable by users of the user devices. The method also includes obtaining one or more metrics representing each user's activeness in customizing one or more device settings of the user device associated with the user based on the device usage log data. The method further includes clustering the users into multiple groups based on the metric(s) and identifying at least one of the groups as being at least one valid user group. In addition, the method includes providing one or more recommendations of at least one customized device setting to one or more of the user devices based on the device usage log data associated with the valid user group(s).