Recommendation Settings Using User Dissatisfaction Pattern Detection
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Solution Overview
Problem
Users face difficulties in finding optimal device settings manually, leading to inefficiency and dissatisfaction due to the time-consuming process of adjusting multiple settings for content consumption.
Innovation Solution
An electronic device analyzes user behavior patterns through short-term and long-term usage data to identify dissatisfaction points, and automatically recommends settings adjustments based on content and context analysis using AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually adjust settings to find optimal configurations, then settings can be customized to personal preferences, but the process is time-consuming and inconvenient
Solution Approach 1:
The system automatically analyzes user behavior patterns and device usage data to determine optimal settings without requiring manual user input. The electronic device serves itself by collecting usage information, identifying dissatisfaction patterns, and autonomously adjusting settings to improve user experience.
Solution Approach 2:
The system proactively identifies potential user dissatisfaction by analyzing usage patterns before actual complaints occur. By detecting anomalies in behavior patterns (such as repeated adjustments or prolonged viewing sessions), the system preemptively adjusts settings to prevent dissatisfaction rather than reacting after problems arise.
2Reliability
If users manually try different settings combinations, then optimal settings can be found, but the process is inefficient and frustrating
Solution Approach 1:
The system continuously monitors user behavior patterns and uses this feedback to refine settings recommendations. By analyzing how users interact with the device over time (watching duration, adjustment frequency, time of day), the system learns what settings actually work best for each user and automatically applies them without requiring manual optimization.
Solution Approach 2:
The patent replaces the mechanical process of manual settings adjustment with an automated data-driven system. Instead of users physically navigating menus and adjusting sliders, the system uses algorithms that process usage data and automatically modify settings parameters, substituting human effort with computational analysis.
3Measurement precision
If the system collects detailed usage data to identify dissatisfaction patterns, then accurate recommendation settings can be provided, but data processing complexity increases
Solution Approach 1:
The system divides complex usage data into distinct analytical segments: short-term usage patterns (immediate behavior), long-term usage patterns (habitual behavior), and dissatisfaction indicators (anomaly detection). By segmenting the analysis process and focusing on specific behavioral metrics rather than processing all raw data at once, the system achieves high detection accuracy while managing computational complexity.
Data Source
AI summary
A method performed by an electronic device, of providing recommendation settings may include: obtaining a first usage pattern based on a usage score obtained from behavioral data over a first time period, corresponding to an interaction of a user with the electronic device, obtaining a second usage pattern, based on behavioral data over a second time period longer than the first time period, corresponding to an interaction of the user with the electronic device, and stored usage pattern data, and controlling outputting of recommendation settings based on a time point of dissatisfaction with use of the electronic device, identified based on the first usage pattern and the second usage pattern.