Context-Aware Application Control for Adaptive Screen Time
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Solution Overview
Problem
Existing computer-implemented tools for controlling user interaction with computing devices lack flexibility and adaptability to new applications and devices, failing to adequately manage screen time based on evolving user behaviors and contexts.
Innovation Solution
A computer-implemented technique that uses context input signals and rule logic to dynamically control interaction time with applications, considering location, activity, time of day, and supervisee's actions, employing machine-trained models and heuristic logic to classify applications and adapt to new contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing control tools are used to manage application interaction, then basic control functionality is provided, but the system lacks adaptability to new applications and devices
Solution Approach 1:
The system automatically discovers and classifies new applications using machine learning models without requiring manual configuration. The supervisor device self-adapts to new applications by analyzing their characteristics and automatically determining appropriate time limits, eliminating the need for manual rule creation for each new application.
Solution Approach 2:
The system dynamically adjusts control parameters such as time limits and contextual rules based on the classified application type and current usage patterns. The machine learning model continuously refines classification parameters and control parameters as new applications are encountered, enabling automatic adaptation.
2Productivity
If coarse control mechanisms are used, then implementation is simple, but the system fails to keep abreast of evolving user interactions
Solution Approach 1:
The control mechanism transitions from static, pre-defined rules to dynamic, context-aware control. The system continuously monitors usage patterns, updates application classifications in real-time, and adjusts time limits based on current context, making the control mechanism adaptive to evolving user interactions.
Solution Approach 2:
The system incorporates feedback loops where usage data from supervisees is continuously collected, analyzed, and used to refine application classifications and adjust control parameters. This feedback mechanism enables the system to learn from actual usage patterns and improve control effectiveness over time.
3Adaptability or versatility
If manual rule creation is required for each application, then control precision can be high, but the system cannot adapt to new applications quickly
Solution Approach 1:
The system performs preliminary classification of applications using machine learning models before actual usage occurs. By pre-analyzing application characteristics and predicting appropriate control parameters, the system is ready to enforce precise control immediately when the application is first used, eliminating the adaptation delay.
Solution Approach 2:
The system creates classification profiles and control rule templates based on patterns from previously classified applications. When a new application is encountered, the system copies and adapts existing classification logic and control parameters from similar applications, enabling rapid precision control without manual configuration.
4Ease of operation
If context-aware control is implemented, then control flexibility is improved, but the system complexity increases
Solution Approach 1:
The system segments the control functionality into distinct modular components: application classification module, context detection module, rule generation module, and enforcement module. Each module handles a specific aspect of context-aware control, making the overall complex system manageable and maintainable while providing flexible control.
Data Source
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AI summary
A computer-implemented technique controls consumption of applications by a supervisee (e.g., a child). The technique detects when a supervisee attempts to interact with an application. In response, the technique receives context input signals that describe a current context affecting the supervisee. The technique then generates an output result based on the current context information and a set of rules expressed by rule logic. The technique then controls interaction by the supervisee with the application based on the output result. In one implementation, the technique automatically generates the rule logic, which may correspond to a set of discrete rules and/or a machine-trained model that implicitly expresses the rules. At least some of the rules specify amounts of time allocated to the supervisee for interaction with the plural applications in plural contexts. According to another illustrative aspect, the technique uses a machine-trained model to automatically classify a new application.