Digital Inaptitude Detection via Access Request Analysis
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Solution Overview
Problem
Current solutions lack an automatic method to detect digital inaptitude situations, which hinders the ability of telecommunication operators to provide targeted assistance to users struggling with digital skills, particularly those experiencing electronic illiteracy.
Innovation Solution
A method that analyzes access request data from user terminals to identify patterns of incorrect requests, such as frequent failed service access attempts, short call durations, and password reset requests, to determine a digital inaptitude score, triggering assistance actions when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to evaluate digital skills, then detection accuracy can be maintained, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The system enables automatic self-assessment of digital skills by analyzing objective usage data from terminal devices. The detection mechanism operates autonomously without requiring manual intervention from operators, thereby maintaining detection accuracy while reducing system complexity and operational resources.
Solution Approach 2:
The patent replaces manual assessment mechanisms with automated data analysis systems. By substituting human evaluation with algorithmic processing of usage patterns, the system achieves comparable or superior detection accuracy while eliminating the complexity associated with manual assessment procedures.
2Difficulty of detecting and measuring
If frequent monitoring of access requests is implemented to detect digital inaptitude, then detection capability is improved, but user experience and system performance may deteriorate
Solution Approach 1:
The system implements selective monitoring of access requests rather than comprehensive analysis of all user activities. By focusing only on specific patterns indicative of digital inaptitude (such as repeated failed access attempts), the system maintains high detection capability while minimizing the impact on user experience and system performance.
Solution Approach 2:
The system establishes detection thresholds and criteria in advance, allowing it to identify digital inaptitude situations proactively based on pre-defined patterns. This preliminary configuration enables the system to detect issues before they significantly impact user experience, while avoiding unnecessary monitoring of normal usage patterns.
3Measurement precision
If comprehensive data collection from multiple services is performed, then detection accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system extracts and analyzes only the most relevant features from access request data, such as frequency of failed attempts, types of services accessed, and error patterns. By selecting only the critical data elements needed for detection, the system maintains high detection accuracy while significantly reducing data processing time and computational resource requirements.
Solution Approach 2:
The detection process is divided into distinct stages: data collection from multiple services, pattern recognition, and final assessment. Each service's data is processed independently according to specific detection criteria, allowing parallel processing that reduces overall computation time while maintaining comprehensive detection accuracy across multiple services.
Data Source
AI summary
Detecting a user having a digital inaptitude (for example electronic illiteracy), where the user has at least one terminal and requires assistance suited for the use of at least one digital service from the terminal is disclosed. The following are provided: querying a database storing service-access requests from the terminal; among the requests, identifying at least incorrect requests relating to failed attempts by the user to access the service; estimating a frequency of incorrect requests for evaluating an electronic illiteracy score of the user; comparing the evaluated score to a preset threshold; and in case the evaluated score exceeds the threshold, detecting the user as having an electronic illiteracy in order to propose at last one specific surface suited to the user.