Passive Digital Repertory Grids for Scalable Personal Construct Extraction
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Solution Overview
Problem
Traditional repertory grid techniques are inefficient for large-scale application due to the need for face-to-face interviews, time-consuming processes, and lack of anonymity, making it difficult to collect users' personal constructs for wellbeing improvement recommendations.
Innovation Solution
A method and system for passive digital repertory grids (PDRG) that extracts users' personal constructs through interactions with electronic devices, such as smartphones, without explicit questioning, allowing for scalable and resource-efficient data collection and integration with a recommender engine for wellbeing improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional repertory grid interviews are conducted face-to-face with a psychiatrist, then accurate personal constructs can be extracted, but the process is time-consuming and not scalable to large numbers of users
Solution Approach 1:
The patent creates a digital copy of the repertory grid interview process through an automated system. The electronic device captures user responses to repertory grid questions via an application, replicating the psychiatrist's data collection function without requiring actual human interaction. This digital copy enables scalable deployment while maintaining the core assessment functionality.
Solution Approach 2:
The system enables users to complete repertory grid assessments independently through their own electronic devices. The application guides users through the questioning process and automatically captures their responses, eliminating the need for a psychiatrist to conduct each interview manually. This self-service approach dramatically increases scalability while preserving assessment accuracy.
2Reliability
If repertory grid interviews are conducted in a predefined location such as a psychiatrist's office, then controlled environment is achieved, but user convenience and accessibility are reduced
Solution Approach 1:
The patent transitions the interview environment from physical space to digital space. Instead of requiring users to travel to a psychiatrist's office, the assessment is delivered through an electronic application that users can access from any location with an electronic device. This dimensional shift from physical to digital enables both controlled environment and universal accessibility simultaneously.
3Measurement precision
If repertory grid interviews last 45 minutes to over an hour, then comprehensive personal constructs are captured, but user time commitment and dropout rates increase
Solution Approach 1:
The system divides the repertory grid assessment into periodic, manageable segments that users can complete at different times. The application presents questions in structured sequences that can be paused and resumed, allowing users to spread the assessment across multiple sessions rather than requiring a single continuous hour-long commitment. This periodic approach maintains data completeness while reducing perceived time burden.
4Measurement precision
If users must feel comfortable telling their psychiatrist personal views, then honest responses are obtained, but anonymity and user privacy are compromised
Solution Approach 1:
The electronic device and application serve as intermediaries between the user and the assessment system. Users interact with the assessment through the application interface rather than direct human confrontation, which reduces social pressure and increases honesty. The digital intermediary automatically captures and processes responses without human judgment, preserving user privacy while maintaining response authenticity.
Data Source
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AI summary
A method and system for efficient repertory grid data extraction and well-being improvement recommendations. This method and system propose a novel way to collect users' data from user application interactions (in electronic devices) that enables to extract repertory grids or personal constructs from one or more users, in such a way that the users do not explicitly has to fill the repertory grid data. This structured way of collecting data allows to extract Individual Personal Constructs in a massively scalable fashion with minimum resources. Additionally, the extracted data may be recurrently input into a novel recommender system or engine which would result in higher engagement efficacy for the user, helping the individual on his/her decision-making process.