Digital Survey Response Prediction System
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Solution Overview
Problem
Conventional digital survey systems face issues with generating inaccurate responses due to excessive questions, inefficiency in resource utilization, and inflexibility, leading to low-quality and incomplete responses.
Innovation Solution
A response prediction system that reduces the number of digital survey questions by identifying and excluding similar or irrelevant questions, generating predicted responses based on respondent attributes and relationships, and providing customized surveys to improve accuracy, efficiency, and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems generate digital surveys with many digital survey questions to collect comprehensive information, then the quantity of response information increases, but the accuracy of survey responses deteriorates due to respondent fatigue and time constraints
Solution Approach 1:
The system performs preliminary actions by generating predicted responses for unanswered survey questions before finalizing the survey results. The server predicts responses using machine learning models trained on historical survey data, respondent attributes, and question characteristics, allowing comprehensive information collection without requiring respondents to answer every question
Solution Approach 2:
The system creates copies of response information by generating predicted responses that replicate the pattern and content of actual respondent answers. These predicted responses are derived from historical data patterns and respondent profiles, enabling the system to reconstruct complete survey datasets without collecting every original response
2Quantity of substance
If conventional systems generate digital surveys with many digital survey questions to ensure sufficient responses are collected, then the completeness of survey data improves, but the time required to complete surveys deteriorates
Solution Approach 1:
The system performs preliminary prediction of survey responses using trained machine learning models before surveys are administered or during data processing. This preliminary action allows the system to determine which questions can be answered through prediction rather than direct respondent input, significantly reducing the time respondents need to spend while maintaining data completeness
Solution Approach 2:
The system enables self-service by using automated machine learning models to generate predicted responses without requiring manual respondent input for every question. The models autonomously analyze respondent attributes, historical data patterns, and question characteristics to produce accurate predictions, eliminating the need for respondents to answer redundant or predictable questions
3Stability of the object's composition
If conventional systems generate uniform sets of digital survey questions for all respondents to ensure consistency, then the standardization of data collection improves, but the adaptability to individual respondent preferences deteriorates
Solution Approach 1:
The system applies local quality by customizing survey question selection and prediction strategies for each individual respondent based on their unique attributes, preferences, and historical behavior patterns. While maintaining standardized data collection frameworks, the system adapts which specific questions are presented to and predicted for each respondent, optimizing the survey experience for local individual characteristics
Solution Approach 2:
The system introduces dynamics by making survey question assignment and response prediction adaptive rather than static. The machine learning models dynamically adjust which questions require actual respondent input versus which can be predicted, based on real-time analysis of respondent attributes, survey context, and historical data patterns, allowing the survey structure to flexibly adapt to each respondent
4Quantity of substance
If conventional systems generate and process large numbers of digital survey questions to collect comprehensive data, then the completeness of collected information increases, but the computational efficiency deteriorates due to excessive processing requirements
Solution Approach 1:
The system extracts and processes only the essential features and patterns from historical survey data and respondent attributes that are most predictive of survey responses. By identifying and focusing on key determining factors rather than processing all possible data points, the system reduces computational complexity while maintaining prediction accuracy and information completeness
Solution Approach 2:
The system changes parameters by transforming raw survey data and respondent attributes into optimized feature representations that are more efficient for machine learning processing. This includes dimensionality reduction, feature selection, and parameter optimization that maintain information completeness while significantly reducing the computational resources required for processing and analysis
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a predicted response to a digital survey question and identifying digital survey questions to remove from a digital survey. For example, the disclosed systems can reduce the number of digital survey questions distributed as part of a digital survey by identifying and removing similar digital survey questions. In addition, the disclosed systems can generate a predicted response to an unprovided digital survey question based on determining relationships between respondents. Further, based on respondent relationships, the disclosed systems can identify digital survey questions that a respondent is likely to answer and can provide the digital survey questions to a respondent device of the respondent.