Multimodal Follow-Up Survey Inquiries for Real-Time Response Quality
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Solution Overview
Problem
Conventional survey systems face issues with inaccurate, incomplete, and irrelevant survey responses, leading to inefficient processing and storage of poor-quality data, and lack flexibility in survey administration, often resulting in long surveys that fail to elicit relevant information from respondents.
Innovation Solution
A customized follow-up survey system that utilizes a multimodal model to determine response quality classifications during survey administration, generating tailored follow-up inquiries in real-time based on administrator-defined parameters, improving response accuracy and efficiency by only prompting relevant questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional survey systems collect a large variety of survey response data, then the quantity of data collected is increased, but the quality of data deteriorates with many inaccurate and irrelevant responses
Solution Approach 1:
The system performs preliminary quality assessment of survey responses using a multimodal model to classify responses as complete, incomplete, or nonsense before downstream processing. This preliminary classification enables the system to identify and exclude poor-quality data early in the workflow, preventing waste of processing resources on unusable data while maintaining collection of comprehensive response data.
Solution Approach 2:
The system implements feedback mechanisms where the multimodal model continuously evaluates response quality and provides real-time classification feedback. This feedback loop allows the system to dynamically adjust processing based on detected response quality, ensuring that only high-quality responses proceed to analysis while maintaining high-volume data collection capabilities.
2Reliability
If conventional survey systems process all collected survey responses, then completeness of data processing is improved, but processing efficiency deteriorates due to wasting resources on poor-quality data
Solution Approach 1:
The system performs preliminary quality assessment of survey responses using a multimodal model to classify responses as complete, incomplete, or nonsense before downstream processing. This preliminary classification enables the system to identify and exclude poor-quality data early in the workflow, preventing waste of processing resources on unusable data while maintaining collection of comprehensive response data.
Solution Approach 2:
The system extracts and separates high-quality responses from low-quality responses using the multimodal model's classification capabilities. By taking out only the relevant, complete, and accurate responses for downstream processing, the system eliminates waste of computational resources on nonsense or incomplete data while ensuring thorough processing of valuable responses.
3Ease of operation
If conventional survey systems administer identical surveys to all respondents, then ease of operation is improved, but adaptability to individual respondent needs deteriorates
Solution Approach 1:
The system dynamically adapts survey administration by using the multimodal model to evaluate respondent answers in real-time and automatically adjust subsequent questions. This dynamic approach allows the survey to transform from a static, identical experience for all respondents to a flexible, adaptive process that responds to individual answer quality and relevance, while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system changes survey parameters such as question selection, sequencing, and depth based on real-time assessment of response quality. By dynamically modifying survey parameters according to multimodal model classifications of respondent answers, the system achieves flexible adaptation to individual respondent needs while keeping the administration process automated and simple.
4Loss of information
If conventional survey systems use long surveys to cover all topics, then comprehensiveness of survey coverage is improved, but response quality deteriorates due to respondent fatigue and incomplete answers
Solution Approach 1:
The system dynamically adapts survey administration by using the multimodal model to evaluate respondent answers in real-time and automatically adjust subsequent questions. This dynamic approach allows the survey to transform from a static, identical experience for all respondents to a flexible, adaptive process that responds to individual answer quality and relevance, while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system extracts and identifies high-value information from responses using the multimodal model's quality assessment capabilities. By detecting complete and relevant answers, the system can determine when sufficient information has been collected and safely terminate or shorten the survey, eliminating the need for unnecessarily long questionnaires while maintaining comprehensive data collection on essential topics.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and customized follow-up survey inquiries in response to digital survey responses. In particular, in one or more embodiments, the disclosed systems receive prompts during survey creation defining goals and/or triggers for follow-up survey inquiries. Further, in some embodiments, the disclosed systems process survey responses utilizing a multimodal model to determine a survey response quality classification and any corresponding triggers in a survey response. Accordingly, the disclosed systems can generate a customized follow-up inquiry based on the survey question, the survey response, and the survey response quality classification.