Cognitive Dynamic Goal Survey System for Adaptive Questioning
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Solution Overview
Problem
Online surveys face challenges such as lower response rates and the risk of receiving fake or unhelpful answers, which reduces the usefulness of the data collected compared to traditional survey methods.
Innovation Solution
A cognitive system dynamically adapts survey questions based on user responses, shifting between primary and secondary survey goals if the affinity level is below a threshold, using multiple decision trees and text recognition to ensure valuable data is collected by adjusting the survey objective to match user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If online surveys are used to reduce costs and improve speed, then survey administration efficiency is improved, but response rates and data quality deteriorate
Solution Approach 1:
The survey system dynamically adapts the questionnaire content based on real-time analysis of respondent answers using cognitive computing. The system modifies subsequent questions, branching paths, and survey focus according to the affinity level detected between respondent responses and primary survey goals, creating a flexible, responsive survey experience that maintains engagement and data quality
Solution Approach 2:
The system implements continuous feedback loops where cognitive computing analyzes respondent answers in real-time, determines affinity levels with survey goals, and uses this feedback to dynamically adjust the survey content. This closed-loop approach ensures the survey adapts to maintain relevance and quality while proceeding online
2Reliability
If traditional survey methods are used to improve response rates, then data usefulness is improved, but cost and time consumption increase
Solution Approach 1:
The patent replaces traditional mechanical survey administration methods (paper surveys, phone interviews, in-person questioning) with cognitive computing systems that use artificial intelligence, natural language processing, and automated analysis to conduct and adapt surveys online, eliminating the need for physical survey materials and manual data processing
Solution Approach 2:
The system changes the parameter of survey adaptability from static (traditional fixed questionnaires) to dynamic (cognitive computing-adjusted questionnaires that modify content based on real-time analysis of respondent affinity levels, answer patterns, and engagement metrics)
3Ease of operation
If static survey questionnaires are used to simplify administration, then ease of operation is improved, but adaptability to user interests deteriorates
Solution Approach 1:
The survey system transitions from static, fixed questionnaires to dynamic, cognitively-adapted questionnaires that automatically adjust their content, structure, and focus based on real-time analysis of respondent answers and affinity levels with survey goals, maintaining ease of online administration while achieving high adaptability
Data Source
AI summary
An approach is provided that transmit a first set survey questions to a user with each of the first set questions corresponding to a primary survey goal. Survey answers are then received from the user. A cognitive system is then used to determining an affinity level between the user and the primary survey goal. If the affinity level reaches a threshold, then a second set of questions is transmitted to the user with these questions also corresponding to the primary survey goal. However, if the first affinity level fails to reach the threshold, then the second set of questions are based on a secondary survey goal and these questions are transmitted to the user.