Video-Based Questionnaire Display for Targeted User Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems require significant processing effort for collecting, reviewing, and inputting questionnaire results, leading to increased user burden and low response rates due to the large number of items and complexity of questionnaires.
Innovation Solution
An information processing apparatus that utilizes machine learning models to analyze video data from store environments, identifying relationships between customers and products, and generating targeted questionnaires based on customer actions and psychological evaluations, reducing the need for manual input and increasing response rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional questionnaire collection methods are used, then comprehensive user feedback can be obtained, but the processing burden increases significantly and response rates decrease
Solution Approach 1:
The system automatically generates questionnaires based on video analysis of user behavior, eliminating the need for manual questionnaire design and distribution. The system serves itself by using captured video data to identify users, analyze their actions, and automatically create personalized questionnaire content, thereby reducing processing burden while maintaining comprehensive feedback collection.
Solution Approach 2:
The patent replaces manual mechanical processes of questionnaire collection, review, and input with automated video analysis and machine learning systems. Computer vision algorithms automatically track user behavior, identify relevant actions, and generate questionnaire items, substituting the complex manual processing system with an automated digital system that reduces overall complexity.
2Loss of information
If comprehensive questionnaires with many items are used, then more complete user feedback is collected, but user burden increases and response rates decrease
Solution Approach 1:
The system applies local quality by generating different questionnaire content tailored to each user's specific behavior patterns observed in video data. Instead of using a uniform comprehensive questionnaire for all users, the system analyzes individual user actions and creates customized questionnaire items that are relevant to each user's experience, reducing burden while maintaining feedback completeness.
Solution Approach 2:
The system uses partial action by selectively generating questionnaire items based on specific user behaviors that are actually observed in the video data. Rather than presenting all possible questionnaire items to every user, the system only generates questions related to actions that the specific user performed, creating a partial but highly relevant questionnaire that reduces user burden while capturing essential feedback.
3Loss of information
If manual questionnaire processing is performed, then detailed user feedback can be collected, but significant processing effort and time are required
Solution Approach 1:
The system performs preliminary action by automatically analyzing user video data and pre-generating questionnaire content before the actual feedback collection process. Video analysis occurs in advance to identify user behaviors, and questionnaire items are prepared based on this analysis, eliminating the need for manual processing during the feedback collection phase and significantly improving processing efficiency.
Solution Approach 2:
The patent replaces manual mechanical processing of questionnaire data with automated computer vision and machine learning systems. The system automatically captures video data, analyzes user actions through algorithms, generates questionnaire items, and processes responses without human intervention, substituting the slow manual processing system with an efficient automated digital system that maintains detailed data collection capability.
Data Source
AI summary
An information processing apparatus identifies a state of a specific user with respect to an object among a plurality of persons included in video data by analyzing the video data obtained by capturing a first area including a person or the object, generates a questionnaire related to the person or the object, identifies positions and orientations of users among the plurality of persons with respect to a display device by analyzing video data obtained by capturing a second area including the display device, and displays the questionnaire related to the specific user on the display device when the specific user is in a state of being closest to the display device and facing the display device and another user is away from the specific user and from the display device and does not face the display device.