Indirect Feedback System for Retail Item Selection
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Solution Overview
Problem
Existing systems fail to effectively capture and utilize indirect user feedback, such as mood and preferences, to guide item selection processes, particularly in retail environments, as they often rely on direct feedback methods that do not fully capture subtle user responses.
Innovation Solution
An indirect feedback system that uses image and audio capturing technology to detect user mood and preferences through facial expressions, posture, and audio cues, processing this data to identify relevant items from a catalog and provide personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct feedback methods are used to capture user preferences, then the feedback collection process is simple, but the accuracy and completeness of user preference information is insufficient
Solution Approach 1:
The patent introduces indirect feedback indicators (facial expressions, posture, gestures) as intermediaries to capture user preferences. These intermediaries serve as mediators between the user's internal preferences and the external detection system, allowing the system to infer preferences without direct user input. This resolves the contradiction by enabling more accurate preference detection through multiple subtle cues rather than relying solely on simple direct feedback.
Solution Approach 2:
The patent replaces traditional mechanical feedback collection methods (direct user input, surveys) with optical and acoustic detection systems. Image capturing devices and audio sensors substitute for manual feedback collection, enabling automated detection of indirect feedback indicators. This substitution increases measurement precision while the automation reduces the operational complexity burden on users.
2Loss of information
If indirect feedback detection is implemented, then user preference information accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the feedback detection into multiple independent indicator types (facial expressions, posture, gestures, audio cues). Each indicator is detected and processed separately through specialized algorithms, then integrated to form a comprehensive preference profile. This segmentation reduces information loss by capturing multiple dimensions of user response while managing system complexity through modular processing of each indicator type.
Solution Approach 2:
The patent creates a multi-functional feedback detection system that can identify multiple types of indirect feedback indicators using a unified framework. The system simultaneously processes facial expressions, posture, gestures, and audio cues through integrated algorithms, enabling comprehensive preference detection without requiring separate specialized systems for each indicator type. This universality reduces overall system complexity while maximizing information completeness.
3Measurement precision
If multiple feedback indicators are analyzed, then the precision of item identification improves, but the processing time increases
Solution Approach 1:
The patent implements preliminary processing of feedback indicators by detecting and recording multiple indicators simultaneously as the user interacts with items. The system captures facial expressions, posture, and gestures in real-time during the shopping experience, preparing the data for analysis before the user completes their selection. This preliminary action enables comprehensive analysis of multiple indicators without adding significant delay to the item identification process.
Solution Approach 2:
The patent uses iterative feedback processing where the system continuously monitors multiple indicators and updates item recommendations in real-time based on accumulated evidence. As more indicators are detected and analyzed, the system refines its confidence in item selections, allowing for progressively more accurate identification without requiring complete analysis of all indicators before providing results. This feedback mechanism balances precision improvement with acceptable processing time.
Data Source
AI summary
Features are disclosed for identifying indirect user feedback and providing content such as item descriptions based on the indirect user feedback. An indirect feedback system may receive sensed data such as images or audio and identify indicators of indirect feedback for the subject shown, heard, or otherwise detected in the sensed data. For example, a user's facial expression and/or body language can provide indirect feedback as to how the user is feeling (e.g., mood). Based on the detected mood, features for suggesting additional items that should appeal to the user are described.