Gesture-Based Feature Identification Using Distance and Facial Scoring
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Solution Overview
Problem
Conventional computer-based gesture monitoring systems struggle to accurately distinguish between different features of complex objects and interpret customer preferences effectively, especially when customers cannot verbally communicate their preferences or are not interested in commenting on every feature, leading to inefficiencies in sales interactions.
Innovation Solution
A computer-implemented method that uses machine learning models to analyze image data, identify customer gestures, and determine relevant features based on distance from the object, integrating facial expression scoring to provide insights into customer interest levels, thereby improving the interpretation of gestures and emotions associated with specific features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer-based gesture monitoring systems are used to identify customer interactions with objects, then the system can detect basic gestures, but it cannot distinguish between different features of complex objects
Solution Approach 1:
The system segments the gesture recognition task into multiple components: detecting gross body movements, identifying hand gestures, determining line-of-sight direction, and classifying specific features based on gesture type and orientation. This segmentation allows the system to distinguish between different features of complex objects without requiring a single overly complex recognition model.
Solution Approach 2:
The system introduces an intermediary classification layer that maps detected gestures to potential features of objects. This intermediary step translates raw gesture data into meaningful feature associations, enabling the system to distinguish between different object features while maintaining manageable system complexity.
2Loss of information
If salespersons manually monitor and interpret customer reactions to multiple product features, then they can understand customer preferences, but it consumes considerable time and effort
Solution Approach 1:
The system enables self-service by automatically detecting and interpreting customer gestures and reactions without requiring salesperson intervention. The computer vision system autonomously monitors customer interactions, identifies gestures, and determines customer preferences, freeing salespersons from time-consuming manual observation while preserving complete customer preference information.
Solution Approach 2:
The system replaces the mechanical process of manual monitoring and interpretation with an automated computer vision system. Machine learning models process visual data to detect gestures and infer customer preferences, substituting human cognitive effort with computational analysis, thereby eliminating time loss while maintaining information quality.
3Measurement precision
If machine learning models are trained on customer interactions based on immediate proximity to objects, then they can identify basic interactions, but they cannot distinguish different aspects or features of the object
Solution Approach 1:
The system applies local quality by associating specific gesture types with specific object features based on the customer's line-of-sight and gesture orientation. Different regions of the customer's body (eyes, hands, head orientation) provide different information about which specific feature is being examined, allowing precise feature identification while maintaining adaptability to various interaction patterns.
Solution Approach 2:
The system employs dynamic adaptation by using machine learning models that can adjust their interpretation based on the specific context of each interaction. The models learn to associate different gesture patterns with different features, allowing flexible interpretation that adapts to various objects, customers, and interaction styles while maintaining high feature identification accuracy.
Data Source
AI summary
A system, method, and computer-readable medium for associating a person's gestures with specific features of objects is disclosed. Using one or more image capture devices, a person's gestures and the location of that person in an environment is determined. Using determined distances between the person and objects in the environment and scales associated with features of those objects, the list of specific features in the person's field-of-view may be determined. Further, a facial expression of the person may be scored and that score associated with one or more specific features.