User Experience Quantification System Using Emotion Data
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Solution Overview
Problem
Current systems fail to provide a systematic approach for personalizing user experience based on user movements and gestures, and they lack effective tools for quantifying user experience and evaluating market readiness for products and services.
Innovation Solution
A system that processes and aggregates user emotion data through a classification, rating, and calibration framework to quantify user experience across the product or service lifecycle, including modules like UE factor selector, variance classifier, emotion translator, and artefact generator to enhance user experience and market readiness analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional control systems use gross body movements for user control input, then the system is simple to operate, but it cannot provide sufficient personalization based on dexterous gestures
Solution Approach 1:
The patent segments user movements into distinct categories: gross body movements for basic navigation and dexterous hand gestures for detailed control. This segmentation allows the system to process different movement types through specialized algorithms, enabling personalization without requiring complete reconstruction of the control system.
Solution Approach 2:
The patent introduces sensors and machine learning models as intermediary components between the user's movements and the system responses. These intermediaries capture and interpret dexterous gestures, translating them into personalized control commands without directly modifying the core control system architecture.
2Measurement precision
If a comprehensive user experience monitoring system is implemented to capture multiple data sources, then user experience quantification improves, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources (sensor data, application data, user feedback) into a unified user experience model. By combining these diverse inputs through standardized processing pipelines and aggregation algorithms, the system achieves comprehensive measurement without proportionally increasing complexity.
Solution Approach 2:
The patent creates a universal monitoring framework that can handle multiple data types and sources through common processing mechanisms. The system uses standardized data collection, processing, and analysis components that work across different contexts, enabling multi-functional capability without requiring separate systems for each data source.
3Speed
If real-time processing of multiple data sources is performed to generate user experience scores, then responsiveness improves, but computational load increases
Solution Approach 1:
The patent implements partial processing by prioritizing certain data sources and processing them in full detail, while other data sources receive summarized or sampled processing. This approach maintains responsiveness for critical user experience metrics while reducing overall computational energy consumption through selective processing intensity.
Data Source
AI summary
The present invention provides system and method for quantifying the user experience which consider data from various stages in the product or a service lifecycle and derive the relevant key KPIs from the same which are then aggregated and calibrated accordingly. The main components of the present invention are UE factor selector (150), UE rating calibrator (180), UE variance classifier (160), UE emotion translator (190) and artefact generator (100). The present invention is used to evaluate the end user's experience thereby understand the type of emotion an end user might experience or express after using the service, application or the product.