Emotion Data Representation via Multi-Modal Sensor Fusion
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Solution Overview
Problem
Current computing technologies are inadequate in representing and recreating nuanced emotional experiences, as they primarily focus on facial expressions and external inputs, failing to capture the full spectrum of human emotional states and sensations, which limits their ability to model complex emotional events and interactions in virtual environments.
Innovation Solution
The development of methods and systems for editing, storing, converting, encoding, and generating representations of subjective experiences, including physiological responses and emotional states, using a non-transitory computer readable medium and machine, allowing for the creation of immersive first-person emotion models that can simulate and manage complex emotional scenarios and transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current techniques use facial expressions and external input devices to represent emotion data, then data entry is simplified, but the representation is limited to rudimentary emotional symbolism and cannot capture nuanced emotional experiences
Solution Approach 1:
The patent segments emotional experience into multiple dimensions including physiological responses (heart rate, skin conductance, muscle tension), sensory inputs (visual, auditory, tactile), cognitive interpretations, and contextual factors. Each dimension is captured separately through different sensors and processing modules, then integrated to create a comprehensive emotional state representation that preserves nuanced information while maintaining ease of data entry through automated multi-channel collection.
2Ease of manufacture
If existing techniques rely on visual and electronic external input devices, then data collection is straightforward, but the system cannot capture internal physiological responses and subjective emotional sensations
Solution Approach 1:
The patent merges multiple data collection modalities including wearable physiological sensors (ECG, EEG, GSR, accelerometers), environmental sensors (microphones, cameras, temperature sensors), and user interface inputs into a unified emotion detection system. This combination enables simultaneous capture of internal physiological responses and external contextual information, greatly expanding the system's adaptability to capture diverse emotional states while maintaining straightforward data collection through integrated hardware-software architecture.
3Extent of automation
If software applications use rule-based decision making, then problem solving is systematic, but the system cannot adequately model complex emotional events or recreate human state experiences
Solution Approach 1:
The patent implements feedback loops where detected emotional states and physiological responses continuously inform and adjust the rule-based decision-making system. The system monitors emotional state changes over time, uses this feedback to refine emotional event models, and adapts its problem-solving strategies based on detected emotional contexts. This feedback mechanism enables the system to progressively improve its accuracy in modeling complex emotional events while maintaining systematic automation through iterative learning and model refinement.
Data Source
AI summary
This document discloses methods, systems, computer readable medium, and a machine for describing, mapping, modeling, generating, recreating, maintaining, archiving, and incorporating emotion or the immersive first-person experiences of self-awareness as data in a computing environment. In a preferable embodiment of the present invention, the method includes analyzing a body, obtaining information regarding at least some of the one or more relationships corresponding to location, topological, directional, distance, or temporal references, and generating a representation of the experience. In a preferable embodiment of the present invention, one or more methods also include the representation of immersive first-person experiences of emotion with other systems and data, the step of using comprising at least one of editing, generating, storing, converting, encoding, transmitting, displaying, editing, and incorporating data from input, output, outcome, result, or derivative values with applications, systems, or instance relevant computing environments.