Emotion Inference via Likelihood Probabilities
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Solution Overview
Problem
Current conversational user interfaces struggle to accurately determine the emotions experienced by individuals, as expressed emotions may not align with internal feelings, making it difficult for humans and AI to understand and respond appropriately in emotional support or communication contexts.
Innovation Solution
A method that analyzes text strings to identify expressed emotions and their probabilities, using a set of likelihood probabilities to infer experienced emotions, allowing for the selection of the most likely experienced emotion and presenting it to human listeners or AI systems for improved communication and support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional emotion detection methods are used to analyze text messages, then the system can identify expressed emotions from words, but it cannot accurately determine the true experienced emotions behind the messages
Solution Approach 1:
The patent introduces an intermediary statistical model that bridges expressed emotions and experienced emotions. This model uses likelihood probabilities as a mediator to infer the relationship between what users express and what they truly experience, allowing indirect detection of internal emotional states through observable text data without requiring direct access to users' internal feelings
Solution Approach 2:
The patent replaces direct emotional measurement (which would require accessing internal mental states) with a statistical inference system. Instead of mechanically or directly measuring experienced emotions, the system uses probability models and mathematical relationships to substitute and estimate these unobservable states from observable expressed emotions and language patterns
2Reliability
If the system only relies on expressed emotions from text, then the analysis is simple, but it fails to capture the true emotional state which may differ from expressed emotions
Solution Approach 1:
The patent transforms the emotion analysis from a single-parameter system (expressed emotions only) to a multi-parameter system that includes expressed emotions, experienced emotions, and likelihood probabilities. By changing the parameters being measured and analyzed, the system achieves more reliable emotional state understanding while managing complexity through structured probabilistic relationships
3Loss of information
If the system implements comprehensive emotion analysis to distinguish expressed and experienced emotions, then emotional understanding improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the emotion analysis process into distinct components: identifying expressed emotions from text, modeling experienced emotions separately, calculating likelihood probabilities, and integrating these elements through mathematical relationships. This segmentation allows comprehensive emotion analysis to be broken down into manageable processing steps, reducing overall system complexity while preserving emotional nuances
Data Source
AI summary
A method may include obtaining data input including one or more words. The method may include determining a set of expressed emotions and a set of expressed emotion probabilities based on the one or more words. The method may include obtaining a set of likelihood probabilities. Each likelihood probability may correspond to a conditional probability of an expressed emotion given an experienced emotion of a group of experienced emotions. The method may include determining a set of experienced emotions of the group of experienced emotions and a set of experienced emotion probabilities based on the set of expressed emotion probabilities and the set of likelihood probabilities. The method may include selecting an experienced emotion of the set of experienced emotions based on the selected experienced emotion corresponding to the highest experienced emotion probability of the set of experienced emotion probabilities. The method may include presenting the selected experienced emotion.