Psychological State Inference via Continuous Space Mapping
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Solution Overview
Problem
Existing techniques are limited in predicting a wide range of psychological states across different cultures and contexts due to the lack of suitable affectual datasets and the challenge of mapping incongruent datasets into a continuous space for accurate inference.
Innovation Solution
The method involves jointly mapping incongruent affectual datasets into a continuous space using regression and machine learning models, allowing for context-specific inferences by indexing discrete psychological labels and adapting to different cultural and contextual cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing techniques are used to predict psychological states, then a narrow set of discrete psychological states can be predicted, but the range of predictable psychological states is limited and cannot accommodate different cultures and contexts
Solution Approach 1:
The patent transforms discrete psychological labels into continuous space coordinates, allowing the system to represent and predict a continuous spectrum of psychological states rather than limited discrete categories. This parameter transformation enables the model to capture subtle variations in psychological states across different cultures and contexts while maintaining prediction accuracy through the use of continuous-valued representations.
Solution Approach 2:
The patent creates a universal continuous space framework that can accommodate multiple incongruent affectual datasets from different cultures and contexts. By mapping diverse psychological labels into a common continuous coordinate system, the system achieves cultural and contextual universality while preserving the unique characteristics of each dataset through their respective label mappings.
2Adaptability or versatility
If incongruent affectual datasets are mapped into a continuous space, then context-specific inferences can be made, but the complexity of mapping and indexing datasets increases
Solution Approach 1:
The patent introduces continuous space coordinates as an intermediary representation layer between discrete psychological labels from different datasets. This intermediary continuous coordinate system serves as a universal language that bridges incongruent datasets, enabling context-specific inferences without requiring direct mapping between disparate label systems. The Voronoi plot structure acts as an organizational intermediary that simplifies the complexity of managing multiple datasets.
Solution Approach 2:
The patent transitions from one-dimensional discrete label categories to multi-dimensional continuous space coordinates. By representing psychological states in a continuous multidimensional space rather than flat discrete categories, the system can capture complex relationships and nuances across different cultures and contexts. This dimensional elevation allows for more sophisticated mapping of incongruent datasets while providing structured access through the Voronoi partitioning.
Data Source
AI summary
Methods, systems, apparatus, and computer-readable media (transitory or non-transitory) are described herein for inferring psychological states. In various examples, data indicative of a measured affect of an individual may be processed using a regression model to determine a coordinate in a continuous space. The continuous space may be indexed based on a plurality of discrete psychological labels. In a first context, the coordinate in the continuous space may be mapped to one of a first set of the discrete psychological labels associated with the first context. In a second context, the coordinate in the continuous space may be mapped to one of a second set of the discrete psychological labels associated with the second context.