Automated Theming of Thought Objects Using Unsupervised Clustering
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Solution Overview
Problem
Existing systems face challenges in effectively handling, evaluating, and theming qualitative responses from multiple user devices due to limitations in transferring manually themed thought objects across different exchanges and insufficient training data for comprehensive modeling, leading to inefficient categorization of thought objects.
Innovation Solution
A system and method that automatically learns from a limited number of themed thought objects in a supervised framework and uses an unsupervised framework to theme un-themed objects, employing weighted combinations of text-analysis techniques to group thought objects into clusters based on stable topics, with stem representation and frequent keyword usage for accurate labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual theming is used in each exchange based on specific exchange requirements, then theming accuracy for that exchange is improved, but the complexity and time required to manually theme each exchange increases
Solution Approach 1:
The system performs preliminary action by automatically theming thought objects using unsupervised learning algorithms before human reviewers need to manually theme them. This preliminary automated theming reduces the initial workload and time required for manual theming while maintaining accuracy through subsequent human review and feedback mechanisms.
Solution Approach 2:
The system implements feedback loops where human reviewers correct automated theming errors, and these corrections are used to refine and improve the automated theming model over time. This feedback mechanism allows the system to learn from mistakes and continuously improve theming accuracy across different exchanges without requiring proportional increases in manual theming effort.
2Adaptability or versatility
If a supervised NLP approach with significant training samples is used, then theming model comprehensiveness is improved, but the data requirements and system complexity increase
Solution Approach 1:
The system employs unsupervised learning algorithms that can automatically discover themes and patterns in thought objects without requiring extensive pre-labeled training data. The model serves itself by autonomously identifying thematic structures through clustering and association analysis, reducing dependency on large volumes of manually annotated training data while maintaining adaptability across different exchanges.
Solution Approach 2:
The theming system is designed to be universal and adaptable to multiple exchanges with different domains and requirements. By using unsupervised learning techniques, the same core system can handle diverse thought objects across various exchanges without requiring separate training datasets for each exchange, thereby achieving comprehensiveness with reduced overall data requirements.
3Productivity
If general themes are provided in systems known in the art, then theming speed is improved, but the specificity and usability of themes in thought object exchanges decreases
Solution Approach 1:
The system applies local quality by generating themes that are specific to each exchange and its particular domain requirements. Rather than applying uniform general themes across all exchanges, the system adapts its theming output to reflect the local characteristics, vocabulary, and contextual nuances of each specific exchange, thereby maintaining both speed and specificity.
Solution Approach 2:
The theming system is dynamic and adaptable, adjusting its theme generation based on the characteristics of each exchange. The unsupervised learning model can dynamically identify and create exchange-specific themes rather than relying on static general themes, allowing it to maintain high theming speed while producing specific, usable themes tailored to each exchange's needs.
Data Source
AI summary
A system and method for automated theming of thought objects is disclosed. In a preferred embodiment, an object theming computer creates aggregated text by aggregating text associated with a first thought object, the aggregated text comprising descriptive and important text associated with the first thought object. The object theming computer then tokenizes the aggregated text into tokens. Further, a theme is associated to thought objects using one or more previously themed thought objects. If one or more thought objects have no associated theme, then a theme is generated and associated to the un-themed thought objects using a current plurality of the one or more thought objects.