Interactive Content Analysis Using AI for Faster, Objective Classification
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Solution Overview
Problem
Conventional techniques for analyzing user-provider interactions are time-consuming, labor-intensive, and suffer from reduced accuracy due to their subjective and limited nature, making it difficult to characterize interactions accurately and efficiently.
Innovation Solution
A system utilizing artificial intelligence and natural language processing to automate the analysis of alphanumeric content data, performing concentration, subject classification, and sentiment analysis to generate identifiers that characterize interactions, allowing for the identification of system and service problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual analysis techniques are used to characterize user-provider interactions, then the analysis can be performed with simple tools, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated electronic processing systems. The system uses computers to automatically perform concentration analysis, subject classification, and sentiment analysis on interaction data, eliminating the need for manual review while dramatically increasing analysis speed and productivity.
Solution Approach 2:
The system enables self-service automated analysis where the computer system independently performs data processing, classification, and characterization without human intervention. The automated generation of interaction driver identifiers, subject identifiers, and sentiment identifiers allows the system to serve its own analysis needs efficiently.
2Ease of manufacture
If manual review and summarizing of alphanumeric content is performed, then the analysis can be conducted with basic tools, but accuracy is reduced due to subjective interpretation
Solution Approach 1:
The patent replaces subjective human interpretation with objective automated processing. The system uses computer algorithms to consistently apply classification rules and sentiment analysis, eliminating human bias and subjectivity while maintaining analytical depth through multi-stage processing including concentration analysis, subject classification, and sentiment analysis.
3Device complexity
If discrete sets of descriptors are used to characterize interactions, then the analysis framework remains simple, but the ability to accurately capture interaction nuances is limited
Solution Approach 1:
The patent segments the analysis process into distinct stages: concentration analysis to identify key communication elements, subject classification to categorize interaction topics, and sentiment analysis to determine emotional tone. This segmentation allows each stage to focus on specific aspects of the interaction, improving overall characterization accuracy while maintaining a structured framework.
Solution Approach 2:
The system adds multiple analytical dimensions beyond simple discrete descriptors. It incorporates concentration weights to prioritize important communication elements, subject classification dimensions to categorize topics, and sentiment dimensions to capture emotional nuance, thereby enriching the characterization without overwhelming complexity.
4Productivity
If automated AI and NLP technology is used to analyze interactions, then analysis accuracy and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex automated analysis into manageable segments: concentration analysis using weight quantifiers, subject classification using trained models, and sentiment analysis using separate processing. This segmentation reduces the apparent complexity by breaking down the overall system into independent, well-defined modules that can be developed and maintained separately.
Solution Approach 2:
The system performs preliminary concentration analysis to identify and weight important communication elements before proceeding to subject and sentiment classification. This preliminary action simplifies subsequent processing by focusing computational resources on the most relevant data, thereby managing complexity while maintaining high efficiency.
Data Source
AI summary
Disclosed are systems and methods that automate the process of analyzing interactive content data using artificial intelligence and natural language processing technology to generate subject matter identifiers and sentiment identifiers that characterize the interaction represented by the content data. The automated processing classifies, reduces, segments, and filters content data to accurately, automatically, and efficiently characterize the content data. The results of the analysis in turn allow for identification of system and service problems and the implementation of system enhancements.