Automated Interactive Content Equivalence Detection via NLP
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Solution Overview
Problem
Conventional techniques for categorizing and analyzing interactive content data from user-provider interactions are manual, time-consuming, and prone to subjective errors, making it difficult to efficiently detect and quantify equivalence between shared experiences.
Innovation Solution
A system that uses artificial intelligence and natural language processing to automatically process and analyze interactive content data by converting it into alphanumeric format, measuring co-occurrence of n-grams, and determining equivalence values, allowing for efficient detection of similarities and trends in user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and summarizing of interactive content data is performed, then equivalence detection can be conducted, but the process becomes time and labor intensive
Solution Approach 1:
The patent replaces manual mechanical review processes with automated natural language processing and machine learning algorithms. The system converts interactive content data into structured formats, extracts key features automatically, and uses computational models to detect equivalence, eliminating the need for human reviewers to manually analyze each interaction while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-service automated analysis where the interactive content data is processed independently without human intervention. The automated system performs data conversion, feature extraction, equivalence detection, and result generation autonomously, allowing the organization to scale analysis capacity without proportionally increasing manual labor resources.
2Ease of operation
If manual categorization using discrete descriptors is used, then interactive content data can be analyzed, but accuracy is reduced due to subjective analysis and limited descriptor selection
Solution Approach 1:
The patent transforms the categorization approach by changing from discrete, pre-defined descriptors to continuous, data-driven features extracted through natural language processing. The system automatically identifies and extracts relevant semantic features, topics, and patterns from the interactive content, dynamically adapting to the data rather than forcing data into fixed categories, thereby improving measurement precision while maintaining operational simplicity through automation.
3Productivity
If automated processing using AI and NLP technology is implemented, then processing efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent segments the automated processing system into distinct modular components: data conversion module, feature extraction module, equivalence detection module, and result generation module. Each module performs a specific function and can be independently optimized, maintained, and scaled. This segmentation manages system complexity by breaking down the complex AI/NLP processing pipeline into manageable, well-defined stages with clear interfaces between them.
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. The interactive content data is converted to machine encoded communication elements that can be further grouped into machined encoded n-grams. The co-occurrence of machine encoded communication elements or n-grams in the interactive content data is compared against communication elements or n-grams in a seed set of concentrated content files to determine an equivalence value. In this manner, the system can automate the process of determining the equivalence of interactive content data files.