Interactive Content Interface for AI Sentiment and Topic Analysis

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional techniques for analyzing user-provider interactions are time and labor intensive and suffer from reduced accuracy due to the subjective nature of manual analysis and limited descriptors, making it difficult to characterize interactions effectively.

Innovation Solution

A system utilizing artificial intelligence and natural language processing to automate the classification, segmentation, and filtering of alphanumeric content data, including concentration analysis, subject classification, and sentiment analysis, to identify interaction drivers and sentiment, and display the results on an Interaction GUI.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual selection of discrete descriptors is used to characterize user-provider interactions, then the analysis process is simple to implement, but the accuracy and objectivity of the characterization is reduced

Engineering Contradiction:

Improveaccuracy of interaction characterizationVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical analysis with automated artificial intelligence and natural language processing systems. The AI-based subject classification and sentiment analysis automatically generate interaction driver identifiers and sentiment identifiers from content data, eliminating the need for manual selection of discrete descriptors while improving accuracy and objectivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary AI processing layer between the raw content data and the final interaction characterization. This intermediary system performs concentration analysis, subject classification, and sentiment analysis to transform unstructured content data into structured interaction driver identifiers and sentiment identifiers, resolving the contradiction between accuracy and complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If manual review and summarization of alphanumeric content is performed, then the analysis can be customized, but the process becomes time and labor intensive

Engineering Contradiction:

Improveprocessing speed of content dataVSAvoidtime required for analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements self-service automated processing where the AI system independently performs concentration analysis, subject classification, and sentiment analysis on content data without human intervention. The system automatically generates interaction driver identifiers and sentiment identifiers, enabling high-speed processing of large volumes of content data while eliminating manual labor and time consumption.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual review and summarization processes with automated AI-based natural language processing. The AI system rapidly analyzes content data to extract meaningful interactions, subjects, and sentiments, dramatically increasing processing speed and productivity compared to manual methods while reducing time and labor requirements.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If discrete descriptors are used to characterize interactions, then the analysis framework is simple, but the ability to accurately capture nuanced sentiments and topics is limited

Engineering Contradiction:

Improvecapability to characterize diverse interactionsVSAvoidcomplexity of classification system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic AI-based classification systems that adapt to diverse interaction types and content. The subject classification and sentiment analysis automatically adjust to different topics, sentiments, and interaction contexts, enabling the system to accurately characterize a wide variety of user-provider interactions without being constrained by fixed discrete descriptors.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent creates a universal AI-based analysis framework that can handle multiple types of content data (audio transcriptions, written communications, chat messages) and generate various identifiers (interaction driver identifiers, sentiment identifiers). This multi-functional system replaces the need for multiple specialized discrete descriptor frameworks, increasing adaptability while managing complexity through unified AI processing.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12632159B2Interface for display of interactive content

Publication Date: 2026.05.19 TRUIST BANK

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.