Dynamic Graph Object Generation for Real-Time Insight Extraction
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Solution Overview
Problem
Conventional methods for collecting, analyzing, and presenting data are costly, time-consuming, and limited to specific snapshots in time, often requiring focus groups and platform-specific formats, which restrict their effectiveness and relevance over time.
Innovation Solution
A graph generation system that uses a combination of rule-based and machine learning models to extract key concepts from digital content across various platforms, determining associations between them, and generating a searchable graph object, enabling dynamic updates and flexible domain-specific analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use focus groups and surveys for data collection and analysis, then meaningful insights can be obtained, but significant costs in time and manpower are required
Solution Approach 1:
The patent replaces manual focus groups and surveys with automated machine learning models and natural language processing systems. These computational systems automatically extract, analyze, and synthesize data from digital content, eliminating the need for human facilitators and reducing time and resource requirements while maintaining or improving insight quality.
Solution Approach 2:
The system creates computational models that replicate and automate the analytical functions previously performed by human focus groups. By training machine learning models on historical data and patterns, the system can simulate human analytical capabilities at scale, providing meaningful insights without the associated time and manpower costs.
2Measurement precision
If conventional techniques collect and analyze data, then insights can be obtained, but the data becomes obsolete over time as it is limited to specific snapshots
Solution Approach 1:
The patent implements dynamic, continuously updating data collection and analysis systems that process digital content in real-time or near-real-time. Rather than relying on static snapshots, the system continuously ingests new data, re-trains models, and updates insights, ensuring that the analyzed information remains current and relevant to changing conditions and trends.
Solution Approach 2:
The system establishes continuous automated data collection, processing, and analysis operations that run ongoing rather than periodically. This continuous operation ensures that insights are consistently updated and remain valid over time, eliminating the obsolescence problem inherent in periodic snapshot approaches.
3Ease of manufacture
If conventional techniques require specific content formats or platform-specific origins, then data collection can be structured, but the scope and versatility of analysis are limited
Solution Approach 1:
The patent develops universal machine learning models and natural language processing systems that can analyze diverse content formats (text, images, video, audio) from multiple platforms (social media, news sites, blogs, forums) using the same underlying technology. This multi-functional approach maintains structured analysis capabilities while dramatically expanding the scope of acceptable data sources beyond platform-specific limitations.
Data Source
AI summary
The present disclosure relates to extracting key concepts from digital content items and determining associations between the key concepts and candidate terms for use in generating and presenting a correlation graph object based on the determined associations. For example, systems described herein involve determining frequency of co-occurrence between various key concepts and applying a classification model (e.g., a zero-shot classification model) to the key concepts and candidate terms to determine associations between the key concepts and candidate terms for a given domain of interest. The systems further involve generating a graph object and processing graph queries in a way that enables fast and efficient presentation of slices of the graph object that provide a visual depiction of key concepts and edges representing associations between pairs of the key concepts.