Automatic Situation Extraction via Pruned Knowledge Graphs
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Solution Overview
Problem
Current natural language processing technologies are limited in extracting and identifying situations, issues, and scenarios from text documents without human agent involvement, as they rely on predefined situations and lack scalability in capturing human variance.
Innovation Solution
The introduction of robotic control theory in artificial intelligence to generate an abstract knowledge graph from user interactions, which is then pruned and segmented to create a situation image, enabling the extraction of scenarios without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If supervised situation extraction using predefined words and phrases is used, then situation extraction can be performed with some structure, but the system is limited by predefined situations and cannot capture human situation variance, reducing scalability
Solution Approach 1:
The system performs self-service by automatically generating situation classes and situation models without requiring human agent involvement in categorization. The unsupervised learning approach enables the system to autonomously identify and classify situations from conversation data, eliminating the need for manual predefined categories while maintaining scalability.
Solution Approach 2:
The system transitions from static predefined situation categories to dynamic situation modeling. By using probabilistic context-free grammars and continuously updating situation models based on new conversations, the system adapts to capture emerging situation types and human variance without requiring manual reconfiguration of predefined categories.
2Measurement precision
If human agents manually categorize undefined situations, then all situations can be captured accurately, but substantial time and resources are consumed
Solution Approach 1:
The system performs self-service by automatically generating situation classes and situation models without requiring human agent involvement in categorization. The unsupervised learning approach enables the system to autonomously identify and classify situations from conversation data, eliminating the need for manual predefined categories while maintaining scalability.
Solution Approach 2:
The patent replaces the mechanical process of manual human categorization with an automated computational system. Using natural language processing, probabilistic context-free grammars, and machine learning algorithms, the system automatically extracts and classifies situations, substituting human cognitive effort with computational processes that operate at much higher speeds and scale indefinitely.
3Extent of automation
If no predefined situation classes are assumed, then the system achieves unsupervised automatic extraction, but the complexity of generating meaningful situation models increases
Solution Approach 1:
The system segments the complex task of situation extraction into distinct processing stages: conversation parsing, knowledge graph generation at multiple textual levels, manifest extraction through pruning, and situation image generation. This segmentation allows each component to handle specific aspects of the problem independently, managing overall complexity while achieving complete automation.
Solution Approach 2:
The patent introduces intermediate representations (knowledge graphs and manifests) that bridge the gap between raw conversation data and final situation models. These intermediaries structure the unstructured data in a way that facilitates automatic situation extraction, reducing the complexity of directly generating situation models from raw text without predefined classes.
Data Source
AI summary
Automatic extractions of situations includes creating a situation image includes accessing a conversation between a first user and a second user, and generating an abstract knowledge graph at one or more textual levels. The method also includes generating one or more manifests by pruning the abstract knowledge graph and segmenting the pruned abstract knowledge graph. The method further includes converting the one or more manifests into the situation image.