Knowledge Driven Solution Inference for Unstructured Data
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Solution Overview
Problem
Existing customer service and support systems face challenges in effectively utilizing and retrieving relevant solutions from large volumes of unstructured data, making it difficult to provide quality service and retain customers.
Innovation Solution
A knowledge-driven solution inference system that extracts unstructured data from various sources, processes it into structured format, and builds a knowledge base for decision-making, using extraction engines, clustering engines, and inference engines to provide relevant solutions to customer queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unstructured data is stored in information sources, then data volume increases, but retrieval efficiency deteriorates
Solution Approach 1:
The patent segments unstructured data into structured formats by extracting specific information elements (problem descriptions, solutions, resolutions) from unstructured text. This segmentation transforms the data organization from undifferentiated mass to categorized, searchable units, enabling efficient retrieval without sacrificing data volume capacity.
Solution Approach 2:
The patent introduces an intermediary processing layer (extraction engine, clustering engine, knowledge base) between the unstructured data sources and the retrieval system. This intermediary structures and indexes the data, creating a bridge that allows fast querying of what would otherwise be unsearchable unstructured content.
2Loss of information
If unstructured data is utilized, then information completeness improves, but processing complexity increases
Solution Approach 1:
The extraction engine automatically identifies and extracts relevant information elements from unstructured data without requiring manual structuring. The system self-organizes the data into standardized formats, reducing the complexity burden on operators while maintaining complete information utilization.
Solution Approach 2:
The patent changes the structural parameters of unstructured data by applying extraction rules and clustering algorithms. This transforms the data from an unmanageable state to a structured state with defined parameters (categories, relationships, hierarchies), making processing systematic rather than ad-hoc.
3Device complexity
If manual information retrieval is used, then system simplicity is maintained, but service quality deteriorates
Solution Approach 1:
The system performs preliminary structuring and indexing of unstructured data in advance, creating a prepared knowledge base before queries are submitted. This preliminary action ensures that when retrieval is needed, the system can quickly access pre-organized information, delivering high service quality without requiring complex real-time processing.
Data Source
AI summary
Various embodiments of systems and methods to provide a knowledge driven solution inference are described herein. In one aspect, unstructured data is retrieved from one or more information sources. Data segments corresponding to a plurality of categories are identified in the extracted unstructured data by natural language processing. Further, the data segments are grouped into a plurality of data clusters based on scores between the data segments. The structured knowledge base is generated by linking the associated plurality of data clusters. The knowledge driven solution inference is provided based on the generated knowledge base.