Conversational Analytics Ontology Expansion
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Solution Overview
Problem
Traditional analytic systems are unable to automatically derive and evolve ontologies from disparate systems, applications, and content sources, limiting their ability to understand and process information beyond their specific domain, such as expanding from airline reservations to hotel or train reservations.
Innovation Solution
A computing device with an interface that processes natural language inputs to provide users with interactive conversational and visual information management, generating ontologies by correlating content items and retrieving data from various sources, enabling the expansion of understanding and retrieval of related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional analytic systems are purpose-built for a specific business need, then they can provide specialized functionality, but they are unable to automatically expand their domain of understanding to process information from other domains
Solution Approach 1:
The patent implements a universal ontology learning framework that enables a single analytic system to handle multiple domains (airline reservations, hotel reservations, car rentals, train reservations) through automatic ontology derivation. The system uses natural language processing and machine learning to adapt to different domains without requiring separate purpose-built systems for each domain, thus achieving multi-functionality while maintaining manageable complexity through automated processes.
2Adaptability or versatility
If traditional systems cannot automatically derive and evolve ontologies from disparate systems, then development time is reduced, but the system cannot understand concepts beyond its specific domain
Solution Approach 1:
The patent implements self-service ontology learning where the analytic system automatically derives, evolves, and expands its own ontologies from disparate content sources without human intervention. The system uses natural language processing to automatically understand new domains, extract concepts and relationships, and update its knowledge base autonomously, eliminating the need for manual ontology development while enabling cross-domain understanding.
Solution Approach 2:
The patent employs preliminary action by pre-processing and indexing content from disparate sources before they are needed for analysis. The system continuously learns and evolves ontologies in advance, building a comprehensive knowledge base that can quickly respond to new domains and queries without requiring time-consuming manual ontology creation when needed.
3Productivity
If the system processes natural language inputs to generate ontologies and retrieve data from various sources, then information retrieval capability is enhanced, but processing complexity increases
Solution Approach 1:
The patent introduces an intermediary natural language processing layer that mediates between user queries and the complex data retrieval processes. This intermediary layer translates natural language inputs into structured ontology queries, automatically identifies relevant concepts and relationships, and coordinates data retrieval from multiple sources, thereby enhancing information retrieval efficiency while managing processing complexity through intelligent abstraction.
Data Source
AI summary
Natural language can be used to conduct a conversation that yields analytic results. An interactive user interface can present suggestive content associated with a natural language input to a user. The suggestive content can guide assist the user in obtaining analytic data/information relating to a business, industry, and/or the like. An ontology comprising one or more content items associated with the natural language input can generated. The ontology can be represented as and/or associated with a data structure that temporal correlates a plurality of content items to the natural language input.