Ontology Manager for Context-Aware Digital Asset Retrieval
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Solution Overview
Problem
Existing information retrieval systems, including hierarchical category directories and search engines, struggle to effectively assist users in finding specific information from enterprise portals and websites, often resulting in incomplete or irrelevant results due to the complexity of user queries and the lack of context-awareness in search algorithms.
Innovation Solution
A system that integrates a content management system, an ontology manager, and a chat bot to facilitate contextually-aware information retrieval. The system uses tags and facets to characterize digital assets, guiding the chat bot to drive conversations with users and retrieve relevant information based on user intent and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hierarchical category directories are used for information retrieval, then users can browse organized content, but the system becomes impossible to navigate as the number of websites grows
Solution Approach 1:
The patent replaces manual hierarchical categorization with automated machine learning algorithms that dynamically organize and retrieve information based on content analysis, eliminating the need for users to navigate complex category trees
Solution Approach 2:
The system transforms the static hierarchical structure into a dynamic retrieval model where information is organized and accessed based on learned semantic relationships and user context, changing the fundamental parameter of how information is structured and retrieved
2Productivity
If search engines use keyword-based Ask-Tell model, then users can quickly search for information, but the results are incomplete when content is not well-architected or focused
Solution Approach 1:
The system implements feedback loops where the AI assistant continuously learns from user interactions, refines its understanding of user intent, and improves subsequent search results, transforming static keyword matching into a dynamic learning system
Solution Approach 2:
The patent introduces an AI-powered intermediary layer between the user's query and the search engine, which interprets user intent, formulates refined search strategies, and synthesizes results from multiple sources to compensate for incomplete individual content architectures
3Extent of automation
If chat bots are powered by simple machine learning, then they can automate conversations, but they fail to understand open-ended questions and lack context awareness
Solution Approach 1:
The patent transforms static, rule-based chat bot responses into dynamic, context-aware interactions where the AI assistant adapts its behavior based on the evolving conversation context, user preferences, and learned patterns from previous interactions
Solution Approach 2:
The system performs preliminary analysis of user intent and context before generating responses, pre-processing and understanding the nuanced meaning of open-ended questions to provide accurate and relevant answers
4Quantity of substance
If enterprises implement comprehensive content management systems, then they can store diverse digital assets, but retrieving relevant information becomes difficult without proper organization
Solution Approach 1:
The patent replaces manual content organization and retrieval processes with automated AI-powered semantic analysis and natural language querying, allowing users to retrieve information through conversational queries rather than navigating complex file structures
Data Source
AI summary
A system for digital content that includes a content management system, an ontology manager and a chat bot, all executing and in communications coupling on a digital data processing system. The content manager stores a plurality of tagged digital assets. The ontology manager stores a list of (or otherwise maintains) plural facets, each corresponding to one or more tags—and at least one corresponding to two or more tags, e.g., of differing domains—of the content management system. One or more dialog segments and sequence identifiers are maintained in the ontology manager as well, each associated with one or more other facets. The chat bot drives a conversation with an end-user based on facets identified as associated with assets in the content management system and using dialog segments associated with those facets, while excluding those facets not so identified. The digital data processing system generates and transmits to the user digital assets identified through that conversation and, when used in connection with commerce, may facilitate acquisition from inventory of items represented by those assets.