Context Map for Sharing Server Conversational Context
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Solution Overview
Problem
Natural language processing in server administration lacks context understanding, making it difficult for systems to accurately execute user queries and predict conversational flows, as user inputs often lack necessary context information.
Innovation Solution
A system that utilizes cognitive engines to analyze user-entered query statements, updates a context map to reflect the query context, and routes requests to appropriate cognitive engines for resolution, leveraging APIs like IBM Watson to provide context and aggregate responses for accurate query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used in server administration, then ease of operation is improved, but context understanding accuracy deteriorates
Solution Approach 1:
The patent introduces a context map as an intermediary data structure that stores and manages conversational context information. This context map acts as a mediator between the natural language input and the cognitive engines, enabling accurate context understanding while maintaining ease of operation through natural language interfaces.
Solution Approach 2:
The system performs preliminary actions by maintaining and updating a context map that stores conversational history and context information before processing new queries. This preliminary context preparation enables accurate understanding of user intentions without requiring users to provide complete contextual information each time.
2Measurement precision
If multiple cognitive engines are used to resolve queries, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple cognitive engines into a unified system that shares a common context map. This consolidation allows the system to leverage the capabilities of multiple engines while reducing overall complexity through shared context management and coordinated operation.
Solution Approach 2:
The context map serves as a universal data structure that is shared across multiple cognitive engines, enabling them to function cooperatively. This multi-functional approach allows different engines to access and contribute to the same context, improving query resolution accuracy without proportionally increasing system complexity.
3Reliability
If context information is maintained across conversations, then reliability is improved, but loss of information increases
Solution Approach 1:
The patent extracts and stores essential context information in a structured context map, separating critical conversational state from complete conversation history. This extraction maintains reliability by preserving necessary context while reducing information loss by focusing on essential elements rather than attempting to retain all possible details.
Data Source
AI summary
A method, computer system, and computer program product for determining a server conversational state in an interactive dialog between a server and an administrator is provided. The embodiment may include receiving a query statement from a user. The embodiment may also include updating a context map to reflect a context of the received query statement. The embodiment may further include transmitting a request to one or more cognitive engines capable of resolving the received query statement. The embodiment may also include processing the transmitted request in each of the one or more cognitive engines. The embodiment may further include updating the context map with a resolution operation performed by the one or more cognitive engines. The embodiment may also include displaying a response to the user.