Intent and Bot Query Guidance via Domain-Specific Language
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Solution Overview
Problem
Existing systems face challenges in accurately analyzing queries to determine the appropriate set of bots needed to respond, particularly in domains with extensive models, where subject matter experts struggle to translate user queries into actionable guidance due to lack of software development skills.
Innovation Solution
An intent and bot-based query guidance system that identifies intent from queries by processing context, temporal hints, and entities, generating a domain-specific language representation to assemble a chain of bots that can provide guidance, utilizing natural language processing and domain models to map queries to relevant bot functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter experts manually translate user queries into actionable guidance, then domain expertise can be leveraged, but the process becomes time-consuming and requires software development skills that experts lack
Solution Approach 1:
An automated query processing system acts as an intermediary between user queries and bot chains. The system includes a query processor that analyzes natural language queries, extracts intent and parameters, and automatically generates bot chain configurations without requiring subject matter experts to manually translate queries, thus resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The system enables self-service by allowing users to submit natural language queries that are automatically processed by the query processor. The processor independently extracts intent, identifies parameters, and configures bot chains without human intervention, eliminating the need for subject matter experts to spend time translating queries while maintaining high accuracy through automated natural language processing
2Adaptability or versatility
If a comprehensive set of bots is assembled to handle all possible queries, then query response capability is improved, but system complexity increases
Solution Approach 1:
The system segments the bot ecosystem into specialized, modular bot chains that handle specific query types or domains. Each bot chain is independently configured and can be selectively activated based on query intent, avoiding the need to deploy a monolithic complex system while maintaining versatility through modular composition of smaller, manageable bot units
Solution Approach 2:
The system dynamically assembles and configures bot chains based on the specific intent and parameters extracted from each query. Rather than maintaining a static comprehensive bot set, the system adapts the bot chain configuration in real-time to match query requirements, reducing unnecessary complexity while preserving adaptability through dynamic selection and configuration
3Ease of operation
If natural language processing is used to analyze queries, then ease of use is improved, but processing time and computational resources increase
Solution Approach 1:
The query processor applies partial natural language processing by focusing extraction efforts on critical elements such as intent identification and key parameter extraction rather than performing exhaustive linguistic analysis. This selective processing approach maintains ease of use for users while reducing computational overhead and resource consumption by processing only the most essential query components
Data Source
AI summary
According to an example, intent and bot based query guidance may include receiving a query associated with a domain, and identifying, based on an analysis of the query, an intent of the query by extracting an action associated with the query and an entity associated with the query. An intent model associated with the query may be generated based on a mapping of the action and the entity with a domain model of the domain. An intent domain specific language representation of the intent model associated with the query may be generated. Based on an analysis of the intent domain specific language representation, a plurality of bots may be identified, and a parameterized bot chain may be generated to respond to the query. A runtime binding of bots of the parameterized bot chain may be performed, and invoked to generate the response to the query.