Intelligent Search Engine with Domain-Aware Intent Routing
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Solution Overview
Problem
Existing chatbots struggle to accurately interpret user intent and locate relevant answers in broad databases due to the difficulty in predicting user intent and managing complex user interactions across diverse domains.
Innovation Solution
A system utilizing a transformer-based natural language processing model that considers user history and domain-specific abbreviations to identify intent, generate recommendations, and provide precise responses through a graph data structure and A* search algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a chatbot serves a broad population with diverse inquiry areas, then the coverage of user intents increases, but the accuracy of intent prediction decreases
Solution Approach 1:
The patent segments the broad user intent space into multiple domains (e.g., healthcare, finance, technology) and further divides each domain into subdomains. This hierarchical segmentation allows the chatbot to handle diverse inquiries accurately by routing queries to domain-specific processing paths, thereby maintaining high intent prediction accuracy across broad coverage areas.
Solution Approach 2:
The patent introduces domain identification as an intermediary step between receiving user input and generating responses. The system first determines which domain the query belongs to, then uses domain-specific knowledge and processing methods to interpret the intent. This intermediary mechanism enables accurate intent prediction across diverse domains by adapting the interpretation process to each domain's characteristics.
2Measurement precision
If the chatbot considers user history and domain-specific context, then the accuracy of intent identification improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary domain identification and context extraction before full intent analysis. By pre-processing the query to determine the domain and extract relevant contextual features early in the pipeline, the system reduces the computational burden of subsequent intent classification while maintaining high accuracy through domain-adapted processing.
Solution Approach 2:
The patent applies different processing strategies and levels of complexity locally to different domains and query types. Instead of using a uniform high-complexity analysis for all queries, the system adapts the depth and method of context analysis based on the identified domain and query characteristics, optimizing computational resources while maintaining accuracy where needed.
3Reliability
If the system performs comprehensive keyword searching in large databases, then the completeness of answers improves, but the search time increases
Solution Approach 1:
The patent segments the large database into domain-specific subdatabases and organizes information hierarchically by domain, subdomain, and topic. This segmentation allows the system to search only relevant portions of the database based on the identified query domain, ensuring comprehensive answer retrieval while significantly reducing search time by avoiding unnecessary searches in unrelated areas.
Solution Approach 2:
The patent performs preliminary domain identification and keyword extraction before executing the database search. By pre-filtering the search space based on domain context and key terms identified from the query, the system ensures that the comprehensive search is focused on relevant areas only, maintaining answer completeness while minimizing search time.
Data Source
AI summary
Systems, methods and interfaces are provided for processing natural language queries. A machine learning-powered search interface may process complex natural language queries across specialized domains. The method may utilize a transformer-based natural language processing model that analyzes user intent by examining both current and historical user inputs. A graph data structure may be used to represent interconnected domains like patient records, insurance claims, and business metrics enables dynamic information retrieval. The system may use an A* search algorithm to navigate domain nodes and identify relevant answers. An abbreviation expansion mechanism resolves technical shorthand by referencing domain-specific mapping databases, ensuring precise interpretation of user queries. The method may identify keywords, traces optimal answer pathways, and adapt by updating graph relationships and abbreviation mappings. By generating contextually rich natural language summaries, the system delivers precise, comprehensive responses through an intuitive search interface.