Natural Language Intent Matching via Fast-Slow Path Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine-driven information retrieval systems struggle to handle unstructured natural language requests and provide structured responses, are limited to interpreting a few languages, and lack the ability to dynamically update backend technology for auto-correction of responses.
Innovation Solution
A system and method that include an intent creation subsystem, a natural language message handling subsystem, and a response handling subsystem, which identify intents, match them with predefined intents using fast and slow path processing techniques, and rectify messages to generate structured responses, enabling handling of both structured and unstructured inputs and learning from user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine-driven information retrieval systems interpret structured incoming user requests, then they can provide relevant responses, but they are unable to handle unstructured incoming user requests or provide structured responses
Solution Approach 1:
The system segments the natural language processing task into distinct modules: an intent identification module that extracts meaning from unstructured input, an intent matching module that compares against predefined intents, and a response generation module that creates structured outputs. This segmentation allows the system to handle unstructured requests while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary intent representation layer that translates unstructured natural language requests into structured intent formats. This intermediary layer acts as a bridge between unstructured input and the structured response system, enabling the system to handle diverse input types without requiring complete system redesign.
2Adaptability or versatility
If conventional systems are configured to interpret incoming user requests in a limited number of languages, then system complexity is reduced, but the system is incapable of upgrading backend technology to modify or auto-correct responses
Solution Approach 1:
The system implements dynamic response handling where the backend technology can be upgraded and modified without system redesign. The response generation module is designed to accept updated intent definitions, matching algorithms, and correction rules, allowing the system to adapt to new languages and improve accuracy over time through incremental backend enhancements.
Solution Approach 2:
The patent incorporates feedback mechanisms where system responses are monitored and evaluated, allowing for automatic correction and continuous improvement. The feedback loop enables the system to learn from interactions, identify errors, and automatically adjust response generation parameters, facilitating backend technology upgrades without disrupting overall system operation.
3Speed
If the system uses a first message handling scheme when intents match predefined intents, then response speed is improved, but the system may provide irrelevant responses when intents are unmatched
Solution Approach 1:
The system applies partial action by using the fast first message handling scheme only for confidently matched intents, while directing unmatched or uncertain intents to a more thorough second handling scheme. This selective application of processing depth ensures rapid responses for clear matches while maintaining reliability for ambiguous cases through additional verification steps.
Solution Approach 2:
The patent implements local quality by applying different handling schemes to different intents based on their match confidence. High-confidence matches receive fast processing with standard validation, while low-confidence or unmatched intents receive enhanced processing including additional validation layers and alternative response strategies, ensuring appropriate response quality for each specific case.
Data Source
AI summary
A system and method to interpret natural language requests and handle natural language responses in conversation is disclosed. The system includes an intent creation subsystem to receive one or more predefined intents to create one or more corresponding intent databases; a natural language message handling subsystem to receive a plurality of natural language messages from a user to identify one or more intents, to match one or more identified intents associated with the plurality of received natural language messages with the one or more predefined intents, handle the one or more identified intents by using a first message handling scheme when a similar match is found and a second message handling scheme in case of a dissimilar match; a natural language response handling subsystem to extract information from plurality of received natural language messages, to rectify the plurality of received natural language messages to handle a structured natural language response.