Natural Language Interaction Engine for Fluid User Input
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Solution Overview
Problem
Current virtual assistants are limited by their rigid scripting and high development costs, making it difficult for organizations of all sizes to deploy natural language interaction capabilities that can interpret fluid and freely expressed user inputs, including casual conversational elements.
Innovation Solution
A system comprising a dialog interface module, a natural language interaction engine, a solution data repository with domain and language models, and flow elements, which preprocesses user requests, interprets them using language recognition rules, and generates appropriate responses or takes actions based on determined user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If virtual assistants use rigid scripting to carry out limited activities, then development cost is reduced, but natural language interpretation capability deteriorates
Solution Approach 1:
The system segments natural language processing into distinct modules: dialog interface module for input processing, natural language interaction engine for interpretation, and solution data repository for knowledge storage. This segmentation allows each module to be developed and maintained independently, reducing overall development cost while maintaining advanced NLP capabilities.
Solution Approach 2:
The patent introduces a dialog interface module as an intermediary between the user and the natural language interaction engine. This intermediary handles preprocessing and formatting of user inputs, shielding the core NLP engine from complexity and reducing the development burden while enabling sophisticated language interpretation.
2Ease of operation
If virtual assistants are equipped with advanced natural language interpretation capability, then user experience is improved, but development cost increases
Solution Approach 1:
The natural language interaction engine is designed as a universal platform that can handle multiple languages, domains, and interaction types through a single system. The solution data repository stores domain models and language models that can be reused across different applications, reducing development cost while providing excellent user experience.
Solution Approach 2:
The system allows dynamic adjustment of processing parameters such as interpretation depth, response generation complexity, and domain specificity. This enables the virtual assistant to adapt its resource consumption based on user needs, providing high-quality user experience when required while reducing development and operational costs during standard operations.
3Ease of operation
If virtual assistants interpret fluid and freely expressed natural language, then communication naturalness is improved, but system complexity increases
Solution Approach 1:
The system divides complex natural language processing into manageable segments: the dialog interface module handles input normalization, the natural language interaction engine performs semantic interpretation, and the solution data repository manages knowledge retrieval. This segmentation reduces system complexity while enabling fluid natural language communication.
Solution Approach 2:
The natural language interaction engine incorporates self-service mechanisms including automatic disambiguation, context-aware interpretation, and adaptive learning from user interactions. This reduces the need for manual configuration and simplifies system management while maintaining high communication naturalness.
Data Source
AI summary
A system for delivering advanced natural language interaction applications, comprising a dialog interface module, a natural language interaction engine, a solution data repository component operating comprising at least one domain model, at least one language model, and a plurality of flow elements and rules for managing interactions with users, and an interface software module. Upon receipt of a request from a user via a network, the dialog interface module preprocesses the request and transmits it to the natural language interaction engine. The natural language interaction engine interprets the request using a plurality of language recognition rules stored in the solution data repository, and based at least determined semantic meaning or user intent, the natural language interaction engine forms an appropriate response and delivers the response to the user via the dialog module, or takes an appropriate action based on the request.