Connected Environment Interface Using LLMs for Natural Language Queries
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Solution Overview
Problem
Users with limited technical proficiency face challenges in communicating with enterprise applications due to the need for sophisticated queries and keyword matching, leading to inefficiencies and underutilization of platform capabilities.
Innovation Solution
A communication system that uses a front-end interface allowing users to input requests in natural language, augmented with requestor information, processed by a large language model to generate accurate and relevant responses, and allows for user interaction and modification of responses to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-matching algorithms are used to search for objects in the connected environment, then the system can retrieve relevant results, but the users must provide exact keywords and manually filter vast numbers of irrelevant results, making the process complex and cumbersome
Solution Approach 1:
The patent introduces a natural language processing intermediary between the user and the keyword-matching search system. Users provide intent descriptions in natural language, and the NLP system automatically generates optimized search queries, filters results, and presents relevant objects. This intermediary eliminates the need for users to manually filter vast numbers of results while maintaining search accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of keyword selection and result filtering with an automated natural language processing system. Instead of users manually selecting keywords and filtering results, the NLP system automatically interprets user intent, generates search queries, and filters results based on the described intent.
2Measurement precision
If sophisticated queries and programming knowledge are required to communicate with enterprise applications, then accurate information retrieval is possible, but users with limited technical proficiency face challenges and the platform capabilities remain underutilized
Solution Approach 1:
The patent replaces the complex mechanical system of SQL queries and programming knowledge with a natural language interface. Users can describe their information needs in everyday language, and the system automatically translates this into accurate database queries and retrieves the correct information, eliminating the barrier of technical proficiency requirements.
Solution Approach 2:
The patent creates a universal interface that works for all users regardless of their technical background. The natural language processing system adapts to different user proficiency levels, allowing both technical and non-technical users to access enterprise application capabilities through a common, intuitive interface.
3Measurement precision
If exact keywords are required for searching objects in the connected environment, then precise results can be obtained, but users struggle to identify the exact object needed and must further filter search results on multiple factors
Solution Approach 1:
The patent replaces the manual process of identifying exact keywords and iteratively filtering results with an automated natural language processing system. Users describe what they are looking for in natural language, and the system automatically generates search queries, retrieves results, and filters them based on the described intent, eliminating the time-consuming iterative filtering process.
Solution Approach 2:
The patent performs preliminary actions by automatically generating optimized search queries and pre-filtering results based on user intent before presenting them to the user. This preliminary processing eliminates the need for users to manually filter results on multiple factors, saving time while maintaining precision.
Data Source
AI summary
Techniques are disclosed to generate relevant responses for requests received in a connected environment. A user communicating with the connected environment may request, in natural language, an operation to be performed. The request may be augmented with the information related to the requesting user to generate an augmented query. The augmented query may be processed by a large language model to generate a response to the augmented query. The response is returned in natural language.