Natural Language Request Translation via Adaptive Templates
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Solution Overview
Problem
Natural language data applications face inefficiencies in processing user requests due to ambiguity, requiring time-consuming and repetitive interrogation processes to determine user intent when interfacing with diverse data sources having different domain-specific languages.
Innovation Solution
A method that performs intent inference on natural language requests, selects appropriate templates, generates inquiries, and applies domain-specific language requests to data storage systems, reducing user effort and increasing accuracy through adaptable templates and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language data applications use traditional interrogation processes to determine user intent, then they can handle ambiguous requests, but the process becomes time-consuming and repetitive
Solution Approach 1:
The system performs preliminary actions by maintaining user profiles that store inferred preferences, contextual information, and historical data from previous interactions. When a new natural language request arrives, the system retrieves and applies this pre-computed information to quickly determine user intent without requiring time-consuming interrogation processes, thus resolving the contradiction between accurate intent determination and processing time
Solution Approach 2:
The system implements feedback mechanisms by continuously learning from user responses to inquiries and automatically updating user profiles with inferred preferences and contextual information. This feedback loop enables the system to improve its intent determination accuracy over time while reducing the need for repetitive clarification questions, thereby addressing both reliability and time efficiency
2Measurement precision
If the system requests disambiguating information from the user for each request, then intent accuracy improves, but user effort and interaction complexity increase
Solution Approach 1:
The system practices self-service by automatically inferring user preferences, contextual information, and intent from the natural language request itself and from historical data stored in user profiles. Instead of requiring users to manually provide disambiguating information, the system autonomously retrieves relevant information from stored profiles and uses it to interpret user intent accurately, thereby improving precision while maintaining ease of operation
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing user preferences, contextual information, and domain-specific knowledge in user profiles before requests are made. When a request arrives, this pre-computed information is immediately applied to interpret intent without requiring additional user input, thus achieving both high precision and ease of operation
3Productivity
If the system stores and utilizes user profiles with inferred preferences, then processing speed improves, but system complexity increases
Solution Approach 1:
The user profile data structure is designed to be universal and multi-functional, serving multiple purposes: storing user preferences, contextual information, historical interaction data, and domain-specific knowledge. This single versatile data structure eliminates the need for multiple separate data management systems, thereby improving processing speed while managing complexity through consolidation rather than proliferation of separate components
Data Source
AI summary
In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.