Predictive Natural Language Request Completion via Sequence Models
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Solution Overview
Problem
Natural language data applications face inefficiencies in processing requests due to incomplete information, requiring users to provide additional context, leading to time-consuming and repetitive interactions, especially when interfacing with diverse data sources using different domain-specific languages.
Innovation Solution
A method is developed to generate and execute predicted next natural language requests using a machine learning algorithm based on a data dependency model and request prediction model, allowing for automatic recommendation of request completions and follow-on requests by analyzing historical data and intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a natural language data application waits for complete user input or performs interactive interrogation to determine user intent, then the accuracy of request processing is improved, but the user experience deteriorates due to time-consuming and repetitive interactions
Solution Approach 1:
The system performs preliminary actions by generating multiple predicted follow-on requests and presenting them to the user before the user actually needs to provide complete input. This allows the system to proactively anticipate user needs based on the partial request and context, reducing the iterative back-and-forth interaction time while maintaining accurate request processing through contextual understanding.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user responses to predicted requests and using this information to refine future predictions. The contextual information from previous requests and responses is fed back into the prediction model, improving the accuracy of subsequent request completions while reducing the time needed for clarification interactions.
2Manufacturing precision
If a natural language data application requires complete and contextually accurate requests from users, then the quality of data retrieval is improved, but the ease of operation deteriorates due to the need for users to provide detailed information
Solution Approach 1:
The system enables self-service by automatically generating predicted follow-on requests that complete the user's intent based on contextual analysis. Instead of requiring users to manually provide all necessary details, the system serves itself by inferring missing information from the conversation context and presenting completion options, thereby maintaining high data retrieval quality while significantly improving ease of operation.
Solution Approach 2:
The system acts as an intermediary between the user's partial request and the complete data retrieval operation. It mediates by analyzing the partial request, generating predicted completions, and presenting these to the user for selection or refinement. This intermediary role allows the system to bridge the gap between simple user input and high-quality data retrieval without requiring users to directly provide complete detailed requests.
3Productivity
If a natural language processing system processes partial requests immediately without waiting for completion, then the productivity is improved by reducing user waiting time, but the reliability deteriorates due to incomplete information
Solution Approach 1:
The system performs preliminary processing of partial requests by generating multiple predicted follow-on requests immediately upon receiving user input. This preliminary action allows the system to start preparing response options while the user is still thinking, improving productivity by reducing perceived waiting time. The reliability is maintained through contextual analysis that accurately interprets the partial request intent before full completion is provided by the user.
Solution Approach 2:
The system uses feedback from contextual analysis to continuously refine its interpretation of partial requests. By analyzing the conversation history, data source context, and semantic meaning of the partial input, the system provides feedback to its prediction model to improve accuracy. This feedback mechanism ensures reliable request interpretation even when processing begins before the user completes their input.
Data Source
AI summary
In various embodiments, a natural language (NL) application receives a partial NL request associated with a first context, and determining that the partial NL request corresponds to at least a portion of a first next NL request prediction included in one or more next NL request predictions generated based on a first natural language (NL) request, the first context associated with the first NL request, and a first sequence prediction model, where the first sequence prediction model is generated via a machine learning algorithm applied to a first data dependency model and a first request prediction model. In response to determining that the partial NL request corresponds to at least the portion of the first next NL request prediction, the NL application generates a complete NL request based on the first NL request and the partial NL request, and causes the complete NL request to be applied to a data storage system.