Natural Language Application Control Through Feedback-Based Action Selection
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Solution Overview
Problem
Natural language based control of computer applications is often rigid, requiring significant computational overhead, preventing the automatic determination of new control functions and leading to erroneous actions that do not align with user intent.
Innovation Solution
A system that generates a request embedding from natural language input, incorporating domain-specific knowledge and current application state, to determine and execute a sequence of actions using machine learning models, allowing for robust and flexible control of computer applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language control functions are fully integrated into the computer application, then the control functionality is reliable and precise, but the computational overhead and labor overhead during application development increase significantly
Solution Approach 1:
The patent introduces an intermediary natural language processing system that sits between the user and the application control functions. This intermediary translates natural language inputs into actionable commands without requiring the application itself to be modified, thus maintaining reliability while reducing development complexity. The system uses language models and embedding techniques to bridge the gap between user intent and application actions.
Solution Approach 2:
The patent segments the natural language control functionality into separate, modular components including embedding generation, language model processing, and action execution. This segmentation allows the control system to be developed and maintained independently from the core application, reducing the burden on application developers while maintaining reliable control functionality through specialized processing modules.
2Measurement precision
If the computer application fully integrates control functions, then precise control is achieved, but new control functions cannot be automatically determined based on user input
Solution Approach 1:
The patent implements a dynamic control system where the available control functions are not fixed but can be automatically determined and adapted based on user input patterns. The system uses machine learning models to learn from user interactions and dynamically expand its repertoire of control functions, allowing precise control while maintaining adaptability to new user needs without requiring application reintegration.
Solution Approach 2:
The natural language control system performs self-service by automatically determining and defining new control functions based on user input, without requiring manual integration by application developers. The system uses autonomous learning and processing to expand its functionality, maintaining precision through learned patterns while gaining adaptability to emerging user requirements.
3Ease of manufacture
If rigid natural language control is implemented, then development overhead is reduced, but the system can only support preconfigured inputs and fails for complex or unanticipated user inputs
Solution Approach 1:
The patent changes the fundamental parameters of natural language processing by using embedding-based representations and flexible language models instead of rigid keyword matching. This allows the system to maintain ease of implementation through standardized processing pipelines while achieving high adaptability by interpreting the semantic meaning of diverse user inputs rather than requiring exact preconfigured matches.
Solution Approach 2:
The patent implements a universal natural language processing framework that can handle multiple types of user inputs (simple commands, complex multi-step requests, semantic variants) through a single flexible system. This universal approach maintains ease of implementation by using consistent processing methods while achieving versatility in handling unanticipated and diverse user inputs across different application contexts.
4Ease of operation
If voice based control sends message on semantic variants, then user convenience is improved, but erroneous actions may be taken that do not align with user intent
Solution Approach 1:
The patent incorporates feedback mechanisms where the system generates candidate actions from natural language inputs, presents them to the user for confirmation, and uses the user's response to refine future interpretations. This feedback loop maintains user convenience by accepting semantic variants while ensuring reliability by verifying that the interpreted actions align with user intent before execution.
Solution Approach 2:
The patent applies preliminary anti-action by generating multiple candidate interpretations of user input and selecting the most appropriate one before execution, rather than directly acting on the first interpretation. This preliminary selection process prevents erroneous actions by anticipating potential misinterpretations and correcting them before they affect the application state, thus maintaining both convenience and accuracy.
Data Source
AI summary
This specification is generally directed to techniques for robust natural language (NL) based control of computer applications. In many implementations, the NL control is at least selectively interactive in that the user feedback input is solicited, and received, in resolving action(s), resolving action set(s), generating domain specific knowledge, and/or in providing feedback on implemented action set(s). The user feedback input can be utilized in further training of machine learning model(s) utilized in the NL based control of the computer applications.