Neural Network GUI Customization via NLP Intent Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing graphical user interfaces (GUIs) do not effectively consider a user's intent when determining which icons or responses to display, leading to user frustration and inefficiency.
Innovation Solution
A system that uses a re-trained natural language processing (NLP) algorithm as part of a neural network to interpret user utterances and determine the user's intent, thereby customizing the GUI to display relevant icons or responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If every command is made accessible on a GUI without sub-menus, then ease of operation is improved, but device complexity increases and area requirements exceed available screen space
Solution Approach 1:
The GUI dynamically reconfigures its layout based on detected user intent. When a user's goal is identified through analysis of their actions and inputs, the system automatically reorganizes command visibility and positioning, bringing relevant commands to prominent locations while maintaining a compact overall structure that adapts to screen size constraints.
Solution Approach 2:
Different regions of the GUI are assigned different levels of accessibility based on user intent. Commands relevant to the detected user goal are placed in easily accessible areas with larger display prominence, while less relevant commands are positioned in secondary areas or maintained in condensed formats, creating a spatial hierarchy that optimizes both accessibility and space utilization.
2Ease of operation
If predictive algorithms use historical usage patterns to customize GUI, then ease of operation is improved, but adaptability to current user intent deteriorates
Solution Approach 1:
The system continuously monitors user interactions and feeds this real-time data back into the intent detection algorithm. By analyzing sequences of user actions, input patterns, and interaction timing, the system dynamically adjusts the GUI configuration to reflect the user's current goal, thereby maintaining both historical learning benefits and real-time adaptability.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns to anticipate upcoming actions. By detecting intent early in the interaction sequence, the GUI can proactively reconfigure itself before the user needs to access relevant commands, ensuring that anticipated commands are already in optimal positions rather than requiring reactive adjustments.
Data Source
AI summary
The system includes one or more processors and one or more non-transitory computer-readable storage devices storing instructions that, when executed, cause the one or more processors to perform receiving user utterances, and utilizing a trained natural language processing (NLP) algorithm as one or more layers in a neural network to generate from the one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network. The instructions, when executed, also can cause the one or more processors to perform using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network. The at least one first output layer of the neural network can be different than the at least one second output layer of the neural network. The instructions, when executed, also can cause the one or more processors to perform coordinating displaying a customized graphical user interface (GUI) using the combined output of the neural network. Other embodiments and variations are disclosed herein.