Dynamic User Interface Input Classification Adaptation
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Solution Overview
Problem
Existing user interface systems fail to accurately predict and adapt to different user input preferences, leading to inefficiencies and errors, as they rely on proximity detection alone without considering individual user preferences for voice, touch, remote control, and recognition inputs.
Innovation Solution
A dynamic user interface system that classifies inputs based on type and adapts the user interface format accordingly, using input classifications to determine the most suitable format for subsequent inputs, incorporating user account settings and historical data for improved prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proximity-based prediction is used to determine user interface format, then the system can automatically adapt to user distance, but the prediction accuracy is insufficient because it does not consider individual user input preferences
Solution Approach 1:
The system implements feedback by storing historical input data and user preferences, then using this feedback to continuously improve prediction accuracy. The machine learning model learns from past interactions to predict future input types more accurately, resolving the contradiction between automatic adaptation and prediction precision.
Solution Approach 2:
The system performs preliminary action by pre-collecting and analyzing user preference data and historical input patterns before making predictions. This preparatory data collection and model training enables more accurate predictions when proximity-based adaptation is needed, balancing automation with precision.
2Ease of operation
If a single user interface format is used for all input types, then the system is simple to implement, but it reduces usability and efficiency for different input methods
Solution Approach 1:
The system applies dynamics by making the user interface format changeable and adaptive based on predicted input types. Instead of a static interface, the system dynamically adjusts UI characteristics such as button sizes, spacing, and layout according to the predicted input method, improving usability without requiring multiple fixed interface systems.
Solution Approach 2:
The system uses parameter changes by modifying specific UI parameters (button size, spacing, layout configuration) based on the predicted input type. This allows the interface to adapt its characteristics dynamically while maintaining the same underlying system architecture, balancing usability improvement with controlled complexity.
3Measurement precision
If the system collects and processes user preference data and historical inputs, then prediction accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The system implements self-service by using machine learning models that automatically learn from collected data without requiring manual configuration or complex processing rules. The model self-adjusts its parameters and predictions based on the data it collects, reducing the need for complex external data processing systems while maintaining high prediction accuracy.
Data Source
AI summary
In some embodiments, a method receives an input for a user interface and determines whether an input classification that classifies the input in one of a plurality of input classifications is included with the input. When the input classification is not included with the input, performing: determining an input classification for the input and sending the determined input classification and the input to a server system. When the input classification is included with the input, sending the input and the input classification that is included with the input to the server system. Then, the method receives a user interface format that is based on the input classification from the server system and causes output of content in the user interface format on the user interface.