Neural Network Task UI Layout for Semantic Property Grouping
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Solution Overview
Problem
The conventional method of creating task user interfaces (UIs) for workflows is time-consuming and inefficient, as developers manually layout UIs, ignoring the semantic relationships between properties, which affects readability and usability.
Innovation Solution
A system that uses a neural network trained on semantic groups to automatically calculate task UI layouts, preserving the semantical relations of context properties, and provides an initial framework for developers to modify or render the UI automatically at runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If developers manually layout UIs, then customization and control are improved, but development time and efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically generating UI layouts before developers need to customize them. The neural network model pre-calculates property groupings and layout structures based on semantic relationships, providing a ready-to-modify framework that eliminates time-consuming manual layout work while maintaining full customization capability.
Solution Approach 2:
The invention introduces an intermediary neural network-based automatic layout generation system between the data model and the final UI. This intermediary automatically translates property semantics into layout structures, serving as a bridge that reduces manual intervention while preserving developer control through subsequent customization options.
2Device complexity
If developers manually layout UIs, then layout control is improved, but development time and costs worsen
Solution Approach 1:
The system enables self-service by allowing the automatic layout generation mechanism to handle the initial UI creation without developer intervention. The neural network model independently analyzes property semantics and generates appropriate layouts, freeing developers from routine layout tasks while maintaining the ability to override or customize results when needed.
Solution Approach 2:
The system performs preliminary layout generation based on semantic analysis before developers begin customization work. This preliminary action establishes a semantically-aware layout framework that respects property relationships, reducing the time and effort required for subsequent manual adjustments while maintaining layout control.
3Adaptability or versatility
If semantic relationships are ignored in manual layout, then layout flexibility is improved, but readability and usability deteriorate
Solution Approach 1:
The system applies local quality by enhancing specific areas of the UI layout where semantic relationships exist. The neural network model identifies and groups properties with semantic connections, applying specialized layout treatments to these local regions while maintaining overall layout flexibility. This ensures that semantically related properties are visually grouped, improving readability without constraining global layout adaptability.
Solution Approach 2:
The invention changes the parameter of layout organization from arbitrary manual placement to semantically-based grouping. The neural network model transforms layout parameters by analyzing property semantics and automatically adjusting groupings, positions, and hierarchies to reflect semantic relationships, thereby improving readability and usability while maintaining flexibility through configurable parameters.
4Productivity
If automatic layout generation is implemented, then development efficiency is improved, but customization capability may worsen
Solution Approach 1:
The system implements dynamics by making the layout generation process adaptive and modifiable. The automatic layout generation is not rigid but provides a dynamic framework that developers can adjust, override, or refine based on specific customization needs. The neural network model generates initial layouts that can be dynamically modified, preserving full customization capability while maintaining high development efficiency.
Solution Approach 2:
The system performs preliminary layout generation to establish a semantically-aware framework, but this preliminary action serves as a starting point rather than a final constraint. Developers retain the ability to modify and customize the generated layouts, with the preliminary generation merely eliminating the most time-consuming manual work while preserving full customization freedom.
Data Source
AI summary
A method and system including at least one data set including one or more properties in a task; a task UI module; a semantic grouping module including a neural network and a property cluster module; a display; and a processor in communication with the task UI module and the semantic grouping module and operative to execute processor-executable process steps to cause the system to: receive the data set at the semantic grouping module; calculate a property vector for each property in the data set, wherein the property vector includes a location of the property vector in a vector space; determine one or more property clusters, via the property cluster module, for all of the property vectors; and automatically generate a section in a user interface for each of the one or more property clusters via the task user interface module. Numerous other aspects are provided.