Semi-Transparent Data Masking for Focused Viewing
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Solution Overview
Problem
Current data presentation methods overwhelm users with large amounts of data, making it difficult to identify important or changed data, and highlighting techniques often increase complexity and cognitive load.
Innovation Solution
A system and method that uses a semi-transparent layer to mask unselected data, reducing complexity and focusing attention on the selected subset, which may have changed or require attention, by suppressing highlighting and emphasis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If data values or objects are highlighted using different colors or fonts, then important data becomes more visible, but the complexity and cognitive load on the user increases
Solution Approach 1:
The patent extracts the distracting visual characteristics (colors, fonts, highlighting) from the majority of data items and applies them selectively only to the subset of interest. This removes the complexity burden from most elements while preserving the ability to highlight important data when needed.
Solution Approach 2:
The patent applies different visual properties to different parts of the data presentation: the subset of interest receives enhanced visual treatment (different colors, fonts, or other distinguishing characteristics) while the remaining data maintains a uniform, simplified appearance. This local differentiation improves visibility of important data without globally increasing complexity.
2Difficulty of detecting and measuring
If multiple distinguishing characteristics are applied to data items, then important data can be identified, but the clarity of data presentation decreases
Solution Approach 1:
The patent extracts multiple distinguishing characteristics from the overall data set and concentrates them exclusively on the subset of interest. This extraction prevents the scattering of visual cues across all data items, thereby maintaining clarity in the presentation of the majority of data while still providing rich visual identification for the important subset.
Solution Approach 2:
The patent applies multiple distinguishing characteristics (colors, fonts, icons, or other visual properties) locally to the subset of interest rather than distributing them across all data items. This localized application maintains overall presentation clarity while enabling effective identification of important data through concentrated visual cues.
3Loss of information
If all data is displayed with equal emphasis, then complete information is available, but user focus on important data is lost
Solution Approach 1:
The patent segments the data presentation into two distinct groups: the subset of interest and the remaining data. The subset of interest is visually differentiated to capture user focus, while the remaining data maintains its informational content but with reduced visual emphasis. This segmentation allows complete information to remain available while directing user attention to important data.
Solution Approach 2:
The patent applies enhanced visual properties locally to the subset of interest (such as different colors, fonts, or visual effects) while the remaining data maintains a neutral, uniform appearance. This local quality differentiation ensures that complete information remains accessible to users while naturally directing their focus to the important subset without overwhelming them with uniform visual complexity across all elements.
Data Source
AI summary
A system, method and graphical user interface for focusing a view of displayed data upon a subset of the data. In a view of multiple values, fields, objects or other data, a subset of the data is selected because it has changed, because it is associated with a recommended action, because it warrants a user's attention, or for some other reason. The remaining data is then masked or covered with a semi-transparent layer that suppresses the data and obscures any highlighting, emphasis or other complexity among the covered data. The user's attention is thus focused upon the selected subset of data without increasing the cognitive load forced on the user.