Map Feature Confidence Assessment via Progressive Image Unveiling
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Solution Overview
Problem
Existing mapping and navigation applications lack the ability to assess individual users' varying perceptual abilities for map feature identification, leading to a poor user experience due to inappropriate information presentation.
Innovation Solution
A method and apparatus that determine a user's confidence level for map feature identification by progressively unveiling a map feature image and calculating the percentage visible at identification, allowing for personalized application settings based on this confidence level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If map features are fully visible from the start, then users can quickly identify features, but users with lower perceptual abilities may be overwhelmed by too much information
Solution Approach 1:
The patent segments map feature information by progressively revealing portions of map features based on user confidence levels. Instead of displaying all map features simultaneously, the system divides information presentation into stages, showing only relevant portions to each user based on their assessed perceptual ability, thereby preventing information overload while maintaining quick identification for capable users.
Solution Approach 2:
The patent applies local quality by customizing the visibility and detail of map features according to individual user characteristics. Each user receives a tailored view where map features are revealed to the appropriate degree based on their specific perceptual abilities, ensuring that information presentation matches local user needs rather than applying a uniform display to all users.
2Object-affected harmful factors
If map features are progressively revealed, then information overload is reduced, but feature identification time increases
Solution Approach 1:
The patent implements dynamics by making the information revelation process adaptive and configurable. The system dynamically adjusts the progression of map feature disclosure based on real-time user interactions and assessed confidence levels. Users can control the pace and extent of information revelation, allowing the system to optimize between reducing information overload and minimizing identification time based on individual user preferences and abilities.
Solution Approach 2:
The patent employs feedback mechanisms where user responses to progressively revealed map features are used to assess and update user confidence levels. This feedback loop allows the system to learn from user performance and adjust future information presentation accordingly, optimizing the balance between preventing information overload and maintaining efficient feature identification through continuous adaptation.
3Adaptability or versatility
If the application is personalized to each user, then user experience is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling users to actively participate in their own personalization process. Users can adjust their own confidence levels and control information revelation parameters directly, reducing the need for complex automated personalization systems. The system provides tools for users to self-manage their information presentation preferences, thereby achieving adaptability while limiting the complexity of automated personalization infrastructure.
Data Source
AI summary
An approach is provided for determining a confidence level for map feature identification in a map application. The approach involves, for instance, presenting an image of a map feature in a user interface of a device. The image is initially presented with a content of the image obscured from view. The approach also involves progressively un-obscuring the image in the user interface until an input identifying the map feature is received from a user via the user interface. The approach further involves determining a percentage of the image that is visible at a time the input is received from the user. The approach further involves personalizing an application to the user based on the feature identification confidence level.