Mapping App Predictive Destination Notifications
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile devices require users to manually input requests to access information, such as routes, which is inefficient given the vast amount of data they can store or access over networks.
Innovation Solution
A mapping application that predicts future destinations and provides dynamic notifications with route information, allowing users to select options for navigation without explicit input, including turn-by-turn or non-prompting modes, and updates information in real-time using GPS and other location services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the mapping application provides automatic predicted destination notifications, then user convenience and navigation efficiency are improved, but device complexity and computational requirements increase
Solution Approach 1:
The mapping application performs preliminary actions by continuously analyzing travel data, location history, and calendar events to predict future destinations before the user explicitly requests them. The system proactively computes likely destinations and prepares route information in advance, presenting predicted destination notifications to users without requiring manual input requests.
Solution Approach 2:
The application serves itself by automatically monitoring device location, analyzing travel patterns, and generating predicted destination notifications without user intervention. The system self-manages the complexity of data collection, analysis, and route computation internally, freeing the user from needing to understand or configure these processes.
2Loss of time
If the mapping application continuously monitors and updates destination predictions, then information timeliness is improved, but energy consumption increases
Solution Approach 1:
The mapping application implements periodic action by updating predicted destination notifications at intervals based on device location changes, time elapsed since last update, and confidence levels in current predictions. Rather than continuously monitoring all parameters, the system periodically re-evaluates travel data and refreshes notifications when significant changes occur, balancing timeliness with energy efficiency.
Solution Approach 2:
The system uses feedback mechanisms where user interactions with predicted destination notifications (selection, dismissal, or modification) provide signals that adjust future prediction behavior. The application learns from user responses to refine its prediction algorithms, improving accuracy over time while potentially reducing unnecessary computations and energy consumption.
Data Source
AI summary
Some embodiments provide a mapping application that provides a variety of UI elements for allowing a user to specify a location (e.g., for viewing or serving as route destinations). In some embodiments, these location-input UI elements appear in succession on a sequence of pages, according to a hierarchy that has the UI elements that require less user interaction appear on earlier pages in the sequence than the UI elements that require more user interaction. In some embodiments, the location-input UI elements that successively appear in the mapping application include (1) selectable predicted-destination notifications, (2) a list of selectable predicted destinations, (3) a selectable voice-based search affordance, and (4) a keyboard. In some of these embodiments, these UI elements appear successively on the following sequence of pages: (1) a default page for presenting the predicted-destination notifications, (2) a destination page for presenting the list of predicted destinations, (3) a search page for receiving voice-based search requests, and (4) a keyboard page for receiving character input.