Waypoint Identification via Transaction Data Patterns
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Solution Overview
Problem
Identifying relevant waypoints for route navigation is technically challenging, especially for users who do not consistently use navigation systems, as it requires historical route information that may not be available for routine or home-based trips.
Innovation Solution
A system using machine learning to identify patterns of recurring events from user transaction data, predicting upcoming events, and recommending physical locations as waypoints along a route, thereby conserving computing resources and reducing wear and tear on vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical route information is used to identify waypoints, then waypoint identification accuracy is improved, but data availability deteriorates for users who do not consistently use navigation systems
Solution Approach 1:
The patent introduces transaction data from external sources (e.g., purchase records, service appointments) as an intermediary data source. This mediator provides location and timing information about user activities without requiring consistent navigation system usage, thereby resolving the contradiction between needing accurate historical route data and the unavailability of such data for intermittent users.
Solution Approach 2:
The system performs preliminary analysis of transaction data to identify patterns of user activities and predict future locations before navigation is needed. By pre-processing external transaction data to extract location patterns, the system prepares waypoint information in advance, eliminating the need for accumulated navigation history while maintaining accurate waypoint identification.
2Adaptability or versatility
If machine learning models analyze transaction data to predict user locations, then waypoint relevance is improved, but computing resource consumption increases
Solution Approach 1:
The system applies partial action by using simplified machine learning models that analyze only the most relevant features of transaction data (location, time, frequency) rather than processing complete transaction records. This partial analysis achieves sufficient waypoint prediction accuracy while significantly reducing computing resource consumption compared to comprehensive data analysis.
Solution Approach 2:
The patent extracts only the essential elements from transaction data (geographic location, timestamp, transaction type) needed for waypoint prediction, discarding unnecessary transaction details. This extraction process reduces the data volume requiring machine learning processing while maintaining the ability to identify user location patterns and generate relevant waypoint recommendations.
3Productivity
If predicted events are integrated into navigation routes as waypoints, then route efficiency is improved by conserving energy and reducing wear, but route flexibility deteriorates
Solution Approach 1:
The system dynamically adjusts the integration of predicted event locations into navigation routes based on user needs and route conditions. Rather than rigidly forcing waypoints at all predicted locations, the system adaptively selects which predicted events to incorporate as waypoints, balancing route efficiency gains with necessary flexibility for user-defined destinations and preferences.
Data Source
AI summary
In some implementations, a system may identify, using a machine learning model, a pattern of events based on account information associated with a user. The system may generate a prediction of a time period during which a predicted event, associated with the pattern of events, is predicted to occur. The system may receive an indication that a vehicle, associated with the user, is to travel a route from a starting location to an ending location during the time period. The system may identify a physical location associated with the predicted event based on a location associated with the vehicle or based on the route. The system may generate a new route that includes the starting location, the physical location as a waypoint along the new route, and the ending location. The system may transmit information that identifies the new route to a device associated with the vehicle.