Predictive Travel Notifications Using User Profile History
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Solution Overview
Problem
Conventional GPS-enabled devices collect traffic data anonymously and do not effectively predict users' driving routes or provide personalized traffic notifications, leading to inefficiencies in navigation and missed opportunities for targeted advertising.
Innovation Solution
Collecting and associating user travel data with their accounts to predict intended routes and provide real-time traffic notifications and personalized recommendations based on historical patterns, using GPS, GLONASS, and other location-determining technologies to offer alternative routes and promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user travel data is collected and associated with user accounts to enable personalized predictions, then prediction accuracy and personalization improve, but data privacy concerns and system complexity increase
Solution Approach 1:
The patent segments user data into multiple components: travel patterns, location history, and preference data are separately collected and processed. This segmentation allows the system to manage complex data types independently while maintaining overall prediction accuracy, reducing the burden on any single data management component.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw data collection and final prediction output. This intermediary layer anonymizes and aggregates data where appropriate while preserving necessary personalization signals, thereby reducing privacy concerns and simplifying downstream processing without sacrificing prediction accuracy.
2Productivity
If real-time traffic notifications and personalized recommendations are provided, then user experience and navigation efficiency improve, but energy consumption and processing requirements increase
Solution Approach 1:
The patent implements periodic updates of traffic notifications and recommendations based on user behavior patterns and traffic condition thresholds, rather than continuous real-time updates. This periodic approach maintains navigation efficiency by providing timely information while significantly reducing the energy consumption associated with constant data processing and transmission.
Solution Approach 2:
The patent performs preliminary processing of travel data and prediction model training during low-usage periods when the device is not actively navigating. By pre-computing prediction models and caching recommended routes, the system reduces real-time processing requirements and energy consumption during actual navigation events while maintaining high productivity.
3Reliability
If anonymous traffic data collection is used, then user privacy is protected, but personalized predictions and targeted advertising opportunities are lost
Solution Approach 1:
The patent applies different data processing qualities to different data elements: highly sensitive personal information is anonymized or encrypted, while less sensitive travel pattern data is processed with greater detail for personalization purposes. This local differentiation allows the system to protect user privacy where critical while retaining sufficient information for personalized predictions and targeted advertising.
Solution Approach 2:
The patent dynamically adjusts the level of data anonymization based on the specific prediction task and user preferences. For certain predictions requiring high personalization, the system uses less anonymized data with appropriate consent, while for general traffic patterns, fully anonymized aggregated data is used. This parameter adjustment balances privacy protection with information retention for personalization.
Data Source
AI summary
Various embodiments can predict a user's intended driving route in order to provide the user with traffic warnings for traffic conditions along the same. A user's driving route, in at least one embodiment, is predicted by collecting travel data, such as information associated with the date, time, location, and direction for trips made within a network of roads over time. Instead of keeping the travel data anonymous, the travel data is associate or linked to the user's account or stored in a user profile in order to build a history of travel patterns for the user over time. The travel patterns can then be used to predict when a user is going to travel or make a trip and, upon identifying a context indicative of a travel pattern, traffic information for a route associated with the pattern is obtained and provided to the user's computing device.