4D Trajectory Prediction System for Scheduling
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Solution Overview
Problem
Conventional location and scheduling systems are limited by their three-dimensional nature, failing to incorporate a temporal component, which makes them reactive rather than predictive, and thus unable to accurately determine a user's availability or location over time.
Innovation Solution
A system and method that generate a user's four-dimensional (4D) location by aggregating data from multiple sources, including calendars, GPS, transportation history, and social media, to create a trajectory that combines location and time, providing a reliability component to indicate the accuracy of the user's calculated 4D position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D location systems are used, then the system structure is simple, but the system cannot predict user location and availability accurately
Solution Approach 1:
The patent transitions from three-dimensional location tracking to four-dimensional trajectory prediction by adding a temporal dimension. The system combines spatial location data with time information to create 4D trajectories, enabling predictive capabilities that determine not just where a user is, but when they will be at specific locations, thereby resolving the contradiction between simple 3D structure and accurate prediction capability.
Solution Approach 2:
The system merges multiple data sources including calendar information, GPS location data, transportation history, and social media check-ins into a unified 4D trajectory model. This integration of diverse data types allows the system to accurately predict user location and availability by combining temporal and spatial information that would be insufficient when used separately.
2Productivity
If 3D location tracking is used, then the system is reactive, but it cannot provide predictive capabilities for scheduling
Solution Approach 1:
The system performs preliminary actions by continuously aggregating and processing data from multiple sources to build predictive 4D trajectory models in advance. By pre-calculating probable locations and availability times based on historical patterns and scheduled events, the system enables proactive scheduling decisions without requiring real-time reactive responses, thus improving productivity while managing time efficiently.
3Reliability
If multiple data sources are integrated, then the accuracy of 4D trajectory prediction improves, but the data processing complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct modules: collecting data from multiple sources (calendars, GPS, transportation records, social media), processing and normalizing the data, calculating 4D trajectories, and determining reliability metrics. This segmentation allows each component to handle specific data types and processing requirements independently, making the overall system more manageable while maintaining high reliability through comprehensive data integration.
Data Source
AI summary
A system and method for maintaining a user's four dimensional (4D) trajectory is disclosed. The system can comprise a plurality of databases related to the user's personal preferences, historical actions, and travel data, among other things. The system can receive requests for appointments, or trajectory information requirements (TIRs), via a user interface, by e-mail, text message, or by other communication means. The TIR can include a date, location, time, and duration. The system can compare the TIR to the locations and timespans of adjacent calendar events to determine if the TIR conflicts with adjacent event. If the TIR does not conflict, the system can add the TIR to the user's overall 4D trajectory. If, on the other hand, the TIR does conflict, the system can delete the TIR and/or send a cancellation message via the user interface.