Predictive Ephemeral POI System for Dynamic Location Tracking
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Solution Overview
Problem
Conventional wireless location applications are limited to providing only static, pre-recorded locations for points of interest, unable to pinpoint movable objects at a specific time or predict their future locations, restricting their functionality in dynamic scenarios such as tracking rare wildlife or events.
Innovation Solution
A predictive ephemeral point-of-interest (PEPOI) system that uses a predictive model to generate likely future locations of objects based on historical data, allowing users to retrieve maps of past and predicted future locations of specific interest, incorporating a process to gather data, select appropriate models, and apply timestamps for location requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional fixed-location POI databases are used, then location information can be provided quickly and easily, but the system cannot track or predict locations of movable objects
Solution Approach 1:
The patent transforms the static POI database into a dynamic system that can handle movable objects. The predictive model continuously updates location predictions based on historical data and movement patterns, allowing the system to adapt to changing positions of objects like wildlife or vehicles while maintaining database structure.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing historical location data and movement patterns before they are needed for prediction. This allows the predictive model to generate future location estimates quickly without real-time computation overhead, resolving the complexity issue.
2Loss of information
If static pre-recorded locations are provided, then the system is simple to operate, but it cannot provide future location predictions or current positions of moving objects
Solution Approach 1:
The patent introduces a predictive model as an intermediary layer between the static database and the user query. This model processes historical location data and generates predicted positions, acting as a mediator that transforms static data into dynamic predictions without requiring complete system redesign.
Solution Approach 2:
The system creates simplified copies of complex movement patterns by storing historical location sequences and extracting movement characteristics. These copied patterns are then reused for predictions, reducing the complexity of real-time analysis while maintaining prediction accuracy.
3Measurement precision
If historical location data is collected and analyzed, then predictive accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing historical location data to extract movement patterns, speed, and directional information before prediction is needed. This preprocessing creates ready-to-use predictive features that accelerate real-time location estimation while maintaining high accuracy.
Solution Approach 2:
The system extracts only the essential predictive features from historical data, such as movement vectors, speed patterns, and temporal characteristics, rather than processing complete historical trajectories. This extraction reduces computational burden while preserving prediction accuracy.
Data Source
AI summary
Predicted ephemeral Points of Interest (PEPOI) are provided to wireless application users, as are likelihood maps. Wireless application users are provided with the ability to record locations and retrieve maps of past locations and predicted future locations of PEPOIs of specific interest. To predict a location for a PEPOI, data about previous reported locations are gathered, along with variable values associated with variations in location. The variables gathered may differ based on the type of PEPOI in question. For instance, a person has different variables associated with its locations than does a storm cloud. Once there is enough data recorded to provide predictable patterns, desirable methods of statistical analysis are chosen depending of the behavior model of PEPOI, which can be used to present a visual guide to finding the PEPOIs at a particular time in the future.