Sensor Data Coordinate Prediction for Service Requests
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Solution Overview
Problem
Current networked computer systems face inaccuracies in predicting geographical locations for service requests, particularly in transportation services, due to incomplete or imprecise address information and GPS errors.
Innovation Solution
The system utilizes sensor data from mobile devices to generate predicted geographical locations by analyzing pick-up and drop-off paths from multiple requests, applying clustering algorithms to determine peak concentrations, and storing these locations in a database for accurate service provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If address information is used for location prediction, then the system can process service requests, but the accuracy of coordinate prediction deteriorates due to imprecise address data
Solution Approach 1:
The patent introduces sensor data from mobile devices as an intermediary to bridge the gap between address information and actual geographical locations. By collecting and analyzing sensor data (GPS, Wi-Fi, cellular tower data) from multiple service requests, the system creates a mapping between address-based location predictions and actual sensor-derived locations, thereby improving prediction accuracy while maintaining service request processing capability
Solution Approach 2:
The system uses feedback from actual service delivery locations to refine future predictions. By comparing the predicted location (from address data) with the actual location where the service was provided (derived from sensor data), the system learns and adjusts its prediction models, progressively improving accuracy without sacrificing processing efficiency
2Measurement precision
If GPS data from mobile devices is collected to improve location accuracy, then coordinate prediction accuracy improves, but device complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing sensor data from multiple service requests before it is needed for prediction. The system aggregates and cleans sensor data in advance, creating ready-to-use location mappings that can be quickly queried during service requests, thereby reducing real-time processing complexity while maintaining high accuracy
Solution Approach 2:
The system creates a simplified representation or copy of the complex sensor data relationships in the form of pre-computed location mappings and prediction models. This allows the system to query accurate predicted locations without reprocessing the full complexity of raw sensor data from multiple devices, effectively managing system complexity
Data Source
AI summary
Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.