Spatiotemporal Data Processing via Signal Classification
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Solution Overview
Problem
Current systems for determining real-time wait times and crowd anticipation in public spaces are inefficient and prone to inaccuracy, requiring manual observation and failing to account for various influencing factors.
Innovation Solution
A method involving user devices that transmit unique identifiers, detected by signal processing devices and computing systems, to determine spatiotemporal measures such as wait time and dwell time, using signal classification and timestamp analysis to generate accurate and automated real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and tracking methods are used to determine wait times, then human judgment can account for various influencing factors, but the process requires significant employee time and effort, reducing productivity
Solution Approach 1:
The system enables self-service measurement by having users automatically transmit their own location data via mobile devices. Users carry smartphones that continuously send signals, allowing the system to automatically track and calculate wait times without requiring employee observation or manual tracking, thus eliminating the trade-off between measurement accuracy and employee productivity
Solution Approach 2:
The patent replaces the mechanical human observation and manual tracking system with an automated electronic signal processing system. Mobile devices transmit electronic signals that are automatically received, processed, and analyzed by computing systems to determine wait times, eliminating the need for employee involvement in data collection while maintaining or improving measurement precision
2Productivity
If automated signal processing from user devices is implemented, then real-time data collection is achieved without manual effort, but system complexity increases due to signal processing requirements
Solution Approach 1:
The system uses universal mobile devices (smartphones) that users already possess and carry throughout the facility. These multi-functional devices serve both as communication tools and as location tracking sensors, eliminating the need for specialized tracking hardware and reducing overall system complexity while maintaining high data collection efficiency
Solution Approach 2:
The patent introduces a computing system as an intermediary that handles the complex signal processing, classification, and analysis tasks. This centralizes the complexity in a dedicated processing system rather than distributing it across multiple components, simplifying the overall architecture while enabling real-time automated data collection from numerous user devices
3Measurement precision
If continuous signal reception from multiple user devices is processed, then accurate real-time spatiotemporal measures are obtained, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification of signals into different types (begin signals, end signals, intermediate signals) as they are received. This pre-processing and categorization of incoming signals enables more efficient subsequent analysis and reduces the computational time required for generating accurate spatiotemporal measures, as data is organized and ready for specific processing tasks before analysis begins
Data Source
AI summary
Systems and methods for detecting and processing spatiotemporal data are disclosed. Signals are received from user devices, where each of the signals includes an identifier of a respective user device that transmitted the signal. A first subset of the signals is determined, where each signal in the first subset was received from a first user device. A first signal in the first subset is identified to classify as a begin signal based on a configuration associated with a first physical area, and a second signal in the first subset is identified to classify as an end signal based on the configuration associated with the first physical area. An amount of time that elapsed between receiving the begin signal and receiving the end signal is determined, and an estimated spatiotemporal measure is generated based at least in part on the determined first amount of time.