Tempo-Spatial Data Extraction from Network Devices
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Solution Overview
Problem
Current methods for determining the number of idle network-connected devices in cellular networks are inadequate, as they lack a method to accurately identify and record idle devices by provider affiliation, leading to inaccurate spatial and temporal data.
Innovation Solution
A computer-implemented data processing system that collects and aggregates data from network-connected devices to estimate the number of users in specific locations, using a unique ID system, association module, processing unit, and estimation module to calculate dynamic ratios and create anonymous aggregated profiles associated with geographical locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistic methods or large scale marketing research are used, then data collection can be performed, but the process becomes human labor intensive, expensive, extensive and time consuming
Solution Approach 1:
The patent replaces traditional mechanical data collection methods (human labor, physical surveys) with an automated electronic system that extracts tempo-spatial data directly from network-connected devices. The system automatically identifies devices, extracts their location and time information, and processes this data through computerized algorithms, eliminating manual labor and significantly reducing time consumption while maintaining or improving data accuracy.
Solution Approach 2:
The system creates copies of existing data that are already being collected by network operators for other purposes (telecom billing, network management). By extracting and reusing this existing data for demographic and spatial analysis, the system avoids the need for separate data collection efforts, thereby reducing time and resource expenditure while achieving accurate spatial-temporal measurements.
2Measurement precision
If traditional statistic methods are used, then data can be collected, but the sample size is relatively small and data is not up-to-date or available for small granularity of time-space units
Solution Approach 1:
The patent segments the population into individual network-connected devices, allowing analysis at the level of single devices rather than aggregated groups. This segmentation enables the system to achieve fine granularity in both space (specific cell sectors) and time (specific time stamps), while simultaneously accessing a very large sample size since nearly every mobile device in the network can be included in the analysis.
Solution Approach 2:
The system adds temporal dimensionality to spatial data by extracting both location and time information from network device records. This creates true tempo-spatial data that captures not just where devices are located but also when they were located there, enabling analysis at fine granularities of both space and time that were not achievable with traditional methods.
3Measurement precision
If network data is extracted and processed, then accurate tempo-spatial data can be obtained, but the system complexity increases
Solution Approach 1:
The patent leverages the existing multi-functional nature of cellular network infrastructure. The same network elements (base stations, location registers, billing systems) that handle voice and data communication are also used to collect and process location information for demographic analysis. This universal use of existing infrastructure avoids the need for separate dedicated measurement systems, thereby reducing overall system complexity while achieving accurate user population estimation.
4Measurement precision
If detailed device tracking is performed, then accurate location data is obtained, but network load increases
Solution Approach 1:
The system extracts location data from records that are already being created and stored by the network for other purposes (billing, authentication, network management). By performing the extraction from pre-existing data records rather than generating new measurement signals, the system obtains accurate location information without adding significant network load or energy consumption.
Solution Approach 2:
The patent uses existing network infrastructure elements (base stations, location registers, network elements) as intermediaries to provide location information. These intermediaries already have the capability to identify and track devices as part of their normal operation, so they can supply location data without requiring additional measurement infrastructure or generating extra network traffic.
Data Source
AI summary
A computer implemented data processing system for estimating an amount of people situated in a specific location and their geo-demographic classification within time range is provided herein. The system is combined of a collector that is configured to collect data on signals and each signal is given a unique ID; an association module configured to associate each signal with a respective location, namely, place of origin; a processing unit configured to calculate total number of users subscribed to a specific network service provider situated in a specific location and time range; calculate a dynamic ratio by research and statistical data; and an estimation module configured to estimate the amount of people originated from a specific location and the overall amount of people in a location within a time range, by applying the calculated dynamic ratio, that was calculated to each time stamp separately.