Distributed Sensor Network for Demographic Profile Accuracy
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Solution Overview
Problem
Conventional systems using stationary sensors are limited in their ability to determine characteristics of people in a given area, as they can only detect a limited sample of individuals within their range and accuracy, leading to skewed results due to limited sampling and active avoidance by some individuals, which does not accurately represent the overall mood or demographics of a location.
Innovation Solution
The implementation of distributed sensor-enabled electronic devices, both stationary and mobile, that collect and analyze demographic data to generate a comprehensive demographic profile for a context, defined by spatial and temporal components, allowing for flexible and accurate determination of characteristics across various contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stationary sensors are used to detect characteristics of people in a location, then the system can determine demographic information and mood for people within sensor range, but the sampling is limited and results are skewed because only a limited sample of people can be detected
Solution Approach 1:
The system segments the population into multiple groups based on detected characteristics (demographics, mood, health) and uses distributed sensors to sample different segments. This allows comprehensive coverage of diverse populations while maintaining adequate sample sizes for each segment, resolving the contradiction between measurement precision and sample quantity.
Solution Approach 2:
The patent introduces temporal dimension by continuously collecting data over time and spatial dimension by distributing sensors across multiple locations. This multi-dimensional approach enables the system to accumulate sufficient sample sizes while maintaining precision through statistical analysis across different dimensions.
2Reliability
If stationary sensors are deployed in specific locations, then the system can collect data at those locations, but the results do not accurately represent the overall population because some people actively avoid the sensors
Solution Approach 1:
The system merges data from multiple stationary sensors located in different environments (venues, public spaces, transportation) to create a comprehensive population profile. By combining datasets from diverse locations, the system achieves both reliability through cross-validation and adaptability to represent different population segments including those who might avoid any single sensor location.
Solution Approach 2:
The patent uses mobile devices as intermediaries that bridge the gap between stationary sensors and people who avoid them. Mobile devices carry sensor data collection capabilities to locations where people are present, ensuring that even individuals who avoid fixed sensor locations contribute to the demographic data, thereby improving representativeness and coverage.
3Measurement precision
If mobile devices with sensors are used to collect user information, then the system can gather data for individual users, but the system can only gather information for one user at a time on a single device
Solution Approach 1:
The system designs mobile devices with universal sensor capabilities that can serve multiple functions: collecting individual user data, aggregating population-level statistics, and operating in both standalone and networked modes. This multi-functionality allows the same device to contribute to both precise individual measurement and high-productivity population-wide data collection.
Solution Approach 2:
The patent merges individual mobile device data collection capabilities with networked aggregation to simultaneously serve multiple users. By combining data from numerous mobile devices through a networked system, the platform achieves high productivity by processing data from many users concurrently while maintaining the measurement precision of individual sensor readings.
Data Source
AI summary
Systems, methods, and devices for determining contexts and determining associated demographic profiles using information received from multiple demographic sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of time and location. Demographic sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The demographic sensor data can include demographic sensor readings that are implicit indications of a demographic for the context. The sensor data can also include user reported data with explicit descriptions of a demographic for the context. The demographic sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine a demographic profile for the context that can be output to one or more users or entities.