Crowdsourced Sound Origin Prediction via Temporompatial Pattern Analysis
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Solution Overview
Problem
Current methods fail to accurately determine the physical origin of sounds and predict events based on sound patterns, limiting their effectiveness in controlling hardware devices in response to predicted occurrences.
Innovation Solution
A system and method that utilize crowdsourced sound capture from multiple mobile devices to analyze sound patterns, determine the location and temporospatial data of sound sources, and predict recurrence, modifying hardware systems accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to determine sound origins, then the process is simple, but the accuracy of determining physical origin is insufficient
Solution Approach 1:
The system divides the sound capture task into multiple segments by deploying numerous mobile devices across different locations. Each device captures sound from its local position, and the combined data from all segments enables accurate triangulation of sound origins without requiring a single complex centralized system
Solution Approach 2:
The patent merges data from multiple independent mobile devices to achieve accurate sound origin determination. By combining location data, timestamp data, and sound capture data from numerous distributed devices, the system achieves high measurement precision through data aggregation and correlation
2Reliability
If sound patterns are analyzed in real-time, then event prediction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously capturing and storing sound data, location data, and timestamp data from mobile devices before events occur. This pre-captured data is then analyzed to predict future events, allowing the system to prepare predictions in advance without requiring real-time processing when events are actually happening
Solution Approach 2:
The patent creates copies of sound capture capabilities across multiple mobile devices rather than relying on a single processing system. Each device independently captures and records sound patterns, creating redundant data copies that can be analyzed collectively to improve prediction reliability without overloading a single processing unit
Data Source
AI summary
One or more processors identify multiple mobile devices for crowdsourcing a sound capturing activity. The processor(s) receive sounds captured by the multiple mobile devices, and analyze the captured sounds to determine a location of a source of the sounds. The processor(s) identify a date and time that the source produced the sounds, and then identify a temporospatial sound pattern for the sounds based on the location of the source of the sounds and the date and time that the source produced the sounds. The processor(s) plot the temporospatial sound pattern on a digital map that depicts the location of the source of the sounds while the source produced the sounds. The processor(s) predict a recurrence of the sounds generated by the source based on the temporospatial sound pattern plotted on the map, and then modify a hardware system based on the predicted recurrence of the sounds generated by the source.