Simulated Animal Data Generation With Adjustable Subject Parameters
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Solution Overview
Problem
The challenge of obtaining customized and cost-effective animal data sets with specific characteristics for targeted subjects and applications such as healthcare, insurance, wellness monitoring, and gaming is hindered by the time-consuming and costly nature of data collection, with existing systems failing to provide tailored data sets.
Innovation Solution
A method and system for generating simulated animal data from real data using sensors, which involves receiving real animal data, modifying parameters, and generating simulated data through techniques like neural networks and generative adversarial networks to create tailored data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real animal data is collected through sensors for targeted subjects and applications, then data accuracy and relevance are improved, but data collection becomes time-consuming and costly
Solution Approach 1:
The patent creates simulated animal data that copies the statistical properties and patterns of real animal data without requiring actual sensor collection. The system generates synthetic datasets that replicate the characteristics of real data, thereby avoiding time-consuming field collection while maintaining data accuracy for simulations and modeling purposes.
Solution Approach 2:
The system performs preliminary data generation by creating simulated datasets in advance that can be used for multiple simulations and modeling applications. This preliminary action eliminates the need for repeated real-time data collection, saving time while maintaining data relevance for various targeted applications.
2Adaptability or versatility
If real animal data is collected with specific characteristics for targeted subjects, then data customization is improved, but data collection cost increases
Solution Approach 1:
The system allows customization of simulated animal data by adjusting parameters such as species type, environmental conditions, sensor configurations, and activity levels. By changing these parameters, the system can generate data tailored to specific applications (healthcare, insurance, wellness monitoring, gaming) without incurring additional collection costs, as the same generation engine adapts to different requirements.
Solution Approach 2:
The simulated data generation system serves multiple functions across different industries and applications. A single system can generate data suitable for healthcare research, insurance risk assessment, wellness monitoring, and gaming simulations simultaneously, providing versatile customization without proportionally increasing costs.
3Reliability
If more real animal data is collected to improve data set quality, then simulation model accuracy is improved, but data acquisition complexity increases
Solution Approach 1:
Instead of collecting more real data through complex sensor deployments, the system copies the essential statistical properties and patterns from existing real data to generate additional simulated datasets. This approach maintains simulation model accuracy by preserving data characteristics while avoiding the complexity of expanded data acquisition infrastructure.
Data Source
AI summary
A method for generating and distributing simulated animal data includes a step of receiving a set of real animal data at least partially obtained from one or more sensors that receive, store, or send information related to one or more targeted individuals. Simulated animal data is generated from at least a portion of real animal data or one or more derivatives thereof. Finally, the simulated animal data is provided to a computing device. Characteristically, one or more parameters or variables of the one or more targeted individuals can be modified.