17938299. ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS simplified abstract (Dell Products L.P.)
Contents
- 1 ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS
Organization Name
Inventor(s)
Paulo Abelha Ferreira of Rio de Janeiro (BR)
Vinicius Michel Gottin of Rio de Janeiro (BR)
Pablo Nascimento Da Silva of Niterói (BR)
ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17938299 titled 'ROBUST MULTI-MODEL EVENT DETECTION WITH UNRELIABLE SENSORS
Simplified Explanation
Model selection is a process where features are scored based on their importance and health, and these scores are combined to generate a model score for each model. The model with a score above a certain threshold is then selected and deployed.
- Features are scored based on importance and health
- Scores are combined to generate a model score
- Model with score above threshold is selected and deployed
Potential Applications
This technology could be applied in various fields such as:
- Healthcare for predicting patient outcomes
- Finance for risk assessment
- Marketing for customer segmentation
Problems Solved
This technology helps in:
- Improving model selection process
- Enhancing accuracy of predictions
- Streamlining decision-making process
Benefits
The benefits of this technology include:
- Increased efficiency in model selection
- Improved performance of predictive models
- Better decision-making based on data
Potential Commercial Applications
This technology has potential commercial applications in:
- Data analytics companies
- Financial institutions
- Healthcare organizations
Possible Prior Art
One possible prior art for this technology could be:
- Existing model selection algorithms
- Previous methods for feature scoring
Unanswered Questions
How does this technology compare to existing model selection methods?
This article does not provide a direct comparison with existing model selection methods.
What are the specific industries that could benefit the most from this technology?
The article mentions potential applications in healthcare, finance, and marketing, but does not delve into specific industries within these sectors that could benefit the most.
Original Abstract Submitted
Model selection is disclosed. Features used as inputs to models are scored in terms of importance and health. The importance and health scores are combined in order to generate a model score for each model. The model with a score above a threshold score is selected and deployed.
