18060101. SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM simplified abstract (Dell Products L.P.)
Contents
- 1 SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM - 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
SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM
Organization Name
Inventor(s)
OFIR Ezrielev of Beer Sheva (IL)
JEHUDA Shemer of Kfar Saba (IL)
SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18060101 titled 'SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL DISTRIBUTIONS IN DYNAMIC SYSTEM
Simplified Explanation
Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, the conditions to which the data processing systems are likely to be exposed are identified. The identified conditions may be used to select operating modes for inference models hosted by the data processing systems. The operating modes may define when and under which conditions the inference models hosted by the data processing systems are automatically modified. The automatic modifications may adapt the inference models to the conditions to which the data processing systems are exposed.
- Identification of conditions for data processing systems
- Selection of operating modes for inference models
- Automatic modification of inference models based on conditions
- Adaptation of inference models to changing conditions
Potential Applications
The technology described in this patent application could be applied in various fields such as:
- Predictive maintenance in manufacturing
- Fraud detection in financial transactions
- Personalized recommendations in e-commerce
Problems Solved
This technology helps in:
- Ensuring optimal performance of inference models
- Adapting to changing environmental conditions
- Enhancing the accuracy of predictions
Benefits
The benefits of this technology include:
- Improved efficiency in data processing systems
- Enhanced adaptability of inference models
- Increased reliability of predictions
Potential Commercial Applications
The potential commercial applications of this technology could be seen in:
- Data analytics companies
- Financial institutions
- E-commerce platforms
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms to optimize inference models based on changing conditions. Another prior art could be the implementation of adaptive systems in data processing to improve model performance.
=== What are the specific conditions that trigger automatic modifications in the inference models? The specific conditions that trigger automatic modifications in the inference models are identified based on the environmental factors to which the data processing systems are exposed. These conditions could include changes in data patterns, system performance, or external factors affecting the inference models.
=== How are the operating modes selected for the inference models? The operating modes for the inference models are selected based on the identified conditions and the desired outcomes. These modes define the rules and thresholds for when the models should be automatically modified to adapt to the changing conditions.
Original Abstract Submitted
Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, the conditions to which the data processing systems are likely to be exposed are identified. The identified conditions may be used to select operating modes for inference models hosted by the data processing systems. The operating modes may define when and under which conditions the inference models hosted by the data processing systems are automatically modified. The automatic modifications may adapt the inference models to the conditions to which the data processing systems are exposed.
