Dell products l.p. (20240177027). SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS
Organization Name
Inventor(s)
OFIR Ezrielev of Beer Sheva (IL)
JEHUDA Shemer of Kfar Saba (IL)
SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177027 titled 'SYSTEM AND METHOD FOR MANAGING INFERENCE MODEL PERFORMANCE THROUGH PROACTIVE COMMUNICATION SYSTEM ANALYSIS
Simplified Explanation
The abstract of the patent application describes methods and systems for managing the execution of inference models hosted by data processing systems. The system includes an inference model manager and multiple data processing systems. The manager communicates system data to link the data processing systems and uses this data to determine if the communication system meets the inference generation requirements of the downstream consumer. If not, the manager obtains an inference generation plan to bring the system back into compliance.
- The patent application involves managing the execution of inference models hosted by data processing systems.
- The system includes an inference model manager and multiple data processing systems.
- The manager communicates system data to link the data processing systems and ensure they meet the inference generation requirements of the downstream consumer.
- If the requirements are not met, the manager obtains an inference generation plan to rectify the situation.
Potential Applications
This technology could be applied in various industries such as healthcare, finance, and e-commerce for optimizing data processing systems and ensuring efficient execution of inference models.
Problems Solved
This technology solves the problem of managing and ensuring the proper execution of inference models hosted by data processing systems, ultimately improving the accuracy and efficiency of data processing tasks.
Benefits
The benefits of this technology include improved performance of data processing systems, enhanced accuracy of inference models, and better compliance with downstream consumer requirements.
Potential Commercial Applications
Potential commercial applications of this technology include data analytics platforms, machine learning services, and AI-driven decision-making systems.
Possible Prior Art
One possible prior art for this technology could be systems that manage the execution of machine learning models in cloud computing environments.
Unanswered Questions
1. How does the system handle real-time data processing and inference model execution? 2. What security measures are in place to protect the communication system data and ensure compliance with data privacy regulations?
Original Abstract Submitted
methods and systems for managing execution of inference models hosted by data processing systems are disclosed. to manage execution of inference models hosted by data processing systems, a system may include an inference model manager and any number of data processing systems. the inference model manager may communication system data for the communication system linking the data processing systems. the inference model manager may use the communication system data to determine whether the communication system meets inference generation requirements of the downstream consumer. if the communication system does not meet inference generation requirements of the downstream consumer, the inference model manager may obtain an inference generation plan to return to compliance with the inference generation requirements of the downstream consumer.
