Dell products l.p. (20240249165). SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION simplified abstract
Contents
SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION
Organization Name
Inventor(s)
PHILIP E. Hummel of San Jose CA (US)
DHARMESH M. Patel of Round Rock TX (US)
SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249165 titled 'SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION
The abstract of the patent application describes methods and systems for providing computer implemented services using inference models obtained through federated learning. These models are used to generate output for the services, with some instances of the models being generated using siloed data with distribution restrictions. Selected instances of the models continue learning to obtain a final inference model for generating the output.
- Inference models are obtained through federated learning.
- Siloed data with distribution restrictions is used to generate instances of the inference models.
- Selected instances of the models continue learning to obtain a final inference model.
- The final inference model is used to generate output for computer implemented services.
Potential Applications: - Personalized recommendations in e-commerce platforms. - Predictive maintenance in industrial settings. - Fraud detection in financial transactions.
Problems Solved: - Efficiently utilizing siloed data for model training. - Improving the accuracy of inference models through continued learning. - Providing personalized and accurate services to users.
Benefits: - Enhanced accuracy in generating output for computer implemented services. - Increased efficiency in utilizing data for model training. - Improved user experience through personalized services.
Commercial Applications: Title: "Enhanced Personalization Technology for E-commerce Platforms" This technology can be used to provide personalized product recommendations to customers, leading to increased sales and customer satisfaction in e-commerce platforms. Market implications include improved customer retention and higher conversion rates for online retailers.
Questions about Federated Learning: 1. How does federated learning differ from traditional centralized model training methods? Federated learning involves training models on decentralized data sources without exchanging raw data, while traditional methods require centralizing data for training.
2. What are the privacy implications of using federated learning for model training? Federated learning helps preserve data privacy by keeping data on local devices and only sharing model updates, reducing the risk of exposing sensitive information.
Original Abstract Submitted
methods and systems for providing computer implemented services using inference models are disclosed. the inference models may be obtained through federated learning, and may be used to generate output used in the computer implemented services. during the federated learning, instances of inference models may be generated using siloed data with distribution restrictions. some of the instances of the inference models may be selected for continued learning to obtain a final inference model used to generate the output.
