View source for VMware, Inc. (20240211901). BLOCKCHAIN HOSTED MACHINE LEARNING simplified abstract

=BLOCKCHAIN HOSTED MACHINE LEARNING=


==Organization Name==

[[:Category:VMware, Inc.|VMware, Inc.]]


[[Category:VMware, Inc.]]

==Inventor(s)==

[[:Category:Sean James Huntley of Sydney (AU)|Sean James Huntley of Sydney (AU)]][[Category:Sean James Huntley of Sydney (AU)]]

==BLOCKCHAIN HOSTED MACHINE LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240211901 titled 'BLOCKCHAIN HOSTED MACHINE LEARNING

The abstract describes a method for providing machine learning services to users of a blockchain network. A machine learning model is hosted off the blockchain, and a smart contract is deployed to the blockchain with a reference to the model and weighted values for classifications. Successful models accumulate more cryptocurrency, allowing for an orchestration service to identify and adjust weighted values for better performance.

*Simplified Explanation:
- Machine learning services are provided to blockchain users.
- A model is hosted off the blockchain, with a smart contract on the blockchain.
- The smart contract includes weighted values for classifications.
- Successful models earn more cryptocurrency.
- Orchestration service adjusts weighted values for better performance.

Key Features and Innovation:
- Hosting machine learning models off the blockchain.
- Using smart contracts on the blockchain for references and weighted values.
- Accumulating cryptocurrency rewards for successful models.
- Orchestration service for adjusting weighted values.

Potential Applications:
- Enhancing machine learning capabilities on blockchain networks.
- Improving classification accuracy through weighted values.
- Incentivizing the development of successful machine learning models.

Problems Solved:
- Providing machine learning services on blockchain networks.
- Rewarding successful models with cryptocurrency.
- Optimizing weighted values for better performance.

Benefits:
- Improved machine learning capabilities.
- Increased accuracy in classifications.
- Incentivized development of successful models.

Commercial Applications:
- Optimizing machine learning services for blockchain applications.
- Enhancing data analysis and decision-making processes.
- Potential market implications in finance, healthcare, and other industries.

Prior Art:
- Further research may be needed to explore prior art related to machine learning services on blockchain networks.

Frequently Updated Research:
- Stay informed on advancements in machine learning services on blockchain networks for potential updates and improvements.

Questions about Machine Learning Services on Blockchain Networks:
1. How does hosting machine learning models off the blockchain impact performance?
2. What are the potential risks associated with using smart contracts for machine learning services on the blockchain?


==Original Abstract Submitted==

disclosed are various embodiments for providing machine learning services to users of a blockchain network. a machine learning model can be hosted off the blockchain. a smart contract can then be deployed to the blockchain. the smart contract can include a reference to the machine learning model and weighted values that can be used by the machine learning model to make classifications. successful machine learning models will tend to accumulate more cryptocurrency coins or tokens due to their continued use and execution, allowing for an orchestration service to identify successful sets of weighted values that can be adjusted as the basis for attempting to evolve better sets of weighted values for better performance by the machine learning model.



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