17985239. DISTRIBUTED EVALUATION PLATFORM FOR NONFUNGIBLE TOKENS USING VIRTUAL TOKEN CLONING simplified abstract (Bank of America Corporation)

DISTRIBUTED EVALUATION PLATFORM FOR NONFUNGIBLE TOKENS USING VIRTUAL TOKEN CLONING

Organization Name

Bank of America Corporation

Inventor(s)

Sakshi Bakshi of New Delhi (IN)

Durga Prasad Kutthumolu of Hyderabad (IN)

Shilpi Choudhari of Hyderabad (IN)

Ankit Kumar of Gurugram (IN)

Saurabh Rajpal of Hyderabad (IN)

Hwee Leng Toh of Singapore (SG)

DISTRIBUTED EVALUATION PLATFORM FOR NONFUNGIBLE TOKENS USING VIRTUAL TOKEN CLONING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17985239 titled 'DISTRIBUTED EVALUATION PLATFORM FOR NONFUNGIBLE TOKENS USING VIRTUAL TOKEN CLONING

Simplified Explanation

The abstract of the patent application describes a distributed evaluation platform that utilizes historical nonfungible tokens to train a machine learning model, generate NFTs and soft tokens based on client information, apply test cases and remediation actions, and refine the machine learning model.

• The distributed evaluation platform trains a machine learning model using historical nonfungible tokens.
• Client information is received from a client device and used to generate NFTs and soft tokens.
• Test cases are applied to the soft tokens, and remedial tokens are generated based on the results.
• The NFTs are overwritten using the remedial tokens to improve the machine learning model.
• An event processing request is sent to an event processing system to further refine the model.

Potential Applications

The technology described in this patent application could be applied in various industries such as finance, healthcare, and marketing for data analysis, pattern recognition, and predictive modeling.

Problems Solved

This technology helps in automating the evaluation and refinement process of machine learning models, making it more efficient and accurate. It also enables the generation of NFTs and soft tokens to represent and analyze client information effectively.

Benefits

The benefits of this technology include improved accuracy in machine learning models, faster evaluation and refinement processes, and better utilization of historical data for training purposes.

Potential Commercial Applications

One potential commercial application of this technology could be in the field of personalized marketing, where machine learning models are used to analyze customer data and generate targeted advertising campaigns.

Possible Prior Art

One possible prior art for this technology could be the use of machine learning models in financial trading algorithms, where historical data is used to predict market trends and make investment decisions.

How does the distributed evaluation platform handle data privacy and security concerns?

The patent application does not specifically address how data privacy and security concerns are managed within the distributed evaluation platform. It would be important to understand the measures in place to protect client information and prevent unauthorized access.

What are the scalability limitations of the distributed evaluation platform?

The abstract does not mention the scalability limitations of the platform, such as the maximum number of clients it can handle or the size of data it can process. Understanding the scalability of the technology is crucial for its practical implementation in large-scale applications.

Original Abstract Submitted

Aspects of the disclosure relate to a distributed evaluation platform. The distributed evaluation platform may train a machine learning model based on historical nonfungible tokens. The distributed evaluation platform may receive client information from a client device. The distributed evaluation platform may generate NFTs corresponding to the client information. The distributed evaluation platform may generate soft tokens corresponding to each NFT. The distributed evaluation platform may apply test cases to the soft tokens. The distributed evaluation platform may generate remedial tokens based on the soft tokens and remediation actions. The distributed evaluation platform may apply the test cases to the remedial tokens. The distributed evaluation platform may overwrite the NFTs using the remedial tokens. The distributed evaluation platform may send an event processing request to an event processing system. The distributed evaluation platform may refine the machine learning model based on the NFTs.