Unlike the use of external data in conventional analysis, the value of AI and machine learning differs greatly when external data changes its shape and persists as a learning result.
This consortium will tackle various issues surrounding AI, including annotations.
Intellectual Property and Contract Process WG
Data utilization in AI (deep learning) / ML (machine learning) differs from conventional data utilization such as sales analysis, and there is a big difference in that the data used is made permanent as a training result. When data is transformed and persisted, it is nothing less than bringing data to a value that is significantly different from traditional data utilization.
Data commerce in AI / ML has many similarities to technology patents. While technology patents are contracted and prices change depending on the commercial flow and the number and unit price of the final product, in addition to the characteristics of patents, data rarity, domain, freshness, etc. are more when using AI / ML for data Contracts and prices vary depending on the elements, development and user scenarios. In addition, it is necessary to consider not only the intellectual property of the data itself, but also consistency with various laws and guidelines such as personal information and privacy.
The Intellectual Property / Contract Review WG aims to realize contracts that take into account commercial flow and final product forms, contract templates, and smart contracts used in the data distribution infrastructure.
Contracts and prices differ depending on the commercial flow and target
Contracting data for use with AI is difficult
For technology patents, licensing fees vary depending on various conditions. Like technology patents, the value of AI varies greatly depending on distribution and target systems. In addition, sufficient consideration should be given to the rights of the data used for learning.
- Use condition
- Purpose of use
- Business flow
- Promise content and Quantity
Conventional static contract procedures cannot handle various conditions including dynamic changes. We will examine smart contracts that respond to dynamic factors, complex commercial flows, and changing data values.