Optimizing the Intellectual Property Management in Accordance with a Process-Functional Approach

Abstract

The article is aimed at developing a way to optimize the management of intellectual property (IP) objects by a process-functional approach based on the use of neural networks in combination with planning networks in conditions of uncertainty. When analyzing the works of various scholars, conceptual approaches to the formation of IP management according to both the process and the functional approaches to management were considered. The use of artificial neural networks in intellectual property management at industrial enterprises in combination with network planning in conditions of uncertainty is systematized. Neural networks consist of different architectures, but to manage intellectual property it is advisable to use either Self-Organizing Maps (SOM) by Kohonen, or Generative Pre-trained Transformer 3 (GPT-3), or Rumelhart Multilayer Perceptron, or an combination of the above. It is proved that the proposed scientific approach (instrumentarium) in the form of neural networks and network planning allows reducing the time for implementation of works related to the management of intellectual property at industrial enterprises on the grounds of a process-functional approach. Based on the carried out study, the computation of spent time was carried out, which confirmed the efficiency of the implementation of neural networks in combination with network schedule for the management of intellectual property in industrial enterprises. Prospects for further research in this direction are the development and construction of a universal instrument using neural networks and network schedule. Further development of intellectual property management will increase production efficiency and profitability of enterprises.

Keywords

• intellectual property
• network model
• neural network
• management of intellectual property
• scientific approaches to intellectual property management