Genetic Algorithms and AI Patent Strategy: Lessons from Industrial Applications

Genetic algorithms are established methods in AI and optimization. Inspired by evolution, they generate a population of candidate solutions and progressively improve it through selection, crossover and mutation.

In the past, it was primarily used for academic research and experimental systems, but in recent years, it has rapidly grown to be applied to real industrial problems. As a result, technologies using genetic algorithms are being applied and registered as patents, and genetic algorithms are now managed as core technology assets for companies beyond simple theoretical techniques.

The biggest advantage of genetic algorithms is that they can effectively deal with very large problems in search space. With the explosion of numbers, such as manpower scheduling, production process planning, and logistics route optimization, it is difficult to find an optimal solution in the traditional way. Because genetic algorithms can explore multiple possibilities at the same time, they serve as a practical solution to these problems.

In industrial applications, genetic algorithms can converge prematurely around solutions that appear promising early in the search. Random mutation and recombination do not necessarily prevent the population from losing diversity before better solutions are found.

An approach to address these limitations is proposed to combine deterministic logic with genetic algorithms. The KR 2022-0066982 patent is a concrete implementation of this idea, adopting a structure that sets the selection criteria for the problem's objectives before running the genetic algorithm right away.

In this patent, we note that different criteria are required depending on the goal of the problem, such as speed, cost reduction and stability. Define it as a deterministic logic framework, select the appropriate framework based on your goals, and select the resources to use based on those criteria. It then utilizes the selected resources as inputs to the genetic algorithm, giving direction to the starting point of the search itself.

An important concept in this process is the resource genome. The resource genome refers to a combination of resources selected to achieve a specific task or goal, and the resources include all the elements necessary to perform the task, including people, machines, tools, space, and software. From a genetic algorithm perspective, a resource genome corresponds to a single answer candidate, and the results of evaluating how effectively the combination performed the actual task are calculated as a value score. This corresponds to the concept of fitness in a general genetic algorithm.

When you apply it to a real industrial environment, the structure becomes clearer. For example, when producing products in a factory, there are several machines and several workers, each with different speeds, costs, and stability. If your goal is to shorten your delivery time, set the criteria for choosing a resource that is faster to work, configure the resource genome according to those criteria, and improve your schedule with a genetic algorithm. Conversely, if cost reduction is the goal, the same process is carried out around low-cost resources. Setting the starting point for search according to your goals is a key feature of this patent.

Rather than removing the randomness of the genetic algorithm, this structure aims to give search a clear direction. This reduces the need for a wide-ranging search space and allows more efficient solutions to meet your goals. In order to reduce computational time and resource consumption, but also to achieve real-world results, the patent strategy for protecting genetic algorithms as the core logic of a service or system is expanding.

Artificial intelligence technologies, such as genetic algorithms, are complex mathematical and computer engineering concepts in themselves, but in terms of patent, the key is how they solve problems and what is different from existing ones. In particular, AI-related inventions are often subject to rigorous judgment during the screening process due to abstraction, unclearness, and inventive step problems.

In this environment, IPLEX IP Law Firm has developed patent strategies for various artificial intelligence technologies such as genetic algorithms, machine learning, data processing and automation systems. The case of the genetic algorithm patent, which combines deterministic logic, is also characterized by the specific technical challenges of scheduling and resource placement and the clear structure of its solutions.

Artificial intelligence patent requires a deep understanding of technology and patent practice. If you don’t fully understand the technology, it’s easy to miss the core of the invention, and, conversely, if you lack practical experience, it’s hard to reliably design a range of rights. The IPLEX IP Law Firm balances these two elements to help artificial intelligence technology lead to real patent rights, not just ideas.

In the future, AI technology will become more complex and applications will continue to expand. The patent strategy should also be more precise. IPLEX IP Law Firm plays a continuous role in enabling companies and researchers to achieve long-term competitiveness through professional patent response that takes into account the nature of AI technology and its judging flow.