From Manual to Machine: The Impact of AI on Patent Classification

Aug 30, 2024

Conventional methods of patent classification may have worked at one point. However, with the burgeoning volume of patent applications, these need to be done away with not only because of the complexity of the process but also considering time taken to complete it. What is required is for such methods to be replaced with automation. When we think of automation, we think of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML), which have been playing a pivotal role in revolutionizing the patent classification process compared to manual methods. Algorithms driven by AI and ML, have significantly contributed towards improving the accuracy, efficiency, and consistency of patent classification. This in turn, has led to less time being spent on patent categorization while enhancing workflow.

While AI possesses a plethora of functionalities, one among them is its proficiency in assessing and processing massive amounts of data at unrivaled speeds. AI systems are able to rapidly recognize core concepts, technologies, and innovations within parent documents with the help of technologies like Natural Language Processing (NLP) and models driven by Deep Learning. Additionally, these systems further go on to uncover patterns and similarities among an array of varied patents, courtesy of their training using comprehensive labeled patent data. As a result of the pattern detection process, AI models can perfectly and accurately categorize patents into suitable classes. So, how does this exercise help inventors, legal professionals, and researchers? Due to the precision levels of the indexing process, these professionals find it easier to search for and extract the patents and their accompanying data without hassles.

Let us understand the part played by machine learning models in this process. The ML models also enhance their learning from new patents while finetuning their classification techniques. We can categorize this learning into two classes: Supervised learning and unsupervised learning. When harnessing the power of labeled patent data to teach models to correctly classify patents into predefined categories, we call it supervised learning. But when new and emerging categories, which might have been invisible earlier, are revealed, we refer to it as unsupervised learning. Using these learning techniques leads to the uncovering of new technological trends while being able to categorize the patents across a gamut of advanced fields in time. In a word, organizations are able to surge ahead of their competition.

To drive home our point, let us share a quintessential example of the application of AI and ML in patent classification. The European Patent Office (EPO) uses an AI-driven system to categorize patents, based on the Cooperative Patent Classification (CPC) system. The EPO has benefited in two ways from the usage of an AI-powered system: One, the classification accuracy levels have been augmented, and two, the time taken to allot patents to their respective categories has gone down. In other words, the system is so robust that it has proved immensely helpful to human classifiers, maintaining homogeneity among various patent offices, thereby resulting in an integrated and efficient classification system.

In conclusion, by leveraging the potential of AI, organizations can improve their patent management strategies, drive innovation, and systematize the process to access crucial data related to intellectual property.

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