In the era of current technological advancement, Intellectual property (IP), in particular patents, has grown to be a cornerstone of innovation, competitive advantage, and strategic decision-making. Valuable insights into technical capabilities, Research and development priorities, and market positioning are provided by a patent portfolio - that is, a collection of patents held by a person, company, or organization. Effective IP portfolio management is vital for maximizing asset value, protecting innovations, and staying competitive. With the growing complexity and volume of patent data, conventional manual or semi-automated techniques are not sufficient sometimes. This has given rise to AI-driven patent portfolio analysis, a revolutionary method using artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) to obtain useful insights from patent datasets. The integration of AI into Patent portfolio management process offers new ways to analyse, optimize and leverage intellectual property.  

Need of AI in patent analysis

Patents are specifically highly structured technical documents containing technical descriptions, classification codes, citations and metadata. Every year, WIPO, USPTO, EPO and other countries' patent offices publish millions of patent documents. This much volume of information creates serious challenges in thoroughly and accurately examining patent portfolios using conventional methods. AI can easily process and analyse vast amounts of patent data in a small amount of time with reduced human error by using some of its core technologies described below.

Core technologies of AI used in Patent Portfolio Analysis

There are four major core technologies: Natural Language Processing (NLP), Machine Learning (ML), OCR and Knowledge graphs.

1. Natural Language Processing (NLP) uses some word embedding techniques, such as Word2Vec, BERT, etc., for summarizing the textual content of the patent documents, identifying keywords, and classifying different technical domains of patents.
2.  Machine Learning (ML) algorithms are used to determine patent quality, valuation, invention trends, as well as infringement risks.
3. Optical Character Recognition (OCR) allows AI model to fully understand the patented invention by analysing technical diagrams and drawings.
4.  Knowledge graphs based on AI model represent potential licensing opportunities, relationships between inventors, assignees and different technologies.  

Applications Area

• By analyzing millions of documents and mapping patent claims with different technology trends, AI detects potential infringement risks, licensing opportunities, and declining innovation areas. This directs the R&D cell of any organization to align with future technologies.
• ML models can easily identify high and low value patents by categorizing and providing ranks to the patents within a portfolio based on some factors, such as technology, citation analysis, litigation history, and relevancy. It can also compare competitors’ portfolio with the company’s portfolio.
• AI models perform pre-market analysis by identifying Freedom-to-operate (FTO) risks.
• AI also suggests new innovation areas or filing opportunities by identifying the technological gap, i.e. white space.

Future of AI in Patent Portfolio Analysis

Looking forward, AI will only become a more integral part of patent portfolio management. Just imagine a world in which AI not only manages your patent portfolio but also drafts patents, demonstrates future technology trends and interacts with other business processes. Further, AI with real-time patent databases will allow for the evolution of a dynamic IP strategy.       

Conclusion

AI-driven patent portfolio analysis is a game-changer for the landscape of IP management. By automating difficult tasks, providing useful insights, and optimizing your portfolio, AI helps companies to maximize the value of their patent assets. Hence, the ongoing evolution of AI technologies could help the patent portfolio analysis to be more robust and insightful than ever before.