By Gargee Patankar, VP-Sales and Strategic Partnerships, PatSeer Technologies Pvt Ltd
Every startup is keen to present the next big idea to the world. And to safeguard their idea from copying or corrupting, it’s always a good idea to get a patent in place. By default, a patent is novel, which means startups need to be 100% certain that their idea is indeed one of a kind. This can be accomplished by conducting a thorough patent search beforehand – and given that speed and accuracy are paramount, there is a clear and compelling case for AI to supercharge the patent search process for startups.
Importance of patent search and analysis
● Gaining an understanding of the existing patent applications is a good way to know the market trends. If there are multiple patents in one category of products, it could indicate a competitive market catering to the various needs of consumers in that domain. Patent search and analysis is also a good way to identify potential technology gaps and white spaces to explore. For instance, the startup might find that their original idea has been explored, but that there’s an opportunity to protect and commercialize an auxiliary product that would appeal to the same target audience.
● By conducting a patent search in advance, startups can save time and money by ensuring that their idea (a) doesn’t already exist as a patent or as a viable product, or (b) doesn’t belong to a category of products that is not patentable. This will save them the legal hassles of dealing with patent infringement further down the line, including the associated costs.
Understanding the challenges of patent search
● Patent search in any field is a highly time-consuming and complex process. One must search across all the available patent data by preparing complex Boolean queries, studying it, and distill the essentials, including the flagging of grey areas for a lawyer to advise on. This can take a lot of time, which can be a disadvantage in terms of seizing immediate market opportunities.
● The need for accuracy in patent search and the subsequent analysis cannot be overstated. Any imprecision could lead to patent rejection, legal battles on grounds of patent infringement, and a significant waste of time and resources. It becomes more difficult when determining the criteria for patent duplicity, – such as whether the requested patent is essentially a copy of an existing filing done by someone else, or a version with
discernible modifications.
The role of AI in patent search and analysis
AI can search for and analyse data of any kind exponentially faster than humans can. Older natural language search algorithms were not capable of understanding the meaning and intent behind the invention description provided by the user. However, with pre-trained Large Language Models, AI-driven patent search has now come of age and can provide far better search relevance and highly accurate results. Therefore, it is an ideal technology for areas like patent search and analysis, which inherently are a time-consuming process. Several AI search and result analysis tools are already speeding things up and taking startups closer to their
eventual patent registration.
● Search efficiency – The most obvious advantage of AI is the efficiency it brings to the search process. Instead of manually preparing complex search queries to dig through patent data, the user can “rely” on the AI search algorithm to deliver highly accurate results in seconds.
● Natural language processing – AI trained in NLP can conduct semantic analyses of patents. This enables them to make sense of regional differences in language and interpret any vaguely worded sections accurately. This is particularly useful when it comes to studying patent claims for different versions of the same invention.
● Algorithms for classification – Not all patent-related data is likely organised in a way the technology is seen by the startup in question. ML algorithms can be trained to sort the data based on relevance, presenting a ranked and classified result to the end user.
● Visualisation tools – AI can organise and summarise the data in an easy-to-understand visual report, highlighting and categorising essentials. This will make it easier to present findings to relevant stakeholders and make informed decisions.
Future trends in AI for patent search and analysis
The applications of AI in patent search and analysis are manifold. Currently, integrating AI with blockchain and IoT is being explored to create a single and transparent chain of information. While some of these AI applications can be expensive, new options are being developed every day, which will ultimately lower cost for startups with limited budgets. This is just the beginning for AI algorithms – and with their immense potential to speed up the patent registration process, startups that embrace them now will be the first to see their unique ideas become viable realities.
With its extensive Boolean and AI search functions, PatSeer, an AI-based patent search engine, leads the way in innovation by enabling users to navigate the IP landscape with never-before-seen ease. Startups may run comprehensive patent searches utilizing the platform’s user-friendly interface, to ensure that their ideas are unique and patentable.
