This paper talks about how artificial intelligence (AI) could help patent examiners with prior art searches. The proof-of-concept enabled researchers to test various AI techniques for suggesting search terms, retrieving the most relevant documents, ranking them, and visualizing  their content. AI is less effective at formulating search queries, but it can speed up and reduce the cost of sifting through a large number of patents. In prior art searching, emphasize  the importance of the human-in-the-loop approach and the need for better tools for human-centered decision and performance support.


Artificial Intelligence (AI) is a series of artificial learning algorithms fed to AI systems in such a way that it exhibits characteristics consistent with a human mind and is designed to mimic human intelligence to  accomplish a certain objective with little or limited human interference.[1]

According to WIPO, a patent is an exclusive right granted for an invention, which is a product or a process that provides, in general, a new way of doing something, or offers a new technical solution to a problem. In other words, patent protection means that the invention cannot be commercially made, used, distributed, imported, or sold by others without the patent owner’s consent.[2]

The standards of ingenuity and non-obviousness must be followed for an invention to be patented. A prior art search is performed to determine whether or not an invention is novel and non-obvious. A prior art search aids in the differentiation of what is already known (prior art) from what is new (invention).[3]

The manual method of conducting a prior art search can be a repetitive and time-consuming activity, artificial intelligence can be used to simplify the time-consuming and burdensome method of patent search and patent-based  discovery, potentially increasing performance, precision, and consistency.


Rapid developments in artificial intelligence have far-reaching consequences for the economy and society as a whole. These advancements have the potential to have a direct impact on the development and characteristics of a broad range of goods and services, with significant consequences for efficiency, jobs, and competition. But, as critical as these effects are likely to be, artificial intelligence also has the ability to alter the innovation process itself, with equally significant implications that may eventually outweigh the direct impact. While it is possible that future breakthroughs will lead to technology that can meaningfully replicate the essence of human subjective intelligence and emotion, the recent developments that have gotten scientific and commercial attention are far from these domains.[4]


Prior art searches include searching patent documents. The patent office performs a prior art check after filing a patent application and systematic review. A prior art search involves looking up any related technical material that was publicly available at the time of the patent application’s filing, or if appropriate, at the time of the priority filing. This entails gathering and compiling information about an invention that has been made public by a certain date, with the help of various national and international patent offices, such as the Indian Patent Office (IPO), the Chinese Patent Office (SIPO), and the Japanese Patent Office (JPO)European  Patent  Office  (EPO),  Korean  Intellectual Property  Office  (KIPO),  United  States  Patent and Trademark  Office  (USPTO),  World  Intellectual Property  Organization  (WIPO)  etc. Non-patent literature, such as journals, magazines, books, manuals, conference reports, research articles, product literature, and other public archives, may be valuable sources of prior art knowledge.[5]

USPTO,  EPO  and  JPO are  the  three  major  patent offices, which together account for about 90 percent of  the  patent  applications.  These  patent  offices have their  independent  patent  application  databases.[6]

Patent offices in different countries compile, retain, and archive all data related to patent applications submitted and granted in their respective fields. This data is held in patent files, which are publicly accessible online. These lists also provide links to other countries’ patent databases. The most extensive archive of patent documents available in the world is multinational patent databases.

In  India,  the  IPR  related  issues  came  into prominence  after  a  while  when the global  community started implementing it.

  • INPAIRS  Version  2  –  It  is a freely  accessible  online patent  search  engine  maintained  by the Government  of India  to  search for Indian  patents.
  • MCPaIRS  (Molecular  Connections  Patent  Information Retrieval  System)  – It  is  a  commercial  patent  database maintained  by  Molecular  Connections. MCPaIRS helps  to  search  the  full  text  of  patents  published  in India.
  • EKASWA  A,  B, and  C  Database  – These  are  the first Indian  patent  searchable  databases  available  in  CD-ROM  and  Web.[7]


In the case of Ericsson vs. Xiaomi[8], Ericsson filed a lawsuit in India accusing Xiaomi of infringing on eight standard-essential patents. The Delhi High Court issued an ex parte injunction prohibiting Xiaomi from selling, manufacturing, advertising, or importing its products.

In Vringo. Inc vs. ZTE Corp[9], Vringo and Vringo Infrastructure filed a patent infringement suit against ZTE in the Delhi High Court, alleging infringement of its patent IN200572.

The Delhi High Court issued an ad-interim ex-parte injunction in February 2014, prohibiting ZTE from importing, distributing, advertising, installing, or running devices that contain infringing components.

In Enercon vs. Dr.AloysWobben[10],The Hon’ble Supreme Court of India tackled multiple patent-related cases including invalidation, challenge, and revocation. This decision paved the way for India’s evolving patent litigation practice .


Prior Art Patent Search is a must-do move and can be done with the help of a patent attorney even before filing a patent application to  in order to satisfy the patentability requirements. Patent lawyers and researchers often rely on keyword-based search engines to perform patent searches, which frequently provide erroneous results that have a vast amount of false positives and false negatives. False positives increase the workload on patent examiners by requiring them to remove irrelevant records, while false negatives can result in a patent being granted incorrectly. It is important to use AI techniques for prior art patent searches  to overcome the challenges associated with manual prior art search techniques. As a result, more accurate and appropriate outputs are obtained. Artificial Intelligence and Machine Learning methods are extremely useful for performing patent searches because they reduce manual labor, improve the precision and reliability of search results, and save time by considering the researcher’s purpose.

[1]AyushVerma, Artificial intelligence: Simplifying the process of prior art patent search iPleaders (2020), (last visited Mar 12, 2021).

[2] WIPO, Patents, (last visited Mar 12, 2021).

[3] IPTel, Prior Art Search: Intellectual Property and Technology Licensing (IPTeL) IPTeL (2018), (last visited Mar 12, 2021).

[4]Iain M. Cockburn, Rebecca Henderson & Scott Stern, The Impact of Artificial Intelligence on Innovation NBER (2018),

[5]Vikram Singh, Kajal Chakraborty & Lavina Vincent, Patent Database: Their Importance in Prior Art Documentation and Patent Search, 21 Journal of Intellectual Property Rights 42–56 (2016),

[6]Alfons Palangkaraya, Patent Application Databases Australian Economic Review (1970),

[7]Vikram Singh, Kajal Chakraborty & Lavina Vincent, Patent Database: Their Importance in Prior Art Documentation and Patent Search, 21 Journal of Intellectual Property Rights 42–56 (2016),

[8]CS (OS) no. 3775 / 2014

[9]Vringo, Inc. v. ZTE Corp., 14-cv-4988 (LAK) (S.D.N.Y. Jun. 3, 2015)

[10] Miscellaneous Petition No. 21/2010, 30/2010 &Amp; 46/2011 In Ora/07/2009/Pt/Ch | 31-05-2013

Enercon (India) Limited v. AloysWobben

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