The Top 20 AI Inventors and their Most Cited Patents

If our era is the next Industrial Revolution, as many claim, AI is surely one of its driving forces.

 – Fei-Fei Li

AI is no more limited to sci-fi movies. The endeavor to replicate or simulate human intelligence in machines has made AI mainstream in the last decade. As a result, AI has left a lasting impact on all our lives. From being a figment of our imaginations to becoming an intrinsic part of our every day, the AI revolution is real and is here to stay. 

We looked back at 2020 and put together a list of the top 20 inventors of Artificial Intelligence. 

Top 20 AI inventors
  1. Sarbajit Rakshit

An Application Architect and seeker of solutions, Sarbjit Rakshit, is an IBM Master Inventor with a degree in mechanical engineering from the Indian Institute of Engineering, Science and Technology.

Source – Forbes

He was awarded 163 US patents in 2019, the highest ever awarded to a citizen of India in a single year. His patent portfolio contains 359 patents in Artificial Intelligence globally, belonging to 271 unique patent families.

The most valuable patent in Sarbajit’s portfolio is US20160070439A1 – Electronic commerce using augmented reality glasses and a smartwatch. This patent family is the most cited (47 times) by companies Ariadne’s Thread (USA) Inc., Microsoft Technology Licensing Llc, Siemens Ag, Ebay Inc, and Lucyd Ltd.

Source – US20160070439A1

Source – Verdict

Before we look at the rest of the list, here’s a fascinating insight. 11 of the top 20 AI inventors are either currently at StradVision or have worked there previously. Ten of these inventors are co-inventors on a patent. Not just any patent; it’s their most cited patent. Let’s find out what StradVision does and their most cited patent.

StradVision is a fairly new company, founded in late 2014. Their goal is to bring powerful and safe ADAS (Advanced driver-assistance systems) & self-driving technology to the masses. StradVision’s technology utilizes a novel perception algorithm allowing autonomous vehicles to reach the required level of safety, accuracy, and driver convenience. This is achieved through safe & reliable real-world object detection, tracking, segmentation, and classification. In addition, they have an auto-labeling system that produces training data with minimal human input and a semi-supervised learning-based training tool, enabling autonomous vehicles to detect and perceive environments in real-time.

StradVision - AI Assisted Driving For everyone

Source – StradVision

The most cited patent for these ten inventors is US10169679B1. The patent is for – “Learning method and learning device for adjusting parameters of CNN by using loss augmentation and testing method and testing device.


Source: StradVision

Yongjoong Kim, Woonhyun Nam, Sukhoon Boo, Myungchul Sung, Donghun Yeo, Wooju RYU, Taewoong Jang, Kyungjoong Jeong, Hongmo Je, Hojin Cho are co-inventors of the said patent.

The said patent family has been cited 27 times by companies Didi Res America Llc, Stradvision Inc, and Beijing Didi Infinity Technology. The patent’s geographical coverage extends to the United States, China, Japan, and Korea. 

  1. Wooju Ryu

Wooju Ryu is a Korean inventor with a master’s degree in Computer Engineering from Pohang University of Science and Technology. 

Wooju Ryu AI Inventor

Source: Twitter

He is presently an Algorithm Engineer at StradVision and works in Deep Learning, Computer Vision, ADAS, Text Recognition and Automatic Driving. He has been associated with Intel, Olaworks, and Samsung as a Senior Researcher between 2007 and 2016. 

Wooju Ryu - Technology wise Patents Distribution

His patent portfolio consists of 831 patents in the AI domain globally, which belong to 267 unique patent families.

  1. Woonhyun Nam

Woonhyun Nam is a Korean inventor with a bachelor’s degree in Computer Science Engineering and a Doctor of Philosophy (Ph.D.) Computer Science and Engineering from the Pohang University of Science and Technology.

Woonhyun Nam - AI inventor

Source: ResearchGate

He is presently the Director, Lead of Algorithm Engineering at StradVision, Inc. His work profile is deeply seated in AI, with him being responsible for engineering, researching, investigating, and deploying algorithms across company products and services. 

Woonhyun Nam - Technology Area Patent famility count

His portfolio consists of 826 patents in the AI domain globally, which belong to 266 unique patent families. Most of his inventions are in the field of Instruments Technology.

  1. Hongmo Je

Hongmo Je is a Korean inventor with a Computer Science degree from the Pohang University of Science and Technology. 

Hongmo Je AI Inventor

Source: Crunchbase

Presently, he is the CTO of Stradvision and leads the RnD Integration/Engineering Team developing camera-based perception SW stack for ADAS/Autonomous Driving applications. He was previously the Engineering Manager at Intel and the head of RnD at Olaworks. 

HongMo Je - Technology wise Patent family count

Hongmo Je’s patent portfolio consists of 824 patents in the Artificial Intelligence (AI) domain globally which belong to 264 unique patent families. In addition, he holds 256 patents in the Instruments domain. 

  1. Donghun Yeo

Donghun Yeo is a Korean inventor with a bachelor’s degree in Computer Science and a Ph.D. in Computer vision from Pohang University of Science and Technology. 

Donghun Yeo - AI Inventor

Source: NIST

Yeo is presently a Senior Researcher at the Hana Institute of Technology. Previously, he was an algorithm engineer at StradVision.

Yeo’s patent portfolio comprises 824 patents in the Artificial Intelligence domain globally, belonging to 264 unique patent families. A significant chunk of his portfolio consists of innovations in Instrument Technology (255). 

  1. Myungchul Sung

Myungchul Sung is a Korean inventor with a master’s degree in Computer Science Engineering from the Pohang University of Science and Technology. He is an Algorithm Engineer at StradVision. 

He holds 824 patents in the Artificial Intelligence domain globally, which belong to 264 unique patent families. However, the most significant chunk of his patent portfolio is innovations in the Instruments Technology domain, amounting to 255. 

  1. Yong-Joong Kim

Yong-Joong Kim is a Korean inventor with a master’s degree in Computer Science from Yonsei University. He is presently an algorithm engineer at Stradvision. In the past, he has been a researcher at the Pohang University of Science and Technology and an IT coordinator at the National Institute for International Education. In addition, he has interned at the MARG Lab at Seoul National University.

  1. Taewoong Jang

Taewoong Jang is a Korean inventor with a bachelor’s degree in Physics & Math who graduated Magna Cum Laude from the Pohang University of Science and Technology. He was an Algorithm Engineer at StradVision and is now a Software Engineer at Coinone. 

He holds 824 patents in the Artificial Intelligence domain globally across 264 unique patent families. The majority of his patent portfolio (255 patents) are innovations related to Instruments Technology. 

  1. Kyungjoong Jeong

Kyungjoong Jeong is a Korean inventor who is an Algorithm Engineer at Stradvision. He graduated from Ulsan University as an Electrical Engineer as the Dean’s Honoured Graduate. He has previously been at Samsung Techwin and a Researcher at POSTECH, where he earned his Master’s degree. Deep Learning, Computer Vision, and Machine Learning are his research interests.

Kyungjoong Jeong’s patent portfolio has 824 patents in the Artificial Intelligence (AI) domain globally, belonging to 264 unique patent families. 255 of these patents are innovations in the field of Instruments Technology. 

  1. Hojin Cho

Hojin Cho is a Korean inventor with a degree in Computer Science Engineering and a Doctor of Philosophy (Ph.D.) Image Processing, Computer Graphics, and Computer Vision from the Pohang University of Science and Technology. He is an Algorithm Engineer at StradVision.

His portfolio consists of 824 patents in AI belonging to 264 unique patent families, of which 255 are in the sub-domain of Instruments Technology.

  1. Sukhoon Boo

Sukhoon Boo is a Korean inventor associated with StradVision Inc. His portfolio consists of 824 patents in AI belonging to 264 unique patent families, of which 255 are in the sub-domain of Instruments Technology.

  1. Hak-Kyoung Kim

Hak-Kyoung Kim is a Korean inventor and an algorithm engineer affiliated with Stradvision Inc. 

His portfolio consists of 758 patents in Artificial Intelligence globally, belonging to 251 unique patent families. In addition, he has 242 innovations in the domain of Instruments Technology.

The most valuable patent in Hak-Kyoung Kim’s portfolio is US10229346B1 – Learning method, learning device for detecting object using edge image and testing method. This is his most cited patent, having been cited 13 times. The patent’s geographical coverage is in the United States, China, Korea, and Japan.


  1. Kye-Hyeon Kim

Kye-Hyeon Kim is a Korean inventor with a bachelor’s degree in Computer Science and a Ph.D. in Computer Science (Machine Learning) from the Pohang University of Science and Technology.

Currently, he is the Chief Research Officer at Superb AI Inc. He has previously been associated with StradVision as an Algorithm Engineer, SK Telecom as a Research Scientist, and Intel and Samsung as a Senior Software Engineer. 

He holds 754 patents in the Artificial Intelligence domain globally, which belong to 251 unique patent families. However, the most significant chunk of his innovations is in the realm of Instruments Technology (242).

The most valuable patent in his portfolio is US10229346B1, the same as Hak-Kyoung Kim. They are co-inventors with a few more inventors on this patent.

  1. John M Ganci Jr

John M Ganci Jr is an American inventor affiliated with IBM. His patent portfolio has 223 patents filed globally which belong to 145 unique patent families. In addition, he holds 102 patents in the Instruments Technology domain.

John M Ganci Jr.’s most cited patent is US20160070439A1, the same as Sarbajit Rakshit. They are co-inventors on this patent with a few others.

  1. Craig Trim

Craig Trim is an American inventor with a Bachelor’s degree in Computer and Information Sciences from Cal Poly Pomona and a Master of Science, MS, Data Analytics from Capella University.

Source – TheOrg

He is currently with Causality Link as a Senior Engineer. His past experiences include being at IBM as a Lead Data Scientist and Dristi as a CTO.

Trim’s patent portfolio consists of 223 patents in the AI domain, belonging to 144 unique patent families. In addition, he holds 116 patents in the Instruments Technology domain. 

Craig’s most cited patent is US20160070439A1. Craig is a co-inventor on this with Sarbajit Rakshit, John Gangci, and a few others.

  1. Corville O Allen

Corville Allen is an American inventor with a degree in Computer Science, Mathematics from Lona College. He has 17 years of experience in Enterprise Software Development, including web-based software, Application Server infrastructure, Business Application Integration, and Cognitive Systems. He is a Senior Technical Staff Member and Master Inventor, a 5-time North Carolina Inventor of the Year at IBM. 

Source: IBM News Room

His specialties include Application Integration, API Development, Agile Methodologies, SDLC, WebSphere, Connectivity, and Architecture.

His patent portfolio consists of 232 patents belonging to 142 unique patent families. In addition, he holds 120 patents in the domain of Instruments Technology.

Allen’s most valuable patent is US9369488B2 – Policy enforcement using natural language processing. The said patent family has been cited 119 times by the company Onetrust Llc. The patent’s geographical coverage extends to the United States and China.

The core idea of the patent is to automatically identify if the user is violating the “terms of use” policy for devices like computers. For example, the user may attempt to use the device camera to photograph an object within a physical location governed by the term of the use policy document. Based on the procedure disclosed in the patent, the user’s computing device may take appropriate action, e.g., policy enforcement, restricting or disabling functionality, alerting or warning the user to non-compliance, or the like.

  1. Martin G Keen

Martin Keen is an American inventor with a degree in Computer Science from Southampton Solent University. He has been associated with IBM as a Technical Content Creation Leader & Video Production Leader.

Source: The Marketplace Podcast

Martin is an IBM Master Inventor and was conferred the Honorary award in 2016 by IBM. He holds over 200 patent applications, specializing in big data, cognitive systems, mobile devices, and predictive analytics. In addition, Martin is a Technical Content Creator Leader, including developing dozens of published books. He is also a Videographer and Video Production Lead specializing in corporate video creation and online learning course development. 

His patent portfolio has 201 globally filed globally belonging to 138 unique patent families. In addition, he holds 90 patents in the domain of Instruments Technology. 

The most valuable patent in Martin Keen’s portfolio is US9473819B1 – Event pop-ups for video selection. The patent family has been cited 16 times by companies IBM, Sony Interactive Entertainment Llc, Amazon Tech Inc, and Dish Network Llc.

Source – US9473819B1

  1. Jeremy Fox

Jeremy Fox is an American inventor with a BBA, Computer Information System degree from the University of Texas at El Paso. He has been associated with IBM since 2001. As a result, he has been accorded the title of Master Inventor at IBM. 

Jeremy has also served as the IBM Commerce IDT Chair for over three years.

His patent portfolio consists of 128 patents in AI globally, belonging to 110 unique patent families. In addition, 68 patents have been filed in Instruments Technology. 

The most valuable patent in Jeremy Fox’s portfolio is US9826500B1 – Preventing driver distraction from incoming notifications – cited 8 times by Nocell Technologies Llc.

Source – MyPolice #LeaveThePhoneAlone

Don’t we agree – those smartphone notifications while driving can be dangerously distractive? Jeremy Fox’s thought process behind this patent is quite appreciable. His ingenious idea to adjust the intensity of notification alerts based on the driving conditions is remarkable. For example: changing a loud beep to just a vibration alert for a particular type of notification. A few examples of conditions include driving:

  • in fair/poor/good weather
  • during day/night
  • familiar/unfamiliar route
  1. Yasuaki Yamagishi

Yasuaki Yamagishi is a Japanese inventor currently Senior Research Scientist at Sony Corporation.

His patent portfolio consists of 614 patents belonging to 104 unique patent families. In addition, he has 99 patents in the domain of Electronics Communication Techniques.

His patent US10178148B2 – Content supply device, content supply method, program, and content supply system – is his most cited (13 times), by Sony Corporation, Saturn Licensing LLC. The patent’s geographical coverage extends to the United States, Brazil, India, China, and Russian Federation.

  1. Joydeep Ray

Joydeep Ray is an American inventor with a master’s degree in Computer Engineering from Carnegie Mellon University. He is a Graphics Architect at Intel Corporation. He has previously been associated with AMD as an MTS Design Engineer, Standard Performance Evaluation Corporation as a Technical Representative in CPU Sub-committee, Carnegie Mellon University as a Research Assistant, and IBM as a Design Engineer.

Ray’s patent portfolio has 293 patents in the Artificial Intelligence domain globally, belonging to 84 unique patent families. In addition, 77 inventions are related to instruments belonging to the Instruments Technology domain. 

His patent US10108850B1 – Recognition, reidentification, and security enhancements using autonomous machines – is his most valuable. It has been cited 10 times and has geographical coverage in the United States and China. 

Let’s Sum it Up

It was interesting to note that most inventors among the top 20 AI inventors across the globe are Korean. 12 out of 20 are either working at StradVision or have worked at StradVision in the past. It’s intriguing to know what StradVision is up to. There is a commonality in many of these inventors’ most cited patents. It’s the object recognition in a video.

InventorCountry of OriginPresent Place of WorkPast Places of Work
Sarbajit RakshitIndianIBMN/A
Wooju RyuKoreanStradVision Inc.Intel, Olaworks, Samsung
Woonhyun NamKoreanStradVision Inc. N/A
Hongmo JeKoreanStradVision Inc.Intel, Olaworks
Donghun YeoKoreanHana Institute of TechnologyStradVision Inc.
Myungchul SungKoreanStradVision Inc.N/A
Yong-Joong KimKoreanStradVision Inc. N/A
Taewoong JangKoreanCoinoneStradVision Inc.
Kyungjoong JeongKoreanStradVision Inc. Samsung Techwin
Hojin ChoKoreanStradVision Inc. N/A
Sukhoon BooKoreanStradVision Inc. N/A
Hak-Kyoung KimKoreanStradvision Inc.N/A
Kye-Hyeon KimKoreanSuperb AI Inc. StradVision, SK Telecom, Intel and Samsung
John M Ganci JrAmericanIBMN/A
Craig TrimAmericanCausality LinkIBM, dristi
Corville O AllenAmericanIBMN/A
Martin G KeenAmericanIBMN/A
Jeremy FoxAmericanIBMN/A
Yasuki YamagishiJapaneseSony CorporationN/A
Joydeep RayAmericanIntel CorporationAdvanced Micro Devices Inc., IBM

What kindled your interest in this article? Are you currently working on any AI projects?

Since you were interested in this article, we wish to share an AI-based initiative. It’s called Patent Quality through Artificial Intelligence. The initiative is focused on inventors, and the core value that drives the initiative is “Prior Art Search for Everyone.” At PQAI, we studied patent rejection stats. We observed that most patents receive 102/103 type rejections. This means the invention described in the patent is either not new or obvious based on a combination of one or more previous inventions/literature. Unfortunately, many inventors apply for patents without a thorough prior art search. Usually, this is because there is a lack of budget or patent searching skills. Also, it’s challenging to search for non-patent literature while performing a prior art search. These reasons triggered an urge to develop an inventor-friendly prior art search engine. And what better than AI to turn to for help?

If you feel the pain inventors go through on receiving a patent rejection, we urge you to join the initiative and contribute the best way only you can!

Prior Art Search Navigation made Easy with PQAI

Prior Art Search Navigation made Easy with PQAI

Informative Snippets in Results for Efficient Relevance Judgement during the Prior Art Search.

“An attempt to help people separate the wheat from the chaff efficiently.”

Traditional Prior Art Search | Recursive & Time-Taking

Prior art search is a recursive process. You begin by:

  • articulating a technical idea in the form of a query,
  • feed it to a search engine,
  • wait for it to spit out the results
  • and then you go through the results one by one.

The relevance of results generally drops as you go down the list. So you refine your query to steer towards more relevant results, and the process repeats. 

As you would know, only a few of these results are relevant, and the rest are irrelevant, often termed “noise.” It is not uncommon to see a 50:1 noise-to-relevance ratio in your results.

Going through hundreds of documents to find one relevant piece of information takes most of your time. Mostly you are just spending time reading documents that you will eventually discard. Therefore, judging a document’s irrelevance is generally beneficial as soon as possible. Unfortunately, most search tools put less emphasis on this part. Either they don’t help you evaluate the relevancy quickly or go only as far as showing some highlighted keywords. But these approaches are seldom sufficient to inform you about the document’s relevance.

With PQAI, however, we are putting a lot of emphasis on this. We believe that enabling searchers to judge the relevance of documents quickly is one of the most impactful areas where prior art search engines need improvement.

In this article, let us analyze this problem in some detail, ponder upon possible solutions, and see how PQAI aims to be of help.

Notorious Titles & Irrelevant Text

Let’s assume we are doing a prior art search through patent literature to keep things simple. Unfortunately, patent titles are notorious for being vague and non-informative. See this patent, for example – The title of this patent is – “Method,” that’s it. I admit this is an extreme example, but judging patents by their titles is generally challenging. Even abstracts, more often than not, are difficult to understand. In fact, abstracts may not even relate directly to your query when you are running a search through claims/descriptions. For many results, therefore, you have to judge the relevance by opening those documents in a separate tab and then going through the complete text, trying to pin down the relevant sections, if any.

A typical patent contains about 10-12 pages of text. However, we routinely bump into patents that are longer and have 50-60 pages of text! When looking for prior art, the information you’re looking for could be anywhere within that text. Even expert searchers spend 90% of their time searching for that crucial piece within the text.

How PQAI helps in Time-Efficient Prior Art Search?

When PQAI identifies results, it goes one step beyond. It also picks out relevant parts of the documents matching your query. We call these “snippets” or “passages” – they are complete sentences or select parts of sentences that make sense on their own. They allow you to judge the relevance of a result directly from the search results page. Thus, you spend much less time sifting through irrelevant results is reduced. Of course, you may still need to read lengthy documents, but only the relevant ones. The ones for which snippets aren’t sufficiently informative, but overall, the number is greatly reduced.

The figure below shows you what PQAI snippets look like.

“A head mounted device” can be described as “an apparatus that fits on a user’s head.” Or a “housing positioned in front of eyes” can be described as “device that covers the eyes like a set of goggles

A Quick Recap

While searching for prior art, you need to spend a lot of time reviewing irrelevant results versus the relevant ones. Getting an idea of the irrelevance of the search result without having to read it all can help you save a lot of time. To reduce this time, PQAI brings the query element mapping feature into it.

When you search with PQAI, each result is accompanied by a query mapping table. The first column shows a part of the invention query, and the second column shows the relevant text from the search result. This mapping is not just word-to-word, but it’s highly contextual. Example: “A head mounted device” is intelligently mapped with “an apparatus that fits on a user’s head.”

Now, doesn’t that sound interesting? So do give PQAI a try. We look forward to hearing about your experience.

How to Do A Patent Search Yourself (Without Worrying About Complex Key Strings)?

Prior Art Search Made Easy With PQAI

Artificial Intelligence is changing the world around you. For example, from suggesting videos, you may like to drive cars. But can AI accompany you on your patent search spree? Let’s find out.

To Pursue or Not to Pursue? – That is the question

If you are one of the inventive types, you must have many ideas as you go about your day, as if problems are waiting for you to arrive and provide a solution. However, you also know the power and perils of ideas. Pursue the right one, and you can make a fortune; pursuing the wrong can lead to wasted effort.

So it would help if you pursued the ideas most likely to give you high returns. But how do you know in advance?

There is no simple answer to this question but a few loose rules. One is that it is better to pursue ideas that are new and never thought of before. This is important because if you market your idea, you can also get a patent. On the other hand, if your idea is not new, you won’t have exclusive access to it and may not even be able to market it.

Many inventors don’t pay sufficient attention to it. Or they assume that if an idea has not been turned into a product, they have no risk in bringing it to market. It couldn’t be farther from the truth. Only a tiny fraction of the actual ideas that have been patented are realized in the products. Therefore, it is vital to run a prior art check before you begin to pursue any idea and be sure that you would be able to patent and, thus, have exclusive rights to market it.

Patent Search | Challenges

Plenty of free resources are available for you to run a patent search. These give you access to thousands of patents. But navigating through that heap of documents is a task of days. Not just that, these search engines require you to create sophisticated search strings. Here is what a sophisticated boolean search string looks like:

The state-of-art patent search tools cater to those who know what to look for and how and where to look for them. But you are an inventor who might not have a legal background. Don’t worry, though. There is a patent search engine that understands natural language and is super easy to navigate through search results. It’s PQAI – Patent Quality Artificial Intelligence. When AI can drive cars, it can make the patent search less complex.

PQAI – An AI-Powered Patent Search Tool

When using PQAI, you don’t have to worry about keywords and search strings. You don’t have to worry about using operators to sieve your results. PQAI also helps you locate prior art without a classification search. Enter your idea into PQAI in plain English. And PQAI shall present to you only the top 10 results closest to your invention. The best part is, each result shows the relevant texts from within the document matching your query. This saves you from reading the patent documents or research papers in full detail.

It’s So Easy You Can Do It Yourself

Let’s assume that your idea is to create a lightweight, portable Bluetooth speaker with an in-built light that glows like a real flame together with your music.

Before investing time and resources into this venture, let’s check for related prior art using PQAI. Go to and enter the description of this invention in plain English. We did it for you, as shown below.

When we ran this query through PQAI, the AI algorithm curated the top ten most relevant representative results. And at the fourth position, we found a patent close to the invention in question. It’s titled – “Portable Bluetooth Camping Light.” Presented below is the snapshot of the result. It also contains a table showing query element mapping with the relevant text from the patent document.

Here are a few drawings from the patent mentioned earlier that match our invention query.

Look at the prior art shown by PQAI. The invention seems to be already patented by someone else. This means it might not be wise to pursue the idea any further.

It’s time for you to look for prior art matching your invention for real. Based on the results you receive, you can choose to modify your query. You can also save the results you like to view later. We are sure you would be surprised to see the insightful results matching your invention.

You can further modify the results by adding filters. For example, you can filter the results based on publication date, document type, and source.

How The Dataset Of PQAI looks like?

The results that PQAI curates for you are not limited to just patents. This tool gives you results that include articles, research papers, R&D, and more. PQAI’s database currently stands at 11 million US patents and applications and nearly 11.5 million research papers in the fields of engineering and computer science.

What sets this apart and allows you more time is how you consume the results you are given. The tool will provide representative results from different sectors relevant to your idea. Further, it extracts relevant snippets and maps them to different parts of your query. This saves the time you spend reviewing or analyzing an entire document to locate possible prior art

Let’s Sum It Up

PQAI has been created after mindful research and is still a work in progress. We have considered the concerns of inventors and are continuously training the AI engine to provide even better results. Easy, curated access to millions of documents and easy search navigation make this the ideal place to begin your patenting/entrepreneurial journey. Patent searches don’t need to be a chore anymore, especially for inventors like you! Happy inventing!

The Ultimate Guide To Prior Art For Inventors

The Ultimate Guide To Prior Art For Inventors PQAI

Inventors get many queries like: Can I get a patent on my invention? What is prior art? Can a YouTube video count as prior art? We have prepared this guide to prior art to help inventors succeed in their patent-seeking journey.

Can I Get A Patent On My Invention?

You came up with a breakthrough idea, a cot that can put babies to sleep using a particular vibration pattern and soothing music. Many parents can’t get enough sleep if their babies don’t sleep well at night. You have solved a problem that a lot of parents face. A lot of parents might be interested in buying such a cot. You see a possibility of a significant revenue stream. And because you feel your invention is novel, you see that it has the potential to be patented.

However, there is a possibility that someone else has already come up with a similar invention and received a patent on it. Now that patent is a “prior art” that can stop you from receiving a patent on your invention.

I hope this example gave you the basic idea of prior art. In this post, we have provided a detailed, visual, and very clear explanation of everything you need to know about it. We have also shared how you can conduct a “Zero Budget Prior Art Search.”

What is Prior Art?

Prior art is any evidence that an invention is already in existence or publicly available prior to the patent application filing date. The invention does not need to be commercially available or exist physically to be prior art. It suffices that the invention has been previously described or shown to be something that contains the use of technology like your invention.

So, if you file a patent without searching for prior art and the patent examiner finds that your invention is not novel (new), you receive ‘§102 type rejection’: “Non-novel or not new.”

21.28% of patent applications got rejected over a period of ~4 years from 2017 to September 2020 because they did not meet the ‘novelty’ criterion.”

There is one more common reason for rejection: ‘§103 type rejection’: “Obvious improvement over the prior art.”

46.95% of patent applications got rejected over a period of ~4 years from 2017 to September 2020 because they did not meet the ‘non-obviousness’ criterion.”

Note: Stats are based on the rejections (Final +Non Final) given by the Patent Examiners for the US applications from 2017 to September 2020

The §102 and §103 constitute 68.23% of the total rejections. This indicates that either an examiner found a prior art questioning the novelty of an invention disclosed or an examiner combined two or more references to prove that an invention disclosed is obvious.

Inventors must invest in a thorough search of past and present products and patents before they conclude on the novelty and non – obviousness of their invention.

Does this count As Prior Art?

A similar invention is available in a video on Youtube. Does this mean we cannot obtain a patent for that invention?

It certainly can, depending on the similarity with your invention.

Credits: r/patents

Inventors often have queries about what counts as prior art and if a piece of certain public information can be the reason for the rejection of their patent application. Such disclosures can act as a prior art depending upon the level of similarity with your invention.

Any invention publicly disclosed or made publicly available in any language or any part of the world may be considered prior art. 

It can be a:

  • a product that was available for sale,  
  • an invention used commercially, 
  • printed or electronic forms of articles, 
  • publications, texts, journals, 
  • presentation at a public event, 
  • or any form of public use of the invention.

An existing product or patent is the most obvious form of prior art. Unfortunately, inventors often assume that because they cannot find a current commercial product containing their invention, their invention must be novel. 

This assumption is far from reality. Inventions often never become products, yet there may be public records showing their existence. That record counts as prior art.

Does This Not Count As A Prior Art?

Generally, publicly available information after your patent application’s ‘effective’ filing date (or priority date) would not qualify as prior art.

Also, patent applications filed after yours generally would not qualify as prior art.

#sidenote: A trade secret does not count as prior art.

Also, patent applications that are filed after yours generally would not qualify as prior art.

#sidenote: A trade secret does not count as a prior art.

What Is Prior Art Under AIA:  §102, §103?

The America Invents Act (AIA) is a complex bill that includes a significant change to U.S. patent law. The AIA relates fundamentally to whether or not an invention can be classified as prior art, with arguably the most impactful change being the shift from a “first to invent” system to a “first inventor to file” system on March 16, 2013.

Section 102: First to Invent Vs. First Inventor to File

Pre-AIA Sections 102(a) and 102(e): Patents were granted using the “first to invent” system. The section provides that an inventor is not entitled to a patent if the claimed invention was already patented, described in a patent or is in public use by another inventor before the claimed invention.

For example, under old U.S. patent law, an inventor could rely on the earliest documented date of the invention and obtain priority to another inventor with an earlier-filed application.

AIA Section 102(a): Prevents a patent if the claimed invention was described in a patent or patent application filed before the effective filing date of the invention.

For example, under the AIA, the U.S. Patent and Trademark Office (USPTO) will award a patent to an inventor with the earliest effective filing date. The earliest effective filing date is the original date that the application was filed.

Section 103: Obviousness

Pre-AIA Section 103: Prevents patenting of an invention if it would have been evident at the time the claimed invention was made.

AIA Section 103: A patent may not be obtained if the invention had been obvious before the effective filing date of the claimed invention.

Example: Prior-art reference (1) teaches encryption. Prior-art reference (2) teaches how to send an email. Then sending an encrypted email would not be novel. It would be ‘obvious’ because there is a motivation to send emails in a form that would allow them to be read only by the intended recipient. 

How To Conduct A Prior Art Search?

  • telling your innovative idea to a friend,
  • and asking the friend if he has heard or seen something like it.
  • assuming that the friend knows it all if he says he has not heard of it;
  • your invention is new, and there is a possibility of getting a patent over it.

Because you want to determine if your invention is new or novel enough to get a patent on it.

However, that’s the ideal scenario and far from reality.

Free Prior Art Search Resources

It can be pretty challenging for an inventor who is (often) not skilled to conduct a prior art search. Although many free resources for patent or prior art searching are available, most do not understand natural language queries. To name a few:

Besides, companies often use deceptive language in patents to hide their I.P. activity from competitors. That makes it further challenging to find the relevant prior art.

Inventor’s Plea

Look at the snapshot below; we have picked this from Reddit (r/patents). Clearly, the inventor tried to look for prior art in USPTO and Google patents but faced difficulty with language and search navigation.

Credits: r/patents

Challenges To Prior Art Search 

The Right Keywords (Synonyms)

“Bendable,” “Foldable,” and “Flexible” are some synonyms that can be used to refer to the same aspect of an invention. Of course, different patent drafters would have used other terms. However, you don’t want to miss out on any relevant results; hence, it would be necessary to take care of synonyms while searching.

The Complex Search Queries

The patent databases are a vast set of information. Thousands of results are received for a single query. Complex search queries are the only way to narrow down the results. These queries are nothing but keywords arranged with a specific logic (Boolean (AND/OR) search string) to get relevant prior art results. 

The Patent Language

The patents are usually drafted in precise language, which is difficult for a person with a non-IP background to comprehend. For example, a computer might be written as “an information exchange system” to cover other similar devices like phones, tablets, etc. 

How Much Does It Cost To Get A Prior Art Search Done?

Professional Search Fees

According to UpCounsel if you hire a professional to conduct an in-depth prior art search, it shall cost you anything between $1000 to $3000 based on the complexity of the invention.  

  • $1,000 to $1,250 for simple inventions
  • $1,250 to $1,500 for slightly complex inventions
  • $1,500 to $2,000 for moderately or relatively complex inventions
  • $2,000 to $3,000 for highly complex inventions or software

Government Search Fees

The USPTO fee for conducting a patent search or prior art search varies according to the entity size. The fee is less for small and micro entities, so it’s less for individual inventors like yourself. ($40 to $700)

The snapshot presented below is from USPTO’s website, which shares complete details of patent-related fees.

Depending on the type of patent, the fee is different: Utility, Design, or Plant.

Here are quick definitions for different patents based on which you can decide which category your invention falls.

Utility Patent

It protects a useful machine’s process, manufacture, and composition of matter.

Design Patent

It protects the product’s shape, appearance, patterns, design, layout, or looks.

Plant Patent

It protects a new and unique plant’s key characteristics from being copied, sold, or used by others.

How To Conduct A Prior Art Search Yourself With Zero Budget?

You need money or skill to conduct a comprehensive prior art search. With money, you can hire an I.P. research firm to perform your prior art search. With skill, you can use other prior art search engines that require you to create sophisticated keyword search strings and go through hundreds of documents to find relevant prior art.

However, if you want to determine the novelty aspect of your invention, you neither need money nor skill.

Yes, you read it right!

We would say, don’t go by our words. Try it out yourself!

A quick search on PQAI (An Initiative by AT&T ) is enough for you to determine the novelty of your invention.

Prior Art Search Using PQAI

Here is how you can conduct the search:

  1. Go to
  2. You shall see a search query box as shown below.
PQAI Prior Art Search Tool Snapshot

3. Let’s try a sample query. How about – “A machine learning based system to sort out the waste based on the images captured by the camera in the past”?

PQAI Prior Art Search Tool Snapshot

Now, let’s take a look at the snapshot presented above. We found a prior art similar to the invention query we entered.

4. The best part is each result shows the claim mapping. That means the relevant section in the prior art document matches the invention query. This makes it super easy for the researcher to look for matching elements between the invention and the result.

5. The following options accompany each result:

  • Save – Like you bookmark the web pages, you can save the results. Saved results are available for your future reference.
Saved Results Section - PQAI Prior Art Search Tool Snapshot
  • Find Similar – This option helps in further refining the search results. As the name suggests, you get to see ten more similar results matching that selected result.
  • View Document – With this option, you can view the selected result document in a new tab. Example: If it’s a U.S. patent, you will be directed to the Google patents link for the selected patent.
  • Feedback – With a thumbs up or thumbs down, you can give feedback on the relevancy of the result.

6. If this has given you a decent idea of how you can use PQAI to conduct a prior art search. Go ahead and give it a try!

Once you establish the novelty of your invention, the next step is to fill out the invention disclosure form. An up-to-date invention disclosure form shall help you prepare for a meeting with a patent attorney.

Let’s Sum It Up

You don’t want USPTO to reject your patent application because someone else has already patented an invention like yours. To ensure the same, it’s necessary to find out if your invention is new before filing a patent. The easiest and most cost-effective way is using PQAI Prior Art Search Engine.

Besides determining the novelty of your invention, you need to check your invention for patentability. First, you must ensure that your invention is a patentable subject matter.

Conducting a prior art search benefits you in many other ways than just novelty determination. To name a few:

  1. Conducting a prior art search early in the process shall help you save resources that would have been otherwise used in pursuing it for patenting. It won’t make financial sense if someone else has already patented it.
  2. When you read the patents on inventions like yours, you will be better positioned to refine your idea. And refine it in a manner that it’s new and non-obvious.

If you have any queries about prior art searches using PQAI, feel free to write to us “[email protected]”.