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 led to AI being mainstream in the last decade. 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 to 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 U.S. 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 US20160070439A1Electronic 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, Lucyd Ltd.

Source – US20160070439A1

Source – Verdict

Before we look at the rest of the list, here’s an interesting insight. 11 of the top 20 AI inventors are either currently at StradVision or have worked there previously. 10 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 what their most cited patent is about.

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. 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 10 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.

StradVision

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 on 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 and holds 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 areas of 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 and holds 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 and holds a degree in Computer Science 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 has previously been 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. He holds 256 patents in the Instruments domain. 

  1. Donghun Yeo

Donghun Yeo is a Korean inventor and holds 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 consists of 824 patents in the Artificial Intelligence domain globally belonging to 264 unique patent families. The major chunk of his portfolio consists of innovations in Instrument Technology (255). 

  1. Myungchul Sung

Myungchul Sung is a Korean inventor and holds 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. The largest 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 Pohang University of Science and Technology, and an IT coordinator at the National Institute for International Education. 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 the Ulsan University as an Electrical Engineer as the Dean’s Honoured Graduate. He has previously been at Samsung Techwin and a Researcher at POSTECH from where he earned his Master’s degree. His research interests are in Deep Learning, Computer Vision, Machine Learning.

Kyungjoong Jeong’s patent portfolio has 824 patents in the Artificial Intelligence (AI) domain globally which belong 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 and holds a degree in Computer Science Engineering and 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 is an algorithm engineer affiliated with Stradvision Inc. 

His portfolio consists of 758 patents in Artificial Intelligence globally, belonging to 251 unique patent families. 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.

SourceUS10229346B1

  1. Kye-Hyeon Kim

Kye-Hyeon Kim is a Korean inventor and holds 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, 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. The largest chunk of his innovations is in the domain of Instruments Technology (242).

The most valuable patent in his portfolio is US10229346B1, 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. He holds 102 patents in the Instruments Technology domain.

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

  1. Craig Trim

Craig Trim is an American inventor and holds 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 globally which belongs to 144 unique patent families. 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 few others.

  1. Corville O Allen

Corville Allen is an American inventor and holds 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, 5-time North Carolina Inventor of the Year at IBM. 

Source: IBM News Room

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

His patent portfolio consists of 232 patents globally which belong to 142 unique patent families. He holds 120 patents in the domain of Instruments Technology.

Allen’s most valuable patent is US9369488B2Policy enforcement using natural language processing. The said patent family has been cited 119 times by 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: one example scenario, the user may attempt to use the device camera to take a photograph of an object within a physical location governed by the term of use policy document. Based on the procedure disclosed in teh patent  the user’s computing device may then take an 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 and 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 issued specializing in areas such as big data, cognitive systems, mobile devices, and predictive analytics. Martin is a Technical Content Creator Leader including the development of 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 patents filed globally which belong to 138 unique patent families. 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 said patent family has been cited 16 times by companies IBM, Sony Interactive Entertainment Llc, Amazon Tech Inc, Dish Network Llc.

Source – US9473819B1

  1. Jeremy Fox

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

Jeremy has also been serving as the IBM Commerce IDT Chair for over 3 years.

His patent portfolio consists of 128 patents in AI globally belonging to 110 unique patent families. 68 patents have been filed in the domain of 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 is to adjust the intensity of notification alerts based on the driving conditions is remarkable. For example: changing loud beep to just a vibration alert for a certain 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 who is currently a Senior Research Scientist at Sony Corporation.

His patent portfolio there consists of 614 patents globally which belong to 104 unique patent families. He has 99 patents in the domain of Electronics Communication Technique.

His patent US10178148B2Content 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 the Carnegie Mellon University. He is a Graphics Architect at Intel Corporation and 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. 77 inventions are related to instruments belonging to the Instruments Technology domain. 

His patent US10108850B1Recognition, 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 past. It’s intriguing to know what StradVision is upto. There is a commonaliity in many of these inventors’ most cited patents as well. 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 showed an interest in this article, we wish to share an AI-based initiative with you. It’s called Patent Quality through Artificial Intelligence. The initiative is focussed 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. Many inventors apply for patents without conducting a thorough prior art search. Usually, this is because there is a lack of budget or patent searching skills. Also, it’s quite difficult to search for non-patent literature while performing a prior art search. These reasons triggered in us 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 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 that only a few of these results are actually relevant and the rest are irrelevant, often termed as the “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 ends up taking the majority of your time. Mostly you are just spending time reading documents that you are going to eventually discard. It is therefore generally beneficial to be able to judge the irrelevance of a document as soon as you can. Unfortunately, most search tools put less emphasis on this part. Either they don’t help you judge 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 analyse this problem in some detail, ponder upon possible solutions, and see how PQAI aims to be of help.

Notorious Titles & Irrelevant Text

To keep things simple let’s assume we are doing a prior art search through patent literature. 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. This is an extreme example, I admit, but it is generally difficult to judge patents from their titles. 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/description. For a lot of results, therefore, you have to judge the relevance by opening those documents in a separate tab and then going through the full text, trying to pin down the relevant sections, if any.

A typical patent contains about 10-12 pages of text. 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 selects 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. You may still need to read lengthy documents but only the ones that are relevant. The ones for which the 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 going through irrelevant results versus the relevant ones. Getting an idea on the irrelevance of the search result without having to read it all can help you save a lot of time. With the aim to reduce this time PQAI brings the query element mapping feature in it.

When you search with PQAI, each result is accompanied with a query mapping table. 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, that sounds interesting, doesn’t it? Do give PQAI a try. We look forward to hearing your experience.

Do Prior Art Search Yourself With PQAI!

Prior Art Search Made Easy With PQAI

Artificial Intelligence is changing the world around you. From suggesting videos you may like, to driving cars for you. But can AI accompany you on your prior art 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 be having a lot of ideas as you go about your day, as if problems are just kind of waiting for you to arrive and provide a solution. You also know the power and perils of ideas. Pursue the right one and you can make a fortune, pursue a wrong one and it can lead to wasted effort.

So it is important that you pursue the ideas that are most likely to give you high returns. But how do you know in advance?

Well, there is no simple answer to this question but few loose rules of thumbs. One is that it is better to pursue ideas that are actually new and never thought of before. This is important because if you market your idea, you can also get a patent for it. If your idea is not new you won’t be able to have exclusive access to it and you 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 then they have no risk in bringing it to market. It couldn’t be farther from the truth. In fact, only a small fraction of the actual ideas that have been patented are realized in the products. Therefore, it is important 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.

The Prior Art Search | Challenges

There are plenty of free resources available for you to use to run a prior art 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 how 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 really have a legal background. Don’t worry though, there is a prior art search engine that understands natural language and is super easy to navigate through search results. It’s PQAIPatent Quality Through Artificial Intelligence. When AI can drive cars it can surely make prior art search less complex.

PQAI – An AI Powered Prior Art Search Tool

When using PQAI, you don’t have to worry about keywords and search strings. You also 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 light-weight, portable bluetooth speaker with an in-built light that glows like a real flame together with your music.

Prior Art Search | Bluetooth Speaker shaped like a lantern

Before investing time and resources into this venture let’s check for related prior art using PQAI. Go to projectpq.ai 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 7th position we found a patent that was pretty 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 above mentioned patent document that match with our invention query.

Looking at the prior art shown by PQAI. The invention seems to be already patented by someone else. This means it might not be very 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 that you would be surprised to see the insightful results matching your invention.

You can further modify the results by adding filters. 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 as of today stands at 11 million US patents and applications and nearly 11.5 million research papers in the fields of engineering and computer science. 

What really sets this apart, and allows you more time is how you consume the results you are given. The tool will provide you with representative results from different sectors that have a relevance to your idea. Further, it extracts relevant snippets and maps them to different parts of your query. This saves the time you would spend reviewing or analysing 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 taken the concerns of inventors into consideration 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. Prior art 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 so 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, say a cot that can put babies to sleep using a particular vibration pattern and soothing music. A lot of parents can’t catch 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 great 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.

Can I get A Patent On my Invention? It's too late

Hope this example gave you the basic idea of prior art. In this post, we have brought to you 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 filing date of the patent application. 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 that is like your invention. 

So, if you file a patent without the 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.

It is critical that inventors 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 very 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 a prior art and if a certain public information can be the reason for rejection of their patent application. Such disclosures can definitely act as a prior art depending upon the level of similarity with your invention.

Any invention that has been publicly disclosed or made publicly available in any language or in any part of the world may count as prior art. 

It can be 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. Inventors often assume that because they cannot find an existing 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, information that is disclosed or becomes available to the public after the ‘effective’ filing date (or priority date) of your patent application would not qualify 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. 

First To Invent Vs First To File Representation

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 who has 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 obvious at the time the claimed invention was made.

AIA Section 103: A patent may not be obtained if the invention would have 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?

The easiest way to do the search should be like:

  • 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 to get a patent over it.

Because you just 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

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

Besides that many times companies use deceptive language in patents to hide their IP activity from competitors. That makes it further difficult 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 on reddit

Challenges To Prior Art Search 

The Right Keywords (Synonyms)

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

The Complex Search Queries

The patents databases are a huge 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 a very specific language, difficult for a person with the non-ip background to comprehend. For example: a computer might be written as “an information exchange system” to cover any other similar devices like phone, tablet 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 to say it’s less for individual inventors like yourself. ($40 to $700)

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

USPTO Patent Search Fee Snapshot

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

Here are quick definitions for different types of patents based on which you can decide which category does your invention fall in.

Utility Patent

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

Design Patent

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

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?

To conduct a comprehensive prior art search you either need money or skill. With money you can hire an IP research firm to conduct the prior art search for you. 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 just 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!

PQAI Prior Art Search Tool Advantages: Zero Budget and No Prior Art Search Skills Needed

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 search.projectpq.ai .
  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 actually 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 that matches with the invention query. This makes it super easy for the researcher to look for matching elements between invention and the result.

5. Each result is accompanied with the following options:

  • Save – Just like you bookmark the webpages, 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 10 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 US 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 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 you file a patent over it. The easiest and the most cost-effective way to do so is using  PQAI Prior Art Search Engine.

Besides determining the novelty of your invention, you need to check your invention for patentability. You need to make sure that your invention is a patentable subject matter.

Conducting 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 which would have been otherwise used in pursuing it for patenting. As it won’t make financial sense if someone else has already patented it.
  2. When you read the patents on the invention like yours, you shall be in a better position to refine your idea. And refine it in a manner that it’s new and non-obvious.

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