Blog, India, Industry News, Intellectual Property, IP industry, Tips


Biological material is an alternate to produce renewable energy called Biofuel. Biofuel offers a possible alternative to petroleum-based fuels or fossil fuels. The explosive and healthy growth in the biofuel market has constantly been catching eyes over the past decade.  The main goal to embrace biofuel is to break the rusty chain of environmental pollution and importing oil adding to the county’s economy, subsequently, it will lend an eager hand in replacing the dependency of nonrenewable resources, mitigating greenhouse gas (GHG) emissions, and patronizing economic development. Biofuel is playing a significant role in sectors like aviation, navy or maritime transportation, etc. which is leading to expansion in consumer demand. A glimpse of biofuel advancement is started from corn starch, sugarcane, rapeseed, soy, etc. as a first generation biofuel, and followed by non-lignocellulose feedstock as second generation biofuel. Feedstock such as Jatropha oil (third-generation biofuel) and genetically modified microorganism (fourth-generation biofuel) are trigger point in cutting edge technology for increasing the biofuel production. Overall, the third and fourth generation biofuel technology will unlock an opportunity to bring forth a large-scale production potential across the globe.  The article will be witnessing the curve of market outlook and forecast, Jurisdictions ladder, competitive landscape in the biofuel market.

The global biofuel market spike has been predicted to jump up at an approx. rate of 2.24 percent over 10 years between the years from 2016 to 2026. Accordingly, an estimation to touch USD 11.59 trillion by the year 2026 owing to the hint of how it will be rapidly growing. Feedstock like coarse grain and vegetable oils are expected to be highly used in biofuel production. The global consumption of biofuel reached about 153.4 billion per liter in 2016, further it has been projected to increase modestly by the period of 2026, which might be around 173.5 billion per liter at the growth rate of 1.2 percent. Factors strengthening biofuel market are:

  • Biomass-based fuels for transportation has a great potential to cut down on emissions produced by vehicles
  • Price is lower than any other nonrenewable resources
  • Sustainability in biofuels as their various uses or applications have increased for the past years as a way to increase energy self-sufficiency
  • Reducing net trade costs, biofuels is getting a grip on several industrial sectors.

The eye-catching healthy rapid growth has become the strategic focus for many business firms for gaining profits over the past decade. It can be noticed the concrete proof of a drastic increase in the biofuel market in terms of its use in aviation, marine transport domains.

Sustainable fuel mostly drives the demand for bioenergy in the transportation domain across the world. It has been studied that biofuel consumption in most of the countries will be interlinked to local or domestic demand. Additionally, in regards to Biofuels Renewable Energy Directive, the policy framework in the European Union has already mentioned that the consumption of renewable energy including non-liquids would jump up to 10 percent of total transport fuel consumption in 2020 on an energy equivalent basis. Also, as per Fuel Quality Directive, fuel producers would require to reduce the greenhouse gases (GHG) intensity of transport fuels.

Europe is continuously holding the top position in biofuel production, while on the flick side, the US is capitalizing on a great deal of raw material availability and playing a dominant inning in the market. The two countries – US and Brazil are the key ethanol suppliers. Brazil is emerging as the main producer of biofuel energy followed by Argentina and Indonesia showing prominent growth. The policies in all these countries influence the biofuel production patterns.

In the European market, biodiesel rules the fuel segment. The biodiesel is certainly preferred over ethanol, diesel and petrol due to the existing energy taxation policy, which brings about a heavy dependence on biofuel indicating around 70 percent of its transport fuel market. Therefore, biodiesel accounts for 80 percent whereas ethanol stands at 20 percent of the biofuel market. Europe, the leading producer of biodiesel, is constantly tightening the grip on the biofuel market among the key players like Brazil, the US, Argentina, and Indonesia. In the current year 2020, European biodiesel production has been expected to have a substantial rise in production.

The leading US biofuel market is expected to be growing at a rate of 4.6 percent and will reach around USD 7336 billion by the end of the year 2026. The industry analysis reports portrayed a different picture, it is speculated that the US market might not come to amply fulfill the biofuel requirement, and the compliance gap could stand out till the year 2030. The US lays great emphasis on the EISA (The Energy Independence and Security Act) Renewable Fuel Standard (RFS) policy. The policy aims to boost the required volumes of renewable fuel being used in vehicles.

Major key players like GEVO, POET, DuPont, Neste, and Bluefire are producing biofuel to fulfill the global demand of fuel. Australia based Algae Tec company cultivates algae for the production of biofuel, for which the company is using marginal land or industrialized locations. Furthermore, it is more productive as oil and hydrocarbons per landmass than any other terrestrial crop. The US based company Butamax Advanced Biofuels has invented bio-isobutanol production technology with reduced production cost in order to provide a high-value drop-in biofuel for transportation fuel supply.

Recent patent filing trends support the market growth of biofuel, wherein patent filing originating from European countries take first place across the globe, followed by US. Also, it has been noticed that innovation is more focused on genetic development for enhanced biofuel production through using different microbial strains. Apart from the genetic development, other secondary areas of innovation cover enzymatic hydrolysis process, waste to biofuel, etc.

In conclusion, it seems biofuel is emerging area that will lead automotive and renewable energy sector. Biotechnological advancement, waste management, strict environmental regulation for gas emission, high demand of electric vehicle, rise in patent activity are the key driving factors that will support the biofuel market to tremendously grow in near future.

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AUTOMOTIVE INDUSTRIES, Blog, Industry News, Intellectual Property, IP industry, Tips


Emission from the transport sector has major contribution to climate change. It forms about 15% of annual emissions which also includes non-CO2 gases as well. 72% of the global transmission come from road vehicles. The top 10 countries with largest transportation emissions in 2014 were: United States, China, Russia, India, Brazil, Japan, Canada, Germany, Mexico and Iran. These countries in conjugation form 53% of the global transport emissions in 2014. Therefore, an eco-friendlier energy is required for the transport sector.

Electric vehicles i.e. vehicles which use electric motors or traction motors for propulsion. They provide a better substitute for petrol/diesel vehicles. An electric vehicle can be powered through various self-contained power collectors like battery, solar panels, fuel cells or an electric generator as well. Fuel cells have greatest energy density as compared to others. According to U.S. Department of Energy’s February 2019 report, the number of fuel cell vehicles on the road in America grew to 6,500 from 4,000 in 2015.

A fuel cell is an electrochemical cell that converts the chemical energy of a fuel (often hydrogen) and an oxidizing agent (often oxygen) into electricity through a pair of redox reactions. There are various fuel cell combinations which have high energy density like: Silicon-air, aluminium-air and other metal-air fuel cells. Since, fuel cells have large energy density, higher charge can be stored and can be used for variety of applications from large scale to small scale which is also evident from the patents landscape application areas as shown in figure. Such as:

  • Stationary fuel cells can be used for primary and backup power generation in commercial and residential uses.
  • Fuel cell electric vehicle have high efficiency between 42-59% with only 10% degradation. A fuel cell that runs on hydrogen produced natural gas could use about 40% less energy and emit 45% less greenhouse gasses than an internal combustion vehicle. The scope can be extended to buses, trains, boats, submarines, airplanes etc.
  • Portable fuel systems can be made with micro fuel cells primarily for phones and laptops.

Additionally, due to the fact that hydrogen fuel cells emit only water and heat, they emit less pollutants as compared to combustion engines. Due to lesser moving parts, less heat is generated and a quieter engine operation is obtained.

Since 1932, GE has been adding to the development of fuel cell. General motors developed Chevrolet Electrovan in 1966, which was first fuel cell road vehicle. Advancements in Fuel cells have come a long way. The first commercially produced hydrogen fuel cell automobile, the Hyundai Tucson FCEV, was introduced in 2013, Toyota Mirai followed in 2015 and then Honda entered the market.

Strengthened by the beliefs of the growth in this field. The application area has been extended to Submarines, Aircrafts, Ships and buses.

  • “HY4” is the hydrogen fuel cell powered passenger aircraft which was launched in 2016.
  • “Forze VII” is a student made racing vehicle made in 2016, which competes against petrol powered cars with a LMP3.
  • “Energy Observer” is a first of its own kind. It can generate and be powered by hydrogen. It was launched in April 2017 for a world tour lasting 6 years in order to optimize its technologies.

What future landscape holds?

A significant surge in total in number of patents filed can be seen in above mentioned graph. Also, it is clearly visible that Toyota has been a major player in the formation of the today’s landscape of Fuel cells, whereas GM has lost its roots.

Toyota and the Japanese Aerospace Exploration Agency (JAXA) announced plans for a hydrogen fuel-cell lunar rover – Toyota lunar Cruiser. Toyota said the rover would be able to operate on the moon for up to six weeks, with a range of 1,000 kilometres (about 621 miles) per tank of hydrogen. Solar panels will provide supplementary electricity.

NYK Line, Toshiba Energy Systems & Solutions Corporation, Kawasaki Heavy Industries Ltd., Nippon Kaiji Kyokai (ClassNK), and ENEOS Corporation will develop an about 150-ton class (passenger capacity approximately 100) high-power FC vessel that will function as a medium-sized tourist ship.

Toyota & Honda have partnered on fuel cell bus mobile power generation venture to supply electricity in disasters. It can be used to supply power during disaster such as typhoons and rainstorms. When a power grid is damaged people suffer from an interruption in the supply of power to their homes and evacuation centres this bus will provide electricity in an affected area.

For Further Information: –

Author: Hitesh Dhiman

Effectual Knowledge Services Pvt Ltd.

View website

Email :

Tel: +44 207 993 8632

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Artificial Intelligence, Blog, Industry News, Intellectual Property, IP industry


Artificial Intelligence (AI) is a branch of science that simulates human intelligence in computer like machines that are programmed to think like humans and exhibit traits like learning and problem-solving. AI uses different digital inputs to gather multiple arrays of data. The data is then processed by AI analytical models for fast and effective output of information with human-like intelligence. Data processing is usually executed with the assistance of two sophisticated technological subsets of AI, which are Deep Learning and Machine Learning that help the machines to constantly learn and develop its knowledge.

With the advent of AI, the collective output of machines has greatly improved. Some of the most primitive and tedious machine based functions are largely simplified with the use of complex programming algorithms used in AI. Currently, AI has impacted almost every industry sector. In the past few years, the Healthcare sector has also seen a gradual upsurge in the adoption of AI based technology for the different healthcare divisions. AI healthcare is broadly employed for segments like patient care, drug discovery, and personalized treatment. Medical imaging has emerged as one of the most potential healthcare segment with a major technological AI upgradation. This upgradation has proved effectiveness in improving the quality of internal imaging using various radiological imaging techniques such as X-ray radiography, magnetic resonance imaging (MRI), medical ultrasonography or ultrasound, computed tomography (CT), and nuclear medicine functional imaging techniques like positron emission tomography (PET). Integration of AI with such imaging techniques can facilitate reviewing of an image and identify potential findings within it by searching a patient’s history or other parameters related to the particular anatomy scanned.


AI in the healthcare sector has not only improved the accuracy of disease detection but has also reduced the treatment time. The potential of AI in improving medical imaging lies in:

  1. Improved Automation: The work flow can be synchronized, i.e., the individual radiological instruments can be collectively regulated.
  2. Better image interpretation: Well taught machines can analyze minute details more effectively.
  3. Effective diagnosis: AI can predict about the diseases more accurately. Studies show high competence of AI in predicting early stages of cancer.
  4. Help doctors: AI can represent only the relevant parts in brief. This helps the doctor conclude quickly.

Some really interesting case studies where AI was employed to improve the diagnostic efficacy through medical imaging are as follows:

  1. Harvard Medical School’s deep learning system can diagnose breast cancer with an accuracy of 97 percent compared to 96 percent of a radiologist. Aided by the diagnostic system, the radiologist’s accuracy improved to 99 percent.
  2. McMaster University’s deep learning system detects Alzheimer’s disease with an accuracy of 98 percent to 99 percent by using magnetic resonance images, compared to 84 percent accuracy of previous computer vision algorithms.

The case studies above are indicative towards a collective approach, where AI can assist the radiologists to increase the value they provide. This is usually done by training the AI system to recognize normal anatomy through typical scans from CT, MRI, ultrasound or nuclear imaging. The AI algorithms used in machine automation help the machines to read medical images by identifying patterns within the image the way radiologists do. Patent trend analysis of the AI in medical imaging field highlights patent filing growth in the field, wherein companies (Philips, Siemens, Smith & Nephew) originating from USA has been top patent filers in the domain. Other countries like India, Germany, Great Britain are also active in the domain. It has been observed that the companies are filing patents in collaboration with academic institutions, like Yale University and British Columbia University.

The market for diagnostic imaging equipment and devices is relatively mature. The market is controlled by four global conglomerates with a combined market share of 80 percent in diagnostic imaging: GE Healthcare, Siemens, Philips, and Toshiba. These four companies dominate the market in the field of Radiological Information System and Picture Archiving and Communication System (RIS/PACS), and Advanced Visualization (AV). Toshiba is endeavoring to expand to the PACS/image analytics market through collaborations with other companies. The AV market remains highly fragmented, with a large number of small companies competing in niche markets. AI is consistently improving both (RIS/PACS and AV) the approach and general access to reliable and accurate medical image analysis, with help from digital image processing, combined with pattern recognition and machine learning AI platforms. For example, a start-up called Butterfly Network has developed a handheld 3D-ultrasound tool that creates 3D images of the medical image in real time and sends the data to a cloud service, which then identifies image characteristics and automates a diagnosis. This kind of clinical support from AI is expected to have a significant impact on the overall medical imaging diagnosis market and its growth. In further instance, Arterys developed an AI algorithm using MR images to draw up the contours of the heart’s four chambers, measuring precisely how much blood they move with each contraction. Cardiologists usually need 30 to 60 minutes to calculate the volume of blood transported with each pump, but Arterys’ AI comes up with the answer within seconds.

The implementation of AI technology into the healthcare sector has following challenges:

  1. a) Availability of Structured and Standardized Data: The lack of sufficient quantities of high quality structured and standardized data, as the data in the healthcare industry comes from different sources such as electronic medical records, laboratory and imaging systems, physician notes, and health-insurance claims.
  2. b) Eating Away Jobs: As Artificial Intelligence gains deeper access to work and personal life, it demonstrates that the biggest threat to mankind is the replacement of humans with machines.
  3. c) Patient Hesitation: There is a degree of patient pushback against being “forced” to engage with a “machine,” whether the AI system assists with patient engagement/communication or provides high-level clinical related services.
  4. d) Data security: Security and data privacy is also a significant challenge for Artificial Intelligence. e) The structures of the human body present great variation in terms of normal dimensions and textures, and this variation potentially masks pathological conditions.

The current business strategy among many large companies in diagnostic imaging is to leverage licensing agreements and work collaboratively with technology suppliers, rather than to acquire these companies outright. In order to make up for the lack of commercial funding available from traditional venture capital resources for imaging technology, most of the key imaging OEM’s have established corporate venture funds. For example, Siemens Venture Capital Healthcare, Philips Healthcare Incubator, and the GE Healthymagination Fund.

An important trend in diagnostic medical imaging is a growing interest in fusion and multimodality imaging. As the market for diagnostic imaging equipment matures, new opportunities are emerging for imaging modalities that can be used by mobile doctors or health-care workers in the field. Another major trend is the idea of smaller, portable imaging machineries. The global medical imaging market is facing increasing competition from refurbished systems due to the high cost of devices and installation in developing markets. IBM/Merge, Philips, Agfa, and Siemens have already started incorporating AI into their medical imaging software systems.

The growth of the global Healthcare AI market is directly correlated with the existing economic conditions and health status (e.g. COVID-19 pandemic situation) across the globe. The rising level of disposable income has propelled the spending trends on healthcare. In addition, the improving global economy is expected to take a step further in the years ahead and catalyze the growth of AI in healthcare industry. Increased global investment in the healthcare sector has facilitated AI based developments world-wide. AI has emerged as a potential alternative to the traditional healthcare sector by improving and standardizing the primitive technologies. It is believed that the advent of AI in medical sciences has revolutionized its basis by inculcating a cooperative and synchronized association between machines and doctors.

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Blog, India, Industry News, Intellectual Property, IP industry, Patent

The growing role and potential implications of AI on patents

As artificial intelligence systems start to co-author patents, how they are treated as part of the organization is going to be a challenge for companies using them for patents.

AI is without doubt the latest buzzword in town. Although it’s not as recent a phenomenon as one thinks, it has definitely penetrated our lives in the recent years more than ever. Be it Google assistant, Amazon’s Alexa, or Apple’s Siri – all of them are powered by a backend AI engine, which is ever learning and ever improving.

Although, AI is penetrating every field now, pharma was one of the earliest adopters of the technology utilizing the AI engines in drug discovery, diagnosis of disease. In 2017, the researchers at Stanford university trained an AI engine using 129,450 clinical images, and then tested its performance against 21 certified dermatologists in identifying Skin diseases. The AI was proven to be as efficient as human dermatologists in recognizing skin cancer. When it comes to patents and AI, there are several issues as discussed in following paragraphs.
Patentability of AI based inventions
As much as it is complex in nature, and ever learning, and ever improving, AI is a software, and software patentability has been frowned upon by law across the globe. In Europe, Article 52 of the European Patent Convention clearly states that “The following in particular shall not be regarded as inventions: schemes, rules and methods for performing mental acts, playing games or doing business, and programs for computers”.

Similarly, the Indian Patent act in Section 3(k) also states the computer programs as not being patentable per se. Although, the law seems to be united against the patenting the AI, but there have been workarounds for patenting the software, for example, in India, the software patents have been allowed in case they are tied up with a hardware, for example, the computer processor on which the software executes, so AI related inventions will not likely be very different.

AI as an Inventor

Perhaps, the most interesting issue in the patenting industry is of the AI being a named inventor of the algorithms it develops. DABUS (“Device for the Autonomous Bootstrapping of Unified Sentience”) is an AI system developed by Dr. Stephen Thaler, which was also named as an inventor in two patent applications on the technology that was developed by the AI. The patent applications, with DABUS as inventor, were filed in US, UK and European Patent Office.

All the three patent offices rejected the patent applications that named DABUS as inventor. The key grounds for rejecting the patent applications were that the laws, as written, envision a natural person to be an inventor and not an AI.

Clearly, there’s a gap in the current legislations which the law makers across the globe need to ponder over and address in case AI systems were to become inventors.

But things do not stop there, in case an AI invented something, who owns the technology and the intellectual property generated thereby?Most of the organizations own the intellectual property generated by the employees, as they are working for the organization for emoluments. Is it safe to consider that DABUS, was working under the supervision of Dr. Stephen and his organization, therefore, the intellectual property belongs to the organization? That would that make DABUS an employee of the organization, giving the AI legal rights.AI being an ever learning system, what if DABUS further evolves and says no?

AI assisted patent intelligence

AI has impacted many industries, and Patents is no different. The major benefit of the AI would be to make the process more efficient and faster across all levels – the patent searching, patent examination and grant process, and even patent licensing. The patents data is very structured across the globe – there is a defined way to draft a patent, there are defined sections in patents that contain specific information.

If AI systems can help consume unstructured data and observe trends from it, absorbing patents data to identify patterns will be a relatively easy task for an AI engine. AI is already integrated in may patent searching tools today, and are making the searching process more and more efficient.

For the same search query, a search engine backed by AI results in less false positive results.AI based translation engines are making more and more patents accessible to public by translating patents to English. The Indian Patent Office is also interested in leveraging the AI based engines to make the patent granting process more efficient.


From the standpoint of patents, the AI is still in the nascent stage and we are yet to witness the full extent of the benefits it can provide. There is no denying that the AI systems will prove to be tremendously beneficial to everyone by making the process more efficient and faster. Also, the legislation across the globe seems to be the lagging behind, as most of the patent laws today were written in an era when AI was just a science fiction, which will need to be updated to match the recent phenomenon.

The author is Vice President, Effectual Services- Advisory firm that offers IP support solutions

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Blog, Industry News, Intellectual Property, IP industry

Blockchain- Transforming And Revolutionising Intellectual Property Rights in India

“The ultimate aim of making the innovational sphere a fair playfield to nurture as well as promote innovation, depends solely on the amount on reliability as well as transparency we can offer. The introduction of the Blockchain to the supply chain is the catalyst we need to achieve all of that.”

The spark towards the information revolution kindled by Blockchain is the modern-day counter-part of what steam engine and other technologies did for the industrial revolution back then. The distributed ledger introduced to the world back in 2008, is pushing the boundaries of the digitised world by revolutionising every industry from Fintech to real estate and has the potential of bringing another radical transformation due for the mankind since the advent of Internet. The biggest uncertainty regarding the digital ledger paying back to the IPR industry for the protection it has offered to the notion since its conception is no longer followed by a question mark, as PTOs all over the world are exploring the different applications of the immutability, reliability, transparency and security offered by Blockchain to the various aspects of the life cycle of IP rights.

Blockchain: Applications in IP

The first and foremost application of the technology in the domain takes the form of a “Smart register” for maintaining an online registry of IP assets and registering patents, trademarks etc. to put an end on the slowpoke and money sinkhole disputes surrounding the ownership of IP assets. Another application lies in tracking the forgery of protected goods by mounting a Blockchain base tag to track the entire lifecycle of goods. Further, in the PTOs, the technology can redefine the working of every stage from filing-to-grant of the application by minimizing the human effort required and can hence reduce the time-to-grant for the applications, something, the inventors and PTOs have collectively wished for throughout the years.

Blockchain in IP: The Indian Landscape

Protection of the assets and IPR rights has always been a worrisome area in India and has even obstructed or atleast impeded the foreign investments in certain sectors. For the same reason, the Central Government has been keenly working on strengthening the IPR regime in the country and the recent steps by the government, like expediting time-to-grant, promoting IPR in the educational sector, increasing the number of examiners etc. have all been in the same direction. But the greatest step towards the notion can be seen in the form of the recent tender issued by the IPO exploring the use of AI and Blockchain to form the platform “IndiaChain”. It is expected to be the world’s largest blockchain implementation program in governance.

The IPO aims to revolutionise the IP process via the infrastructure by using the hash-based technology to improve the experience for inventors as well as examiners. By streamlining the registration process via Blockchain, the IPO purports to be able to foretell the timelines for the inventors regarding the different actions of the office as well as rectify the disparities surrounding the first to file rule among the applicants.


Despite the concerns surrounding the enormous task of inter-connecting IP registries and the massive power requirements of a Blockchain-based infrastructure, IPO has laid down the foundation stone for the amalgamation of the technology into the IP ecosystem. If the prophecy regarding the technology being even more beneficial to the innovational sphere than the finance domain, turns out to be true, we might see IPO moving towards more advanced uses of the technology like ledger management, a supervisory authority for tracking the use of IP assets in the market and their commercialization for investors via a bidding system; providing a central and government-backed market place for innovators to catch the eyes of the tech-titans etc. and many other potential applications bringing the revolution promised by the ledger to the table.


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Blog, Copyright, IP industry

Copyright and the Fair use doctrine

Definition of Copyright

Copyright is a type of intellectual property, that provides exclusive rights to its owner’s to make copies of the original work. The very basic definition of copyright is the right to copy. This means that work can be reproduced only after the consent of its original creators or anyone they have given authorization. The creative work can be in a literary, artistic, educational, or musical form.

Fair Use & The Four Factors for Determining the Fair Use

The fair use doctrine is described more precisely as the troublesome and the most problematic doctrine in all of copyright law. It’s because it is an open-ended doctrine. The Federal courts of the United States created Fair Use doctrine from a very famous 19th decision by Justice Joseph Story known as Folsom versus Marsh. The case was related to copywriting a publication relating to George Washington’s letters and copies of it. Justice Story formed the basis of the modern fair use doctrine which considers various factors to determine whether the copied property amounts to infringement or not, while considering the purpose and overall circumstances.

It is codified at 17 U.S.C. §107 and states that “the fair use of a copyrighted work, including such use by reproduction in copies or phono records or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright.” Therefore, there are no hard-and-fast rules, only general guidelines and varied court decisions, this was done to provide expansive meaning to the definition of fair use.

Judges use four factors for measuring and determining the fair use to resolve the disputes related to fair use. These factors are not definitive; these are only the guidelines that courts are free to adapt to a particular situation on case-by-case basis. Alternatively, a judge has freedom when determining the fair use, therefore the outcome can be hard to predict in any given case. In each instance, all four factors have to be applied to the copying, and then once the factors are applied and weighed, one can determine whether it’s fair use or not. The four factors judges consider are:

  1. What is the purpose and character of the use: The fair use doctrine in most cases will be fair for Non- Commercial uses and Non-profit educational uses, whereas, this is not entirely true all Non-profit education and Non- Commercial uses. Transformative use may qualify for fair use. Transformative use may be for purpose such as scholarships, research, or education. Also, parody may classify as fair use because the parodist transforms the original work.


  1. What is the nature of the copyrighted work: When a new work is being made from a copyright it’s better to copy from published work than unpublished work. The scope of fair use is quite less for an unpublished work as compared to published work, since the author has right to control the first appearance of their expression in public. More particularly if the facts are copied from any copyrighted material it still may come under fair use.


  1. What is the amount and substantiality of the portion used: This factor is defined on the basis of the amount that is copied. The amount copied is directly proportional to the chance of fair use of any copyrighted work. More the amount copied lesser will be the chance of getting under fair use, similarly, lesser the amount copied more will be the chance of fair use of product. Further, even if you take a small portion of a work, your copying might not be fair if you copy the core or the most important aspect of the work.

  1. What is the effect of the use upon the potential market of the copyrighted work: This factor considers if there is any depreciation in the income of the copyright owner or undermines a new or potential market for the copyrighted work. If the work competes directly with the original work a law suit may be filled.



Fair use doctrine is a very important aspect of the copyright. It helps to strengthen the protection that is given to the citizens. The fair use doctrine helps to provide wider scope to the federal courts and provide better judgement. It helps to provide a more adaptive approach on the case-by-case basis.


Hitesh Dhiman

Effectual Knowledge Services Pvt Ltd

View website

Email :

Tel: +44 207 993 8632


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Blog, Intellectual Property, IP industry, IP Litigation, Patent, Patent Infringement

Efficient use of search tools for prior art and patent searching

Potential applicants are recommended to carry out a prior art search to check whether their invention is novel. These searches can create stronger claim development, as well as verifying that the invention does not infringe another’s intellectual property, thereby avoiding any potentially expensive litigation further down the line.

When performing a prior art search, patents are the main (and best) source for identifying patent documents (non-patent literature can also be useful). A patent database search will look for particular ideas and technologies by keyword, classification, date, inventor and assignee, among other things, and can be carried out on numerous free and paid databases.

Free patent databases

Since each country carries out the patent examination procedure before granting exclusivity rights to an invention, most have developed their own search portals in order to carry out an initial patentability examination and simplify the examination process.

To further enhance the efficiency of these portals and keep up with the rapid pace of development, most countries have moved towards the amalgamation of machine learning and AI to organise patent documents (eg, into International Patent Classification (IPC) and/or Cooperative Patent Classification (CPC) categories, along with other docketing processes based on technical divisions).

The top free databases with research and analytics functions are provided by:

  • Google Patents;
  • the EPO;
  • the USPTO;
  • the Japan Patent Office;
  • the Korean Intellectual Property Office;
  • the Canadian Intellectual Property Office;
  • the China National IP Administration; and
  • IP Australia.

Third-party databases

There are several paid databases that operate on deep-learning AI and natural language processing, which provide a comprehensive guide to portfolio analysis, document comparison and high-performance searches across a large number of countries. These are equipped with machine translation to translate all non-English patents into English for easier understanding. The AI ​​behind these databases further enhances other important features, such as filtering and sorting the shifting of patented datasets. Another interesting function of these tools is the similarity search, which looks at phrases or paragraphs in the patent and showcases a list of similar patents based on the input.

The formation of a query depends on the analyst’s know-how of the concept and its iterations. These searches are built with synonyms, proximity and Boolean operators and different types of classification (eg, IPC, CPC and US and F-terms) to obtain the exhaustive set of prior art documents related to the concept disclosed in the invention.

The abovementioned features are common to most paid databases. However, the most appropriate and unique specifications of several paid third-party databases are listed below.

Orbit, Questel

This is a highly respected database to perform patent searching for prior-art searches and landscape analysis. It can access more than 54 million patent families, 100 million patents and 12 million design patents. It also provides worldwide patent coverage.It is possible to search for non-patent literature as it provides access to 108 million scientific publications, including books, research papers, journals and articles.Further,efficient resource sharing is available due to its sub-account feature. Here, multiple sub-accounts can be associated with a single primary account, which allows for the export of a shortlisted patent dataset to these sub-accounts for further analysis, while the primary account remains free for subsequent formation and running of search queries.

Derwent Innovation, Clarivate

This is an excellent tool for projects that require long and complex search queries, as it allows significant flexibility to a patent search process, especially in the life sciences and wireless sectors. Data intelligence provides a stunning graphical representation of the patent dataset for results interface based on assignees, inventors and legal states, among other things, which can come in handy in landscape projects. Further, it provides advanced filtering features such as forward and backward citations, legal status and INPADOC patent family searches.

Patseer, Gridlogics

For a service provider or an innovation-driven company, Patseer can function an optimum decentralised solution, as it allows the creation of a group where multiple members can work, customise and manage their project online.It can work as a boon in case of a landscape project, as different members can create an online taxonomy where they can add or remove patents in that category and can directly export the dataset or charts based on this later on. Sorting and filtering the relevant patents is much easier with this database, as it provides an outstanding dashboard for visualisation and analytics where it is possible to play with the results in multiple ways. There is also a filtering feature based on the number of occurrences of particular keywords used to search for a patent.


To be a unicorn in the field of ideas and technologies, you need to keep an eagle eye on competitors, which the insights feature of Patsnap helps to achieve. This is useful in identifying the patent value, top authorities, patent type and filing trends of any desired company.For an individual patent attorney or strategist, the most tedious task can be done with a single click by using the playbook list feature, which analyses patent value, litigation threat and litigation history, and simulates a merger and licensee locator. This databaseprovides a quick check on whether a patent has contributed to any standard.


R&D professionals and inventors need tools to efficiently analyse the disclosures closest to their innovation from a pool of ideas presented to them. Although free tools provide easy access to every individual, they also have drawbacks, which may affect the accuracy and efficiency of the search process and potentially risk the project. These holdbacks can be corrected by including features such as Word Intelligence to automatically highlight the synonyms of keywords used in the search strings, graphical and statistical analysis of datasets, and patent sorting and filtering based on the high relevancy order. It is hoped that some of these features will be included in subsequent updates to other patent databases.

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Nitesh Chaurasiya
Effectual Knowledge Services Pvt Ltd
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Blog, Copyright, India, Intellectual Property, IP industry

Protecting AI inventions

When AI was first introduced, one of the founding fathers of the technology Professor John McCarthy said that: “AI is the science and engineering of making intelligent machines, especially intelligent computer programs.” Since then, there has been a  vast amount of breakthrough research in the industry, which is evident from the fact that almost 386,428 related patent applications have been filed in this field (according to the Derwent database).

AI as an inventor

From tools and services to products and consultancies, AI has created a number of revenue-generating opportunities. It has already simplified a number of tasks and now, with the help of neural networks, it is inventing new ways to solve problems. Further, certain privileges have been granted to corporate entities (eg, Facebook and Google) so that they can defend themselves in court. It therefore follows that AI should be able to own its patents. However, debate is ongoing and requires considering where the line between creation by human and machine should be drawn and how much (or little) human input or guidance is required.

Recently, there was a case where the EPO refused European patent applications EP18275163 and EP18275174, which designated DABUS – a machine described as “a type of connectionist artificial intelligence” – as an inventor. One application was for a new type of beverage container based on fractal geometry and the other was for a device for attracting enhanced attention signals, which could be helpful in search and rescue operations. Similarly, the USPTO and UKIPO have disqualified patent applications on the grounds that a non-human cannot hold inventorship as per these countries’ laws.

Protection for AI inventions

When IP laws were formed, it was beyond imagination to consider naming a machine as an inventor. So how can AI inventions be protected in the current IP system? For the time being, this falls under copyright and trade secret laws.


According to basic guidance in the Compendium of US Copyright Office Practices, works produced by a machine with no creative input or intervention from a human cannot be given authorship. Though the computer programs responsible for autonomously generating works are the result of human ingenuity, their source code may be copyrighted as a literary work under the US Copyright Act. In other recent cases, the Shenzhen Court ruled that AI-generated articles are entitled to copyright protection. The Shenzhen Nanshan District People’s Court recently ruled in favour of plaintiff Shenzhen Tencent Computer System Co Ltd in its claim for copyright infringement against Shanghai Yingmou Technology Co Ltd for an article written by AI software Dreamwriter. Ownership and accountability of AI for copyright IP protection is still under debate and is being discussed in many IP offices.

Trade secrets

AI models, tools and data can be kept as trade secrets. Currently, trade secret laws may be used to protect any derived data or additional software code created by the AI. This offers the competitive advantage of avoiding a need for public disclosure of information. Further, proprietary technology lasts longer than patent protection. Trade secrets may provide the broadest scope of IP protection, including information such as algorithms, source code, methods, techniques, processes and the way a business utilises AI to implement machine learning. Also, trade secrets provide immediate protection, without the costly or lengthy registration process required by other forms of intellectual property. Trade secret protection might be well-suited for rapidly developing and improving AI inventions.

Reaching new heights in the AI world

The right AI innovation can bring a company leagues above the competition. Trade secret and copyright protection should be considered when developing an effective AI protection strategy. As more organisations are investing in the development of a robust and well-structured AI protection strategy, they will be in a leading position to enforce their IP rights. Further, when infringement and misappropriation occur, they will be prepared and – more importantly – they will be in an advantageous position to prevent unlawful conduct.

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Prashant Singhal
Effectual Knowledge Services Pvt Ltd
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Blog, India, Intellectual Property, IP industry

Protecting AI and machine-learning inventions

“Artificial Intelligence is a new digital frontier that will have a profound impact on the world, transforming the way we live and work” – Francis Gurry, WIPO director general

Technological advances in smart machines and computers are having a huge impact on the banking, business, communication, defence, education, internet, medical and transport sectors. They are becoming dependent on AI technology to collect and analyse historical data. Further, neural networks are being developed to use the processing power of computers to replicate the intelligence and learning capabilities of the human brain. Examples of these processes are self-driving vehicles, product recommendations on e-commerce websites and fraud detection.

Growing competition to develop AI has forced major jurisdictions to amend their patent laws – for example, it led India to provide statutory protection to software through the Copyright Act.

AI is the future

According to WIPO, machine learning is the most dominant feature of AI and is mentioned in more than 40% of all AI-related patents filed worldwide, with a very high growth rate of 28% between 2013 and 2016. Further, use of the term ‘neural network’ grew at a rate of 46% over the same period. The top three fields in which machine learning-related patent applications were filed were telecommunications (more than 51, 273), transport (50,861) and life and medical sciences fields (40,758). This shows that there is a future in AI and the protection of AI-based inventions is therefore of utmost importance to inventors and innovators around the globe.

‘Computer program per se’ versus protecting AI inventions

‘Computer program per se’ means a computer program without hardware implementation, which is considered to be a mathematical model, business method, presentation of information or a scheme. As AI falls into this category, it is therefore deemed unpatentable in all major jurisdictions. However, these inventions can be protected by linking the computer program to a hardware or computer network since it may include certain other things, which are ancillary to or developed through the program. Therefore, when drafting AI or machine learning-based inventions, it is worth showing real-world application rather than an abstract idea.

For instance, a machine learning model may be deemed a mathematical model or abstract idea and is therefore unpatentable. However, the model embedded in a self-driving vehicle for automatic detection of a route can be considered to provide a technical enhancement to self-driving vehicles and thus meets the patentability criteria. Inventors around the globe are encouraged to link AI with practical applications and innovate AI-assisted technology.

Since AI-based inventions can be categorised as abstract ideas, a solution-based approach should be kept in mind when drafting patent applications. Here are some tips:

  • Link the solution to a practical application.
  • Include a system architecture, which illustrates that all hardware elements are connected via a network, which can provide additional support for any objection on unpatentability during prosecution stages. Inclusion of the system architecture proves the hardware and/or the computer network link, thus making it patentable.
  • Draft a system claimshowing that limitations to hardware provide additional proof of the hardware limitations of the AI-based invention. The system claim may include a memory, an interface and a processor configured to implement an algorithm stored in the memory.

Advantages of patenting AI inventions rather than protection under copyrights

Patenting can be expensive and, while it has its advantages over copyright protection, AI-based computer programs can be protected under copyright law as they can be considered as literary works. Patenting an AI-based invention provides a broader scope of protection and covers the logic of the invention, while copyright merely protects the inventor against an entity copying the literary work (computer program). A patented technology is considered to have commercial value, particularly if it leads to acquisition or licensing deals (eg, Vertex.AI (which had a strong AI-related patent portfolio)), and was later acquired by Intel Labs, citing key benefits as IP rights owned by Vertex.AI.

Patenting activity for AI and machine learning-based applications has steadily increased in the last few years. In fact, the number of AI-based patent publications nearly doubled in 2018 and 2019 as compared with the previous years, as Table 1 illustrates.

AI/machine learning-based patent publications per year (USPTO)

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Amit Goel
Effectual Knowledge Services Pvt Ltd
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Blog, India, Intellectual Property, IP industry

Will Blockchain and AI revolutionize Patent Analysis in India?

Indian Patent office is working on using Blockchain, AI and IoT to make the patent process smoother. Will it be the right time to do so?

Blockchain and AI are the buzzwords off late, the former quickly rose to the limelight due to the Bitcoin saga and the latter is finding its way into people’s life as we speak – be it through smartphone cameras or while browsing the Internet.

Simply speaking – blockchain is an indestructible ledger associated with an asset, that builds over time and remains with the asset.

This opens up a variety of applications – tracking the ownership, checking for any sale and transaction of the asset, tracking whether an asset was illegally sold etc. With so many industries experimenting with the blockchain technology to make it work for them – Patent industry is no different.

Some notable early starters who seem to have started service offerings in this area are – an Estonian company, Agrello, is trying to implement blockchain in creating smarter contracts. Another company, Binded is trying to leverage blockchain to serve as a proof of copyright. Another player, Bernstein is trying to leverage blockchain for management of Intellectual Property such as patents.

In addition to blockchain, the other upcoming technology – the AI, is also quickly percolating in the field of Intellectual Property and patents. Some early adopters were the companies that provide searchable databases for patents across the globe – for example, an Australian player Ambercite, uses AI engine to search for patents, that allegedly throws little or no noise, making the overall process of searching and analyzing patents very efficient.

Not only the commercial players, the Patent offices across the globe are also leveraging the technologies to unlock potential benefits. For example, The US patent office implemented AI engine to suggest patent classes that should be searched to make the process more efficient. The European Patent Office implemented a tool that is able to translate patents published in 32 languages into English.

The Indian patent office seems to be no exception – there was tender announced by IPO, “Expression of Interest for Making use of Artificial Intelligence, Blockchain, IoT and other latest technologies in Patent Processing system of IPO”.

In this tender, the IPO has expressed interest in leveraging technologies, including Blockchain, AI and IoT (Internet of Things) to make the patent process smoother. When the envisioned system is ready it will enable a Blockchain-AI based ecosystem for managing IP protection in India, which will be much more efficient, smooth and faster.

But this is all but a beginning and the road to be traveled is very long – the fundamental principle of an AI powered system is one in which a “training set” and an “expected output set” is provided to every AI engine for it to calibrate itself – thereafter, the AI based engine can thus operate on another dataset, in real time, to produce the same desired output.

All this sounds good for a highly structured data, but the patents are very unique – they are tehnno-legal documents that can be very diverse, even though they relate to same technical domain.

The question is, would we need an AI-backed engine for every class of technology? If yes, what is the level at which we need to define technology – Is “automobile” a suitable level technology for AI or one needs an AI engine for each component of an automobile such as Engine etc.

Also, with fast moving technologies such as Electronics and Computer Science – the terminology included in patents also changes pretty quickly – how frequently such AI engines needs to be recalibrated?

All in all, we can safely say that both AI and blockchain are in very early stages – and though not without potential, it will be some time before we see the full extent of the benefits it can extend to the patent sector.

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