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Artificial intelligence patents. Law practice. 

 

The topic of artificial intelligence is still not present in Bulgarian law as something normative and practical, which is on the legislative agenda in the 21st century, not only for technological and legal reasons, which undoubtedly determines the future of the innovative sector and through it the public, business and personal life of every modern person. The latest research on the subject in many of the world's advanced technology countries has shown that it is not far off the time when besides the afternoon chess game with some electronic device, artificial intelligence will be issued with instant visas, will be approved faster and secure (personal data - finger, face recognition) bank credits, national and cross-border (eg European) elections will be held and health services will be provided. Other studies have shown that artificial intelligence will replace many professions - lawyers, notaries, bailiffs, judges, revolutionize medical precision and monitoring, robotize our industry, services and lifestyle, thus putting new intellectual, philosophical and psychological challenges to everyday life and perhaps to the relationships between us - human beings. Realizing the inevitability of all this, many companies operating in different spheres of social and business life began to develop dynamic and focused inventions based entirely on artificial intelligence. Taking this into account, I want to pay close attention to this statement of these patents, driven by my belief that today's inventions in the area of ​​Artificial Intelligence (AI) are the basis of our more interesting tomorrow.

1.Historical development. Artificial Intelligence (AI) appeared in the 1950s, with the first mention of the term coming from a summer 1956 research project of Dartmouth College, New Hampshire, USA. A year earlier, in 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, decided to organize a group for exploring and developing digital thinking machines. McCarthy selects the name "Artificial Intelligence" as a "new field" of scientific search. It presumes mostly neutral neutrality in order to avoid focusing on the narrow theory of automation and cybernetics, as already known achievements of analog technology. In early 1955, Mr. McCarthy turned to Robert Morrison, director of biological and medical research at the Rockefeller Foundation, to request funding for the Dartmouth summer seminar for about 10 mathematicians. On 2 September 1955, the project was officially presented to the board members under the notion of "artificial intellect".

McCarthy's team [1] proposes to conduct a two-month study of artificial intelligence by 10 scientists (in fact, 11 mathematicians are involved in the final project) in the summer of 1956 at Dartmouth College in Hanover, New Hampshire on several topics. First of all, the study concerns the assumption that every aspect of learning or any other intelligence characteristic can in principle be so accurately described as to make a machine to simulate it. The experiment also involves attempting machines to use language, to form abstractions and concepts, to solve problems that have so far been a priority of people's mental activity, and to improve the described results. Last but not least, the proposal discusses the development of computers, natural language processing, neural networks, calculus theory, abstraction and creativity - areas in artificial intelligence that are still relevant today. Since the 1950s, innovators and researchers have published over 1.6 million scientific articles related to artificial intelligence, with around 340,000 applications for inventions related to the AI ​​industry being filed. The demand for interest is so great that only in 2013, half of the patent applications on the subject of artificial intelligence have been made.

The McCarthy team and its students created it in the 50 programs that the press describes as "astonishing": computers solve algebraic problems, proving logical theorems, speaking English, and so on. By the mid-1960s, US research was heavily funded by the Ministry of Defence, with artificial intelligence laboratories being established around the world. Progress in the field slowed down in 1974, in response to Sir James Lighthill's criticism [2], and continued pressure from the US Congress to fund more productive projects, and therefore the US and British governments were interrupting exploratory research at the AI sector. The next few years are called "Winter Periods" for Artificial Intelligence, as receiving funding for such projects is extremely difficult.

In the early 1980s, artificial intelligence research was resumed, stimulated by the commercial success of so-called "expert systems" [3], as a form of artificial intelligence program that simulates the knowledge and analytical skills of human experts. Until 1985 the market for artificial intelligence programs reaches over one billion dollars. At the same time, Japan's computing project for fifth-generation computing technology has led US and British governments to rebuild funding for academic research in the AI ​​area. However, starting with the 1987 Lisp market crash, the AI ​​industry once again fell into an unfamiliar market situation, viewed as the second, long-lasting "frost" in research on the subject.

In the late 1990s and early 21st century, AI innovations began to materialize in logistics, data mining, medical diagnostics, and other areas of science and technology. Their tremendous success reaches its peak due to the increasing computational power of Artificial Intelligence computers (see Moore's Law [5]), with emphasis being placed on addressing practical practical problems from business and the economy, new connections between AI and others areas (such as statistics, economics and mathematics, medicine [6]) and the commitment of researchers to new mathematical methods and scientific standards. All in all, he finds his logical technological expression in the first chess system based on artificial intelligence "Deep Blue", which defeated world chess champion Garry Kasparov on May 11, 1997.

A little later, in the beginning of 2015, Jack Clark of Bloomberg notes that the year in question was remarkable for artificial intelligence, with the number of software AI projects on which different types of Google searches are based increased from "sporadic use" in 2012. to more than 2700 projects. Clark also presented factual data showing that the percentage of image processing errors in "Google Images" (artificial intelligence software algorithm) has dropped significantly since 2011. He explains this phenomenon with the increase of available neural computer networks, due to the cloud computing infrastructure being upgraded and the storage of research tools and massive databases in them. Another example in the commented direction is the development by Microsoft of a "Skype" software system [7] which, based on artificial intelligence, can automatically translate from one language to another and "Facebook" software that can describe some images of blind people. In a US study on the subject in 2017, it was found that one in five companies in the country "has included AI technologies in some proposals or processes". In 2016 China also significantly accelerated its state funding for artificial intelligence, taking into account this large database supply and the rapid increase in research results, some observers believe China is about to become the "super power of AI innovation ".

Machine learning is the dominant AI topic covered by multiple patents for artificial intelligence and included in more than a third of all identified inventions on the topic (134,777 patent documents). Patents related to machine training increase annually by 28% annually, and in 2016, 20 195 patent applications were filed (compared to 9,567 in 2013).

Some of the most popular machine learning techniques that revolutionize AI technologies are so-called "deep learning and neural networks." They are also the fastest growing achievements at the technological level reflected in patent applications: so-called "deep learning" shows an impressive average annual growth rate of 175% from 2013 to 2016. , reaching 2,399 patent applications in 2016. Neural networks grew at a rate of 46% over the same period, with 6,506 patent applications in 2016.

Among the AI ​​functional applications subject to patents, the so-called "computer vision" (8), which includes image recognition, is most popular. Computer vision is mentioned in 49% of all patents related to AI technologies (167 038 patent documents), increasing annually by 24% on average (21 011 patent applications filed in 2016). Other AI functional applications with the highest growth rates in patent applications in 2013 - 2016 are the artificial intelligence for robotics and control methods that grow on average by 55% per year.

2.Challenges in front of European Patents for Artificial Intelligence. The European Patent Office or abbreviated as "EPO" is increasingly receiving patent applications that include the term "programmed computer" as a key part of the invention described. Moreover, this increase in document submission is observed in technical areas that are traditionally not considered computer-centric. For example, according to EPO statistics, 40% of new patent applications filed in healthcare have an AI or machine learning aspect.

In recognition of the growing importance of artificial intelligence and machine learning for patent applications in all fields, the EPO has devoted time and place to the updated "Expertise Guidelines 2018" [9] to focus specifically on the patentability of inventions that have AI elements and / or affect aspects of machine learning. This shows a particularly detailed view of the EPO on the most up-to-date trends in software patents, which will actually assist applicants and patent attorneys across Europe.

The new "Expertise Guidelines" mentioned clearly show that EPO intends to treat AI technologies and machine learning as a form of mathematical method. Mathematical methods are part of the objects listed in the list of non-patentable inventions defined in Art. 52, para 2 of the European Patent Convention (EPC), and in this line of thought are essentially non-registrable "as such". However, the mathematical method that is related to the control of a technical system or process may be technical in nature, thus overcoming its "exclusion" as a non-patentable invention.

This has always been the constant position of EPO when commenting on exceptions to patentability, and so it is not surprising that the Artificial Intelligence and Machine Learning section of the new "Guidelines for Expertise" is largely based on the practice of the Office. It was therefore accepted that inventions involving artificial intelligence and machine training would be patentable as far as they are described and stated in the context of working in a technical system or controlling a technical process. Careful preparation of the application in this context will be sufficient to ensure that this requirement is met - describing and claiming artificial intelligence or machine learning component in the context of the technical system in which they work or maintain technically, such as any abstraction in the opposite context is excluded. Only such an approach according to the EPO would lead to the issuance of a European patent. Artificial intelligence or machine learning algorithms that work in the context of non-technical systems, such as business processes and models, are unlikely to be accepted as patentable.

The shared motives are reflected in EPO Board of Appeal Decision T1510 / 10, issued in December 2013, which shares the view that the use of machine learning (as well as AI technologies) is not in itself sufficient to make an invention is patentable. This means that the conventional use of machine learning or artificial intelligence to solve a problem that may or may not be solved by that means does not, a priori, mean that a technical effect has been achieved even if the problem it decides technically on its merits.

In Section "G - Patentability", Chapter II, "Inventions", section 3.3.1 "Artificial Intelligence and Machine Training", the new "Guidelines” of the EPO from 2018, lead to the following conclusions:

Artificial intelligence and machine learning based on computational models and algorithms for classification, clustering, regression and reduced dimensionality such as neural networks, genetic algorithms, support vector machines, k-means regression and kernel discriminant analysis. Such computational models and algorithms have per se an abstract mathematical nature, whether they can be "trained" on the basis of certain existing bases. Consequently, the guidelines presented in G-II, 3.3 [10] are generally applicable to such computational models and algorithms.

In considering whether the patentability of the invention is technical in its entirety (Article 52 (1) (2) and (3) of the EPC), it expressed as a "support vectors machine", a "reasoning engine" or "neural network" should be interpreted and interpreted very precisely and carefully by the expertise because they usually refer to abstract models of a technical nature.

On the other hand, it should be noted correctly that artificial intelligence and machine learning are applied in various fields of technology. For example, using a neural network in a heart rate monitor to identify irregular heartbeats is a process of technical input. Classification of digital images, video, audio or speech signals based on features at a low level (eg edges or attributes of pixels for images) are other typical technical applications of mathematics, computer AI algorithms for classification. However, the classification of textual documents only with regard to their textual content is not considered a technical objective in itself but as a linguistic one [11]. The classification of abstract data records or even "data records for telecommunications networks", without specifying the technical use of the resulting classification is not a technical purpose, even if a classification algorithm can be considered to have valuable mathematical properties such as such as sustainability [12]. When the classified method (the subject of the patent) maintains a specific technical purpose, the steps for its generation can also contribute to the technical nature of the invention if they support the same objective.

The detailed analysis carried out by the EPO in order to understand the depth of discussions theoretical problem and the correct interpretation of the patentability of artificial intelligence, could lead to significant legal development of the practice, as it will open the door to the possibility of being received European patent protection on training methodologies algorithms, AI innovations or machine learning as well as mechanisms for generating sets of data that are used for the intended purpose.

In my opinion, because of the said European patent, a method of training an artificial intelligence or machine learning algorithm or a method of generating training data for this purpose would be provided if it is possible to make a reliable justification of the proven, and a repeatable technical effect. For example, a training method that makes the neural network "converge" faster with technology or uses a smaller set of data can be credited as serving to solve a technical problem and thus meet the legal requirements for a European patent protection.

The mentioned patentability analysis of AI innovations introduces a well-known aspect of patent law that is commonly found only in the pharmaceutical and biotechnology spheres - "credibility". For example, it may be proven that a particular untrained software AI model has "converged" faster when "trained" using a specific method and a specific set of databases for its "learning", but only such evidence will not be sufficient to make a plausible claim. As it has already become clear, "credibility" itself as a criterion is also conditioned by whether an AI patent will result in a real and guaranteed technical result or not. Any abstractness of the claim would result in a lack of sufficient plausibility, which in turn will end with an opinion of the lack of patentability.

3.AI Artificial Intelligence in the US - Trends and Legal Framework. Several recent reports from America show that patentability of research objects in AI technologies has been extremely active over the past few years. In December 2016, Google and Elon Musk [13] opened their AI platforms publicly, Uber launched the Uber AI Lab69 project, and Apple announced that it would be publishing its research in the field of artificial intelligence for the first time.

Significant interest in the US also exists in future applications of artificial intelligence with other "intertwined" parallel technologies such as robotics, virtual reality, autonomous vehicles, block, 3-D printing and IoT.62 [14]. There is fierce competition for leadership in the AI ​​sector among several leading companies, which helps to stimulate artificial intelligence-based innovation, along with accelerating progress in current and future applications. Technology giants such as Google, IBM, Microsoft, Intel, Facebook, Amazon, Baidu, Samsung and Apple have patented hundreds of AI patents and some of the industrial multinational companies like Boeing have and to acquiring start-up AI companies. The deep learning sub-sector [15] is currently also promoting innovation pioneering as an investment activity.

In the United States, most artificial intelligence technologies can be protected by a patent. But some inventions of artificial intelligence are faced with the increased legal control of the US Patent and Trademark Office (USPTO) expertise, especially with regard to whether the invention is included in the criterion of patentability". The USPTO follows a two-step analysis to determine whether a patent claim is permissible for a patent. First, the USPTO determines whether the patent application is focused on a concept that meets the patent requirements. Some areas are not patent admissible: abstract ideas, mathematical models, natural laws, and natural phenomena. If the patent application addresses one of these areas, the USPTO examines in its essence whether the claim under investigation as a whole amounts to "more than" the above-mentioned concepts, in which case it is decisive whether the formulated claims reach a real technical result and not just an abstraction. Inventions based on AI technologies related to autonomous vehicles or robots that aim to control, move or manipulate a tangible object (for example, a vehicle or a postal package) are generally subject to a comparatively minimal analysis of the expertise. These technologies are generally considered to be eligible for patenting, as it is assumed that they lead to a tangible technical result and are therefore not abstract.

By contrast, an artificial intellect-based invention that is not aimed at controlling material objects - such as a software algorithm - may face increased control in the expertise of whether it is focused on an abstract idea or not. However, many technical aspects of even an invention based on AI innovations can overcome stringent substantive patent requirements in the United States. To the extent that the USPTO as an institution has not provided explicitly a legal definition of the term "abstract idea", this loophole in US case law has been filled by a number of judgments that have been sufficiently illustrative in this respect. For example, the Federal Court of Justice has held that patent claims relating to a particular data arrangement are permissible for patents, stating in particular that the "self-refering table" set forth in the claims is a specific type of data structure. In this context, the compilation of specific data structures, specific rules, specific combinations of technical steps, or specific hardware configurations that improve computer performance are accepted as eligible claims, while most cases of usable use of a general-purpose computer are often considered as non-putative.

On the basis of these criteria, the US Patent Office (USPTO) identifies patentable AI objects, and in this line of thought, the AI-based technology developers are required to go beyond expected user scenarios (for example, patenting method for the use of conventional technologies to solve a common problem), the latter attempting to identify the unique technical features of its AI patent application by improving the performance of computer. These technical features may include the following components:

- pre-processing of training data (so-called "taxonomy");

- the learning process itself (e.g., neural network topology, configuration of parameters, termination conditions, etc.);

-use of trained classifiers or solutions (eg, sequence using classifiers, modeling of a space for solution of a genetic algorithm);

- "end-to-end" workflow (eg user interfaces);

- hardware (integration of artificial intelligence algorithms into hardware components, hardware acceleration);

Section 35 of the Civil Code of the United States, Section 101 (hereafter "35 USC § 101") limits the patentable objects to "new, useful technical processes, machinery, production or composition of matter, or any new and useful improvement in her ". As I have explained several times, patent claims that are aimed at abstract ideas (eg mathematical algorithms), natural phenomena or laws of nature are not eligible for patent protection in the United States. The Supreme Court of the United States motivates this constant view with the fact that these objects "are the main tools for scientific and technological work", and the granting of monopolies on these instruments through patent rights can seriously hamper innovation.

An example of the requirement of US case law, and in particular the hypothesis of 35 U.S.C. § 101 that a patentable AI invention should not be "aimed at an abstract idea" or should include an "inventive concept" that goes beyond an "abstract idea" – it has emblematic case law of Alice v. SEEels Bank Alice v. CLS Bank "[16] just on the topic. In 2016 however, the US Federal Court has examined another case - Enfish v. Microsoft (17), which significantly disproved the motives of the Alice v. SEEels Bank Decision. At the turn of the century, the “Enfish” firm registered U.S. Patent No. 6,151,604 and 6,163,775 which claimed a logical model for a computer database. The logical model is a computer database system that explains how the various elements of the information in the database are linked together. Unlike conventional logical models, “Enfish” includes all data in a single table, with column definitions provided by rows in the same table. Patents describe this as a "stand-alone" property of the database. In a standard, conventional relational database, each unit (ie, each type of thing) that is modeled is provided in a separate table. For example, a corporate file replication model may include the following tables: document table, face table, and company table. The document table may contain information about the documents stored, the persons table may contain information about the authors of the documents, and the company table may contain information about the companies that employ the persons. “Enfish” patents describe a table structure that allows information normally appearing in several different tables to be stored in one. Columns are defined by rows in the same table. Initially, the “Enfish” case against Microsoft was misinterpreted under the hypothesis of the "abstract idea" of the precedent “Alice”. In fact, the Federal Court of Justice ruled in its reasoning that “Enfish” patent claims are aimed at a specific improvement in the way computers work, embodied in the claimed "self-referencing table" for a database that the prior art does not contain. An interesting fact is that this case is often used as an example of one of the first cases concerning key details in understanding the patentability of AI technologies.

4.Conclusion. At the end of this presentation, I would like to point out that, in my opinion, the implementation of highly specialized legal norms regulating artificial intelligence in patent law at national and cross-border level as well as the development of legal regulation of AI technologies would have a profound impact on innovation, the economy and society. Given the global, explosive development of the AI ​​sector, it is of paramount importance that relevant stakeholders - patent specialists and businesses actively participate in further research and discussions among themselves, as well as in a more comprehensible presentation of this particularly complex topic of the public to find the most appropriate ways for artificial intelligence to promote innovation while minimizing any potentially negative social, business, legal and ethical implications.

 

 

Author: Mr.Atanas Kostov – patent attorney

 

 

[1] See. Russell, Stuart, J .; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2., Page 17, over the next 20 years, artificial intelligence will be dominated by these people and their students. "

[2] British scientist, mathematician, pioneer in the field of aeroacoustics;

[3] For them again Russell, Stuart J .; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2., Pages 22-24;

[4] "Lisp" machines are general purpose computers designed for effective performance through “Lisp” as the primary software and programming language, usually through hardware support. They are an example of hi-tech computing architecture and, in a sense, the first unified workstations. “Lisp” machines are commercial pioneers in many of the most common technologies, including laser printing, computer mice, high resolution raster graphics, and so on.

[5] Moore's law is expressed in the observation that the number of transistors in a dense integrated circuit is doubled every two years. The observation is named after Gordon Moore, co-founder of Fairchild Semiconductor and CEO of Intel, whose 1965 study. describes doubling the number of components for an integrated circuit each year by predicting that this growth rate will continue.

[6] See the Google DeepMind Health project, which was launched in 2016. The project has successfully started working with the National Healthcare System (NHS) in England.

[7] The product is called "Microsoft translator API" and is protected in 2010. with United States Patent US20110307244A1 registered. The owner of the patent is Microsoft Corporation, but the interesting moment is that one of the two inventors is Bulgarian Christina Nikolova Tautanova, graduated from Stanford University. Ms.Tautanova as an inventor and computer scientist is at the heart of several other patents in the field of artificial intelligence with Microsoft Corporation.

[8] See PCT / EP2014 / 071032 "Method for determining a property belonging to at least part of a real environment" Applicant Apple Inc.

[9] A special scientific conference - "Patenting Artificial Intelligence" - was held on 30 May 2018 at the headquarters of EPO, Munich, Germany.

[10] This point G-II, 3.3 commented on the lack of patentability of mathematical methods.

[11] Argument of decision of the EPO Board of Appeal T 1358/09;

[12] This is the decision of the EPO Board of Appeal T 1784/06.

[13] The creator of the PayPal electronic payment system and the car giant for electric cars - "Tesla".

[14] The "LoT" abbreviation comes from the "The Internet of things". This is an Internet connection extension to physical devices and everyday objects.

[15] "Deep learning" is a form of machine learning in which neural networks provide computer-based information for decision-making, training, and process correction based on what the computer has learned within certain parameters.

[16] Alice Corp. v. CLS Bank International, 573 U.S.A. 208, 134 S. Ct. 2347 (2014) is a decision since 2014. of the United States Supreme Court on patent patentability. The case concerns the question whether certain claims for a computer-implemented electronic escrow service to facilitate financial transactions embrace abstract ideas that do not qualify for patent protection. The patent has been declared invalid, as the claims are formulated as an abstract idea, and their application to the functionality of a computer is not enough to make this idea a patentable object;

[17] See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed Cir., 2016).

 

Blockchain software patents. Blockchain attorney.

 

Introduction. The liberalisation of the banking sector to crypto currencies and, above all, the blockchain technology behind them, shows that the financial market slowly breaks the shell of misunderstanding into the new online legal mechanisms that lead to software verifying each link in a legal deal. In my opinion, the crypto currencies themselves (as a speculative tool) in the near future are doomed to a total collapse of a single dollar denomination for a specific crypto (for example, "bitcoin"), but blockchain technology as an innovative business model and legal tool for doing business online, will soon develop technologically and enter various spheres of public life - finance, law, health, education, creative industries, public sector. A proof of this statement is that at present, perhaps the largest bank in the world - the US Bank of America - has a total of 50 patents in the field of blockchain technology serving the banking and financial sector. Оn the second position by applications for this kind of patents in US it is the giant in the computer industry IBM, which logically declare interest in this industry, as it has been for years the main hardware supplier (of various types of computer equipment) for thousands of banks around the world. 

Financial Instruments concerning cryptocurrencies. Blockchain attorney. 

 

 

Cryptocurrencies are legally digital financial assets designed to work as a means of exchange by using the cryptographic method of securing transactions in order to control the creation of additional currency units. In this context, cryptocurrencies can be classified as a subset of digital currencies and / or as subspecies of alternative currencies, in particular virtual ones. In the present, the cryptocurrencies also serve to buy the so-called "tokens", which I think can be qualified as a type of electronic bonds serving to raise capital through the so-called "ICO" concept (Initial coin offering) is a type of electronic crediting of startup companies from third parties. This feature has turned cryptocurrencies into a particularly dynamic financial instrument, which are different from the different banking and stock exchange analogues, leads to incredible flexibility and speed in financial operations. The present statement aims to give a greater focus on all the mentioned processes - their positive, speculative nature, but also their role as an ever-increasing real financial instrument that literally blows the financial sector and places many legal issues. 

Trademark opposition. Trademark attorney in Bulgaria and EUIPO.

 

1.Opposition procedure in Bulgaria. With one of the last amendments of the Trade Mark and Geographical Indications Law (the Bulgarian abbreviation of this act is ZMGO and comes from “Закон за марките и географските означения”) of 26.02.2010 - SG, no. 19 of 2010 26.02.2010, the National Assembly of Republic of Bulgaria adopted an interesting and practically new change in the procedure of registration of trademarks, by introducing the so-called opposition procedure.

Under the new system of trademark registration, the examination of registration applications will be done on an absolute basis only on absolute grounds.

If the application submitted meets the requirements of Article 11 of the ZMGO, it will be published in the Official Bulletin of the Patent Office. The new relative grounds for refusal of registration presented in Article 12 of the ZMGO refer to the fact that when an opposition is filed, the trade mark will not be registered if: it is identical to an earlier trade mark and its goods or services are identical to the ones of the earlier mark; because of its identity or similarity to an earlier trade mark and the identity or similarity of the goods or services of the two marks, there is a likelihood of confusion by the consumers, including the possibility of connection with the earlier mark.

A hot topic which concerns a lot of online businesses is copying (retrieving or stealing) website content - its texts, graphics, designs, photos and commercial concepts. The trend on a global and national level indicates that within a few years, any good, including author's works - books, musical works, architectural projects, computer programs, etc. will be offered predominantly online. This fact calls for increased attention paid to the adequate copyright protection of the content of the Internet sites as well as to the formulation of the clearer general terms and conditions for their use in order to avoid unscrupulous practices carried out by third parties.

This statement will address the main guidelines that should be marked as a means of protection and prevention against the illegal "copying or theft" that is done with respect to the content or functionality of the Internet sites.

 

The theory and practice of patent law overlap with the view that the doctrinal and factual three-dimensional mark and design actually achieve the same legal result - the defense of the image of an object. However, the differences in the legal effects of the registration of the two objects of intellectual property are significant and should be explored in depth, with the idea to properly structure a strategic decision, on the most adequate means of defense for dealing with in a particular case. From this point of view, I find this topic particularly interesting because it is the basis for solving legal issues of a theoretical and practical nature that concern the correct registration of a product's vision, depending on several important criteria: 

- how much it has been used and has become commercially recognizable prior to the date of its filing as an industrial property;

 - how many images should the protection cover;

 - target time as the speed of the registration;

 - what is the duration of registration required by the applicant; 

- should the pre-selected remedy be in line with any possible future legal disputes that would have been sorted out when there is an improperly chosen intellectual property subject to the application or registration.

 

In the 21st century, literally every social, business, political, and cultural aspect of life is mediated by online space. This means that behind every everyday activity stands an online platform - a website, a blog, an Internet mobile application, and so on. The Bulgarian social reality shows that offenses / crimes aimed at copying, using, reproducing, distributing, hacking, breaking the integrity and functioning of online works are a practice that is completely neglected, legally unsettled, and therefore devoid of any clearly structured legislative sanction, therefore prevention. It is correct to note that there are some legal norms in the Bulgarian legislation which concern the commented subject, but they are in different material laws and provide for separate abstract hypotheses that do not communicate with each other and thus remain some "mutilated" legal constructions without real practical significance.

All of this naturally leads to the total unproductiveness and almost zero efficiency in the work of specialized prevention bodies in the face of, for example, in the computer crime sector of so called “GDBOP”, due in particular to the lack of an adequate legal framework from a substantive point of view and respectively the lack of powers from a procedural point of view. This article aims to precisely identify these problems and to define what has been achieved so far internationally to justify the real need for such a legislative initiative in Bulgaria, which is justified not only in the present but also especially in the future, given the obvious all-round digitization of public life. Here I would like to point out the fact that the legal focus on online space and its problems has been placed in Western Europe and the US in the mid-1990s, i.e. the Bulgarian legislation is late for nearly 20 years to place emphasis on lawmaking in this sphere. I would like to mention that when in 2009 at a intellectual property conference, I started to speak on the subject of this article, I was greeted with silence and misunderstanding by a strictly professional audience that made me feel like a "stranger in my own." Unfortunately, there is no feedback between the online industry and lawyers, intellectual property specialists, which leads to a lack of communication and an option for a meeting of opinions, concepts and views to identify the specific problems to be solved. That is why, up to this day, I continue to insist that an "Intellectual Property Law on the Internet" must exist in the Bulgarian legislation, taking into account the balanced interests of users and rights holders, and in this context, the different options to the positive legal behavior of the different legal entities and a corresponding sanction in respect of offenses and crimes directed against copyright works and various technical and software platforms, object of intellectual property based on the Internet.

The open source software changes the content of the concept of licensing. Currently, this institute is governed by a new sphere of copyright law (mostly in the Anglo-Saxon legal doctrine), which means that it is developing rapidly and its legal qualification is relatively complex due to theoretical and technical considerations. The Source Code is a work similar to a literary and resembling a written book created by one or several authors, which also complies with the wording of Article 10 of the Berne Convention ("computer programs, whether original or object-oriented, are protected as literary works in the framework of the Convention "), the same opinion being later reflected in Article 10 (1) of the Agreement on Trade-Related Aspects of Intellectual Property Rights. Thus, the source code becomes an author's work automatically with its creation, with the respective authors, regardless of whether it is registered somewhere or not, and the author decides whether to distribute it in the public and in what way. This distribution does not give users of the source code any rights other than to use it.

Some authors use this way: for example, the Qmail Internet Mail is developing in a similar way. Users can download the main content and use it but cannot create similar works and distribute them. That is why the authors of Qmail distribute it through the so-called patches of the original source code, which is the equivalent of an added chapter from a book without the original content; therefore, the final user is responsible for collecting the individual pieces in a block that makes sense. The key point here is that the added chapters, the so-called source code patches, are copyrights of the respective "relative" followers. So, they cannot redistribute Qmail with their patches, as Qmail's primary author cannot spread patches through Qmail without the patch writer. The foundation of the software-licensing concept is the Free Software Foundation (FSF), which was created in 1985 in the United States, and it is committed to promoting the rights of use, study, copying, modifying and distributing of computer programs among computer users. FSF presents the development and use of free software, especially the so-called GNU (GUI) operating system, widely used in its GnU / Linux version. The GJU project started in 1984 with the idea to develop and complete Unix as an operating system representing free software of the GI-U system. The types of GJU operating systems that use the kernel called Linux are the most widely used at the moment. That's why these systems are often referred to as "Linux," but it's right to call them "GnU / Linux" systems. Contemporary practice shows that all major companies are turning to investing in free software, even the computer mastodon “Microsoft”, who was particularly conservative in this direction. Some authors are on the opinion that in this context copyright is a serious obstacle, but to me it is the opposite - it is the legal way to protect and guarantee the creation of works on the basis of cooperation (co-authorship), because each author has copyrights and control over the distribution policy with regard to its individual parts of the "common" work. In this hypothesis, there is a so-called divided co-authorship in which each of the co-authors has participated in the creation of a part of the work, and this part can be distinguished as an independent work. If all the authors agree on the licensing rules, a co-author work is produced and everything is fine, but if not, a complex legal situation occurs. The "Open Source" license is this agreement that puts things in place regarding the distribution policy so that before they start distributing, the authors agree that they will work in a joint co-author project. Each author has to accept the terms and conditions before the project is distributed, and each individual author must be technically involved in the outcome of the license agreement whenever it is changed. 

 

Fashion business is one of the industries in the modern world, behind which hundreds of billions of dollars of money come from the global economy. Statistics show that some of the richest companies in the world are fashion giants such as ZARA (valued at 66 billion dollars), Luxottica Group (the largest glass-making company under the brands Hut, Ray -Ban "and" Oakley "," Burberry "," Chanel "," Prada " and " Versace "- estimated at $ 30 billion), LVMH (70 luxury brands like Louis Vuitton and Hennessy, - estimated at $ 28 billion), H & M (estimated at $ 26 billion), and so on. Behind the commercial success of these companies naturally stands the protection afforded by their intellectual property, which is strictly defended all over the world. The way this protection is structured as a legislative basis and practice is the subject of this article.

1.Copyright Law and Fashion. In accordance with the principles of the Berne Convention, any original work of art is automatically protected by copyright worldwide, and this also applies to the works of fashion design under Article 2, paragraph 7, of the Convention. Obviously, the copyright object of protection that concerns fashion is the artistic design of any garment, shoe, hat or belt, and parts of it. Copyright laws worldwide, including the Bulgarian Copyright and Related Rights Act, protect original prints and patterns, unique colors and new combinations of graphic elements used for clothing and accessories. Each legislation builds in this context certain specific rules. 

1.1. Copyright Law on Fashion in the United States. US Copyright Act advocates that fashion design can be protected only if and only to the extent that the protected design includes identifiable picture, graphic, or sculptural elements or exist separately and are capable of being perceived independently by the utilitarian aspects of the article itself - such as a belt. The American courts accept in this context that an element of the design is virtually separable in two cases:

- when the design itself can be removed from the garments and sold separately (e.g. belt buckle);

- where the design as such is a conceptually separable part, consisting of artistic and artistic features which do not contribute to the immaterial aspect of clothing and entail its different functionality (for example, a Halloween costume).

In 2006, The US Congress was introduced to discuss the "Innovative Design Protection Act", better known as the "Fashion Bill". Until 2012 He underwent six changes as soon as his final version is expected. 

Trademark registration in European Union. Trademark attorney in EUIPO.

 

A European Union Trade Mark (EUTM) is any trademark which is pending registration or has been registered in the European Union as a whole (rather than on a national level within the EU).

The EUTM system creates a unified trademark registration system in Europe, whereby one registration provides protection in all 28 member states of the EU. The EUTM system is unitary in character. Thus, an objection against an EUTM application in any member state can defeat the entire application, an EUTM registration is enforceable in all member states.

The EUTM system is administered by the European Union Intellectual Property Office (EUIPO), which is located in Alicante, Spain (see also trade mark law of the European Union).

The Community trademark gives its proprietor a uniform right applicable in all 28 Member States of the European Union on the strength of a single procedure which simplifies trademark policies at European level. It fulfils the three essential functions of a trademark at European level: it identifies the origin of goods and services, guarantees consistent quality through evidence of the company's commitment to the consumer, and is a form of communication, a basis for publicity and advertising.