Year: 2018

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How to write a successful argument

STEPS to a SUCCESSFUL ARGUMENT

How to write a successful argument

THE OVERVIEW

An Argumentative essay takes a position on a debatable issue, such as abortion rights, same-sex marriage, gun control, tax increases, and so forth.

In writing your argumentative essay, you think out the issues and take a position, which is called your thesis. You use the body of your essay to defend and explore your position. The goal is to persuade the reader to agree with your side of the issue.

THE STEPS

  1. Create a Strategy by exploring both sides of the argument and considering who will be your audience. To do this you will LIST the arguments on both sides of the issue and take notes on whom your readers will be.
  2. Consider the Merits and Weaknesses of Both Sides by looking over the lists of arguments and weighing them against how you previously felt about the issue. You also reconsider the audience as you do this.
  3. Take a Position by writing out your thesis. This is your firm stance on the issue.
  4. Create an Outline by using the items in your list that support your side of the issue.
  5. Draft an Introduction that clearly states your thesis, your position, and invites the reader into your argument.
  6. Use the Body of the Essay to Support Each Point in Your Argument with specific evidence. You may want to use statistics, evidence, examples from real life, expert opinions, and other reasonable sources of evidence.
  7. Present and Refute Opposing Arguments by drawing from you original lists. Show the weaknesses and problems in the other side’s position. This will bolster your case.
  8. Build a Link to Your Readers by finding some common ground between the two sides. This is often done by sharing the common values underlying a position on an issue, such as a sense of justice or fairness.
  9. Be Sure to Avoid Common Mistakes in Reasoning by carefully using inductive and deductive reasoning. Inductive reasoning means: you arrive at a conclusion based on several facts. Deductive reasoning means you reach a conclusion based on premises, which may or may not be proven true. Also be sure to avoid logical fallacies, such as making hasty generalizations, using circular language, or employing biased language.
  10. Always provide a thorough bibliography for your well-researched argument essay.

(taken from Rules for Writers, by D. Hacker [pp. 348-361])

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Vulnerability to Natural Hazards

Vulnerability to Natural Hazards

The topic of the society’s vulnerability to hazards and natural disasters is one that captivated further interest and attention after brief analysis on the issue. Vulnerability in this case refers to the potential of a group of people to be able to cope with a natural hazard (Wisner, Blaikie, Cannon, & Davis, 2003). When a hazard causes a disaster, numerous people get injured and killed. The situations tend to be gruesome to the people affected but it does not affect everybody equally. There are people who are more susceptible to certain natural hazards than another group of people. Vulnerability to natural hazards differs from region to region. For example, people who live in an area that is a plateau and is near a large water body they are most likely to suffer from floods. Regions that are close to tectonic plates are more likely to experience earthquakes than other places. My research would like to involve what factors lie behind the vulnerability, what affects other areas more than others and how can the issue be improved or how can the people protect themselves. My interest in this segment was sparked by various factors that I came across. Most researches and work done on natural hazards do not look at the vulnerability angle of the society but rather concentrate on the triggers (Wisner, Blaikie, Cannon, & Davis). Events that took my keen notice into the research were to specific ones: Haiti’s natural hazard and disasters experience and the Dhaka area in Bangladesh that is populated with squatters within the area. Both these regions have something in common: They have been key witnesses of natural hazards which have led to insurmountable amounts of damage that have set them back. I was curious to the factors that made these areas more vulnerable to the hazards as compared to other places which have fairly similar experiences but are able to maintain their progress. The research topic is well connected with content we are learning in class that is related to natural hazards and its geography. Through the content I have learnt in class I have been able to apply it and to understand how to approach a topic of research. Vulnerability of these regions gives a deeper scope of knowledge that I have learnt about natural hazards and disaster in our class.

For me to be able to learn more about vulnerability and how its related to natural hazards I had to first go through the historical experience of Haiti and disaster. Throughout time, the region has experience a continuous torrent of earthquakes and floods. Based on archived information and newspaper articles, I was able to really dig deep into articles providing the statistics of Haiti’s experience of with hazards (Jones, 2016). In 2008, Haiti was badly devastated by four storms which left almost 75% of its farm land destroyed and cost the life of 800 people (Jones). In 2010, they experienced one of the worst disasters in history. An earthquake left around 90,000 people killed and over 1.5 million people lost their homes. The information was able to really put into perspective the tragedy that the hazard had upon the people and the disaster they experienced. The research was able to provide to me information on the vulnerability that made Haiti vulnerable by the natural hazard. It was more than just their geographical location but factors such as instability in their political circle and high amounts of corruption (Jones). For information on Dhaka and the factors that also made it vulnerable to the disasters I was able to find reliable sources of information from research done on the area (Wisner, Blaikie, Cannon, & Davis).

While undergoing this research I was able to find certain important factors that have been given considerable thought. In relation to vulnerability to hazardous natural factors, one key component that determines how much impact the disaster will have upon the region are certain human factors (“Vulnerability to Natural Hazards”). Some of the factors involved are the wealth of the people in the region. Rich people are able to access medical help and have more stable housing as compared to impoverished people which will make them experience the disaster differently. Education is a major component as people who have knowledge on dealing with hazardous situations can be able to protect themselves more adeptly than those who do not. Governance was also shown to be of key importance as they are the ones who develop policies that can reduce the vulnerability of the people to potential natural hazards. Other important factors include age of the people affected and the technological advancement of the area.

To limit my coverage of the topic I have decided to only look at vulnerability and factors that propagate it to make the society less protected to natural hazards. As significant as the hazards and disasters are by themselves, I will not delve into that but concentrate on how the people make themselves vulnerable to the situations. I will dig deeper and uncover more analysis on various areas that have lower vulnerability to hazard compared to those that have a higher amount like Haiti, and compare what the safer areas are doing to protect themselves.

 

References

“Vulnerability to Natural Hazards”. (n.d.). Retrieved May 6, 2017, from https://www.e-education.psu.edu/geog030/node/379

Jones, S. (2016, October 4). Why is Haiti vulnerable to natural hazards and disasters? Retrieved May 6, 2017, from https://www.theguardian.com/world/2016/oct/04/why-is-haiti-vulnerable-to-natural-hazards-and-disasters

Wisner, B., Blaikie, P., Cannon, T., & Davis, I. (2003). t Risk: natural hazards, people’s vulnerability and disasters 2nd Edition.

 

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Writing your Research Report

Writing your Research Report

Topic: The topic of the research final paper is open to the interests of the students. Students should find a question they would like to answer using the statistical techniques learnt in class.

To find the topic of your final report you need to start from a broad perspective looking at some general idea that you would like to explore. For example, let’s say that you would like to look at the relationship between interest rates and the economy. This is a very broad topic and you need to start doing research looking to specify your main research question. In figure 1 we can see all the different sub-topics, or more specific areas, in which we can divide our broad topic.

Interest Rate and the Economy

Figure 1

We could list more sub-topics than the ones we represented in Figure 1. However, we need to pick one so we can have a more focused research topic. Let’s assume that we choose to research the question regarding the impact of interest rates on the growth of construction in the United States. Once that we decided what our topic is going to be, we need to solve three main issues regarding our research project:

  1. Review of literature: we need to research what other people have done previously. Especially, we need to find peer-reviewed publications of researchers that have worked in this specific area and look at their results. To find other research you should go to the website of the UNF Library and search for research papers in this specific area. The following is a guide on how to search the library at UNF:
    1. Enter the Library website and in that website you click on Databases by Subject
    1. Once you click that link a new page will open, which will show you all the different databases of research articles according to the disciplines of study. In our case, we will be interested in the Business (Finance, Investment, International, Management) and the Economics and Geography databases. The specific database will depend on the research topic you choose.
    1. In our specific example, we should click on the Economics and Geography link. There a list of databases will appear. You should select one of them. Let’s say that we pick EconLit.
    1. Once we picked EconLit, a new page will open, where we can start searching for research reports. Then, if we find one that we believe is germane to the selected topic, we can download it and read it and use it in our review of literature and reference sections.

It is important to remember that when we read other people’s work we are looking for the following:

  1. How they addressed the research question
  2. What data they used?
  3. What kind of statistical technique they utilized?
  4. What conclusions they reached?

This will allow us to move further in our research project and to be able to understand how to perform our task.

  1. Data Availability: once we do a review of literature we should be able to tell what kind of data we need to do our research project. At this time, it is very important to be able to find out if the data is available and where we can find them. Many times a research project cannot continue because of the lack of datasets or because the information does not exist yet. It is very important that you can find the data. Useful sites to check out:
    1. Census Bureau: www.census.gov
    2. Bureau of Labor Statistics: www.bls.gov
    3. Bureau of Economic Analysis: www.bea.gov
    4. Federal Reserve Database: http://www.research.stlouisfed.org/fred2/
    5. U.S. Government Open Data: www.data.gov
    6. International Data (World Bank): http://data.worldbank.org/
  1. Statistical Methodology: finally, after the review of literature is done and the dataset is located we need to determine what the methodology that we are going to use is. That means we need to decide which technique that we learnt in class is appropriated to answer the research question. Accordingly, you can use your review of literature to inform this decision, the textbook, class notes, etc.

Once you performed these three tasks you are ready to use excel to calculate your model and show your results. Accordingly, we are ready to write our research report.

Format Research Report

The following are the main sections of your research report and a short explanation of what the contents of each section should be:

  1. Cover Page: contains the title, authors and class section.
  2. Introduction: this is a short section of your paper, no more than one page. However, this is a very important part of your report, as this is the section that most people are going to read first. As a result, you need to make sure that people are convinced of the worthiness of your research. You should emphasize the following:
    1. Why is this research topic important? Why the reader should keep reading? Why should the reader care about this paper?
    2. What are the main findings of your research?
    3. Why these findings are important?
  3. Review of literature: Explains the previous work on this topic, if any, and how the topic of this paper fits that body of work. You need to explain, very shortly what other people have found with regards to this research question and how their findings compare to your findings. By mentioning other people’s work you discuss the problem at hand and how other people have addressed this problem. If your research topic is about your company you need to discuss the nature of the problem and the different solutions that were offered and why you think that your empirical analysis could improve those proposed solutions. This section should have 2-3 pages.
  4. Data: in this section you need to describe the data you found, show a table with the main statistics, like the mean and the standard deviation for each variable. Explain what the sources of your data are. This sections should be 2 pages at the most.
  5. Methodology and results: In this section you need to proceed as follows: first, explain the statistical techniques that you are going to use to answer your research question. For example, if you are using a regression model you need to show the theoretical equation with the independent variables and why are you using the variables that you are using. Second, you need to show your results and do the tests to show that your model, if it is a regression model, satisfies the assumptions. If you introduced some modifications to satisfy the assumptions, then you need to explain those changes. Finally, you need to explain your results main results and conclusions. This section should be 4-5 pages long.
  6. Conclusions: Explains what the results are and why they are important. This sections should be 1 page long.
  7. References should be listed at the back of the paper.
  8. Appendixes after the references (if needed)

Length and Format: The length of the paper is 12-15 pages (12 pages minimum), including title page, references and figures, but excluding appendixes.

The format of the page should be 1.5 spacing, font size 12 Times New Roman. The margins should be Right:1.25”, Left:1.25”, Top:1” and Bottom:1”

Format Standard: you can use any style: APA, MLA, Chicago, etc. The library has a great guide for citation styles at http://libguides.unf.edu/citationguide

Still stuck with your research report, get more help here:

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Big Data Patents (Digital Intellectual Property Law)

Article By Sandro Sandri 

1- BIG DATA

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.1 “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”

In another way Big Data is an evolving term that describes any voluminous amount structured, semistructured and unstructured data that has the potential to be mined for information. It is often characterized by 3Vs: the extreme Volume of data, the wide Variety of data types and the Velocity at which the data must be processed. Although big data doesn’t equate to any specific volume of data, the term is often used to describe terabytes, petabytes and even exabytes of data captured over time. The need for big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate compute power to big data tasks to achieve the desired velocity. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively. Achieving such velocity in a cost-effective manner is also a headache. Many enterprise leaders are reticent to invest in an extensive server and storage infrastructure that might only be used occasionally to complete big data tasks. As a result, public cloud computing has emerged as a primary vehicle for hosting big data analytics projects. A public cloud provider can store petabytes of data and scale up thousands of servers just long enough to accomplish the big data project. The business only pays for the storage and compute time actually used, and the cloud instances can be turned off until they’re needed again. To improve service levels even further, some public cloud providers offer big data capabilities, such as highly distributed Hadoop compute instances, data warehouses, databases and other related cloud services. Amazon Web Services Elastic MapReduce is one example of big data services in a public cloud.

Ultimately, the value and effectiveness of big data depends on the human operators tasked with understanding the data and formulating the proper queries to direct big data projects. Some big data tools meet specialized niches and allow less technical users to make various predictions from everyday business data. Still, other tools are appearing, such as Hadoop appliances, to help businesses implement a suitable compute infrastructure to tackle big data projects, while minimizing the need for hardware and distributed compute software know-how.

a) BIG DATA AND THE GDPR

The General Data Protection Regulation, which is due to come into force in May 2018, establishes a few areas that have been either drafted with a view to encompass Big Data-related issues or carry additional weight in the context of Big Data, lets analyse just two aspects.

– Data processing impact assessment

According to the GDPR, where a type of processing in particular using new technologies, and taking into account the nature, scope, context and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons, the controller shall, prior to the processing, carry out an assessment of the impact of the envisaged processing operations on the protection of personal data. This criterion is most likely going to be met in cases of Big Data analytics, IoT or Cloud operations, where the processing carries high privacy risks due to the properties of either technology or datasets employed. For example, linking geolocation data to the persons name, surname, photo and transactions and making it available to an unspecified circle of data users can expose the individual to a higher than usual personal safety risk. Involving data from connected IoT home appliances or using a Cloud service to store and process such data is likely to contribute to this risk.

– Pseudonymisation

 

According to the GDPR, ‘pseudonymisation’ means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person. At least two aspects link pseudonymisation to Big Data. First, if implemented properly, it may be a way to avoid the need to obtain individual consent for Big Data operations not foreseen at the time of data collection. Second, paradoxically, Big Data operations combining potentially unlimited number of datasets also makes pseudonymisation more difficult to be an effective tool to safeguard privacy.

b) BIG DATA APPLICATIONS

Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year: about twice as fast as the software business as a whole. Developed economies increasingly use data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion people accessing the internet. Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn lead to information growth. The world’s effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 20073 and predictions put the amount of internet traffic at 667 exabytes annually by 2014. According to one estimate, one third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. This also shows the potential of yet unused data (i.e. in the form of video and audio content).

2 “Data, data everywhere”. The Economist. 25 February 2010. Retrieved 9 December 2012.

3 Hilbert, Martin; López, Priscila (2011). “The World’s Technological Capacity to Store, Communicate, and Compute Information”. Science. 332 (6025): 60-65. doi:10.1126/science.1200970. PMID 21310967.

 

While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company’s problem at hand if the company has sufficient technical capabilities.

2- PATENTS

A patent is a set of exclusive rights granted by a sovereign state to an inventor or assignee for a limited period of time in exchange for detailed public disclosure of an invention. An invention is a solution to a specific technological problem and is a product or a process. Being so, Patents are a form of intellectual property.

A patent does not give a right to make or use or sell an invention.5 Rather, a patent provides, from a legal standpoint, the right to exclude others from making, using, selling, offering for sale, or importing the patented invention for the term of the patent, which is usually 20 years from the filing date6 subject to the payment of maintenance fees. From an economic and practical standpoint however, a patent is better and perhaps more precisely regarded as conferring upon its proprietor “a right to try to exclude by asserting the patent in court”, for many granted patents turn out to be invalid once their proprietors attempt to assert them in court.7 A patent is a limited property right the government gives inventors in exchange for their agreement to share details of their inventions with the public. Like any other property right, it may be sold, licensed, mortgaged, assigned or transferred, given away, or simply abandoned.

The procedure for granting patents, requirements placed on the patentee, and the extent of the exclusive rights vary widely between countries according to national laws and international agreements. Typically, however, a granted patent application must include one or more claims that define the invention. A patent may include many claims, each of which defines a specific property right.

4 WIPO Intellectual Property Handbook: Policy, Law and Use. Chapter 2: Fields of Intellectual Property Protection WIPO 2008

A patent is not the grant of a right to make or use or sell. It does not, directly or indirectly, imply any such right. It grants only the right to exclude others. The supposition that a right to make is created by the patent grant is obviously inconsistent with the established distinctions between generic and specific patents, and with the well-known fact that a very considerable portion of the patents granted are in a field covered by a former relatively generic or basic patent, are tributary to such earlier patent, and cannot be practiced unless by license

thereunder.” – Herman v. Youngstown Car Mfg. Co., 191 F. 579, 584-85, 112 CCA 185 (6th Cir. 1911)

6 Article 33 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS).

7 Lemley, Mark A.; Shapiro, Carl (2005). “Probabilistic Patents”. Journal of Economic Perspectives, Stanford Law and

Economics Olin Working Paper No. 288. 19: 75.

 

relevant patentability requirements, such as novelty, usefulness, and non-obviousness. The exclusive right granted to a patentee in most countries is the right to prevent others, or at least to try to prevent others, from commercially making, using, selling, importing, or distributing a patented invention without permission.

Under the World Trade Organization’s (WTO) Agreement on Trade-Related Aspects of Intellectual Property Rights, patents should be available in WTO member states for any invention, in all fields of technology,9 and the term of protection available should be a minimum of twenty years.10 Nevertheless, there are variations on what is patentable subject matter from country to country.

a) EUROPEAN PATENT LAW

European patent law covers a wide range of legislations including national patent laws, the Strasbourg Convention of 1963, the European Patent Convention of 1973, and a number of European Union directives and regulations in countries which are party to the European Patent Convention. For certain states in Eastern Europe, the Eurasian Patent Convention applies.

Patents having effect in most European states may be obtained either nationally, via national patent offices, or via a centralised patent prosecution process at the European Patent Office (EPO). The EPO is a public international organisation established by the European Patent Convention. The EPO is not a European Union or a Council of Europe institution.[1] A patent granted by the EPO does not lead to a single European patent enforceable before one single court, but rather to a bundle of essentially independent national European patents enforceable before national courts according to different national legislations and procedures.[2] Similarly, Eurasian patents are granted by the Eurasian Patent Office and become after grant independent national Eurasian patents enforceable before national courts.

8 Lemley, Mark A.; Shapiro, Carl (2005). “Probabilistic Patents”. Journal of Economic Perspectives, Stanford Law and Economics Olin Working Paper No. 288. 19: 75. doi:10.2139/ssrn.567883.

9 Article 27.1. of the TRIPs Agreement.

10 Article 33 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS).

 

European patent law is also shaped by international agreements such as the World Trade Organization’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs Agreement), the Patent Law Treaty (PLT) and the London Agreement.

3- BIG DATA PATENTS

11 Patent Analytics Solutions That Help Inventors Invent”, Outsell Inc, June 3 2016

Patent data is uniquely suited for big data tools and techniques, because of the high volume, high variety (including related information) and high velocity of changes. In fact, patents are leading the way with big data and analytics in many ways. “The patent space offers a fascinating insight into the potential of big data analytics, rich visualization tools, predictive and prescriptive analytics, and artificial intelligence”.11 Especially recently, big data tools and technologies are being used in several ways in the patent world to transform and improve patent analysis.

Patents and Intellectual Property are gradually gaining significance around the world. This is leading to a bottleneck-large databases and ever growing information. A new way around the innovation problem is to acquire patents. With examples such as Nokia, Motorola, Twitter, the patent purchases seem rather straightforward. Nokia sold a large chunk of its company to Microsoft, but held on to the crucial patents by signing a licensing deal. They can now earn a revenue using patents licensed to Microsoft. Google bought Motorola and its patents and later sold the company to Lenovo while holding on to the patents. There are ample such examples in the industry.

Transactions of Intellectual Property (IP) are rather complex. Per example, a basic component to be verified before a patent is granted, is novelty. In other words, if a priorart describing the invention is found, the application stands to be rejected. A prior-art could be in the form of a publication, a blog post, a lecture, a video, or a book. With a massive amount of information generated, that doubles every 18 months, it is extremely difficult to found prior-art. One way, some organizations follow, is crowdsourcing the prior art search. Details about the patent are published on a website asking IP professionals from around the world to find a prior-art. The emergence of Big Data analytics, on the other hand, has provided a clear solution. In addition, the outcomes through this method get better and precise with each operation.

Since Big Data analytics is still not commonly used by most government authorities, prior-art gets overlooked and many false patents are granted. This comes out when-in litigation-the opposing parties put all their efforts in looking for a prior-art to invalidate each other’s patents. More often than not, a prior-art is found or there is an out of court settlement. Hence, a concept called patent wall has gained traction. It is very common for companies to file as well as acquire a number of patents around the technology they are working on. This serves as a defence against litigators and allows the companies to market and sell their products/services without any fear of litigation.

The core value of patents is that the invention must be publicly disclosed in exchange for a time-limited monopoly on the invention. Patents are not only a legal asset that can block competitors, they are potentially a business and financial asset. For market participants, patents can provide direct insight into where competitors are headed strategically.

Big Data is the key to unlocking this inherent value. Patent information is comprised of vast data sets of textual data structures involving terabytes of information. When unlocked through Big Data techniques and analysis, the insights are compelling, revealing the direction a technology is headed and even uncovering the roadmap for a specific company’s product plans. But, deriving these insights from the proliferation of information requires truly sophisticated Big Data analysis.

While Big Data is quickly growing as a trend, what’s delivering more value these days are Big Data services that optimize specific data sets and create specialized analysis tools for that data. Technology teams that are dedicated to certain data sets will curate and improve the data, learn the specifics of that data and how best to analyze it, and create selfservice tools that are far more useful than generic Big Data technologies.

A key part of the Big Data service is a specialized analysis engine tailored to particular data. For example, a patent analysis engine must understand the dozens of metadata items on each patent in order to group patents correctly and traverse the references. To be most effective, Big Data services need to automatically keep up with the data updates, as patents are living documents that change over time. Even after the patent Big Data Patents is finalized and issued, it can be reclassified, assigned to a new owner, reexamined and updated, attached to a patent family or abandoned.

Most importantly, Big Data services are only as good as the insights they deliver – a Big Data service should provide a specialized user interface that allows real-time, userdriven analysis with search, correlations and groupings, visualizations, drill down and zooms. The patent data analysis must be presented in a manner that is compelling and consistent.

There are more than 22,000 published patent applications between 2004 and 2013 relating to big data and efficient computing technologies, resulting in almost 10,000 patent families. Patenting activity in this field has grown steadily over the last decade and has seen its highest increases in annual patenting over the last two years (2011-2012 and 2012-2013) of the present data set. The growth has continually been above the general worldwide increase in patenting, showing a small increase of 0.4% over worldwide patenting for the 2005-2006 period and showing a maximum increase of 39% for 2012-13.~

“Using” a patent effectively means suing a competitor to have them blocked access to market, or charge them a license for allowing them to sell. When a patent holder wishes to enforce a patent, the defendant often can invoke that the patent should not have been granted, because there was prior art at the time the patent was granted. And, while patent offices do not seem to have a clear incentive to take into account actual reality, including the exponentially available information created by Big Data, when reviewing the application, the situation is very different for a defendant in a patent lawsuit. They will have every incentive to establish that the patent should never have been granted, because there was pre-existing prior art, and the information in the patent was not new at the time of application. And one important consequence of Big Data will be that the information available to defendants in this respect, will also grow exponentially. This means that, the probability of being able to defend against a patent claim on the basis of prior art, will grow significantly. Because of the lag of time between patent applications and their use in court, the effect of the recent explosion of information as a result of Big Data is not very visible in the patent courts yet.

A patent is, of itself, an algorithm. It describes the process of a technical invention – how it works (at least, that’s what a patent is theoretically supposed to be doing). It is therefore quite possible that a lot of algorithms around analysis of Big Data will become patented themselves. It could be argued that this will act as a counterweight against the declining value and potential of patents.

Many of these algorithms are, in fact, not technical inventions. They are theoretical structures or methods, and could therefore easily fall into the area of non-patentable matter. Algorithmic patents are particularly vulnerable to the ability by others to “innovate” around them. It is quite unlikely that a data analysis algorithm would be unique, or even necessary from a technical point of view. Most data analysis algorithms are a particular way of doing similar things, such as search, clever search, and pattern recognition. There is, in actual fact, a commoditization process going on in respect of search and analytical algorithms. Patents are “frozen” algorithms. The elements of the algorithm described in a patent are fixed. In order to have a new version of the algorithm also protected, the patent will either have to be written very vague (which seriously increases the risk of rejection or invalidity) or will have to be followed up by a new patent, every time the algorithm is adapted. And the key observation around Big Data algorithms is that, in order to have continued business value, they must be adapted continuously. This is because the data, their volume, sources and behaviour, change continuously.

The consequence is that, even if a business manages to successfully patent Big Data analytical algorithms, such patent will lose its value very quickly. The reason is simple: the actual algorithms used in the product or service will quickly evolve away from the ones described in the patent. Again, the only potential answer to this is writing very broad, vague claims – an approach that does not work very well at all.

80% of all big data and efficient computing patent families (inventions) are filed by US and Chinese applicants, with UK applicants accounting for just 1.2% of the dataset and filing slightly fewer big data and efficient computing patents than expected given the overall level of patenting activity from UK applicants across all areas of technology.

Against this, however, it should be borne in mind that many of the potential improvements in data processing, particularly with regard to pure business methods and computer software routines, are not necessarily protectable by patents and therefore will not be captured by this report. UK patenting activity in big data and efficient computing has, on the whole, increased over recent years and the year-on-year changes are comparable to the growth seen in Germany, France and Japan.12

12 Intellectual Property Office, Eight Great Technologies Big Data A patent overview

 

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