Guest blog by: Elicet Cruz PhD. of IFI Claims partner IALE Tecnologia (Spain)
A recent article by the BBC News talked about the surge in research into the novel material graphene to reveal an intensifying global contest to lead a potential industrial revolution. According to the BBC: “Latest figures show a sharp rise in patents filed to claim rights over different aspects of graphene since 2007, with a further spike last year China leads the field as the country with the most patents. The South Korean electronics giant Samsung stands out as the company with most to its name.”
IALE used the data from Treparel’s partner IFI Claims and the KMX technology to provide more insights in the recent development oft he Graphene patent landscape.
Graphene is a material which through its extraordinary properties has attracted the attention of both scientists and industry worldwide.
It is an extremely thin sheet composed of a thick carbon atom with a networked, or hexagonal honeycomb structure, containing 50 million atoms per centimeter. In this regard is considered a two-dimensional material. When a graphene layer is placed one above the other, we obtain graphite. When wound forming spheres we obtain fullerene, and when wound forming tubes, carbon nanotubes are obtained. All of these three-dimensional shapes are materials from the same family.
In addition to its thinness, graphene stands out for its high transparency, flexibility, strength, impermeability and high electrical conductivity. Its conductivity is superior to any known metal. Furthermore, it is considered an environmentally friendly material and is relatively cheap to produce.
Due to these characteristics, graphene is considered a material with great future market potential, with applications in telecommunications (mobile telephony …), electronics (chip manufacturing …), medical – pharmaceutical, energy (solar panels), etc.
Through a general search on graphene in the IFI CLAIMS Global Database, we obtain 12,878 granted patents and applications worldwide through December 2012. Figure 3 shows a patent landscape produced by the KMX patent analytics tool. The figure shows graphene patents, clustered according to main areas and technological development lines. As a next step, we create a KMX free classifier. We do this by labeling a few patents based on their location in the landscape and a review by the analyst. Then we train the classifier using the KMX Support Vector Machine algorithm developed by Treparel Information Solutions. The training process takes the labeled patents and uses them as a training set. Based on the full text of the patents, KMX applies labels to the entire collection based on their similarity to the training set. This interactive process of labeling, training and classifying can be repeated over and over again until we obtain the best classification.
The top part of figure 3 shows the patents used as the training set; the lower part shows the classification results after training and applying the classifier.
The two biggest clusters (“film, graphene, subtrate” containing 1,519 patents and “nanotubes, carbon, nanostructures” containing 2,096 patents) were extracted to create two new data sets. These were further classified, in order to explore the applications and developments specifically related to them (Figure 4).
The development of graphene across the years, shows a growing trend in patentability in the latest 10 years, with more than the 57% of the patents published between 2011 and 2012 (Figure 5).
The number of graphene related patents have shown rapid growth over the last 10 years (Figure 5). More than 57% of the patents were published in 2011 and 2012.
Looking at International Patent Classification (IPC) codes, there are 4,935 codes covering graphene patents. The most common are shown in Figure 6.
The main patented contents are related to carbon preparation (C01B 31/02), graphite, including modified graphite (C01B 31/04), manufacture of carbon filaments (D01F 9/12), and to nanotechnologies for materials or surface science (B82K 30/00). There is a growing trend for all codes in the latest 3 years (2010-2012).
Patents have been published in more than 30 priority countries. 96% of the patents during the period 1994 through 2012 were filed in only 8 countries. The United States (US), China (CN) and Japan (JP) are the most prominent countries in the period (Fig. 7).
The covered patents belong to 6,831 families. 55% are single patent families. 7 large families stand out due to their size (13-33 patents). Figure 8 shows the largest families, the subject areas claimed and the organizations which are the assignees of these families.
Graphene was discovered in 2004 by two Russian-born researchers Andre Geim (Sochi, 1958) and Konstantin Novoselov (Nizhny Tagil, 1974), professors at the University of Manchester (UK). They were awarded with the Nobel Prize in Physics in 2010.
Until 2010, Geim and Novoselov, had not applied for any patents on this material, according to the article “Andre Geim: in praise of graphene” published in Nature News in October, 2010.
In this interview Geim explained the reasons:
“We considered patenting; we prepared a patent and it was nearly filed. Then I had an interaction with a big, multinational electronics company. I approached a guy at a conference and said, “We’ve got this patent coming up, would you be interested in sponsoring it over the years?” It’s quite expensive to keep a patent alive for 20 years. The guy told me, “We are looking at graphene, and it might have a future in the long term. If after ten years we find it’s really as good as it promises, we will put a hundred patent lawyers on it to write a hundred patents a day, and you will spend the rest of your life, and the gross domestic product of your little island, suing us.” … I considered this arrogant comment, and I realized how useful it was. There was no point in patenting graphene at that stage. You need to be specific: you need to have a specific application and an industrial partner.”
However, after this interview, Geim and Novoselov decided to patent graphene innovations associated with specific applications, as shown in Figure 9.
Geim (5 patents, 3 families) and Novoselov (7 patents, 4 families) have patented both together (5 patents, 3 families) and separately (3 patents, 1 family). The jointly filed patents are presented in the top part of Figure 9. Novoselov’s patents appear in dark blue in the bottom part of Figure 9. These patents are associated with the cluster “fiber, polymer, composite.”
There is no doubt that graphene has great potential within multiple industries. This potential was validated by a Nobel Prize for the researchers who first synthesized it. The high level of patent activity, especially in the years 2010-2012, reinforces this view.
This article was originally published by IFI CLAIMS.
Delft, December 31, 2012: At the very end of 2012, only hours away from the new year , while the politicians of the republican party and the democratic party still work on a solution for the fiscal cliff in the US, it is good to look back and important to look forward. In 2012 the term “Big Data” certainly gained a lot of attention and although it may already many things to many people, it is clear that in 2013 we will hear more about analyzing very large complex data sets. Also cloud computing has become very important as approach to process large amounts of data when needed. Now we have scale-able approaches for very large data sets and compute jobs one would expect that we have made big steps forward in being able to learn more from more data.
Since there is a strong demand for quantitative decision making supported by proving that one can make the best solution (most accurate with a minimal risk) one expects that we will see a convergence of big data technologies and cloud computing technology. There is much activity happening and every week we can read even about start-ups being well funded by investments to explore, develop and bring to the market new technologies in both domains. But many technology development activities do not guarantee convergence towards technology that enables large scale knowledge discovery. One thing that is needed is focus on the essentials to enable this. Of course many aspects are important such as technology standards and economics of scale and defining proper approaches to deal with privacy of data.
One essential component is to be able to take into account all relevant data needed for analyzing all knowledge on a certain topic. This means that we need technology to enable us to analyze data from tables, text, images and graphs (network data) in whatever size (tera to petabytes) and number of observations (variables) as well as being able to combine static data with dynamic (temporal) data. We also need technology to detect and extract all patterns (regularities) in a data set and build automatically accurate and reliable mathematical models describing such a patterns. Even if we have this and are able to run this in a large scale setting, we still have another “essential nut to crack” and that is determining the meaning of the model that describe an pattern or a trend in a data set. In other words – we need large scale support for providing the semantic meaning of patterns and trends in data.
Given the strong progress in research and development coming from the area of big data analytics and cloud computing and a market demand for this – it is logical to expect that in 2013 we will see much progress in the area of automatic model generation of patterns in data sets with support of semantics of these models. This will enable intelligent decision making – based on also understanding the context of multiple patterns in a dataset – and is a step further than decision making based only on a accurate model of a single pattern or trend in a complex data set.
In the old days reading news was easy. We read what the journalists wrote and we trusted them that they analyzed the news and selected the most important developments assuming proper fact finding before they publish what was about to be the truth. The internet has changed this forever. The internet is a ‘free’ source of information for journalists as well as their readers. The journalist needs to demonstrate he did his fact finding using up-to-date sources of information and important news.
Data driven journalism is bringing news a level further with insight from data. The journalist is becoming a data scientist and analyzes available data sources to be able to support a story with indepth analysis of relevant data and thus add much more value to the story compared to the free information on the internet. Additionally data driven journalism provides the readers an insight look on important trends and developments in society based on the analysis and interpretation of public data often provided by goverment agencies.
The Guardian (UK) is one of the most well know quality news papers and supports data driven journalism strongly by providing access to their data.
The quality of news is determined by the quality of the data that has to take in to account many aspects including accuracy and completeness of data. Data Driven Journalism is going more in the direction of analyzing Big Data; to derive signal from noise a journalist needs to have access to professional reliable data, analysis methods and software tools.
To demonstrate how data journalism could work using advanced search tools we at Treparel have used The Guardian API to extract data sets of specific topics to analyze them. As an example we searched in the The Guardian API for all documents related to “big data” which resulted in a total of 10.521 documents. We have to analysed this in more detail as you can image the amount of noise in the data.
From this landscape visualization we immediately determine important topic areas (called ‘clusters of text’). These clusters with the most important words (‘annotations’) help us to easily identify the most addressed topics.
Based on this visualization we notice that Google, Apple and Microsoft are mentioned often in ‘big data’ articles. We decide to filter where in these clusters these companies are mentioned to understand the relationship between the company and the topics of the clusters.
Google is mentioned in 1274 out of the 10.521 news articles.
Microsoft is mentioned 928 times as shown by the red dots in the visualization where these documents are more concentrated around games and Facebook, video and search and mobile internet. The articles where Google is dominantly mentioned are focussed more around search/video and mobile internet.
Apple is mentioned 1051 times as shown by the blue documents which is about 10% of the full set of 10521 articles. Apple is mentioned much broader with a focus on mobile phone and internet.
To exclude the least relevant documents we decide to select all news articles documents that have a calculated relevance ranking above 80%: this helps us limiting the set of articles to the most important (or relevant) articles on ‘big data’ about Google, Apple (white/yellow dots) and Microsoft (red dots) (in total 215 articles).
Now we have excluded irrelevant articles we can much better analyze what the most important articles are in respect to internet technologies.
Through these analysis we are finding some insightful relevant articles about a general thema like ‘big data’.
The is a cluster on ‘cloud computing’ and on ‘videos of Youtube’ but dominant in the centre are the articles about Apple’s technologies on tablets and mobile phones (iPad and iPhone). Related to this are the articles on patents where Nokia is important because they own many basic patents. When we look for the topics that are important in relation to Microsoft we see that this consists of their OS Windows but also games and the Xbox (where Sony pops up as well). If we then look what are the important articles related to Google we find ‘search technologies’ and ‘privacy’ related topics.
We could ask ourselves now how this evolved over the years from 2000 to 2012. Since the most talked topics are related to Apple we select Apple and visualize all articles over time using blue rings for Apple and a color mapping from red (2000) to white (2012) which gives the final visualization shown below.
By looking at both visualizations we noticed that ‘big data’ is getting rapidly more media attention. But it also shows that Apple is gaining more interest from the The Guardian versus Google and Microsoft since 2010. Given the fact that the articles are about technology this demonstrates that also in ‘big data’ competition in gearing up.
(Disclaimer: for this analysis we used KMX on a simple Window desktop PC. It took us les then 10 minutes to do the analysis of over 10.000 documents)
Guest blog by: Aalt van de Kuilen, President CEPIUG.
For many years, Patent Searchers retrieved patent information by using classical hosts (STN, Questel Dialog and ORBIT in the old days). A specific command language was essential for performing searches with these tools and good understanding of this language was a basic requirement.
This was very useful for retrieving accurate legal information and finding relevant information for patentability, patent clearance as well as material for oppositions and due diligence. This information was mainly intended to be used by Patent Attorneys, to give a legal opinion on certain projects and/or products.
Over the last years, a new group of clients coming from the “business” has become more and more interested in patent information (as well as trends in patenting) So, the more specific requests for patent information not only for legal opinions, but also for more business related decisions became a fact. These “new clients” also want to receive a different kind of information out of patent data.
We see a growing need for information relevant for more strategic/marketing decisions.
To retrieve this information, new tools are needed, to create more landscape like output. On the other hand the possibility to handle bigger sets of data and perform more statistical analysis is a requirement.
New initiatives has been undertaken to satisfy the needs of the searchers and several tools has been developed like tools for semantic searching.
Also Treparel has come up with the KMX tool to handle larger sets of patent data. The basic principle by feeding the system with some relevant documents (which are particular relevant to the topic) makes it possible to retrieve “similar” documents almost immediately and with a high precision.
First trials with the software look very promising, with a relevancy of over 90% for certain topics. Although we are not yet there, we think KMX is a product with future potency and is worth to be further developed and improved.
We are looking forward to see the next steps in the development of this product. As president of the CEPIUG I will strongly support these initiatives and all of those who are looking to new ways of accessing bulk sets of patent data.
The CEPIUG (Confederacy of European Patent Information User Groups) was founded in 2008 and it aims to be a platform for cooperation for the Patent Information User Groups in Europe.
Guest blog by: Enric Escorsa O’Callaghan of IFI Claims partner IALE Tecnologia (Spain).
In 2006 Fuji Photo Film Co., Ltd. entered into the cosmetics sector by launching a series of new antiaging products. Surely for some it was shocking to see how a company operating for decades in the photography industry had moved now into a completely different sector such as the healthcare one.
Fuji Photo Film has many years of experience studying the properties of Collagen. As explained by Andrzej Brylak, Fujifilm’s Europen Director, “Collagen is a key ingredient in the emulsion film and a material widely used by the cosmetics industry. It prevents oxidation from exposure to light and this is a major problem for protecting rolls of film as well as for preventing skin damage”
Apart from Collagen, Fuji also focuses its research on other healthcare related areas such as the control of free radicals or the improvement of absorption and penetration processes.
Let’s study Fuji’s patent portfolio over the last few years to track this diversification process.
We search by applicant/assignee on IFI CLAIMS Global Patent Database and obtain all patents from Fujifilm. We import the text fields of the patent (title, abstract, claims, description) into the KMX Text Analytics tool. Within KMX, we can search and highlight documents containing specific terms such as “collagen”, “free radicals”, “reducing agent”, “skin”, etc. A representative landscape visualization is shown below.
As expected, Fujifilm had been working on collagen for several years. But when did Fujifilm begin developing other potential applications for collagen? When did that shift to another radically different market begin? To answer those questions, the landscape map is a start, but perhaps it is not the most accurate approach, as some related processes can be described with other synonym or more abstract terms.
Today’s economy is more and more a global economy where information is available very fast. Technological innovation has become an important driver for many economies and global companies. Increased competition from more companies lead to lower margins on many products and shorter product life cycles. Many companies outsource their manufacturing processes to low wage countries and invest them in R&D to stay competitive. Companies look to obtain a faster return on their R&D investment directly driving the importance of a solid IP strategy. The irreversible change that has taken place is that many global companies shifted from production based to knowledge based competitiveness.
Revenues from the R&D investments were obtained from selling products but now there is an additional value extraction point coming from IP licensing. This requires an attractive IP portfolio and provides the opportunity to obtain revenues directly after the R&D process. Value creation of IP and maximizing ROI from R&D investments needs a strategy and this needs a thorough SWOT analysis of all IP around the relevant technologies.
The number of patent filings is still growing which leads to a growing need for analysis of large patent collections to optimize revenue from IP licensing.
In a global economy stimulation of innovation is essential for todays economies and the European Union 7th Framework Program (FP7) is very important in this. Treparel is part of a consortium called Fusepool.net that was granted a two year project. In the fusepool project will be delivering a software platform to help Small and Medium Enterprises (SME) in Europe to analyze their innovative capabilities and opportunities but also to find possible partners and funding opportunities. The analysis of large text document sets is a key component in the project.
Treparel is contributing to the project with text analytics and visualization technology that will enable a SME’s to perform patent landscaping analysis on their technology area and to learn where their strength and weaknesses are and also where opportunities and threads can be identified. In such a SWOT analysis the opportunities and threads are important also for partner matching and finding possible research funding opportunities which also relies on large scale text analytics techniques. The other participating partners are the Bern University (BUAS) who is coordinator and Xerox Research, Searchbox, Geox and the European Network of Living Labs.
You can watch the project evolving over time on the special website >> Fusepool.net