HKH Prinses Maxima op Week van de Ondernemer 2013

Utrecht, 11 april 2013. De ‘Week van de Ondernemer’ is al 16 jaar lang een groot en goed gewaardeerd evenement voor ondernemers in Nederland. Het biedt een programma met inspirerende gasten en topsprekers uit het bedrijfsleven, de politiek, de wetenschap en de media. Nieuw dit jaar is de introductie van het deelthema Ondernemend Financieren.

Dit jaar heeft HKH Prinses Máxima, in haar rol als lid van het Comité voor Onderschap en Financiering, een speciaal werkbezoek gebracht aan de WvdO en in het bijzonder deel genomen aan een ronde tafelgesprek over de mogelijkheden van alternatieve financiering van het MKB.

Het Ronde tafelgesprek ging over de mogelijkheden die alternatieve financiering biedt nu met name het MKB steeds meer moeite ondervindt bij het aantrekken van financiering. Uit de Financieringsmonitor die Economische Zaken eind vorig jaar naar de Tweede Kamer heeft gezonden blijkt dat bij kleine bedrijven 52% van de aanvragen voor vreemd vermogen wordt afgewezen (was 34% in de monitor over de tweede helft van 2011), en 4% bij grootbedrijf (was 10%).  Uit de frequente contacten met (potentiële) aanbieders van alternatieve financieringsvormen blijkt dat één van de knelpunten die zij ondervinden de onbekendheid is bij de categorieën bedrijven waar men zich op richt. In het regeerakkoord is de volgende passage opgenomen: “Nieuwe alternatieve financieringsvormen zoals kredietunies, crowdfunding en MKB-obligaties zullen worden ondersteund via promotie, wegnemen van belemmeringen in de regelgeving en door de inzet van kennis en bestaande instrumenten.” Het ronde tafelgesprek tussen enkele alternatieve financieringsaanbieders en ondernemers uit het MKB, onder gastheerschap van het Ministerie van Economische Zaken en in aanwezigheid van HKH Prinses Máxima, is een middel om de promotie van alternatieve financieringsvormen te bevorderen.

Tijdens de Ronde Tafel kwamen onder leiding van dhr. Rinke Zonneveld, Directeur Onderschap bij het Ministerie van EZ, vier ondernemers en vier aanbieders van alternatieve financiering aan het woord. TIIN Capital werd vertegenwoordigt door Hans Biesheuvel, Voorzitter MKB Nederland, Commissaris en informeel investeerder in TIIN Capital. Hans gaf een enthousiast pleidooi om als ondernemer informeel te investeren met kapitaal, kennis en netwerk.

Treparel CEO Jeroen Kleinhoven in gesprek met HKH Prinses Maxima

Jeroen Kleinhoven, recentelijk aangesteld als algemeen directeur, introduceerde Treparel als ‘een pareltje van big data innovatie in het Nederlandse ondernemingslandschap’ en deelde ervaringen in de zoektocht van het bedrijf naar groei financiering. HKH Prinses Máxima, luisterde met interesse naar de verschillende mogelijkheden en toonde zich betrokken in de voor- en nadelen van de verschillende financieringsvormen in aanvulling op de traditionele rol van banken. Jeroen gaf voorbeelden van zijn positieve ervaring met TIIN in de ‘hands en eye-on samenwerking’ waarbij het bedrijf kan vertrouwen op mede-ondernemers als gesprekspartners bij TIIN bij het werven van nieuwe klanten en partnerships. HKH Prinses Máxima bedankte de deelnemers na afloop en gaf aan het gesprek als leuk en interessant te ervaren. Dit betekent dat ze veel nieuwe zaken heeft gehoord, in een prettige setting. Ze wenste bij vertrek Treparel persoonlijk een welgemeend succes toe.

Meer over de Week van de Ondernemer en dit Ronde Tafel gesprek op: Nu Zakelijk

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Analyzing the Graphene Patent landscape reveals a global race

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.

Figure 1: Graphene could find uses in computing, energy, medicine and other fields

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.

Patents on Graphene

Figure 2. Graphene, graphite, carbon nanotubes and fullerene

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.

Figure 3. Graphene patents used to train the classsifier (above), and the entire collection after training and applying the KMX classifier (below).

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).

Figure 4. Data sets for “film, graphene, substrate” and “nanotubes, carbon, nanostructures”. Produced by KMX with data provided by IFI CLAIMS Patent Services.

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).

Figure 5. Evolution of patents on graphene. Source: KMX and IFI CLAIMS Global Database.

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.

Figure 6. Main IPC codes related to Graphene. Source: IFI CLAIMS Global Database.

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).

Figure 7. Main countries during the period 1994-2012. Source: IFI CLAIMS Global Database.

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.

Figure 8. Largest patent families. Source: IFI Claims Global Database. Created with KMX.

Geim and Novoselov – The Original Researchers

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.

Figure 9 Patents on graphene with Geim y Novoselov as inventors (INV) or applicants (PA). Source: IFI CLAIMS Global Database. Created with KMX.

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.

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“Big Data” in Google Search Trends

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.

Demand for Quantitative Decision Making

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.

“Cloud Computing” in Google Search Trends

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.

Automatic Model Generation in Data Patterns

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 figure below we show this in a “picture” where one can see that building on the rapid advancement on big data and cloud computing technology it is expected that large scale analysis of multiple related trends and patterns will become available in the near future.

Figure showing how cloud computing and big data analytics technology can enable finding semantic relations between patterns in large complex data sets

Using Big Data and Cloud Computing to analyzing complex data at the semantic level

At Treparel we are focusing on  the described developments to build technology to enable data scientist to set-up  ”analytic pipelines” and run these using cloud computing technology to extract many models from different data sets and determine relations between. This step is needed to go from decision making using accurate and reliable models to intelligent decision making using all models that have semantic relationships in the context of the data. Much R&D is still needed in this direction but with the fast progress in big data and cloud computing technology and a broad demand from multiple industries the future in 2013 certainly looks promising and challenging.

Since this is the last blog of 2012 – let me wish you a very good and exciting 2013.

Dr. Anton Heijs, CTO Treparel.

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A new Era in News finding and creation

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.

Analysing Big News from The Guardian

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.

The annotation terms on top of each cluster provide an overview of the different topic areas where big data is mentioned in the news articles provided by the Guardian.

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.

A closer look on Google vs Microsoft

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.

All articles on ‘big data’ where Google is mentioned (shown as green dots)

Google is mentioned in 1274 out of the 10.521 news articles.

All articles on big data where Microsoft is mentioned (shown as red dots)

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.

All articles on big data where apple is mentioned (shown as blue dots)

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).

News articles ranked by relevance on internet technology.

Now we have excluded irrelevant articles we can much better analyze what the most important articles are in respect to internet technologies.

Is there a story?

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.

Trend of all articles from 2000 (red) to 2012 (white) related to Apple (blue rings) and all other news articles.

Conclusion? Microsoft is entering the market late ….

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)

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Patent discovery: making specialist tools available for business users

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.

Information overload leads to growing need for business user friendly tools

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.

Growing need for patent information

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.

Over 90% relevancy is promising

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.

Aalt van de Kuilen, President CEPIUG

About CEPIUG

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.

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Fujifilm’s Astalift Anti Ageing Skin Care

Antiaging at Fujifilm

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.

In this article we try to understand the diversification process followed by the Japanese company by using IFI CLAIMS Direct patent database and Treparel KMX Patent Analytics Software.

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.

Patent portfolio diversification

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.

1. Create an IP Landscape

Figure 1: Brushing and coloring patents according to textual matches with KMX.

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.

Figure 2: Evolution of the term “Collagen” in Fujifilm patents over the 10 last years (2003-2012)

When searching generally for health related patents we will very likely retrieve patents linked to medical image or photographic technologies used for medicine. But it would be very interesting to identify all patents related to applications for healthcare, cosmetics, anti-aging in one group and separate these clearly from those related to medical image devices and image based diagnostics (endoscopies, radiographies, thermographies, radiologies, electrocardiograph, electoencephalograph, magnetoencephalograph, etc.). The KMX free classifier allows us to do that in a couple of simple steps.

2. Labeling a small set of relevant documents

We identify some patents related to the topics of healthcare, cosmetics, medical imaging and so on. We ensure, by reading these few patents attentively, that in fact they are related to these topics.  In KMX, we label the sample documents appropriately.

Figure 3: Highlighting and labeling patents with KMX Classifier technology.

3. Score the full set of patents and auto-assign a classification for each

After defining the training set, we train a free classifier.  KMX will score all patents in the studied set and assign a classification for each. We can sort the list on highest scores, check the classifications and iteratively train the classifier until we are satisfied with the results.

Figure 4: KMX free classification applied to Fujifilm’s patents

The classifier we have created with KMX allows us to automatically classifying any patent according to our specific needs. With IFI CLAIMS and KMX any organization can establish a continuous monitoring and classification pipeline for all patents being registered in their area of activity.

Figure 5: Landscape of Fujifilm’s patents published in 2011 automatically classified in 4 different classes

4. Apply the Fujifilm classifier on a competitor portfolio

Once we have created a Fujifilm classifier in KMX, we can apply it to other data sets, including competitor portfolios.  Let’s look at KODAK’s portfolio and apply the Fujifilm classifier to it.

Figure 6: IP landscape of KODAK’s patents published in 2011 (using Fujifilm classification)

The comparison of Figures 5 and 6 shows clearly that FUJI’s patent portfolio, last year, is much stronger than KODAK’s. Both FUJI and Kodak have protected technologies in medical image and in vehicles. But FUJI’s shift towards areas such as cosmetics and the food industry can be appreciated, compared to Kodak’s inactivity in these areas.

Table 1: Compare patent documents from Fujifilm and Kodak (related to 4 areas of interest)

5. Patent evolution comparison of Fujifilm and Kodak portfolios

Fuji Photo Film has followed over the years an intensive IP orientated strategy with thousands of patents being registered every year. Figure 7 below compares the overall evolution in the number of patents during the last ten years between the two companies.

Figure7: Patent documents published by Kodak and Fuji over the last 10 years (2003-2012).

Our short study shows two things.

1. Fujifilm and KODAK have been working for years in areas traditionally related to the photography industry, such as medical imaging.
2. Only Fujifilm has been investing in new market opportunities such as cosmetics and the food industry.

As pointed out in an article in the Economist, the Japanese company reacted earlier and more efficiently to the threat of digital photography.  They did this by designing a consistent strategic plan that included the development of new business lines.

About the writer:

Enric Escorsa O’Callaghan (Iale Tecnologia)

Enric Escorsa O’Callaghan Secretary General at IALE Tecnologia (Spain).

Eric is a agricultural engineer specializing in horticulture and landscaping in the College of Agriculture of Barcelona. Postgraduate in Landscape Architecture from the University of Greenwich, London. He worked for eight years as a landscape architect in London and Barcelona. Since March 2006 is Project Engineer in IALE Technology participates in projects where intellectual property, European projects and development of online courses.

This article was originally published by IFI CLAIMS.

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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.

Past and Present processes to fund R&D and IP development

The traditional IP development process (revenue from product sales generates money to invest in R&D)

The new process where revenues from IP generate revenue directly after the innovation (to be invested directly in R&D).

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.

From Information Gap to Information Democracy

Although there is more data available (patent and non-patent literature) and there is a growing need for data driven analysis the paradox the search and analysis work is still done with limited resources; the effect? We call it the “information gap”. But unlimited computer resources (by cloud computing) promisses  ”information democracy” and the use of Big Data Text Analytics.

The traditional role of the searcher, performing analysis answering specific requests.

The information professional in the role of the analyst setting up analytic pipelines to automatically analyse large document sets.

The expertise of the information professionals will still be needed in the future for on request search and analysis tasks providing detailed answers. The information professionals have the expertise of the data to define analytic pipelines that can process large amounts of data and generate results and more generic analysis tasks. When these tasks are automated the results can be provided to many people in the business and they can even benefit from sharing the information.

The information professional support multiple business requests obtaining the best information, answering specific analysis requests and managing recurring analysis on large document collections.

In  this paradigm shift the  information professionals (searchers) become “information creators” and by defining analytic pipelines they help the information consumers in a proactive way which improves the competitiveness of the organization in the knowledge driven economy.

Use Case: SWOT Analysis on eBook technologies

One of the use cases that will benefit from this are SWOT analysis since the analysis of large sets can be automated using advanced text analytics functionality. In the next picture we analysed the landscape of electronic paper technologies that are related to ebooks. For this KMX clustered the eBook landscape and performed an automatic ranking (classification) on the most relevant technologies for ebooks. Visualization like these can be built in a short period of time (minutes) and can become part of  auto generated reports in a browser.

Landscape analysis on the owners of 4277 patents in electronic paper technology related to ebooks.

Patent landscape of 4277 patents around electronic paper technology. (in purple the highly relevant patents in screen technology, in red dots low relevant patents).

Treparel’s KMX technology focus on big data text analytics developments exactly to enable information professionals to handle analytics needs on the growing amount data in two ways.

1. It supports the specific analysis tasks based on specific questions from the business
2. it enables more proactive analysis on the more generic analysis tasks with analytic pipelines that are processed in the cloud and generate rich reports with visualizations in a browsers.

The following link provides a presentation related to this blog:

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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

What is fused?

High quality data is the basis of sound decision making. Fusepool focuses on six core areas:

• Information & communication e.g. matching business opportunities and potential partners
• Transport & mobility (e.g. merging and delivery of emergency data)
• Goods & services (e.g. analyzing patents for product development)
• Finance & insurance (e.g. mashup of insurance costs and crime rate)
• Real estate & housing (e.g. house price correlation with salary progression, inflation etc.)
• Energy & utilities (e.g. mashup of internet costs and avg. speed)

Why a data pool?

Data is an integral element of the business value chain. However, many barriers impede leveraging the full potential of that data for more efficient, digitally supported and data-driven business insights and actions.

Fusepool refines and enriches raw data using common standards and provides tools for analyzing and visualizing data so that end users and other software receive timely, context-aware and relevant information.

To ensure that data and results are of high quality, Fusepool combines the well-defined but error-prone (semantic) Web 3.0 with controlled supervision and the collaborative but often messy (social) Web 2.0.

More reading on the www.semanticweb.com: Dive Into Linked Data At Fusepool

Nov 28, 2012: Update on Open Call for Users: Data for Business and Product development

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