Category: Amberblog

Patent vs scientific literature – a comparison

We have had some questions recently from the likes of universities and even come companies about why they should look in the patent literature when assessing inventions,  when they could look in the scientific literature instead.

To me this is a false dichotomy – ideally a diligent searcher would look at both. But putting this aside – how do they compare? To help answer this question, I have prepared the table below:

 

Measure

Scientific literature

Patent literature

Total number of publications

~50 to 60 million.

 

This is based on an estimate of ‘up to 50 million’ published in 2010, and allowing for growth since there

~93 million  in 51 million families

 

As at September 2015. They may be more than this, but this figure is based on publications available online

Annual number of publications

(in 2014)

~3 million

~4.4 million in 3.4 million families

Types of disclosures

Scientific papers are published for a variety of reasons. Sometimes these disclose new concepts or inventions, often they contain new data or reviews of old data.

Patents in every case contain inventions, and are published in a form designed to make the scope of the invention easy to understand

Ease of online access

Scientific papers can be more difficult to obtain online, and where available online can require access to paid databases.

There are a number of free and paid online databases that can give full and immediate access to 93 million patent records

Indexing

(ability to apply a unique identifier to support further analysis)

Scientific papers can have inconsistent bibliographical details, meaning that they can hard to index.

Patents publications have a (more or less) standardised numbering system, meaning that it is possible to fully index them

Commercial relevance

Scientific papers for a whole range of reasons, many of them for non-commercial purposes such as advancing in their academic career.

Patents cost more money to file, and so only tend to be filed for inventions where the inventor or applicant thought at the time there was commercial merit in the invention

Which organisations publish their ideas?

Generally written by staff or students working in research organisations and universities.

 

Companies may baulk at publishing their best ideas

Companies of all sizes, research organisations, universities and individual inventors.

 

Even the most secretive of companies can publish plenty of patents, for example the likes of Apple.

Language of publication

Can vary, although English language abstracts of non-English papers can be published

 

Even where English language abstracts are published, the rest of the paper may only be available in PDF format and machine translation may be difficult

Can vary, although English language abstracts of non-English papers can be published.

 

In addition the most important patents tend to be published in multiple countries, which can be multiple languages if not originally in English.

 

The free patent search database Espacenet now includes on-demand machine translation, to/from multiple languages for many of the patents in its database

Publication and filing delay

Can be immediate for self-publications, although peer review can take slow publication

 

There can also be a delay if the invention is commercialised to allow for patents to be filed first

Generally 18 months from date of filing, although some utility patents are published soon after filing.

 

Patents are generally filed before the commercialisation of inventions.

 

Looking at this table,  the benefits of also searching in the patent literature for universities and companies considering new ideas become pretty clear.

A true story can also confirm this point. A patent attorney colleague told me of a university client who had spent three years working on an invention, and had taken it to the  patent attorney to be filed as an new patent.  But a simple patent search quickly revealed knockout prior art, being some patents filed in Japan.

The inventor was upset of course – and also surprised. He recognised the name of the Japanese patent filer, as being a leading academic in a related field. The university inventor was fully across the scientific literature in the specific field he was inventing in, but had not seen anything published by the Japanese academic in this specific field.

The reason why is that the japanese academic had not published any scientific papers in the area of his patents. Instead he had chosen to protect his obviously valuable invention with patents.

 

Is patent searching difficult?

Patent seaching can be as simple and fast as using Google. In addition Ambercite has developed Cluster Searching, which in a matter of seconds can find similar patents to relevant patents you have already found. Cluster Searching is available for a free and confidential trial – please contact us to arrange a demonstration.

 

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Do Segway have a valid patent over the self balancing wheel? A 1 minute patent search casts doubt on this.

Verve has just reported that Segway, maker of the Human Transporter

 

Segway.jpg

 

has just sued Hovertrax, maker of the popular Solowheel,

 

Hovertrax.jpg

 

for infringement of five of their patents.

These patents are 

  • US6302230
  • US6651763
  • US7023330
  • US7275607
  • US7479872

The complaint specifically notes that: The ‘230 patent discloses and claims a personal transporter with a balance monitor and a method for using such a transporter”. These patents are all owned by the company Deka Products LP.

The earliest of these patents, US6302230 for a Personal mobility vehicles and methods  was filed in June 1999. But is this patent valid?

For a patent to be valid, it must be novel and inventive over the ‘prior art’. As part of the patent examination the examinder does a comprehensive search for earlier patent and non-patent publications. The applicant too needs to declare all of the prior art they would have known about.  In this case the US630.. patent has 126 prior art patents (backward citations) listed. These include 16 patents identified by the examiner (after an extensive search no doubt) and 110 nominated by the applicant.

Given this extensive existing prior art search, we would expect that finding new and relevant prior art would take many hours if not days?

This would of course depend on the method you use to search. Ambercite has recently developed Cluster Searching, which determines similar patents using algorithms that consider the similarity of patent citations to patents you may be interested in. The search returns a combination of ‘direct citations”‘ (already known) and indirect citaitons (new and not already cited prior art).

Arguably all of the known prior art has already been considered by the examiner, so it is the indirect prior art that may be most interesting.

Cluster Searching can work best if we start with a cluster of similar patents, so in this case we will use as starting patents the five asserted patents. We will restrict results to patents filed before the priority date of the US6302.. patent of June 1999. The search query looks like this

US6302230-search-query.jpg

The results initially look like this, which shows you the most similar patents in order of likely similarity (only the five most similar are shown). Many of these are also filed by Deka

a1sx2_Original1_US6302230-search-result.jpg

 

Of note is the last column, which shows whether the patent was an indirect or direct citation. From above we are mostly interested in indirect citations, so we will sort the results by this column (results can be sorted by clicking the column heading). Once sorted by this column heading, we also sort the first column Rank so that it the patents are sorted by the most likely be similar, to the worst. In the image below, only the four most likely to be similar patents are shown

a1sx2_Original1_US6302230-search-result-sorted.jpg

 

Of note is the fourth listed patent, namely WO1998006117, filed for a  Process for stabilizing a single-axle wheeled vehicle and vehicle so stabilized. This was published in 1989, and discloses a:

A single-axle vehicle with one or two wheels arranged on the axle is characterized by high manoeuvrability. To stabilize the vehicle, a sensor produces a signal corresponding to the actual position, which controls in a closed control loop the direction and magnitude of the additional forces exerted on the vehicle

Uniaxle.jpg

Or in other words, a uniaxle vehicle with a balance monitor. This patent is clearly relevant to the inventive concept of the Segway patent, and yet has not been recognised as such by any of the prior art searches. We do not know why, but this may be due to the inherent limitations of keyword searching.

 

Want to try Cluster Searching for yourself?

Cluster Searching is a very fast and easy to use web application. Free demonstrations and confidential trials are available to qualified applicants – please contact us to arrange a short demonstration and trial. You may be surprised (and impressed) by just how much time and money you may save.

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How to quickly find potential licensing options for patent portfolios – Case study on Aurora Algae

Licensing managers are often called upon to identify potential licensing options for portfolios of patent they might manage. There are a variety of ways of doing this, but perhaps the easiest way, and a very simple starting point, is to start with the patents themselves.

Ambercite has recently developed Cluster Searching, which is a web application for finding the most similar patents to one or more starting patents. This is done using advanced analytics which are applied to patent citations. Each patent citation is an opinion by an expert (being the patent applicant or examiner) that two patents are similar. The advanced analytics within Cluster Searching then combine these opinions to work out which clusters of patents are most similar to the starting patents, irrespective of the keywords or patent class codes that are use. Date filters can also be applied to focus the search. The search can take seconds to run working from the patent numbers themselves.

As an example of this, consider Texan company Aurora Algae, which builds and operates algae farms, as shown below. Aurora claims that its algae can be used to produce pharmaceuticals, health foods, fish feed and biofuels.

AlgaeFarm.jpg

 

Aurora have filed a significant number of patent applications to protect their technology – we estimate that in the key US market they have filed 14 granted patents and 21 patent applications. There are also 9 WO patents and 2 EP patents.

EP, US and WO patents or patent applications are ideal starting patents to be used for Cluster Searching. These can be pasted and copied into a data entry box process, as we have done in the image below, which only shows some of the known Aurora patents. Note the selection of a date filter – in the case we limited the search for similar patents to those filed after November 2007, being the filing date of the earliest Aurora patent we found. We also put an upper limit of the most similar 1000 patents.
 

AuroraPatents.jpg

 

The five most similar patents found, out of the 408 returned by this query, are shown here (click on image to get full size version):

AuroraPatents-Search-result_full-size.jpg

 

You will note a series of columns:

  • Rank shows our overall ranking of results
  • Similarity shows the predicted similarity of the patents found to the seed patents
  • L.P. is short for ‘Licensing Potential’. This value suggests which patents found have the best combination of similarity to the seed patents, and commercial importance of the patent found, to be worthwhile investigating further as a licensing option. Higher values are better, and Licensing Potential values for different patents owned by the same applicant can be added together
  • Number is patent number, Owner and Title are exactly that, and Filed is filing date
  • Citation tells you if the patents found have direct citation connections to the starting patents, or instead are indirect ‘citations of citations’.  Both can be relevant. In this case, we returned 106 direct citations and 302 indirect citations. The highest ranked indirect citation was US8967402, filed by Solazyme for a process to convert algae into biodiesel.
  • Amberscore is our citation based prediction of the commercial importance of a patent. Higher values are better, and anything above 1 is above average.

Of these values, Licensing Potential is perhaps the most efficient way of reviewing the results. If we add the values together (this is easily done by exporting the results table to Excel, and using an Excel function such as Pivot-tables to add together Licensing Potential values), we can see that the following companies are among those with the best Licensing Potential for the Aurora patent portfolio:

Company

Total Licensing Potential from Aurora patents

Number of similar patents found

About the company

Haliae   (Arizona)

1540.0

51

“Heliae is an applied life sciences and technology company focused on researching and developing algae and other cutting edge biological platforms for commercial scale production of useful products.”

Solazyme (California)

605.0

44

“Starting with microalgae, the world’s original oil producer, Solazyme creates new, sustainable, high-performance products. These include renewable oils and powerhouse ingredients that serve as the foundation for healthier foods; better home, personal care and industrial products; and more sustainable fuels.”

DuPont Pioneer

85.0

20

Producer of glyphosate resistant seeds – and Aurora has developed glyphosate resistant algae

Genifuel  (Utah)

61.5

3

“Genifuel Corporation produces equipment to make renewable fuels from wet organic material by a highly efficient process known as Hydrothermal Processing (HTP).”

DSM (Netherlands)

49.7

6

Has filed patents for the production of fatty acids from algae

Sapphire Energy (California)

46.3

67

“Sapphire is a venture capital backed company founded in 2007 for the purpose of growing and processing micro-algae into products that serve very large and diverse markets where the unique attributes of algae provide valuable solutions.”

Bionavitas (Washington state)

41.2

2

“Innovations for more efficient growth of algae for use in biofuels, nutraceuticals, and environmental remediation.”

Livefuels (California)

31.8

1

“LiveFuels taps the power of natural aquatic life to produce renewable fuels from algae.”

Algae Systems (US)

23.6

2

“Algae Systems’ technology captures what others discard -untreated wastewater and atmospheric CO2 – and produces renewable fuels and fertilizers, leaving behind only clean water for beneficial reuse”

Pond Biofuels (Ontario)

17.1

3

“Pond Biofuels converts raw smokestack emissions from heavy industry into algal biomass”

 

As you can see, a range of companies using similar technologies is quickly found. In most cases the links back to the technologies being patented by Aurora Algae are quite obvious.

The link to Du Pont Pioneer is less obvious, but comes because a number of Aurora patents deal with the use of glyphosate to help manage weed growth in algae farms – and Du Pont Pioneer is filing patents and developing technologies for the use of glyphosate to help manage weed growth in agriculture. This example proves how this sort of analysis can easily be used to find ‘parallel’ technologies, i.e. similar technologies that may not be picked up conventional patent searching – but still can be relevant to your licensing objective.

 

How does this analysis compare to other patent searching techniques?

There are other vendors that offer searching systems that can be used to find licenisng opportunites. In comparison to these other systems, our customers have been telling us that Cluster Searching is:

  • Easy to use – there is no need to spend time building complex queries that require you to try to guess what the most appropriate queries are
  • Easy to learn – There is no need to spend many hours learnng how to use complex multi-screen systems. Instead Cluster Searching simply requires a list of starting patents, which are often to easy find. This in turn can lead to much lower training costs 
  • Cost effective – Ambercite offers corporate subscriptions that are very competitive compare to some other searching systems, and allow unlimited usage within your corporate environments
  • Capable of finding patents missed by other techniques – which comes from being to able to avoid the limitations and risk of keyword searching

 

Want to try this for yourself?

Ambercite offers free and completely confidential two week trials to Cluster Searching – please contact us to arrange a demonstration and trial. Testimonials for Cluster Searching are found here, including:

“Ambercite Clustering has managed to create a tool to enhance chemical patent due diligence in a remarkable way…revealing art which may cover compounds in discovery within claims of a generic scope.  Congratulations!” – Patent Analyst, global pharmaceutical company, USA

 

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Why can’t patent searchers rely on keyword or semantic searching to find all relevant patents?

Most patent searching is based on keyword or semantic searching. I understand this fully and use keyword searching a lot myself.  

However from long experience, I have learnt that relying on keyword searching alone will see a lot of highly relevant patents missed or incorrectly ranked in a relevancy list of patents found.  For this reason, we recommend to all searchers that they supplement their keyword or semantic searches with a citation based search such as Cluster Searching (where the seed patents for the Cluster Search could be the best hits they find using other search methods). 

There are four good reasons for this.

1) Patent applicants can be very inconsistent with their use of technical words. 

2) Semantic or keyword searching can return thousands or hundreds of thousands of irrelevant hits

3) Semantic searching can be misled by different languages 

4) Even if keyword searching finds relevant hits, the order of result can be misleading

 

1) Patent applicants can be very inconsistent with their use of technical words 

As one example alone, consider this following list synonyms for the humble cardboard ‘box’ :

  • carton
  • carrier 
  • container
  • package 
  • packaging
  • receptacle
  • case 
  • pack  
  • crate

In some cases, it might be possible for search engines to understand every possible synonym for some technical concepts, but this is unlikely for all technical concepts. As one real world client example from a real world example a key prior citation for a patent that referred to ‘carbon dioxide’ was missed by USPTO examiners and a major patent law firm because it instead referred to the very rare synonym ‘carbonic acid gas’.

In contrast, the citation searching approach used by Cluster Searching relies the expertise of a number of patent examiners and applicants in the immediate field to recognise rare synonyms for technical concepts  – this greatly increases the probability that at least one of the searchers makes the link between patents that would be missed by semantic analysis alone.

 

2) Semantic or keyword searching can return thousands or hundreds of thousands of irrelevant hits 

Again if we return to the simple example of cardboard boxes, a search for prior art based on the main technical keywords used for cardboard boxes (for example ‘box’, ‘lid’, ‘flap’, etc) will return thousands or tens of thousands of patents – almost all not very relevant to the patent application that you may be looking for – as is shown by the results for this query shown by the seach engine “The Lens” 

boxlidfapquery.gif

 

In order to deal with these large amounts of irrelevant hits, searchers and examiners are forced to apply assumptions about which keywords are important – and every such assumption risks filtering out irrelevant patents.

As an example of this, consider the feedback from a patent examiner for a major patent office after trialling Cluster Searching:

The top ranked [prior art] patent, USxxxxxxx, was very good because it found an element that is hard to search for, i.e. ‘independent power settings’.  The ‘independent power settings’ are hard to search for because searching for something like “power near2 settings” would return several thousand hits and most of them would not be for independent power settings and limiting that search with an adjective like “independent” would filter out a lot of good results.  And it’s amazing that it came up first in your search…   .”  – Patent Examiner of a world leading patent office

 

3) Semantic searching can be misled by different languages

Semantic based searching is much less likely to pick up similar patents in different languages – self-evidently semantic searching will likely struggle to recognise synonyms in foreign languages. Returning to the earlier, it is worthwhile considering some of its foreign language equivalents for the word ‘packaging’.

  • verpackung             (German)
  • conditionnement     (French)
  • imballaggio             (Italian)
  • embalaje                 (Spanish)
  • パッケージング         (Japanese)
  • 包装                         (Simplified Chinese)
  • 포장                         (Korean)

In contrast, we like to believe that Cluster Searching is ‘language agnostic’ as it relies on the ability of patent examiner to recognise similarities between patents that may even be filed in foreign languages. 

 

4) Even if keyword searching finds relevant hits, the order of result can be misleading

Semantic based searching tends to rank results based on the overall similarity of keywords. So even if a keyword semantic search tool will recognise that a box is also a carton and a carrier, it may rank patents that refer to ‘cartons’ and ‘carriers’ as being much less relevant than patents that refer to ‘boxes’. However In practice, many of the patents that refer to cartons may be much less similar than many patents that refer to boxes.

In contrast, patent examiners and applicants will identify and cite similar patents even these patents use different synonyms for the same concepts.

 

>>>>>>>>>>>

For all of the above reasons, we recommend to all clients that they use Cluster Searching to complement their existing semantic or keyword searching processes. Case studies have shown that it will produce results that are different and yet still very relevant, when compared to conventional searching. An example, consider the case study found here. While originally published in August 2014, these results are still very applicable to Cluster Searching. 

An example of a query from a recent blog on Cluster Searching is found below:

 

ClusterQuery_20150714-041021_1.jpg

 

But doesn’t this produce a lot more work?

Cluster Searching has been carefully designed to be very easy and fast to use. You can obtain and review usable results in minutes. Considering the importance of many patent searches to their clients, and the genuine importance of not missing relevant patents in these searches, we think that this few minutes is well worth spending.

 

Want to know more?

Ambercite offers free and completely confidential two week trials to Cluster Searching – please contact us to arrange a demonstration and trial. Testimonials for Cluster Searching are found here, including:

“Ambercite Clustering has managed to create a tool to enhance chemical patent due diligence in a remarkable way…revealing art which may cover compounds in discovery within claims of a generic scope.  Congratulations!” – Patent Analyst, global pharmaceutical company, USA

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Viagra – boosting the patent literature

Viagra, which is a trade name for the drug sildenafil, has been a major bestseller for Pfizer earning it up to US$2 billion annually. Sildenafil was originally tested for use in dealing with hypertension, but the side effects on penile erections were soon noticed. 

The first patent for sildenafil was US5250534, which was filed in 1992, expired in 2012 and covered sildenafil itself, but netheir its two family members cover its effect on erectile disfunction or impotence. Pfizer followed up this up with US6469012, filed in 1994 for Pyrazolopyrimidinones for the treatment of impotence and hence clearly targeted at impotence. Equivalants to US6469012 have been filed in a number of other countries, including in Europe and as a PCT patent. US6469012 is slated to expire in 2019. 

Not suprisingly given the huge commercial impact of sildanafil, there has been many later patents claiming similar compounds or their applications. There are various means of identifying these later patents, but among the easist method is the recently released web application of Cluster Searching. With Cluster Searching, we can run instant patent searches using little more than the numbers of some relevant ‘seed patents’. Cluster Searching uses advanced altogorihms that looks for similarity in citaiton profiles to suggest similar patent to one or more starting (or seed) patents, and then ranks the found patents according to predicted similarity. The overall concept is shown below:

Cluster-searching-concept_20150701-042028_1.jpg

 

In this case some good seed patents would be US5250534, US6469012, and the EP and WO equivalents of US6469012. Since we are looking for later patents, we might limit the search to patents filed after the priority date for US5250543, which is June 1990.

The resultant query would like this (see below), and takes a few seconds to run.

Viagra-query.gif

This query produced over 1700 similar direct and indirectly results. Along with metrics for similarity (to the seed patents) and likely commercial importance (our metric AmberScore) we also calculate “Licensing Potential“, which ranks patents based on both similarity and AmberScore. Licensing Potential values can be added together, and different patent owners can be compared based on total AmberScore values.

This is what we see below.

Viagra_followon-patents_20150701-034620_1.jpg

 

Not surprisingly, Pfizer was the leading owner of these later patents – it is well known for pharmaceutical companies to try to evergreen their own patents. Behind Pfizer came a mixture of large pharmaceutical companies, led by Boehringer Ingelheim, but also including Bayer, OSI Pharmaceuticals, Merck and GSK.

It can also be helpful to look at the leading patent (in term of Licensing Potential) filed by each of these companies. These are shown in the table below.

Applicant

Total Licensing Potential

Number of direct and indirect forward citation patents

Forward cite patent with highest licensing potential

Title

Filing date

Direct forward citation?

Licensing Potential of leading patent

Pfizer

551.8

161

EP0526004

Pyrazolopyrimidinone antianginal agents

1992-07-02

Direct

23.6

Boehringer Ingelheim

277.9

66

US7645763

 

8-[3-amino-piperidin-1-yl]-xanthines, their preparation and their use as pharmaceutical composition

2005-02-22

Indirect

8.4

Bayer

161.5

71

US6362178

 

2-phenyl substituted imidazotriazinones as phosphodiesterase inhibitors

2000-07-21

Direct

15.4

OSI Pharmaceuticals

133.4

35

US5696159

Lactone compounds for treating patients with precancerous lesions

1994-08-03

Indirect

6.6

Merck

129.8

55

US6821978

Xanthine phosphodiesterase V inhibitors

2001-08-28

Direct

14.8

Vivus Inc

114.6

30

US6403597

Administration of phosphodiesterase inhibitors for the treatment of premature ejaculation

2001-06-21

Direct

13.6

GSK

111.9

38

WO1997003675

Use of CGMP-phosphodiesterase inhibitors to treat impotence

1996-07-11

Indirect

17.7

Alexza

94.2

41

US7488469

Delivery of caffeine through an inhalation route

2006-07-18

Indirect

13.7

Eli Lilly

86.9

34

US5859006

Tetracyclic derivatives, process of preparation and use

1996-07-16

Indirect

9.0

Nitromed

82.8

30

US5958926 

Nitrosated and nitrosylated phosphodiesterase inhibitor compounds, compositions and their uses

1998-09-01

Indirect

8.0

 

It should be noted that not all of these patents will be directly related to sildenafil patents. However each patent has both a Licensing Potential for a reason, and each should have something in common with sildenafil patents – and each has been filed after the original priority date for the sildenafil patents.

It shoudl also be noted that the patents are a combination of directly and indirectly connected patents – so not all of these patents would have identified by a traditional forward citation analysis.

 

Want to know more?

This simple example shows how easy it can be to explore the patent landscape following on from some important patents such as the original patents filed for sildenafil . There are many other ways we could have run this analysis – for example we could have included some of later patents filed by Pfizer for sildenafil. Nonetheless, this does show what is possible.

Ambercite offers free and completely confidential two week trials to Cluster Searching – please contact us to arrange a demonstration and trial. Testimonials for Cluster Searching are found here, including:

“Ambercite Clustering has managed to create a tool to enhance chemical patent due diligence in a remarkable way…revealing art which may cover compounds in discovery within claims of a generic scope.  Congratulations!” – Patent Analyst, global pharmaceutical company, USA 

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Mapping Innovation Hotspots and Collaboration in Australia

Background

Ambericte recently published a report on the top 10 patents in Australia, which has been well received. For another perspective on innovation in Australia, we have been collaborating with Andre Vermeij of Kenedict in the Netherlands. Andre has developed an innovative approach for mapping collaboration and collaboration opportunities, and was interested to apply this to Australian patents. The resulting study is shown below – and is a practical example of what is possible when disparate companies collaborate (note that this blog is jointly published by Kenedict here).

Introduction

As Australia starts to look beyond the recent mining boom to see what is next on the horizon, it obvious to many that innovation and technology will be a key part of its future success, and that a key part of this is increased collaboration between the different parts of the economy. The Australian Technology Network has been on the front foot on this, releasing a report in March 2015 claiming that Australia ranks just 29th out of 30 in the OECD in terms of the proportion of businesses collaborating with universities on innovation.

This report was launched by the Federal Minister for Industry and Science, who noted:

“Lifting the rate of collaboration between industry and research is essential to maintaining Australia’s economic growth”

Collaboration though is not just between universities and companies – it can be between any groups, even within a company. And it can be very worthwhile, with a 2014 report commissioned by Google estimating that collaborative companies are worth $46 billion per year to the Australian economy.

But how exactly is Australia doing right now in innovation and collaboration? Where is it strong?

In a practical example of international collaboration, Dutch based innovation analyst Kenedict has worked together with Australian based developer of patent search and analytics software Ambercite to throw a new light on

  • Where are Australia’s best areas of innovation?
  • Who is collaborating with whom?

This analysis is based on patents filed by Australian innovators in the last five years. Most serious innovators filed patents, and so patents can be an excellent proxy of innovation, and in areas where data is otherwise very difficult to get.

The results are provided in 3 sections:

  • A static map is provided that provides an overview of what areas were patents filed in recent years
  • Further details are provided of four of the leading areas, being Biotechnology, E-Commerce, Medical Devices and Sports. These details are provided in fully interactive maps as well.
  • Having shown all of the patents filed in these areas, we then provide a final map showing the central network of collaboration in Australia. CSIRO is a major player in this network, but it includes links to many of the leading universities and companies big and small.

The Data

To get a view on innovative activity in Australia, we started by compiling a dataset containing all patent applications published between 2010 and 2014 listing Australian applicants. The data originates from the European Patent Office’s patent database, which contains patent data from over 90 countries. We focused on patent families, where a patent family is where the same patent is filed in more than one country, to avoid double-counting unique inventions in the final analyses and visualizations.

Subsequently, we further cleaned and enriched the data by combining applicant names (for instance identifying ‘CSIRO’ and ‘Commonwealth Scientific and Industrial Research Organisation’ as the same organisation) and adding organisation types (Corporate, SME, University, Research Institute and Government). The final dataset consisted of 12,774 patent families.

Applying Network Analytics to Uncover Relationships

We then applied the principles of Social Network Analysis to analyse and visualize Australia’s innovation hotspots and associated collaboration networks. Network analysis focuses on the mapping of actors and their relationships in a specific context – in this case, the context of technology and innovation. Networks consist of nodes (here, patents or patent applicants) and edges (connections between patents or applicants) and can be analysed and visualized using a wide variety of indicators and interactive maps. A significant advantage of this perspective in the context of innovation is that it allows gaining insight into the relationships between technologies and organizations, which can serve as a basis for a renewed look on Australia’s innovative activity over the past five years.

Innovation Hotspots in Australia

To get a first visual overview of innovation hotspots in Australia, we constructed a map including all interconnected technology areas based on patenting activity over the past five years. Patents list technology classification codes on their front pages, which are assigned during the examination process to make it easier to locate the patent at a later point in time. We can use these classifications to connect inventions and show clusters of related technology in Australia. For instance, when two patents share a classification code related to a specific technology field, we can assume that these patents at least partly overlap in their underlying technologies. Using this perspective, we can build a network consisting of patents which share one or more classifications.

This allows us to visually obtain answers to a wide variety of questions: Which technologies dominate the Australian technology landscape? How are technology areas connected, and which organisations have leading roles within these clusters? Are there up and coming technology areas which may become more important in the near future?

 

AustraliaTechnologyNetwork.jpg

 

The map above shows the largest interconnected component of technologies, consisting of 9,231 patents and over 125,000 connections between them. Colours were assigned to patents to show clusters of activity in the network. We examined the contents of each cluster and added relevant technology descriptions and key organizations to the map as well:

Australia’s innovation hotspots and related organisations can be distinguished directly from this map. The largest cluster by far is Biotechnology (bottom left hand corner), which consists of various sub-clusters and key activity by various universities, CSIRO and CSL. At the top of the map, we see the medical technology leaders Cochlear and Resmed operating in relatively secluded clusters, suggesting that they are alone within their spheres within Australia. We also see gaming technology companies Aristocrat and Ainsworth, which share the green cluster at the bottom right (Aristocrat was founded by Lens Ainsworth, who later left the company and founded Ainsworth). Silverbrook, which dominates the cyan cluster at the bottom, mostly focuses on inkjet/print head technology.

Next to this, we see various clusters of interrelated technologies which are not necessarily dominated by one or a few organisations, but which help to connect the other clusters of activity. The E-Commerce cluster consists of a wide variety of technologies related to online shopping, advertisements and consumer interaction technology; the sports equipment cluster contains all kinds of inventions related to exercise devices and golf; and the contents of the medical devices cluster mainly relates to all kinds of diagnostic and imaging technology (outside of the aforementioned Cochlear and Resmed).

Zooming in on clusters

To get a better view on actual activity, we extracted four of the aforementioned clusters and mapped them in an interactive visualization app. These areas are:

  • Biotechnology
  • E-Commerce
  • Medical devices
  • Sports

The app provides the opportunity to click, zoom and pan around these networks and draw further conclusions. Each patent in the clusters is sized by its Amberscore value, which predicts the likely commercial importance of a patent based on the number and strength of its forward, backward and indirect citations. This can help suggest the importance of a patent and aids in visually determining the most important patents in these clusters.

The different colours in these maps show areas of related activity, which relate back directly to the main map showed above. When using these maps, please note that the Biotechnology cluster may take a few seconds to load due to the large amount of data. Clicking a patent shows its direct connections; clicking white space again returns to the full network. Further information regarding each patent can be found upon click in the right-hand pane; the search box can be used to locate specific patents based on keywords.

Perhaps the best way of looking at these maps is by zooming into the maps, which you should be able to do using the usual zoom functions on your computer, including a mouse wheel if you have one. By doing so, the titles of the individual patents become visible, such as seen in this extract below:

MedicalExample.jpg

As a second step, it is possible to select any of the patents, which hides all of the other patents that do not share a patent classification code. In this way, only the patents that do a share a patent classification code are shown, so that the most similar patents are shown. For example, if we select the “Tenodesis System” (tendon anchoring) patent shown above, its immediate patent network becomes clear:

a1sx2_Original1_Zoomed-in.jpg


Practically this allows these patent owners to identify potential partners for collaboration. In this case the Tenodesis patent is owned by Lumaca Orthopeadics, a company based in Melbourne. Lumaca appear to be in the same technical area as the patent titled “Prosthetic menisci and method..’, which is filed by Prosthexis Pty Ltd, a company based in Romsey, which is just outside of Melbourne.

Patent Collaboration in Australia

The map and interactive visuals in the previous sections provide a strong overview of Australia’s key technology areas and the relationships between them based on overlapping technology classifications. Although many of the clusters were directly relatable to activity by one or a few innovators, the map does not show the actual collaborations taking place between organisations (instead showing areas of shared activity). Especially in light of the recent interest in further stimulating partnerships academia and industry, this warrants further interest.

We used our modified applicant data to construct a network of all patent collaborations (patents shared by more than one applicant) between corporates, small- and medium-sized enterprises, universities, research institutes and governmental bodies. This provides a unique glimpse into the actual state of innovation collaboration in Australia, by showing key players, their influence in terms of patenting activity, and the intensity of their collaborations with others.

The interactive visual below shows the largest interconnected cluster of collaborating organizations based on co-patenting over the past five years:

 

Using the interactive versions of the visualisations, it is possible to obtain a view on the actual connectivity of key organisations in the network. One of the best connected nodes in this network is CSIRO (Commonwealth Scientific and Industrial Research Organisation). Just like in the Cluster Maps, selecting any node hides all but the directly connected applicants. By selecting the CSIRO node, we can see that its strongest collaborations (shared patent filings) are with Monash University, the University of Melbourne, and the Grains Research and Development Corporation, as is shown in the network excerpt below:

 

CSIROQueensland.jpg

 

Another well connected applicant is the University of Queensland. The strongest connection from the University of Queensland is to Syngenta of Switzerland. By looking up patents jointly owned by the University of Queensland and Syngenta, it becomes clear that these patents are predominately around the treatment or pre-treatment of sugar cane, an important crop for Queensland. However, this strong connection to a big corporate (Syngenta) is relatively rare – instead the majority of connections in the collaboration map are between universities, other universities and research institutes. There is nothing wrong with these collaboration per se, except that universities and research institutes are less likely to be able to commercialise the inventions they produce. This is in contrast to the general capability of large corporates to successfully commercialise new ideas (even if not all such commercialisation is successful).

 

What have we learnt?

A review of recent Australian patent filings has shown that Australian applications are focused on the following areas:

  • Biotechnology
  • Gaming (Aristocrat and Ainsworth)
  • Printers (Silverbrook Research)
  • Cochlear devices (Cochlear)
  • Sleep apnea devices (Resmed)
  • Plants and plant science
  • E-commerce
  • Data security
  • Sports equipment
  • Management systems
  • Construction
  • Solar cells
  • Medical Devices (outside of Cochlear and Resmed)
  • Water Treatment
  • CO2 capturing

 

Some of these areas represent  Australia’s traditional strengths (‘Plants and plant science’), other areas are well known in Australia innovation circles (biotechnology, Cochlear, Resmed, Silverbrook) while other areas of strength show that Australian innovators are working in what could be the key technologies of the 21st century (E-commerce, CO2 capture and solar cells).

Perhaps the most surprising omission from this map is mining. While the Australian mining industry can be a great innovator, they do not appear to be filing many patents to reflect this.

The value in this sort of analysis is that Australian companies can identify other Australian companies working in this same space, and therefore potential opportunities for collaboration. Next to this, the map shows how various technology areas are connected to each other through other clusters of technology, which provides insight into how technologies can be combined to generate new potentially interesting ideas. On the country level, maps like this one can provide a new perspective on Australia’s innovation strengths – with some good examples being the clusters mentioned in the list above.

And there is certainly scope for more collaboration. A map showing direct collaboration in Australia showed that this collaboration was predominately between research organisations (lead by CSIRO) and other research organisations and universities. Although various Australian and international corporates and SMEs also appear in the network, they do not hold central positions and are often connected to just one or a few research organisations or universities. This is indeed an indication that collaboration between businesses and public research organisations could be improved in Australia, further corroborating the results of the Australian Technology Network’s recent report.

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