Show me new prior art! Case study on Cuozzo speedometer patent currently in front of US Supreme Court

US6778074 was filed by Cuozzo Speed Technologies back in 2002, and claims a car speedometer which can change colour of its markings depending on the local speed limit, as determined by GPS.


This humble little patent has made its way to the US Supreme court, which is currently hearing arguments in relation to :

  • Whether the court of appeals erred in holding that, in IPR proceedings, the Board may construe claims in an issued patent according to their broadest reasonable interpretation rather than their plain and ordinary meaning.
  • Whether the court of appeals erred in holding that, even if the Board exceeds its statutory authority in instituting an IPR proceeding, the Board’s decision whether to institute an IPR proceeding is judicially unreviewable

Here at Ambercite we will leave the resolution of these arguments to the esteemed justices, although Chief Justice has already started expressing his opinon: “A bizarre way to … decide a legal question” and a “very extraordinary animal in legal culture to have two different proceedings addressing the same question“.

As is often the case when considering a patent, we are curious to know whether it is truly novel or not. And this is an ideal opportunity to test our just improved interface in Cluster Searching, which makes it even easier to to focus the results on ‘unknown’ prior art(not listed as a patent citation), say compared to ‘known’ citations (previously listed as a patent ciation)

As always, the search process can be very fast – we simply enter the patent number and a date filter cutt-off, in this caes the filing date of the Cuozzo patent:



The first few results look like this, ranked in order of predicted similarity.



Note the last column, which shows the usual combination of ‘known’ and ‘unknown’ citations. There is a litte antenna like symbol on top of this column, which opens up a filter for this column.



If we select this button, we can perhaps the select the “unknown citations” . These prior art documents have have not been nominated as patent citations previously – and so perhaps be used as the basis for both a USPTO or US Supreme Court review.



This will produce the following set of results:



Third on this list is US6633811, filed by Bosch in the year 2000 for a Method of automatically adjusting a vehicle speed display according to vehicle location, and disclosing:

…speed limits at the current location may be displayed on the speed scale itself by highlighting an appropriate scale mark or producing a scale mark of a different length or color. The current location may be obtained from an on-board GPS

How easy was that?

(Note – If I was acting for the opponents for Cuozzo, I would now run a second an Cluster Search based on both US6778074 and US6633811).


Want to try this for yourself at home or work?

Please contact us for a free trial, or even a free patent search for patent litigators – you may be very surprised to see what we can do for you, and how easy it all is.

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Apple pays $24.9 million to settle Siri based patent infringement allegation – who else could be at risk?

Apple has agreed to pay out $24.9 million to the Dynamic Advances, licensee of US7177798, Natural language interface using constrained intermediate dictionary of results, which they allege that Apple infringes with its Siri voice command tool.


Patent infringement is beyond the realm of Ambercite, but our Cluster Searching tools can in a very short period of time answer two key questions in this case:

  • Is there are any relevant prior art, not cited by the USPTO, which should be considered by anybody trying to invalidate this patent?
  • Who else might be at risk of a patent infringement suit by the patent licensee?

We might answer the first question first.


What other prior art should be considered?

Patent citations are those listed by examiners or applicants in relation to a patent. If we look up this patent say using Google patent, 76 patent citations are listed.
All of these patent citations have been presumably considered by the examiner in their decision to grant the patent. But this in tyurns raises a couple of questions

1) 76 patents is a lot – if you were short of time, which patents would you start with?
2) What other patents could you also consider?

Both of these questions can be answered in a matter of seconds by entering the patent number in Cluster Searching, as shown below

The resulting query looks like this:


In this case, we used a Filing Date filter set to May 2001 as this was the filing date of the patent.

The most similar five patents are shown below:



Note that the 5th column in is “Similarity”, our metric of how similar the found patents are to the asserted patent. The patents are listed from the most similar patent downwards

The second to last column shown in “Citation”. In these results there are two types of citation”

Known citations– one of the 76 known prior art citations for this patent. It follows from this that the highest ranked , i.e. the predicted to be most similar patent, is US5794050 for a Natural language understanding system – followed by US7050977.

Unknown citations – These are predicted to be similar patents not recognised as prior art by the examiners or applicants – but in many cases they are. Heading this list are

  • US5727950, Agent based instruction system and method, which to be honest is not a perfect match, although there are similarities
  • US6233559 Speech control of multiple applications using applets, which starts to become quite relevant.

Note that these patents listed are only the start of a long list. Just like any conventional patent search* not all will be 100% relevant, but many will.

(*have you ever run a keyword based search and been returned some results which were not 100% relevant? You too?)


Who else may be at risk of assertion for this patent?

This is a very similar process to the prior art search – except that we are looking for patents filed after the filing date of the asserted patent. Also we might need to return a few more than 50 results. The resulting query is shown below:



The list below shows the most similar patents – including a combination of known forward citation, and other patents not listed as a forward citation – but still likely to be important.


Of note, the 6th column is Licensing Potential, which considers both the commercial potential of the forward citation as well as its similarity to the asserted patent.

These values can be added, both to work out the “Net Licensing Potential” of the asserted patent, and also the most likely licensing prospects.  But to do this, we might need a qualifying criteria:

  • Forward citation Patents to be included in this analysis need to
  • Have a minimum similarity of 2
  • Not be owned by the patent owner or licensee

The results in this table can be downloaded into a spreadsheet , and the net Licensing Potential calculated.

In this case, we calculate the net Licensing Potential to be 779.3 – a very impressive figure and suggestive of a very valuable patent.

But it is the leading companies in the forward citation network that are perhaps of most interest, as shown below:


Many of these companies are immediately recognisable (IBM, Microsoft, Google, Apple, Amazon) while others may need an introduction:

Nuance Communication is developing speech and imaging applications
• VCVC III LLC could be a patent holding company – and shares a business address with Intellectual Ventures.
Veveo “is pioneering technologies that enable natural interfaces that support true conversational capabilities for connected devices and applications.”, and is now owned by Rovi Corporation

For each of these patent owners, it is possible to work out which patent of theirs is most similar to the asserted patent – for example for Microsoft this is US8289283 Language input interface on a device


What next?

The simple analysis, which can be prepared quickly and objectively, show what is possible with advanced patent analytics. Cluster Searching is now being routinely for analysis of the value and licensing potential of patents or portfolios of patents – and we invite you to see this for yourself. Please contact us for a review of patents you may be interested in – or a free trial.

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Three new drugs that could fight type 2 diabetes – what can patent analytics tell us?

Type 2 diabetes (too much sugar in the blood) is a disease that is thought to affect 360 million people world wide, and can shorten life expectancy by 10 years. For this reason the concept of a drug that may help reduce Type 2 diabetes by increasing the amount of sugar removed from the body with urine has great promise, and early results suggest this can reduce mortality, blood sugar, blood pressure and even help with weight loss.

This drug is “Jardiance” (technical name of empagliflozin) and known to be a “SGLT2 inhibitor”. This is the third known such inhibitor, along with “Invokana” (canagliflozin) and “Forxiga” (dapagliflozin). 

Ambercite are not medical doctors and so suggest that you consult your doctor for advice on whether any of these drugs would work for you or not.  

But we are patent analysts, and so were intrigued on what the patent analytics would look like for these three potentially very valuable drugs.

When looking at this sorts of analyses, the first issue is identifying the patents involved. The likes of Orange Book and similar sites can help identify the patents underpinnign commercially available drugs, and is so so often the case, each of these drugs was protected by multiple patents, with mutlitple filing and expiry dates. However to make things manageable we have chosen to simply analyse the first granted US patent for each drug. In a commercial assessment you would of course look at all of the family members, but this is intended to make things manageable.

And how did we analyse these three patents? We used our product Cluster Searching. This has a number of features, and most important for this analysis, these includes

  1. We assign to every patent in our database an AmberScore value, which is a general predictor of patent importance. Amberscore has been normalised so that the average AmberScore value for granted US patents in 1
  2. We assess the Total Licensing Potential for each patent. This is a simple predictor of the relative commercial importance of each patent. 

This is best shown in an example. We identified that the first granted US patent for empagliflozin was US6303661, and added this number into Cluster Searching. 




When we ran this query, the Patents searched part of our website showed  that;



So the patent was originally filed by Priobiodrug on 6th October 1998, and has an AmberScore value of 27.51. or more than 27 times as high as the average US patent. This is a good patent, but I suspect the owners know this already.

Arrmed with this information, we can reset the filing date filter in the query area



Which in turn will suggest the most similar prior art patents. In this case, this is US5939560, filed by Ferring BV in 1996 – and some others are shown below. Note that the 4th column shown in our “Similarity metric” – a higher value suggests greater similarity. 



From a commercial viewpoint, perhaps the patents that followed on from this are the most interesting. To find this, we reset the date filter so that it is showing patents filed after this filing date



This will produce a different set of reults, with the top three shown below



You will note from these results several things

  • The similarity values range from 59 downwards. We suggest that any similarity score of 2 or more is worth noting, so that there are a number of similar patents in these
  • We supply some values in the “L.P.” column. This is “LIcensing Potential”, which is a combination of the similarity and AmberScore of each patent in the list. 
  • The Citation types (second to last column) include a combination of Known (direct forward citaitons) and “Unknown” citations – the latter includes patents which would not be picked up in a general search, but which we still believe to be relevant
  • The list of owners include both related parties to the patent owner (Priobiodrug, the original applicant) and unrelated parties (Novarits)
  • You can export the results to Excel

From this table, we can now compute a total “Net Licensing Potential” – which we could define as

Net Licensing Potential = The sum of the Licensing Potential values for patents

  • filed after the original filing date of a patent, and
  • where the owner is not the original party or related party, and
  • where there is a minimum similarity score of 2


For this patent, there are a lot of patents in this list that meet these criteria – 512 in fact, and we can calculate the Net Licensing Potential”to be 2329, an exceptionally high value. 


Having done all of this analysis, it is very easy to repeat this for the other two drugs listed. The results are summarised below – we have also copied the expiry date  from the listing of this patent in OrangeBook, athough of  course these drugs are likely to be protected by multiple patents with different expiry dates. 


Brand name




Technical name




Earliest granted US patent number


US 6414126

US 7943788

Priority date

24 Apr 1997

12 Oct 1999

1 Aug 2003

Most similar prior art patent

US5939560, filed by Ferring BV in 1996

WO1998031697, filed by Sankyo Company Limited in 1998

US7094763, filed by  Janssen Pharmaceuticals in 2004

Known patent applicants / licensees

Probiodrug, Prosidion / Boehringer Ingelheim, Eli Lilly and Company

AstraZeneca, Bristol-Myers Squibb

Mitsubishi Tanabe Pharma, Janssen Pharmaceutical

Expiry date for listed patent

24 Apr 2017

4 Oct 2020

14 Jul 2027





Net Licensing Potential




Leading owners of similar and later patents – Total Licensing Potential in forward citation network)

Merck – 208

Novartis – 185

Takeda Pharma – 185

Boehringer Ingelheim – 580

Kissei Pharma – 138

Janssen Pharma – 94

Boehringer Ingelheim – 363

Theracos – 645

Kissei Pharma – 43


This table suggests that empagliflozin has the highest scoring patent. Of note, it was the first patent filed.


What we have we learnt about patent analytics in general?

In this case study, to some extent, we have cheated by looking out for patents for likely to be very important drugs. Putting this aside, we can see that it is possible to quantify the potential value of any patent using these patent analytics tools. We can imagine many applications for this capability, including

  • portfolio reviews
  • aqusition due diligence
  • patent valuation

These processse can be done quickly and efficiently, and so provide a robust means of assessing patent and IP value. And indeed Cluster Searching is being used all of the time for these sorts of analyses. 

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