Patent landscaping in seconds – case study on “Seeing Machines” driver monitoring

How long does a patent landscape take to do? 

I asked this question once of a specialist employed by a French multinational, and she said up ‘up to one month’. And I am sure that she did a great job.

But what happens if you do not have a month? Maybe a client is about to walk in the door, all you know is their patent  numbers, and you are trying to get a feel for the area they are in?  What then?

Imagine, for example, you are were asked to look at the innovative Australian company Seeing Machines, which produces driver monitoring technology. How might you do this? 

The answer is surprisingly easy

The (unrelated to Ambercite) patent search tool Patentlens allows you to easily run owner searches. The query would look something like this

SeeingMachinesQuery.jpg

 

Which produce a list of results like this:

SeeingMachinesResults.jpg

 

The results can be exported into a spreadsheet, where the 4th column is a list of patent numbers.

ExportQuery.jpg

 

These numbers can be copied and pasted into Family Cluster Searching, without any editing required.

SeeingMachinesQueryFCS.jpg

 

Cluster Searching will report the patents searched, but in some case as a ‘duplicate’, as shown below. This duplicate means that the patent concerned is in the same family as an earlier patent in this list. In this way, we avoid running duplicate searches within the same family.

FirstResults.jpg

 

All patents in this list are shown with AmberScore values – patents can be sorted by this metric to deterrmine the most important familes

SeeingMachinesPatentFamilies.jpg

With the highest ranked family being that of DE60140067, which also includes US7043056 and other family members.

 

What the most similar patents found?

So that is a ranking of the patents searched. But what about the most similar patents found?

The top 20 of these patent families are shown below, and we can show up to the top 2000 most similar. But even from this top 20 list we can start to see the leading applicants in this area, what specifically they are filing patents, and when the prority dates were.

SeeingMachinesTop20.jpg

 

If you want to present the results in graphical form, it is easy to export the results into Excel, and to manipulate them within Excel.

ExportToExcel.jpg

 

So, in a few short seconds, you can

  • Find and list the relevant Seeing Machines patents
  • Rank them by family importance
  • Find the most similar patents, who is filing them, and when they were filed

All of a sudden, the challenge of producing a patent landscape, that does more than a show of list of patents in the portfolio, becomes very achievable.

 

Unknown vs known citations

Unlike other citation based searches, Cluster Searching shows both ‘known’ (already recognised as a forward or backward citation to any of the family members of the query patents) and ‘unknown’ citations – which have not been recognised as citaiton but which still may be relevant.

For example, US5293427, ranked  33rd in our list, and filed by Nissan with a priority year of 1990 for a Eye position detecting system and method therefor.

 

Are are results reliable?

No patent search is perfect, but the results in this case are based on the citations found against  45 patents in 18 families – and the unknown citations linked to these citations. This meand that there would have between 18 and 45 independent patent examination in this group, probably by the same number of examiners – and many searches for the citations of these citations.

While no search is perfect, by the time all of these independent experts have had a search, they shoud have picked up almost all of the relevant prior art.  This in turn would have led to the forwards citations from both the Seeing Machines portfolio and its prior art.

The collective wisdom of all of these searchers would have meant that while not perfect – this search would be pretty good…and we believe more than comparable to other types of patent searching.

 

Would you like to learn more?

For further information on this analyis, including the other search engines tested, please contact us. 

 

 

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