Co-occurrence Matrix – Beyond just a chart generator..

I have been asked by many researchers on how an Excel pivot table is different from the co-occurrence matrix that we provide in Patent iNSIGHT Pro since both are primarily used to generate charts and trends between two or three analysis points.

While Efficiency, Ease-of-use, direct integration between unstructured and structures fields, are perhaps obvious reasons, I feel two key capabilities make Patent iNSIGHT Pro co-occurrence analyzer a lot more powerful for the end researcher:

1) Capability to Drill Down, quickly read-through and analyze patents behind the numbers in the matrix

Consider the sample matrix shown below:

When you are analyzing a matrix like the one above your first instinct is – What are the patents behind a particular cell?

Researchers I speak to tell me that they would like to quickly jump to the Bibl. & abstract or Claims of patents behind a cell. This capability to quickly go through patents in context of an analyzed segment makes a big difference to the quality of interpretation made. In Patent iNSIGHT Pro all you need to do is right click and select “View records”.

2) Capability to create subsets from the matrix and slice the subset further by a different dimension

Lets say if your question was “In a particular space, which companies were most active at the peak of the technology lifecycle and at that time what countries did they focus on for protecting their inventions?”

For this, one would first look at the Assignee-Filing Year spread and see which year(s) saw the peak filing activity. Then only for those patents in the peak years, analyze the coverage (Assignee – Family Countries).

So what you intend to do is pick up a subset of patents from the results of one relationship and apply another relationship to the subset. In Patent iNSIGHT Pro you can select a couple of cells in the Assignee- Filing Year relationship, right-click to group the patents and then jump to the Assignee-Family Coverage relationship for the subgroup all within 4-5 clicks.  I will leave it to you on how one could achieve that in Excel.

In sum, if you think of it, both the capabilities appear as must-have if you think of a co-occurence matrix as a powerful analysis tool to manipulate, slice-dice and dig through the patent data and not just as a precursor to generating a chart.

Visual Patent Analysis – What and Why ?

For companies it is critical to detect white-spaces and patent minefields early in the R&D cycle each of which can perhaps generate or save millions of dollars at a later stage. Traditional graphs and charts are good for displaying the results of an analysis activity but not for quickening and improving the analysis process.

An increasingly used mechanism is visual analysis of patents that involves 2-Dimensional spatial patent visualization and leverages the capability of the human visual system to identify patterns and anomalies. The key advantages of visual patent analysis are that you can drastically reduce the time-to-insights and explore IP-congested technology spaces in a swift but efficient fashion.

The ease of use and intuitive nature of visual analysis tools makes it easy for even business and R&D teams to use for their analysis needs. (Usually in organizations, the R&D, the patent information team, the legal team, the marketing cum business strategy team and the licensing team are involved in various parts of the IP strategy driving a product.)

Other important benefits which visual patent analysis provides:

  • Ease of navigation across relationships- Can be used for exploring through networks of relationship between companies, inventors and their research or you can also explore semantic relationships between patent content.
  • Quick interpretation – 2-Dimensional spatial mapping of technology clusters remains as one of most comprehensible ways to represent a landscape and can be easily interpreted
  • The Peripheral vision advantage – You can benefit from being cognizant of the clusters around your focus area. In some case these “peripheral clusters” may contain the golden nugget you seek.
  • Powerful highlighting, search and dissection tools combined with a rich intuitive display makes is easy to detect patterns and irregularities within the patent landscape. Such capabilities make the visualization many times more powerful. For instance Google Maps would’t be as powerful without it’s built in geographical search, highlighting and other navigational options.
  • Clusters that are co-located based on semantic similarity are very useful when conducting infringement analysis. (Ofcourse, visual analysis tools must allow for clusters to be generated specifically from the claims section for undertaking infringement analysis)

To sum up, visual analysis is a powerful method to address challenges posed by patent information overload.