(The key question)

How does it actually work?

Technology mapping and quality scoring
How did we get here?

Patent analytics
problems today

  • Unreliable patent to technology mapping
    Using keywords and CPC codes for technology mapping creates inaccurate, incomplete, and inconsistent results. Yet, they are still used today by almost every patent analytics tool. Because of this, it is very difficult to get reliable insights within a reasonable amount of time.
  • No reliable patent scoring
    Many patents are not very important, so without a reliable quality score, we still know very little about the competition. Unfortunately, currently available scores are based on flimsy evidence, do not measure quality well, and are thus not used for decision making often.
  • Bad intelligence leads to poor decision making
    Without a reliable map of the environment, it is very difficult to arrive at your destination. Bad intelligence leads to poor decision making, limited value creation, and in the worst case, disaster.

What makes Focus different

Making patent analytics strategic

Tech mapping

Use your domain knowledge to teach Focus' AI what to look for by example. You can train Focus to learn your internal taxonomy, or any other technological category, and let it mine through any scope of patents for matches.

Scoring

Focus' quality scoring algorithm accurately predicts real-life events such as when a patent will lapse and whether it is likely to be used in litigation. Allowing you to accurately estimate risk, value, impact, etc, in any portfolio or tech domain.

Strategic

Communicate the strategic value of IP by condensing extremely large volumes of data into board level insights that go beyond patents alone.

Time

Very little manual effort is required to operate Focus' algorithms and arrive at the insights you need. Leaving you with more time for strategy.
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Beyond counting citations

Most patent scoring methods are based on citing rates one way or another, but patent quality depends on many other factors as well. Claim broadness and strength, family size, prosecution history, etc, all tell you something about the likely quality of a patent. Focus developed over 30 proprietary metrics that measure different aspects of patent quality. These metrics are calculated for every patent published since 1995.

Deep learning for scoring

For many of the patents that were published since 1995, we know when they lapsed and whether they were ever litigated. This gives us the opportunity to learn what set of characteristics leads to what outcome. Rather than deciding the weight for every variable by ourselves, Focus uses neural networks to model what combination of characteristics makes a patent have a certain outcome. Neural networks are perfect for a task like this, because they can model very complex relationships. The resulting model is then applied to make predictions about patents for which the outcome is yet unknown.
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How do we compare to the competition?

 
Focus
The Rest
Learns your taxonomy
Tech mapping
   2-5X more patents     avg. 97% accurate
Incomplete
 and noisy
Reliable scoring
Nope
Easily obtain strategic insights
Save time and money
Nope
Common Questions

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