(The key question)

How do we measure  importance?

Taking citation analysis to the next level
Past to present

The problem with current methods

  • Practiced since the 60's
    Researchers have found correlations between citations and expert judgement, auction values, litigation likelihood, and much more, but the signal has always been noisy.
  • Structural biases cause noise
    There are many structural biases in the citation network that artificially manipulate citation counts. To make citation analysis work, we neet to take the finger off the scale.
  • Wrong conclusions
    Without properly correcting for structural biases, any citation based metric will deliver noisy results at best and plain wrong results at worst.

Making citation analysis worth your time

What structural biases are we dealing with?

Tech domain

Citation practices and average citation counts vary strongly per domain. The better we are at determining a patent's topic, the more reliably we can compare it to its true peers. Here, Focus uses NLP models rather than IPC/CPC codes.

Region and size

Because of regulatory differences, U.S. patents tend to get cited more than their EU counterparts. There are similar differences in other regions. Also, the larger the family, the more often it tends to get cited for reasons unrelated to quality.


Examiners tend to cite patents they have examined or cited in the past. There are also large differences between individual examiners. To get a fair score, all of this must be corrected.


Applicants tend to cite patents from the same applicant and the same goes for inventors. Some cite a lot, and some very little. This must be corrected to fairly compare patents.
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Beyond counting citations

Correcting citation counts for biases alone already results in a much more reliable metric than what is available today. But why stop there? Rather than counting citations, we measure intergenerational impact. This means that we weigh the importance of a patent's ancestors, the patent itself, and its offspring.

Better, sooner

Altogether, this results in major improvements and allows you to spot important patent families significantly better and years earlier than previously possible. Focus helps you identify important assets before they become obvious to everybody else.
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If subsequent generations die out, only counting direct citations can lead to wrong conclusions.
By also taking indirect citations into consideration, we can measure patent importance more accurately.

How do we compare to the competition?

The Rest
Intergenerational influence
Topic determination
NLP algorithm
IPC/CPC codes
Corrects applicant bias
Corrects examiner bias
Corrects inventor bias

Common pitfalls

For IP teams

  • Paying maintenance fees for weak patents.
  • Overlooking important out-licensing candidates.
  • Signing unfair licensing deals.

For your Business

  • Missing out on partnering opportunities.
  • Failing to spot major technological developments.
  • Misidentifying key inventors.
Common Questions

Let's put your mind at ease.

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