  This work proposes to measure the scope of a patent claim as the reciprocal
of self-information contained in this claim. Self-information is calculated
based on a probability of occurrence of the claim, where this probability is
obtained from a language model. Grounded in information theory, this approach
is based on the assumption that an unlikely concept is more informative than a
usual concept, insofar as it is more surprising. In turn, the more surprising
the information required to define the claim, the narrower its scope. Seven
language models are considered, ranging from simplest models (each word or
character has an identical probability) to intermediate models (based on
average word or character frequencies), to large language models (LLMs) such as
GPT2 and davinci-002. Remarkably, when using the simplest language models to
compute the probabilities, the scope becomes proportional to the reciprocal of
the number of words or characters involved in the claim, a metric already used
in previous works. Application is made to multiple series of patent claims
directed to distinct inventions, where each series consists of claims devised
to have a gradually decreasing scope. The performance of the language models is
then assessed through several ad hoc tests. The LLMs outperform models based on
word and character frequencies, which themselves outdo the simplest models
based on word or character counts. Interestingly, however, the character count
appears to be a more reliable indicator than the word count.
