Patent-generator is a Github-hosted python script that turns literary texts into patent applications, with descriptions of the accompanying diagrams (here's Kapital, AKA "A method and device for comprehending, theoretically, the historical movement"; and here's Heidegger's The Question Concerning Technology, AKA "A device and system for belonging to bringing-forth").
The program operates in four parts. First it generates a title for the invention, then an abstract, then a list of illustrations, and finally a more detailed description of the “embodiments” of the invention.
In general, my methodology is to find common grammatical structures in patent applications, and then extract sentences containing similar grammatical structures from my input texts. To do this, I make heavy use of the Pattern library, which, among many other wonderful features, allows you to perform regular-expression-like searches using parts of speech. For example, here’s how you can use pattern to to search through a text for all instances of an adjective followed by a plural noun.
Transform any text into a patent application
Charles Duan from Public Knowledge sends us “a video we put together for Fair Use Week about copyright and fair use, to the tune of ‘Let It Go’ from Frozen, and full of clips of other fair use videos.”
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Python is immensely popular in the data science world for the same reason it is in most other areas of computing—it has highly readable syntax and is suitable for anything from short scripts to massive web services. One of its most exciting, newest applications, however, is in machine learning. You can dive into this booming […]
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