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
Businesses like Adobe Stock use large, visible watermarks to deter copyright infringement; a new paper presented by Google Researchers to the Computer Vision and Pattern Recognition shows that these watermarks can be reliably detected and undetectably erased by software.
US court records are not copyrighted, but the US court system operates a paywall called “PACER” that is supposed to recoup the costs of serving text files on the internet; charging $0.10/page for access to the public domain, and illegally profiting to the tune of $80,000,000/year.
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