Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems, methods, and storage media for extracting patent document templates from a patent corpus are disclosed. Exemplary implementations may: obtain a patent corpus; receive one or more parameters; determine one or more subsets of the patent corpus by filtering the patent corpus based on the one or more parameters; identify one or more document clusters within individual ones of the one or more subsets of the patent corpus; obtain a patent document template corresponding to the first document cluster; and/or perform other operations.

Abstract: Systems and methods for facilitating editing of a confidential document by a non-privileged person by stripping away content and meaning from the document without human intervention such that only structural and/or grammatical information of the document are conveyed to the non-privileged person are disclosed. Exemplary implementations may: receive an electronic document including text conveying one or more confidential concepts; provide a content-stripped version of the electronic document to a human editor; receive an edited content-stripped version of the electronic document; and provide an edited electronic document based on the edited content-stripped version such that human-editor-provided changes were effectuated without the human editor ever being exposed to the content and meaning contained in the electronic document.

Abstract: Systems and methods for providing adaptive surface texture in auto-drafted patent documents are disclosed. Exemplary implementations may: obtain a plurality of style attributes, a given style attribute being descriptive of a type of document characteristic that is often-repeated or habitual in patent documents; obtain values for the plurality of style attributes, a given value of a style attribute being descriptive of a specific document characteristic that is often-repeated or habitual in one or more sample patent documents; and provide an auto-drafted patent document exhibiting the values for the plurality of style attributes.

Abstract: Systems and methods for automatically creating a patent application based on a claim set such that the patent application follows a document plan inferred from an example document are disclosed. Exemplary implementations may: obtain one or more example documents, a given example document including a patent document; identify common formatting features among the one or more example documents; determine a document plan for a patent application based on information gained from the one or more example documents; receive one or more previously unseen patent claims; and use computerized natural language generation to automatically create a new patent application based on both the document plan and the one or more patent claims such that the new patent application reflects subject matter of the one or more patent claims and is otherwise consistent with the one or more example documents.

Abstract: Systems and methods for enhancing the depth and richness of content in computer-generated patent applications by providing non-explicit support for individual claim features are disclosed. Exemplary implementations may: receive a previously unseen claim feature, the previously unseen claim feature being absent from the previously received patent documents; provide one or more sentences of never-been-seen-before computer-generated text using the trained machine learning model and the previously unseen claim feature as input; and insert the one or more sentences of non-explicit support in a draft patent application proximal to explicit support for the previously unseen claim feature.

Abstract: Systems and methods for facilitating editing of a confidential document by a non-privileged person by stripping away content and meaning from the document without human intervention such that only structural and/or grammatical information of the document are conveyed to the non-privileged person are disclosed. Exemplary implementations may: receive an electronic document including text conveying one or more confidential concepts; provide a content-stripped version of the electronic document to a human editor; receive an edited content-stripped version of the electronic document; and provide an edited electronic document based on the edited content-stripped version such that human-editor-provided changes were effectuated without the human editor ever being exposed to the content and meaning contained in the electronic document.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.