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.

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.

A text data analysis information generation device (1) includes: a dependence relationship tree information generation unit (12) configured to generate dependence relationship tree information that indicates, in a tree structure, a dependence relationship between words in text data to be analyzed; a graph information generation unit (13) configured to extract a subject, a predicate, and an object from the text data based on the generated dependence relationship tree information, and generate graph information that indicates, in a graph structure, triple information of ontology that consists of them; and a hierarchical concept information adding unit (14) configured to extract two suitable components from the text data, acquire broader concept information that is common between the two components, and add the acquired broader concept information to the graph information as a parent node that is common between the two components so as to generate text data analysis information.

A text data analysis information generation device (1) includes: a dependence relationship tree information generation unit (12) configured to generate dependence relationship tree information that indicates, in a tree structure, a dependence relationship between words in text data to be analyzed; a graph information generation unit (13) configured to extract a subject, a predicate, and an object from the text data based on the generated dependence relationship tree information, and generate graph information that indicates, in a graph structure, triple information of ontology that consists of them; and a hierarchical concept information adding unit (14) configured to extract two suitable components from the text data, acquire broader concept information that is common between the two components, and add the acquired broader concept information to the graph information as a parent node that is common between the two components so as to generate text data analysis information.

A document analysis apparatus 10 includes: a candidate generation unit 11 that, for each line included in a document that is a target of structural analysis, specifies another line in a parallel relationship with the line by performing extraction of a marker indicating a hierarchy, and generates a candidate for a hierarchical structure of the document that is the target based on the result of the specification of each line; and a candidate evaluation unit 12 that, if two or more candidates have been generated, performs evaluation on each candidate for the hierarchical structure, and selects one candidate for the hierarchical structure as the hierarchical structure of the document that is the target based on the evaluation result.

A document analysis apparatus 10 includes: a candidate generation unit 11 that, for each line included in a document that is a target of structural analysis, specifies another line in a parallel relationship with the line by performing extraction of a marker indicating a hierarchy, and generates a candidate for a hierarchical structure of the document that is the target based on the result of the specification of each line; and a candidate evaluation unit 12 that, if two or more candidates have been generated, performs evaluation on each candidate for the hierarchical structure, and selects one candidate for the hierarchical structure as the hierarchical structure of the document that is the target based on the evaluation result.

Provided is a font generating method including generating an intermediate code by adding attributes for METAFONT to code of an outline font, generating a font in the METAFONT by parsing the intermediate code, hierarchizing the font into a whole set representing a whole of a character and a partial set representing a part of the character, and changing a style of the font according to a relational equation representing a relationship between the whole set and the partial set.

Provided is a font generating method including generating an intermediate code by adding attributes for METAFONT to code of an outline font, generating a font in the METAFONT by parsing the intermediate code, hierarchizing the font into a whole set representing a whole of a character and a partial set representing a part of the character, and changing a style of the font according to a relational equation representing a relationship between the whole set and the partial set.

Systems and methods are disclosed herein for contextual data analysis and proactive intervention in industrial plant processes. Each of multiple data streams in an industrial plant are mapped to a common hierarchical data structure, wherein the data streams correspond to respective values or states associated with unit operations, assets, and process streams in the plant. The mapped data streams define hierarchical process relationships between subsets of the unit operations, assets, and process streams. Real-time data is collected to populate at least one level of the hierarchical data structure for certain data streams, wherein future outcomes are predicted for downstream operations based on the collected real-time data for at least one data stream, and at least one other data stream having a defined hierarchical process relationship therewith. Upon ascertaining that predicted future outcomes correspond to issues requiring intervention, output signals are generated based thereon for operator alerts and/or automated control.

Systems and methods are disclosed herein for contextual data analysis and proactive intervention in industrial plant processes. Each of multiple data streams in an industrial plant are mapped to a common hierarchical data structure, wherein the data streams correspond to respective values or states associated with unit operations, assets, and process streams in the plant. The mapped data streams define hierarchical process relationships between subsets of the unit operations, assets, and process streams. Real-time data is collected to populate at least one level of the hierarchical data structure for certain data streams, wherein future outcomes are predicted for downstream operations based on the collected real-time data for at least one data stream, and at least one other data stream having a defined hierarchical process relationship therewith. Upon ascertaining that predicted future outcomes correspond to issues requiring intervention, output signals are generated based thereon for operator alerts and/or automated control.