Patent Publication Number: US-11651160-B2

Title: 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

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure relates to systems and methods for providing a data structure representing patent claims, modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese, providing a data structure with ordered content derived from patent claims, identifying and naming software components described by functional patent claim language, and 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. 
     BACKGROUND 
     Patent applications are documents prepared by licensed patent practitioners. These professionals are either patent attorneys (scientists/engineers with a law degree) or patent agents (scientists/engineers without a law degree). Once prepared, a patent application is filed with the United States Patent &amp; Trademark Office (USPTO) where it is examined by a Patent Examiner. Each application is ultimately rejected or allowed to issue as a U.S. Patent. 
     A patent application has three main parts: claims, specification, and figures. The claims are a numbered list of sentences that precisely define what is being asserted as the invention. In other words, the claims attempt to define the boundary between what is regarded as prior art and what is considered as inventive (i.e., useful, new, and non-obvious). The specification is the longest section. It explains how to make and use the claimed invention. Finally, the figures complement the specification and depict the claimed features. 
     The profitability of patent preparation for law firms has been in decline due to a number of factors. More than ever, it is market forces rather than practitioner experience and competence that tend to drive fee amounts for preparing patent applications. The collision of these market-rate fee amounts with escalating hourly rates for practitioners creates a climate where often only entry-level and non-attorney practitioners can yield profitability. In some major general practice law firms, patent preparation is even viewed as a loss-leader practice to gain a position for licensing and litigation work. Complicating things further, a talent shortage is emerging with client demand for patent drafting ever increasing while the number of new patent practitioners minted each year trending downward. 
     SUMMARY 
     Exemplary implementations augment law firm leverage with cutting-edge machine learning and natural language generation technologies. Some implementations facilitate automated generation of complete patent application drafts based on concise practitioner inputs such as claim sets and/or drawing figures. Practitioners can now maximize their time and expertise by focusing on the client experience and only key aspects of the patent preparation process. Exemplary implementations handle the rest with near-instantaneous turnaround. For example, except for the background section and this paragraph, the present disclosure was automatically generated without human intervention based only on a single method claim set prepared by a patent practitioner. 
     One aspect of the present disclosure relates to a system configured for providing a data structure representing patent claims. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. The processor(s) may be configured to process a claim line of the claim set. The claim line may be a unit of text having an end indicated by a presence of one or more end-of-claim line characters. The processor(s) may be configured to identify one or more features in the claim line to be stored in the data structure. The one or more features may include one or both of a main feature or a sub feature. The processor(s) may be configured to store the one or more features in the data structure. The main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. The sub feature may describe or expands on an aspect of a main feature. 
     Another aspect of the present disclosure relates to a method for providing a data structure representing patent claims. The method may include obtaining a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. The method may include processing a claim line of the claim set. The claim line may be a unit of text having an end indicated by a presence of one or more end-of-claim line characters. The method may include identifying one or more features in the claim line to be stored in the data structure. The one or more features may include one or both of a main feature or a sub feature. The method may include storing the one or more features in the data structure. The main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. The sub feature may describe or expands on an aspect of a main feature. 
     Yet another aspect of the present disclosure relates to a system configured for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain a data structure representing a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. The processor(s) may be configured to perform a natural language generation operation on the data structure to provide a modified data structure. The data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. The data structure may include language units from the claim set. The language units may be in patentese. The language units may be organized in the data structure according to one or more classifications of individual language elements. The modified data structure may have the same dimensions as the data structure representing the claim set such that a given data structure element at a given position within the data structure representing the claim set corresponds to a given data structure element at the same position within the modified data structure. 
     Still another aspect of the present disclosure relates to a method for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese. The method may include obtaining a data structure representing a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. The method may include performing a natural language generation operation on the data structure to provide a modified data structure. The data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. The data structure may include language units from the claim set. The language units may be in patentese. The language units may be organized in the data structure according to one or more classifications of individual language elements. The modified data structure may have the same dimensions as the data structure representing the claim set such that a given data structure element at a given position within the data structure representing the claim set corresponds to a given data structure element at the same position within the modified data structure. 
     Even another aspect of the present disclosure relates to a system configured for providing a data structure with ordered content derived from patent claims. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain a first data structure representing a claim set. The first data structure may include language units from the claim set. The language units may be in patentese. The language units may be organized in the first data structure according to one or more classifications of individual language elements. The processor(s) may be configured to obtain a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. The processor(s) may be configured to identify main features in the first data structure. The main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. Identify sub features in the second data structure that correspond to individual ones of the main features in the first data structure. A given sub feature may describe or expands on an aspect of a corresponding main feature. The processor(s) may be configured to store the identified sub features from the second data structure in a third data structure such that a given identified sub feature is associated in the third data structure with a corresponding main feature. The processor(s) may be configured to, based on identified initial mentions and identified subsequent mentions, store, in the third data structure and in association with a corresponding main feature, the sub features from the second data structure that do not correspond to individual ones of the main features in the first data structure. 
     A further aspect of the present disclosure relates to a method for providing a data structure with ordered content derived from patent claims. The method may include obtaining a first data structure representing a claim set. The first data structure may include language units from the claim set. The language units may be in patentese. The language units may be organized in the first data structure according to one or more classifications of individual language elements. The method may include obtaining a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. The method may include identifying main features in the first data structure. The main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. Identify sub features in the second data structure that correspond to individual ones of the main features in the first data structure. A given sub feature may describe or expands on an aspect of a corresponding main feature. The method may include storing the identified sub features from the second data structure in a third data structure such that a given identified sub feature is associated in the third data structure with a corresponding main feature. The method may include, based on identified initial mentions and identified subsequent mentions, storing, in the third data structure and in association with a corresponding main feature, the sub features from the second data structure that do not correspond to individual ones of the main features in the first data structure. 
     A yet further of the present disclosure relates to a system configured for identifying and naming software components described by functional patent claim language. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain a data structure representing a claim set. The data structure may include language units from the claim set. The language units may be in patentese. The processor(s) may be configured to obtain a data structure element from the data structure. The data structure element may include a main feature. The main feature may include a step of a claimed process. The processor(s) may be configured to identify a main verb to be associated with a software component name that corresponds to the main feature. The main verb may describe a primary function of the main feature. The processor(s) may be configured to, in response to identify the main verb, identifying a noun or noun phrase to be associated with the software component name. The processor(s) may be configured to determine the software component name based on the identified main verb and the identified noun or noun phrase. 
     A still further aspect of the present disclosure relates to a method for identifying and naming software components described by functional patent claim language. The method may include obtaining a data structure representing a claim set. The data structure may include language units from the claim set. The language units may be in patentese. The method may include obtaining a data structure element from the data structure. The data structure element may include a main feature. The main feature may include a step of a claimed process. The method may include identifying a main verb to be associated with a software component name that corresponds to the main feature. The main verb may describe a primary function of the main feature. The method may include, in response to identifying the main verb, identifying a noun or noun phrase to be associated with the software component name. The method may include determining the software component name based on the identified main verb and the identified noun or noun phrase. 
     An even further aspect of the present disclosure relates to a system configured 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. The system may include one or more hardware processors configured by machine-readable instructions. The processor(s) may be configured to obtain a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims, each dependent claim in the claim set depending on the independent claim by referring to the independent claim or an intervening dependent claim. The claim set was prepared by a human. The processor(s) may be configured to obtain a first data structure representing the claim set. The first data structure may include language units from the claim set. The language units may be in patentese. The processor(s) may be configured to obtain a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. The processor(s) may be configured to obtain a third data structure. The third data structure may include ordered content derived from the claim set. The ordered content of the third data structure may be ordered based on one or more of claim structure of the claim set, antecedent basis in the claim set, or claim dependency in the claim set. The processor(s) may be configured to determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure. 
     And another aspect of the present disclosure relates to a method 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. The method may include obtaining a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims, each dependent claim in the claim set depending on the independent claim by referring to the independent claim or an intervening dependent claim. The claim set was prepared by a human. The method may include obtaining a first data structure representing the claim set. The first data structure may include language units from the claim set. The language units may be in patentese. The method may include obtaining a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. The method may include obtaining a third data structure. The third data structure may include ordered content derived from the claim set. The ordered content of the third data structure may be ordered based on one or more of claim structure of the claim set, antecedent basis in the claim set, or claim dependency in the claim set. The method may include determining one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure. 
     These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a system configured for providing a data structure representing patent claims, in accordance with one or more implementations. 
         FIG.  2    illustrates a method for providing a data structure representing patent claims, in accordance with one or more implementations. 
         FIG.  3    illustrates a system configured for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese, in accordance with one or more implementations. 
         FIG.  4    illustrates a method for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese, in accordance with one or more implementations. 
         FIG.  5    illustrates a system configured for providing a data structure with ordered content derived from patent claims, in accordance with one or more implementations. 
         FIG.  6    illustrates a method for providing a data structure with ordered content derived from patent claims, in accordance with one or more implementations. 
         FIG.  7    illustrates a system configured for identifying and naming software components described by functional patent claim language, in accordance with one or more implementations. 
         FIG.  8    illustrates a method for identifying and naming software components described by functional patent claim language, in accordance with one or more implementations. 
         FIG.  9    illustrates a system configured 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, in accordance with one or more implementations. 
         FIG.  10    illustrates a method 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, in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    illustrates a system  100  configured for providing a data structure representing patent claims, in accordance with one or more implementations. In some implementations, system  100  may include one or more servers  102 . Server(s)  102  may be configured to communicate with one or more client computing platforms  104  according to a client/server architecture and/or other architectures. Client computing platform(s)  104  may be configured to communicate with other client computing platforms via server(s)  102  and/or according to a peer-to-peer architecture and/or other architectures. Users may access system  100  via client computing platform(s)  104 . 
     Server(s)  102  may be configured by machine-readable instructions  106 . Machine-readable instructions  106  may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a claim set obtaining module  108 , a claim line processing module  110 , a claim line determination module  112 , a claim line storing module  114 , a portion storing module  116 , a feature identifying module  118 , a marker classification module  120 , and/or other instruction modules. 
     Claim set obtaining module  108  may be configured to obtain a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim number indicated a position of a corresponding claim in the numbered list of sentences of the claim set. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. 
     Claim line processing module  110  may be configured to process a claim line of the claim set. Determining whether the claim line may belong to an independent claim or a dependent claim includes determining whether the claim line includes a reference to another claim. The reference may indicate that the claim line belongs to a dependent claim. The claim line may be a unit of text having an end indicated by a presence of one or more end-of-claim line characters. By way of non-limiting example, the one or more end-of-claim line characters may include one or more of a colon, a semi-colon, or a carriage return. 
     Claim line determination module  112  may be configured to determine whether the claim line is a first claim line of a claim. Determining whether the claim line may be the first claim line of a claim includes determining whether the claim line begins with a claim number. 
     Claim line determination module  112  may be configured to, responsive to a determination that the claim line is the first claim line of a claim, determine whether the claim line belongs to an independent claim or a dependent claim. 
     Claim line determination module  112  may be configured to determine whether there are more claim lines in the claim set to be iterated on. 
     Claim line storing module  114  may be configured to, responsive to a determination that the claim line belongs to an independent claim, store the claim line as an independent claim preamble in a data structure. The independent claim preamble may convey a general description of the invention as a whole. By way of non-limiting example, the data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. 
     The data structure may include language units from the claim set. The language units may be in patentese. Patentese may include text structure and legal jargon commonly used in patent claims. The language units may be organized in the data structure according to one or more classifications of individual language elements. By way of non-limiting example, a language element may include one or more of a word, a phrase, a clause, or a sentence. A claim may be a single sentence. By way of non-limiting example, a sentence may include a set of words that is complete and contains a subject and predicate, a sentence including a main clause and optionally one or more subordinate clauses. By way of non-limiting example, a clause may include a unit of grammatical organization next below a sentence, a clause including a subject and predicate. A phrase may include a small group of words standing together as a conceptual unit, a phrase forming a component of a clause. By way of non-limiting example, a word may include a single distinct meaningful element of language used with others to form a sentence, a word being shown with a space on either side when written or printed. By way of non-limiting example, the one or more classifications may include one or more of independent claim, dependent claim, preamble, main feature, sub feature, claim line, clause, phrase, or word. 
     Portion storing module  116  may be configured to, responsive to a determination that the claim line belongs to a dependent claim, store a portion of the claim line as a dependent claim preamble in the data structure. The dependent claim preamble may include a reference to a preceding claim. Identify one or more clauses in the claim line. Identifying the one or more clauses in the claim line may include applying a machine learning model to the claim line. By way of non-limiting example, the machine learning model may be based on one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a regression algorithm, an instance-based algorithm, a regularized algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. Applying the machine learning model to the claim line may result in one or more aspects of a given clause being labeled. By way of non-limiting example, identifying the one or more clauses in the claim line may include determining whether the claim line includes one or more markers, a given marker being a trigger word, a trigger phrase, or a trigger punctuation. 
     Feature identifying module  118  may be configured to identify one or more features in the claim line to be stored in the data structure. The one or more features may include one or both of a main feature or a sub feature. By way of non-limiting example, the main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. The sub feature may describe or expands on an aspect of a main feature. 
     Marker classification module  120  may be configured to, responsive to a determination that the claim line includes one or more markers, classify individual ones of the one or more markers. In some implementations, by way of non-limiting example, classifying the given marker may include determining whether the given marker exists within a clause, whether the given marker indicates a boundary between two clauses, or whether the given marker indicates a clause containing a list. 
     In some implementations, server(s)  102 , client computing platform(s)  104 , and/or external resources  122  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  102 , client computing platform(s)  104 , and/or external resources  122  may be operatively linked via some other communication media. 
     A given client computing platform  104  may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given client computing platform  104  to interface with system  100  and/or external resources  122 , and/or provide other functionality attributed herein to client computing platform(s)  104 . By way of non-limiting example, the given client computing platform  104  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  122  may include sources of information outside of system  100 , external entities participating with system  100 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  122  may be provided by resources included in system  100 . 
     Server(s)  102  may include electronic storage  124 , one or more processors  126 , and/or other components. Server(s)  102  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  102  in  FIG.  1    is not intended to be limiting. Server(s)  102  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  102 . For example, server(s)  102  may be implemented by a cloud of computing platforms operating together as server(s)  102 . 
     Electronic storage  124  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  124  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  102  and/or removable storage that is removably connectable to server(s)  102  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  124  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  124  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  124  may store software algorithms, information determined by processor(s)  126 , information received from server(s)  102 , information received from client computing platform(s)  104 , and/or other information that enables server(s)  102  to function as described herein. 
     Processor(s)  126  may be configured to provide information processing capabilities in server(s)  102 . As such, processor(s)  126  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  126  is shown in  FIG.  1    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  126  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  126  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  126  may be configured to execute modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 ,  120 , and/or other modules. Processor(s)  126  may be configured to execute modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 ,  120 , and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  126 . As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and  120  are illustrated in  FIG.  1    as being implemented within a single processing unit, in implementations in which processor(s)  126  includes multiple processing units, one or more of modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120  may be implemented remotely from the other modules. The description of the functionality provided by the different modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120  described below is for illustrative purposes, and is not intended to be limiting, as any of modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120  may provide more or less functionality than is described. For example, one or more of modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120  may be eliminated, and some or all of its functionality may be provided by other ones of modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120 . As another example, processor(s)  126  may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules  108 ,  110 ,  112 ,  114 ,  116 ,  118 , and/or  120 . 
       FIG.  2    illustrates a method  200  for providing a data structure representing patent claims, in accordance with one or more implementations. The operations of method  200  presented below are intended to be illustrative. In some implementations, method  200  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  200  are illustrated in  FIG.  2    and described below is not intended to be limiting. 
     In some implementations, method  200  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  200  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  200 . 
     An operation  202  may include obtaining a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. Operation  202  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim set obtaining module  108 , in accordance with one or more implementations. 
     An operation  204  may include processing a claim line of the claim set. The claim line may be a unit of text having an end indicated by a presence of one or more end-of-claim line characters. Operation  204  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim line processing module  110 , in accordance with one or more implementations. 
     An operation  206  may include determining whether the claim line is a first claim line of a claim. Operation  206  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim line determination module  112 , in accordance with one or more implementations. 
     An operation  208  may include, responsive to a determination that the claim line is the first claim line of a claim, determining whether the claim line belongs to an independent claim or a dependent claim. Operation  208  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim line determination module  112 , in accordance with one or more implementations. 
     An operation  210  may include, responsive to a determination that the claim line belongs to an independent claim, storing the claim line as an independent claim preamble in a data structure. Operation  210  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim line storing module  114 , in accordance with one or more implementations. 
     An operation  212  may include, responsive to a determination that the claim line belongs to a dependent claim, storing a portion of the claim line as a dependent claim preamble in the data structure. Identify one or more clauses in the claim line. Operation  212  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to portion storing module  116 , in accordance with one or more implementations. 
     An operation  214  may include identifying one or more features in the claim line to be stored in the data structure. The one or more features may include one or both of a main feature or a sub feature. Operation  214  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to feature identifying module  118 , in accordance with one or more implementations. 
     An operation  216  may include determining whether there are more claim lines in the claim set to be iterated on. Operation  216  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim line determination module  112 , in accordance with one or more implementations. 
       FIG.  3    illustrates a system  300  configured for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese, in accordance with one or more implementations. In some implementations, system  300  may include one or more servers  302 . Server(s)  302  may be configured to communicate with one or more client computing platforms  304  according to a client/server architecture and/or other architectures. Client computing platform(s)  304  may be configured to communicate with other client computing platforms via server(s)  302  and/or according to a peer-to-peer architecture and/or other architectures. Users may access system  300  via client computing platform(s)  304 . 
     Server(s)  302  may be configured by machine-readable instructions  306 . Machine-readable instructions  306  may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a data structure obtaining module  308 , a language generation operation performance module  310 , a data structure element obtaining module  312 , a data structure element determination module  314 , a claim preamble determination module  316 , a preamble base determination module  318 , a preamble base storing module  320 , a data structure element modification module  322 , a data structure element storing module  324 , and/or other instruction modules. 
     Data structure obtaining module  308  may be configured to obtain a data structure representing a claim set. By way of non-limiting example, the data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. 
     The data structure may include language units from the claim set. The language units may be in patentese. Patentese may include text structure and legal jargon commonly used in patent claims. The language units may be organized in the data structure according to one or more classifications of individual language elements. By way of non-limiting example, a language element may include one or more of a word, a phrase, a clause, or a sentence. A claim may be a single sentence. By way of non-limiting example, a sentence may include a set of words that is complete and contains a subject and predicate, a sentence including a main clause and optionally one or more subordinate clauses. By way of non-limiting example, a clause may include a unit of grammatical organization next below a sentence, a clause including a subject and predicate. A phrase may include a small group of words standing together as a conceptual unit, a phrase forming a component of a clause. By way of non-limiting example, a word may include a single distinct meaningful element of language used with others to form a sentence, a word being shown with a space on either side when written or printed. 
     By way of non-limiting example, the one or more classifications may include one or more of independent claim, dependent claim, preamble, main feature, sub feature, claim line, clause, phrase, or word. A preamble of an independent claim preamble may convey a general description of the invention as a whole. A preamble of a dependent claim may include a reference to a preceding claim. By way of non-limiting example, a main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. A sub feature may describe or expands on an aspect of a main feature. 
     A claim line may be a unit of text having an end indicated by a presence of one or more end-of-claim line characters. By way of non-limiting example, the one or more end-of-claim line characters may include one or more of a colon, a semi-colon, or a carriage return. 
     Language generation operation performance module  310  may be configured to perform a natural language generation operation on the data structure. The natural language generation operation may be based on a machine learning model. By way of non-limiting example, the machine learning model may be based on one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a regression algorithm, an instance-based algorithm, a regularized algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. 
     By way of non-limiting example, the natural language generation operation may include one or more of paraphrase induction, simplification, compression, clause fusion, or expansion. Paraphrase induction may include preserving original meaning. By way of non-limiting example, paraphrase induction may include rewording and/or rearranging one or more of phrases, clauses, claim lines, or entire claims. Simplification may include preserving original meaning. Simplification may include splitting up a claim line for readability. Compression may include preserving important aspects. Compression may include deleting content for summarization. Fusion may include preserving important aspects. Fusion combining language elements for summarization. Expansion may include preserving original meaning and embellishing on the original content. Expansion may include introducing new content that supports or broadens the original meaning. Sentence semantics may be lossless with paraphrasing and simplification. Sentence semantics may be lossy with compression and fusion. 
     A one-to-one language element transformation may occur with paraphrasing and compression. A one-to-many language element transformation may occur with simplification. A many-to-one language element transformation may occur with fusion. The natural language generation operation may be performed according to a set of rules. 
     Data structure element obtaining module  312  may be configured to obtain a data structure element. 
     Data structure element determination module  314  may be configured to determine whether the data structure element includes a claim preamble. 
     Data structure element determination module  314  may be configured to, responsive to a determination that the data structure element does not include a claim preamble, determine whether the data structure element includes a main feature. 
     Data structure element determination module  314  may be configured to, responsive to a determination that the data structure element does not include a main feature, determine whether the data structure element includes a list of list items. 
     Data structure element determination module  314  may be configured to determine whether there are more data structure elements to be iterated on. 
     Claim preamble determination module  316  may be configured to, responsive to a determination that the data structure element includes a claim preamble, determine whether the claim preamble is from an independent claim or a dependent claim. 
     Preamble base determination module  318  may be configured to, responsive to a determination that the claim preamble is from an independent claim, determine a preamble base from the claim preamble. The preamble base may include the claim preamble without leading generic terms or transition words. By way of non-limiting example, a leading generic term may include system, method, device, apparatus, or composition. 
     Preamble base storing module  320  may be configured to store the preamble base in a modified data structure. The modified data structure may have the same dimensions as the data structure representing the claim set such that a given data structure element at a given position within the data structure representing the claim set corresponds to a given data structure element at the same position within the modified data structure. 
     Data structure element modification module  322  may be configured to, responsive to a determination that the data structure element does include a main feature, modify the data structure element by converting a verb, a gerund, or a present participle to a corresponding lemma. In some implementations, by way of non-limiting example, a lemma may include a canonical form, a dictionary form, or a citation form of a set of words. 
     Data structure element modification module  322  may be configured to, responsive to a determination that the data structure element includes a list of list items, modify the data structure element by converting the data structure element to a permissive list format. The permissive list format may convey an unrestricted list of allowed list items but not obligatory list items. 
     Data structure element modification module  322  may be configured to, responsive to a determination that the data structure element does not include a list of list items, modify the data structure element by converting the data structure element to prose. The prose may include an ordinary form of written language, without structure of claim language, as distinguished from patentese. The prose may include permissive prose. In some implementations, the permissive prose conveying allowed but not obligatory concepts. 
     Data structure element storing module  324  may be configured to store the modified data structure element in the modified data structure. 
     In some implementations, server(s)  302 , client computing platform(s)  304 , and/or external resources  326  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  302 , client computing platform(s)  304 , and/or external resources  326  may be operatively linked via some other communication media. 
     A given client computing platform  304  may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given client computing platform  304  to interface with system  300  and/or external resources  326 , and/or provide other functionality attributed herein to client computing platform(s)  304 . By way of non-limiting example, the given client computing platform  304  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  326  may include sources of information outside of system  300 , external entities participating with system  300 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  326  may be provided by resources included in system  300 . 
     Server(s)  302  may include electronic storage  328 , one or more processors  330 , and/or other components. Server(s)  302  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  302  in  FIG.  3    is not intended to be limiting. Server(s)  302  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  302 . For example, server(s)  302  may be implemented by a cloud of computing platforms operating together as server(s)  302 . 
     Electronic storage  328  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  328  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  302  and/or removable storage that is removably connectable to server(s)  302  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  328  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  328  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  328  may store software algorithms, information determined by processor(s)  330 , information received from server(s)  302 , information received from client computing platform(s)  304 , and/or other information that enables server(s)  302  to function as described herein. 
     Processor(s)  330  may be configured to provide information processing capabilities in server(s)  302 . As such, processor(s)  330  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  330  is shown in  FIG.  3    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  330  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  330  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  330  may be configured to execute modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 ,  324 , and/or other modules. Processor(s)  330  may be configured to execute modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 ,  324 , and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  330 . As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and  324  are illustrated in  FIG.  3    as being implemented within a single processing unit, in implementations in which processor(s)  330  includes multiple processing units, one or more of modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324  may be implemented remotely from the other modules. The description of the functionality provided by the different modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324  described below is for illustrative purposes, and is not intended to be limiting, as any of modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324  may provide more or less functionality than is described. For example, one or more of modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324  may be eliminated, and some or all of its functionality may be provided by other ones of modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324 . As another example, processor(s)  330  may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules  308 ,  310 ,  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324 . 
       FIG.  4    illustrates a method  400  for modifying data structures representing patent claims such that the modified data structures include language elements in prose rather than patentese, in accordance with one or more implementations. The operations of method  400  presented below are intended to be illustrative. In some implementations, method  400  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  400  are illustrated in  FIG.  4    and described below is not intended to be limiting. 
     In some implementations, method  400  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  400  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  400 . 
     An operation  402  may include obtaining a data structure representing a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. Operation  402  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  308 , in accordance with one or more implementations. 
     An operation  404  may include performing a natural language generation operation on the data structure. Operation  404  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to language generation operation performance module  310 , in accordance with one or more implementations. 
     An operation  406  may include obtaining a data structure element. Operation  406  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element obtaining module  312 , in accordance with one or more implementations. 
     An operation  408  may include determining whether the data structure element includes a claim preamble. Operation  408  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element determination module  314 , in accordance with one or more implementations. 
     An operation  410  may include, responsive to a determination that the data structure element includes a claim preamble, determining whether the claim preamble is from an independent claim or a dependent claim. Operation  410  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim preamble determination module  316 , in accordance with one or more implementations. 
     An operation  412  may include, responsive to a determination that the claim preamble is from an independent claim, determining a preamble base from the claim preamble. Operation  412  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to preamble base determination module  318 , in accordance with one or more implementations. 
     An operation  414  may include storing the preamble base in a modified data structure. Operation  414  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to preamble base storing module  320 , in accordance with one or more implementations. 
     An operation  416  may include, responsive to a determination that the data structure element does not include a claim preamble, determining whether the data structure element includes a main feature. Operation  416  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element determination module  314 , in accordance with one or more implementations. 
     An operation  418  may include, responsive to a determination that the data structure element does include a main feature, modifying the data structure element by converting a verb, a gerund, or a present participle to a corresponding lemma. Operation  418  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element modification module  322 , in accordance with one or more implementations. 
     An operation  420  may include storing the modified data structure element in the modified data structure. Operation  420  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element storing module  324 , in accordance with one or more implementations. 
     An operation  422  may include, responsive to a determination that the data structure element does not include a main feature, determining whether the data structure element includes a list of list items. Operation  422  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element determination module  314 , in accordance with one or more implementations. 
     An operation  424  may include, responsive to a determination that the data structure element includes a list of list items, modifying the data structure element by converting the data structure element to a permissive list format. Operation  424  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element modification module  322 , in accordance with one or more implementations. 
     An operation  426  may include, responsive to a determination that the data structure element does not include a list of list items, modifying the data structure element by converting the data structure element to prose. Operation  426  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element modification module  322 , in accordance with one or more implementations. 
     An operation  428  may include storing the modified data structure element in the modified data structure. Operation  428  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element storing module  324 , in accordance with one or more implementations. 
     An operation  430  may include determining whether there are more data structure elements to be iterated on. Operation  430  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element determination module  314 , in accordance with one or more implementations. 
       FIG.  5    illustrates a system  500  configured for providing a data structure with ordered content derived from patent claims, in accordance with one or more implementations. In some implementations, system  500  may include one or more servers  502 . Server(s)  502  may be configured to communicate with one or more client computing platforms  504  according to a client/server architecture and/or other architectures. Client computing platform(s)  504  may be configured to communicate with other client computing platforms via server(s)  502  and/or according to a peer-to-peer architecture and/or other architectures. Users may access system  500  via client computing platform(s)  504 . 
     Server(s)  502  may be configured by machine-readable instructions  506 . Machine-readable instructions  506  may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a data structure obtaining module  508 , a feature identifying module  510 , a software component name determination module  512 , a duplicate identifying module  514 , a software component name storing module  516 , a sub feature storing module  518 , a mention identifying module  520 , a second sub feature storing module  522 , and/or other instruction modules. 
     Data structure obtaining module  508  may be configured to obtain a first data structure representing a claim set. In some implementations, by way of non-limiting example, a given data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. 
     The first data structure may include language units from the claim set. The language units may be organized in the first data structure according to one or more classifications of individual language elements. The language units may be in patentese. The patentese may include text structure and legal jargon commonly used in patent claims. By way of non-limiting example, a language element may include one or more of a word, a phrase, a clause, or a sentence. By way of non-limiting example, the one or more classifications may include one or more of independent claim, dependent claim, preamble, main feature, sub feature, claim line, clause, phrase, or word. 
     A claim may be a single sentence. By way of non-limiting example, a sentence may include a set of words that is complete and contains a subject and predicate, a sentence including a main clause and optionally one or more subordinate clauses. By way of non-limiting example, a clause may include a unit of grammatical organization next below a sentence, a clause including a subject and predicate. A phrase may include a small group of words standing together as a conceptual unit, a phrase forming a component of a clause. By way of non-limiting example, a word may include a single distinct meaningful element of language used with others to form a sentence, a word being shown with a space on either side when written or printed. 
     Data structure obtaining module  508  may be configured to obtain a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. The prose may include an ordinary form of written language, without structure of claim language, as distinguished from patentese. The prose may include permissive prose. In some implementations, the permissive prose may convey allowed but not obligatory concepts. 
     Feature identifying module  510  may be configured to identify main features in the first data structure. In some implementations, by way of non-limiting example, the main feature may include a step of a claimed process, a physical part of a claimed machine or article of manufacture, or a component of a claimed composition of matter. In some implementations, a given sub feature may describe or expands on an aspect of a corresponding main feature. 
     Software component name determination module  512  may be configured to determine software component names associated with individual ones of the main features. In some implementations, a given software component name may describe a functional aspect of a software program. 
     Duplicate identifying module  514  may be configured to identify duplicates among the software component names to obtain unique software component names. 
     Software component name storing module  516  may be configured to store the unique software component names in a third data structure along with corresponding main features such that a given main feature is associated in the third data structure with a given unique software component name. Identify sub features in the second data structure that correspond to individual ones of the main features in the first data structure. 
     Sub feature storing module  518  may be configured to store the identified sub features from the second data structure in the third data structure such that a given identified sub feature is associated in the third data structure with a corresponding main feature. 
     Mention identifying module  520  may be configured to identify initial mentions of content words in the main features and sub features stored in the third data structure. 
     Mention identifying module  520  may be configured to identify subsequent mentions of content words in sub features in the second data structure that do not correspond to individual ones of the main features in the first data structure and that are not yet stored in the third data structure. The content words may be words that have meaning as opposed to grammatical or structural words. By way of non-limiting example, a given content word may be a noun, a main verb, an adjective or an adverb. By way of non-limiting example, a given grammatical or structural word may be an auxiliary verb, a pronoun, an article, or a preposition. An initial mention of a given content word may be the first time the given content word is mentioned in the third data structure. The initial mention of the given content word may be not preceded by a definite article. The subsequent mentions may be identified based on a presence of a definite article preceding individual ones of the subsequent mentions. 
     Second sub feature storing module  522  may be configured to, based on the identified initial mentions and the identified subsequent mentions, store, in the third data structure and in association with a corresponding main feature, the sub features from the second data structure that do not correspond to individual ones of the main features in the first data structure. If a given sub feature may correspond with more than one main feature based on the identified initial mentions and the identified subsequent mentions, the given sub features is stored in the third data structure is association with the corresponding main feature that fall last in the third data structure among all the corresponding main features. 
     In some implementations, one or more operations may include applying a machine learning model. In some implementations, by way of non-limiting example, the machine learning model may be based on one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a regression algorithm, an instance-based algorithm, a regularized algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. 
     In some implementations, server(s)  502 , client computing platform(s)  504 , and/or external resources  524  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  502 , client computing platform(s)  504 , and/or external resources  524  may be operatively linked via some other communication media. 
     A given client computing platform  504  may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given client computing platform  504  to interface with system  500  and/or external resources  524 , and/or provide other functionality attributed herein to client computing platform(s)  504 . By way of non-limiting example, the given client computing platform  504  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  524  may include sources of information outside of system  500 , external entities participating with system  500 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  524  may be provided by resources included in system  500 . 
     Server(s)  502  may include electronic storage  526 , one or more processors  528 , and/or other components. Server(s)  502  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  502  in  FIG.  5    is not intended to be limiting. Server(s)  502  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  502 . For example, server(s)  502  may be implemented by a cloud of computing platforms operating together as server(s)  502 . 
     Electronic storage  526  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  526  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  502  and/or removable storage that is removably connectable to server(s)  502  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  526  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  526  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  526  may store software algorithms, information determined by processor(s)  528 , information received from server(s)  502 , information received from client computing platform(s)  504 , and/or other information that enables server(s)  502  to function as described herein. 
     Processor(s)  528  may be configured to provide information processing capabilities in server(s)  502 . As such, processor(s)  528  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  528  is shown in  FIG.  5    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  528  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  528  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  528  may be configured to execute modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 ,  522 , and/or other modules. Processor(s)  528  may be configured to execute modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 ,  522 , and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  528 . As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and  522  are illustrated in  FIG.  5    as being implemented within a single processing unit, in implementations in which processor(s)  528  includes multiple processing units, one or more of modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522  may be implemented remotely from the other modules. The description of the functionality provided by the different modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522  described below is for illustrative purposes, and is not intended to be limiting, as any of modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522  may provide more or less functionality than is described. For example, one or more of modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522  may be eliminated, and some or all of its functionality may be provided by other ones of modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522 . As another example, processor(s)  528  may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules  508 ,  510 ,  512 ,  514 ,  516 ,  518 ,  520 , and/or  522 . 
       FIG.  6    illustrates a method  600  for providing a data structure with ordered content derived from patent claims, in accordance with one or more implementations. The operations of method  600  presented below are intended to be illustrative. In some implementations, method  600  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  600  are illustrated in  FIG.  6    and described below is not intended to be limiting. 
     In some implementations, method  600  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  600  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  600 . 
     An operation  602  may include obtaining a first data structure representing a claim set. The first data structure may include language units from the claim set. The language units may be in patentese. Operation  602  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  508 , in accordance with one or more implementations. 
     An operation  604  may include obtaining a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. Operation  604  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  508 , in accordance with one or more implementations. 
     An operation  606  may include identifying main features in the first data structure. Operation  606  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to feature identifying module  510 , in accordance with one or more implementations. 
     An operation  608  may include determining software component names associated with individual ones of the main features. Operation  608  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to software component name determination module  512 , in accordance with one or more implementations. 
     An operation  610  may include identifying duplicates among the software component names to obtain unique software component names. Operation  610  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to duplicate identifying module  514 , in accordance with one or more implementations. 
     An operation  612  may include storing the unique software component names in a third data structure along with corresponding main features such that a given main feature is associated in the third data structure with a given unique software component name. Identify sub features in the second data structure that correspond to individual ones of the main features in the first data structure. Operation  612  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to software component name storing module  516 , in accordance with one or more implementations. 
     An operation  614  may include storing the identified sub features from the second data structure in the third data structure such that a given identified sub feature is associated in the third data structure with a corresponding main feature. Operation  614  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to sub feature storing module  518 , in accordance with one or more implementations. 
     An operation  616  may include identifying initial mentions of content words in the main features and sub features stored in the third data structure. Operation  616  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to mention identifying module  520 , in accordance with one or more implementations. 
     An operation  618  may include identifying subsequent mentions of content words in sub features in the second data structure that do not correspond to individual ones of the main features in the first data structure and that are not yet stored in the third data structure. Operation  618  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to mention identifying module  520 , in accordance with one or more implementations. 
     An operation  620  may include, based on the identified initial mentions and the identified subsequent mentions, storing, in the third data structure and in association with a corresponding main feature, the sub features from the second data structure that do not correspond to individual ones of the main features in the first data structure. Operation  620  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to second sub feature storing module  522 , in accordance with one or more implementations. 
       FIG.  7    illustrates a system  700  configured for identifying and naming software components described by functional patent claim language, in accordance with one or more implementations. In some implementations, system  700  may include one or more servers  702 . Server(s)  702  may be configured to communicate with one or more client computing platforms  704  according to a client/server architecture and/or other architectures. Client computing platform(s)  704  may be configured to communicate with other client computing platforms via server(s)  702  and/or according to a peer-to-peer architecture and/or other architectures. Users may access system  700  via client computing platform(s)  704 . 
     Server(s)  702  may be configured by machine-readable instructions  706 . Machine-readable instructions  706  may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a data structure obtaining module  708 , a data structure element obtaining module  710 , a verb identifying module  712 , a data structure element tokenizing module  714 , a token determination module  716 , a verb determination module  718 , a word determination module  720 , a comma skipping module  722 , a noun phrase identifying module  724 , a parse obtaining module  726 , a noun phrase identifying module  728 , a noun phrase determination module  730 , a noun identifying module  732 , a noun determination module  734 , a noun storing module  736 , a software component name determination module  738 , and/or other instruction modules. 
     Data structure obtaining module  708  may be configured to obtain a data structure representing a claim set. By way of non-limiting example, a given data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. 
     The data structure may include language units from the claim set. The language units may be organized in the data structure according to one or more classifications of individual language elements. By way of non-limiting example, the one or more classifications may include one or more of independent claim, dependent claim, preamble, main feature, sub feature, claim line, clause, phrase, or word. By way of non-limiting example, a language element may include one or more of a word, a phrase, a clause, or a sentence. 
     A claim may be a single sentence. A sentence may include a set of words that is complete and contains a subject and predicate. A sentence may include a main clause and optionally one or more subordinate clauses. A clause may include a unit of grammatical organization next below a sentence. A clause may include a subject and predicate. A phrase may include a small group of words standing together as a conceptual unit, a phrase forming a component of a clause. A word may include a single distinct meaningful element of language used with others to form a sentence. A word may be shown with a space on either side when written or printed. 
     A given main feature may include a step of a claimed process. A given sub feature may describe or expands on an aspect of a corresponding main feature. The language units may be in patentese. The patentese may include text structure and legal jargon commonly used in patent claims. 
     Data structure element obtaining module  710  may be configured to obtain a data structure element from the data structure. The data structure element may include a main feature. The main feature may include a step of a claimed process. 
     Verb identifying module  712  may be configured to identify a main verb to be associated with a software component name that corresponds to the main feature. Identifying the main verb may include identifying a present participle in the data structure element. Determining the software component name may include determining a lemma of the identified verb. Determining the software component name may include determining a nominalization of the identified verb. The main verb may describe a primary function of the main feature. 
     Verb identifying module  712  may be configured to, in response to a determination that the main verb has not been identified, identify the main verb. 
     Data structure element tokenizing module  714  may be configured to tokenize the data structure element by breaking up a stream of text in the data structure element into tokens, a given token being a word, a phrase, a symbol, or a punctuation. 
     Token determination module  716  may be configured to determine whether a next token is a comma. 
     Verb determination module  718  may be configured to, in response to a determination that the next token is not a comma, determine whether the main verb has been identified. 
     Word determination module  720  may be configured to, in response to a determination that the next token is a comma, determine whether a word after the comma is a trigger word. 
     Comma skipping module  722  may be configured to, in response to a determination that the word after the comma is a trigger word, skip to a next comma in the data structure element. The trigger word may be a word that identifies a phrase that qualifies the main feature. By way of non-limiting example, the trigger word may include one or more of by, if, after, when, based, at, upon, in, in response, while, for, during, on, prior, subsequent, from, with, using, to, or via. 
     Noun phrase identifying module  724  may be configured to, in response to identify the main verb, identifying a noun or noun phrase to be associated with the software component name. 
     Parse obtaining module  726  may be configured to obtain a parse of the tokenized data structure element. By way of non-limiting example, the parse of the tokenized data structure element may include one or more of grammatical constituents, parts of speech, syntactic relations, or inflectional form. 
     Noun phrase identifying module  728  may be configured to, based on the parse, identify a noun phrase that follows the main verb in the data structure element. 
     Noun phrase determination module  730  may be configured to determine whether the identified noun phrase is a terminal noun phrase or a parent of a qualifier phrase. 
     Noun identifying module  732  may be configured to, in response to a determination that the identified noun phrase is a terminal noun phrase or a parent of a qualifier phrase, identify one or more nouns in the identified noun phrase. The terminal noun phrase may be a noun phrase that lacks smaller constituent phrases. The qualifier phrase may be a phrase that precedes an adjective or adverb. 
     Noun determination module  734  may be configured to determine whether any nouns were identified in the identified noun phrase. 
     Noun storing module  736  may be configured to, in response to a determination that one or more nouns were identified in the identified noun phrase, store the one or more nouns to be used in determining the software component name. 
     Software component name determination module  738  may be configured to determine the software component name based on the identified main verb and the identified noun or noun phrase. By way of non-limiting example, determining the software component name may include listing the identified noun or noun phrase, listing the identified verb, and listing a term describing a type of software component. 
     In some implementations, one or more operations may include applying a machine learning model. In some implementations, by way of non-limiting example, the machine learning model may be based on one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a regression algorithm, an instance-based algorithm, a regularized algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. 
     In some implementations, server(s)  702 , client computing platform(s)  704 , and/or external resources  740  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  702 , client computing platform(s)  704 , and/or external resources  740  may be operatively linked via some other communication media. 
     A given client computing platform  704  may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given client computing platform  704  to interface with system  700  and/or external resources  740 , and/or provide other functionality attributed herein to client computing platform(s)  704 . By way of non-limiting example, the given client computing platform  704  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  740  may include sources of information outside of system  700 , external entities participating with system  700 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  740  may be provided by resources included in system  700 . 
     Server(s)  702  may include electronic storage  742 , one or more processors  744 , and/or other components. Server(s)  702  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  702  in  FIG.  7    is not intended to be limiting. Server(s)  702  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  702 . For example, server(s)  702  may be implemented by a cloud of computing platforms operating together as server(s)  702 . 
     Electronic storage  742  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  742  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  702  and/or removable storage that is removably connectable to server(s)  702  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  742  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  742  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  742  may store software algorithms, information determined by processor(s)  744 , information received from server(s)  702 , information received from client computing platform(s)  704 , and/or other information that enables server(s)  702  to function as described herein. 
     Processor(s)  744  may be configured to provide information processing capabilities in server(s)  702 . As such, processor(s)  744  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  744  is shown in  FIG.  7    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  744  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  744  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  744  may be configured to execute modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 ,  738 , and/or other modules. Processor(s)  744  may be configured to execute modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 ,  738 , and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  744 . As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and  738  are illustrated in  FIG.  7    as being implemented within a single processing unit, in implementations in which processor(s)  744  includes multiple processing units, one or more of modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738  may be implemented remotely from the other modules. The description of the functionality provided by the different modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738  described below is for illustrative purposes, and is not intended to be limiting, as any of modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738  may provide more or less functionality than is described. For example, one or more of modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738  may be eliminated, and some or all of its functionality may be provided by other ones of modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738 . As another example, processor(s)  744  may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules  708 ,  710 ,  712 ,  714 ,  716 ,  718 ,  720 ,  722 ,  724 ,  726 ,  728 ,  730 ,  732 ,  734 ,  736 , and/or  738 . 
       FIG.  8    illustrates a method  800  for identifying and naming software components described by functional patent claim language, in accordance with one or more implementations. The operations of method  800  presented below are intended to be illustrative. In some implementations, method  800  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  800  are illustrated in  FIG.  8    and described below is not intended to be limiting. 
     In some implementations, method  800  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  800  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  800 . 
     An operation  802  may include obtaining a data structure representing a claim set. The data structure may include language units from the claim set. The language units may be in patentese. Operation  802  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  708 , in accordance with one or more implementations. 
     An operation  804  may include obtaining a data structure element from the data structure. The data structure element may include a main feature. The main feature may include a step of a claimed process. Operation  804  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element obtaining module  710 , in accordance with one or more implementations. 
     An operation  806  may include identifying a main verb to be associated with a software component name that corresponds to the main feature. The main verb may describe a primary function of the main feature. Operation  806  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to verb identifying module  712 , in accordance with one or more implementations. 
     An operation  808  may include tokenizing the data structure element by breaking up a stream of text in the data structure element into tokens, a given token being a word, a phrase, a symbol, or a punctuation. Operation  808  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure element tokenizing module  714 , in accordance with one or more implementations. 
     An operation  810  may include determining whether a next token is a comma. Operation  810  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to token determination module  716 , in accordance with one or more implementations. 
     An operation  812  may include, in response to a determination that the next token is not a comma, determining whether the main verb has been identified. Operation  812  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to verb determination module  718 , in accordance with one or more implementations. 
     An operation  814  may include, in response to a determination that the main verb has not been identified, identifying the main verb. Operation  814  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to verb identifying module  712 , in accordance with one or more implementations. 
     An operation  816  may include, in response to a determination that the next token is a comma, determining whether a word after the comma is a trigger word. Operation  816  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to word determination module  720 , in accordance with one or more implementations. 
     An operation  818  may include, in response to a determination that the word after the comma is a trigger word, skipping to a next comma in the data structure element. Operation  818  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to comma skipping module  722 , in accordance with one or more implementations. 
     An operation  820  may include, in response to identifying the main verb, identifying a noun or noun phrase to be associated with the software component name. Operation  820  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun phrase identifying module  724 , in accordance with one or more implementations. 
     An operation  822  may include obtaining a parse of the tokenized data structure element. Operation  822  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to parse obtaining module  726 , in accordance with one or more implementations. 
     An operation  824  may include, based on the parse, identifying a noun phrase that follows the main verb in the data structure element. Operation  824  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun phrase identifying module  728 , in accordance with one or more implementations. 
     An operation  826  may include determining whether the identified noun phrase is a terminal noun phrase or a parent of a qualifier phrase. Operation  826  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun phrase determination module  730 , in accordance with one or more implementations. 
     An operation  828  may include, in response to a determination that the identified noun phrase is a terminal noun phrase or a parent of a qualifier phrase, identifying one or more nouns in the identified noun phrase. Operation  828  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun identifying module  732 , in accordance with one or more implementations. 
     An operation  830  may include determining whether any nouns were identified in the identified noun phrase. Operation  830  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun determination module  734 , in accordance with one or more implementations. 
     An operation  832  may include, in response to a determination that one or more nouns were identified in the identified noun phrase, storing the one or more nouns to be used in determining the software component name. Operation  832  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to noun storing module  736 , in accordance with one or more implementations. 
     An operation  834  may include determining the software component name based on the identified main verb and the identified noun or noun phrase. Operation  834  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to software component name determination module  738 , in accordance with one or more implementations. 
       FIG.  9    illustrates a system  900  configured 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, in accordance with one or more implementations. In some implementations, system  900  may include one or more servers  902 . Server(s)  902  may be configured to communicate with one or more client computing platforms  904  according to a client/server architecture and/or other architectures. Client computing platform(s)  904  may be configured to communicate with other client computing platforms via server(s)  902  and/or according to a peer-to-peer architecture and/or other architectures. Users may access system  900  via client computing platform(s)  904 . 
     Server(s)  902  may be configured by machine-readable instructions  906 . Machine-readable instructions  906  may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a claim set obtaining module  908 , a data structure obtaining module  910 , a title determination module  912 , a field determination module  914 , a background determination module  916 , a summary determination module  918 , a description determination module  920 , a system claim determination module  922 , a method claim determination module  924 , a patent specification determination module  926 , a figure text determination module  928 , and/or other instruction modules. 
     Claim set obtaining module  908  may be configured to obtain a claim set. The claim set was prepared by a human. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims. Each dependent claim in the claim set may depend on the independent claim by referring to the independent claim or an intervening dependent claim. 
     Data structure obtaining module  910  may be configured to obtain a first data structure representing the claim set. In some implementations, by way of non-limiting example, a given data structure may include a specialized format for organizing and storing data, the data structure including one or more of an array, a list, two or more linked lists, a stack, a queue, a graph, a table, or a tree. 
     The first data structure may include language units from the claim set. The language units may be in patentese. The patentese may include text structure and legal jargon commonly used in patent claims. 
     By way of non-limiting example, a language element may include one or more of a word, a phrase, a clause, or a sentence. A claim may be a single sentence. By way of non-limiting example, a sentence may include a set of words that is complete and contains a subject and predicate, a sentence including a main clause and optionally one or more subordinate clauses. By way of non-limiting example, a clause may include a unit of grammatical organization next below a sentence, a clause including a subject and predicate. A phrase may include a small group of words standing together as a conceptual unit, a phrase forming a component of a clause. By way of non-limiting example, a word may include a single distinct meaningful element of language used with others to form a sentence, a word being shown with a space on either side when written or printed. 
     The language units may be organized in the first data structure according to one or more classifications of individual language elements. By way of non-limiting example, the one or more classifications may include one or more of independent claim, dependent claim, preamble, main feature, sub feature, claim line, clause, phrase, or word. In some implementations, a given main feature may include a step of a claimed process. In some implementations, a given sub feature may correspond to a given main feature. In some implementations, a given sub feature may describe or expands on an aspect of a corresponding main feature. 
     Data structure obtaining module  910  may be configured to obtain a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. In some implementations, prose may include an ordinary form of written language, without structure of claim language, as distinguished from patentese. The prose may include permissive prose. In some implementations, the permissive prose conveying allowed but not obligatory concepts. 
     In some implementations, the ordered content may include one or more software component names. In some implementations, a given software component name may describe a functional aspect of a software program. In some implementations, the ordered content may be arranged according to the one or more software components. In some implementations, a given software component may be associated with one or more main features. The one or more main features may be obtained from the first data structure. In some implementations, a given software component may be associated with one or more sub features. The one or more sub features may be obtained from the second data structure. 
     Data structure obtaining module  910  may be configured to obtain a third data structure. The third data structure may include ordered content derived from the claim set. By way of non-limiting example, the ordered content of the third data structure may be ordered based on one or more of claim structure of the claim set, antecedent basis in the claim set, or claim dependency in the claim set. 
     Title determination module  912  may be configured to determine a title of the patent specification. The title may be determined based on a preamble base stored in the second data structure. The preamble base may include the claim preamble without leading generic terms or transition words. By way of non-limiting example, a leading generic term may include system, method, device, apparatus, or composition. By way of non-limiting example, a transition word may include including, including, or consisting. The title may be determined based on a preamble base stored in the second data structure in combination with one or more leading generic terms and one or more transition words. 
     Field determination module  914  may be configured to determine a field of disclosure of the patent specification. The field of disclosure may be determined based on a preamble base stored in the second data structure. The field of disclosure may be determined based on a preamble base stored in the second data structure in combination with one or more leading generic terms and one or more transition words. 
     Background determination module  916  may be configured to determine a background of the patent specification. The background may be determined based on information provided in an invention disclosure from one or more inventors. The invention disclosure may be obtained separately from the claim set. By way of non-limiting example, the background may be determined based on one or more of the first data structure, the second data structure, or the third data structure. By way of non-limiting example, the background may be determined based on information obtained from a website, an article, a news article, a journal article, or online publication. The background may be intentionally left blank for post editing. 
     Summary determination module  918  may be configured to determine a summary of the patent specification. By way of non-limiting example, the summary may include a first summary portion, a second summary portion, and a third summary portion. The first summary portion may be associated with an independent system claim. The first summary portion may be determined based on the first data structure and the second data structure. The second summary portion may be associated with an independent method of claim. The second summary portion may be determined based on the first data structure and the second data structure. The third summary portion may include boilerplate language associated with summary. 
     Description determination module  920  may be configured to determine a brief description of the drawings of the patent specification. The brief description of the drawings may be determined based on a preamble base stored in the second data structure. The brief description of the drawings may be determined based on a preamble base stored in the second data structure in combination with one or more leading generic terms. 
     Description determination module  920  may be configured to determine a detailed description of the patent specification. The detailed description may include a first description portion and a second description portion. The first description portion may be associated with a system claim set. The first description portion may be determined based on the third data structure. The first description portion may be not determined based on the first data structure or the second data structure. The first description portion may include boilerplate text associated with the first description portion. The second description portion may be associated with a method of claim set. By way of non-limiting example, the second description portion may be determined based on the first data structure, the second data structure, and the third data structure. The second description portion may include boilerplate text associated with the second description portion. 
     System claim determination module  922  may be configured to determine system claims for the patent specification. The system claims may be determined based on the first data structure and the second data structure. 
     Method claim determination module  924  may be configured to determine method of claims for the patent specification. The method of claims may be determined based on the first data structure. 
     Patent specification determination module  926  may be configured to determine an abstract for the patent specification. The abstract may be determined based on the first data structure and the second data structure. 
     Figure text determination module  928  may be configured to determine figure text for the patent specification. The figure text may include first figure text and second figure text. In some implementations, the first figure text may be associated with a first figure. The first figure may illustrate a system. The first figure text may be determined based on the third data structure. In some implementations, the second figure text may be associated with a second figure. The second figure may illustrate a method. The second figure text may be determined based on the second data structure. 
     In some implementations, one or more operations may include applying a machine learning model. In some implementations, by way of non-limiting example, the machine learning model may be based on one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a regression algorithm, an instance-based algorithm, a regularized algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. 
     In some implementations, server(s)  902 , client computing platform(s)  904 , and/or external resources  930  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  902 , client computing platform(s)  904 , and/or external resources  930  may be operatively linked via some other communication media. 
     A given client computing platform  904  may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given client computing platform  904  to interface with system  900  and/or external resources  930 , and/or provide other functionality attributed herein to client computing platform(s)  904 . By way of non-limiting example, the given client computing platform  904  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  930  may include sources of information outside of system  900 , external entities participating with system  900 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  930  may be provided by resources included in system  900 . 
     Server(s)  902  may include electronic storage  932 , one or more processors  934 , and/or other components. Server(s)  902  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  902  in  FIG.  9    is not intended to be limiting. Server(s)  902  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  902 . For example, server(s)  902  may be implemented by a cloud of computing platforms operating together as server(s)  902 . 
     Electronic storage  932  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  932  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  902  and/or removable storage that is removably connectable to server(s)  902  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  932  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  932  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  932  may store software algorithms, information determined by processor(s)  934 , information received from server(s)  902 , information received from client computing platform(s)  904 , and/or other information that enables server(s)  902  to function as described herein. 
     Processor(s)  934  may be configured to provide information processing capabilities in server(s)  902 . As such, processor(s)  934  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  934  is shown in  FIG.  9    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  934  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  934  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  934  may be configured to execute modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 ,  928 , and/or other modules. Processor(s)  934  may be configured to execute modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 ,  928 , and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  934 . As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and  928  are illustrated in  FIG.  9    as being implemented within a single processing unit, in implementations in which processor(s)  934  includes multiple processing units, one or more of modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928  may be implemented remotely from the other modules. The description of the functionality provided by the different modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928  described below is for illustrative purposes, and is not intended to be limiting, as any of modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928  may provide more or less functionality than is described. For example, one or more of modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928  may be eliminated, and some or all of its functionality may be provided by other ones of modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928 . As another example, processor(s)  934  may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules  908 ,  910 ,  912 ,  914 ,  916 ,  918 ,  920 ,  922 ,  924 ,  926 , and/or  928 . 
       FIG.  10    illustrates a method  1000  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, in accordance with one or more implementations. The operations of method  1000  presented below are intended to be illustrative. In some implementations, method  1000  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  1000  are illustrated in  FIG.  10    and described below is not intended to be limiting. 
     In some implementations, method  1000  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  1000  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  1000 . 
     An operation  1002  may include obtaining a claim set. The claim set may include a numbered list of sentences that precisely define an invention. The claim set may include an independent claim and one or more dependent claims, each dependent claim in the claim set depending on the independent claim by referring to the independent claim or an intervening dependent claim. The claim set was prepared by a human. Operation  1002  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to claim set obtaining module  908 , in accordance with one or more implementations. 
     An operation  1004  may include obtaining a first data structure representing the claim set. The first data structure may include language units from the claim set. The language units may be in patentese. Operation  1004  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  910 , in accordance with one or more implementations. 
     An operation  1006  may include obtaining a second data structure. The second data structure may have the same dimensions as the first data structure such that a given data structure element at a given position within the first data structure corresponds to a given data structure element at the same position within the second data structure. The second data structure may include language elements associated with the claim set. The language elements of the second data structure may be in prose rather than patentese. Operation  1006  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  910 , in accordance with one or more implementations. 
     An operation  1008  may include obtaining a third data structure. The third data structure may include ordered content derived from the claim set. The ordered content of the third data structure may be ordered based on one or more of claim structure of the claim set, antecedent basis in the claim set, or claim dependency in the claim set. Operation  1008  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to data structure obtaining module  910 , in accordance with one or more implementations. 
     An operation  1010  may include determining one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure. Operation  1010  may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to one or more of title determination module  912 , field determination module  914 , background determination module  916 , summary determination module  918 , description determination module  920 , system claim determination module  922 , method claim determination module  924 , patent specification determination module  926 , and/or figure text determination module  928 , in accordance with one or more implementations. 
     Although the present technology has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the technology is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present technology contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.