Patent Publication Number: US-11663677-B2

Title: System and method for automatically generating calculations for fields in compliance forms

Description:
CROSS-REFERENCE TO RELATED CASES 
     This Patent Application is a continuation of and claims priority to U.S. patent application Ser. No. 15/384,907 entitled “SYSTEM AND METHOD FOR AUTOMATICALLY GENERATING CALCULATIONS FOR FIELDS IN COMPLIANCE FORMS” and filed on Dec. 20, 2016, which claims priority to U.S. Provisional Patent Application No. 62/362,688 entitled “SYSTEM AND METHOD FOR MACHINE LEARNING OF CONTEXT OF LINE INSTRUCTIONS FOR VARIOUS DOCUMENT TYPES” and filed on Jul. 15, 2016. This Patent Application also claims priority to India Foreign Patent Application 201631035834, entitled “SYSTEM AND METHOD FOR AUTOMATICALLY GENERATING CALCULATIONS FOR FIELDS IN COMPLIANCE FORMS” and filed Oct. 20, 2016, all of which are assigned to the assignee hereof. The disclosures of all prior Applications are considered part of and are incorporated by reference in this Patent Application. 
    
    
     BACKGROUND 
     Many people use electronic document preparation systems to help prepare important documents electronically. For example, each year millions of people use electronic tax return preparation systems to help prepare and file their tax returns. Typically, electronic tax return preparation systems receive tax related information from a user and then automatically populate the various fields in electronic versions of government tax forms. Electronic tax return preparation systems represent a potentially flexible, highly accessible, and affordable source of tax return preparation assistance for customers. However, the processes that enable the electronic tax return preparation systems to incorporate new tax forms into the tax return preparation systems often utilize large amounts of human and computing resources. 
     For instance, due to changes in tax laws, or due to updates in government tax forms, tax forms can change from year to year, or even multiple times in the same year. If a tax form changes, or if a new tax form is introduced, it can be very difficult to efficiently update the electronic tax return preparation system to correctly populate the various fields of the tax forms with the requested values. For example, a particular line of a newly adjusted tax form may request an input according to a function that requires values from other lines of the tax form and/or values from other tax forms or worksheets. These functions range from very simple to very complex. Updating the electronic tax return preparation system often includes utilizing a combination of tax experts, software and system engineers, and large amounts of computing resources to incorporate the new form into the electronic tax return preparation system or to learn the behavior of an existing system. This can lead to delays in releasing an updated version of the electronic tax return preparation system as well as considerable expenses. These expenses are then passed on to customers of the electronic tax return preparation system, as are the delays. Furthermore, these processes for updating electronic tax return preparation systems can introduce inaccuracies into the tax return preparation systems. 
     These expenses, delays, and possible inaccuracies can have an adverse impact on traditional electronic tax return preparation systems. Customers may lose confidence in the electronic tax return preparation systems. Furthermore, customers may simply decide to utilize less expensive options for preparing their taxes. 
     These issues and drawbacks are not limited to electronic tax return preparation systems. Any electronic document preparation system that assists users to electronically fill out forms or prepare documents can suffer from these drawbacks when the forms are updated or new forms are released. 
     What is needed is a method and system that efficiently and accurately incorporates updated forms into an electronic document preparation system. 
     SUMMARY 
     Embodiments of the present disclosure address some of the shortcomings associated with traditional electronic document preparation systems by providing methods and systems for incorporating new or updated forms, or for learning the behavior of existing systems, by utilizing multiple independent analysis processes, evaluating the results from each analysis process, and selecting the results that are most accurate. In particular, embodiments of the present disclosure receive form data related to a new form that includes data fields to be completed in accordance with specific functions designated by the new form. Embodiments of the present disclosure utilize multiple separate analysis techniques to generate candidate functions for each selected data field of the new form. The purpose of generating the candidate functions is to find an acceptable candidate function for each data field of the new form. An acceptable candidate function is one that reliably produces data values in accordance with the context or description related to a selected field of the new form. After candidate functions have been generated by the multiple analysis techniques, embodiments of the present disclosure evaluate each of the candidate functions in order to determine which candidate functions most reliably provide correct data values for the selected data field. When an acceptable candidate function has been found for a selected data field, embodiments of the present disclosure can repeat the process for each selected data field of the new form until acceptable candidate functions have been found for each selected data field of the new form. In this way, embodiments of the present disclosure provide a more reliable electronic document preparation system that quickly, efficiently, and reliably learns and incorporates new forms. 
     In one embodiment, an electronic document preparation system utilizes, for each selected data field of the new form, a machine learning module to generate a first set of candidate functions, a natural language parsing module to generate a second set of candidate functions, and a historical instructions analysis module to generate a third set of candidate functions. The electronic document preparation system tests, for each selected data field of the new form, each of the candidate functions from the first, second, and third set of candidate functions until one or more acceptable candidate functions has been found. 
     In one embodiment, the electronic document preparation system gathers training set data to assist in evaluating the candidate functions. The training set data includes previously filled forms related to the new form. The previously filled forms include data fields that have already been completed. The electronic document preparation system can utilize the previously filled forms in order to test the accuracy of the candidate functions. 
     In one embodiment, the electronic document preparation system includes an evaluator module that tests the candidate functions and identifies the acceptable candidate functions. For example, for a first selected data field of the new form, the evaluator module receives the candidate functions from the machine learning module, the natural language parsing module, and the historical instructions analysis module. The evaluator module then generates test data for each candidate function by applying the candidate function to the training set data. The evaluator module then generates matching data for each candidate function by comparing the test data to the completed data fields of the previously filled forms in the training set data. The evaluator module then generates ranking data that ranks each of the candidate functions based on how closely they match the training set data. If one or more of the highest ranked candidate functions matches the training set data within a threshold error tolerance, then the evaluator module identifies these one or more candidate functions as acceptable candidate functions. 
     In one embodiment, the electronic document preparation system includes an interface module that receives form data related to the new form. The form data can include an electronic version of the new form such as a PDF, a JPG, or other type of file that can represent an electronic version of the new form. 
     Typically, the form data is not in a structured form in which the data fields, and the text or context that describes them, are not segmented in a way that they can be identified and catalogued. In one embodiment, the electronic document preparation system includes a form analysis module. The form analysis module is configured to analyze the form data and to generate structured form data. The structured form data separates out each line and data field of the new form and all the associated information relevant to the data field. The associated information can be spread across multiple sources including the new form, other forms, instructions related to the forms, publications, etc. Variables are also mapped to each line of the form. The various facts related to each data field can be extracted from the structured form data. These facts include dependencies, constants, mapping tables, etc. These dependents, constants, mapping tables, etc. can be used to generate candidate functions using one or more of the analysis techniques or modules. 
     In one embodiment, the machine learning module utilizes the dependencies extracted from the structured form data in order to generate candidate functions for each selected data field of the new form. The dependencies can also come from the historical instructions analysis module. The machine learning module utilizes these dependencies, in combination with a library of operators, to generate many candidate functions. The machine learning module can also utilize the training set data in order to find candidate functions that provide data values that closely match the training set data. 
     In one embodiment, the machine learning module provides to the evaluator module only those candidate functions that have been preliminarily deemed to be more likely to be accurate. Alternatively, the machine learning module can provide all of the generated candidate functions to the evaluator module. 
     In one embodiment, the historical instructions analysis module analyzes software instructions related to previous versions of an electronic document preparation system. The previous version of the electronic document preparation system already includes software instructions that define functions that compute data values for data fields of historical forms, such as previous versions of the new form. The historical instructions analysis module analyzes the software instructions to generate dependencies data indicating dependencies between variables and then passes the dependencies data to the machine learning module. Additionally, the historical instructions analysis module analyzes the software instructions in greater detail and determines the function used by the previous version of the electronic document preparation system to calculate the data value for a selected field of a previous form that corresponds to the new form. The historical instructions analysis module generates one or more candidate functions from the function used by the previous electronic document preparation system and passes the one or more candidate functions to the evaluator module for further analysis. 
     In one embodiment, the natural language parsing module analyzes the context data related to the selected data field as contained in the structured form data. The context data may include text or other contextual indications as to what the correct function is for providing data values for the selected data field. The natural language parsing module can use techniques such as semantic parsing in order to determine what are the dependencies in the correct function and what their likely relationships are. The natural language parsing module then generates one or more candidate functions based on the natural language parsing analysis of the context data related to the selected data field. The natural language parsing module then passes the one or more candidate functions to the evaluator module. 
     In one embodiment, the evaluator module combines the candidate functions from each analysis module and then tests and ranks them in order to determine which is the best candidate function for each selected data field of the new form. The ranking data can indicate, for each candidate function, which analysis module or analysis technique generated the candidate function. The evaluator module can generate the final list of acceptable functions for each of the selected data fields of the new form. 
     In one embodiment, it is possible that one or more of the analysis modules or techniques will not provide a candidate function for a selected data field of the new form. In this case, the evaluator module will still rank the available candidate functions and determine if one or more of the candidate functions is an acceptable candidate function. 
     In one embodiment, the form analysis module includes historical structured form data related to a previous version of the new form. The form analysis module can compare the structured form data to the historical structured form data to determine if the description or context data related to a selected data field of the new form is identical to the description or context data related to a corresponding data field in the previous version of the form. If the description or context data related to the selected data field of the new form is not identical to the description or context data related to the corresponding data field in the previous version of the form, then the evaluator module can discard candidate functions from the machine learning module and the historical instructions analysis module. In this case, the ranked list of candidate functions includes only solutions from the natural language parsing module. If the description or context data related to the selected field of the new form is identical to the description or context data related to the corresponding data field in the previous version of the form, then the evaluator module ranks all of the candidate functions from all the analysis modules. 
     In one embodiment, if two or more candidate functions have identical rankings, then the evaluator module can give priority to candidate functions generated by one of the analysis modules over candidate functions generated by the other analysis modules. For example, the evaluator may give priority to candidate functions generated by the natural language parsing module over candidate functions generated by the historical instructions analysis module and the machine learning module. The evaluator module may also give priority to candidate functions generated by the historical instructions analysis module over candidate functions generated by the machine learning module. 
     In one embodiment, a method and system for learning and incorporating new forms in an electronic document preparation system can include learning the behavior of existing systems. 
     Embodiments of the present disclosure address some of the shortcomings associated with traditional electronic document preparation systems that do not adequately and efficiently incorporate new forms. An electronic document preparation system in accordance with one or more embodiments provides efficient and reliable incorporation of new forms by multiple analysis techniques in conjunction with training set data in order to quickly and accurately incorporate and learn new forms. The various embodiments of the disclosure can be implemented to improve the technical fields of data processing, resource management, data collection, and user experience. Therefore, the various described embodiments of the disclosure and their associated benefits amount to significantly more than an abstract idea. In particular, by utilizing multiple analysis techniques and training set data to learn and incorporate new forms in an electronic document preparation system, users can save money and time and can better manage their finances. 
     Using the disclosed embodiments of a method and system for learning and incorporating new forms in an electronic document preparation system, a method and system for learning and incorporating new forms in an electronic document preparation system more accurately is provided. Therefore, the disclosed embodiments provide a technical solution to the long standing technical problem of efficiently learning and incorporating new forms in an electronic document preparation system. 
     In addition, the disclosed embodiments of a method and system for learning and incorporating new forms in an electronic document preparation system are also capable of dynamically adapting to constantly changing fields such as tax return preparation and other kinds of document preparation. Consequently, the disclosed embodiments of a method and system for learning and incorporating new forms in an electronic document preparation system also provide a technical solution to the long standing technical problem of static and inflexible electronic document preparation systems. 
     The result is a much more accurate, adaptable, and robust method and system for learning and incorporating new forms in an electronic document preparation system, but thereby serves to bolster confidence in electronic document preparation systems. This, in turn, results in: less human and processor resources being dedicated to analyzing new forms because more accurate and efficient analysis methods can be implemented, i.e., fewer processing and memory storage assets; less memory and storage bandwidth being dedicated to buffering and storing data; less communication bandwidth being utilized to transmit data for analysis. 
     The disclosed method and system for learning and incorporating new forms in an electronic document preparation system does not encompass, embody, or preclude other forms of innovation in the area of electronic document preparation system. In addition, the disclosed method and system for learning and incorporating new forms in an electronic document preparation system is not related to any fundamental economic practice, fundamental data processing practice, mental steps, or pen and paper based solutions, and is, in fact, directed to providing solutions to new and existing problems associated with electronic document preparation systems. Consequently, the disclosed method and system for learning and incorporating new forms in an electronic document preparation system, does not encompass, and is not merely, an abstract idea or concept. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of software architecture for learning and incorporating new forms in an electronic document preparation system, in accordance with one embodiment. 
         FIG.  2    is a block diagram of a process for learning and incorporating new forms in an electronic document preparation system, in accordance with one embodiment. 
         FIG.  3    is a flow diagram of a process for learning and incorporating new forms in an electronic document preparation system, in accordance with one embodiment. 
         FIG.  4    is a block diagram of software architecture for learning and incorporating new forms in an electronic document preparation system, in accordance with one embodiment. 
     
    
    
     Common reference numerals are used throughout the FIGs. and the detailed description to indicate like elements. One skilled in the art will readily recognize that the above FIGs. are examples and that other architectures, modes of operation, orders of operation, and elements/functions can be provided and implemented without departing from the characteristics and features of the invention, as set forth in the claims. 
     DETAILED DESCRIPTION 
     Embodiments will now be discussed with reference to the accompanying FIGs., which depict one or more exemplary embodiments. Embodiments may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein, shown in the FIGs., and/or described below. Rather, these exemplary embodiments are provided to allow a complete disclosure that conveys the principles of the invention, as set forth in the claims, to those of skill in the art. 
     Herein, the term “production environment” includes the various components, or assets, used to deploy, implement, access, and use, a given application as that application is intended to be used. In various embodiments, production environments include multiple assets that are combined, communicatively coupled, virtually and/or physically connected, and/or associated with one another, to provide the production environment implementing the application. 
     As specific illustrative examples, the assets making up a given production environment can include, but are not limited to, one or more computing environments used to implement the application in the production environment such as a data center, a cloud computing environment, a dedicated hosting environment, and/or one or more other computing environments in which one or more assets used by the application in the production environment are implemented; one or more computing systems or computing entities used to implement the application in the production environment; one or more virtual assets used to implement the application in the production environment; one or more supervisory or control systems, such as hypervisors, or other monitoring and management systems, used to monitor and control assets and/or components of the production environment; one or more communications channels for sending and receiving data used to implement the application in the production environment; one or more access control systems for limiting access to various components of the production environment, such as firewalls and gateways; one or more traffic and/or routing systems used to direct, control, and/or buffer, data traffic to components of the production environment, such as routers and switches; one or more communications endpoint proxy systems used to buffer, process, and/or direct data traffic, such as load balancers or buffers; one or more secure communication protocols and/or endpoints used to encrypt/decrypt data, such as Secure Sockets Layer (SSL) protocols, used to implement the application in the production environment; one or more databases used to store data in the production environment; one or more internal or external services used to implement the application in the production environment; one or more backend systems, such as backend servers or other hardware used to process data and implement the application in the production environment; one or more software systems used to implement the application in the production environment; and/or any other assets/components making up an actual production environment in which an application is deployed, implemented, accessed, and run, e.g., operated, as discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing. 
     As used herein, the terms “computing system”, “computing device”, and “computing entity”, include, but are not limited to, a virtual asset; a server computing system; a workstation; a desktop computing system; a mobile computing system, including, but not limited to, smart phones, portable devices, and/or devices worn or carried by a user; a database system or storage cluster; a switching system; a router; any hardware system; any communications system; any form of proxy system; a gateway system; a firewall system; a load balancing system; or any device, subsystem, or mechanism that includes components that can execute all, or part, of any one of the processes and/or operations as described herein. 
     In addition, as used herein, the terms computing system and computing entity, can denote, but are not limited to, systems made up of multiple: virtual assets; server computing systems; workstations; desktop computing systems; mobile computing systems; database systems or storage clusters; switching systems; routers; hardware systems; communications systems; proxy systems; gateway systems; firewall systems; load balancing systems; or any devices that can be used to perform the processes and/or operations as described herein. 
     As used herein, the term “computing environment” includes, but is not limited to, a logical or physical grouping of connected or networked computing systems and/or virtual assets using the same infrastructure and systems such as, but not limited to, hardware systems, software systems, and networking/communications systems. Typically, computing environments are either known environments, e.g., “trusted” environments, or unknown, e.g., “untrusted” environments. Typically, trusted computing environments are those where the assets, infrastructure, communication and networking systems, and security systems associated with the computing systems and/or virtual assets making up the trusted computing environment, are either under the control of, or known to, a party. 
     In various embodiments, each computing environment includes allocated assets and virtual assets associated with, and controlled or used to create, and/or deploy, and/or operate an application. 
     In various embodiments, one or more cloud computing environments are used to create, and/or deploy, and/or operate an application that can be any form of cloud computing environment, such as, but not limited to, a public cloud; a private cloud; a virtual private network (VPN); a subnet; a Virtual Private Cloud (VPC); a sub-net or any security/communications grouping; or any other cloud-based infrastructure, sub-structure, or architecture, as discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing. 
     In many cases, a given application or service may utilize, and interface with, multiple cloud computing environments, such as multiple VPCs, in the course of being created, and/or deployed, and/or operated. 
     As used herein, the term “virtual asset” includes any virtualized entity or resource, and/or virtualized part of an actual, or “bare metal” entity. In various embodiments, the virtual assets can be, but are not limited to, virtual machines, virtual servers, and instances implemented in a cloud computing environment; databases associated with a cloud computing environment, and/or implemented in a cloud computing environment; services associated with, and/or delivered through, a cloud computing environment; communications systems used with, part of, or provided through, a cloud computing environment; and/or any other virtualized assets and/or sub-systems of “bare metal” physical devices such as mobile devices, remote sensors, laptops, desktops, point-of-sale devices, etc., located within a data center, within a cloud computing environment, and/or any other physical or logical location, as discussed herein, and/or as known/available in the art at the time of filing, and/or as developed/made available after the time of filing. 
     In various embodiments, any, or all, of the assets making up a given production environment discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing, can be implemented as one or more virtual assets. 
     In one embodiment, two or more assets, such as computing systems and/or virtual assets, and/or two or more computing environments, are connected by one or more communications channels including but not limited to, Secure Sockets Layer communications channels and various other secure communications channels, and/or distributed computing system networks, such as, but not limited to: a public cloud; a private cloud; a virtual private network (VPN); a subnet; any general network, communications network, or general network/communications network system; a combination of different network types; a public network; a private network; a satellite network; a cable network; or any other network capable of allowing communication between two or more assets, computing systems, and/or virtual assets, as discussed herein, and/or available or known at the time of filing, and/or as developed after the time of filing. 
     As used herein, the term “network” includes, but is not limited to, any network or network system such as, but not limited to, a peer-to-peer network, a hybrid peer-to-peer network, a Local Area Network (LAN), a Wide Area Network (WAN), a public network, such as the Internet, a private network, a cellular network, any general network, communications network, or general network/communications network system; a wireless network; a wired network; a wireless and wired combination network; a satellite network; a cable network; any combination of different network types; or any other system capable of allowing communication between two or more assets, virtual assets, and/or computing systems, whether available or known at the time of filing or as later developed. 
     As used herein, the term “user” includes, but is not limited to, any party, parties, entity, and/or entities using, or otherwise interacting with any of the methods or systems discussed herein. For instance, in various embodiments, a user can be, but is not limited to, a person, a commercial entity, an application, a service, and/or a computing system. 
     As used herein, the term “relationship(s)” includes, but is not limited to, a logical, mathematical, statistical, or other association between one set or group of information, data, and/or users and another set or group of information, data, and/or users, according to one embodiment. The logical, mathematical, statistical, or other association (i.e., relationship) between the sets or groups can have various ratios or correlation, such as, but not limited to, one-to-one, multiple-to-one, one-to-multiple, multiple-to-multiple, and the like, according to one embodiment. As a non-limiting example, if the disclosed electronic document preparation system determines a relationship between a first group of data and a second group of data, then a characteristic or subset of a first group of data can be related to, associated with, and/or correspond to one or more characteristics or subsets of the second group of data, or vice-versa, according to one embodiment. Therefore, relationships may represent one or more subsets of the second group of data that are associated with one or more subsets of the first group of data, according to one embodiment. In one embodiment, the relationship between two sets or groups of data includes, but is not limited to similarities, differences, and correlations between the sets or groups of data. 
     Hardware Architecture 
       FIG.  1    illustrates a block diagram of a production environment  100  for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. Embodiments of the present disclosure provide methods and systems for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. In particular, embodiments of the present disclosure receive form data related to a new form having data fields to be completed according to functions set forth in the new form. Embodiments of the present disclosure utilize multiple separate analysis techniques and/or analysis modules in conjunction with training set data to correctly learn the functions for each data field and incorporate them into the electronic document preparation system. Embodiments of the present disclosure gather training set data including previously filled forms related to the new form. Embodiments of the present disclosure utilize the multiple analysis techniques to generate a plurality of candidate functions for each data field to be learned. Embodiments of the present disclosure generate test data for each candidate function from the various analysis techniques by applying the candidate function to the training set data. Embodiments of the present disclosure compare the test data to the data values in the corresponding fields of the previously filled forms of the training set data. Embodiments of the present disclosure generate matching data indicating how closely the test data matches the values in the previously filled forms of the training set data. Embodiments of the present disclosure can identify, for each selected data field, one or more of the highly ranked candidate functions as acceptable candidate functions. An acceptable candidate function can include a candidate function that produces test data that exactly matches the training set data. Alternatively, an acceptable candidate function can include a candidate function that produces test data that matches the training set data within an acceptable error tolerance. 
     Embodiments of the present disclosure can generate results data that indicates the acceptable candidate functions for each data field of the new form. Embodiments of the present disclosure can output the results data for review by experts who can review and approve the correct functions. Additionally, or alternatively, embodiments of the present disclosure can determine when an acceptable candidate function has been found or when the new form has been entirely learned and can incorporate the new form into a user document preparation engine so that users or customers of the electronic document preparation system can utilize the electronic document preparation system to electronically prepare documents using the new form. By utilizing multiple analysis techniques to learn and incorporate new forms, efficiency of the electronic document preparation system is increased. 
     Embodiments of the present disclosure can include as analysis modules, two or more of a machine learning module, a natural language parsing module, and a historical instructions analysis module. Embodiments of the present disclosure can include other types of analysis modules than those listed above, as will be apparent to those of skill in the art in light of the present disclosure. All such other analysis modules and analysis techniques fall within the scope of the present disclosure. 
     In addition, the disclosed method and system for learning and incorporating new forms in an electronic document preparation system provides for significant improvements to the technical fields of electronic document preparation, data processing, data management, and user experience. 
     In addition, as discussed above, the disclosed method and system for learning and incorporating new forms in an electronic document preparation system provide for the processing and storing of smaller amounts of data, i.e., more efficiently acquire and analyze forms and data; thereby eliminating unnecessary data analysis and storage. Consequently, using the disclosed method and system for learning and incorporating new forms in an electronic document preparation system results in more efficient use of human and non-human resources, fewer processor cycles being utilized, reduced memory utilization, and less communications bandwidth being utilized to relay data to, and from, backend systems and client systems, and various investigative systems and parties. As a result, computing systems are transformed into faster, more efficient, and more effective computing systems by implementing the method and system for learning and incorporating new forms in an electronic document preparation system. 
     The production environment  100  includes a service provider computing environment  110 , user computing environment  140 , third party computing environment  150 , and public information computing environment  160 , for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. The computing environments  110 ,  140 ,  150 , and  160  are communicatively coupled to each other with one or more communication channels  101 , according to one embodiment. 
     The service provider computing environment  110  represents one or more computing systems such as a server, a computing cabinet, and/or distribution center that is configured to receive, execute, and host one or more electronic document preparation systems (e.g., applications) for access by one or more users, for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. The service provider computing environment  110  represents a traditional data center computing environment, a virtual asset computing environment (e.g., a cloud computing environment), or a hybrid between a traditional data center computing environment and a virtual asset computing environment, according to one embodiment. 
     The service provider computing environment  110  includes an electronic document preparation system  111 , which is configured to provide electronic document preparation services to a user. 
     According to one embodiment, the electronic document preparation system  111  can be a system that assists in preparing financial documents related to one or more of tax return preparation, invoicing, payroll management, billing, banking, investments, loans, credit cards, real estate investments, retirement planning, bill pay, and budgeting. The electronic document preparation system  111  can be a tax return preparation systems or other type of electronic document preparation system. The electronic document preparation system  111  can be a standalone system that provides financial document preparation services to users. Alternatively, the electronic document preparation system  111  can be integrated into other software or service products provided by a service provider. 
     The electronic document preparation system  111  assists users in preparing documents related to one or more forms that include data fields to be completed by the user. The data fields request data entries in accordance with specified functions. Once the electronic document preparation system has learned the functions that produce the requested data entries for the data fields, the electronic document preparation system can assist individual users in electronically completing the form. 
     In many situations, such as in tax return preparation situations, state and federal governments or other financial institutions issue new or updated versions of standardized forms each year or even several times within a single year. Each time a new form is released, the electronic document preparation system  111  may need to learn the specific functions that provide the requested data entries for each data field in the new form. If these data fields are not correctly completed, there can be serious financial consequences for users. Furthermore, if the electronic document preparation system  111  does not quickly learn and incorporate new forms into the electronic document preparation system  111 , users of the electronic document preparation system  111  may turn to other forms of financial document preparation services. In traditional electronic document preparation systems, new forms are learned and incorporated by financial professionals and/or experts manually reviewing the new forms and manually revising software instructions to incorporate the new forms. In some cases, this can be a slow, expensive, and unreliable system. Thus, the electronic document preparation system  111  in accordance with principles of the present disclosure advantageously utilizes multiple analysis techniques in conjunction with training set data in order to quickly and efficiently learn the functions related to each data field of a form and incorporate them into the electronic document preparation system  111 . The use of multiple analysis techniques helps to ensure that accurate candidate functions are found. 
     In one embodiment, the electronic document preparation system  111  includes a user interface module  112 , a form analysis module  113 , a natural language parsing module  114 , a historical instructions analysis module  115 , a machine learning module  116 , a data acquisition module  117 , and an evaluator module  118 . 
     The interface module  112  is configured to receive form data  121  related to a new form. The interface module  112  can receive the form data  121  from an expert, from a government agency, from a financial institution, or in other suitable ways. According to one embodiment, when a new form or new version of a form is released, an expert or other personnel of the electronic document preparation system  111  can upload an electronic version of the form to the interface module  112 . The interface module  112  can also receive the form data in an automated manner such as by receiving automatic updates or in another way. 
     In one embodiment, a new form can include any form to be analyzed by the electronic document preparation system  111 . In one embodiment, the new form may be new in the sense that it is a form that has been produced or released very recently. In one embodiment, the new form may also be new in the sense that it has not been previously analyzed by the by the electronic document preparation system  111 , even though the new form was not recently produced or released. In one embodiment, the new form can also be any form that will be analyzed by the electronic document preparation system  111  for any reason. In one embodiment, the new form can include a form that has been previously analyzed and/or incorporated by the electronic document preparation system  111  and that will be analyzed anew. In some cases, there is doubt as to whether the new form has been previously incorporated into the system and thus the new form may or may not have been previously analyzed or incorporated by the electronic document preparation system  111 . 
     In one embodiment, the form data  121  may be in a visual form such as a PDF or in another format that does not readily enable cataloging of the individual data fields and corresponding context data of the new form. Accordingly, the electronic document preparation system  111  includes a form analysis module  113  that is configured to analyze the form data  121  and generates structured form data  119 . The structured form data  119  can include data related the data fields, limiting values, tables, dependencies, variables, text descriptions, or other data related to the new form and its data fields. 
     The interface module  112  can also output results data  122  indicating the results of an analysis and evaluation process for finding acceptable candidate functions for the various data fields. The interface module  112  can also output learned form data related to the finalized learned functions of the new form. An expert can obtain and review the results data  122  and the learned form data from the interface module  112 . Results data  122  or other test data can also be utilized by an expert and/or an automated system to use for other purposes. For example: results data  122  or other test data can be used by electronic document preparation systems to test software instructions of the electronic document preparation system before making functionality associated with the software instructions available to the public. 
     In one embodiment, the natural language parsing module  114  is an analysis module that utilizes natural language parsing analysis techniques in order to generate natural language parsing data  123  and candidate function data  124 . 
     In one embodiment, the natural language parsing module  114  performs natural language parsing analysis on the structured form data  119 . The structured form data  119  may include text description or other context data related to each data field. The natural language parsing module  114  generates natural language parsing data  123  by performing natural language parsing analysis on the context data related to a selected data field of the structured form data  119 . The natural language parsing module  114  can identify the words used in the structured form data  119  to describe the proper function for generating a data value for the selected data field. The natural language parsing module  114  can identify keywords in the context data that describe operators such as “add”, “sum”, “subtract”, “multiply”, “divide”, “combine”, “if”, “then”, “else”, “greater”, “lesser”, “maximum”, “minimum”, or other words that suggest operators or relationships between dependencies or variables. The natural language parsing module  114  can also identify keywords that denote dependencies, variables, or other factors in a correct function for providing the data value in the selected data field. For example, the natural language parsing module  114  can identify when data values from other lines or data fields are referenced in the context of the selected data field. The natural language parsing module  114  can identify when other forms or data fields from other forms are being referenced in the context of the selected data field. The natural language parsing module  114  can analyze the relationships between the operators and dependencies in order to generate one or more candidate functions for the selected data field. 
     In one embodiment, the natural language parsing module  114  generates candidate function data  124  that includes the one or more candidate functions based on a natural language parsing analysis of the context data related to the selected data field. In some cases, the candidate function data may include only a single candidate function. In other cases, ambiguities in the context data may cause the natural language parsing module  114  to generate multiple candidate functions for the selected data field. 
     In one example, the electronic document preparation system  111  is learning an appropriate function for line 5 of a new tax form. The text in the tax form related to line 5 says “This value is the lesser of $6000 and the sum of line 3 and line 4.” The natural language parsing module  114  generates natural language parsing data  123  by analyzing the text related to line 5. The natural language parsing module identifies keywords such as the constant “6000”, the relationship indicator “lesser”, the operator “sum”, and the dependencies “line 3” and “line 4”. From these keywords and other knowledge of the syntax of English sentences, the natural language parsing module  114  generates one or more candidate functions for line 5 of the new tax form. 
     In one embodiment, the natural language parsing module  114  can use natural language parsing techniques such as semantic parsing in order to determine dependencies, operators, syntax, and relationships between the dependencies and operators in order to generate candidate functions. The natural language parsing module  114  then generates one or more candidate functions based on the natural language parsing analysis of the context data related to the selected data field. 
     In one embodiment, when the natural language parsing module  114  has generated candidate function data  124  including one or more candidate functions for the selected data field of a new form, the natural language parsing module  114  passes the candidate function data  124  to the evaluator module  118 . 
     In one embodiment, the natural language parsing module  114  can improve in accuracy based on feedback from the evaluator module  118 . For example, when the evaluator module  118  indicates that one or more candidate functions are acceptable or not acceptable, the natural language parsing module can learn and increase the accuracy of the natural language parsing analysis for future analyses based on the feedback from the evaluator module  118 . Additionally, or alternatively, the natural language parsing module  114  can receive feedback, updates, revisions, etc. from systems other than the evaluator module  118 , or even from experts that help maintain the electronic document preparation system  111 . 
     Those of skill in the art will recognize, in light of the present disclosure, that the natural language parsing module  114  can utilize many other natural language parsing techniques than those described herein. All such other natural language parsing analysis techniques fall within the scope of the present disclosure. 
     In one embodiment, the historical analysis module  115  is an analysis module that utilizes a historical instructions analysis technique to generate candidate functions for a selected data field of the new form. 
     In one embodiment, the electronic document preparation system  111  has access to historical electronic document preparation systems that previously generated data values for data fields of historical forms that are related to the new form. The historical electronic document preparation system can include previous versions of the historical document preparation system  111 . The historical electronic document preparation system includes software instructions for calculating the data values of data fields of various forms, including forms related to the new form. By analyzing the previous functions used by the historical electronic document preparation system for generating data values for a data field corresponding to the selected field of the new form, the historical instructions analysis module  115  can generate one or more candidate functions for the selected data field of the new form. 
     In one embodiment, the historical instructions analysis module  115  analyzes the software instructions and generates the historical document instruction data  125 . The historical document instructions data  125  can include dependency data that identifies one or more dependencies used by the historical electronic document preparation system in calculating data values for a data field of a previous form related to the selected data field of the new form. The historical document instructions data  125  can also indicate operators and relationships between the dependencies. 
     In one embodiment, the historical instructions analysis module  115  generates candidate function data  126  indicating one or more candidate functions for the selected data field of the new form. In one embodiment, the historical instructions analysis module  115  may generate candidate function data  126  that includes a candidate function that is identical to a function used in the historical software instructions for generating a data value for a data field in a previous form related to the new form. Alternatively, or additionally, the historical instructions analysis module  115  may generate candidate function data  126  that includes more than one candidate functions due to inability to decisively determine the function used in the historical software instructions. 
     In one embodiment, the historical instructions analysis module  115  generates an abstract syntax tree when analyzing the historical software instructions related to the historical electronic document preparation system. The historical instructions analysis module  115  can generate historical document instructions data  125  by parsing the abstract syntax tree. The parsing of the abstract syntax tree can provide dependency data related to dependencies in the function for generating the data value in the data field of the historical form related to the new form. The parsing of the abstract syntax tree can also result in one or more candidate functions to be included in the candidate function data  126 . 
     Those of skill in the art will recognize, in light of the present disclosure, that the historical instructions analysis module  115  can utilize many other historical instruction analysis techniques than those described herein. All such other historical instructions analysis techniques fall within the scope of the present disclosure. 
     In one embodiment, after the historical instructions analysis module  115  has generated candidate function data  126  including one or more candidate functions, the historical instructions analysis module  115  provides the candidate function data to the evaluator module  118 . 
     In one embodiment, the machine learning module  116  is an analysis module that uses machine learning analysis techniques to generate candidate functions for the selected data field of the new form. 
     In one embodiment, the machine learning module  116  generates a large number of candidate functions based on dependency data that includes dependencies related to the selected data field. The machine learning module  116  can receive dependency data from the historical instructions analysis module  115 , the natural language parsing module  114 , or the form analysis module  113 . The machine learning module  116  utilizes the dependency data to generate candidate functions as part of a machine learning process to generate more accurate candidate functions. 
     In one embodiment, the machine learning module  116  generates machine learning data  127 . The machine learning module  116  utilizes the machine learning data  127  to generate a plurality of candidate functions  128 . 
     In one embodiment, the machine learning module  116  generates candidate functions for the selected data field based on the dependency data and one or more operators from a superset of operators. Thus, the candidate functions generated by the machine learning module  116  can include dependencies from the dependency data and operators from a superset of operators. 
     In one embodiment, the machine learning module  116  utilizes training set data  129  to test the candidate functions. In particular, the machine learning module  116  generates a plurality of candidate functions, generates test data for each candidate function by applying the candidate function to the training set data, then compares the test data to the training set data in order to determine which of the candidate functions are most accurate. The machine learning module  116  can continue generating additional candidate functions for the selected data field until one or more candidate functions is found that matches the training set data  129  within a threshold error tolerance. 
     In one embodiment, the machine learning module  116  is able generate and test thousands of candidate functions very rapidly in successive iterations. The machine learning module  116  can utilize one or more algorithms to generate candidate functions based on many factors. The machine learning module  116  can generate new candidate functions based on previously tested candidate functions. The machine learning module  116  can utilize analysis of the structured the form data  119 , the historical document instruction data  125 , the natural language parsing data  123 , and/or other data to learn the likely dependencies/components of the correct function for a particular data field and can generate candidate functions based on these likely components. 
     In one embodiment, the electronic document preparation system  111  uses the data acquisition module  117  to acquire the training set data  129 . The training set data  129  includes previously prepared documents for a large number of previous users of the electronic document preparation system  111  or fictitious users of the electronic document preparation system  111 . The training set data  129  can be used by the machine learning module  116  and the evaluator module  118  in order to learn and incorporate the new form into the electronic document preparation system  111 . 
     In one embodiment, the training set data  129  can include historical data  130  related to previously prepared documents or previously filled forms of a large number of users. The historical data  130  can include, for each of a large number of previous users of the electronic document preparation system  111 , a respective completed copy of the new form or a completed copy of a form related to the new form. The completed copies of the form include data values in the data fields. 
     In one embodiment, the training set data  129  can include fabricated data  131 . The fabricated data  131  can include copies of the new form that were previously filled using fabricated data. The fabricated data can include real data from previous users or other people but that has been scrubbed of personal identifiers or otherwise altered. 
     In one embodiment, the historical data  130  and/or the fabricated data  131  also includes all of the related data used to complete the forms and to prepare the historical document. The historical data  130  can include previously prepared documents that include or use the completed form and which were filed with and/or approved by a government or other institution. In this way, the historical data  130  can be assured in large part to be accurate and properly prepared, though some of the data related to the previously prepared documents may include errors. Typically, the functions for computing or obtaining the proper data entry for a data field of a form can include data values from other forms resources related to each other and sometimes complex ways. Thus, the historical data  130  can include, for each historical user in the training set data, a final version of a previously prepared document, the form that is related to the new form to be learned, other forms used to calculate the values for the related form, and other sources of data for completing the related form. 
     In one embodiment, the electronic document preparation system  111  is a financial document preparation system. In this case, the historical data  130  can include historical financial data. The historical financial data can include, for each historical user of the electronic document preparation system  111 , information, such as, but not limited to, a name of the user, a name of the user&#39;s employer, an employer identification number (EID), a job title, annual income, salary and wages, bonuses, a Social Security number, a government identification, a driver&#39;s license number, a date of birth, an address, a zip code, home ownership status, marital status, W-2 income, an employer&#39;s address, spousal information, children&#39;s information, asset information, medical history, occupation, information regarding dependents, salary and wages, interest income, dividend income, business income, farm income, capital gain income, pension income, IRA distributions, education expenses, health savings account deductions, moving expenses, IRA deductions, student loan interest, tuition and fees, medical and dental expenses, state and local taxes, real estate taxes, personal property tax, mortgage interest, charitable contributions, casualty and theft losses, unreimbursed employee expenses, alternative minimum tax, foreign tax credit, education tax credits, retirement savings contribution, child tax credits, residential energy credits, and any other information that is currently used, that can be used, or that may be used in the future, in a financial document preparation system or in the preparation of financial documents such as a user&#39;s tax return, according to various embodiments. 
     In one embodiment, the data acquisition module  117  is configured to obtain or retrieve historical data  130  from a large number of sources. The data acquisition module  117  can retrieve, from databases of the electronic document preparation system  111 , historical data  130  that has been previously obtained by the electronic document preparation system  111  from a plurality of third-party institutions. Additionally, or alternatively, the data acquisition module  117  can retrieve the historical data  130  afresh from the third-party institutions. 
     In one embodiment, the data acquisition module  117  can also supply or supplement the historical data  130  by gathering pertinent data from other sources including the third party computing environment  150 , the public information computing environment  160 , the additional service provider systems  137 , data provided from historical users, data collected from user devices or accounts of the electronic document preparation system  111 , social media accounts, and/or various other sources to merge with or supplement historical data  130 , according to one embodiment. 
     The data acquisition module  117  can gather additional data including historical financial data and third party data. For example, the data acquisition module  117  is configured to communicate with additional service provider systems  137 , e.g., a tax return preparation system, a payroll management system, or other electronic document preparation system, to access financial data  136 , according to one embodiment. The data acquisition module  117  imports relevant portions of the financial data  136  into the electronic document preparation system  111  and, for example, saves local copies into one or more databases, according to one embodiment. 
     In one embodiment, the additional service provider systems  137  include a personal electronic document preparation system, and the data acquisition module  117  is configured to acquire financial data  136  for use by the electronic document preparation system  111  in learning and incorporating the new or updated form into the electronic document preparation system  111 . Because the services provider provides both the electronic document preparation system  111  and, for example, the additional service provider systems  137 , the service provider computing environment  110  can be configured to share financial information between the various systems. By interfacing with the additional service provider systems  137 , the data acquisition module  117  can supply or supplement the historical data  130  from the financial data  136 . The financial data  136  can include income data, investment data, property ownership data, retirement account data, age data, data regarding additional sources of income, marital status, number and ages of children or other dependents, geographic location, and other data that indicates personal and financial characteristics of users of other financial systems, according to one embodiment. 
     The data acquisition module  117  is configured to acquire additional information from various sources to merge with or supplement the training set data  129 , according to one embodiment. For example, the data acquisition module  117  is configured to gather from various sources historical data  130 . For example, the data acquisition module  117  is configured to communicate with additional service provider systems  137 , e.g., a tax return preparation system, a payroll management system, or other financial management system, to access financial data  136 , according to one embodiment. The data acquisition module  117  imports relevant portions of the financial data  136  into the training set data  129  and, for example, saves local copies into one or more databases, according to one embodiment. 
     The data acquisition module  117  is configured to acquire additional financial data from the public information computing environment  160 , according to one embodiment. The training set data can be gathered from public record searches of tax records, public information databases, property ownership records, and other public sources of information. The data acquisition module  117  can also acquire data from sources such as social media websites, such as Twitter, Facebook, LinkedIn, and the like. 
     The data acquisition module  117  is configured to acquire data from third parties, according to one embodiment. For example, the data acquisition module  117  requests and receives third party data  126  from the third party computing environment  150  to supply or supplement the training set data  129 , according to one embodiment. In one embodiment, the third party computing environment  150  is configured to automatically transmit financial data to the electronic document preparation system  111  (e.g., to the data acquisition module  117 ), to be merged into training set data  129 . The third party computing environment  150  can include, but is not limited to, financial service providers, state institutions, federal institutions, private employers, financial institutions, social media, and any other business, organization, or association that has maintained financial data, that currently maintains financial data, or which may in the future maintain financial data, according to one embodiment. 
     In one embodiment, the electronic document preparation system  111  utilizes the machine learning module  116  to generate candidate functions in conjunction with training set data  129 . The machine learning module  116  generates a plurality of candidate functions for each data field of the new form to be learned and applies the candidate functions to the training set data  129  in order to find candidate functions that produces data values that more closely within a threshold error tolerance match the corresponding data values in the completed data fields of the training set data  129 . The machine learning module  116  can continue to generate new candidate functions until the machine learning module  116  finds a candidate function that, when applied to the training set data  129 , produces data values that match the data values in the completed data fields of the training set data  129 . 
     In one embodiment, the dependency data utilized by the machine learning module  116  can include possible dependencies such as one or more data values from other data fields of the new form, one or more data values from another related form or worksheet, one or more constants, or many other kinds of possible dependencies that can be included in a correct function for a particular data field. 
     In one embodiment, the machine learning module  116  generates candidate functions based on the dependency data  129  and one or more operators selected from a set or superset of operators. The operators can include arithmetic operators such as addition, subtraction, multiplication, or division operators. The operators can include logical operators such as if-then operators. The operators can include existence condition operators that depend on the existence of a data value in another data field of new form, in a form other than the new form, or in some other location or data set. The operators can include string comparisons. Each candidate function can include one or more of the operators operating on one or more of the possible dependencies. 
     In one embodiment, when the machine learning module  116  has generated candidate function data  128  including one or more candidate functions, the machine learning module  116  provides the candidate function data  128  to the evaluator module  118 . In one embodiment, the machine learning module  116  only passes to the evaluator module  118  a selected number of the candidate functions that most closely match the training set data  129 . In some cases, the machine learning module  116  may pass only a single candidate function to the evaluator module  118 . In one embodiment, the machine learning module  116  may provide candidate function data  128  that includes a large number of candidate functions to the evaluator module  118 . 
     In one embodiment, the machine learning module  116  utilizes one or more algorithms, analysis techniques, or processes that can assist in the machine learning process for generating candidate functions for the selected data field. In one embodiment, the machine learning module  116  utilizes genetic programming techniques as part of the machine learning process. In one embodiment, the machine learning module  116  utilizes genetic algorithms in the machine learning process. 
     Those of skill in the art will recognize, in light of the present disclosure, that the machine learning module  116  can utilize many other types of machine learning analysis techniques other than those described above. All such other machine learning analysis techniques fall within the scope of the present disclosure. 
     In one embodiment, the electronic document preparation system  111  utilizes the evaluator module  118  to evaluate the candidate function is generated by each of the natural language parsing module  114 , the historical instructions analysis module  115 , and the machine learning module  116 . The evaluator module  118  can evaluate, for each selected data field of the new form, which of the candidate functions is an acceptable candidate function. 
     In one embodiment, the evaluator module  118  receives candidate function data  124  from the natural language parsing module  114 , candidate function data  126  from the historical instructions analysis module  115 , and candidate function data  128  from the machine learning module  116 . Each of the candidate function data  124 ,  126 , and  128  can include one or more candidate functions. The candidate function data  124  can include a first set of candidate functions generated by the natural language parsing module  114 . The candidate function data  126  can include a second set of candidate functions generated by the historical instructions analysis module  115 . The candidate function data  128  can include a third set of candidate functions generated by the machine learning module  116 . Each set of candidate functions can include one or more candidate functions. 
     In one embodiment, the evaluator module  118  generates combined candidate function data  132  by combining the candidate function data  124 , the candidate function data  126 , and the candidate function data  128 . Thus, the combined candidate function data  132  includes candidate functions from each of the natural language parsing module  114 , the historical instructions analysis module  115 , and the machine learning module  116 . 
     In one embodiment, the evaluator module  118  evaluates the accuracy of each of the candidate functions in the combined candidate function data  132 . In particular, the evaluator module  116  generates test data  133  for each candidate function in the combined candidate function data  132 . In particular, the evaluator module  118  generates the test data  133  by applying each candidate function to the training set data  129 . In other words, for a selected data field of the new form, the evaluator module  118  generates test data  133  by retrieving the data values from the training set data  129  that correspond to the dependencies in the candidate functions and generates test data values by plugging the dependencies into the candidate functions. The dependencies can include constants, values from other data fields in the previously prepared forms, values from data fields in other forms, values from data fields in worksheets used to prepare the previously filled forms, or other kinds of dependencies. Thus, the evaluator module  118  generates test data by pulling data values from the training set data  129  that correspond to the dependencies in the candidate functions and plugging them into the candidate functions. 
     In one embodiment, the evaluator module  118  generates matching data  134  for each candidate function by comparing the test data  133  to the training set data  129 . In particular, the test data  133  will include, for each candidate function, a test value. The evaluator module  118  generates matching data  134  by comparing the test values in the test data  133  to the data values in the data fields of the previously filled forms that correspond to the selected data field of the new form for which the candidate functions were generated. The matching data  134  indicates, for each candidate function, how closely the test values of the test data  133  match the data values in the data field of the previously filled forms that corresponds to the selected data field in the new form. 
     In one embodiment, the evaluator module  118  generates ranking data  135  that ranks each of the candidate functions based on how well the respective test data  133  matches the training set data  129 . In particular, the matching data  134  lists each candidate function in order of how accurate the candidate function is. The accuracy of the candidate function indicates how well the test data generated by the candidate function matched the training set data  129 . 
     In one embodiment, generating ranking data  134  includes generating accuracy scores based on the matching data  134 . The accuracy scores can include scores between 0 and 1 and can also include an indicator of which analysis module or analysis techniques were used to generate the scores. 
     In one embodiment, the evaluator module  118  can select one or more of the most highly ranked candidate functions as acceptable candidate functions for the selected data field. In one embodiment, the evaluator module  118  selects only the highest ranked candidate function as an acceptable candidate function. Alternatively, the evaluator module  118  can select multiple candidate functions as being acceptable candidate functions for the selected data field. In one embodiment, the evaluator module  118  only selects a candidate function as an acceptable candidate function if it&#39;s test data  133  matches the training set data  129  within a threshold error tolerance. 
     In one embodiment, the evaluator module  118  generates results data  122  that indicates one or more of the ranking data  135  and any candidate functions that are selected as acceptable candidate functions. The results data  122  can include the list of all ranked candidate functions. Alternatively, the results data  122  may include only selected number of the highest ranked candidate functions. Alternatively, the results data  122  can include only results data having test data  133  that matches the training set data  129  within a threshold error. 
     In one embodiment, it is possible that one or more of the analysis modules will not provide a candidate function for a selected data field of the new form. In this case, the evaluator module will still rank the available candidate functions and determine if one or more of the candidate functions is an acceptable candidate function. 
     In one embodiment, the form analysis module  113  can compare the structured form data  119  to the historical structured form data to determine if the description or context data related to a selected data field of the new form is identical to the description or context data related to a corresponding data field in a previous version of the form. If the description or context data related to the selected data field of the new form is not identical to the description or context data related to the corresponding data field in the previous version of the form, then the evaluator module  118  can discard candidate functions from the machine learning module  116  and the historical instructions analysis module  115 . In this case, the ranking data  135  includes only solutions from the natural language parsing module  114 . If the description or context data related to the selected field of the new form is identical to the description or context data related to the corresponding data field in the previous version of the form, then the evaluator module  118  ranks all of the candidate functions from all the analysis modules. 
     In one embodiment, if two or more candidate functions have identical rankings, then the evaluator module  118  can give priority to candidate functions generated by one of the analysis modules over candidate functions generated by the other analysis modules. For example, the evaluator may give priority to candidate functions generated by the natural language parsing module  114  over candidate functions generated by the historical instructions analysis module  115  and the machine learning module  116 . The evaluator module  118  may also give priority to candidate functions generated by the historical instructions analysis module  115  over candidate functions generated by the machine learning module  116 . 
     In one embodiment, the evaluator module  118  identifies acceptable functions for the selected data fields of the new form one at a time. In other words, if the form data  121  indicates that a form has 10 data fields to be learned, the evaluator module  118  will begin by learning the correct function for a first data field of the new form. The first data field is not necessarily the data field that appears first in the form. In particular, the evaluator module  118  will cause a machine learning module  116 , the historical instructions analysis module  115 , and the natural language parsing module  114  to each generate candidate function data including one or more candidate functions for the first data field of the new form. The evaluator module  118  will generate combined function data  132  from the candidate function data  124 ,  126 , and  128 . The evaluator module  118  will then generate test data  133 , matching data  134 , and ranking data  135  for the first data field as described previously. The evaluator module  118  will determine one or more acceptable functions for the first data field. The evaluator module  118  will then cause the natural language parsing module  114 , the historical instructions analysis module  115 , and the machine learning module  116  to generate candidate function data  124 ,  126  and  128  each including one or more candidate functions for a second data field of the new form. The evaluator module  118  will again generate combined candidate function data  132 , test data  133 , matching data  134 , and ranking data  135 . The evaluator module  118  will select one or more candidate functions as acceptable functions for the second data field of the new form. The evaluator module  118  will continue in this manner until all respective accepted candidate functions have been found for each data field of the new form. 
     It is expected that the training set data  129  may include some errors in the completed data values for the data field under test. Thus, an acceptable function may result in test data  133  that does not perfectly match the completed data fields in the training set data  129 . Thus, the correct candidate function will result in test data that matches the training set data within an error tolerance. 
     In one embodiment, the evaluator module  118  generates confidence score data  122  based on the matching data  134 . The confidence score data can indicate, for each candidate function, how confident the machine learning module  116  is that the candidate function is a correct function. The confidence score data can be based on the matching data  134  and recurrence data. 
     In one embodiment, the evaluator module  118  generates results data  122 . The results data  122  can include matching data  134  and/or confidence score data for each candidate function that has been tested for particular data field of the new form to be learned. Alternatively, the results data  122  can include data indicating that one or more of the candidate functions is possibly correct based on the matching data  134  and/or the confidence score. Alternatively, the results data  122  can indicate that an acceptable candidate function has been found. The results data  122  can also indicate what the acceptable candidate function is. The results data  122  can be provided to the interface module  112 . The interface module  112  can output the results data  122  to an expert or other personnel for review and/or approval. 
     In one embodiment, the evaluator module  118  outputs results data  122  indicating that a candidate function has been found that is likely correct. The results data  122  can indicate what the candidate function is, the matching data  134  or confidence score data related to the candidate function, or any other information that will be useful for review by an expert. The evaluator module  118  can cause the interface module  112  to prompt an expert to review the results data  122  and to approve the candidate function as acceptable or to indicate that the candidate function is not acceptable and that the analysis modules should continue generating candidate functions for the data field currently under test. The evaluator module  118  awaits input from the expert or other personnel approving the candidate function. If the candidate function is approved by the expert or other personnel, the evaluator module  118  determines that an acceptable function has been found and moves on to finding an acceptable candidate function the next data field of the new form. 
     In one embodiment, the evaluator module  118  does not wait for the approval of an expert before determining that the correct candidate function test and found. Instead, when the evaluator module  118  determines that an acceptable function has been found based on the matching data, the confidence score data, and/or other criteria, the evaluator module  118  moves onto the next data field of the new form under test. 
     In one embodiment, when the evaluator module  118  has learned an acceptable function for each data field of the new form, then the evaluator module  118  generates learned form data. The learned form data indicates that the new form has been learned. The learned form data can also indicate what the acceptable functions are for each of the data fields of the new form. The interface module  112  can output the learned form data for review and/or approval by expert. In one embodiment, once the expert or other personnel has approved the learned form data, the evaluator module  118  ceases analysis of the new form and awaits form data  121  related to another form to be learned. 
     In one embodiment, the user computing environment  140  is a computing environment related to a user of the electronic document preparation system  111 . The user computing environment  140  includes input devices  141  and output devices  142  for communicating with the user, according one embodiment. The input devices  141  include, but are not limited to, keyboards, mice, microphones, touchpads, touchscreens, digital pens, and the like. The output devices  142  include, but are not limited to, speakers, monitors, touchscreens, and the like. The output devices  142  can display data related to the preparation of the financial document. 
     In one embodiment, the electronic document preparation system  111  is a tax return preparation system. Preparing a single tax return can require many government tax forms, many internal worksheets use by the tax return preparation system in preparing a tax return, W-2 forms, and many other types of forms or financial data pertinent to the preparation of a tax return preparation system. For each tax return that is prepared for a user, the tax return preparation system maintains copies of all of the various tax forms, internal worksheets, data provided by the user and any other relevant financial data used to prepare the tax return. Thus, the tax return preparation system maintains historical tax return data related to millions of previously prepared tax returns. The tax return preparation system can utilize the historical tax return data to gather or generate relevant training set data  129  that can be used by the evaluator module  118 . 
     In one embodiment, a state or federal agency releases a new tax form that is simply a new version of a previous tax form during tax return preparation season. an expert upload form data  121  to the interface module  112 . The form data  121  corresponds to an electronic version of the new tax form. Many or all of the data fields of the new tax form may be similar to those of the previous tax form. The evaluator module  118  begins to learn the new tax form starting with a first selected data field of the new tax form. The first selected data field corresponds to a first selected line of the new tax form, not necessarily line 1 of the new tax form. The evaluator module  118  causes the data acquisition module  117  to gather training set data  129  that includes a large number of previously prepared tax returns and the tax related data associated with the previously prepared tax returns. In particular, the training set data  129  will include previously prepared tax returns that use the previous version of the new form. The evaluator module  118  generates a plurality of candidate functions for the first selected data field and applies them to the training set data  129 . For each candidate function, the machine learning module generates matching data  134  and/or confidence score data indicating how well the test data  133  matches the training set data  129 . The evaluator module  118  generates results data  122  indicating the matching data  134  and/or the confidence score data of one or more of the candidate functions. The results data  122  can also indicate whether a candidate function is deemed to be an acceptable function for the first selected data field. 
     The evaluator module  118  moves onto a second selected data field after an acceptable function has been found for the first selected data field. The data fields correspond to selected lines of the new tax form. The evaluator module  118  continues in this manner until all selected data fields of the new tax form have been found. When all selected data fields of the new tax form have been learned, the evaluator module  118  generates learned form data indicating that all selected fields of the new form have been learned. The interface module  112  can present results data  122  or learned form data for review and/or approval by an expert or other personnel. Alternatively, the evaluator module  118  can move from one data field to the next data field without approval or review by an expert. 
     Embodiments of the present disclosure address some of the shortcomings associated with traditional electronic document preparation systems that do not adequately learn and incorporate new forms into the electronic document preparation system. An electronic document preparation system in accordance with one or more embodiments provides more reliable financial management services by utilizing machine learning and training set data to learn and incorporate new forms into the electronic document preparation system. The various embodiments of the disclosure can be implemented to improve the technical fields of data processing, data collection, resource management, and user experience. Therefore, the various described embodiments of the disclosure and their associated benefits amount to significantly more than an abstract idea. In particular, by utilizing machine learning to learn and incorporate new forms in the electronic document preparation system, electronic document preparation system can more efficiently learn and incorporate new forms into the electronic document preparation system. 
     Process 
       FIG.  2    illustrates a functional flow diagram of a process  200  for learning and incorporating new forms in an electronic document preparation system, in accordance with one embodiment. 
     At block  202  the user interface module  112  generates structured form data by analyzing form data related to a new form having a plurality of data fields, the structured form data identifying the data fields of the new form and context data related to context describing the data fields of the new f that expect data values in accordance with specific functions, according to one embodiment. From block  202  the process proceeds to block  204 . 
     At block  204  the form analysis module  113  generates structured form data from by analyzing and structuring the form data related to the new form, according to one embodiment. From block  204  the process proceeds to blocks  206 . 
     At block  206 , the natural language parsing module  114 , the historical instructions analysis module  115 , and the machine learning module  116  each generate candidate function data including one or more candidate functions for a first selected data field of the new form, according to one embodiment. From block  206  the process proceeds to block  208   
     At block  208  the evaluation module  118  generates combined candidate function data by combining the candidate function data from the natural language parsing module  114 , the historical instructions analysis module  115 , and the machine learning module  116 , according to one embodiment. From block  208  the process proceeds to block  210 . 
     At block  210  the evaluation module  118  generates test data by applying the candidate functions to training set data including a plurality of previously filled forms each having a data value in a data field that corresponds to the first selected field of the new form, according to one embodiment. From block  210  the process proceeds to block  212 . 
     At block  212  the evaluation module  118  generates matching data indicating how closely the test data for each candidate function matches the training set data, according to one embodiment. From block  212  the process proceeds to block  214 . 
     At block  214 , the evaluation module  118  generates ranking data based on the matching data, according to one embodiment. From block  214  the process proceeds to block  216 . 
     At block  216  the evaluation module  118  generates results data indicating one or more acceptable candidate functions for the first selected data field of the new form, according to one embodiment. From block  216  the process proceeds to block  218 . 
     At block  218 , the interface module  112  outputs the results data for review by an expert or other personnel, according to one embodiment. 
     Although a particular sequence is described herein for the execution of the process  200 , other sequences can also be implemented. For example, in one embodiment, blocks  206 - 218  can be repeated for each additional selected data field of the new form until each selected data field of the new form has a corresponding acceptable candidate function. 
       FIG.  3    illustrates a flow diagram of a process  300  for learning and incorporating new forms in an electronic document preparation system, according to various embodiments. 
     In one embodiment, process  300  for learning and incorporating new forms in an electronic document preparation system begins at BEGIN  302  and process flow proceeds to GENERATE STRUCTURED FORM DATA BY ANALYZING FORM DATA RELATED TO A NEW FORM HAVING A PLURALITY OF DATA FIELDS, THE STRUCTURED FORM DATA IDENTIFYING THE DATA FIELDS OF THE NEW FORM AND CONTEXT DATA RELATED TO CONTEXT DESCRIBING THE DATA FIELDS OF THE NEW FORM  304 . 
     In one embodiment, at GENERATE STRUCTURED FORM DATA BY ANALYZING FORM DATA RELATED TO A NEW FORM HAVING A PLURALITY OF DATA FIELDS, THE STRUCTURED FORM DATA IDENTIFYING THE DATA FIELDS OF THE NEW FORM AND CONTEXT DATA RELATED TO CONTEXT DESCRIBING THE DATA FIELDS OF THE NEW FORM  304  process  300  for learning and incorporating new forms in an electronic document preparation system generates structured form data by analyzing form data related to a new form having a plurality of data fields, the structured form data identifying the data fields of the new form and context data related to context describing the data fields of the new form, according to one embodiment. 
     In one embodiment, once process  300  for learning and incorporating new forms in an electronic document preparation system generates structured form data by analyzing form data related to a new form having a plurality of data fields, the structured form data identifying the data fields of the new form and context data related to context describing the data fields of the new form at GENERATE STRUCTURED FORM DATA BY ANALYZING FORM DATA RELATED TO A NEW FORM HAVING A PLURALITY OF DATA FIELDS, THE STRUCTURED FORM DATA IDENTIFYING THE DATA FIELDS OF THE NEW FORM AND CONTEXT DATA RELATED TO CONTEXT DESCRIBING THE DATA FIELDS OF THE NEW FORM  304  process flow proceeds to GATHER TRAINING SET DATA RELATED TO PREVIOUSLY FILLED FORMS, EACH PREVIOUSLY FILLED FORM HAVING COMPLETED DATA FIELDS THAT EACH CORRESPOND TO A RESPECTIVE DATA FIELD OF THE NEW FORM  306 . 
     In one embodiment, at GATHER TRAINING SET DATA RELATED TO PREVIOUSLY FILLED FORMS, EACH PREVIOUSLY FILLED FORM HAVING COMPLETED DATA FIELDS THAT EACH CORRESPOND TO A RESPECTIVE DATA FIELD OF THE NEW FORM  306 , process  300  for learning and incorporating new forms in an electronic document preparation system gathers training set data related to previously filled forms, each previously filled form having completed data fields that each correspond to a respective data field of the new form, according to one embodiment. 
     In one embodiment, once process  300  for learning and incorporating new forms in an electronic document preparation system gathers training set data related to previously filled forms, each previously filled form having completed data fields that each correspond to a respective data field of the new form at GATHER TRAINING SET DATA RELATED TO PREVIOUSLY FILLED FORMS, EACH PREVIOUSLY FILLED FORM HAVING COMPLETED DATA FIELDS THAT EACH CORRESPOND TO A RESPECTIVE DATA FIELD OF THE NEW FORM  306 , process flow proceeds to GENERATE, FOR A FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, FIRST CANDIDATE FUNCTION DATA INCLUDING A FIRST SET OF CANDIDATE FUNCTIONS BY PERFORMING A MACHINE LEARNING PROCESS  308 . 
     In one embodiment, at GENERATE, FOR A FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, FIRST CANDIDATE FUNCTION DATA INCLUDING A FIRST SET OF CANDIDATE FUNCTIONS BY PERFORMING A MACHINE LEARNING PROCESS  308 , process  300  for learning and incorporating new forms in an electronic document preparation system generates, for a first selected data field of the plurality of data fields of the new form, first candidate function data including a first set of candidate functions by performing a machine learning process, according to one embodiment. 
     In one embodiment, once process  300  for learning and incorporating new forms in an electronic document preparation system generates, for a first selected data field of the plurality of data fields of the new form, first candidate function data including a first set of candidate functions by performing a machine learning process at GENERATE, FOR A FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, FIRST CANDIDATE FUNCTION DATA INCLUDING A FIRST SET OF CANDIDATE FUNCTIONS BY PERFORMING A MACHINE LEARNING PROCESS  308 , process flow proceeds to GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, SECOND CANDIDATE FUNCTION DATA INCLUDING A SECOND SET OF CANDIDATE FUNCTIONS BY PERFORMING A NATURAL LANGUAGE PARSING PROCESS ON THE CONTEXT DATA RELATED TO THE FIRST SELECTED DATA FIELD  310 . 
     In one embodiment, at GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, SECOND CANDIDATE FUNCTION DATA INCLUDING A SECOND SET OF CANDIDATE FUNCTIONS BY PERFORMING A NATURAL LANGUAGE PARSING PROCESS ON THE CONTEXT DATA RELATED TO THE FIRST SELECTED DATA FIELD  310 , process  300  for learning and incorporating new forms in an electronic document preparation system generates, for the first selected data field of the plurality of data fields of the new form, second candidate function data including a second set of candidate functions by performing a natural language parsing process on the context data related to the first selected field, according to one embodiment. 
     In one embodiment, once process  300  for learning and incorporating new forms in an electronic document preparation system generates, for the first selected data field of the plurality of data fields of the new form, second candidate function data including a second set of candidate functions by performing a natural language parsing process on the context data related to the first selected field at GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, SECOND CANDIDATE FUNCTION DATA INCLUDING A SECOND SET OF CANDIDATE FUNCTIONS BY PERFORMING A NATURAL LANGUAGE PARSING PROCESS ON THE CONTEXT DATA RELATED TO THE FIRST SELECTED DATA FIELD  310 , process flow proceeds to GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, THIRD CANDIDATE FUNCTION DATA INCLUDING A THIRD SET OF CANDIDATE FUNCTIONS BY ANALYZING HISTORICAL SOFTWARE INSTRUCTIONS FOR PROVIDING A DATA VALUE FOR A DATA FIELD OF A HISTORICAL FORM RELATED TO THE NEW FORM  312 . 
     In one embodiment, at GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, THIRD CANDIDATE FUNCTION DATA INCLUDING A THIRD SET OF CANDIDATE FUNCTIONS BY ANALYZING HISTORICAL SOFTWARE INSTRUCTIONS FOR PROVIDING A DATA VALUE FOR A DATA FIELD OF A HISTORICAL FORM RELATED TO THE NEW FORM  312  the process  300  generates, for the first selected data field of the plurality of data fields of the new form, third candidate function data including a third set of candidate functions by analyzing historical software instructions for providing a data value for a data field of a historical form related to the new form, according to one embodiment. 
     In one embodiment, once process  300  generates, for the first selected data field of the plurality of data fields of the new form, third candidate function data including a third set of candidate functions by analyzing historical software instructions for providing a data value for a data field of a historical form related to the new form at GENERATE, FOR THE FIRST SELECTED DATA FIELD OF THE PLURALITY OF DATA FIELDS OF THE NEW FORM, THIRD CANDIDATE FUNCTION DATA INCLUDING A THIRD SET OF CANDIDATE FUNCTIONS BY ANALYZING HISTORICAL SOFTWARE INSTRUCTIONS FOR PROVIDING A DATA VALUE FOR A DATA FIELD OF A HISTORICAL FORM RELATED TO THE NEW FORM  312 , process flow proceeds to GENERATE, FOR EACH CANDIDATE FUNCTION OF THE FIRST, SECOND, AND THIRD SETS, TEST DATA BY APPLYING THE CANDIDATE FUNCTION TO THE TRAINING SET DATA  314 . 
     In one embodiment, at GENERATE, FOR EACH CANDIDATE FUNCTION OF THE FIRST, SECOND, AND THIRD SETS, TEST DATA BY APPLYING THE CANDIDATE FUNCTION TO THE TRAINING SET DATA  314  the process  300  for learning and incorporating new forms in an electronic document preparation system generates, for each candidate function of the first, second, and third sets, test data by applying the candidate function to the training set data, according to one embodiment. 
     In one embodiment, once the process  300  for learning and incorporating new forms in an electronic document preparation system generates, for each candidate function of the first, second, and third sets, test data by applying the candidate function to the training set data at GENERATE, FOR EACH CANDIDATE FUNCTION OF THE FIRST, SECOND, AND THIRD SETS, TEST DATA BY APPLYING THE CANDIDATE FUNCTION TO THE TRAINING SET DATA  314 , process flow proceeds to COMPARE THE TEST DATA TO THE TRAINING SET DATA  316 . 
     In one embodiment, at COMPARE THE TEST DATA TO THE TRAINING SET DATA  316  the process  300  for learning and incorporating new forms in an electronic document preparation system compares the test data to the training set data, according to one embodiment. 
     In one embodiment, once the process  300  for learning and incorporating new forms in an electronic document preparation system compares the test data to the training set data at COMPARE THE TEST DATA TO THE TRAINING SET DATA  316 , process flow proceeds to SELECT ONE OR MORE OF THE CANDIDATE FUNCTIONS AS ACCEPTABLE CANDIDATE FUNCTIONS FOR THE FIRST SELECTED DATA FIELD BASED ON HOW CLOSELY THE TEST DATA FOR EACH CANDIDATE FUNCTION MATCHES DATA VALUES IN THE COMPLETED DATA FIELDS OF THE PREVIOUSLY FILLED FORMS THAT CORRESPOND TO THE FIRST SELECTED DATA FIELD  318 . 
     In one embodiment, at SELECT ONE OR MORE OF THE CANDIDATE FUNCTIONS AS ACCEPTABLE CANDIDATE FUNCTIONS FOR THE FIRST SELECTED DATA FIELD BASED ON HOW CLOSELY THE TEST DATA FOR EACH CANDIDATE FUNCTION MATCHES DATA VALUES IN THE COMPLETED DATA FIELDS OF THE PREVIOUSLY FILLED FORMS THAT CORRESPOND TO THE FIRST SELECTED DATA FIELD, the process  300  for learning and incorporating new forms in an electronic document preparation system selects one or more of the candidate functions as acceptable candidate functions for the first selected data field based on how closely the test data for each candidate function matches data values in the completed data fields of the previously filled forms that correspond to the first selected data field, according to one embodiment. 
     In one embodiment, once the process  300  for learning and incorporating new forms in an electronic document preparation system selects one or more of the candidate functions as acceptable candidate functions for the first selected data field based on how closely the test data for each candidate function matches data values in the completed data fields of the previously filled forms that correspond to the first selected data field at SELECT ONE OR MORE OF THE CANDIDATE FUNCTIONS AS ACCEPTABLE CANDIDATE FUNCTIONS FOR THE FIRST SELECTED DATA FIELD BASED ON HOW CLOSELY THE TEST DATA FOR EACH CANDIDATE FUNCTION MATCHES DATA VALUES IN THE COMPLETED DATA FIELDS OF THE PREVIOUSLY FILLED FORMS THAT CORRESPOND TO THE FIRST SELECTED DATA FIELD  318  proceeds to OUTPUT RESULTS DATA INDICATING THE ONE OR MORE ACCEPTABLE FUNCTIONS  320 . 
     In one embodiment, at OUTPUT RESULTS DATA INDICATING THE ONE OR MORE ACCEPTABLE FUNCTIONS  320  the process  300  for learning and incorporating new forms in an electronic document preparation system outputs results data indicating the one or more acceptable functions. 
     In one embodiment, once the process  300  for learning and incorporating new forms in an electronic document preparation system outputs results data indicating the one or more acceptable functions at OUTPUT RESULTS DATA INDICATING THE ONE OR MORE ACCEPTABLE FUNCTIONS  320 , process flow proceeds to END  322 . 
     In one embodiment, at END  322  the process for learning and incorporating new forms in an electronic document preparation system is exited to await new data and/or instructions. 
       FIG.  4    illustrates a block diagram of a production environment  400  for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. 
     The production environment  400  includes a service provider computing environment  410 , user computing environment  440 , third party computing environment  450 , and public information computing environment  460 , for learning and incorporating new forms in an electronic document preparation system, according to one embodiment. The computing environments  410 ,  440 ,  450 , and  460  are communicatively coupled to each other with one or more communication channels  401 , according to one embodiment. 
     The service provider computing environment  410  includes an electronic document preparation system  411 , which is configured to provide electronic document preparation services to a user. 
     In one embodiment, the electronic document preparation system  411  includes a user interface module  412 , a form analysis module  413 , a first analysis module  414 , a second analysis module  415 , a third analysis module  416 , a data acquisition module  417 , and an evaluator module  418 . 
     The interface module  412  is configured to receive form data  421  related to a new form. In one embodiment, the form data  421  may be in a visual form such as a PDF or in another format that does not readily enable cataloging of the individual data fields and corresponding context data of the new form. Accordingly, the electronic document preparation system  411  includes a form analysis module  413  that is configured to analyze the form data  421  and generates structured form data  419 . The structured form data  419  can include data related the data fields, limiting values, tables, dependencies, variables, text descriptions, or other data related to the new form and its data fields. 
     In one embodiment, the first analysis module  414  generates, for a first data field of the new form, first candidate function data  424  including the first set of one or more candidate functions. The first analysis module  414  is configured to generate the first candidate function data  424  based on a first analysis technique or analysis process. The first analysis module  414  provides the first candidate function data  424  to the evaluator module  418 . 
     In one embodiment, the second analysis module  415  generates, for the first data field of the new form, second candidate function data  426  including a second set of one or more candidate functions. The second analysis module  415  is configured to generate the second candidate function data  426  based on a second analysis technique or analysis process different than the first analysis technique or process. The second analysis module  415  provides the second candidate function data  426  to the evaluator module  418 . 
     In one embodiment, the third analysis module  416  generates, for the first data field of the new form, third candidate function data  428  including a third set of one or more candidate functions. The third analysis module  416  is configured to generate third candidate function data  428  based on a third analysis technique or analysis process different than the first and second analysis techniques or processes. The third analysis module  416  provides the third candidate function data  428  to the evaluator module  418 . 
     In one embodiment, the data acquisition module  417  is configured to gather training set data  429 . The training set data  429  includes a large number of previously filled forms related to the new form. Each of the previously filled forms includes completed data fields correspond to the data fields of the new form. In one embodiment, the previously filled forms can include previous versions of the new form. 
     In one embodiment, the evaluator module  418  generates candidate function data  432  by combining the first candidate function data  424 , the second candidate function data  426 , and the third candidate function data  428 . The evaluator module  418  generates, for each candidate function, test data  433  by applying the candidate function to the training set data  429 . The evaluator module  418  then generates matching data  434  for each candidate function by comparing the test data for that candidate function to the training set data  429 . The matching data indicates how closely the test data  433  matches the training set data  429 . Evaluator module  418  then generates ranking data  435  by ranking of each of the candidate functions based on how closely their respective test data  433  matches the training set data  429 . The evaluator module  418  then generates results data  436  indicating one or more acceptable candidate functions based on the ranking data  435 . In one embodiment, a candidate function is an acceptable function if the test data  433  exactly matches the training set data  429 . Alternatively, a candidate function can be an acceptable function if the test data  433  matches the training set data  429  within a threshold error tolerance. The evaluator module can cause the interface module  412  to output the results data  422 . 
     In one embodiment, after the electronic document preparation system  411  finds one or more acceptable candidate functions for the first selected data field, the electronic document preparation system  411  can continue to find acceptable candidate functions for other selected data fields of the new form in a similar manner. 
     In one embodiment, the first, second, and third analysis modules  414 ,  415 ,  416  include one or more of a machine learning module, a natural language parsing module, and a historical instructions analysis module. The first, second, and third analysis modules can include analysis modules or techniques other than those listed above. Those of skill in the art will recognize, in light of the present disclosure, that many other analysis modules or analysis techniques can be used in an electronic document preparation system  411 . All such other analysis modules and techniques fall within the scope of the present disclosure. 
     As noted above, the specific illustrative examples discussed above are but illustrative examples of implementations of embodiments of the method or process for learning and incorporating new forms in an electronic document preparation system. Those of skill in the art will readily recognize that other implementations and embodiments are possible. Therefore, the discussion above should not be construed as a limitation on the claims provided below. 
     In one embodiment, a computing system implements a method for learning and incorporating new forms in an electronic document preparation system. The method includes generating structured form data by analyzing form data related to a new form having a plurality of data fields. The structured form data identifies the data fields of the new form and context data related to context describing the data fields of the new form. The method also includes gathering training set data related to previously filled forms, each previously filled form having completed data fields that each correspond to a respective data field of the new form and generating, for a first selected data field of the plurality of data fields of the new form, first candidate function data including a first set of candidate functions by performing a machine learning process, and generating, for the first selected data field of the plurality of data fields of the new form, second candidate function data including a second set of candidate functions by performing a natural language parsing process on the context data related to the first selected field. The method also includes generating, for the first selected data field of the plurality of data fields of the new form, third candidate function data including a third set of candidate functions by analyzing historical software instructions for providing a data value for a data field of a historical form related to the new form. The method also includes generating, for each candidate function of the first, second, and third sets, test data by applying the candidate function to the training set data. The method also includes comparing the test data to the training set data, selecting one or more of the candidate functions as acceptable candidate functions for the first selected data field based on how closely the test data for each candidate function matches data values in the completed data fields of the previously filled forms that correspond to the first selected data field, outputting results data indicating the one or more acceptable functions. 
     In one embodiment, a computing system implements a method for learning and incorporating new forms in an electronic document preparation system. The method includes generating structured form data by analyzing form data related to a new form having a plurality of data fields. The structured form data identifies the data fields of the new form and context data related to the data fields of the new form. The method also includes gathering training set data related to previously filled forms. Each previously filled form has completed data fields that each correspond to a respective data field of the new form. The method also includes generating, for a first selected data field of the plurality of data fields of the new form, first candidate function data including a first set of candidate functions based on a first analysis process. The method also includes generating, for the first selected data field of the plurality of data fields of the new form, second candidate function data including a second set of candidate functions based on a second analysis process. The method also includes generating, for each candidate function of the first and second sets, test data by applying the candidate function to the training set data. The method also includes comparing the test data to the training set data. The method also includes selecting one or more of the candidate functions as acceptable candidate functions for the first selected data field based on how closely the test data for each candidate function matches data values in the completed data fields of the previously filled forms that correspond to the first selected data field and outputting results data indicating the one or more acceptable functions. 
     In one embodiment, a system for learning and incorporating new forms in an electronic document preparation system includes at least one processor and at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process. The process includes receiving, with an interface module of a computing system, form data related to a new form having a plurality of data fields and generating, a form analysis module of a computing system, structured form data by analyzing the new form. The structured form data identifies the data fields of the new form and context data related to context describing the data fields of the new form. The process also includes gathering, with a data acquisition module of a computing system, training set data related to previously filled forms. Each previously filled form has completed data fields that each correspond to a respective data field of the new form. The process also includes generating, with a machine learning module of a computing system and for a first selected data field of the plurality of data fields of the new form, first candidate function data including a first set of candidate functions by performing a machine learning process. The process also includes generating, with a natural language parsing module of a computing system and for the first selected data field of the plurality of data fields of the new form, second candidate function data including a second set of candidate functions by performing a natural language parsing process on the context data related to the first selected field. The process also includes generating, with a historical instructions analysis module of a computing system, for the first selected data field of the plurality of data fields of the new form, third candidate function data including a third set of candidate functions by analyzing historical software instructions for providing a data value for a data field of a historical form related to the new form. The process also includes generating, with an evaluator module of a computing system, for each candidate function of the first, second, and third sets, test data by applying the candidate function to the training set data and comparing, with the evaluator module, the test data to the training set data. The process also includes selecting, with the evaluator module, one or more of the candidate functions as acceptable candidate functions for the first selected data field based on how closely the test data for each candidate function matches data values in the completed data fields of the previously filled forms that correspond to the first selected data field and outputting, with the evaluator module, results data indicating the one or more acceptable functions 
     In one embodiment, a non-transitory computer-readable medium has a plurality of computer-executable instructions which, when executed by a processor, perform a method for learning and incorporating new forms in an electronic document preparation system. The instructions include an interface module configured to receive form data representing to a new form having a plurality of data fields and a form analysis module configured to generate structured form data by analyzing the new form, the structured form data identifying the data fields of the new form and context data related to context describing the data fields of the new form. The instructions also include a data acquisition module configured to gather training set data related to previously filled forms, each previously filled form having completed data fields that each correspond to a respective data field of the new form. The instructions also include a first data field analysis module configured to generate first candidate function data including one or more candidate functions for each selected data field from the plurality of data fields of the new form. The instructions also include a second data field analysis module configured to generate second candidate function data including one or more candidate functions for each selected data field from the plurality of data fields of the new form. The process also includes an evaluator module configured to identify, for each selected data field of the new form, one or more acceptable candidate functions from the first and second candidate function data by generating test data by applying the candidate functions to the training set data and ranking the candidate functions for each selected data field based on how closely the test data for each candidate function matches corresponding data values in the training set data. 
     In the discussion above, certain aspects of one embodiment include process steps and/or operations and/or instructions described herein for illustrative purposes in a particular order and/or grouping. However, the particular order and/or grouping shown and discussed herein are illustrative only and not limiting. Those of skill in the art will recognize that other orders and/or grouping of the process steps and/or operations and/or instructions are possible and, in some embodiments, one or more of the process steps and/or operations and/or instructions discussed above can be combined and/or deleted. In addition, portions of one or more of the process steps and/or operations and/or instructions can be re-grouped as portions of one or more other of the process steps and/or operations and/or instructions discussed herein. Consequently, the particular order and/or grouping of the process steps and/or operations and/or instructions discussed herein do not limit the scope of the invention as claimed below. 
     As discussed in more detail above, using the above embodiments, with little or no modification and/or input, there is considerable flexibility, adaptability, and opportunity for customization to meet the specific needs of various parties under numerous circumstances. 
     In the discussion above, certain aspects of one embodiment include process steps and/or operations and/or instructions described herein for illustrative purposes in a particular order and/or grouping. However, the particular order and/or grouping shown and discussed herein are illustrative only and not limiting. Those of skill in the art will recognize that other orders and/or grouping of the process steps and/or operations and/or instructions are possible and, in some embodiments, one or more of the process steps and/or operations and/or instructions discussed above can be combined and/or deleted. In addition, portions of one or more of the process steps and/or operations and/or instructions can be re-grouped as portions of one or more other of the process steps and/or operations and/or instructions discussed herein. Consequently, the particular order and/or grouping of the process steps and/or operations and/or instructions discussed herein do not limit the scope of the invention as claimed below. 
     The present invention has been described in particular detail with respect to specific possible embodiments. Those of skill in the art will appreciate that the invention may be practiced in other embodiments. For example, the nomenclature used for components, capitalization of component designations and terms, the attributes, data structures, or any other programming or structural aspect is not significant, mandatory, or limiting, and the mechanisms that implement the invention or its features can have various different names, formats, or protocols. Further, the system or functionality of the invention may be implemented via various combinations of software and hardware, as described, or entirely in hardware elements. Also, particular divisions of functionality between the various components described herein are merely exemplary, and not mandatory or significant. Consequently, functions performed by a single component may, in other embodiments, be performed by multiple components, and functions performed by multiple components may, in other embodiments, be performed by a single component. 
     Some portions of the above description present the features of the present invention in terms of algorithms and symbolic representations of operations, or algorithm-like representations, of operations on information/data. These algorithmic or algorithm-like descriptions and representations are the means used by those of skill in the art to most effectively and efficiently convey the substance of their work to others of skill in the art. These operations, while described functionally or logically, are understood to be implemented by computer programs or computing systems. Furthermore, it has also proven convenient at times to refer to these arrangements of operations as steps or modules or by functional names, without loss of generality. 
     Unless specifically stated otherwise, as would be apparent from the above discussion, it is appreciated that throughout the above description, discussions utilizing terms such as, but not limited to, “activating”, “accessing”, “adding”, “aggregating”, “alerting”, “applying”, “analyzing”, “associating”, “calculating”, “capturing”, “categorizing”, “classifying”, “comparing”, “creating”, “defining”, “detecting”, “determining”, “distributing”, “eliminating”, “encrypting”, “extracting”, “filtering”, “forwarding”, “generating”, “identifying”, “implementing”, “informing”, “monitoring”, “obtaining”, “posting”, “processing”, “providing”, “receiving”, “requesting”, “saving”, “sending”, “storing”, “substituting”, “transferring”, “transforming”, “transmitting”, “using”, etc., refer to the action and process of a computing system or similar electronic device that manipulates and operates on data represented as physical (electronic) quantities within the computing system memories, resisters, caches or other information storage, transmission or display devices. 
     The present invention also relates to an apparatus or system for performing the operations described herein. This apparatus or system may be specifically constructed for the required purposes, or the apparatus or system can comprise a general purpose system selectively activated or configured/reconfigured by a computer program stored on a computer program product as discussed herein that can be accessed by a computing system or other device. 
     Those of skill in the art will readily recognize that the algorithms and operations presented herein are not inherently related to any particular computing system, computer architecture, computer or industry standard, or any other specific apparatus. Various general purpose systems may also be used with programs in accordance with the teaching herein, or it may prove more convenient/efficient to construct more specialized apparatuses to perform the required operations described herein. The required structure for a variety of these systems will be apparent to those of skill in the art, along with equivalent variations. In addition, the present invention is not described with reference to any particular programming language, and it is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references to a specific language or languages are provided for illustrative purposes only and for enablement of the contemplated best mode of the invention at the time of filing. 
     The present invention is well suited to a wide variety of computer network systems operating over numerous topologies. Within this field, the configuration and management of large networks comprise storage devices and computers that are communicatively coupled to similar or dissimilar computers and storage devices over a private network, a LAN, a WAN, a private network, or a public network, such as the Internet. 
     It should also be noted that the language used in the specification has been principally selected for readability, clarity and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the claims below. 
     In addition, the operations shown in the FIGs., or as discussed herein, are identified using a particular nomenclature for ease of description and understanding, but other nomenclature is often used in the art to identify equivalent operations. 
     Therefore, numerous variations, whether explicitly provided for by the specification or implied by the specification or not, may be implemented by one of skill in the art in view of this disclosure.