Information processing apparatus, information processing method, and information processing program

Abstract

To provide an information processor, an information processing method, and an information processing program capable of improving the efficiency of work for business improvement by materializing the resolution of information, decomposing an AsIs model into data elements, reconstructing a ToBe model with the whole in view, and advancing arrangement.SOLUTION: An information processing device includes an acquisition unit that acquires a predetermined document, an extraction unit that extracts a plurality of natural languages from content of an item in the predetermined document and stores the plurality of natural languages in a data storage unit, an organization unit that classifies and organizes the plurality of natural languages accumulated in the data storage unit, determines similarity between the natural languages, clarifies at least one of a common attribute and a lack / difference attribute including a dependency relation and / or an attribute relation, and tags each of the plurality of natural languages, and a registration unit that registers the plurality of natural languages tagged with the common attribute and the lack / difference attribute including the dependency relation and / or the attribute relation in a data set.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] When improving business processes, such as increasing efficiency, a series of tasks are carried out for the business process, such as visualizing the AsIs model (current process model), identifying and analyzing issues, defining the ToBe model (improved process model), and deciding on the items to be improved in order to realize the ToBe model process.

[0003] Technologies for streamlining this work have been disclosed (see, for example, Patent Documents 1 to 3). Patent Document 1 discloses a method for efficiently providing multifaceted analysis / evaluation by using two pieces of input information: current business design document information and ideal business design document information, analyzing each design document information at the same time, and comparing and evaluating the two design documents, and outputting the results in a single file. Patent Document 2 discloses a system that, when systematizing a process flow diagram created from a user's perspective, can feed back KPI values ​​onto a process flow diagram created from a user's perspective without writing program code for obtaining KPI values. Patent Document 3 discloses a device that improves human work, visualizes information to motivate people, and leads to business stability. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-65711 [Patent Document 2] Japanese Patent Application Laid-Open No. 2008-299646 [Patent Document 3] Japanese Patent Publication No. 2020-77424 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in many cases, business efficiency improvements, such as the use of AI, are considered by focusing on a single business issue, without considering the overall picture, and the system is not designed to solve the issues by expanding the scope of application of AI or other business efficiency improvements. Furthermore, when business design is considered, the majority of cases are considered at the existing business process or report level, and the current situation is that design and consideration are not done in a way that connects to data.

[0006] Due to the problems mentioned above, it was not easy to switch between the AsIs model and ToBe model for business processes and examine them from various perspectives, or to organize the AsIs, organize the data, link them, change the way they are displayed, or make them more detailed or more abstract.When proceeding with the above series of tasks, it was necessary to repeat and recreate the series of tasks in order to change the way they were displayed, to make them more detailed, or to make them more abstract, and because the process from organization to implementation was separated, it was difficult to work seamlessly.

[0007] In view of the above problems, the present invention aims to provide an information processing device, an information processing method, and an information processing program that can concretize or abstract the resolution of information, including correlations, to decompose the AsIs model into data elements and reconstruct a ToBe model that takes the whole into consideration, thereby facilitating organization and making business improvement work more efficient. [Means for solving the problem]

[0008] A first aspect of the present invention is an information processing device comprising: an extraction unit that extracts a plurality of natural languages ​​from the contents of items in a specified document and stores the extracted natural languages ​​in a data storage unit; an organization unit that classifies and organizes the plurality of natural languages ​​stored in the data storage unit, determines the similarities between the plurality of natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifies and tags each of the plurality of natural languages ​​with at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships, between the plurality of natural languages; and a registration unit that registers the plurality of natural languages, each tagged with at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships between the plurality of natural languages, in a dataset.

[0009] In the first aspect of the present invention, the extraction unit may further extract at least one of the name of an item in the predetermined document, the business process to which the predetermined document belongs, and the type of the predetermined document.

[0010] In the first aspect of the present invention, the registration unit may register at least one of the item names, business processes, and predetermined document types further extracted by the extraction unit in the data set.

[0011] In the first aspect of the present invention, the organizer may further tag each of the plurality of natural languages ​​with the process category to which the natural language belongs.

[0012] In the first aspect of the present invention, a UI display unit may be further provided that uses data registered in the data set to generate data for display in a user interface with a different appearance and presents the data to the user.

[0013] In a first aspect of the present invention, the attribute correlation may be a correlation between a given natural language and a natural language expressing an attribute that describes the given natural language.

[0014] In a first aspect of the present invention, the dependency correlation may be a correlation having at least one of a mechanism, a parent-child relationship, or a cause-and-effect dependency correlation relationship between a predetermined natural language and a natural language that appears around the predetermined natural language.

[0015] In a first aspect of the present invention, the process partition may be any of the following processes: requirement, function, logical, physics, logic, and parametric.

[0016] In the first aspect of the present invention, the organizing section may determine whether a specific piece of data among the plurality of pieces of data stored in the data storage section is similar to data excluding the specific piece of data.

[0017] In the first aspect of the present invention, the one or more criteria may be composed of at least one of a criterion for determining whether business processes are similar, a criterion for determining whether types of specified documents are similar, a criterion for determining whether item names are similar, a criterion for determining whether process classifications are similar, a criterion for determining whether dependency correlations are similar, a criterion for determining whether attribute correlations are similar, and a criterion for determining whether natural language character strings are similar.

[0018] In the first aspect of the present invention, the organizing section may quantify one or more similarities, which are degrees of similarity based on one or more criteria.

[0019] In the first aspect of the present invention, the organizing section may determine that the information is similar by comparing one or more similarities with one or more thresholds.

[0020] In the first aspect of the present invention, the registration unit may register one or more similarities in the dataset.

[0021] In the first aspect of the present invention, a receiving unit that receives an input from a user may be further provided, and the receiving unit may receive a selection of a UI to be displayed by the comparison unit.

[0022] In the first aspect of the present invention, the form of the data generated by the generation unit may be at least one of a detailed design process diagram, a CRUD diagram, a deliverable list, a data list, an ER diagram, a business flow diagram, and a tree diagram.

[0023] In the first aspect of the present invention, the UI display unit may perform calculations based on desired logic and generate data to be displayed on the user interface using the calculation results.

[0024] A second aspect of the present invention is summarized as follows: a computer executes an acquisition step of acquiring a predetermined document; an extraction step of extracting multiple natural languages ​​from the contents of items in the predetermined document and storing them in a data storage unit; an organization step of classifying and organizing the multiple natural languages ​​stored in the data storage unit, determining the similarities between the multiple natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifying at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships, between the multiple natural languages, and tagging each of the multiple natural languages; and a registration step of registering the multiple natural languages, each tagged with at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships between the multiple natural languages, into a dataset.

[0025] A third aspect of the present invention is an information processing program that causes a computer to realize an acquisition function for acquiring a predetermined document, an extraction function for extracting multiple natural languages ​​from the contents of items in the predetermined document and storing them in a data storage unit, an organization function for classifying and organizing the multiple natural languages ​​stored in the data storage unit, determining the similarities between the multiple natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifying at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships, between the multiple natural languages, and tagging each of the multiple natural languages, and a registration function for registering the multiple natural languages, each tagged with at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships between the multiple natural languages, in a dataset.

[0026] According to the present invention, by concretizing or abstracting the resolution of information, including correlations, it is possible to decompose the AsIs model into data elements and reconstruct a ToBe model that takes the whole into consideration, thereby providing an information processing device, information processing method, and information processing program that can facilitate organization and make business improvement work more efficient. [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 10 is a diagram illustrating the organization of an AsIs model and a ToBe model. [Figure 2] FIG. 1 is a process diagram showing the relationship between process requirements in a product development operation. [Figure 3] FIG. 3 is a diagram for explaining the process to which the requirements shown in the process diagram of FIG. 2 belong. [Figure 4] FIG. 3 is a diagram for explaining the process to which the requirements shown in the process diagram of FIG. 2 belong. [Figure 5] FIG. 3 is a diagram for explaining the process to which the requirements shown in the process diagram of FIG. 2 belong. [Figure 6] FIG. 3 is a diagram illustrating character strings that are highly similar to the requirements of the process diagram shown in FIG. 2. [Figure 7]FIG. 2 is a schematic diagram illustrating the relationship between a process and a data model. [Figure 8] FIG. 8(a) is a diagram explaining the data model of business process P, FIG. 8(b) is a structural diagram of business processes P1 to P3, and FIG. 8(c) is a table expressing the dependency correlations of business processes P1 to P3. [Figure 9] FIG. 2 is a schematic diagram illustrating an example of a form. [Figure 10] Figure 10(a) is a diagram explaining the data model of the report UI, Figure 10(b) is a structural diagram of reports UI1 and UI2, and Figure 10(c) is a table expressing the dependency correlation between reports UI1 and UI2. [Figure 11] Figure 11(a) is a diagram explaining a data model showing the correlation between business processes and the report UIs used in the business processes, Figure 11(b) is a structural diagram of business process P and the report UI, and Figure 11(c) is a table expressing the dependency correlation between business process P and the report UI. [Figure 12] 10 is a data model of each item ID-n of a form in a design basis process used in the information processing apparatus according to the embodiment. [Figure 13] FIG. 13 is a structural diagram of each item ID-n shown in FIG. [Figure 14] Figure 14(a) shows the data model of the report UI and each item ID-n of the report in the design basis process, Figure 14(b) shows a part of the structural diagram of the report UI and each item ID-n shown in Figure 12, which are in a mutually dependent correlation, and Figure 14(c) is a table expressing the dependent correlation between the report UI in Figure 14(b) and each item ID-n. [Figure 15] 1 is an example of a business process. [Figure 16] 16 is a diagram showing an example of a form used in each business process of FIG. 15. FIG. [Figure 17] An example of a data model corresponding to each business process shown in FIG. 15 and each report shown in FIG. 16 is shown below. [Figure 18] FIG. 2 is a schematic diagram summarizing the relationships among processes, UIs, and data in the information processing apparatus according to the embodiment. [Figure 19]1 is a block diagram illustrating an example of an information processing apparatus according to an embodiment of the present invention. [Figure 20] FIG. 2 is a diagram illustrating an example of an operation of the information processing device according to the embodiment. [Figure 21] FIG. 2 is a schematic diagram illustrating an example of a form. [Figure 22] 22 is a schematic diagram of a form that displays the process categories to which the items belong in the form shown in FIG. 21. FIG. [Figure 23] FIG. 23(a) is a structural diagram of items R1 to R9, and FIG. 23(b) is a table expressing the dependency correlations of items R1 to R9. [Figure 24] 24(a) and 24(b) are diagrams in which attributes of each item shown in FIG. 23(a) and FIG. 23(b) are added. [Figure 25] FIG. 10 is a diagram showing a deliverable list and a data list. [Figure 26] 1 is an example of a correlation diagram. [Figure 27] 1 is an example of a business process diagram. [Figure 28] 1 is a graph showing the man-hours for each task. [Figure 29] This is an example of a CRUD diagram. [Figure 30] This is an example of an ER diagram. [Figure 31] 1 is an example of a business flow diagram. [Figure 32] This is an example of a tree diagram. [Figure 33] 23 is a schematic diagram of the form shown in FIG. 22, further displaying the processes to which the items belong. FIG. [Figure 34] 10 is a flowchart illustrating the operation of the information processing device according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0028] Next, an embodiment of the present invention will be described with reference to the drawings. In the description of the drawings relating to the embodiment, the same or similar parts are designated by the same or similar reference numerals. However, it should be noted that the drawings are schematic, and the relationship between planar dimensions and the like may differ from the actual ones. Therefore, specific dimensions should be determined with reference to the following description. Furthermore, it goes without saying that the drawings may include parts with different dimensional relationships and ratios.

[0029] Furthermore, the embodiments are merely examples of devices and methods for embodying the technical idea of ​​the present invention, and the technical idea of ​​the present invention does not limit the configuration, arrangement, layout, etc. of each component to those described below. The technical idea of ​​the present invention can be modified in various ways within the technical scope defined by the claims.

[0030] (Embodiment) According to the information processing device of this embodiment, when proceeding with the visualization of a process model, understanding of issues, analysis, definition, etc. in improving business operations, it is possible to concretely or abstract the resolution of information, including correlations, while controlling the scope of the process, etc., attribute correlation relationships, and / or dependency correlation relationships, thereby decomposing the AsIs model into data elements, reconstructing a ToBe model with an overall perspective, and proceeding with the organization, and it is possible to quickly and efficiently organize the AsIs model while adding attribute correlations and dependency correlations. Here, the AsIs model is the current process model of a business process, and the ToBe model is the improved process model.

[0031] The information processing device according to this embodiment acquires data used in the current process model under consideration, and uses the data model used in this embodiment to display the data of the current process model in a desired user interface (UI) with different presentation styles, thereby organizing the data, visualizing the AsIs model, and promoting the understanding and analysis of issues. The data model used in the information processing device according to this embodiment is a data model consisting of processes and design basis data, and is based on attribute correlations and dependency correlations. Here, the term "process" refers to the sequence and combination of tasks, and the term "design basis data" refers to the purpose of the design, logical thinking in the design work, the expression of the design object, the realization of the design, the means, and all other background information on the design.

[0032] An overview of a method for organizing an AsIs model and a ToBe model by an information processing device according to this embodiment will be described with reference to FIG. 1. FIG. 1 shows an example of the order in which business processes are organized from the AsIs model to the ToBe model when the information processing device according to this embodiment organizes the business processes. As shown in FIG. 1, when organizing a business process, first, a process is specified in the AsIs model. Next, a user interface (UI) associated with the designated process is selected. The UI may be, for example, a form, a tool, a system, or the like, but here it is assumed to be a form describing the contents of the designated process. Furthermore, data described in the UI is acquired. By organizing the AsIs model, the AsIs model is visualized, analyzed, and issues are identified. In the AsIs model, organization proceeds along arrow 101 in the order of process, UI, and data.

[0033] When proceeding with the reorganization into the ToBe model, the order is reverse to the order of the reorganization of the AsIs model, that is, the order is data, UI, and process along arrow 102. By proceeding with the reorganization into the ToBe model, the definition of the ToBe model and the determination of the maintenance items for realizing the ToBe model process are made.

[0034] In both the AsIs model and the ToBe model, the resolution increases in the order of process, UI, and data. Conversely, abstraction progresses in the order of data, UI, and process. Furthermore, processes are created from a user's perspective, and data is created from a system's perspective. According to the information processing device of this embodiment, in both the AsIs model and the ToBe model, it is possible to move back and forth between the process, UI, and data and organize these models. When moving back and forth between the process, UI, and data, the data model in the information processing device of this embodiment is used.

[0035] Furthermore, as shown in FIG. 1, when the resolution of a business process is raised from the UI (form) level to the data level, the resolution is too high to organize at the data level, making it difficult to handle. Therefore, it is easier to organize the process if there are concepts and categories that are abstracted to a certain extent. In this embodiment, as described below, the business process is classified into R, F, L, P, l, and p as concepts and categories with a certain degree of abstraction. While the AsIs model has a concept of UI (form), this does not necessarily mean the same UI (form) in the ToBe model. Therefore, the information processing device of the present invention sets a concept with a high level of abstraction that summarizes data at the same level as the UI (form) in the AsIs model, making it easier to consider the ToBe model. In other words, by raising the resolution of the AsIs model to the data level and then classifying the data into categories with a level of abstraction corresponding to the UI (form), it becomes easier to consider the ToBe model.

[0036] The data model used in the information processing device of the present invention is a model that represents the attribute correlations and dependency correlations that exist between a given natural language and the natural languages ​​that appear around it. In this embodiment, the natural language refers to a language that expresses requirements in a business process, and is composed of sentences, words, numbers, mathematical expressions, symbols, or a combination of these. The natural languages ​​that appear around the given natural language express requirements that are related to the requirements expressed in the given natural language in the business process, and details of these will be described later.

[0037] Attribute correlation is a correlation with a certain level of abstraction or resolution between a given natural language and the natural languages ​​that appear around it, and is a correlation between natural languages ​​that express attributes that explain requirements expressed in a given natural language, and is a correlation that explains natural languages ​​or requirements internally, i.e., a correlation that explains and defines mainly the internal connections of natural languages.

[0038] Dependency correlation is a correlation that has a dependency relationship between a given natural language and the natural languages ​​that appear around it, either through mechanism, parent-child relationship, or cause-and-effect relationship, and it is a correlation that externally explains the natural language or requirements, that is, it is a correlation that mainly explains and defines the external connections of the natural language. Attribute correlation and dependency correlation will also be discussed in detail later.

[0039] The data model used in the information processing device of the present invention will be described. FIG. 2 shows a process diagram illustrating the relationships between requirements for each process division related to product development work. The diagram shown in FIG. 2 is a diagram related to the product development work of an electric kick scooter, as an example. A process diagram visualizes the flow of tasks in a business process. In FIG. 2, each requirement in the diagram (e.g., requirements 31 to 39), and each requirement in each process division of the business process, is shown with solid or dashed lines indicating the relationship between each requirement. The relationship between each requirement shown with dashed lines is a dependency correlation, and the relationship between each requirement shown with solid lines is an attribute correlation. The data model used in the information processing device according to this embodiment is a data model based on these dependency correlations and attribute correlations.

[0040] In Figure 2, requirements that are mutually dependent are connected with dashed lines and arranged horizontally. Requirements that are mutually attributed are connected with solid lines and arranged vertically. Furthermore, the horizontal axis lists the business process divisions along a rough timeline 21: "Requirements," "Performance / Functions," "Logic," "Product," and "Means of Security."

[0041] Here, the business process divisions shown in FIG. 2 (referred to as process divisions in this embodiment) are obtained by classifying each business in a business process according to the content of the business. In FIG. 2, as examples of process divisions into which each business in a business process is classified, a requirement process division, a performance / function process division, a logic process division, a product process division, and a guarantee means process division are shown along the horizontal axis (time axis 21). As will be described later, in the information processing device of the present invention, the business process divisions are classified into R (requirements), F (functions), L (logical), P (physics), l (logic), and p (parameters). The requirement process division, performance / function process division, logic process division, product process division, and guarantee means process division in FIG. 2 correspond to R, F, L, P, and l, respectively. Each requirement belongs to one of the process divisions R, F, L, P, l, and p. In Figure 2, the requirement process category is described as "requirement," the performance / function process category is described as "performance / function," the logic process category is described as "logic," the product process category is described as "product," and the security means process category is described as "security means." Also, in Figure 2, p (parameter) is not described.

[0042] In this embodiment, business processes are classified into R (requirements), F (functions), L (logical), P (physics), l (logic), and p (parameters), but this is not limited to this format, and the business process may be displayed in a format such as RPp, with some parts omitted.

[0043] A dependency relationship is a relationship in which one natural language or requirement indicates the mechanism, parent-child relationship, cause and effect, etc. of another natural language or requirement, and one natural language or requirement externally explains the other natural language or requirement, that is, it is a relationship that explains and defines mainly the external connections of natural languages. Below, we will explain dependency relationships in detail.

[0044] When developing a product, development work generally begins with the requirements process section, as shown in Figure 2. Specifically, as an example, in the development of a kick scooter, the development begins with requirement 31, a user requirement for "making local travel easier." In Figure 2, requirements 31 to 33 are included in the requirements process section, requirements 34, 35, and 37 to 39 are included in the performance / function process section, and requirement 36 is included in the product process section.

[0045] Requirement 34 lists "portability" as a function necessary to satisfy the user's request in requirement 31, "I want to make local travel easier." Requirement 35 lists "people" as the target of the "portability" in requirement 34. Since the object to be transported is "people" in requirement 35, requirement 36 lists an "electric board." Requirements 31, 34, and 35 are in a parent-child relationship or a cause-and-effect relationship. In this embodiment, such a parent-child relationship or cause-and-effect relationship is called a dependency correlation.

[0046] An attribute correlation is a relationship in which one requirement embodies another requirement. Multiple requirements that are in an attribute correlation form a hierarchical structure. This hierarchical structure corresponds to the vertical relationship between the requirements in Figure 2, and as you move downwards in the page of Figure 2, that is, as you progress through the layers, the level of abstraction decreases or the resolution increases, and the content of the requirements becomes more specific. The degree of attribute correlation between requirements in Figure 2 corresponds to this level of abstraction or resolution. Below, we will explain attribute correlation in detail.

[0047] Requirement 32, "Self-propelled means of transport operated by a handle," is listed as one of the specific means for realizing the user request in Requirement 31, "I want to make local travel easier." The specific content of Requirement 32 is listed as "a vehicle weighing 10 kg or less, measuring 100 mm, with a three-wheeled electric motor with a handle, capable of traveling at 10 km / h in two hours."

[0048] Requirements 31, 32, and 33 are mutually attribute-correlated and form a hierarchical structure, with the content becoming more specific as you move up the hierarchy from requirement 31 to requirement 32 to requirement 33. In other words, the level of abstraction decreases and the resolution increases as you move from requirement 31 to requirement 32 to requirement 33.

[0049] Specific details of the "portable function" in requirement 34 include the "steering function" in requirement 37, the "stopping function" in requirement 38, and the "rotation transmission function" in requirement 39. Requirement 34 and requirements 37 to 39 are also mutually attribute-correlated, with requirements 37 to 39 being specific embodiments of requirement 34. Note that requirements 37, 38, and 39 are not mutually dependent.

[0050] Next, we will explain the business processes to which natural language, which is the target of classification by the information processing device according to this embodiment, belongs. As an example, a ledger, which is a document related to a company's management activities, organizes and stores the above-mentioned requirements for each business process. For example, in the case of a product specification, in the early stages of product development, the details of the specification review process enclosed by solid line 41 in FIG. 3 are described in the product specification. The specification review process enclosed by solid line 41 mainly includes information on the requirement process category. In the intermediate stages of product development, the details of the detailed design process enclosed by solid line 42 in FIG. 4 are described in the product specification. The detailed design process mainly includes specific information such as the performance / function process category, logic process category, and product process category. In the final stages of product development, the details of the evaluation process enclosed by solid line 43 in FIG. 5 are described in the product specification. The evaluation process mainly includes information such as the product process category and the security means process category.

[0051] As mentioned above, it is determined in advance which process category (R, F, L, P, l, and p) each requirement recorded on the form used for each business process belongs to, and the elements and process categories included in each business process. This will be explained in detail later.

[0052] Here, the natural language used in the text recorded in a document or the like may differ depending on the company, department, person in charge, etc., even if the natural language relates to the same requirement of the same process. For example, in the case of a specific requirement of a specific process division in product development work, the natural language used in the text recording the content of the requirement may differ depending on the person in charge, even if the content is the same.

[0053] As shown in Figure 6, the natural language expressing each requirement shown in Figure 2 may be replaced with natural language highly similar to that requirement, which is written around the requirement in Figure 6. Figure 6 explains the natural language highly similar to that requirement in the process diagram shown in Figure 2, and is an enlarged version of Figure 2, with natural language highly similar to that requirement written around the requirement in Figure 2. For example, requirement 31, "I want to make local transportation easier," can be expressed using character strings such as "neighborhood," "walking," "easy," "walking," "commute," and "tough." Similarly, requirement 34, "portability," can be expressed using "rideable" and "transport," and requirement 32, "self-propelled transportation means operated by handle," can be expressed using natural language including character strings such as "handle," "automatic machine," "electric drive," and "operating lever."

[0054] In business processes, when data is aggregated to be passed to tools or systems for business execution, variations in spelling, such as those mentioned above, exist. Therefore, in order to classify and organize the data, measures such as normalization are required to address the variations in spelling, which requires humans to read vast amounts of data. While there are name matching technologies that utilize thesaurus data and distributed word representations, their accuracy is still insufficient. The information processing device according to this embodiment classifies and organizes the natural language expressions that express each requirement in the process diagram shown in FIG. 2, as shown in FIG. 6, and natural language expressions that are highly similar to these. In this case, the classification and organization is performed using the business process to which each requirement belongs and the data model.

[0055] The operation of the information processing apparatus according to this embodiment will be described in an abstract manner below.

[0056] FIG. 7 shows a schematic diagram illustrating the relationship between business processes and data models, which will be described later. FIG. 7 shows data models 131 to 135 corresponding to multiple business processes P1, P2, P3, P4, and P5, respectively. As an example, the business processes P1 to P5 shown in FIG. 7 are arranged in the order of the progress of the business processes, with business processes P1 to P3 being a planning process, business process P4 being a design review process, and business process P5 being a testing process. The relationships between the business processes P1 to P5 are as follows: n Business Process P, one step before n-1 is business process P n Parent of Business Process P n Business Process P after one step of n+1 is business process P n For example, in Figure 7, if business process P2 is the process in question, the parent process of business process P2 is business process P1, and the child process is business process P3.

[0057] The process divisions that require attention in each business process differ depending on the order of the stages in the business process and the dependency relationships between the business process and other business processes. Here, the process divisions that require attention refer to the process divisions to which the requirements shown in Figure 1 belong, or the process divisions to which the items shown in Figure 11 belong, that are most frequently included in a given business process, and are any of the process divisions R, F, L, P, l, and p. For example, in Figure 7, the process division that requires attention in business process P1, which is first in the order of the stages in the business process, is R. In the case of a planning process, the process division that requires attention in business processes P1 to P3 is R.

[0058] With reference to Figures 3 to 5, we have already given examples where the product specification, which is a document used in the specification review process, mainly contains information on the requirement process category, the product specification, which is a document used in the detailed design process, mainly contains information on the performance / function process category, logic process category, and product process category, and the product specification, which is a document used in the evaluation process, mainly contains information on the security means process category.The above-mentioned noteworthy items correspond to the requirement process category in the specification review process, the performance / function process category, logic process category, and product process category in the detailed design process, and the security means process category in the evaluation process in the examples shown in Figures 3 to 5.

[0059] Fig. 8(a) shows a data model of business process P used in the information processing device according to this embodiment, Fig. 8(b) shows an example structural diagram of business processes P1 to P3, which are interdependently related to one another, and Fig. 8(c) shows a table expressing the interdependence of business processes P1 to P3 in Fig. 8(b). PID-1, PID-2, ... in Fig. 8(a) represent interdependence correlations between business processes P1 to P3 shown in Fig. 8(b) and correspond to business process correlation_IDs shown in Fig. 8(c). Business process P in Fig. 8(a) corresponds to the process ID in the process item list in Fig. 8(c), and P(parent) and P(child) in Fig. 8(a) correspond to "parent" and "child" in the process correlation table in Fig. 8(c).

[0060] An example of a form used in a business process is shown in Fig. 9. Fig. 9 shows a business process Pm (m is an integer equal to or greater than 1) in which a form UI is used, each item (user interface) UI-n (n is an integer equal to or greater than 1) written in the form UI, and process classifications R, L, and P to which the natural language entered in item UI-n belongs.

[0061] FIG. 10(a) shows a data model of form UIs used in a business process in an information processing device according to this embodiment. FIG. 10(b) shows an example of a structure diagram of forms UI1 and UI2, which are interdependently related to each other. FIG. 10(c) shows a table expressing the interdependence between forms UI1 and UI2 in the business process shown in FIG. 10(b). As with the business process shown in FIG. 16, UI-ID-1, UI-ID-2, ... in FIG. 10(a) represent the interdependence between the forms UI1 and UI2 shown in FIG. 10(b) and correspond to the process UI correlation_ID shown in FIG. 10(c). The form UI in FIG. 10(a) corresponds to the process UI in the process item list in FIG. 10(c), and the UI(parent) and UI(child) in FIG. 10(a) correspond to the "parent" and "child" in the process correlation table in FIG. 10(c).

[0062] As with business process P and form UIs, parent UI-n and child UI-n that are in a dependency correlation with item UI-n may exist for item UI-n that is recorded in a form. Parent UI-n and child UI-n that are in a dependency correlation with item UI-n may be recorded in the same form as item UI-n, or may be recorded in different forms.

[0063] FIG. 11(a) shows a data model used in the information processing device according to this embodiment, illustrating the correlation between a business process and a form UI used in the business process. FIG. 11(b) shows an example of a structure diagram of a business process P and a form UI, which are interdependently correlated with each other. FIG. 11(c) shows a table expressing the interdependent correlation between the business process P and the form UI in FIG. 11(b). Assume that the form used in business process P3 is form U1. P-UI-ID-1, P-UI-ID-2, and so on in FIG. 11(a) represent the interdependent correlation between business process P3 and form U1 shown in FIG. 11(b) and correspond to the process UI correlation_ID shown in FIG. 11(c). The form UI in FIG. 11(a) corresponds to the PU correlation in FIG. 11(c), and the UI(parent) and UI(child) in FIG. 11(a) correspond to the "parent" (P3) and "child" (U1) in the process correlation table in FIG. 11(c).

[0064] Fig. 12 shows a data model indicating the correlation between items of each item ID-n of a form in a design basis process as an example of a business process used in the information processing device according to this embodiment, and Fig. 13 shows a structural diagram of each item ID-n shown in Fig. 12, which are in a mutually dependent correlation. The ID-n shown in Fig. 13 is a dependency correlation between each element shown in Fig. 12.

[0065] Figure 14(a) shows a data model showing the correlation between the report UI used in the business process and each item ID-n of the report in the design basis process. Figure 14(b) shows a portion of a structural diagram of the report UI and each item ID-n shown in Figure 7, which are in a mutually dependent correlation. Figure 14(c) shows a table expressing the dependent correlation between the report UI in Figure 14(b) and each item ID-n. The UI shown in Figure 14(a) is a dependent correlation between each element shown in Figure 14(b). As shown in Figure 14, it can be seen that there is a dependent correlation between the report UI and report item R1.

[0066] As described above, there can be dependency correlations between elements of the same type, such as between business processes of multiple business processes P, between forms of multiple form UIs, and between items of each item ID-n on a form. However, dependency correlations are not limited to between elements of the same type; they can also exist between elements of different types, such as between a business process P and a form UI, or between a form UI and each item ID-n on a form. On the other hand, attribute correlations refer to correlations with attributes that explain specific items. While attribute correlations are not shown in Figures 9 to 14, attribute correlations differ from dependency correlations in that elements in an attribute correlation are of the same type, and attribute correlations do not exist between elements of different types.

[0067] The data model for each business process will be described with reference to Figures 15 to 17. As shown in Figure 15, business processes P1, P2, and P3 are exemplified by a planning business process, a design review business process, and a design verification and assurance business process.

[0068] Figure 16 shows examples of forms used in each business process shown in Figure 15. In the planning business process, design review business process, and design verification and assurance business process, a proposal document, a design review document, and design verification materials are used as forms U1, U2, and U3, respectively. Each of forms U1, U2, and U3 has items U1-1, U1-2..., U2-1, U2-2..., U3-1, U3-1.... As shown in Figure 16, each of forms U1, U2, and U3 has items that are common to other forms and items that are not common to other forms.

[0069] Figure 17 shows an example of a data model corresponding to each business process shown in Figure 15 and each report shown in Figure 16. It shows that each item U1-1, U1-2..., U2-1, U2-2..., U3-1, U3-1... shown in Figure 16 has items belonging to processes R, F, L, P, l, and p. As with Figure 16, each of the data models shown in Figure 17 has items that are common to other data models and items that are not common to other data models. The data model of the entire business process based on the three data models shown in Figure 17 is shown in Figure 15.

[0070] Based on the above explanation, a schematic diagram summarizing the relationships among processes, UIs, and data is shown in Figure 18. As explained above and shown in Figure 18, processes, UIs, and data are each expressed in natural language. The natural language expressing processes, UIs, and data each has a dependency correlation or an attribute correlation with other elements that make up at least one of the processes, UIs, and data. As shown in Figure 18, the process and UI, and the UI and data, respectively, have a dependency correlation.

[0071] Details will be described later, but the registration unit 114 of the information processing device according to this embodiment registers in a dataset the natural language, the name of the item from which the natural language was extracted, the business process to which a specified document belongs, the type of specified document, the process classification R, F, L, P, l, and p to which the item belongs, and items that have a dependency correlation and / or attribute correlation with the item, and once this data is accumulated, it becomes possible to obtain information on the UI linked to the process by a dependency correlation and the data linked to the UI by a dependency correlation.

[0072] As described with reference to FIG. 1 , according to the information processing device of this embodiment, when a user moves back and forth between the AsIs model and the ToBe model to proceed with organization, a process is specified in the AsIs model, a user interface (UI) associated with the specified process is selected, and data described in the UI is acquired. According to the data model used in the information processing device of this embodiment, information on data that has a mutual dependency correlation or attribute correlation can be obtained from data registered in a dataset. When a UI associated with a specified process is selected, information on UIs that can be selected from the specified process can be obtained from information on data that has a mutual dependency correlation or attribute correlation. Similarly, when data described in a UI is acquired, information on the acquired data can be obtained from information on data that has a mutual dependency correlation or attribute correlation. Furthermore, information on UIs in which data can be described and information on processes in which a UI can be selected can also be obtained.

[0073] Moving back and forth between the AsIs model and the ToBe model, that is, moving back and forth between the process, UI, and data, means that by using the dependency correlation or attribute correlation described above, you can obtain information on selectable UIs from a process and information on the data described in the UI, and further obtain information on UIs in which the obtained data may be described and information on processes from which UIs may be selected, and use these to trace the relationships among the process, UI, and data. By tracing the relationships between the process, UI, and data, you can examine the AsIs model and ToBe model from various data and UI perspectives, and refine and abstract them.

[0074] An information processing device according to this embodiment will be described with reference to the drawings. Fig. 19 is a block diagram showing the configuration of the information processing device 10 according to this embodiment. Fig. 20 is a diagram illustrating an example of the operation of the information processing device 10 according to this embodiment.

[0075] As shown in Figure 19, the information processing device 10 is composed of a CPU 11 for performing various calculations, a memory unit 12 for storing processing programs, data, etc., an I / O (input / output interface) 13, a display unit 14, and an input unit 15.

[0076] The I / O 13 is an interface, buffer, etc. for communication (transmission and reception).

[0077] The display unit 14 is a display device such as a display, and displays the results of calculations performed by the CPU 11, etc.

[0078] The input unit 15 is an input device such as a keyboard or a mouse, and is realized by a device that receives input from a user and transmits it to the CPU 11.

[0079] The block diagram in Figure 19 shows the functional units within the CPU 11. When each functional unit of the CPU 11 is realized by software, the CPU 11 realizes it by executing instructions of a program, which is software that realizes each function. In detail, the CPU 11 includes an acquisition unit 111, an extraction unit 113, a registration unit 114, an organization unit 115, a UI display unit 116, etc. The storage unit 12 also includes a dataset storage unit 121 and a data storage unit 122.

[0080] The acquisition unit 111 acquires a predetermined document. In this embodiment, the document acquired by the acquisition unit 111 is a document that can occur in the current process model, such as a form, but is not limited to this and may be, for example, a calculation sheet, a formula, a data file, etc. Furthermore, the document acquired by the acquisition unit 111 does not necessarily have to include two or more words or compound words, and may be a single word or a compound word.

[0081] FIG. 20 shows an example in which the acquisition unit 111 acquires specifications 203 and 204 as predetermined documents from file servers 201 and 202, respectively.

[0082] The predetermined document may be saved not only in text format but also in other formats such as image, audio, etc. The extraction unit 113 may be configured to extract character strings according to the format of each document, and may, for example, recognize a sentence by image recognition, audio recognition, etc., and then extract character strings from the sentence. In this embodiment, the predetermined document is a form.

[0083] The document acquired by the acquisition unit 111 may be tagged with the type of the document and / or the business process to which the document belongs. Alternatively, when the acquisition unit 111 acquires a document, the acquisition unit 111 may acquire the type of the document and / or the business process to which the document belongs, for example, through a user's input via the input unit 15. Alternatively, the formats of the reports that may occur in the business processes often have common items for each business process, and therefore the acquisition unit 111 may automatically determine the type of the acquired document and / or the business process to which the document belongs, based on the acquired document.

[0084] An example of a predetermined document is shown in Figure 21. Figure 21 is a product planning document, which is a form related to the business process of the product planning process.

[0085] In this embodiment, the matters to be described in a specified document are composed of one or more items. An item in a specified document is an item in the specified document that explains, for example, at least a part of a business process, and typically consists of an item name and a field for describing the content of the item. However, the item name may be omitted. Hereinafter, the name of an item will be referred to as the item name, and the content of the item will be referred to as the item content. In the product planning document shown in FIG. 21, the item names of "planning overview" and "cost comparison table" are written, and the respective contents are entered in the fields for describing the respective item contents.

[0086] Each item listed in the form shown in Fig. 21 and the content of the items entered in each item are the same as or similar to the content listed in each requirement in the process diagrams shown in Fig. 2 to Fig. 5. For example, the item names and content of each item in the plan outline and cost comparison table listed in the product planning document shown in Fig. 21 are the contents of requirement 31, requirement 32, and requirement 33 of the request process in the process diagram shown in Fig. 2 rewritten to match each item.

[0087] As described above, the item names and contents of each item written in the forms used in the business process correspond to the contents written in each requirement of the process diagram shown in Figures 2 to 5. As explained above with reference to Figures 3 to 5, the forms organize and store the requirements of the process diagram for each business process, and the requirements of the process diagram that are written as items in the form are roughly predetermined depending on each business process. The requirements of the process diagram are mutually dependently correlated or attribute-correlated, as shown by dashed or solid lines in Figure 2. Therefore, the items and contents of each item of the form are mutually dependently correlated or attribute-correlated, just like the requirements of the process diagram.

[0088] The product planning document shown in FIG. 22 shows information about which process each item in the product planning document shown in FIG. 21 belongs to among the process divisions R, F, L, P, l, and p in the process diagram shown in FIG. 2, and which other items in the document have a dependency correlation with it. In FIG. 22, R indicates that the item belongs to the process division R. The same is true for F, L, P, l, and p. Furthermore, R (parent) and R (child) indicate information about items that have a dependency correlation with the item. In FIG. 22, R (parent) is written as R parent, and R (child) is written as R child.

[0089] As shown in Figure 22, the project overview items show process categories R, F, L, and P, and each process category is further denoted by R (parent) R (child), F (parent) F (child), L (parent) L (child), or P (parent) P (child). This indicates that one or more natural languages ​​included in the content of the project overview items belong to one of the process categories R, F, L, or P, and that information indicating which items they are dependently correlated with is linked. In the cost comparison table, the items corresponding to the previous model are denoted by PP (parent) P (child). This indicates that the previous model belongs to a process category and is related to the process one step earlier and the process one step later as a business process. Furthermore, the items corresponding to price are denoted by RR (parent) R (child). This indicates that each item belongs to the process categories P and R, and that information indicating which items they are dependently correlated with is linked.

[0090] The dependency correlation expressed by R (parent) R (child) of RR (parent) R (child) shown in Fig. 22 will be explained with reference to Fig. 23(a) and Fig. 23(b). Fig. 23(a) shows a structural diagram of items R1 to R9 that belong to process partition R and have a dependency correlation with each other, and Fig. 23(b) shows a table expressing the dependency correlation.

[0091] 23(a) show that the items R1 to R9 are in a parent-child relationship, i.e., a dependency relationship, and for example, item R1 has no parent and its children are items R2, R7, and R8. Item R2 has item R1 as its parent and items R3 and R6 as its children.

[0092] The requirement item list shown in Fig. 23(b) shows at least some of the items belonging to the process classification of requirement (R), and items R1 to R9 are shown. The requirement item correlation table shown in Fig. 23(b) shows some of the dependency correlations between the items shown in the requirement item list. For example, correlation 001 indicates the dependency correlation between items R1 and R2, where item R2 is the child when item R1 is the parent, and correlation 002 indicates the dependency correlation between items R1 and R8, where item R8 is the child when item R1 is the parent.

[0093] The dependency relationships shown in FIGS. 22 and 23 are shown in the process diagram of FIG. 2 as relationships between items connected by dashed lines in the horizontal direction within the page.

[0094] Furthermore, for each item, an attribute that explains the item is called an attribute correlation. This attribute correlation is shown in Fig. 2, but not in Figs. 22 and 23. The attribute correlation will be explained with reference to Figs. 24(a) and 24(b). Figs. 24(a) and 24(b) are diagrams in which the attributes of each item have been added to the structural diagram of items R1 to R9 and the required item list shown in Figs. 23(a) and 23(b).

[0095] Referring to FIG. 24(a), for example, item R1 has the following attributes: "Request source: Customer," "Importance: Level 3," and "Occurrence date: Month / Day / Age, 2024." Similarly, item R2 has the following attributes: "Request source: Customer," "Importance: Level 1," and "Occurrence date: Month / Day / Age, 2024." Referring to FIG. 24(b), the attributes of items R1 to R9 are listed. Note that the origin is the item name, and for R1, the customer is the item content. Similarly, the importance is the item name, and for R1, level 3 is the item content. Similarly, the occurrence date is the item name, and for R1, month / day / Age, 2024 is the item content.

[0096] The attribute correlations shown in FIG. 24 are shown in the process diagram of FIG. 2 as relationships between items connected by solid lines in the vertical direction within the page.

[0097] The extraction unit 113 extracts natural language from a predetermined document and stores it in the data storage unit 122. The extraction unit 113 may extract any word or compound word from the predetermined document, or may extract all or a predetermined number of words and / or compound words contained in the predetermined document. The natural language extracted by the extraction unit 113 from the predetermined document is the content entered in a field for describing the content of an item described in the predetermined document, and may be part or all of the content of the item described in the predetermined document. Alternatively, it may be natural language extracted from the content of the item when the content of the item is a sentence, such as the project summary item shown in FIG. 21.

[0098] When extracting natural language from a predetermined document, the extraction unit 113 may acquire the name of the item from which the natural language was extracted, the business process to which the predetermined document belongs, and the type of the predetermined document, and store them in the data storage unit 122. If the predetermined document is a form, the type of the predetermined document is, for example, a product plan, product specification, product catalog, complaint management sheet, service report, etc., and if the predetermined document is not a form, it is, for example, a calculation sheet, formula, data file, etc.

[0099] When acquiring the business process to which a predetermined document belongs and the type of the predetermined document, if the predetermined document is tagged with the document type and / or the business process to which the document belongs, the extraction unit 113 may acquire the business process and the type of the predetermined document from the information tagged with the predetermined document. If the acquisition unit 111 acquires the document type and / or the business process to which the document belongs via the input unit 15, or if the acquisition unit 111 automatically determines the acquired document type and / or the business process to which the document belongs, the extraction unit 113 may receive the business process and the type of the predetermined document from the acquisition unit 111.

[0100] The extraction unit 113 extracts a natural language from a predetermined document and stores it in the data storage unit 122, and repeats this operation for one or more documents, thereby storing multiple natural languages ​​in the data storage unit 122. Furthermore, the extraction unit 113 extracts a natural language from a predetermined document, the name of the item from which the natural language was extracted, the business process to which the predetermined document belongs, and the type of the predetermined document, and stores these in the data storage unit 122, and repeats this operation for one or more documents, thereby storing data for multiple natural languages ​​in the data storage unit 122: the natural language, the name of the item from which the natural language was extracted, the business process to which the document from which the natural language was extracted belongs, and the type of document from which the natural language was extracted.

[0101] The organizing unit 115 classifies and organizes the multiple pieces of data stored in the data storage unit 122, and determines whether specific data is similar to other data excluding the specific data among the multiple pieces of data stored in the data storage unit 122. The organizing unit 115 determines the similarity between the specific data and other data excluding the specific data for all of the multiple pieces of data stored in the data storage unit 122.

[0102] After determining that specific data and another specific data excluding the specific data are similar, the organizing unit 115 extracts at least one of commonalities, characteristics, similarities, and differences between the specific data and the other specific data, tags the specific data and the other specific data with at least one of the extracted commonalities, characteristics, similarities, and differences, and transmits them to the registration unit 114.

[0103] The organizing unit 115 may determine, based on at least one of the extracted commonalities, characteristics, similarities, and differences, the process class to which specific data belongs and the correlation between the specific data and data excluding the specific data among the multiple data stored in the data storage unit 122. The organizing unit 115 may classify and organize the multiple data stored in the data storage unit 122, tag each of the multiple data stored in the data storage unit 122 with the process class R, F, L, P, l, and p to which each data belongs, the mutual dependency relationships of the multiple data, and / or attribute relationships, and transmit the tagged data to the registration unit 114.

[0104] In addition, after determining that specific data and data excluding the specific data are similar, the organizing unit 115 may determine the correlation between the specific data and data excluding the specific data among the multiple data stored in the data storage unit 122 by natural language processing.

[0105] The criteria for determining whether specific data and data excluding the specific data are similar when the organizing unit 115 determines whether the specific data are similar are specified by one of the following criteria or a combination thereof. The organizing unit 115 may quantify each similarity based on each criterion when determining whether specific data and data excluding the specific data are similar based on each criterion listed below. The organizing unit 115 may determine that the similarity is high, for example, when each of the similarities based on each criterion exceeds one or more predetermined thresholds. Alternatively, the organizing unit 115 may determine that the similarity is high when the sum of the similarities based on each criterion is the total similarity and the total similarity exceeds a second predetermined threshold, and / or may determine that the similarity is low when the total similarity is lower than a third predetermined threshold. Note that the determination threshold may be given as an absolute value. Alternatively, the determination threshold may be derived as a relative value obtained as a result of learning. Furthermore, weights or coefficients such as probability or importance may be applied. The predetermined threshold and the second predetermined threshold may be stored in the storage unit 12, or may be acquired via the input unit 15 by input by the user, for example.

[0106] The criterion for determining whether data is similar is whether the business processes to which a specific piece of data and data other than the specific piece of data, extracted by the extraction unit 113 and stored in the data storage unit 122, belong are the same or similar. Here, "similar" refers to whether the vectors are similar when the natural language constituting the names of the business processes are vectorized, whether the vectors are similar when the natural language and the natural language have attribute correlations and dependency correlations, and / or whether the order of the business processes themselves is similar. Similarly, the term "similar" used in the similarity determination criteria described below refers to whether the vectors are similar when the natural language constituting the names of the objects to be determined for similarity are vectorized, whether the vectors are similar when the natural language and the natural language have attribute correlations and dependency correlations, and / or whether the order of the objects to be determined for similarity is similar. Furthermore, the business processes to which the specific data and the data excluding the specific data belong, among the multiple data extracted by the extraction unit 113 and stored in the data storage unit 122, have a common parent and child of the dependency correlation or a common business process corresponding to the attribute correlation from the perspective of each. For example, in Fig. 7, if the business process to which the predetermined document from which the extraction unit 113 extracted specific data belongs is business process P2, the parent of the dependency correlation is business process P1 and the child is business process P3.

[0107] The criterion for determining that the types of documents from which data are extracted are similar is whether the types of the predetermined documents from which data are extracted by the extraction unit 113 are the same or similar. Furthermore, since the document type is determined by the business process, the criterion for determining the similarity of the types of documents from which data are extracted may be whether the business processes to which the predetermined documents from which the extraction unit 113 extracted natural language belong have a common parent-child relationship in a dependency correlation or a common business process in an attribute correlation.

[0108] The criteria for determining whether the names of items from which data are extracted are similar are whether the names of the items from which the extraction unit 113 extracted natural language are identical or similar. Alternatively, whether the names of the items from which the extraction unit 113 extracted data share at least two or more common parent and child items in a dependency correlation, or high-level and low-level abstract items in an attribute correlation. For example, in the process diagram shown in FIG. 2, high-level and low-level abstract items in an attribute correlation correspond to requirements located above and below a given requirement in the vertical direction on the page, which are directly connected by a solid line and have an attribute correlation with a given requirement. For example, if the name of the item from which the extraction unit 113 extracted natural language is requirement 32, "self-propelled vehicle operated by handle operation," in FIG. 2, the high-level abstract item in the attribute correlation is requirement 33, and the low-level abstract item in the attribute correlation is requirement 31. Hereinafter, high-level and low-level abstract requirements in an attribute correlation are referred to as "before and after" in the attribute correlation.

[0109] The criterion for determining that the process classes to which the data belong are similar is whether the process classes to which the data extracted by the extraction unit 113 belong are the same or close to each other, or whether the process classes are parent and child of a dependency correlation or have a common process class corresponding to an attribute correlation.

[0110] The criterion for determining whether dependency correlations are similar is whether the parent and child requirements of the dependency correlations are close to each other.

[0111] The criterion for determining whether attribute correlations are similar is whether the requirements before and after the attribute correlations are close to each other.

[0112] The criterion for determining that the data extracted by the extraction unit 113 are similar in terms of vectors is whether the data extracted by the extraction unit 113 are similar to each other in terms of distributed representations of words in the existing technology, or whether the data are similar in terms of document vectors.

[0113] The criterion for determining that data strings are similar is whether there is a natural language or item name in which at least a predetermined number of characters are identical among the strings constituting the data or item names extracted by the extraction unit 113 from a specified document, or whether there is a natural language or item name in which at least a predetermined number of characters are identical.

[0114] The above criteria may be weighted or multiplied by a coefficient such as probability or importance. For example, if the dependency correlations or attribute correlations are similar, the data extracted by the extraction unit 113 are considered to have a high degree of similarity with each other. Also, for example, if the business processes are similar, the similarity is considered to be high based on the similarity of the business processes alone. By multiplying the degree of similarity when each of the above criteria is satisfied by a weight or a coefficient, the degree of similarity when multiple criteria are satisfied can be expressed as a numerical value.

[0115] Here, the organizing unit 115 may determine that only information that is determined to have a high degree of similarity is similar information, or may determine that the degree of similarity is high when the total degree of similarity is higher than a second predetermined threshold.

[0116] The organizing unit 115 repeatedly classifies and organizes the multiple pieces of data stored in the data storage unit 122 as described above each time data is added to the data storage unit 122, thereby increasing the number of pieces of data that are identical or similar to each other and clarifying a predetermined concept for the data, i.e., a natural language definition. That is, by repeatedly classifying and organizing the multiple pieces of data stored in the data storage unit 122, the organizing unit 115 determines that specific data is similar to other data that is different from the specific data, and extracts commonalities, characteristics, and / or similarities between the specific data and other data. Each time data is added to the data storage unit 122, the organizing unit 115 repeatedly classifies and organizes the data, thereby clarifying at least one of common attributes between the specific data and other data and deficiency / difference attributes that express the differences between the specific data and other data, thereby clarifying the definition of a predetermined concept for the data. The organizing unit 115 clarifies at least one of common attributes and missing / different attributes between multiple pieces of data stored in the data storage unit 122, thereby clarifying the definition of a predetermined concept for the data, thereby making it possible to clarify the mutual dependencies and / or attribute relationships between the multiple pieces of data stored in the data storage unit 122.

[0117] When the organizing unit 115 clarifies at least one of the common attributes and the missing / different attributes including the dependency and / or attribute relationship between the specific data and the other data, the organizing unit 115 may tag the specific data and the other data with the common attributes and the missing / different attributes including the dependency and / or attribute relationship, and transmit them to the registration unit 114. Furthermore, when the definition of a predetermined concept for the specific data is clarified in this way, the organizing unit 115 may tag the specific data with the definition of the predetermined concept and transmit them to the registration unit 114.

[0118] By repeatedly classifying and organizing data, the organizer 115 constructs theoretically infinite relationships between natural languages, including dependency correlations, which are outward connections, and attribute correlations, which are inward connections. Data acquired through business processes, UIs, etc. can be collected within this structure, and data in the same or similar domains can be gathered. By utilizing a collection (dataset) of natural languages ​​that correlate with important business information, there is no need to store or extract a theoretically infinite number of natural languages. This can improve, for example, the accuracy of predictions for natural language attributes and improve utilization and performance when organizing data from an AsIs model to a ToBe model.

[0119] The registration unit 114 registers, into a dataset, a plurality of data items stored in the data storage unit 122, which are tagged with at least one of a common attribute and a deficiency / difference attribute including a dependency relationship and / or an attribute relationship between specific data and another specific data received from the organization unit 115. The registration unit 114 may register, into a dataset each time it receives data items, specific data items tagged with a common attribute and a deficiency / difference attribute including a dependency relationship and / or an attribute relationship between specific data and another specific data received from the organization unit 115. The registration unit 114 may register, into a dataset, a plurality of data items stored in the data storage unit 122, which are tagged with the process classifications R, F, L, P, l, and p to which the data items belong, and the dependency relationship and / or attribute relationship between the multiple data items received from the organization unit 115.

[0120] In addition to these, the registration unit 114 may register information such as the date and time the natural language was registered, information on the sentence, document, business process, etc. in which the natural language was confirmed, and the number of times the character string was confirmed in the dataset.

[0121] Furthermore, in addition to these, the registration unit 114 may transmit to the registration unit 114 information indicating which data among the multiple data stored in the data storage unit 122 received from the organizing unit 115 was identical, and / or which data it was similar to, and / or the judgment results based on each of the one or more judgment criteria described above, and / or the organizing unit 115 may transmit to the registration unit 114 the similarity of each of the one or more judgment criteria described above, and / or may register the total similarity in the dataset.

[0122] When the registration unit 114 receives from the sorting unit 115 a determination result that identical or similar information does not exist, the natural language, the name of the item from which the natural language was extracted, the business process to which the specified document belongs, and the type of the specified document, the registration unit 114 may register the natural language, the name of the item from which the natural language was extracted, the business process to which the specified document belongs, and the type of the specified document in a dataset.

[0123] FIG. 20 shows an example of a basic UI for organizing a UI (document) from a process. In FIG. 20, as an example, the extraction unit 113 extracts data R1, data R2, data R3, and data R4 from specifications 203 and 204. Furthermore, the organizing unit 115 classifies and organizes the multiple data stored in the data storage unit 122, tags each of the multiple data stored in the data storage unit 122 with the process classifications R, F, L, P, l, and p to which the data belongs, the mutual dependencies and / or attribute relationships between the multiple data, and transmits the tagged data to the registration unit 114. The registration unit 114 registers this information in a dataset. Task 1, Task 2, Person in Charge: Department 1, Element: Part 1, Person in Charge: Department 2, Element: Part 2 are examples of information registered in the dataset.

[0124] In Figure 20, natural language dependency correlations are registered from Task 1 and Task 2, and then natural language dependency correlations and attribute correlations such as UI, data, reference destination, registration destination, tool, INPUT, and OUTPUT are gradually added to them, increasing the resolution and making them more specific in the process.

[0125] The UI display unit 116 uses the data registered in the dataset to generate data for display in a user interface (UI) with different presentation methods, and presents the data to the user. The method of presenting the data to the user may be, for example, a method of displaying the data on the display unit 14.

[0126] When generating data to be displayed on the UI, the UI display unit 116 generates the data to be displayed on the UI by utilizing the relationships among the process, UI, and data based on the dependency correlation and attribute correlation shown in Fig. 18. For example, when a specific UI is acquired by the acquisition unit 111, information about the process to which the UI belongs can be obtained by utilizing the dependency correlation based on the information registered in the dataset, i.e., the relationship shown in Fig. 18, and information about other UIs in the process can be obtained from the obtained process information. The UI display unit 116 can generate data to be displayed on other UIs obtained in this way.

[0127] When a specified UI is acquired by the acquisition unit 111, the UI display unit 116 can use the information registered in the data set, i.e., the relationships shown in Figure 18, to trace relationships not only between processes but also between other elements that have a dependency correlation or attribute correlation with the UI, such as data described in the UI and other elements that have a dependency correlation or attribute correlation with this data, and use the obtained information to generate data to be displayed on the UI.

[0128] When generating data to be displayed on the UI, the UI display unit 116 may use data registered in a data set from the information obtained as described above to, for example, organize identical or similar information as identical information, and then generate data to be displayed on the UI. Alternatively, the UI display unit 116 may perform calculations based on desired logic and use the calculation results to generate data to be displayed on the UI.

[0129] The form of the UI for displaying the data generated by the UI display unit 116 may be, for example, a detailed design process diagram, a list of deliverables, a correlation diagram, a CRUD diagram (Create-Read-Update-Delete diagram), a data list, an ER diagram (Entity Relationship Diagram), a business flow diagram, a tree diagram, a logic tree, etc.

[0130] FIG. 25 shows, as an example, a deliverable list and a data list in which the results of calculations performed by the UI display unit 116 are used as deliverables.

[0131] Figure 26 shows an example of a correlation diagram. The correlation diagram shown in Figure 26 is an RFLP correlation diagram. An RFLP correlation diagram clarifies and organizes the relationships between R (Request), F (Function), L (Logic), and P (Parameter). Note that Figure 26 shows the diagram reorganized into classifications based on process classifications. While the process classification definition and pattern, RFLPlp, is shown, this format is not limiting. It is also possible to omit some of the display, for example, in a format such as RPp. In both cases, a text information table with high resolution is created by registering the connections (dependency correlations and attribute correlations) from requirements to design parameters. The necessary information is then extracted from the text information table at the required resolution for organization, analysis, and judgment. Displaying everything significantly reduces visibility, so the display format can be switched appropriately depending on the business process, etc.

[0132] Figure 27 shows a business process diagram. Figure 28 shows a graph displaying the man-hours for each task. Figure 29 shows an example of a CRUD diagram. Figure 30 shows an example of an ER diagram. A CRUD diagram is a diagram that organizes in tabular form which functions or programs (modules) create (C), read (R), update (U), and delete (D) which data when designing a system or software that handles various types of data. An ER diagram shows the relationships between each element of a business process. For example, while a business flow diagram allows you to grasp individual processes, an ER diagram allows you to grasp the process flow, the relationships between them, and the relationships between each element. Figure 31 shows an example of a business flow diagram. Figure 32 shows an example of a tree diagram.

[0133] 25 to 32 generated by the UI display unit 116 may be configured to be selected by the user. The UI display unit 116 may determine the UI to be displayed via the input unit 15, for example, based on an input by the user. Furthermore, the UI display unit 116 may determine, via the input unit 15, logic for calculations and processing to be executed on information obtained, for example, based on an input by the user. Furthermore, after generating a data UI such as those shown in FIGS. 25 to 32, the UI display unit 116 may edit or correct the data UI, for example, based on an input by the user, or generate a data UI different from the previously generated data UI, etc.

[0134] 20, as an example, the UI display unit 116 extracts task 1, task 2, responsible: department 1, element: part 1, responsible: department 2, and element: part 2 from data R1, data R2, data R3, and data R4 registered in the dataset, and then performs a calculation of the man-hours, obtaining the calculation results that the man-hours for task 1 are 5 hours (5h) and the man-hours for task 2 are 10 hours (10h). Furthermore, calculations are performed using logic L1, logic L2, and logic L3 for calculation, obtaining calculation result 1 (P1, P2, P3) and calculation result 2 (P4). Calculation result 1 and calculation result 2 are saved in the file server.

[0135] The dataset storage unit 121 stores a dataset. The dataset includes natural language, the name of an item from which the natural language is extracted, the business process to which a specific document belongs, the type of the specific document, the process classifications R, F, L, P, l, and p to which the item belongs, and items that have a dependency correlation and / or attribute correlation with the item. The dataset may be created in advance and stored in the dataset storage unit 121, or may be generated by the information processing device according to this embodiment.

[0136] The data storage unit 122 stores the natural language extracted from the predetermined document by the extraction unit 113, the name of the item from which the natural language was extracted, the business process to which the predetermined document belongs, and the type of the predetermined document.

[0137] The product planning document shown in Fig. 33 shows natural language corresponding to each requirement shown in the process diagram, which is included in the sentences written in the planning overview section of the product planning document shown in Fig. 22, and for each of these natural languages, the process categories R, F, L, P, l, and p to which it belongs, and information on items that have a dependency correlation and / or attribute correlation with each item that can be linked to each item. Specifically, for example, it shows that the compound word "market of 20 trillion yen scale" belongs to process category R, ​​and is linked to items that have a dependency correlation with "market of 20 trillion yen scale."

[0138] When extracting natural language corresponding to each requirement shown in the process diagram from the text written in the planning overview section of the product planning document shown in Figure 22, more accurate extraction is possible when natural language processing is performed on the text, and extraction is performed using information on the process category to which the content written in the planning overview section belongs, and items that have dependency correlations and attribute correlations, compared to extraction using only natural language processing on the text.

[0139] Next, the operation of the information processing device according to this embodiment will be described with reference to the flowchart of FIG.

[0140] In step S801, the acquisition unit 111 acquires a predetermined document.

[0141] In step S802, the extraction unit 113 extracts natural language from a predetermined document and stores it in the data storage unit 122.

[0142] In step S803, the organizing unit 115 classifies and organizes the multiple data stored in the data storage unit, determines the similarity between the multiple data stored in the data storage unit, and then clarifies at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships between the multiple data, and tags each of the multiple data.

[0143] In step S804, the registration unit 114 registers, into a dataset, multiple pieces of data, each tagged with at least one of common attributes and missing / different attributes, including dependencies and / or attribute relationships between multiple pieces of data.

[0144] In step S805, the UI display unit 116 uses the data registered in the data set to generate data for display in a user interface (UI) with a different appearance, and presents the data to the user.

[0145] The present invention naturally includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention-specifying matters according to the scope of the claims that are appropriate from the above description. [Explanation of symbols]

[0146] 10. Information processing equipment 11 CPU 12 Storage section 13 I / O (Input / Output Interface) 14 Display section 15 Input section 101, 102 arrows 111 Acquisition Department 113 Extraction part 114 Registration Department 115 Organizing Department 116 UI display section 121 Dataset Storage 122 Data storage unit 201, 202 file servers 203, 204 Specifications 31~39 Requirements 41~43 solid line

Claims

1. an acquisition unit that acquires a predetermined document; an extraction unit that extracts a plurality of natural languages ​​from the contents of the items in the predetermined document and stores the extracted natural languages ​​in a data storage unit; an organizing unit that classifies and organizes the plurality of natural languages ​​stored in the data storage unit, determines the similarities between the plurality of natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifies at least one of common attributes and missing / different attributes, including dependency correlation and / or attribute correlation, between the plurality of natural languages, and tags each of the plurality of natural languages; a registration unit that registers the plurality of natural languages, each tagged with at least one of the common attribute and the missing / different attribute, including the dependency correlation and / or the attribute correlation between the plurality of natural languages, into a dataset; An information processing device comprising:

2. 2. The information processing apparatus according to claim 1, wherein the extraction unit further extracts at least one of the name of the item in the specified document, the business process to which the specified document belongs, and the type of the specified document.

3. 3. The information processing apparatus according to claim 2, wherein the registration unit registers at least one of the item names, the business processes, and the predetermined document types further extracted by the extraction unit in a data set.

4. The information processing apparatus according to claim 1 , wherein the organizing unit further tags each of the plurality of natural languages ​​with a process category to which the natural language belongs.

5. 2. The information processing device according to claim 1, further comprising a UI display unit that generates data to be displayed in a user interface with different appearances using the natural language registered in the data set and presents the data to the user.

6. 2. The information processing apparatus according to claim 1, wherein the attribute correlation is a correlation between a given natural language and a natural language expressing an attribute that describes the given natural language.

7. 2. The information processing device according to claim 1, wherein the dependency correlation is a correlation having at least one of a mechanism, a parent-child relationship, or a cause-and-effect dependency correlation between a predetermined natural language and natural languages ​​that appear around the predetermined natural language.

8. 5. The information processing apparatus according to claim 4, wherein the process classification is any one of a request, a function, a logical, a physics, a logic, and a parameter process.

9. The information processing device according to claim 1, characterized in that the one or more judgment criteria are composed of at least one of a judgment criterion for determining whether business processes are similar, a judgment criterion for determining whether types of specified documents are similar, a judgment criterion for determining whether item names are similar, a judgment criterion for determining whether process classifications are similar, a judgment criterion for determining whether dependency correlations are similar, a judgment criterion for determining whether attribute correlations are similar, and a judgment criterion for determining whether natural language character strings are similar.

10. The information processing apparatus according to claim 1 , wherein the organizing unit quantifies one or more similarities, which are degrees of similarity in each of the one or more criteria, based on the one or more criteria.

11. 11. The information processing apparatus according to claim 10, wherein the organizing unit determines the information to be similar by comparing the one or more similarities with one or more thresholds.

12. The information processing apparatus according to claim 10 , wherein the registration unit registers the one or more similarities in the data set.

13. 6. The information processing apparatus according to claim 5, further comprising a reception unit that receives an input from a user, wherein the reception unit receives a selection of a UI that displays the data generated by the UI display unit.

14. 6. The information processing device according to claim 5, wherein the form of the data generated by the UI display unit is at least one of a detailed design process diagram, a CRUD diagram, a deliverable list, a data list, an ER diagram, a business flow diagram, and a tree diagram.

15. 6. The information processing apparatus according to claim 5, wherein the UI display unit performs calculations based on desired logic and generates data to be displayed on the user interface using the calculation results.

16. The computer an acquisition step of acquiring a predetermined document; an extraction step of extracting a plurality of natural languages ​​from the contents of the items in the predetermined document and storing the extracted natural languages ​​in a data storage unit; an organizing step of classifying and organizing the plurality of natural languages ​​stored in the data storage unit, determining the similarities between the plurality of natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifying at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships, between the plurality of natural languages, and tagging each of the plurality of natural languages; a registration step of registering the plurality of natural languages ​​tagged with at least one of the common attributes and the deficiency / difference attributes, including the dependency relationships and / or the attribute relationships between the plurality of natural languages, into a dataset; An information processing method comprising:

17. On the computer, an acquisition function for acquiring a predetermined document; an extraction function for extracting a plurality of natural languages ​​from the contents of the items in the predetermined document and storing the extracted natural languages ​​in a data storage unit; an organizing function that classifies and organizes the plurality of natural languages ​​stored in the data storage unit, determines the similarities between the plurality of natural languages ​​stored in the data storage unit based on one or more criteria, and then clarifies at least one of common attributes and missing / different attributes, including dependency relationships and / or attribute relationships, between the plurality of natural languages, and tags each of the plurality of natural languages; a registration function of registering the plurality of natural languages, each tagged with at least one of the common attributes and the deficiency / difference attributes, including the dependency relationships and / or the attribute relationships between the plurality of natural languages, into a dataset; An information processing program characterized by realizing the above.

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