Data utilization promotion system, data utilization promotion method, and program

Abstract

We provide a data utilization promotion system, data utilization promotion method, and program that proposes applications that meet user expectations and requests. [Solution] In a data utilization promotion system comprising at least a storage unit and a control unit, the patent information server 800 comprises a use case collection file storing data types and uses in at least one association and / or a database storing data of multiple different users, and the control unit of the data utilization infrastructure server comprises an extraction unit that extracts data types corresponding to uses along with uses based on the use case collection file and / or extracts other users suitable for uses from among multiple different users registered in the database through data linkage, and a presentation unit that presents the extracted data types corresponding to uses and / or other users suitable for uses along with the uses.

Description

[Technical Field]

[0001] The present invention relates to a data utilization promotion system, a data utilization promotion method, and a program. [Background Art]

[0002] With the increasing social awareness of digital transformation (DX), there is a growing trend of collecting and analyzing vast amounts of measurement data related to various phenomena using Internet of Things (IoT) technology.

[0003] For example, in order to solve various problems in operations in the transportation, electric power, manufacturing, and other fields, there have been efforts to collect and analyze data across departments, operations, companies, and even industries, utilize the data, and thereby achieve productivity improvement and new business creation.

[0004] However, it is necessary to understand a large amount of data. For example, when attempting to utilize data across different industries, it is difficult for users to figure out what purposes the data can be used for, which has been one of the factors hindering data utilization.

[0005] Furthermore, even among companies in the same industry, it is difficult for a company to identify which other companies it should cooperate with for data linkage, which has also been one of the factors hindering data utilization and DX implementation.

[0006] Here, Patent Document 1 describes a data utilization system that proposes appropriate data preparation content for the purpose of utilization to users who perform data utilization, so that data can be easily utilized for various purposes using various types of data from a plurality of business systems. More specifically, in the data utilization system of Patent Document 1: (1) the utilization purpose specified by the user is collated with the data information prepared in the system, and the data preparation content items and difficulty level to be implemented for the utilization purpose are calculated and presented. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2019-185582 [Overview of the project] [Problems that the invention aims to solve]

[0008] However, in the prior art described in Patent Document 1, etc., users who intend to utilize data must first specify the purpose and use of data utilization, such as KPIs (key performance indicators). In particular, when users try to create new DX businesses by utilizing data across different industries, it is difficult for them to come up with specific purposes and uses from the outset. Even when the purpose is clear, such as improving productivity, it is difficult for them to come up with what kind of data from what industry should be used, or which other companies in the same industry should collaborate with on data.

[0009] In particular, prior art described in Patent Document 1, etc., collects data linkages for objectives such as KPIs and assesses data usefulness based on actual data utilization results. However, there is a problem in that it is difficult to propose new data linkages when there is little experience in data utilization.

[0010] Therefore, the present invention was devised in view of the above-mentioned problems, and aims to provide a data utilization promotion system, a data utilization promotion method, and a program that enable even users with little knowledge or experience in data utilization to understand new applications and use cases for data utilization, as well as appropriate data linkage partners and collaborators. [Means for solving the problem]

[0011] The data utilization promotion system of the present invention, which solves the above problems, comprises at least a storage unit and a control unit, wherein the storage unit comprises a use case collection file storing data types and uses in at least in association with each other, and / or a database storing data of multiple different users, and the control unit comprises an extraction unit that extracts data types corresponding to uses along with uses based on the use case collection file, and / or extracts other users suitable for the use from among the multiple different users registered in the database through data linkage, and a presentation unit that presents the extracted data types corresponding to uses along with uses, and / or other users suitable for the use.

[0012] Furthermore, the data utilization promotion method of the present invention is a data utilization promotion method executed in a data utilization promotion system comprising at least a storage unit and a control unit, which include a use case collection file storing data types and uses in association with each other, and / or a database storing data of multiple different users, and includes an extraction step executed in the control unit, which extracts data types corresponding to uses along with uses based on the use case collection file, and / or extracts other users suitable for the use from among the multiple different users registered in the database through data linkage, and presents the extracted data types corresponding to uses along with uses, and / or other users suitable for the use.

[0013] Furthermore, the present invention is a program for execution in a data utilization promotion system comprising at least a storage unit and a control unit, which include a use case collection file storing data types and uses in association with each other, and / or a database storing data of multiple different users, wherein the control unit is configured to execute: an extraction step of extracting data types corresponding to uses along with uses based on the use case collection file, and / or extracting other users suitable for the use from among the multiple different users registered in the database through data linkage; and a presentation step of presenting the extracted data types corresponding to uses and / or other users suitable for the use. [Effects of the Invention]

[0014] According to the present invention, even users with limited knowledge or experience in data utilization can receive suggestions for new applications and use cases for data utilization, as well as appropriate data integration partners and collaboration partners, thereby promoting data utilization. [Brief explanation of the drawing]

[0015] [Figure 1] Figure 1 shows an example configuration of a data utilization system including the data utilization infrastructure server of this embodiment. [Figure 2A] Figure 2A shows an example of a use case when implementing the data utilization promotion method in this embodiment. [Figure 2B] Figure 2B shows an example of a use case collection file extracted by the data utilization platform server 200. [Figure 3] Figure 3 shows an example of the module configuration of the data utilization platform server in this embodiment. [Figure 4] Figure 4 shows an example of the relationship between the data to be analyzed, the components to be analyzed, the analysis process, the analysis objective, and the business system in this embodiment, illustrating the analysis relationship information. [Figure 5A]FIG. 5A is a diagram showing an example of an analysis target data association table according to the present embodiment. [Figure 5B] FIG. 5B is a diagram showing an example of an analysis component association table according to the present embodiment. [Figure 5C] FIG. 5C is a diagram showing an example of an analysis component-analysis process association table according to the present embodiment. [Figure 5D] FIG. 5D is a diagram showing an example of an analysis process-analysis objective association table according to the present embodiment. [Figure 5E] FIG. 5E is a diagram showing an example of an analysis objective-business operation association table according to the present embodiment. [Figure 5F] FIG. 5F is a diagram showing an example of a data provision destination industry-business application association table according to the present embodiment. [Figure 5G] FIG. 5G is a diagram showing an example of a data provision destination usage purpose-analysis objective association table according to the present embodiment. [Figure 5H] FIG. 5H is a diagram showing an example of a data type-data association table according to the present embodiment. [Figure 6A] FIG. 6A is a diagram showing an example of meta information of data analysis logic according to the present embodiment. [Figure 6B] FIG. 6B is a diagram showing an example of meta information of data matching logic according to the present embodiment. [Figure 7A] FIG. 7A is a diagram showing a configuration example of an analysis target data DB according to the present embodiment. [Figure 7B] FIG. 7B is a diagram showing a configuration example of an analysis result DB according to the present embodiment. [Figure 7C] FIG. 7C is a diagram showing a configuration example of a performance information DB according to the present embodiment. [Figure 8] FIG. 8 is a diagram showing an example of a processing sequence of a data utilization promotion method according to the present embodiment. [Figure 9] FIG. 9 is a diagram showing a first flow example of the data utilization promotion method according to the present embodiment. [Figure 10] FIG. 10 is a diagram showing a second flow example of the data utilization promotion method according to the present embodiment. [Figure 11] Figure 11 shows an example of a data utilization promotion method in this embodiment, specifically example 3 of the flow chart. [Figure 12] Figure 12 shows an example of a flow chart 4 of the data utilization promotion method in this embodiment. [Figure 13] Figure 13 schematically illustrates the method by which the data utilization platform server 200, based on use case 451, refers to the analysis relationship table 307 to acquire combinations of data 411 and analysis components 412-415 that it proposes to the user. [Figure 14] Figure 14 shows an example of a screen image in this embodiment. [Figure 15] Figure 15 is a block diagram showing an example configuration of the control system 900 of this embodiment. [Figure 16] Figure 16 is a schematic diagram showing the functions of the control unit 920 of the control system 900 in this embodiment. [Figure 17] Figure 17 is a flowchart illustrating an example of the processing of the control system 900 in this embodiment. [Modes for carrying out the invention]

[0016] <System Configuration> Embodiments of the present invention will be described in detail below with reference to the drawings. Figure 1 is a diagram showing an example configuration of the data utilization promotion system of this embodiment. As shown in Figure 1, the data utilization promotion system of this embodiment is configured as a distributed computing system, and as an example, as shown in the figure, it is configured with distributed functions across a gateway device connected to user terminals 100 and asset terminals 400, an edge computer installed in a wireless base station etc. connected to the gateway device by a network, and a cloud server connected to the edge computer by a network. In this embodiment, the data utilization system with functions distributed across multiple devices in this manner will be described as a data utilization infrastructure server 200, but it is not limited to this, and the data utilization system may be configured as a single server device. Furthermore, the data utilization system may also have the properties of a data distribution infrastructure. Note that the data utilization promotion system of this embodiment may include user terminals 100 and asset terminals 400, etc.

[0017] The data utilization platform server 200 is a server device that proposes new uses and use cases for data utilization, as well as data integration partners and collaboration partners. Furthermore, based on the correspondence between the proposed data types and uses, or the correspondence between the proposed data integration partners and other users and their uses, the data utilization platform server 200 can also manage and analyze the analysis components (including applications registered by the ASP server 600) that constitute the analysis process for the data to be analyzed. In other words, it can not only propose data types and uses, or data integration partners and collaboration partners, but also propose to the user specific combinations of data corresponding to those data types and other users, and analysis components and applications corresponding to those uses.

[0018] Furthermore, the data utilization platform server 200 can not only make proposals to data users on the user terminal 100 side, but can also discover, analyze, and evaluate assets on the asset terminal 400 side, which may become data providers, and propose data linkage and data provision. Specifically, the data utilization platform server 200 can estimate the feasibility of data acquisition and the value of the data that can be acquired from information about the resources of potential data providers (customer information, business partner information, logistics information, accounting information such as slips, ledgers, and general ledger data, business model information, human resource information, know-how information, etc.), and propose data provision to the asset terminal 400. In addition, the data utilization platform server 200 may discover various tangible and intangible assets by inferring the raw data from the asset terminal 400's trained models and ontology information, and propose data provision generated from those assets.

[0019] In this embodiment, for the sake of explanation, the terminal on the data user's (our user's) side may be described as the user terminal 100, and the terminal on the data provider's (other user's) side as the asset terminal 400. However, the relationship between our user and other users may be equivalent or interchangeable. For example, from the perspective of other user A, user B of user terminal 100 can be considered another user. Therefore, in this case, other user A can be considered a data user, and user B can be considered a data provider. Consequently, in this embodiment, functions described as those of user terminal 100 can be reinterpreted as functions of terminal 400, and similarly, functions described as those of asset terminal 400 can be reinterpreted as functions of terminal 100. Therefore, the same user can be both a data user and a data provider, and that user's terminal can be considered to simultaneously possess some or all of the functions of user terminal 100 and some or all of the functions of asset terminal 400. The functional configuration of the terminal is arbitrary.

[0020] As shown in Figure 1, the data utilization platform server 200 is connected via a network to user terminals 100 and asset terminals 400, as well as to an ASP (Application Service Provider) system 600 that provides various software as a service, known as SaaS (Software as a Service). In Figure 1, one of each is shown for user terminal 100, asset terminal 400, and ASP system 600, but there may be multiple units of each. Furthermore, a data utilization promotion system may be configured including at least one of the user terminal 100, asset terminal 400, and ASP system 600. In addition, the ASP system 600 may be part of a cloud server or connected to a gateway.

[0021] In the network configuration described above, the user terminal 100 is, in this embodiment, a terminal on the side of the data user (candidate), and receives proposals for combinations of data type and use, or proposals for use and other users, presented by the data utilization infrastructure server 200. Furthermore, it is a terminal for confirming, selecting, and performing operations such as analysis execution and result confirmation of proposals for combinations of data and analysis components (e.g., applications) corresponding to each combination of data type and use, or combination of other users and use.

[0022] In this embodiment, the asset terminal 100 is a terminal on the side of the data provider (candidate), and can receive proposals for data provision from the data utilization platform server 200 through asset mining, etc. When providing data, it provides that data (not limited to original data, but may also be derived data such as simulated data) directly or indirectly via the ASP system 600 or the data utilization platform server 200 to the user of the user terminal 100. The asset terminal 100 may provide cleansed data, such as data from which sensitive information has been removed from the original data, or it may provide derived data such as simulated data created by ontology creation or simulation based on statistical information such as co-occurrence of the original data, or it may provide data as a trained model, knowledge graph, or ontology information. Furthermore, the asset terminal 100 may also provide data by receiving data from the data utilization platform server 200, etc., and then using application software installed on the asset terminal 100 to process the data using the trained model, etc., and returning the processing results, statistical information, etc., to the data utilization platform server 200, etc.

[0023] Furthermore, the ASP system 600 is a system that receives and processes various data from the asset terminal 400 (e.g., driving data for transportation and logistics, operation slip data, observation data such as temperature and vibration of equipment, maintenance history, etc.). In this embodiment, the ASP system 600 becomes the target for problem solving by users of the user terminal 100 through analysis of data provided by the asset terminal 400, that is, it serves as a tool for improving the productivity of existing businesses, developing new businesses, and finding partners for collaboration. Although not shown in Figure 1, the patent document information server 800, which will be described later, is a device equipped with a patent document database connected to the network (it may also be a database that stores web data such as web scraping results), and has the function of providing the data utilization platform server 200 with a collection of use cases based on patent document information and web scraping results. Furthermore, although not shown in Figure 1, the control system 900, which will be described later, is either connected to the network or is a functional module within the data utilization infrastructure server 200, and has the function of evaluating the data registered or distributed to the data utilization infrastructure server 200 and ensuring the reliability of the data (authenticity, usefulness, etc.).

[0024] <Hardware Configuration> An example of the hardware configuration of the data utilization platform server 200 described above will be explained. Specifically, the data utilization platform server 200 includes, as an example, a storage unit 206 that includes appropriate non-volatile storage elements such as SSDs (Solid State Drives) and hard disk drives, and volatile storage elements such as memory; a control unit 202 such as a CPU that executes programs held in the storage unit 206 and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit 204 that connects to the network and handles communication processing with other devices; and an input / output unit 208 which is an input means such as a keyboard and an output means such as a display. As described above, in this embodiment the data utilization platform server 200 is configured as a distributed computing device, so cloud servers, edge computers, and gateways do not need to have at least one of the storage unit 206, control unit 202, and input / output unit 208, and do not need to have one of the functions of the storage unit 206, control unit 202, and input / output unit 208 described later.

[0025] This hardware configuration is the same for the user terminal 100, the asset terminal 400, and the ASP server 600 (not shown). For example, the user terminal 100 includes a storage unit 106 that includes appropriate non-volatile storage elements such as SSDs (Solid State Drives) and hard disk drives, as well as volatile storage elements such as memory; a control unit 102 that executes programs held in the storage unit 106, performs overall control of the device itself, and performs various judgments, calculations, and control processing, such as a CPU; a communication unit 104 that connects to the network and handles communication processing with the data utilization platform server 200, etc.; and an input / output unit 108 that includes input means such as a keyboard and output means such as a display.

[0026] Similarly, the asset terminal 400 includes a storage unit 406 containing appropriate non-volatile memory elements such as SSDs and hard disk drives, and volatile memory elements such as memory; a control unit 402 such as a CPU that executes programs held in the storage unit 406 and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit 404 that connects to the network and handles communication processing with the data utilization platform server 200, etc.; and an input / output unit 408 which is an input means such as a keyboard and an output means such as a display. Although not shown, similarly, the ASP server 600 may also include a storage unit containing appropriate non-volatile memory elements such as SSDs and hard disk drives, and volatile memory elements such as memory; a control unit such as a CPU that executes programs held in the storage unit and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit that connects to the network and handles communication processing with the data utilization platform server 200, etc.; and an input / output unit which is an input means such as a keyboard and an output means such as a display.

[0027] <Processing Sequence> Figure 2A is a sequence diagram showing an example of the processing of the data utilization promotion method in this embodiment. The main components of the processing sequence shown in Figure 2A are: a data utilization platform server 200 that collects and stores patent information from a patent information server 800 and proposes combinations of data types and uses to users of user terminals 100 based on a collection of use cases extracted and stored therefrom; a user terminal 100 that receives proposals for data types (or other users) and uses from the data utilization platform server 200, as well as proposals for corresponding data and applications; an asset terminal 400 that registers data to be provided (which may be derived data, etc.) based on proposals for data provision through asset mining, etc.; and an ASP server 600 that provides applications such as analytical processing as SaaS on the data utilization platform. As mentioned above, in order for the user's own user terminal 100 to identify appropriate partners and collaborators by linking the user's own data with other users' data, the user terminal 100 also has the function of an asset terminal 400 that registers data and provides data to the data utilization platform server 200, and as an asset terminal 400, it may perform data registration in step SA4 below.

[0028] As shown in Figure 2A, first, the patent document information server 800 transmits patent document information to the data utilization platform server 200 (step SA1). For example, the patent document information server 800 may be a server or database of a public institution such as the Japan Patent Office's patent information API (Application Programming Interface) server, platpat server, or espacenet server, or a private patent information database or server. However, it is not limited to this, and the patent document information server 800 may store web data collected and scraped from publicly available web information, such as B2B DX case studies described later, and provide the data utilization platform server 200 with a collection of B2B use cases. The patent document information may include application information (descriptions of patent classification, applicant, inventor, etc.), specification information (descriptions of problems the invention aims to solve, effects, detailed description of the invention, industrial applicability, etc.), claims, and drawings (flowcharts, data flow diagrams, block diagrams, etc.).

[0029] Next, the data utilization platform server 200 stores the received patent information or web data as a patent document file in the storage unit 206, extracts at least a description of the data type (or industry) and application, and stores it in the storage unit 206 as a use case collection file. For example, the data utilization platform server 200 may extract combinations of data type (or industry) and application using the IoT patent classification shown below. For example, the data utilization platform server 200 may determine that a patent document assigned G16Y 10 / 25 can be used for manufacturing purposes, a patent document assigned G16Y 10 / 40 can be used for transportation purposes, a patent document assigned G16Y 10 / 50 can be used for finance and insurance purposes, and a patent document assigned G16Y 40 / 60 can be used for positioning and navigation purposes, and extract combinations of IoT data types (which may be extracted not only from patent classifications, but also from descriptions such as IoT device names and units described in the patent document specifications, etc.) and their uses (which may be extracted not only from patent classifications, but also from descriptions such as industrial applicability, problems / effects, detailed description of the invention, and drawings). Furthermore, the data utilization platform server 200 may learn from the collected use case files to create a thesaurus dictionary, and then automatically extract the data type (or industry) and application by searching for synonyms and related terms in the learned results (in this case, the thesaurus dictionary) against the patent document information. [Table 1]

[0030] Here, Figure 2B shows an example of a use case collection file extracted by the data utilization platform server 200. As shown in Figure 2B, in this example, in addition to the data type (the "B" item indicating the industry of the data provider (data source) and the "data" item indicating the type of data) and the purpose (the "to B" item indicating the industry of the data user (data recipient) and the "objective" item indicating the purpose of data use), the source patent document numbers are listed so that drawings and other information can be accessed via URL links, and this is compiled into a database as a reference table.

[0031] Then, the data utilization platform server 200 presents the user of the user terminal 100 with the application and the corresponding data type (data provider's industry "B", etc.) based on the use case collection file (step SA3). Here, the data utilization platform server 200 may display the use case collection as a list on the input / output unit 108 of the user terminal 100, as shown in Figure 2B, or it may present the list in a way that allows the user to enter any word into the search box via the input / output unit 108 to narrow down the search to any item. For example, if the user specifies the "to B" field and enters "electricity" which is related to their industry, the data utilization platform server 200 may control the list of use cases so that only those in the "to B" field that are "electricity" are displayed. Similarly, if a user specifies the "objective" field for the purpose and enters items they wish to improve through data integration, such as "working hours and sales," the data utilization platform server 200 may control the display of only those related to productivity improvement, such as "working hours" and "sales," from the list of use cases. Note that while many of the use cases illustrated in Figure 2B are DX examples involving collaboration between different industries where "B" and "to B" are in different sectors, the system is not limited to this. Use cases contributing to productivity improvement through collaboration between companies in the same industry where "B" and "to B" are in the same sector are also acceptable.

[0032] Furthermore, the data utilization platform server 200 may include items related to data value, such as data authenticity and usefulness, in the items of the use case collection file (such as annotation of data evaluation by the control system 900 described later), and by presenting data value along with data type and application, users of user terminals can use it as an indicator for selection. For example, the data utilization platform server 200 may evaluate that the value (especially usefulness) of a data type is high if the same combination of data type and application is described in many patent documents and web data, and / or if the same data type has many applications across multiple patent documents and web data. In this embodiment, data value is sometimes explained as a concept that includes data reliability, such as data authenticity and usefulness, but is not limited to this. Evaluation values ​​of data value, including reliability such as authenticity and usefulness, may be attached as metadata to the data type (including industry, etc.) or the data itself. Furthermore, data reliability may include some or all of the following concepts. More specifically, this will be explained in detail in the description of the control system 900, which will be discussed later. • Data reliability: That is, is the data reliable? • Data ownership: Who is the original owner, and who is potentially entitled to payment? • Data quality: Are the sensor readings accurate? • Data integrity: Have the sensor readings been tampered with since the initial capture?

[0033] The above is the basic sequence of this embodiment. Here, the data utilization platform server 200 may not only present combinations of data types and uses to the user, but may also present combinations of data and applications that can actually be used. Note that the processing in steps SA4 to SA6 below may be executed in advance and the processing results may be saved in advance, prior to the proposal of data and applications (step SA6).

[0034] First, the asset terminal 400 registers the data it can provide (which may be derived data) with the data utilization platform server 200 (step SA4). Before this process, the data utilization platform server 200 may have the asset terminal 400 perform a mining process. More specifically, the data utilization platform server 200 estimates the possibility of obtaining the data and / or the value of the obtainable data from the information obtained via the communication unit 204, and presents this to the input / output unit 408 of the asset terminal 400. The user of the asset terminal 400 (a potential data provider) then accepts the provision of the data in consideration of its value (which may be linked to the data sales price), and the registration process in step SA4 described above is executed.

[0035] Next, the ASP server 600 registers the analysis components, which are software / application programs provided in the form of SaaS or similar on the data utilization platform, with the data utilization platform server 200 (step SA5). Note that the order of processes such as steps SA1, SA4, and SA5 does not matter.

[0036] Next, the data utilization platform server 200 generates analysis relationship information (step SA6) that defines the link between the data to be analyzed and the analysis components (all or part of the application's functions) that perform the analysis processing on the data, based on the correspondence between data types and uses stored in the use case collection file. More specifically, the data utilization platform server 200 generates analysis relationship information by identifying combinations of data from the data registered in step SA4 that are suitable for the data type (e.g., a user who is suitable for industry "B" of the data type and whose data is suitable for type "data") and analysis components from the analysis components registered in step SA5 that are suitable for the use (the following formula schematically shows this correspondence). Therefore, a use case, which is a combination of data type and use, can be understood as an abstract and superficial data sequence concept, while analysis relationship information, which is a combination of data and analysis components, can be understood as concrete and internal processing content. [Formula 1] <Use Case> = <Data Type> × <Purpose> ↓↑ ↓↑ ↓↑ <Analysis-related information> = <Data> × <Analysis components>

[0037] Then, when the user of user terminal 100 selects a combination of data type and application from the use case collection via the input / output unit 108 (step SA7), the data utilization server 200 proposes a combination of data and analysis component (application) to the user from the corresponding analysis-related information (step SA8). Then, when the user of user terminal 100 selects a combination of data and analysis component (application) via the input / output unit 108 (step SA9), the data utilization server 200 executes analysis processing using the data and analysis component (application) and sends the analysis results to user terminal 100 (step SA10). Note that steps SA7 to SA10 are two round-trip processes, but they may be one round-trip processes. That is, when presenting use cases of data type and application combinations (step SA3), the combination of data and analysis component (application) corresponding to that combination may also be presented, so that the user can select (adopt) a combination of data corresponding to the data type and analysis component (application) corresponding to the application in one step.

[0038] Furthermore, when proposing a combination of data and analytical components (applications), the analysis results (KPIs, etc.) when the data is executed using the analytical components (applications) may also be presented. This allows, for example, a user to understand the business improvement effect, such as how much productivity and other indicators will improve by partnering with different other users. Moreover, as mentioned above, by first registering derived data such as simulated data, both the user and other users can understand the effects of collaboration without knowing the other party, before concluding business partnership agreements such as non-disclosure agreements (NDAs). This enables the discovery of appropriate partners and collaborators across different industries or within the same industry, thereby promoting data partnerships, collaborations, and digital transformation (DX).

[0039] <Module Configuration> Figure 3 shows the module configuration of the data utilization platform server 200 in this embodiment.

[0040] In this embodiment, the data utilization platform server 200 is equipped with data utilization middleware 300 that stores or receives data to be analyzed in real time from asset terminals 400, etc., manages and analyzes the analysis components that constitute the analysis process for this data to be analyzed, and performs processes such as proposing the analysis components to the user.

[0041] The main components of this data utilization middleware 300 are: a patent document information file 301 that stores patent document information and web data received from a patent document information server 800; a use case collection file 302 that stores data associated with at least data type and purpose; a use case extraction unit 316 that extracts at least data type and purpose from patent document information and stores it in the use case collection file 302; a usefulness determination unit 317 that evaluates data value such as usefulness; an asset estimation unit 318 that estimates the possibility of data acquisition and / or the value of the data that can be acquired; an evaluation module unit 319 that is embedded in the data pipeline of the data utilization platform and accepts evaluation via the communication unit 204; an analysis execution management unit 303 that manages the execution of analysis processing 302 by combinations of analysis components 303; an analysis target data DB 304 that stores data to be analyzed registered from multiple asset terminals 400; an analysis result DB 305 that stores the results of the analysis processing; and a performance information D that stores performance information related to the analysis processing executed by the analysis execution management unit 303. B306 consists of an analysis relationship table 307 that stores analysis relationship information, an analysis relationship information management unit 308 that manages the analysis relationship table 307, a presentation unit 309 that proposes combinations of data types and uses to the user by referring to a use case collection file 302, and further proposes data combinations and / or analysis component combinations to the user by referring to the analysis relationship table 307, a user / business management unit 310 that manages users and business that access the data utilization middleware 300 to perform analysis, a client-facing I / F provision unit 311 that provides an interface for the functions provided by the data utilization middleware 300 to the user terminal 100, an analysis processing performance management unit 312 that manages the performance information DB 306, an analysis component management unit 313 that manages the analysis components 303 executed by the analysis execution management unit 303, a data management unit 314 that manages the data stored in the analysis target data DB 304, and a data communication unit 315 that communicates with the user terminal 100, asset terminal 400, ASP system 600, patent document information server 800, etc. via the network.

[0042] In particular, the evaluation module 319 is an evaluation module embedded in the data pipeline of the data utilization platform and accepts evaluations via the communication unit 204. More specifically, when data is uploaded from the asset terminal 400, the evaluation module 319 is embedded in the gateway or edge computer along the data pipeline path and has the function of scoring the evaluation value of the data and adding it to the data as metadata. For example, the evaluation module 319 may score at least one of the following items (A) to (I) as an evaluation value related to the reliability of the data and embed it in the metadata as a reliability evaluation value. (A) Will the data be verified using a distributed ledger? (B) Is it a secure storage location? (C) Is gateway authentication available? (D) Is there metadata about the origin of the product? (E) Does it include owner information via device signature? (F) Is the data distribution being denoised using the 3-sigma rule? (G) Is the polling frequency above the optimal level according to the Nyquist criteria? (H) Does the authenticity fingerprint match? (I) Does it have Byzantine fault tolerance?

[0043] The evaluation module 319 then accepts evaluation (which may be rephrased as review or audit) via the communication unit 204. Specifically, the evaluation module 319 accepts evaluation via the communication unit 204 to determine whether the data valuation method described above complies with data distribution standards, guidelines, laws and regulations such as the Personal Information Protection Act. For example, the evaluation module 319 may be configured to present an evaluation value scoring program so that an auditing firm or the like can verify the internal processing method of the evaluation module 319. The evaluation module 319 may also be configured to present a comparison between the data uploaded by the asset terminal 400 (data that does not contain sensitive information or ontology data, etc.) and the original data before uploading (data that contains sensitive information or data before ontology creation, etc.). In addition, the evaluation module 319 has a function to automatically check the following items and may accept evaluation (which may be rephrased as review or audit to determine whether it complies with the Securities and Exchange Act or International Accounting Guidelines) via the communication unit 204. (1) When making a payment, conduct due diligence to determine if there is a pipeline connecting the data provider to the data recipient (in a B2B context). (2) Are offsetting operations being properly executed in transactions between multiple companies? (3) Checking for inflated sales figures (fictitious transactions) (e.g., group companies leaking data through other companies) (4) Monitoring of illegal information trading (insider information)

[0044] Specifically, the evaluation module 319 may be connected to the control system 900, as described later, to provide the control system 900 with data and other information, and to accept the evaluation by receiving data evaluation results from the control system 900. The evaluation module 319 itself may also be the control system 900.

[0045] Furthermore, the asset estimation unit 318 performs asset mining by estimating the possibility of obtaining data and / or the obtainable data value from information acquired from the asset terminal 400 via the communication unit 208 (such as input data from the input / output unit 408, data from the storage unit 406, and trained models from the control unit 402). More specifically, the asset estimation unit 318 estimates the possibility of obtaining data and / or the obtainable data value by retrospectively estimating the original data (type and data value) from (1) the resources that are the source of the data, (2) the hardware and software that digitize or convert the data, and (3) the original data (raw data) that has undergone some processing (such as ontological data such as processed data) or data that has been altered (such as trained models). Note that for estimating data value, data values ​​such as authenticity, usefulness, and reliability estimated by the usefulness determination unit 317, the evaluation module unit 319, and the control system 900 may also be used.

[0046] As an example of (1), the asset estimation unit 318 may estimate the data availability and / or the data value that can be obtained from at least one of the customer, the business partner, the logistics, the accounting, the business model, the human resources, and the know-how, based on at least one of the customer, the business partner, the logistics, the accounting, the business model, the human resources, and the know-how obtained via the communication unit 208.

[0047] As an example of (2), the asset estimation unit 318 may estimate equipment or software to be purchased, leased or made available based on at least one of customer information, trading partner information, logistics information, accounting information, business model information, human resource information, and know-how information obtained via the communication unit 208, and estimate the data availability and / or the data value that can be obtained based on such equipment or software.

[0048] As an example of (3), the asset estimation unit 318 may estimate the availability of the data and / or the value of the available data by predicting the trained original data or ontological original data based on the trained model information or ontology information obtained via the communication unit 208. Not limited to receiving the trained model directly from the asset terminal 400, the asset estimation unit 318 may also estimate the type and value of the original data by receiving the output value (an example of trained model information) resulting from giving some input value (such as dummy data) to the trained model of the asset terminal 400.

[0049] <Correspondence between use cases and analytical information> Figure 4 shows use cases 451 corresponding to the application (e.g., the industry of the data provider "to B" and the purpose of use "objective") and data type (e.g., the industry of the data provider user "B" and the type of data), and analysis relationship information 401 corresponding to the data to be analyzed, analysis components, analysis processing, analysis objectives, and business system relationships. In the embodiment of Figure 4, analysis components are sometimes described in a narrow sense as data analysis logic and matching logic, but the analysis components of the present invention (claims) are not limited to these and are a concept that includes software, applications, analysis processing, and analysis objectives such as KPIs (e.g., formulas and reference tables that output indicator values ​​and evaluation values ​​such as productivity).

[0050] As described above, use case 451 of this embodiment is a combination of data type 451 and purpose 450. More specifically, data type 451 includes an item "B" indicating the industry of the data provider and an item "data" indicating the type of data, while purpose 450 includes an item "to B" indicating the industry of the data recipient (data user) and an item "objective" indicating the purpose of use by the data recipient.

[0051] On the other hand, the analysis relationship information 401 in this embodiment represents the relationship between the data and analysis components used for analysis, the analysis processing resulting from their combination, the analysis objective, and the business operations in the business application and the facilities where those operations are carried out for said analysis objective. "Relationship" here means that the related elements can be used in combination, that there is a correspondence with the use case application and data type combination, or that there is a history of use. The analysis relationship information 401 is referenced by the data utilization platform server 200 to select and propose appropriate analysis components for the data to be analyzed, as specified by the user (including the adoption of use case proposals for application and data type).

[0052] The main components are the data to be analyzed 411, the analysis components 412, the analysis process 413, the analysis objective 414, and the business application 415, and each piece of information is managed in a hierarchical structure. Of these, the analysis components 412 are classified into data matching logic 421 and data analysis logic 422. Here, the data matching logic and data analysis logic necessary for performing analysis on the data to be analyzed are reusable as software components that make up the analysis components, and the analysis process is composed of combinations of these components.

[0053] First, in the hierarchy of the data 411 to be analyzed, data from multiple ASP systems 600 to be analyzed and the relationships between such data are managed. The data relationships are created, for example, by the method described in Japanese Patent Publication No. 2016-209063.

[0054] Furthermore, the hierarchy of the analysis component 412 manages the data matching logic 421, which is the processing logic (e.g., SQL statements, etc.) for extracting one or more combinations of data to be analyzed from a storage medium such as a database, and the data analysis logic 422, which is the logic for performing analysis on the aforementioned one or more data to be analyzed (e.g., graphing of series data, correlation analysis, etc.).

[0055] Furthermore, the analysis processing level 413 manages the data analysis processing created by the combination of one or more data points to be analyzed and one or more analysis components mentioned above.

[0056] Furthermore, at the Analysis Objectives 414 level, KPI information, which is the purpose of the analysis, is managed. At the Business Application 415 level, the business processes 4151 and equipment, etc. 4152, which require the aforementioned KPIs for analysis, are managed. In addition, related elements in each level are linked together.

[0057] The analysis-related information management unit 308 generates analysis-related information that defines the link between the data to be analyzed 411 and the application that performs the analysis processing on said data (in a narrow sense, the business application 415 and / or the analysis purpose 414, and in a broad sense, the analysis process 413 and the analysis component 412) based on the correspondence between the data type 451 and the purpose 450 stored in the use case collection file 302.

[0058] More specifically, the analysis-related information management unit 308 searches for and extracts data 411 that corresponds to the "B" item indicating the industry of the data provider and the "data" item indicating the type of data for data type 451, and stores the correspondence in the analysis-related information. For example, since data 411 may include metadata indicating information about the data provider (information representing the user of the data provider, such as company identification information and industry), or metadata indicating the product number and unit of the IoT device, the analysis-related information management unit 308 may search for and extract the "B" item indicating the industry and the "data" item indicating the type of data from the metadata. In this embodiment, the analysis-related information management unit 308 functions as an extraction unit.

[0059] Furthermore, the analysis-related information management unit 308 searches for analysis components 412 to business applications 415 that correspond to the “toB” item 455, which indicates the industry of the data recipient, and the “objective” item 454, which indicates the purpose of data use by the data recipient, among the uses 450, and stores the correspondence in the analysis-related information. For example, the analysis-related information management unit 308 may search and extract the business application 415 that corresponds to the “toB” item 455, which indicates the industry of the data recipient, from the registered applications of the ASP server 600 and store the correspondence in the analysis-related information. Alternatively, the analysis-related information management unit 308 may search and extract the analysis objective 414 and business application 415 that correspond to the “objective” item 454, which indicates the purpose of use by the data recipient, from the registered applications (registered analysis components) of the ASP server 600 and store the correspondence in the analysis-related information.

[0060] Furthermore, since the analysis-related information also defines the vertical correspondence in Figure 4 from the business application 415 and the analysis objective 414 to the data 411, once the horizontal correspondence in Figure 4 is determined—that is, the business application 415 corresponding to toB 455, the analysis objective 414 corresponding to objective 454, and the data 411 corresponding to data type 451—the analysis-related information management unit 308 may determine the analysis processes 413 and analysis components 412 that fill in the gaps based on the analysis-related information regarding the vertical correspondence.

[0061] Figures 5A to 6B show the configuration of the analysis relationship table 307, which stores linking information between elements in the analysis relationship information 401 (i.e., vertical correspondences and vertical correspondences), managed by the data utilization platform server 200 of this embodiment, and the data structure of the metadata of the analysis components.

[0062] In this embodiment, the analysis relationship table 307, which manages the linking information between elements in the analysis relationship information 401, is composed of the following: the analysis target data linking table 501, the analysis component linking table 502, the analysis component-analysis processing linking table 503, the analysis processing-analysis purpose linking table 504, and the analysis purpose-business linking table 505, as well as the data provider industry-business application linking table 508, the data provider usage purpose-analysis purpose linking table 509, and the data type-data linking table 500.

[0063] The data linking table 501 shown in Figure 5A is a table that stores information regarding the link between the data to be analyzed 411 in the analysis relationship information 401 and the data matching logic 421 in the analysis component 412. Its main components are linking identification information 511 that uniquely identifies each link, data to be analyzed identification information 512, data matching logic identification information 513, weight 514, and update date and time 515.

[0064] The aforementioned linking identification information 511 stores information for identifying the link. The analysis target data identification information 512 stores information for identifying the analysis target data that is linked. The data matching logic identification information 513 stores information for identifying the data matching logic that is linked. The weight 514 stores information indicating the weight of the link. For example, the more frequently the combination resulting from the link is used by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 515 stores the date and time when the records of each of the above items 511 to 514 were last updated. Since there can be multiple links for a single analysis target data, the analysis target data linking table 501 may store multiple records for that analysis target data.

[0065] Furthermore, the analysis component linking table 502 illustrated in Figure 5B is a table that stores information regarding the linking between the data matching logic 421 and the data analysis logic 422 in the analysis component 412 of the analysis relationship information 401. Its main components are linking identification information 521, data matching logic identification information 522, data analysis logic identification information 523, weight 524, and update date and time 525.

[0066] Of these, the linking identification information 521 stores information to uniquely identify the link. The data matching logic identification information 522 stores information to identify the data matching logic that is being linked. The data analysis logic identification information 523 stores information to identify the other data analysis logic that is being linked. The weight 524 stores information indicating the weight of the link. For example, the more frequently the combination resulting from the link is used by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 525 stores the date and time when the records of the above data items 521 to 524 were last updated. Since there can be multiple links for one data matching logic and a data analysis logic, the analysis component linking table 502 may store multiple records for that one data matching logic and a data analysis logic.

[0067] Furthermore, the analysis component-analysis process linking table 503 illustrated in Figure 5C is a table that stores information regarding the link between the data analysis logic 422 in the analysis component 412 of the analysis relationship information 401 and the analysis process 413 executed by a user such as an analyst. Its main components are linking identification information 531, data analysis logic identification information 532, analysis process identification information 533, weight 534, and update date and time 535.

[0068] Of these, the linking identification information 531 stores information for uniquely identifying the linking. Furthermore, the data analysis logic identification information 532 stores information to identify the data analysis logic 422 that is linked to it. Furthermore, the analysis process identification information 533 stores information to identify the analysis process 413 that is linked to it. Furthermore, the weight 534 stores information indicating the weight of the link. For example, the more frequently the combination resulting from the link is used by users such as analysts, the larger the numerical value representing the weight will be. Furthermore, the update date and time 535 stores the date and time when the records of each of the above data items 531 to 534 were last updated. Note that there may be multiple links for one data analysis logic 422, so the analysis component-analysis process link table 503 stores information for that one data analysis logic 4 Multiple records may be stored for item 22.

[0069] Furthermore, the analysis process-analysis objective linking table 504 illustrated in Figure 5D is a table that stores information regarding the linking between the analysis processes performed in analysis process 302 and the KPIs in analysis objective 414. Its main components are linking identification information 541, analysis process identification information 542, analysis objective identification information 543, weight 544, and update date and time 545.

[0070] Of these, the linking identification information 541 stores information to uniquely identify the link. The analysis process identification information 542 stores information to identify the analysis process 413 that is being linked. The analysis purpose identification information 543 stores information to identify the analysis purpose 414 that is being linked. The weight 544 stores information indicating the weight of the link. For example, the more frequently the combination resulting from the link is used by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 545 stores the date and time when the records of each of the above data items 541 to 544 were last updated. Since there can be multiple links for a single analysis process 413, the analysis process-analysis purpose linking table 504 may store multiple records for that single analysis process 413.

[0071] Furthermore, the analysis objective-business linkage table 505 illustrated in Figure 5E is a table that stores information regarding the linkage between KPIs in the analysis objective 414 and targets such as businesses and equipment in the business application 415. Its main components are linkage identification information 551, analysis objective identification information 552, business identification information 553, target identification information 554, weight 555, and update date and time 556.

[0072] Of these, the linking identification information 551 stores information to uniquely identify the link. The analysis purpose identification information 552 stores information to identify the analysis purpose 414, which is one of the linked items. The business identification information 553 stores information to identify the business 4151, which is the other item linked. The target identification information 554 stores information to identify the target 4152, which is linked together with business 4151. The weight 555 stores information indicating the weight of the link. For example, the more frequently the combination resulting from the link is used by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 556 stores the date and time when the records of each of the above data items 551 to 555 were last updated. Note that there can be multiple links for one analysis purpose, so the analysis purpose-business linking table 505 may store multiple records for the above-mentioned analysis purpose.

[0073] Furthermore, the data provider industry-business application linking table 508, illustrated in Figure 5F, is a table that stores information regarding the linking between the data provider industry ("toB") 455 of the use case collection file 302, which is stored in the use case collection file 302, and the business application 415 registered in the ASP server 600. Its main components are the linking identification information 581, the data provider industry information "toB" 582, the business application information 583, the weight 584, and the update date and time 585.

[0074] Of these, the linking identification information 581 stores information to uniquely identify the link. The data provider industry information "toB" 582 stores information to identify the industry ("toB") 455 of the data provider to which the link is made. The business application information 583 stores information to identify the business application 415 to which the link is made. The weight 584 stores information indicating the weight of the link. For example, the more useful the combination resulting from the link is, such as being adopted more often by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 585 stores the date and time when the records of each of the above data items 581 to 584 were last updated. Note that there can be multiple links for one data provider industry ("toB") 455, so the data provider industry-business application linking table 508 can store multiple records for that one data provider industry ("toB") 455.

[0075] Furthermore, the data provider usage purpose-analysis purpose linking table 509, illustrated in Figure 5G, is a table that stores information regarding the linking between the usage purpose ("objective") 454 of the data provider stored in the use case collection file 302 and the analysis purpose 414 registered in the ASP server 600. Its main components are the linking identification information 591, the data provider usage purpose information "objective" 592, the usage purpose information 593, the weight 594, and the update date and time 595.

[0076] Of these, the linking identification information 591 stores information to uniquely identify the link. The data provider usage information "objective" 592 stores information to identify the usage purpose ("objective") 454 of the data provider to which the link is made. The usage information 593 stores information to identify the other party to which the link is made, the analysis purpose 414. The weight 594 stores information indicating the weight of the link. For example, the more useful the combination resulting from the link is, such as being adopted more often by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 595 stores the date and time when the records of each of the above data items 591 to 594 were last updated. Note that there can be multiple links for a single data provider usage purpose ("objective"), so the data provider usage purpose-analysis purpose linking table 509 may store multiple records for that single data provider usage purpose ("objective").

[0077] Furthermore, the data type-data linking table 500 illustrated in Figure 5H is a table that stores information regarding the link between data type 451 stored in the use case collection file 302 and data 411 registered in the asset terminal 200. Its main components are linking identification information 501, data type information 502, data information 503, weight 504, and update date and time 505. As an example, as described above, data type 451 may include information on the industry "B" of the data provider for the use case, while data 411 may include information on the industry of the user of the asset terminal 400 that registered the data. Based on this correspondence, the data type-data linking table 500 may define information regarding the link.

[0078] Of these, the link identification information 501 stores information to uniquely identify the link. The data type information 502 stores information to identify the data type 451 that is being linked. The data information 503 stores information to identify the other data 411 that is being linked. The weight 504 stores information indicating the weight of the link. For example, the more useful the combination resulting from the link is, such as being adopted more often by users such as analysts, the larger the numerical value representing the weight will be. The update date and time 505 stores the date and time when the records of each of the above data items 501 to 504 were last updated. Since there can be multiple links for a single data type 451, the data type-data link table 500 may store multiple records for that single data type 451.

[0079] Furthermore, the analysis relationship table 307 in this embodiment also stores metadata 506 for each data analysis logic 422 and metadata 507 for each data matching logic 421.

[0080] Of these, the metadata 506 of the data analysis logic 422, as illustrated in Figure 6A, stores information about the data that will be input to the data analysis logic 422. Its main components are identification information 561, data items 562, type 563, and category 564.

[0081] Of these, the identification information 561 stores information for uniquely identifying each record of the metadata 506. The data item 562 stores the name of the data corresponding to that data item. However, this item may not be specified. The type 563 stores the data type of the data corresponding to the data item 562. The category 564 stores the category of the data corresponding to the data item 562.

[0082] Here, the value for type 564 could be "input," "key," etc. If it is "input," it indicates that the data item is input data for the data analysis logic. If it is "key," it indicates that the data item is a common index that serves as the axis for matching, specifying ranges, and sorting multiple data in the data analysis logic.

[0083] Furthermore, the metadata 507 of the data matching logic 421, as illustrated in Figure 6B, stores information about the data provided by the data matching logic 421. Its main components are identification information 571, data items 572, type 573, size 574, and table 575.

[0084] Of these, the identification information 571 stores information for uniquely identifying each record in the metadata 507. The data item 572 stores the name of the data corresponding to that data item 572. The type 573 stores the data type of the data corresponding to that data item 572. The size 574 stores the size of the data corresponding to that data item 572. The table 575 stores information about the table from which the data corresponding to that data item 572 was extracted.

[0085] Figure 7A shows an example of the configuration of the data to be analyzed DB304 in this embodiment. The data to be analyzed DB304 in this embodiment is data (which may be derived data) that has been uploaded from the asset terminal 200 to the data utilization platform server 200, or stored via a storage medium by an appropriate user. More specifically, it is data managed by the asset terminal 200, and can be assumed to be, for example, sensing data of vehicles and equipment used in the relevant business (of course, it is not limited to this, and various types of data can be assumed). The data to be analyzed DB304 can store information of the user who registered the data and the asset terminal 200 (identification information such as user ID and terminal ID, industry information, company name and personal name, etc.) associated with the data.

[0086] The data DB304 to be analyzed, illustrated in Figure 7A, consists of multiple tables as an example. These tables correspond to the data source tables specified in table 575 of the metadata 507 (data matching logic) shown in Figure 6B. Each table, as illustrated, is a collection of records containing various values, such as the date and time the data was observed, location, result, sensor ID, transmitted protocol, geographical location of the sensor, gateway ID where the measurement was captured, geographical location of the gateway, time the data was read, expected value or scale range, and trust score (data value evaluation value).

[0087] Figure 7B shows an example of the configuration of the analysis results DB 305 in this embodiment. The analysis results DB 305 in this embodiment is a database that stores the contents of the analysis processing actually executed based on analysis components previously proposed by the data utilization platform server 200, or analysis components selected by the analyst 201 (or developer 202) independently of those proposed. Specifically, it is a collection of records that use an ID that uniquely identifies the analysis result as the key, and includes information on the data matching logic and data analysis logic of the analysis components that constitute the analysis processing, as well as the analysis processing performed using these, the purpose of the analysis, the business, the target, and the result (in this case, the data value output as the result of executing the analysis processing).

[0088] Figure 7C shows an example of the configuration of the performance information DB306 in this embodiment. The performance information DB306 in this embodiment is a database that stores performance information of analysis processes that have actually been executed. Specifically, it is a collection of records that associate the analysis process name, which uniquely identifies the analysis process, with values ​​such as the execution frequency of the analysis process (e.g., the number of times it has been executed in the last month), the number of users (the number of users who have adopted it), the number of standard registrations, and the number of modifications (e.g., the number of times the analyst or developer has made modifications regarding the analysis process), the amount (e.g., the average cost incurred by the user to adopt it (e.g., the purchase price of data and the application)), the evaluation value (evaluation from the user who adopted it), the number of improvement requests (the number of improvement requests from the user who adopted it), and the period (e.g., the average period during which the user adopted it). The above-mentioned number of standard registrations indicates the number of times the analysis process is recognized by analysts, etc., as being used frequently or widely, and has been set as a standard analysis process.

[0089] <Example Sequence> The actual procedure for the data utilization promotion method in this embodiment will be described below with reference to the diagram. The various operations corresponding to the data utilization promotion method described below are realized by a program executed by the data utilization infrastructure server 200. This program consists of code for performing the various operations described below.

[0090] Figure 8 shows the processing sequence when implementing the data utilization promotion method in this embodiment. Here, we will show the processing sequence when the sequence already shown in Figure 2 is specifically executed using each element of the data utilization middleware 300.

[0091] Its main components are the asset terminal 400, the data utilization middleware 300 running on the data utilization platform server 200, which includes the data management unit 314, the analysis-related information management unit 308, the analysis processing results management unit 312, the presentation unit 309, the use case collection file 302, and the analysis-related table 307, as well as the user of the user terminal 100.

[0092] At the asset terminal 400, the asset estimation unit 318 proposes data provision through asset mining, etc., and registers the data held by the asset terminal 400 with the data utilization platform server 200 (step 611). In this case, the data management unit 314 of the data utilization platform server 200 accepts the registration of data from the aforementioned asset terminal 400 (step 621). The data management unit 314 also notifies the analysis-related information management unit 308 of the acceptance of the data registration (step 622).

[0093] Then, when the analysis-related information management unit 308 receives a notification from the data management unit 314 mentioned above, it creates an analysis-related table 307 from the data (step 631). As described above, the analysis-related information management unit 308 also stores the linking information with the use case collection file 302 (horizontal correspondence in Figure 4) in the analysis-related table 307.

[0094] On the other hand, the presentation unit 309 refers to the use case collection file 302 and presents candidate combinations of data type (or industry) and purpose (use case) to the user terminal 100 (step 650). The user of the user terminal 100 selects one of the candidate combinations of user type and purpose (use case) (step 661).

[0095] At this time, when the presentation unit 309 of the data utilization platform server 200 receives a specification of a use case (combination of user type and purpose) from the user of the user terminal 100, it refers to the analysis relationship table 307 (step 651) and selects candidate combinations of available data and analysis components (applications, etc.) for the user type (or industry) and purpose that constitutes the use case (step 652). The presentation unit 309 also proposes the selected data (or information of other users who have registered the data) and analysis component (applications, etc.) candidate combinations to the user of the user terminal 100 (step 653).

[0096] The user of user terminal 100 views the proposed combinations of data (or other users who are potential partners) and analysis components (applications, etc.) suggested by the presentation unit 309 (step 662), and decides which combination of data (or other users who are potential partners) and analysis components (applications, etc.) to use (step 663). The user decides which combination of data (or other users who are potential partners) and analysis components (applications, etc.) to use by selecting one of the proposed combinations of data (or other users who are potential partners) and analysis components (applications, etc.), or by changing part of the proposed combination. The user then executes the analysis process using the combination of data (or partners) and analysis components (applications, etc.) decided above (step 664).

[0097] Meanwhile, the Analysis Processing Performance Management Unit 312 confirms the execution of the analysis processing by the user as described above, records the results related to the execution of the analysis processing (combination of analysis components, history of processing execution, analysis results, results of collaboration with other users, evaluations and improvement requests from the user, etc.) (step 641), and notifies the Analysis-Related Information Management Unit 308 of the record of the results (step 642). In this case, when the Analysis-Related Information Management Unit 308 receives the notification from the Analysis Processing Performance Management Unit 312 as described above, it updates the analysis-related table based on the results (step 632).

[0098] <Flowchart example> Figure 9 shows an example of a data utilization promotion method in this embodiment. Specifically, it is a flowchart showing how the data utilization infrastructure server 200 generates an analysis relationship table 307 based on predetermined data registered from the asset terminal 400.

[0099] In this case, the data utilization platform server 200 accepts data registration from the asset terminal 400 (step 701). The data accepted for registration here includes various business data, sensing data, etc., held by each asset terminal 400.

[0100] Furthermore, the data utilization platform server 200 generates data relationships for the registered data obtained in step 701 (step 702). This data relationship generation process can employ existing technologies as appropriate, but for example, it can involve associating data that shares one or more common attributes, such as the location of value observation (e.g., geospatial information), time period, or target, across asset terminals 400 (blockchain technology, distributed ledger technology).

[0101] Next, the data utilization platform server 200 generates data matching logic and metadata 507 to extract one or more related data from the data to be analyzed DB 304 based on the data relationships generated in step 702 above (step 703).

[0102] The generation of data matching logic involves generating SQL statements to retrieve each data item to be analyzed, as indicated by the data relationships described above, from the corresponding tables in the analysis data DB304. This SQL statement generation can be achieved by using a template SQL statement pre-held by the data utilization platform server 200, and setting the target table and data identification information accordingly.

[0103] Furthermore, generating metadata 507 involves reading the following information from each data item being analyzed, as indicated by the data relationships described above: type (string, numeric), size, value (table name where it is stored), sensor ID, transmitted protocol, geographical location of the sensor, gateway ID where the measurement was captured, geographical location of the gateway, time the data was read, expected value or scale range, and trust score (various evaluation values ​​related to data reliability and data usefulness, such as data authenticity, data ownership, data quality, and data integrity). This information is then compiled into a table as metadata 507.

[0104] Furthermore, the data utilization platform server 200 refers to the metadata 507 of the data matching logic generated in step 703 above (step 704), and adds a record to the data matching table 501 regarding the link between the data to be analyzed and the data matching logic (step 705).

[0105] Next, the data utilization platform server 200 determines whether processing has been completed for all data described in the metadata 507 of the data linking logic. If it is determined that processing has not been completed (step 706: NO), it repeats the processes in steps 705 and 706.

[0106] On the other hand, if the determination in step 706 reveals that processing has been completed for all data described in the metadata 507 of the data linking logic (step 706: YES), the data utilization platform server 200 determines whether processing has been completed for all data matching logic (step 707).

[0107] If this determination reveals that processing has been completed for all data matching logic (step 707: NO), the data utilization platform server 200 repeats the processing in steps 704 to 706.

[0108] On the other hand, if the above determination reveals that processing has been completed for all data matching logic (step 707: YES), and that processing has not been completed for all data relationships (step 708: NO), the data utilization platform server 200 repeats the processing in steps 703 to 707.

[0109] On the other hand, if it is determined in step 708 above that processing has been completed for all data relationships (step 708: YES), the data utilization platform server 200 refers to the metadata 506 of the data analysis logic (step 709), and also refers to the metadata 507 of the data matching logic (step 710).

[0110] Next, if, as a result of the references in steps 709 and 710 above, the data utilization platform server 200 does not specify data item 562 in the metadata 506 of the data analysis logic (step 711 :NO), compare the types (563, 573) using metadata 506 of the data analysis logic and metadata 507 of the data matching logic (step 712).

[0111] Next, if the data utilization platform server 200 finds, as a result of the comparison in step 712 above, that the metadata 507 of the data matching logic contains all the data that matches the metadata 506 of the data analysis logic (step 713: YES), it adds a record to the analysis component linking table 502 as a link between the data matching logic and the data analysis logic (step 714).

[0112] On the other hand, if it is found in step 711 above that the metadata 506 of the data analysis logic specifies data item 562 (step 711: YES), the data utilization platform server 200 compares the data items (562, 572) in the metadata 506 of the data analysis logic and the metadata 507 of the data matching logic (step 715).

[0113] Next, if the data utilization platform server 200 finds, as a result of the comparison in step 715 above, that the metadata 507 of the data matching logic contains all the data that matches the metadata 506 of the data analysis logic (step 716: YES), it adds a record to the analysis component linking table 502 as a link between the data matching logic and the data analysis logic (step 717).

[0114] Furthermore, the data utilization platform server 200 determines whether the metadata 507 of the data matching logic linked in step 717 above contains data corresponding to the data for which type 564 is the "key" in the metadata 506 of the data analysis logic (step 718).

[0115] If this determination reveals that the data corresponding to the "key" data is not included (Step 718: NO), the data utilization platform server 200 searches for the data corresponding to the "key" by referring to the data relationships in the metadata 506 of the data analysis logic, where type 564 is "input" (Step 719).

[0116] Furthermore, the data utilization platform server 200 adds a record to the analysis target data linking table 501 as a link to the data retrieved in step 719 above (step 720). Next, the data utilization platform server 200 determines whether processing has been completed for all data matching logic (step 721). If this determination reveals that processing has not been completed for all data matching logic (step 721: NO), the data utilization platform server 200 repeats the processes in steps 710 to 720.

[0117] On the other hand, if it is determined in step 721 above that processing has been completed for all data matching logic (step 721: YES), the data utilization platform server 200 determines whether processing has been completed for all data analysis logic (step 722).

[0118] If this determination reveals that processing is not complete for all data analysis logic (Step 722: NO), the data utilization platform server 200 repeats the processing in Steps 709 to 721. On the other hand, if it is determined that processing is complete for all data analysis logic (Step 722: YES), the data utilization platform server 200 terminates processing.

[0119] <Flow Example 2> Figure 10 is a diagram showing an example of a data utilization promotion method in this embodiment (flowchart 2). Specifically, it is a flowchart showing the process of updating the analysis relationship table 307 based on the execution results of the analysis process.

[0120] First, the data utilization platform server 200 processes the analysis (minutes) performed by the analyst 201. Step 801 checks whether information regarding the combination of analysis components is registered in the analysis relationship table 307. Note that the fact that the analysis process has been performed by analyst 201 can be detected, for example, through a record registration event in the analysis results DB 305.

[0121] Next, if the data utilization platform server 200 confirms, as a result of step 801, that the analysis process has been registered in the analysis relationship table 307 (step 802: YES), it updates the "weight" information of each record that is linked to each other in each table of the analysis relationship table 307 with respect to the relevant analysis process (and the analysis components, analysis target data, etc. that constitute it) (step 803). This update, for example, involves incrementing the "weight" value.

[0122] On the other hand, if the result of step 801 does not confirm that the analysis process has been registered in the analysis relationship table 307 (step 802: NO), the data utilization platform server 200 confirms the link between each analysis component and the data to be analyzed that constitute the analysis process executed above, based on the information of the analysis process (step 804).

[0123] Next, the data utilization platform server 200 registers the information of the analysis process performed above into the analysis relationship table 307 (step 805). In addition, the data utilization platform server 200 adds records to the analysis process-analysis purpose linking table 504 and the analysis purpose-business process linking table 505 as information related to the analysis process performed above (step 806).

[0124] Furthermore, the data utilization platform server 200 determines whether there are existing linked records corresponding to the analysis component-analysis processing linked table 503 (step 807). If this determination reveals that there are existing linked records corresponding to the analysis component-analysis processing linked table 503 (step 807: YES), the data utilization platform server 200 updates the weight information of the corresponding record in the analysis component-analysis processing linked table 503 (step 808).

[0125] Next, if the data utilization platform server 200 determines, as a result of the judgment in 807 above, that there are no existing linking records corresponding to the analysis component-analysis process linking table 503 (step 807: NO), it adds records related to the linking in the analysis process executed above to the analysis component-analysis process linking table 503 (step 809).

[0126] Furthermore, the data utilization platform server 200 determines whether there is an existing matching record in the analysis component matching table 502 (step 810). If this determination reveals that there is an existing matching record in the analysis component matching table 502 (step 810: YES), the data utilization platform server 200 updates the weight information of the corresponding record in the analysis component matching table 502 (step 811).

[0127] On the other hand, if the determination in step 810 above reveals that there are no existing linking records corresponding to the analysis component linking table 502 (step 810: NO), the data utilization platform server 200 adds records related to the linking in the analysis process performed above to the analysis component linking table 502 (step 812).

[0128] Next, the data utilization platform server 200 determines whether there is an existing matching record in the data matching table 501 (step 813). If the result of this determination is that there is an existing matching record in the data matching table 501 (step 813: YES), the data utilization platform server 200 updates the weight information of the corresponding record in the data matching table 501 (step 814).

[0129] On the other hand, if the determination in step 813 above reveals that there are no existing linked records corresponding to the data linked table 501 to be analyzed (step 813: NO), then data utilization The base server 101 adds records related to the linking in the analysis process performed above to the analysis target data linking table 501 (step 815).

[0130] Next, the data utilization platform server 200 stores the analysis results of the analysis process executed above (data values ​​output as a result of the analysis process, for example, KPI values) in the analysis results DB 305, linked to the analysis process executed above (steps 816, 817).

[0131] Furthermore, the data utilization platform server 200 updates the performance information in the performance information DB06 regarding the analysis process performed as described above (step 818). Specifically, it updates values ​​such as the execution frequency, number of users, number of standard registrations, and number of modifications for the relevant analysis process.

[0132] <Flow Example 3> Figure 11 is a diagram showing an example of a data utilization promotion method in this embodiment, specifically a flowchart of the process flow for the data utilization infrastructure server 200 to select and present an appropriate combination of analysis components for performing the corresponding analysis on the data to be analyzed corresponding to the user-specified use case (combination of application and data type (or industry of data provider (partner))) by referring to the analysis relationship table 307 which defines the correspondence with the use case collection file 301.

[0133] In this case, the data utilization platform server 200 receives the user's specification of the data to be analyzed via the user terminal 100 (step 901). This specification of the data to be analyzed by the user may be done by automatically retrieving data associated with the user specifying a use case, or, as explained in Figure 8, by the user selecting from the candidates for data to be analyzed presented by the user specifying a use case, or, as described above, by the user specifying the industry "B" and purpose "objective" of the data provider (partner).

[0134] Furthermore, the data utilization platform server 200 refers to the analysis relationship table 307 (step 902), extracts analysis components (data matching logic, data analysis logic) associated with the user-specified data to be analyzed (step 903), and creates candidate combinations of analysis components (step 904). These candidates are a list of analysis components that were extracted in step 903.

[0135] Next, the data utilization platform server 200 extracts analysis processes similar to the candidate combinations of analysis components created above (step 906). Details of this process will be described later in Figure 12.

[0136] Next, the data utilization platform server 200 extracts the analysis results of similar processes extracted in step 906 from the analysis results DB 305 (step 907). These analysis results are presented to the user as relevant information.

[0137] Next, the data utilization platform server 200 refers to the performance information DB 306 to calculate the execution frequency of the above-mentioned similar processes (step 908). The data utilization platform server 200 also refers to the performance information DB 306 to calculate the number of users of the above-mentioned similar processes (step 909). Furthermore, the data utilization platform server 200 refers to the performance information DB 306 to calculate the number of registered routine tasks for the similar processes described above (step 910).

[0138] Furthermore, the data utilization platform server 200 refers to the performance information DB 306 to calculate the number of modifications for the similar processes described above (step 911). Also, the data utilization platform server 200 refers to the analysis relationship table 307 to calculate the number of similar processes for the similar processes described above (step 912).

[0139] Next, the data utilization platform server 200 uses the results from steps 907 to 912 described above to determine the usefulness of the analysis component combination (step 913). Here, a score is calculated by weighting and adding the results from steps 908 to 912 described above. However, the determination of usefulness (evaluation value of usefulness) is not limited to this, and the data utilization platform server 200 may determine that the more the same combination of data type (or industry) and application is described in many patent documents and web data in the use case collection file 302, the higher the usefulness of the data and analysis component combination corresponding to that same combination of data type and application. Also, the data utilization platform server 200 may determine that the more applications there are for the same data type (or industry) across multiple patent documents and web data in the use case collection file 302, the higher the usefulness of the data corresponding to that data type (or industry).

[0140] Furthermore, the data utilization platform server 200 determines whether processing has been completed for all candidate combinations of analysis components (step 914). If this determination reveals that processing has not been completed for all candidate combinations of analysis components (step 914: NO), the data utilization platform server 200 repeats the processing in steps 906 to 913.

[0141] On the other hand, if the above determination determines that processing has been completed for all candidate combinations of analysis components (step 914: YES), the data utilization platform server 200 outputs the candidate combinations of analysis components, the usefulness determination results, and the analysis results of similar processing to the user terminal 100 (step 915) and presents them to the user.

[0142] <Flow Example 4> Figure 12 shows an example of a data utilization promotion method in this embodiment, specifically a flowchart of the data utilization infrastructure server 200 selecting a suitable combination of analysis components to perform analysis on user-specified data, and selecting a similar existing analysis process.

[0143] In this case, the data utilization platform server 200 refers to the analysis relationship table 307 and compares the candidate combinations of analysis components for the specified data to be analyzed, selected in the flow shown in Figure 11, with existing combinations of analysis components that have been stored as a record of analysis processing (step 1001).

[0144] Next, the data utilization platform server 200 determines whether the input data items match between the candidate analysis component combinations and the existing analysis component combinations (step 1002).

[0145] If, as a result of this determination, it is found that the input data items (e.g., data type, industry of data provider, etc.) do not match between the candidate analysis component combinations and existing analysis component combinations (Step 1002: NO), the data utilization platform server 200 determines that the combinations are not similar (Step 1009) and proceeds to Step 1010.

[0146] On the other hand, if the above determination reveals that the input data items match between the candidate analysis component combination and the existing analysis component combination (Step 1002: YES), the data utilization platform server 200 determines whether the number of matching types of analysis components composed of the candidate analysis component combination and the existing analysis component combination is greater than or equal to a specified threshold (Step 1003).

[0147] If, as a result of the above determination, it is found that the number of matching types of analysis components is not equal to or greater than the specified threshold (step 1003: NO), the data utilization platform server 200 determines that the combinations are not similar (step 1009) and proceeds to step 1010.

[0148] On the other hand, if the above determination reveals that the number of matching types of analysis components is equal to or greater than the specified threshold (Step 1003: YES), the data utilization platform server 200 determines whether the execution order of the analysis components composed of the candidate analysis component combination and the existing analysis component combinations is the same (Step 1004).

[0149] If the above determination reveals that the execution order of the analysis components does not match (Step 1004: NO), the data utilization platform server 200 determines that the combinations are not similar (Step 1009) and proceeds to Step 1010.

[0150] On the other hand, if the above determination results in a finding that the execution order of the analysis components matches (Step 1004: YES), the data utilization platform server 200 determines whether KPIs, business processes, and target information are available as analysis-related information (Step 1005).

[0151] If this determination reveals that there is no information on KPIs, operations, or targets (Step 1005: NO), the data utilization platform server 200 determines that the combinations are only partially similar (Step 1008) and proceeds to Step 1010.

[0152] On the other hand, if the above determination reveals that information on KPIs, operations, and targets exists (Step 1005: YES), the data utilization platform server 200 determines whether the number of matching KPIs, operations, and targets between the candidate analysis component combination and existing analysis component combinations is equal to or greater than the specified threshold (Step 1006).

[0153] If the above determination reveals that the number of matching KPIs, tasks, and targets is not equal to or greater than the specified threshold (Step 1006: NO), the data utilization platform server 200 determines that the combinations are only partially similar (Step 1008) and proceeds to Step 10101.

[0154] On the other hand, if the above determination reveals that the number of matching KPIs, tasks, and targets is greater than or equal to the specified threshold (Step 1006: YES), the data utilization platform server 200 determines that the combinations are similar (Step 1007) and proceeds to Step 1010.

[0155] Next, the data utilization platform server 200 determines whether the comparison has been completed for all candidate analysis component combinations and existing analysis component combinations (step 1010). If this determination reveals that the comparison has not been completed for all candidate analysis component combinations and existing analysis component combinations (step 1010: NO), the data utilization platform server 200 repeats the processes from steps 1001 to 1009 described above.

[0156] On the other hand, if the above determination reveals that comparisons have been completed for all candidate analysis component combinations and existing analysis component combinations (Step 1010: YES), the data utilization platform server 200 terminates processing.

[0157] <How to obtain data and application combinations from use cases> Figure 13 schematically illustrates the method by which the data utilization platform server 200, based on use case 451, refers to the analysis relationship table 307 to obtain combinations of data 411 and analysis components 412-415 that it proposes to the user. In Figure 13, ω represents the weight.

[0158] The data utilization platform server 200 retrieves combinations of data 411 and analysis components 412-415 based on use case 451 stored in use case collection file 302. As shown in Figure 13, this is similar to finding the path (combination of nodes and links) from data type 451 to application 450.

[0159] Therefore, the data utilization platform server 200 may use a known shortest path search method, such as Dijkstra's algorithm, to obtain combinations of data 411 and analysis components 412-415 from use case 441.

[0160] Figure 13 illustrates the bidirectional search method of the graph search algorithm using arrows. Specifically, in the first step, the data utilization platform server 200 refers to the data type-data linking table 500 to obtain data (e.g., data B, C) and weight ω504 corresponding to the data type (e.g., the industry of the data provider "B" and the type of data).

[0161] In parallel, and in reverse, the data utilization platform server 200 refers to the data provider industry-business application linking table 508 to obtain the business application (e.g., application A) and weight 584 corresponding to the data provider industry "toB" 455, and also obtains the analysis purpose (e.g., KPIα) and weight 594 corresponding to the data provider's purpose of use 454, and refers to the analysis purpose-business linking table 505 to add or multiply the obtained business application and the weight ω 555 corresponding to the analysis purpose. Summarized weight = ω584 * ω594 * ω555

[0162] In the second stage, the data utilization platform server 200 refers to the data linking table 501 to be analyzed, identifies the data matching logic 422 (e.g., matching logic a, b) and their respective weights ω 514 that are linked to the data identified in the first stage (data C, D), and then adds or multiplies them. Summarized weight = ω504 * ω514

[0163] In parallel, and working in reverse, the data utilization platform server 200 refers to the analysis process-analysis objective linking table 504 to identify the analysis processes (e.g., analysis processes 1 and 2) and their respective weights ω544 that are linked to the business applications and analysis objectives (e.g., application A and KPIα) identified in the first stage, and then adds or multiplies them. Summarized weight = ω584 * ω594 * ω555 * ω544

[0164] In the third stage, the data utilization platform server 200 identifies the data analysis logics (e.g., analysis logics 1, 2, and 3) linked to the matching logics (e.g., matching logics a and b) identified in the second stage, along with their respective weights ω524, and then adds or multiplies them. Summarized weight = ω504 * ω514 * ω524

[0165] In parallel, and working in reverse, the data utilization platform server 200 identifies the analysis logics (e.g., analysis logics 1, 2, and 3) linked to the analysis processes (e.g., analysis processes 1 and 2) identified in the second stage, along with their respective weights ω534, and then adds or multiplies them. Summarized weight = ω584 * ω594 * ω555 * ω544 * ω534

[0166] In the three steps described above, since the bidirectional searches led to the same data analysis logic node 422, the paths that arrived at the same analysis logic (e.g., analysis logics 1, 2, and 3) through bidirectional searches are connected, and their respective weights are added or multiplied. Summarized weights totalω = ω584 * ω594 * ω555 * ω544 * ω534 * ω504 * ω514 * ω524

[0167] In this example, (app, analysis purpose, analysis process, analysis logic, matching logic, data) = (A,α,1,1,a,B), (A,α,1,2,a,B), (A,α,1,1,a,C), (A,α,2,1,a,C), (A,α,1,2,a,C), (A,α,2,2,a,C), (A, α, 1, 2, b, C), Eight paths (A, α, 2, 3, b, C) are obtained, and the ones with the largest cumulative weight totalω are determined to have the highest value.

[0168] In this way, the data utilization platform server 200 identifies combinations of data and analysis components 412-415 from the use case 451 adopted by the user and presents them to the user based on their weights. For example, the data utilization platform server 200 may display the weight values ​​for each combination, or it may prioritize displaying them in descending order of weight or value.

[0169] Furthermore, each weight ω (ω584, ω594, ω555, ω544, ω534, ω504, ω514, ω524) is not limited to an evaluation value of usefulness (usefulness), but may also be a value based on the value of the data and software analysis components, such as authenticity and reliability by the evaluation module 319 (usefulness and / or reliability). Specifically, the weight ω may be a value that is evaluated as having high value based on at least one of the following: (1) the more users who have adopted it, (2) the longer the period of adoption by users, (3) the greater the cost incurred by users to adopt it, (4) the higher the evaluation from users who have adopted it, (5) the more improvement requests from users who have adopted it, (6) the greater the comprehensiveness of the data, (7) the greater the accuracy of the data, (8) the greater the consistency of the data, (9) the greater the timeliness of the data, (10) the higher the quality of data processing, (11) the greater the reproducibility of derived data from the original data, and (12) the greater the effect of data linkage.

[0170] <Screen example> Figure 14 shows an example of an output screen in this embodiment. Specifically, it is an example of a confirmation screen (step 662) of the data and analysis component combination candidates proposed in step 653, corresponding to the use case (data type, application) selected by the user via the user terminal 100 in step 661 of Figure 8.

[0171] The screen 1101 illustrated in Figure 14 includes, for example, an analysis target data field 1111 that displays the analysis target data specified by the user, and a candidate display field 1112. The analysis target data field 1111 may contain information indicating the data type and data ID, as well as information about the industry of the data provider and the data provider (other users). This allows the user to understand potential partners.

[0172] Furthermore, the candidate display field 1112 lists combinations of data matching logic and data analysis logic that are candidates for analysis processing for the user-specified data to be analyzed, as well as the analysis processing, analysis purpose, business, and target associated with the combination, along with the usefulness of the combination and related analysis results (the ID of the corresponding record in the analysis results DB305 in Figure 7B). If there is no matching information, it will be displayed including blank spaces.

[0173] Although the best mode for carrying out the present invention has been described in detail above, the present invention is not limited thereto and can be modified in various ways without departing from its essence.

[0174] [Control systems as evaluation modules] The evaluation module 319 may, for example, be an API for data transfer, and may be connected to the control system 900 via a network or the like, thereby accepting evaluations from the control system 900. The evaluation module 319 itself may also be the control system 900.

[0175] Although not shown in Figure 1, a control system 900 may be included as a functional module of the data distribution infrastructure, including the data utilization promotion system according to this embodiment. Figure 15 is a block diagram showing an example configuration of the control system according to this embodiment.

[0176] <Overall System Configuration> Embodiments of the present invention will be described in detail below with reference to the drawings. In this embodiment, the control system 900 is a device that mediates the input and output of data via an interface such as an API (Application Programming Interface). Furthermore, the control system 900 can perform data due diligence (DD) on data input and / or on sensors that acquire such data and systems that handle such data, which are input and output via an interface (such as the evaluation module unit 319) connected to an asset terminal 400, a user terminal 100, or an ASP system 600, and / or on the system that handles such data, by storing the evaluation result or valuation result in the storage unit 906 and returning the evaluation result to the evaluation module unit 319.

[0177] This control system 900 is connected via a network to user terminals 100 and asset terminals 400, as well as to a SaaS (Software as a Service) system 600 that provides various software as a service, acting as an ASP (Application Service Provider), enabling data communication between them. In Figure 1, one of each is shown for the user terminal 100, asset terminal 400, and ASP system 600, but there may be multiple units of each. Furthermore, the user terminal 100, asset terminal 400, and ASP system 600 may be configured as a data distribution infrastructure system, including at least one of them. In addition, the ASP system 600 may be part of a cloud server or connected to a gateway.

[0178] In the network configuration described above, the user terminal 100 is, in this embodiment, a terminal on the side of a data user (candidate), and by setting and registering the control rules to be followed with the control system 900 via the input / output unit 208, it becomes a terminal that can receive data that conforms to the control rules mediated by the control system 900 and utilize that data.

[0179] In this embodiment, the asset terminal 400 is a terminal on the side of the data provider (candidate), and when providing data, it provides that data to the user of the user terminal 100 directly or indirectly via the ASP system 600. In this embodiment, the control system 900 mediates the transmission and reception of data from the asset terminal 400 to the user terminal 100, thereby ensuring compliance with control rules such as IT controls. For example, if the data transmitted from the asset terminal 400 contains personal information, the control system 900 may, in order to ensure compliance with the control rules of the Personal Information Protection Act, delete or process personal information and sensitive information from the original data from the asset terminal 400 and output it as anonymized data, or output derived data generated from an ontology using a trained model from the original data or from a knowledge graph as anonymized data, and then output the data to the user terminal 100.

[0180] In addition, the asset terminal 400 may provide data by providing the data processing results, such as a trained model that has been trained on the data, to the control system 900, etc., without transmitting (uploading) the data. In this case, the control system 900 can generate derived data based on the received trained model, etc., and provide the data to the user terminal 100 or the ASP system 600.

[0181] Furthermore, the ASP system 600 is a system that receives and processes various data (e.g., driving data for transportation and logistics, observation data such as temperature and vibration of equipment, maintenance history, etc.) directly from the asset terminal 400 or indirectly via the control system 900. For example, the ASP system 600 becomes the target of problem solving by users of the user terminal 100 through the analysis of data provided by the asset terminal 400, that is, it becomes a tool for improving the productivity of existing businesses, developing new businesses, or for DX. In this case, that is, when users of the user terminal 100 use the data provided by the asset terminal 400, or when they execute applications of the ASP system 600 using the data provided by the asset terminal 400, the control system 900 of this embodiment mediates data input and output, thereby ensuring compliance with various control rules. In other words, the control system 900 of this embodiment can be considered a data hub for realizing various controls.

[0182] <Hardware Configuration> An example of the hardware configuration of the control system 900 described above will now be explained. Specifically, the control system 900 is, for example, a computer equipped with a storage unit 906 that includes appropriate non-volatile memory elements such as SSDs (Solid State Drives) and hard disk drives, and volatile memory elements such as memory; a control unit 902 such as a CPU that executes programs held in the storage unit 906 and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit 904 that connects to a network and handles communication processing with other devices; and an input / output unit 908 which is an input means such as a keyboard and an output means such as a display. As described above, in this embodiment the control system 900 is configured as a distributed computing device, so the cloud server, edge computer, and gateway do not need to have at least one of the storage unit 906, control unit 902, and input / output unit 908, and do not need to have one of the functions of the storage unit 906, control unit 902, and input / output unit 908 described later.

[0183] This hardware configuration is the same for each part of the user terminal 100, asset terminal 400, and ASP system 600. For example, the user terminal 100 includes a storage unit 206 that includes appropriate non-volatile memory elements such as SSDs (Solid State Drives) and hard disk drives, as well as volatile memory elements such as memory; a control unit 202 that executes programs held in the storage unit 206 and performs overall control of the device itself, as well as various judgment, calculation, and control processing such as a CPU; a communication unit 204 that connects to the network and handles communication processing with the control system 900, etc.; and an input / output unit 208 that includes input means such as a keyboard and output means such as a display.

[0184] Similarly, the asset terminal 400 includes a storage unit 406 containing appropriate non-volatile memory elements such as SSDs and hard disk drives, and volatile memory elements such as memory; a control unit 402 such as a CPU that executes programs held in the storage unit 406 and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit 404 that connects to the network and handles communication processing with the control system 900, etc.; and an input / output unit 408 which is an input means such as a keyboard and an output means such as a display. Although not shown, similarly, the ASP system 600 may also include a storage unit containing appropriate non-volatile memory elements such as SSDs and hard disk drives, and volatile memory elements such as memory; a control unit such as a CPU that executes programs held in the storage unit and performs overall control of the device itself, as well as various judgment, calculation, and control processing; a communication unit that connects to the network and handles communication processing with the control system 900, etc.; and an input / output unit which is an input means such as a keyboard and an output means such as a display.

[0185] <Internal configuration of control system 900> Furthermore, in Figure 15, the data distribution infrastructure system including the control system 900 according to this embodiment consists of the control system 900, a plurality of applications 901, and a plurality of microservices 903. The applications 901 run on computers such as asset terminals 400 and ASP systems 600, and the microservices 903 run on a system consisting of computers such as user terminals 100 and ASP systems 600. Note that the present invention is not limited to the types and number of applications 901 and microservices 903. Also, applications 901 and microservices 903 are not limited to those realized by executing advanced software such as simulation programs provided by the ASP system 600, etc., and applications 901 may simply be considered as data transmission applications for asset terminals 400, and microservices 903 may simply be considered as data reception applications for user terminals 100, etc.

[0186] The control system 900, the computers running application 901 (such as asset terminals 400 and ASP system 600), and the systems running microservices 902 (such as user terminals 100 and ASP system 600) are connected to each other via a network. The network can be a WAN (Wide Area Network) or a LAN (Local Area Network). The network connection can be either wired or wireless.

[0187] The control system 900 provides one application 901 with at least one API 905 for connecting to the control system 900. Additionally, one microservice 902 provides at least one API 907 for connecting to the control system 900. The control system 900 may also provide at least one API 907 for connecting to one microservice 902.

[0188] As shown in Figure 15, the control system 900 includes a communication unit 904 (API 905, API 907, etc., which are interfaces for data input / output), a control unit 902 (receiving unit 910, control unit 920, data due diligence (DD) unit 940, transmission unit 911, etc.), and a storage unit 130 (control library 930, knowledge graph database (DB) 931, evaluation result log database (DB) 932).

[0189] Of these, the memory unit 906 includes a control library 930 that stores codes and / or settings corresponding to controls for each control type. For example, the control library 930 stores, in association with control types (various IT controls (e.g., security guidelines, etc.), various physical controls (e.g., stationing of security guards, installation of automatic doors with access cards, etc.), various internal controls (e.g., transaction monitoring, etc.)) and codes and / or settings that can implement those controls. More specifically, the control library 930 stores codes and / or settings corresponding to controls for each control type by storing, in association with codes and / or settings, at least one of the following as control rules: for example, guidelines to be followed, control frameworks, laws, treaties, contract clauses, and risks to be avoided.

[0190] Furthermore, the memory unit 906 includes a knowledge graph database (DB) 931 for storing the knowledge graph. For example, the knowledge graph is composed of edges and nodes, where nodes represent concepts, words, or items, and edges represent the relationships, co-occurrence, and statistics between those nodes. Nodes are not limited to language, but as a more specific example, the knowledge graph may be a large language model (LLM). In addition, the knowledge graph DB 931 may store analysis results (statistical information such as co-occurrence, etc.) and learning results (trained learning models, etc.) from the analysis and learning unit 923.

[0191] Furthermore, the evaluation result log database (DB) 932 stores the evaluation results or valuation results from the data due diligence unit 140, which will be described later.

[0192] Of the control unit 902, the data due diligence unit 940 (data DD unit 940) performs evaluation or valuation (hereinafter, "evaluation or valuation" may be collectively referred to as "evaluation") on the data input and output via interfaces 905 and 907, and stores the evaluation results in the evaluation result log DB 932 of the storage unit 906. The data DD unit 940 may also perform evaluation or valuation based on control verification logs, etc., stored in the evaluation result log DB.

[0193] <Examples of real-time performance evaluation methods> In other words, the data DD unit 940 is an evaluation module embedded in the data pipeline of the data distribution infrastructure and performs evaluation via the communication unit 904. More specifically, when data is uploaded from the asset terminal 400, the data DD unit 940 may be embedded in gateways or edge computers along the data pipeline path and may have the function of scoring the evaluation value of the data and adding it to the data as metadata or storing it in the evaluation result log DB 932. For example, the data DD unit 940 may score at least one of the following items (A) to (J) as an evaluation value related to the reliability of the data, embed it in the metadata as a reliability evaluation value, and / or store it in the evaluation result log DB 932. (A) Will the data be verified using a distributed ledger? (B) Is it a secure storage location? (C) Is gateway authentication available? (D) Is there metadata about the origin of the product? (E) Does it include owner information via device signature? (F) Is the data distribution being denoised using the 3-sigma rule? (G) Is the polling frequency above the optimal level according to the Nyquist criteria? (H) Does the authenticity fingerprint match? (I) Does it have Byzantine fault tolerance? (J) Is the decoding result of satellite data, etc. (e.g., parity check result) correct?

[0194] <Example of a batch-processing-based performance evaluation method> Furthermore, the data DD unit 940 can perform evaluations of data input and output via the communication unit 904 in batch processing, in addition to the near real-time evaluation method described above. For example, the data DD unit 940 may perform evaluations in batch processing based on control verification logs (which may include a history of near real-time evaluation results by the data DD unit 940) stored in the evaluation result log DB by the log storage unit 125 of the control unit 920, which will be described later. As an evaluation method, the data DD unit 940 may score whether or not the data conforms to control rules such as data distribution standards, guidelines, and laws and regulations such as the Personal Information Protection Act.

[0195] <Examples of evaluation methods for design> As described above, the data DD unit 940 may not only evaluate data input / output operations, whether performed in real time or in batches, but may also perform evaluations as a design. For example, the data DD unit 940 may evaluate whether the asset terminals 400, ASP systems 600, user terminals 100, etc., on the data pipeline of the data distribution platform are located in which country, who manages them and how, and whether the design allows for the prohibition of unauthorized access, from the perspective of control rules such as physical controls. Specifically, the data DD unit 940 may score whether the system conforms to physical control rules based on the history of data that senses the presence of physical controls (e.g., entry and exit history using access cards or automatic locking doors, operating status of surveillance cameras, attendance history of security guards).

[0196] <Example of an external evaluation acceptance function> However, the data DD unit 940 may also function as an evaluation module that accepts evaluations from external devices via the communication unit 904. For example, when the data DD unit 940 receives access from an external device owned by an auditing firm, etc., via the communication unit 904, it presents the evaluation verification log of the evaluation result log DB 932 to the auditing firm, etc., and accepts external evaluations (which may be rephrased as review or audits). Specifically, the data DD unit 940 accepts evaluations via the communication unit 904 to determine whether the data valuation method described above conforms to control rules such as data distribution standards, guidelines, and laws and regulations such as the Personal Information Protection Act. For example, the data DD unit 940 may be configured to present an evaluation value scoring program so that auditing firms, etc., can verify the internal processing method of the data DD unit 940. Furthermore, the data DD unit 940 may be configured to present a comparison between the data uploaded by the data presentation device 400 (data that does not contain sensitive information or ontology data, etc.) and the original data before uploading (data that contains sensitive information or data before ontology creation, etc.). In addition, the data DD unit 940 has a function to automatically check the following items and may accept evaluation via the communication unit 204 (which can be rephrased as a review or audit of whether it complies with the Securities and Exchange Act and international accounting guidelines). (1) When making a payment, conduct due diligence to determine if there is a pipeline connecting the data provider to the data recipient (in a B2B context). (2) Are offsetting operations being properly executed in transactions between multiple companies? (3) Checking for inflated sales figures (fictitious transactions) (e.g., group companies leaking data through other companies) (4) Monitoring of illegal information trading (insider information) (5) Monitoring of circular trading

[0197] <Example of evaluation based on international standards> The data DD unit 940 may perform evaluation and rating based on data evaluation criteria pursuant to ISO 25012. For example, the data DD unit 940 may evaluate at least one of the following as assertion items: accuracy, completeness, validity, confidentiality, integrity, interoperability, usefulness, and compliance with laws and regulations. In this case, the data DD unit 940 can quantify the quality for each assertion item using a measurement function such as A / B, where the measured quantity element A is the total quantity B, as shown in Table 2 (see ISO 25012). Therefore, the data DD unit 940 may use this quality value as the evaluation value. Alternatively, the data DD unit 940 may set predetermined thresholds for each assertion item and determine whether the quality is good or bad based on whether these quality values ​​meet the threshold criteria. [Table 2]

[0198] Furthermore, the control unit 920 of the control unit 902 implements the control by executing and / or setting the code stored in the control library 930 that corresponds to the control to be set. The control to be set may be automatically set based on control rules such as laws and regulations that must be complied with in the country or region where the data provision device 600, ASP system 600, or user terminal 100 is installed, or based on the nationality of the user or legal entity of the data provision device 600, ASP system 600, or user terminal 100, or it may be set manually by the user of the data provision device 600, ASP system 600, user terminal 100, or control system 900 via the communication unit 904, input / output unit 908, etc. As shown in Figure 15, the control unit 920 includes a configuration and execution unit 921 that executes and / or configures the settings, debugs, automatically verifies and executes the code stored in the control library 930 corresponding to the set controls; an editing and processing unit 922 that edits and processes data; an analysis and learning unit 923 that analyzes and learns data; a derived data generation unit 924 that generates derived data based on analysis results and learning results such as knowledge graphs; and a log storage unit 125 that stores various histories as control verification logs in the evaluation result log DB 932.

[0199] The control unit 920 can implement the control by automatically configuring and executing a combination of code execution and / or settings corresponding to the control to be set from the control library 930. The specific method of automatic configuration by the control unit 920 will be described later. Furthermore, as a specific example of control for implementing the control, the control unit 102, 1. Select the combination of interfaces (APIs 905, 107, etc.) (i.e., which application 901 will accept data input from, and which microservice 903 will output it to, etc.). 2. Select the data to be input and / or output (i.e., what data to accept as input from application 901 and what data to output to microservice 903), 3. Editing or processing the input data (for example, if the input data contains personal information, deleting or processing the personal information from the data to a level that can be considered pseudonymized information under the Personal Information Protection Act), 4. Control the system to generate and output derived data from the input data (for example, if the input data contains personal information, generate derived data from a knowledge graph that reflects the statistical information of the original data, thereby converting it from personal information under the Personal Information Protection Act to anonymized information). 5. Control the input and / or output of data with high evaluation or valuation results (for example, control the input and output of data with a score above a predetermined threshold based on scoring by the data DD unit 940). 6. Control to prevent data with low evaluation or valuation results from being input and / or output (for example, control to prevent data with scores below a predetermined threshold from being input or output based on scoring by the data DD unit 940). 7. Verify that the data conforms to control rules (e.g., ESG (Environment, Social, Governance) rules, IT control rules to ensure data integrity, security guidelines, risk avoidance rules). 8. Verify data that senses that physical controls are in place (for example, verification using entry / exit history from access cards or automatic locking doors, operation history of surveillance cameras, and attendance records of security guards). Control may be achieved by performing at least one of the following actions.

[0200] Furthermore, the log storage unit 125 of the control unit 920 may store these histories as control verification logs in the evaluation result log DB 932. That is, the log storage unit 125 of the control unit 920, 1. Interface selection history, 2. Data selection history, 3. History of whether the entered data has been edited or processed. 4. Generate derived data from the input data and output the history. 5. History of data with high evaluation or valuation results, 6. History of when low evaluation or valuation results were not entered and / or output. 7. History of verification results regarding whether the data conforms to control rules. 8. Verification history of the data that sensed that physical control was in place. At least one of these may be stored in the evaluation result log DB932 of the storage unit 906 as a control verification log.

[0201] <Specific examples of derived data generation> Here, the derived data generation unit 924 of the control unit 920 converts the input data into derived data. For example, the derived data generation unit 924 generates derived data based on a knowledge graph that reflects the input data. For instance, the analysis and learning unit 923 of the control unit 920 analyzes and learns the input data to generate a knowledge graph with various items and concepts as nodes and statistical data such as the co-occurrence of variance and covariance as edges, and stores it in the knowledge graph DB 131. Then, the derived data generation unit 924 of the statistics unit 120 may read the knowledge graph that reflects the statistical information of the input data from the knowledge graph DB 131, provide a set of random numbers to the knowledge graph, and format it to reflect statistics such as variance and covariance, thereby generating derived data and controlling it to be output. Thus, the derived data generation unit 924 can generate output derived data from input data using known derived data generation methods. For example, if the input data falls under personal information as defined by the Personal Information Protection Act, the unit can generate and output derived data that has been processed to a level that can be considered anonymized information, thereby ensuring compliance with the control rules of the Personal Information Protection Act. The following methods may be used as known derived data generation methods. The derived data generation process may also be performed on an asset terminal 400 or the like. References: 1. "A method for generating pseudo-data with privacy protection using statistical values" https: / / ipsj.ixsq.nii.ac.jp / ej / index.php?active_action=repository_view_main_item_detail&page_id=13&block_id=8&item_id=187369&item_no=1 2. Won first place in the anonymization technology competition at NeurIPS, a prestigious international conference in the field of AI and machine learning. ~Towards the realization of a new method for anonymizing personal data in a safe and user-friendly way~ https: / / group.ntt / jp / newsrelease / 2021 / 03 / 02 / 210302b.html 3. “Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs” Saad, Feras Ahmad Khaled https: / / dspace.mit.edu / handle / 1721.1 / 147226

[0202] <Examples of automated configuration> Here, a specific control example of the control unit 920 in this embodiment will be explained using Figure 16. Figure 16 is a schematic diagram showing the functions of the control unit 920 of the control system 900 in this embodiment. As described above, the control system 900 is connected to the asset terminal 400, the ASP system 600, and the user terminal 100 via the network 300.

[0203] The control system 900 supports the configuration of program code (code development and configuration) that enables control by user U1. The program may be executed using various services provided by asset terminals 400, ASP system 600, etc., or it may be a program executed internally by the control system 900 (such as an OS (operating system) or a data editing and processing program).

[0204] User U1 operates the user terminal 100 to input information regarding the controls (control rules such as the Personal Information Protection Act) that they wish to comply with for the program they wish to use in the asset terminal 400, ASP system 600, control system 900, etc. The control system 900 selects the service to be used by User U1 from several candidate services used for program execution.

[0205] As shown in Figure 16, the control system 900 includes a control library 930 and a control unit 920. The control library 930 stores the control rules 7 and programs 8 acquired by the control unit 920. However, when using programs from the ASP system 600, the control library 930 does not need to store those programs, and may only store some code for functional extensions or other purposes necessary for using those programs, or configuration information for those programs.

[0206] Control Rule 7 is information that indicates restrictions based on User U1's policies (such as compliance with laws and guidelines, and service usage policies) related to the execution of Program 8. Program 8 may have been created without considering IT control rules, such as the settings of asset terminal 400 and user terminal 100, or processing requests via the inter-server network 300.

[0207] The constraints imposed by Control Rule 7 could include, for example, lower limits on scores and performance in evaluation results, and upper limits on the costs associated with using the service. Costs could include, for example, the fees for using the service, the amount of computing resources required for processing the service (processor resources and memory resources), and the CO2 emissions associated with processing. Performance could include the service response time (or an indicator of the speed of the response time) and availability (or an indicator of the service's reliability, such as the frequency of update patch releases for the service).

[0208] Furthermore, the control library 930 pre-stores the rule table 9. The rule table 9 is information that shows the correspondence between code patterns (code patterns) that indicate the processing that can use a specific service among the codes (sets of instructions) that may appear in the program 8, and the candidates for available services (candidates for use). The rule table 9 may also be called rule information. The rule table 9 also has values ​​for predetermined parameters p that correspond to each service. Here, parameter p is an indicator that represents the cost or performance when the relevant service is selected. Parameter p is the input to control rule 7.

[0209] For example, rule table 9 contains a record with code pattern P1, candidate service A1, and parameter p=a1. This record indicates that service A1 is available for code pattern P1 in the program, and that the value of parameter p when service A1 is used is a1.

[0210] Furthermore, rule table 9 contains a record for code pattern P1, candidate service A2, and parameter p=a2. This record indicates that service A2 is available for code pattern P1 in the program, and that the value of parameter p when service A2 is used is a2.

[0211] Rule table 9 also registers the service and parameter p values ​​for other code patterns as potential candidates for use.

[0212] Here, the asset terminal 400 and the ASP system 600 may, for example, be server computers that provide cloud services. In that case, the asset terminal 400 will support a data cloud, and the ASP system 600 will support a SaaS cloud.

[0213] The asset terminal 400 and ASP system 600 execute processing using pre-configured programs and settings information in response to data transmission requests and processing requests received via the network 300. Examples of processing that the asset terminal 400 and ASP system 600 can execute include transmitting data in response to adoptive parent requests, processing data received via the network 300, executing functions in response to requests received via the network 300, and saving data received via the network 300.

[0214] The control unit 920 retrieves program 8 from the control library 930, which includes code 8a and setting 8b corresponding to control rule 7. Again, if a SaaS program is used, the control unit 920 retrieves the specification information for code 8a and setting 8b corresponding to control rule 7 to be used for that program.

[0215] In this process, when executing program 8, the control unit 920 identifies a set of services that satisfy control rule 7 from among several candidate services based on the code patterns included in program 8.

[0216] For example, the control unit 920 detects code 8a corresponding to code pattern P1 from within program 8 through pattern matching. In this case, program 8 can be said to contain code pattern P1. Then, based on the rule table 9, the control unit 920 identifies services A1 and A2 as candidate services for use corresponding to code 8a.

[0217] Furthermore, the control unit 920 detects a configuration code 8b from within program 8 that corresponds to code pattern P2 registered in rule table 9 through pattern matching. In this case, program 8 can be said to contain code pattern P2. Then, based on rule table 9, the control unit 920 identifies services B1 and B2 as candidate services for use corresponding to code 8b.

[0218] Note that only one service is selected for each code that matches a given code pattern. In the example above, either service A1 or A2 is selected for code 8a. Similarly, either service B1 or B2 is selected for code 8b.

[0219] In this way, the control unit 920 detects each code pattern included in program 8 and identifies multiple candidate services. The control unit 920 then obtains a parameter p for each candidate from rule table 9 and identifies a set of services that satisfy control rule 7. As an example, consider a case where the candidate services to be selected are services A1, A2, B1, and B2, and an upper limit Z for the sum of CO2 costs is given as the ESG control rule 7. Also, the parameter p represents the CO2 cost value of the candidate services.

[0220] In this case, there are four possible sets of services to choose from: service A1 and B1, service A1 and B2, service A2 and B1, and service A2 and B2. The total cost when service A1 and B1 is selected is a1 + b1. The total cost when service A1 and B2 is selected is a1 + b2. The total cost when service A2 and B1 is selected is a2 + b1. The total cost when service A2 and B2 is selected is a2 + b2.

[0221] Of these, the set of services A1 and B1 is assumed to be the one that does not exceed the upper limit Z for CO2 costs. In this case, the control unit 920 identifies the set of services A1 and B1 as the set of services that satisfy ESG control rule 7. If there are multiple sets of services that do not exceed the upper limit Z for CO2 costs, the control unit 920 may identify the set of services with the smallest total CO2 costs.

[0222] When the control unit 920 identifies the service A1 and B1 set as a set of services that satisfy control rule 7, it executes program 8 using the service A1 and B1 set. For example, when the control unit 920 executes code 8a in program 8, it may convert code 8a to code that uses service A1 and then execute it. Similarly, when the control unit 920 executes code 8b in program 8, it may convert code 8b to code that uses service B1 and then execute it.

[0223] Alternatively, the control unit 920 may convert program 8 into a group of programs for distributed processing software that combines multiple services. For example, a first template for program conversion may be pre-registered in the rule table 9, associated with code pattern P1. Similarly, a second template for program conversion may be pre-registered in the rule table 9, associated with code pattern P2. "Template" refers to the format (template) of a predetermined program that defines the processing of a service.

[0224] For example, the first template shows the format (template) of a program that specifies that the ASP system 600 should perform a particular process in response to a processing request. The first template may include, for example, the format of a program to be executed by the ASP system 600, or the format of a program to send configuration information to the ASP system 600. Also, for example, the second template shows the format (template) of a program to send a processing request to the ASP system 600 via the network 300.

[0225] In more specific examples, the first template might show the format of a function program, and the second template might show the format of a program that calls the function. Alternatively, for example, the first template might show the format of a program that sets a data storage location, and the second template might show the format of a program that requests data writing or reading.

[0226] In this case, the control unit 920 may generate a first post-conversion program that utilizes service A1 by substituting the value extracted from code 8a into the argument of the first template. For example, the first post-conversion program specifies that the ASP system 600 should use service A1 to perform the processing corresponding to code 8a. The first post-conversion program is applied to the ASP system 600. For example, the first post-conversion program is sent to the ASP system 600, and the ASP system 600 executes the first post-conversion program. Alternatively, for example, predetermined configuration information may be sent to the ASP system 600 according to the procedure specified by the first post-conversion program.

[0227] Furthermore, the control unit 920 may generate a second post-conversion program that utilizes service B1 by substituting the value extracted from code 8b into the argument of the second template. For example, the second post-conversion program specifies that ASP system 600-2 should use service B1 to execute the processing corresponding to code 8b. The second post-conversion program is applied to ASP system 600-2, which is different from ASP system 600-1. For example, the second post-conversion program is sent to ASP system 600-2, and the second post-conversion program is executed by ASP system 600-2.

[0228] Furthermore, ASP system 600-2 may provide other services. In that case, the second template also serves as the format for a program that specifies how ASP system 600-2 should perform a particular process. This allows the control unit 920 to coordinate multiple services on ASP system 600-1 and ASP system 600-2 (for example, services A1 and B1) to execute the processes described in program 8.

[0229] According to the control system 900, control rule 7 and program 8 are acquired, and when the acquired program 8 is executed, a set of services that satisfies control rule 7 is identified from multiple candidate services based on the code patterns contained in program 8. Then, program 8 is executed using the identified set of services.

[0230] This allows for the appropriate identification of services to be used that conform to the control rules set by user U1. For example, ESG control rule 7 may use an upper limit on the sum of costs such as CO2 consumption associated with service use. In this case, the control system 900 can combine services in a way that does not exceed the CO2 costs assumed by user U1 and provide them to user U1 at user terminal 100.

[0231] Furthermore, as Control Rule 7, the evaluation values ​​of the Security Assessment Program (ISMAP: Information System Security Management and Assessment Program) may be used. For example, the lower limit of the security evaluation value registered under ISMAP can be used for multiple cloud services provided by multiple ASP systems 600 and asset terminals 400 as data clouds. By using ASP systems 600 and asset terminals 400 as data clouds that provide cloud services with a security evaluation value higher than the lower limit, and executing cloud services on the data and providing them to user terminals 100, ISMAP's Control Rule 7 can be satisfied.

[0232] Additionally, a lower limit on the average utilization rate may be used as control rule 7. Raising the lower limit on the average utilization rate (or selecting a set of services with a higher average utilization rate) can also improve the reliability of the functionality implemented by program 8.

[0233] Furthermore, the control rule 7 may include conditions for each of a plurality of types of parameters such as CO₂ cost and performance value. The control unit 920 may select a set of services that satisfies all the conditions for each of the plurality of types of parameters. Also, the control unit 920 may accept input of priorities for each type of parameter. When there are a plurality of sets of services that satisfy the control rule 7, the control unit 920 may select a set in which the higher the priority of the type of parameter, the more advantageous the evaluation against the condition. For example, consider a case where the control rule 7 includes conditions for each of CO₂ cost and performance value, and the priority of cost is higher than the priority of performance value. In this case, if there are a plurality of sets of services that all satisfy the respective conditions for cost and performance value, it is conceivable that the control unit 920 specifies, among the plurality of sets, the set with the minimum cost as the set of services to be used.

[0234] Note that the execution subject for processing of various services may be the control system 900. That is, the control system 900 may execute the processing of the service by itself without requesting the asset terminal 400 or the ASP system 600 to process the service. Alternatively, the execution subject for processing of various services may be an information processing system including the control system 900, the asset terminal 400, and the ASP system 600 (other information processing apparatuses). In this case, it can also be said that the execution subject of the functions of the control system 900, various services, and the program 8 (or the converted program) is the information processing system.

[0235] The above is an example of the configuration of the control system 900 of the present embodiment. Regarding each functional unit included in the control system 900, a plurality of functional units may be combined into one functional unit, or one functional unit may be divided into a plurality of functional units for each function.

[0236] <Processing Example of Control System 900> FIG. 17 is a flowchart explaining an example of processing of the control system 900 of the present embodiment.

[0237] As shown in Figure 17, first, the control unit 920 of the control system 900 receives operations via the user input / output unit 208 of the user terminal 100 through processing by the configuration execution unit 921, and controls the user to set control rules and the data types and application types they wish to use (SA1). Here, as a specific example, suppose the user specifies ISMAP and ESG control rules and wants to select a routing application for joint delivery from SaaS software using delivery schedule data from other companies from cloud data.

[0238] Next, the control unit 920, through processing by the configuration execution unit 921, automatically configures the asset terminals 400 that provide data, the ASP system 600 that provides the application, and their codes and / or settings, which satisfy the specified control rules, data types, and application types (SA2). Specifically, the configuration execution unit 921, in accordance with the ISMAP control rules, preferentially selects data clouds (multiple asset terminals 400) and SaaS clouds (multiple ASP systems 600) with high evaluation values ​​registered in ISMAP, while also selecting the ASP system 600 that provides the specified joint delivery routing application and the asset terminals 400 that provide the third-party delivery schedule data for that purpose. At this time, in accordance with the ESG control rules, it preferentially selects joint delivery routing applications with CO2 emission reduction programs. If there are multiple candidates, the one that best fits the control rules, in this example, the one with the lowest CO2 cost or the one with the highest ISMAP evaluation value, may be preferred.

[0239] The control unit 920 then accepts the specification of other control rules (SA3). For example, if in the above step a large number of candidate asset terminals 400 or a large number of candidate ASP system 600 applications are presented (SA3, YES), the process returns to step SA1 for narrowing down the options, and further control rule specifications are accepted from the user of the user terminal 100.

[0240] If no other controls are specified (SA3, NO), the control unit 920, through processing by the configuration execution unit 921, mediates data input / output so that the data output from the asset terminal 400 configured in the above steps is input to the application of the ASP system 600, specifying the code and / or settings configured in the above steps (SA4). Specifically, the control system 900 receives location information, slip information, etc., from a smartphone owned by a delivery truck driver (example of asset terminal 400) via API 905 using the receiving unit 100, checks the authenticity of the data (such as whether the parity check result when decoding satellite data to obtain location information is correct, and whether it has not been tampered with) through processing by the data DD unit 940, and transmits the data via API 907 using processing by the transmitting unit 911 to the ASP system 600 that provides a joint delivery application (example of microservice 903).

[0241] Furthermore, during this data input / output mediation, if the data DD unit 940 determines, for example, that the data contains personal information, the editing and processing unit 922 may process the data by deleting sensitive data before outputting it to the ASP system 600. Alternatively, the derived data generation unit 924 may generate derived data based on a knowledge graph that reflects statistical information on location, and this derived data may be output to the ASP system 600.

[0242] Then, the control unit 920 stores logs such as the data evaluation value and processing details in the evaluation result log DB 932 through processing by the log storage unit 123 (SA5). For example, the quality of the input and output data may be stored in the evaluation result log DB 932 as metadata (such as ownership information and device information such as smartphones), or the data processing process details, derived data processing details, and the operational results of the SaaS application (such as CO2 emissions reduced by joint delivery) may be stored as logs in the evaluation result log DB 932.

[0243] Then, the data DD unit 940 performs an evaluation of the control verification based on the logs stored in the evaluation result log 132 (SA6). This control verification is an evaluation performed as a batch process and differs from the real-time data evaluation performed in step SA4 described above. Furthermore, this control verification may be performed automatically by the data DD unit 940 according to the control rules, or the logs stored in the evaluation result log 132 may be made viewable so that evaluations can be performed manually by auditors or others from other terminals, and evaluations from external terminals may be accepted.

[0244] Then, the control unit 920 determines whether or not the system complies with the controls based on the control verification results from the data DD unit 940 (SA7). If it does not comply with the controls (SA7, NO), it returns to step SA2 and reconfigures the code, settings, etc. (SA2). Therefore, the procedure in the first cycle up to step SA7 can be seen as a test operation or debugging process performed before actual execution.

[0245] On the other hand, if the control verification result from the data DD unit 940 conforms to the control (SA7, YES), the control unit 920 prompts the user to input whether to terminate or not. If the user does not terminate (SA8, NO), the control unit returns to SA4 and starts execution. Therefore, the procedure in the first cycle that reaches step SA8 can be understood as ending the test execution and asking the user whether to proceed with the actual execution.

[0246] On the other hand, if the control unit 920 determines that it is time to terminate the process (SA8, YES), it terminates the above process. The above is an example of the processing of the control system 900 in this embodiment.

[0247] [Example 1] The embodiment described above can be used in various other ways. For example, with the implementation of the Work Style Reform Act in Japan in 2024 in the logistics industry, working hours will be restricted, requiring that existing operations be carried out within limited working hours. In other words, there is a need to optimize transportation efficiency, for example, to improve the productivity (sales minus expenses) of drivers per hour of work.

[0248] Therefore, logistics company A, as a user, has often had empty return trips (i.e., a low utilization rate for return trips). By collaborating with other companies in the same industry to match return trips and increase the utilization rate for return trips, productivity can be improved.

[0249] Therefore, for example, in this embodiment, if both the industry of the data provider "B" and the industry of the data recipient "toB" are specified as "transportation industry," and the purpose "object" is specified as "vehicle utilization rate," "productivity," "load-matching," etc., other users who could be potential collaborators can be found.

[0250] [Example 2] Another example is when a company's services are provided across multiple companies as part of a supply chain. In such cases, the overall quality of the supply chain's services (e.g., improved robustness and resilience) can be improved. For instance, by specifying the group of companies involved in the supply chain as data provider "B" and specifying component values ​​related to robustness and resilience as data type "data," or by specifying "robust" or "resilience" as the purpose "object," data exchange with potential partners can be established to improve the overall quality of the supply chain.

[0251] [Example 3] Another example, Example 3, is its application to ESG-related Scope 3 (greenhouse gas emissions from the entire value chain). For example, as in Example 2 above, by linking data across the entire supply chain, CO2 emissions from the source of the supply chain can be added together, allowing for an evaluation of CO2 emissions across the entire supply chain. In this way, a bottom-up, white-box evaluation method can be used, and it is also possible to improve KPIs such as cost, price, and greenhouse effects.

[0252] [Example 4] Another example is when providing services across multiple cloud services, where evaluations can be performed to ensure compliance with information security service standards such as ISMAP. For instance, by evaluating security measures from a report set provided by a contractor (cloud service provider) as a data linkage destination and providing it to the user, the actual security information of the service provider (contractor) can be collected and evaluated within the contractor company. In addition, risk control information can be automatically identified and acquired.

[0253] The above is a description of this embodiment.

[0254] In this embodiment, a use case collection file is provided in which data types and uses are stored in at least one associated manner, and based on the use case collection file, the user is presented with the use along with the data type corresponding to that use.

[0255] According to this embodiment, since the data type corresponding to the application is presented as a use case along with the application, even users with little knowledge or experience in data utilization can receive suggestions for new applications and use cases for data utilization, thereby promoting data utilization.

[0256] Furthermore, in the present embodiment, a patent document file that stores patent document information may be further provided, and at least descriptions related to data types and usages may be extracted from the patent document information, associated with each other, and stored in the use case collection file.

[0257] According to this configuration, use cases are automatically collected from patent documents. Therefore, even for use cases that have no track record of data utilization, promoting data utilization can be achieved by proposing such use cases as specific use cases to users.

[0258] Furthermore, in the present embodiment, based on the use case collection file, the greater the number of patent documents that describe the same combination of data type and usage, and / or the greater the number of usages across multiple patent documents for the same data type, the evaluation may be made that the value of said data type is higher.

[0259] According to this configuration, even if there is no track record of user utilization, the degree of usefulness can be measured according to the number of use cases or usages in patent documents, and useful use cases can be presented to users.

[0260] Furthermore, in the present embodiment, for combinations of a data type and a usage, and / or combinations of data or other users and a usage, the evaluation that the value is higher is performed based on at least one of the following: (1) the greater the number of users that have adopted the combination; (2) the longer the period for which users have adopted the combination; (3) the greater the cost users have spent to adopt the combination; (4) the higher the evaluation from users that have adopted the combination; (5) the greater the number of improvement requests from users that have adopted the combination; (6) the higher the completeness of the data; (7) the higher the accuracy of the data; (8) the higher the consistency of the data; (9) the higher the timeliness of the data; (10) the higher the quality of data processing; (11) the higher the reproducibility of derived data from original data; and (12) the higher the effect achieved through data linkage. Therefore, the degree of usefulness can be obtained by receiving user utilization records, feedback, and the like, and practical use cases can be presented to users.

[0261] Furthermore, in this embodiment, the possibility of obtaining data and / or the value of obtainable data may be estimated from the information obtained via the communication unit.

[0262] This allows for the discovery of potential data providers through asset mining, thereby promoting data provision and, consequently, data utilization.

[0263] Furthermore, in this embodiment, (1) based on at least one of the customer information, trading partner information, logistics information, accounting information, business model information, human resource information, and know-how information obtained via the communication unit, the possibility of obtaining data from at least one of the customer, trading partner, logistics, accounting, business model, human resources, and know-how, (2) Estimate equipment or software to be purchased, leased or made available based on at least one of the customer information, business partner information, logistics information, accounting information, business model information, human resource information, and know-how information obtained via the communication unit, and estimate the feasibility of data acquisition and / or the value of the data that can be acquired based on said equipment or software, and (3) Based on the trained model information or ontology information obtained via the communication unit, predict the trained raw data or ontological raw data to estimate the data availability and / or the value of the available data. You may perform at least one of the following actions.

[0264] This allows us to estimate the availability and / or value of obtainable data by using at least one of the following: (1) the resources that serve as the source of the data, (2) the hardware and software for digitization and data conversion, and (3) the original data (raw data) that has undergone some kind of processing (such as ontological data like processed data) or data that has been altered (such as trained models), and retrospectively estimate the original data (its type and data value). This promotes data provision and thereby facilitates data utilization.

[0265] Furthermore, in this embodiment, analysis relationship information is generated that defines the link between the data to be analyzed and the analysis component that performs the analysis processing on said data, based on the correspondence between data type and application stored in the use case collection file. Based on the analysis relationship information, a combination of available analysis components may be identified for predetermined data designated as the target of analysis by the user.

[0266] This allows us to identify and propose specific combinations of data and analytical components based on use cases, enabling even users unfamiliar with data utilization software configurations and data structures to understand concrete data utilization methods, thereby further promoting data utilization.

[0267] Furthermore, this embodiment may also include an evaluation module that is embedded in the data pipeline and accepts evaluation via a communication unit.

[0268] This allows for the evaluation of data value, such as reliability and usefulness, along the data pipeline from data provider to data recipient, fulfilling a data due diligence function and enabling the construction of a safe and secure data distribution system, thereby further promoting data utilization.

[0269] Furthermore, in this embodiment, the data type may include the industry of the data provider and the type of data, and the purpose may include the industry of the data recipient and the purpose of use by the data recipient.

[0270] This allows for a concrete understanding of the industries users want to work in, the industries from which they want to receive data, and the objectives they can achieve by utilizing the data, thereby further promoting data utilization.

[0271] Furthermore, according to this embodiment, a system that provides data to applications that utilize data, such as data analysis, can propose reusable components for creating analysis processes from the time of system introduction and operation commencement. This makes it possible to speed up the creation and preparation of analysis applications for analyzing data from multiple business systems, for example, and to reduce the costs required to implement data utilization, including data analysis processing.

[0272] In other words, it becomes possible to propose analysis components regardless of the amount of data analysis experience, and consequently, to improve the efficiency and reduce the cost of creating data analysis software.

[0273] Furthermore, the following is made clear by the description herein: In the data utilization promotion system of this embodiment, the control unit may, in the process of generating the analysis relationship information, relate a plurality of data to be analyzed according to the relationships between the data to be analyzed based on the attributes of each data to be analyzed, and generate a data matching logic for extracting the plurality of data to be analyzed during analysis, as one of the analysis components using a predetermined algorithm. According to this, data obtained from various business systems and other sources can be grouped based on appropriate attributes and used for efficient analysis.

[0274] Furthermore, in the data utilization promotion system of this embodiment, the control unit may, in the process of generating the analysis relationship information, link the data matching logic and the data analysis logic, which is one of the analysis components and performs analysis on the data to be analyzed extracted by the data matching logic, by comparing metadata related to the data handled by the data matching logic and the data analysis logic, respectively.

[0275] According to this, the data matching logic and data analysis logic that constitute the analysis components can be accurately linked based on the compatibility of the data items they handle, enabling the efficient proposal of analysis components.

[0276] Furthermore, in the data utilization promotion system of this embodiment, the control unit may further perform a process to update the analysis-related information based on the actual performance information of the analysis processing performed by the analysis component.

[0277] According to this, it becomes possible to manage information acknowledging the usefulness of the analysis components that users actually select and use from those presented to them, and consequently, to make the subsequent identification of combinations of analysis components more appropriate.

[0278] Furthermore, in the data utilization promotion system of this embodiment, the control unit may link the analysis process with at least one of the following pieces of information when updating the analysis-related information: the purpose of the analysis process, the business operations corresponding to that purpose, and the target of those operations.

[0279] According to this, it becomes possible to manage information that recognizes the usefulness of the analysis components that users actually select and use from the analysis components presented to them, taking into account specific circumstances such as purpose, work, and target of the work. In turn, this makes it possible to further improve the accuracy of identifying combinations of analysis components in the future.

[0280] Furthermore, in the data utilization promotion system of this embodiment, the control unit may, in the process of identifying the combination of analysis components, identify the combination of analysis components based on the actual performance information of the analysis components if it has such performance information. According to this, the accuracy of identifying combinations of analytical parts can be made more appropriate.

[0281] Furthermore, in the data utilization promotion system of this embodiment, the control unit may further perform a process to identify analysis processes similar to the identified combination of analysis components based on performance information of analysis processes using the analysis components, and to determine the usefulness of the identified combination of analysis components based on the actual values ​​of the frequency of occurrence of predetermined events related to the similar analysis processes.

[0282] According to this, the accuracy of identifying combinations of analysis components can be further improved based on the frequency of occurrences of events that demonstrate the usefulness of the analysis components, such as events in which the user actually selected and used the analysis components presented to the user.

[0283] Furthermore, in the data utilization promotion system of this embodiment, the control unit may further perform a process to output to a predetermined device the information regarding the identified combination of analysis parts as information regarding candidate combinations of analysis parts suitable for analyzing predetermined data designated as the target of analysis. This makes it possible to present users with information on analytical components that are suitable for analyzing the data they are trying to analyze.

[0284] Furthermore, in the data utilization promotion method of this embodiment, the information processing system may, in the process of generating the analysis relationship information, relate multiple data to each other according to the relationships between the data based on the attributes of each data to be analyzed, and generate a data matching logic for extracting the multiple data to be analyzed using a predetermined algorithm as one of the analysis components.

[0285] Furthermore, in the data utilization promotion method of this embodiment, the information processing system may, in the process of generating the analysis relationship information, link the data matching logic and the data analysis logic, which is one of the analysis components and performs analysis on the data to be analyzed extracted by the data matching logic, by comparing metadata related to the data handled by the data matching logic and the data analysis logic, respectively.

[0286] Furthermore, in the data utilization promotion method of this embodiment, the information processing system may further perform a process to update the analysis-related information based on the actual performance information of the analysis processing by the analysis component.

[0287] Furthermore, in the data utilization promotion method of this embodiment, when the information processing system updates the analysis-related information, it may link the analysis process with at least one of the following pieces of information: the purpose of the analysis process, the business operations corresponding to that purpose, and the target of those operations.

[0288] Furthermore, in the data utilization promotion method of this embodiment, if the information processing system has performance information on analysis processing using the analysis components when it identifies combinations of analysis components, it may also identify combinations of analysis components based on such performance information.

[0289] Furthermore, in the data utilization promotion method of this embodiment, the information processing system is The process may also involve identifying analytical processes similar to the specified combination of analytical components based on performance data of analytical processes using those components, and further determining the usefulness of the specified combination of analytical components based on the actual frequency of occurrence of predetermined events related to those similar analytical processes.

[0290] Furthermore, in the data utilization promotion method of this embodiment, the information processing system may further perform a process to output to a predetermined device the information regarding the identified combination of analysis parts as information regarding candidate combinations of analysis parts suitable for analyzing predetermined data designated as the target of analysis.

[0291] According to this embodiment, it is possible to discover various tangible and intangible assets such as data assets generated from IoT devices, customer lists of a company, networks such as business partners and logistics, human resources, know-how, and ledger data, analyze and evaluate these assets, automatically detect new use cases to promote B2B collaboration and digital transformation (DX), propose them to potential partners, and even present data trust evaluations to potential partners, making it industrially applicable.

[0292] [Other embodiments] Now, while embodiments of the present invention have been described above, the present invention may be implemented in various other different embodiments within the scope of the technical idea described in the claims, in addition to the embodiments described above.

[0293] For example, while the data utilization promotion system is described as being implemented as a distributed computing system in separate enclosures that implement its functions, it is not limited to this and may be implemented in the same enclosure. In addition, each function such as the control unit and memory unit can be configured to be functionally or physically distributed and integrated according to the functional load or in any arbitrary unit.

[0294] Furthermore, while the example described shows the data utilization promotion system processing requests from client terminals such as user terminal 100 and asset terminal 400 and returning the processing results to the client terminals, the data utilization promotion system may also be configured as an integrated unit that includes user terminal 100 and asset terminal 400, etc., to perform processing in a standalone manner.

[0295] Furthermore, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods.

[0296] In addition, the processing procedures, control procedures, specific names, information including registration data and search conditions for each process, screen examples, and database configuration shown in the above-mentioned documents and drawings may be changed at will unless otherwise specified.

[0297] Furthermore, with respect to the user terminal 100, data utilization platform server 200, asset terminal 400, ASP server 600, etc., each component shown in the diagram is a functional concept and does not necessarily need to be physically configured as shown.

[0298] For example, the processing functions of each device in the data utilization infrastructure server 200, particularly those performed by the control unit 202, may be implemented in whole or in part by a processor such as a CPU (Central Processing Unit) and a program interpreted and executed by that processor, or they may be implemented as a wired logic hardware processor. The program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing a computer to execute the method according to the present invention, as described later, and is mechanically read by the data utilization infrastructure server 200 as needed (it may also be read by the data utilization infrastructure server 200 from an ASP server 600, etc., as needed). That is, a computer program for giving instructions to the CPU and performing various processes in cooperation with the OS (Operating System) is recorded in a storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM and forms a control unit in cooperation with the CPU.

[0299] Furthermore, this computer program may be stored on an application program server connected via any network to the user terminal 100, data utilization infrastructure server 200, asset terminal 400, ASP server 600, patent document information server 800, etc., and it is possible to download all or part of it as needed.

[0300] Furthermore, the program according to the present invention may be stored on a computer-readable recording medium, or it may be configured as a program product. Here, "recording medium" includes any "portable physical medium" such as memory cards, USB memory, SD cards, flexible disks, magneto-optical disks, ROMs, EPROMs, EEPROMs, CD-ROMs, MOs, DVDs, and Blu-ray® Discs.

[0301] Furthermore, "program" refers to a data processing method described in any language or writing method, regardless of its format, such as source code or binary code. Note that "program" is not necessarily limited to a single, monolithic structure; it also includes distributed structures consisting of multiple modules or libraries, and those that work in cooperation with other programs, such as an OS (Operating System), to achieve their functions. Regarding the specific configuration for reading the recording medium in each device shown in the embodiments, the reading procedure, or the installation procedure after reading, well-known configurations and procedures can be used. The present invention may also be defined as a program product recorded on a non-temporary, computer-readable recording medium.

[0302] The various databases stored in memory units 106, 206, 406, etc. (e.g., Patent Document DB301, Use Case Collection File 302) are storage means such as memory devices like RAM and ROM, fixed disk devices like hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processing and website provision.

[0303] Furthermore, the user terminal 100, data utilization platform server 200, asset terminal 400, ASP server 600, patent document information server 800, etc., may be configured as known personal computers, workstations, or other information processing devices, or they may be configured by connecting any peripheral devices to the information processing device. In addition, the user terminal 100, data utilization platform server 200, asset terminal 400, ASP server 600, patent document information server 800, etc., may be realized by implementing software (including programs, data, etc.) that realizes the method of the present invention on the information processing device.

[0304] In this application, the phrase "A and / or B" may be read as "either or both of A and B."

[0305] Furthermore, the specific forms of distribution and integration of the devices are not limited to those shown in the figures, and all or part of them can be functionally or physically distributed and integrated in any unit according to various additions or functional loads. In other words, the embodiments described above may be implemented in any combination, or the embodiments may be implemented selectively. [Explanation of Symbols]

[0306] 7. Control Rules 8 Programs 8a Code 8b Settings (including configuration code) 9 Rule Table U1 User 100 user terminals 200 Data Utilization Platform Servers 400 Asset Terminals 600 ASP systems 800 Patent Document Information Server 106, 206,406 storage section 102, 202, 402 Control Unit 104, 204, 404 Communications Department 108, 208, 408 input / output section 300 Data Utilization Middleware 301 Patent Document Information File 302 Use Case Collection File 303 Analysis Execution Management Department 304 Data database to be analyzed 305 Analysis result DB 306 Performance Information Database 307 Analysis Relationship Tables 308 Analysis-Related Information Management Department 309 Analysis and Proposal Department 310 User / Business Management Department 311 Client Interface Provision Department 312 Analysis Processing Performance Management Department 313 Analysis Parts Management Department 314 Data Management Department 315 Data Communications Department 316 Use Case Extraction Unit 317 Utility Judgment Unit 318 Asset Estimation Department 319 Evaluation Module Section 323 Analytical Components 333 Analysis Processing 401 Analysis-related information 421 Data Matching Logic 422 Data Analysis Logic 451 Use Cases 450 uses 451 Data Types 501 Data linking table for analysis 502 Analysis component linking table 503 Analysis Parts - Analysis Processing Linking Table 504 Analysis Processing - Table Linking Analysis Objectives 505 Analysis Objective - Business Function Linking Table 506 Metadata for Data Analysis Logic 507 Metadata for Data Matching Logic 508 Data provider industry-business application linkage table 509 Data Provider Usage Purpose - Analysis Purpose Linking Table 500 Data Type - Data Linking Table 900 Control System (Control Module) 901 Application 902 Control Unit 903 Microservices 904 Communications Department 905 (Data Input Side) API 906 Storage section 907 (Data output side) API 908 Input / output section 910 Receiver 911 Transmitter 912 Compatibility Operation Control Unit 920 Control Department 921 Configuration Execution Unit 922 Editing and Processing Department 923 Analytical Learning Department 924 Derived Data Generation Unit 930 Control Library 931 Knowledge Graph 932 Evaluation Result Log DB 940 Data DD Section

Claims

1. In a data utilization promotion system comprising at least a storage unit and a control unit, The aforementioned storage unit is It includes a use case collection file that stores data types and their uses in at least in correspondence. The control unit, An extraction unit extracts from the aforementioned use case collection file the use cases corresponding to the data type of data desired by the user, the data type corresponding to the user's use, and / or the data type of other users' data that is suitable for the user's use. A presentation unit that constructs an application corresponding to the extracted combination of the use and data type and presents it to the user, Data utilization promotion system.

2. In the data utilization promotion system described in claim 1, The aforementioned data type includes the industry of the data provider and / or the type of data, The aforementioned uses include the industry of the data recipient and / or the purpose of use by the data recipient. Data utilization promotion system.

3. In the data utilization promotion system according to claim 1 or 2, The application presented by the aforementioned presentation unit is This application is constructed by identifying a combination of analytical components that can handle the aforementioned data types as input data for analytical components while satisfying the aforementioned purpose for analysis. Data utilization promotion system.

4. A method for promoting data utilization, which is executed on a computer having at least a storage unit and a control unit equipped with a use case collection file that stores data types and uses in at least in association with each other, The control unit is executed as follows: Extraction step of extracting from the aforementioned use case collection file the use cases corresponding to the data type of data desired by the user, the data type corresponding to the user's use, and / or the data type of other users' data that is suitable for the user's use, The presenting step includes constructing an application corresponding to the extracted combination of the aforementioned use and the aforementioned data type, and presenting it to the user, Methods for promoting data utilization.

5. In a program for causing a computer to run, which includes at least a storage unit and a control unit, the computer having a use case collection file that stores data types and uses in at least one correspondence, The control unit, Extraction step of extracting from the aforementioned use case collection file the use cases corresponding to the data type of data desired by the user, the data type corresponding to the user's use, and / or the data type of other users' data that is suitable for the user's use, A program for constructing an application corresponding to the extracted combination of use and data type, and for presenting it to the user, and for executing the following steps.

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