Program, method, information processing device and system

Abstract

We support the creation of inventions, new business and product development, business reform and improvement, etc. to address challenges. [Solution] The program causes a processor to perform the following steps: accepting information about a problem from a user; inputting the accepted information about the problem into a trained model constructed based on the information about the problem and outputting first causal structure information including the problem and the cause of the problem; extracting one or more pieces of second causal structure information that match or are similar to the output first causal structure information from a database in which multiple pieces of second causal structure information including the problem and the cause of the problem are stored; presenting one or more problem-solving means corresponding to the extracted one or more pieces of second causal structure information; accepting from the user a selection of the one or more presented problem-solving means; running a simulation based on the accepted one or more problem-solving means; and presenting the results of the simulation.

Description

[Technical Field]

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

[0002] In recent years, ensuring organizational reproducibility of innovation creation has become an urgent issue.

[0003] Patent Document 1 describes a system that extracts cause or effect elements that represent the cause or effect from an event description, converts them into a query, obtains an answer to the created query from a causal relationship database, and automatically presents information indicating other events or phenomena that are causally related to the event related to the query. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2009-516311 Summary of the Invention [Problem to be solved by the invention]

[0005] However, Patent Document 1 does not disclose a series of flows including generating a causal structure including a user's problem and its underlying cause, matching with cases from different fields, presenting means for solving the problem, and performing a simulation.

[0006] The purpose of this disclosure is to support invention creation, new business and product development, business reform and improvement, etc., by generating causal structures for problems, matching, presenting solutions to the problems, and conducting simulations as a series of flows. [Means for solving the problem]

[0007] In order to solve the above problems, one embodiment of the program of the present disclosure is a program to be executed by a computer having a processor and a memory, the program causing the processor to execute the following steps: accepting information about a problem from a user; inputting the accepted information about the problem into a trained model constructed based on the information about the problem, and outputting first causal structure information including the problem and a cause of the problem; extracting one or more pieces of second causal structure information that match or are similar to the output first causal structure information from a database in which multiple pieces of second causal structure information including the problem and the cause of the problem are stored; presenting one or more problem-solving means corresponding to the extracted one or more pieces of second causal structure information; accepting from the user a selection of the presented one or more problem-solving means; performing a simulation based on the accepted one or more problem-solving means; and presenting the results of the simulation. [Effects of the Invention]

[0008] According to this disclosure, it is possible to support invention creation, new business / product development, business reform / improvement, etc. by generating a causal structure for a problem, matching, presenting solutions to the problem, and simulating them as a series of flows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an example of the overall configuration of a system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating an example of the functional configuration of a user terminal 10 according to the present embodiment. [Figure 3] FIG. 2 is a diagram illustrating an example of the functional configuration of a server 20 according to the present embodiment. [Figure 4] FIG. 2 is a diagram showing the data structure of a user information database 2021 according to the present embodiment. [Figure 5] FIG. 2 is a diagram showing the data structure of a usage information database 2022 according to the present embodiment. [Figure 6]FIG. 2 is a diagram showing the data structure of a causal structure database 2023 according to the present embodiment. [Figure 7] 10 is a flowchart showing an example of a learning processing operation of a first causal structure generation tool 2024 according to this embodiment. [Figure 8] 10 is a flowchart illustrating an example of a problem solving support operation according to the present embodiment. [Figure 9] FIG. 2 is a diagram showing an example of a first screen of the user terminal 10 according to the present embodiment. [Figure 10] FIG. 10 is a diagram showing a second example of a screen of the user terminal 10 according to the present embodiment. [Figure 11] FIG. 10 is a diagram showing a third example of a screen of the user terminal 10 according to the present embodiment. [Figure 12] FIG. 10 is a diagram showing a fourth example of a screen of the user terminal 10 according to the present embodiment. [Figure 13] FIG. 10 is a diagram showing a fifth example of a screen of the user terminal 10 according to the present embodiment. [Figure 14] FIG. 2 is a block diagram showing the basic hardware configuration of a computer 90. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated description will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0011] In the following description, a "processor" refers to one or more processors. The at least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may also be another type of processor such as a GPU (Graphics Processing Unit). The at least one processor may be single-core or multi-core.

[0012] Furthermore, the at least one processor may be a processor in the broad sense, such as a hardware circuit (for example, a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) that performs part or all of the processing.

[0013] In the following explanation, information that produces an output for an input may be described using expressions such as "xxx table," but this information may be data of any structure, or may be a learning model such as a neural network that produces an output for an input. Therefore, an "xxx table" may be referred to as "xxx information."

[0014] Furthermore, in the following description, the configuration of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0015] In addition, in the following explanation, processing may be described using the "program" as the subject, but since a program is executed by a processor to perform specified processing while appropriately using a memory unit and / or an interface unit, etc., the subject of the processing may also be the processor (or a device such as a controller that has that processor).

[0016] The program may be installed in a device such as a computer, or may be stored in, for example, a program distribution server or a computer-readable (e.g., non-transitory) recording medium. Also, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0017] Furthermore, in the following description, identification numbers are used as identification information for various objects, but other types of identification information (for example, identifiers including alphabetic characters or symbols) may also be used.

[0018] In addition, in the following description, when describing elements of the same type without distinguishing between them, reference symbols (or common symbols among the reference symbols) may be used, and when describing elements of the same type with distinction between them, the identification numbers (or reference symbols) of the elements may be used.

[0019] In the following description, the control lines and information lines are those that are considered necessary for the description, and do not necessarily represent all the control lines and information lines in the product. All components may be interconnected.

[0020] Each information processing device is configured by a computer equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For each of the user terminal 10 and the server 20, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0021] <Summary> The system according to this embodiment is a problem-solving support system that generates a causal structure including a user's problem and its underlying causes, proposes solutions to the problem based on structurally similar causal structures from case studies in other fields, and then performs simulations. This system accepts information about the problem and generates a causal structure based on the received information. Next, it extracts causal structures from other fields that are similar to the generated causal structure from a database and proposes solutions to the problem based on the extracted causal structure. It then accepts a selection of the proposed solutions, runs a simulation, and presents the results. This allows the system to support invention creation, new business / product development, business reform / improvement, and other activities, regardless of personal qualities such as intuition or experience, by performing a series of processes that include generating a causal structure for the problem, matching, proposing solutions to the problem, and simulation.

[0022] <Configuration example> <1-1 System Configuration> 1 is a block diagram showing an example of the overall configuration of a system 1 according to this embodiment. The system 1 includes, for example, a user terminal 10 and a server 20. The user terminal 10 and the server 20 are communicatively connected via, for example, a network 80.

[0023] 1 shows an example in which the system 1 includes one user terminal 10, the number of user terminals 10 included in the system 1 is not limited to one. The number of user terminals 10 included in the system 1 may be two or more.

[0024] In this embodiment, a collection of multiple devices may be considered as one server. The allocation of multiple functions required to realize the server 20 according to this embodiment to one or more pieces of hardware can be determined appropriately in consideration of the processing capacity of each piece of hardware and / or the specifications required for the server 20.

[0025] The user terminal 10 is, for example, an information processing device operated by a user who uses the problem solving support system. The user terminal 10 accepts various information and instructions from the user and transmits the input information to the server 20. The user terminal 10 also displays problem solving means and simulation results presented by the server 20. The user terminal 10 is realized, for example, by a desktop personal computer (PC), a laptop PC, a smartphone, or a mobile terminal such as a tablet.

[0026] The server 20 is, for example, an information processing device that provides a service to support users in solving problems. The server 20 generates a causal structure based on problem information received from the user. Next, the server 20 searches a causal structure database for a similar causal structure and presents a solution to the problem. Furthermore, the server 20 runs a simulation of the presented solution and presents the results.

[0027] The server 20 is, for example, an information processing device realized by a computer connected to a network 80. As shown in Fig. 1, the server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29. The input / output IF 23 functions as an input device for receiving input operations from a user and as an interface with an output device for outputting information to the user.

[0028] Each information processing device is configured by a computer equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For the user terminal 10 and the server 20, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0029] <1-2 User terminal> Fig. 2 is a diagram showing an example of the functional configuration of the user terminal 10 shown in Fig. 1. As shown in Fig. 2, the user terminal 10 includes a communication unit 120, an input device 13, an output device 14, an audio processing unit 17, a microphone 171, a speaker 172, a storage unit 180, and a control unit 190. The blocks included in the user terminal 10 are electrically connected by, for example, a bus or the like.

[0030] The communication unit 120 performs processing such as modulation and demodulation for the user terminal 10 to communicate with other devices. The communication unit 120 performs transmission processing on signals generated by the control unit 190 and transmits the signals to the outside (for example, the server 20). The communication unit 120 performs reception processing on signals received from the outside and outputs the signals to the control unit 190.

[0031] The input device 13 is a device for a user operating the user terminal 10 to input instructions or information. The input device 13 is realized, for example, by a touch-sensitive device 131 or the like, which inputs instructions by touching the operation surface. If the user terminal 10 is a PC or the like, the input device 13 may be realized by a keyboard, a mouse, or the like. The input device 13 converts instructions input by the user into electrical signals and outputs the electrical signals to the control unit 190. The input device 13 may include, for example, a receiving port that receives electrical signals input from an external input device.

[0032] The output device 14 is a device for presenting information to a user operating the user terminal 10. The output device 14 is realized, for example, by a display 141 or the like. The display 141 displays data according to the control of the control unit 190. The display 141 is realized, for example, by an LCD, an organic EL display, or the like.

[0033] The audio processing unit 17 performs, for example, digital-to-analog conversion processing of an audio signal. The audio processing unit 17 converts a signal provided from the microphone 171 into a digital signal and provides the converted signal to the control unit 190. The audio processing unit 17 also provides the audio signal to the speaker 172. The audio processing unit 17 is realized, for example, by a processor for audio processing. The microphone 171 receives audio input and provides an audio signal corresponding to the audio input to the audio processing unit 17. The speaker 172 converts the audio signal provided from the audio processing unit 17 into audio and outputs the audio to the outside of the user terminal 10.

[0034] The storage unit 180 is realized by, for example, the memory 15 and the storage 16, and stores data and programs used by the user terminal 10. The storage unit 180 stores, for example, user information 181. The user information 181 includes, for example, a user ID, a user name, user information, a usage history, and the like.

[0035] The control unit 190 is realized by the processor 19 reading a program stored in the storage unit 180 and executing instructions included in the program. The control unit 190 controls the operation of the user terminal 10. The control unit 190 functions as an operation reception unit 191, a transmission / reception unit 192, and a presentation control unit 193 by operating in accordance with the program.

[0036] The operation reception unit 191 performs processing for receiving instructions or information input from the input device 13. Specifically, for example, the operation reception unit 191 receives instructions or information input from the touch-sensitive device 131 or the like. The operation reception unit 191 also receives voice instructions input from the microphone 171. For example, the operation reception unit 191 acquires instructions from the user by analyzing the received voice signal and extracting a predetermined noun.

[0037] The transmitting / receiving unit 192 performs processing for the user terminal 10 to transmit and receive data to and from external devices such as the server 20 in accordance with a communication protocol. Specifically, for example, the transmitting / receiving unit 192 transmits information input by the user or instructions from the user to the server 20. The transmitting / receiving unit 192 also receives information provided by the server 20.

[0038] The presentation control unit 193 controls the output device 14 to present to the user information provided from the server 20. Specifically, for example, the presentation control unit 193 causes the display 141 to display the problem-solving means, a report of the simulation results, etc. transmitted from the server 20.

[0039] <1-3 Server> Fig. 3 is a diagram showing an example of the functional configuration of the server 20 shown in Fig. 1. As shown in Fig. 3, the server 20 functions as a communication unit 201, a storage unit 202, and a control unit 203.

[0040] The communication unit 201 performs processing for the server 20 to communicate with external devices.

[0041] The storage unit 202 is realized by the memory 25 and the storage 26, and stores data and programs used by the server 20 to provide the estimation system. The storage unit 202 has, for example, a user information database 2021, a usage information database 2022, a causal structure database 2023, etc. The databases stored in the storage unit 202 are not limited to these. The storage unit 202 also stores, for example, a first causal structure generation tool 2024, a matching tool 2025, a solution generation tool 2026, and a simulation tool 2027 as programs.

[0042] The user information database 2021 is a database that stores information about users, as will be described in detail later.

[0043] The usage information database 2022 is a database that stores information about users' system usage, as will be described in detail later.

[0044] The causal structure database 2023 is a database that stores a plurality of pieces of causal structure information, including issues and their causes, based on past cases. Details will be described later.

[0045] The first causal structure generation tool 2024 generates first causal structure information indicating causal relationships between elements related to a task based on information about the task received from a user. The first causal structure generation tool 2024 has a function of extracting and constructing causal relationships, including tasks and their causes, from input text data and other information, using, for example, natural language processing technology and a machine learning model. The first causal structure generation tool 2024 is, for example, a specific trained model that has been trained in advance, but may also include a generative AI such as a large-scale language model (LLM).

[0046] The matching tool 2025 compares (matches) the first causal structure information generated by the first causal structure generation tool 2024 with a plurality of pieces of second causal structure information stored in the database 2023, and extracts matching or similar second causal structure information. The matching tool 2025 has a function of searching for and selecting optimal past causal structure information from the database using, for example, a similarity determination algorithm or the like.

[0047] The solution generation tool 2026 generates and presents means for solving the user's problem (problem-solving means) based on the second causal structure information extracted by the matching tool 2025. The solution generation tool 2026 has a function of generating specific solutions based on past cases and knowledge included in the extracted second causal structure information, for example, and providing them to the user as multiple options.

[0048] The simulation tool 2027 executes a simulation of what would happen if a problem-solving measure selected by a user were implemented. The simulation tool 2027 has a function to virtually predict and evaluate the results and effects of the selected problem-solving measure, and to present the simulation results to the user. This allows the user to check the effects of the solution in advance.

[0049] The control unit 203 is realized by the processor 29 reading a program stored in the storage unit 202 and executing instructions included in the program. The control unit 203 operates in accordance with the program to perform functions shown as a reception control module 2031, a transmission control module 2032, a service processing module 2033, and a presentation control module 2034.

[0050] The reception control module 2031 controls the process by which the server 20 receives signals from external devices in accordance with a communication protocol. The transmission control module 2032 controls the process by which the server 20 transmits signals to external devices in accordance with a communication protocol. The service processing module 2033 performs main processes such as accepting information about tasks from users, generating causal structures, and executing matching and simulations. The presentation control module 2034 controls the process of presenting information to users.

[0051] Specifically, the service processing module 2033 cooperatively operates functional modules such as a first causal structure generation tool 2024, a matching tool 2025, a solution generation tool 2026, and a simulation tool 2027. The service processing module 2033 accepts information (e.g., text data) about a problem from a user and causes the first causal structure generation tool 2024 to generate first causal structure information including the problem and its cause based on the information. The information about the problem is detailed information about the problem, including, for example, the background (e.g., cause of occurrence, countermeasures taken so far, etc.), constraints (e.g., occurrence timing, occurrence conditions, etc.), and effects (e.g., scope of impact, involved parties). The first causal structure information is a causal model that includes multiple elements (e.g., a problem and one or more causes) extracted based on the information about the problem and indicates the causal relationships between the multiple elements. The first causal structure information is, for example, a directed acyclic graph (DAG) or text data automatically generated using natural language processing technology and a machine learning model (e.g., a trained model or a large-scale language model). The service processing module 2033, for example, uses the matching tool 2025 to search and extract second causal structure information that matches or is similar to the generated first causal structure information from the causal structure database 2023. The second causal structure information is a causal model that includes multiple elements (e.g., a problem and one or more causes) extracted based on past cases and indicates the causal relationships between the multiple elements. The second causal structure information is, for example, a directed acyclic graph (DAG) or text data that is automatically generated using natural language processing technology and a machine learning model (e.g., a trained model or a large-scale language model). The service processing module 2033 uses the solution generation tool 2026 to generate and present a problem-solving solution based on the extracted second causal structure information. The service processing module 2033, for example, uses the simulation tool 2027 to execute a simulation based on the presented problem-solving solution and presents the simulation results to the user.

[0052] <2 Data Structure> 4 to 6 are diagrams showing the data structures of databases stored in server 20. Note that Figures 4 to 6 are merely examples and do not exclude data that is not listed. Furthermore, even data that is listed in the same database may be stored in separate storage areas in storage unit 202.

[0053] Fig. 4 is a diagram showing the data structure of the user information database 2021. The user information database 2021 shown in Fig. 4 has columns such as name, age, contact information, and usage history, with the user ID as a key, for example.

[0054] The item "user ID" is an item for storing an identifier for uniquely identifying a user.

[0055] The item "Name" is an item for storing the name of the user.

[0056] The item "age" is an item for storing the age of the user.

[0057] The item "contact information" is an item for storing the user's contact information, such as a telephone number, email address, and address.

[0058] The "Usage History" item is an item that stores the user's system usage history. The "Usage History" item stores, for example, issues that have been solved using the system in the past, generated causal structures, simulation results, usage dates, etc. The "Usage History" item may store, for example, the usage ID corresponding to FIG. 5.

[0059] Fig. 5 is a diagram showing the data structure of the usage information database 2022. The usage information database 2022 shown in Fig. 5 has columns such as user ID, usage date, and execution result, with the usage ID as a key, for example.

[0060] The item "usage ID" is an item for storing an identifier for uniquely identifying the use of the system.

[0061] The item "user ID" is an item for storing an identifier for uniquely identifying a user who has used the system. The item "user ID" corresponds to, for example, the user ID shown in FIG.

[0062] The item "Date of Use" is an item for storing the date on which the system was used.

[0063] The "Execution Results" item is an item that stores the results of processing executed using the system. For example, the "Execution Results" item stores generated causal structure information, simulation results, presented content of problem-solving means, etc.

[0064] Fig. 6 is a diagram showing the data structure of the causal structure database 2023. The causal structure database 2023 shown in Fig. 6 has columns such as second causal structure information and metadata, with a causal structure ID as a key, for example.

[0065] The item "causal structure ID" is an item for storing an identifier for uniquely identifying a causal structure.

[0066] The item "second causal structure information" is an item that stores a causal structure that indicates the causal relationships between variables related to the issue, generated by analyzing past cases. The second causal structure information is generated by analyzing case information (e.g., technical information and business model information) such as academic papers, patent documents, business cases, and historical cases. The second causal structure information is, for example, an effective acyclic graph or text data. The second causal structure information includes, for example, a model that describes the causal relationships between variables in "a case where a software company switched from a one-time sale to a recurring revenue model and tripled its sales."

[0067] The item "metadata" is an item that stores accompanying information such as the background, constraints, and effects of the second causal structure information. The item "metadata" is detailed information about the issue, and stores information such as the background (e.g., cause of occurrence, measures taken so far, etc.), constraints (e.g., time of occurrence, conditions for occurrence, etc.), and effects (e.g., scope of impact, parties involved), etc.

[0068] <3 Example of operation> <3-1 Learning process of trained model (first causal structure generation tool)> A description will be given of the learning processing operation of the first causal structure generation tool 2024. The first causal structure generation tool 2024 is a model that has been trained so that, when information about a problem is input, it outputs first causal structure information including the problem and the cause of the problem.

[0069] FIG. 7 is a flowchart showing an example of the learning processing operation of the first causal structure generation tool 2024.

[0070] In step S11, the server 20 acquires information about the issue, such as case information on past cases. Specifically, the service processing module 2033 acquires, for example, all kinds of case information (e.g., technical information and business model information) from around the world, such as academic papers, patent documents, business cases, and historical cases, as information about the issue. The service processing module 2033, for example, analyzes these cases, formalizes them as causal structures (e.g., second causal structure information, which will be described later), and accumulates them as learning data in the causal structure database 2023. For example, in the case where "Company A introduced a customer management system to its sales team and recorded detailed contact histories with customers, thereby quickly discovering latent customer needs and increasing sales by 20%," the service processing module 2033 generates a causal structure in which the "introduction of the customer management system" caused the "early discovery of latent customer needs," which in turn led to "increased sales," and accumulates this causal structure as learning data in the causal structure database 2023. That is, information about the issues stored in the causal structure database 2023 (past case examples: second causal structure information) is used as learning data and is also used for matching with the first causal structure information described below.

[0071] In step S12, the server 20 trains the first causal structure generation tool 2024. Specifically, the service processing module 2033 trains the first causal structure generation tool 2024 using, for example, information about the issue acquired in step S11 (the causal structure of the case) as learning data. Through this training, the first causal structure generation tool 2024 acquires, for example, a function to automatically generate a graph (e.g., an effective acyclic graph) and text data indicating a causal relationship. For example, the first causal structure generation tool 2024 learns the relationship extracted as a causal model, from "introduction of a customer management system" to "early detection of latent needs" to "increased sales."

[0072] In step S13, the server 20 stores the trained first causal structure generation tool 2024 in the storage unit 202. As a result, from the next time onwards, this tool can quickly and accurately generate first causal structure information based on information about the task received from the user.

[0073] <3-2 Problem-solving support movements> The problem solving support operation will now be described.

[0074] FIG. 8 is a flowchart showing an example of a problem solving support operation.

[0075] First, a user who uses the problem solving support system operates the user terminal 10 to access the server 20. The user logs in to the problem solving support application provided by the server 20, for example, by inputting their own user ID and password.

[0076] In step S21, the server 20 accepts information about the issue from the user. Specifically, the service processing module 2033 accepts, for example, text data and related materials input by the user from the reception control module 2031 and the user terminal 10. The information about the issue is detailed information about the issue, and includes, for example, information about the background (e.g., cause of occurrence, measures taken so far, etc.), constraints (e.g., time of occurrence, conditions for occurrence, etc.), and effects (e.g., scope of impact, parties involved). The service processing module 2033 accepts, for example, detailed information input by the user, including the issue of "sales target not being achieved" and the cause of "lack of customer management, failure to discover latent needs."

[0077] In step S22, the server 20 generates first causal structure information. Specifically, the service processing module 2033 generates the first causal structure information based on the received information about the problem using, for example, the first causal structure generation tool 2024. The service processing module 2033 inputs the received information about the problem to, for example, the first causal structure generation tool 2024 (trained model), and outputs first causal structure information including the problem and its cause. The first causal structure information is a causal model that includes multiple elements (e.g., a problem and one or more causes) extracted based on the information about the problem and indicates the causal relationships between the multiple elements. The first causal structure information is, for example, a directed acyclic graph (DAG) or text data. When the first causal structure information is a directed acyclic graph, each node corresponds to each element, and the nodes are connected by edges (the direction of the causal relationship). The directed acyclic graph has a structure, for example, of "element 1 (cause 1)" → "element 2 (cause 2)" → "element 3 (problem)." Furthermore, when the first causal structure information is text data, it includes, for example, text data such as "The cause of element 3 (problem) is element 2 (cause 2), and the cause of element 2 (cause 2) is element 1 (cause 1)." For example, the service processing module 2033 extracts a causal relationship in which "insufficient customer management" is the cause of the problem "sales target not achieved," and generates these problems and causes as a directed acyclic graph (DAG) or text data. For example, when the service processing module 2033 determines that there is insufficient information about the problem to generate the first causal structure information, it may accept additional information from the user.

[0078] In step S23, the server 20 extracts second causal structure information including the problem and the cause of the problem. Specifically, the service processing module 2033, for example, uses the matching tool 2025 to search and extract (match) one or more pieces of second causal structure information that match or are similar to the output first causal structure information from the causal structure database 2023. The second causal structure information is a causal model that includes multiple elements (e.g., a problem and one or more causes) extracted based on past cases and indicates the causal relationships between the multiple elements. The second causal structure information is, for example, a directed acyclic graph (DAG) or text data automatically generated using natural language processing technology and a machine learning model (e.g., a trained model or a large-scale language model). The service processing module 2033, for example, uses an algorithm based on graph theory to compare the first causal structure information with each piece of second causal structure information stored in the causal structure database 2023 and evaluate the degree of match or similarity therebetween. This algorithm evaluates the degree of agreement or similarity between nodes (issues, causes) and edges (direction and strength of causal relationships) of a graph (e.g., a directed acyclic graph) using techniques such as graph isomorphism determination, subgraph isomorphism determination, or graph kernel method. The service processing module 2033 may, for example, score the degree of agreement or similarity based on the evaluation. The service processing module 2033 may, for example, extract only second causal structure information whose similarity score is equal to or greater than a predetermined threshold (e.g., 70%). For example, if the similarity score of a causal structure in which "introduction of a customer management system" resulted in "a 20% increase in sales" is 80% from among past cases stored in the causal structure database 2023, the service processing module 2033 extracts this as a similar case. On the other hand, the causal structure in which "brand image improved" due to "development of a new product" has a similarity score of 50% is excluded from the extraction target. At this time, the service processing module 2033 may, for example, present the extracted second causal structure information and similarity score to the user as a matching result, and receive approval as to whether to proceed to the next step based on the presented result.

[0079] In step S24, the server 20 presents a solution to the problem. Specifically, the service processing module 2033, for example, uses the solution generation tool 2026 to infer and generate one or more solution measures corresponding to the one or more pieces of second causal structure information extracted in step S23. The service processing module 2033, for example, presents the generated solution measures to the user. For example, if the extracted second causal structure information is a case of "introducing a customer management system," the service processing module 2033 presents a specific solution measure, "introducing a customer management system," in the form of a report based on the findings. At this time, the service processing module 2033 may, for example, calculate a feasibility score for each solution measure and present it to the user. The feasibility score is calculated taking into account, for example, data availability, infrastructure preparation, technological maturity, and implementation difficulty.

[0080] In step S25, the server 20 accepts a selection of a problem-solving measure from the user. Specifically, the user selects a desired solution from, for example, one or more proposed problem-solving measures. The service processing module 2033 accepts, for example, the solution selected or rejected by the user. For example, the user selects the solution of "introducing a customer management system" from the multiple proposed problem-solving measures.

[0081] In step S26, the server 20 executes a simulation. Specifically, the service processing module 2033 executes a simulation using, for example, the simulation tool 2027, based on one or more selected problem-solving measures. In this simulation, the effectiveness of each of the presented problem-solving measures is estimated by comparing the results when they are applied to the problem with the results when they are not applied. For example, the predicted sales when a solution such as "introducing a customer management system" is applied are compared with the predicted sales when it is not applied, and the difference is calculated as the effect. This makes it possible to numerically indicate the specific benefits of the implementation.

[0082] Furthermore, the service processing module 2033 may perform a more accurate simulation by, for example, verifying comparisons multiple times and fine-tuning parameters in each verification. For example, in a simulation of "introduction of a customer management system," the service processing module 2033 may set the "sales representative training period" to one month in the first verification, fine-tune this to two months in the second verification, and compare the results of each. This allows the service processing module 2033 to search for optimal parameters (for example, training period, implementation cost, implementation timing, etc.) and derive the most effective solution.

[0083] In step S27, the server 20 presents the results of the simulation. Specifically, the service processing module 2033 presents the results of the simulation in the form of a report, for example. This report includes the predicted sales increase rate, implementation cost, implementation period, etc., resulting from applying the proposed solution.

[0084] Furthermore, the service processing module 2033 may, for example, feed back and store the first causal structure information, the problem-solving means, and the results of the simulation in the causal structure database 2023. This allows the entire past problem-solving process to be accumulated in the database as new knowledge, which will be used to make more accurate predictions and propose solutions in the next problem-solving cycle.

[0085] <4 Screen example> An example of a screen used in the system 1 of this embodiment will be described below. Figures 9 to 13 are schematic diagrams showing an example of a display screen of the display 141 of the user terminal 10.

[0086] 9 is a diagram showing an example of a first screen of the user terminal 10 according to this embodiment. Fig. 9 shows an example of a screen displayed to accept information about the assignment in chat format in step S21 of Fig. 8.

[0087] As shown in FIG. 9, the first screen example includes a first area 1411, a second area 1412, and a third area 1413. The first area 1411 is, for example, an area showing the current step. Here, text such as "Step 1: Let's dig deeper into the problem through conversation" is displayed. The second area 1412 is, for example, an area showing guide text that the system displays in response to the user's problem input. Here, text such as "What problem are you having trouble with? Please enter as much detail as possible" is displayed. The third area 1413 is, for example, an area where the user inputs information about the problem. The third area 1413 may display, for example, a send button for sending the input information.

[0088] 10 is a diagram showing an example of a second screen of the user terminal 10 according to this embodiment. Fig. 10 shows an example of a screen displayed when generating the first causal structure information in step S22 of Fig. 8.

[0089] As shown in FIG. 10, the second screen example includes a first area 1421, a second area 1422, and a third area 1423. The first area 1421 is, for example, an area showing the current step. Here, text such as "Step 2: Generate causal structure" is displayed. The second area 1422 is, for example, an area presenting the generated causal structure (first causal structure information). Here, an effective acyclic graph is displayed as the causal structure. The third area 1423 is, for example, an area displaying a button for proceeding to the next step. Here, buttons such as "Check matching results" and "Continue conversation" are displayed.

[0090] 11 is a diagram showing a third example of a screen of the user terminal 10 according to this embodiment. Fig. 11 shows an example of a screen displaying the extraction (matching result) of the second causal structure information in step S23 of Fig. 8.

[0091] As shown in FIG. 11 , the third screen example includes a first area 1431, a second area 1432, and a third area 1433. The first area 1431 is, for example, an area showing the current step. Here, text such as "Step 3: Matching Result" is displayed. The second area 1432 is, for example, an area showing a list of extracted past cases (second causal structure information). Here, "IT Industry: Subscription Model Shift" is displayed in the form of "Similarity: 92%." The second area 1432 may, for example, display the second causal structure information as text, or may display it graphically using an effective acyclic graph or the like. The third area 1433 is, for example, an area showing a button for proceeding to the next step. Here, buttons such as "Proceed to Solution Generation" and "Readjust" are displayed.

[0092] 12 is a diagram showing a fourth example of a screen of the user terminal 10 according to this embodiment. Fig. 12 shows an example of a screen displaying the problem solving means in step S24 of Fig. 8.

[0093] As shown in FIG. 12, the fourth screen example includes a first area 1441, a second area 1442, and a third area 1443. The first area 1441 is, for example, an area showing the current step. Here, text such as "Step 4: Presenting a solution" is displayed. The second area 1442 is, for example, an area showing a list of generated problem-solving measures. Here, a solution summary such as "Solution A: Predictive analysis system using machine learning," a feasibility score, technical details, and the basis for score calculation are displayed. The third area 1443 is, for example, an area showing a button for proceeding to the next step. Here, buttons such as "Run simulation" and "Readjust" are displayed.

[0094] 13 is a diagram showing a fifth example of a screen of the user terminal 10 according to this embodiment. Fig. 13 shows an example of a screen displaying the results of the simulation in step S27 of Fig. 8.

[0095] As shown in FIG. 13, the fifth screen example includes a first area 1451, a second area 1452, and a third area 1453. The first area 1451 is, for example, an area showing the current step. Here, text such as "Step 5: Present simulation results" is displayed. The second area 1452 is, for example, an area showing a report of the simulation results (problem solution proposal). Here, an executive summary of the proposed solution is displayed. The third area 1453 is, for example, an area showing buttons for presenting the details of the report. Here, buttons for "Present report" and "Present in presentation format" are displayed. The user can select the desired display by pressing the button.

[0096] <5 Variations> In the above embodiment, only the first causal structure generation tool 2024 (trained model) is used when generating the first causal structure information, but this embodiment is not limited to this. In this embodiment, when generating the first causal structure information, the causal structure may be generated in two stages: a pre-process and a post-process. For example, in the pre-process, the first causal structure generation tool 2024 (first trained model) generates the first causal structure information based on information about the task input by the user. Then, in the post-process, for example, another trained model that has learned principles such as physical laws or social business models may be used. Specifically, the first causal structure information generated in the pre-process is input to the other trained model, and a more detailed causal structure, i.e., the generated first causal structure information, is simulated in a digital environment based on physical laws, etc., and a faithfully reproduced causal structure (first additional causal structure information) is output. The first additional causal structure information may include, for example, text data, diagrams (e.g., 3D images), videos, etc. This makes it possible to obtain more accurate and detailed causal structure information based on physical and social laws, rather than simply outputting a causal structure. Also, for example, in step S26, the simulation tool 2027 may execute a simulation similar to the simulation executed in the digital environment for the above-described first causal structure information, based on one or more problem-solving means.

[0097] For example, in the case of a problem of noise reduction for bullet trains, in a first step, the first causal structure generation tool 2024 generates abstract first causal structure information, such as the causal relationship between "increased noise" and "increased vehicle speed." In a subsequent step, this first causal structure information is input into a trained model that has learned the laws of physics. The trained model adds an element, such as "friction between the car body (solid) and air (fluid)," which is a cause of noise based on the laws of physics, and outputs the first causal structure information as a more refined causal structure (first additional causal structure information) that reproduces the first causal structure information in a digital environment. The first additional causal structure information may be expressed, for example, as an image (e.g., a 3D image) including the object of the problem (here, a bullet train), and the first causal structure information may be associated with and displayed alongside the corresponding location on the image. In this way, by combining two different trained models, this embodiment can dramatically improve the accuracy of problem solving.

[0098] In the above embodiment, an example has been described in which the first causal structure generation tool 2024 is realized by a trained model or a generation AI, but the present disclosure is not limited to this. In the present disclosure, all or part of the functions of various tools such as the matching tool 2025, the solution generation tool 2026, and the simulation tool 2027 may also be realized by a trained model or a generation AI.

[0099] In the above embodiment, the first causal structure generation tool 2024 automatically generates first causal structure information based on information about the problem input by the user, and the matching tool 2025 extracts second causal structure information similar to the first causal structure information from the causal structure database 2023. However, this embodiment is not limited to this. In this embodiment, the user may directly input and manually create a causal model that indicates the relationship between the problem and its cause. In this case, the causal structure information is input directly to the matching tool 2025 without going through the first causal structure generation tool 2024.

[0100] In the above embodiment, the first causal structure information is a directed acyclic graph (DAG) or text data, but this embodiment is not limited to this. In this embodiment, the causal structure information may be in another data format, such as tabular data or JSON data indicating the relationship between nodes and edges.

[0101] In the above embodiment, the second causal structure information is generated by analyzing past cases, but this embodiment is not limited to this. In this embodiment, the second causal structure information may be a virtual causal model derived from expert knowledge, industry best practices, or simulations.

[0102] In the above embodiment, when performing a simulation, a comparison is made between a case where the proposed problem-solving means is applied and a case where the proposed problem-solving means is not applied. However, this embodiment is not limited to this. In this embodiment, the effect of simultaneously applying multiple different problem-solving means may be simulated. For example, it is possible to predict a synergistic effect when multiple solutions are combined and implemented.

[0103] <6 Summary> As described above, according to this embodiment, the server 20 accepts information about a problem from a user. The server 20 inputs the accepted information about the problem into a trained model constructed based on the information about the problem, and outputs first causal structure information including the problem and its cause. The server 20 extracts one or more pieces of second causal structure information that match or are similar to the output first causal structure information from a database storing multiple pieces of second causal structure information including the problem and its cause. The server 20 presents one or more problem-solving means corresponding to the extracted one or more pieces of second causal structure information. The server 20 accepts a selection from the user of the one or more presented problem-solving means. The server 20 performs a simulation based on the accepted one or more problem-solving means. The server 20 presents the results of the simulation. This allows for support for invention creation, new business / product development, business reform / improvement, and the like, using a series of processes including causal structure generation, matching, presentation of problem-solving means, and simulation.

[0104] Furthermore, according to this embodiment, the first causal structure information is extracted from the received information on the problem, includes a plurality of elements including the problem and its cause, and is an effective acyclic graph or text data indicating the causal relationships of the elements. This allows the relationship between the complex problem and its cause to be visually or structurally expressed, making it easier to understand.

[0105] Furthermore, according to this embodiment, the second causal structure information is generated by analyzing at least one case from academic papers, patent documents, business cases, and historical cases. This allows cross-sectional use of cases from different fields to derive creative solutions to problems that are not bound by common sense.

[0106] Furthermore, according to this embodiment, the server 20 stores the first causal structure information, the problem-solving means, and the results of the simulation in a database. This allows the entire solution process to be accumulated as new knowledge, and the system can continuously learn and improve accuracy.

[0107] Furthermore, according to this embodiment, in the extracting step, the server 20 evaluates the agreement or similarity between the first causal structure information and the second causal structure information using graph isomorphism determination, subgraph isomorphism determination, or graph kernel method, thereby enabling mathematical comparison of abstract causal structures and achieving highly accurate matching.

[0108] Furthermore, according to this embodiment, in the extraction step, the server 20 presents a score indicating the degree of agreement or similarity between the first causal structure information and the second causal structure information, thereby enabling the user to objectively judge the reliability of the matching result and select an appropriate case.

[0109] Furthermore, according to this embodiment, in the step of executing a simulation, the server 20 estimates the effectiveness by comparing the case where one or more of the presented problem-solving means are applied to the problem with the case where they are not applied. This makes it possible to numerically indicate the effectiveness of the proposed solution, thereby supporting decision-making.

[0110] Furthermore, according to this embodiment, in the step of executing the simulation, the server 20 performs multiple comparisons and fine-tunes the parameters in each comparison, thereby further improving the accuracy of the simulation and deriving a more realistic and optimal solution.

[0111] <7 Basic computer hardware configuration> 14 is a block diagram showing the basic hardware configuration of a computer 90. The computer 90 includes at least a processor 91, a main memory device 92, an auxiliary memory device 93, and a communication IF (interface) 99. These are electrically connected to each other by a bus.

[0112] The processor 91 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0113] The main memory device 92 is for temporarily storing programs and data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM.

[0114] The auxiliary storage device 93 is a storage device for saving data and programs, such as a flash memory, HDD, magneto-optical disk, CD-ROM, DVD-ROM, or semiconductor memory.

[0115] The communication IF 99 is an interface for inputting and outputting signals for communicating with other computers via a network using wired or wireless communication standards.

[0116] A network is composed of the Internet, LAN, various mobile communication systems constructed by wireless base stations, etc. For example, networks include 3G, 4G, and 5G mobile communication systems, LTE, and wireless networks (e.g., Wi-Fi) that can connect to the Internet via a specified access point. In the case of wireless connections, communication protocols include, for example, Z-Wave, ZigBee, and Bluetooth. In the case of wired connections, networks also include those that are directly connected using a USB cable, etc.

[0117] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them via a network. In this way, the computer 90 is a concept that includes not only a computer 90 housed in a single housing or case, but also a virtualized computer system.

[0118] <8 Basic Functional Configuration of Computer 90> A description will be given of the functional configuration of a computer realized by the basic hardware configuration of a computer 90 shown in Fig. 14. The computer includes at least the functional units of a control unit, a storage unit, and a communication unit.

[0119] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0120] The control unit is realized by the processor 91 reading various programs stored in the auxiliary storage device 93, expanding them in the main storage device 92, and executing processing in accordance with the programs. The control unit can realize functional units that perform various types of information processing depending on the type of program. In this way, the computer is realized as an information processing device that processes information.

[0121] The storage unit is realized by a main storage device 92 and an auxiliary storage device 93. The storage unit stores data, various programs, and various databases. Furthermore, the processor 91 can allocate a storage area corresponding to the storage unit in the main storage device 92 or the auxiliary storage device 93 in accordance with the programs. Furthermore, the control unit can cause the processor 91 to execute processes for adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0122] A database refers to a relational database, which manages data sets called masters and tables in a tabular format structurally defined by rows and columns, by relating them to each other. In a database, a table is called a table, a master, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables and masters can be set and associated.

[0123] Normally, each table and each master has a column set as a primary key to uniquely identify a record, but setting a primary key to a column is not essential. The control unit can cause the processor 91 to add, delete, or update records in specific tables and masters stored in the storage unit according to various programs.

[0124] Note that the databases and masters in this disclosure may include any data structure in which information is structurally defined (such as a list, dictionary, associative array, or object). The data structure also includes data that can be considered as a data structure by combining data with functions, classes, methods, etc. written in any programming language.

[0125] The communication unit is realized by the communication IF 99. The communication unit realizes the function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 91 to execute information processing on the received information in accordance with various programs. In addition, the communication unit can transmit information output from the control unit to other computers 90.

[0126] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The present invention can also be realized by software program code that implements the functions of the embodiments. In this case, a storage medium on which the program code is recorded is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media for providing such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tape, non-volatile memory cards, and ROMs.

[0127] Furthermore, the program code that realizes the functions described in this embodiment can be implemented in a wide range of program or script languages, such as assembler, C / C++, perl, Shell, PHP, and Java (registered trademark).

[0128] Furthermore, the program code of the software that realizes the functions of the embodiments may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the processor of the computer may read and execute the program code stored in the storage means or storage medium.

[0129] The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes programs stored in memory.

[0130] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0131] If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0132] Although several embodiments of the present disclosure have been described above, these embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and modifications are intended to be included in the scope of the inventions and their equivalents as defined in the claims, as well as in the scope and spirit of the inventions.

[0133] (Addendum) The matters described in the above embodiments will be supplemented below.

[0134] (Appendix 1) A program to be executed by a computer having a processor and a memory, the program causing the processor to: receiving information about the assignment from a user; inputting information about the received assignment into a trained model constructed based on information about the assignment, and outputting first causal structure information including the assignment and a cause of the assignment; extracting one or more pieces of second causal structure information that match or are similar to the output first causal structure information from a database in which a plurality of pieces of second causal structure information including problems and causes of the problems are stored; presenting one or more problem-solving means corresponding to the one or more pieces of extracted second causal structure information; receiving a selection of one or more of the presented problem-solving means from a user; executing a simulation based on the received one or more problem-solving means; presenting the results of the simulation; A program that executes the following.

[0135] (Appendix 2) The program described in (Appendix 1), wherein the first causal structure information is extracted from information about the received problem, includes multiple elements including the problem and the cause, and is an effective acyclic graph or text data that shows the causal relationships between the multiple elements.

[0136] (Appendix 3) The program described in (Appendix 1) to (Appendix 2), wherein the second causal structure information is generated by analyzing at least one example of an academic paper, a patent document, a business case, or a historical case.

[0137] (Appendix 4) The program according to any one of (Supplementary Note 1) to (Supplementary Note 3), which causes a processor to execute a step of storing the first causal structure information, the problem-solving means, and the results of the simulation in a database.

[0138] (Appendix 5) A program described in (Appendix 1) to (Appendix 4), in which in the extraction step, the agreement or similarity between the first causal structure information and the second causal structure information is evaluated using graph isomorphism determination, subgraph isomorphism determination, or graph kernel method.

[0139] (Appendix 6) The program according to any one of (Appendix 1) to (Appendix 5), wherein in the extracting step, a score indicating the degree of agreement or similarity between the first causal structure information and the second causal structure information is presented.

[0140] (Appendix 7) A program according to (Appendix 1) to (Appendix 6), wherein in the step of executing a simulation, an effect is estimated by comparing a case where the one or more presented problem-solving means are applied to the problem with a case where the one or more presented problem-solving means are not applied to the problem.

[0141] (Appendix 8) The program according to (Appendix 7), wherein in the step of executing a simulation, multiple comparisons are verified and parameters are fine-tuned in each verification.

[0142] (Appendix 9) A method executed by a computer having a processor and a memory, wherein the processor executes all of the steps performed in any of the inventions according to (Appendix 1) to (Appendix 8).

[0143] (Appendix 10) An information processing device comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of (Appendix 1) to (Appendix 8).

[0144] (Appendix 11) A system comprising means for performing all steps performed in any of the inventions according to (Appendix 1) to (Appendix 8). [Explanation of symbols]

[0145] 1. Information processing system 10...User terminal 13...Input device 14...Output device 17...Audio processing unit 19...Processor 20...Server 29...Processor

Claims

1. A program to be executed by a computer having a processor and a memory, the program causing the processor to: receiving information about the assignment from a user; inputting the received information about the problem into a trained model constructed based on information about the problem, and outputting first causal structure information including the problem and a cause of the problem; extracting one or more pieces of second causal structure information that match or are similar to the output first causal structure information from a database in which a plurality of pieces of second causal structure information including problems and causes of the problems are stored; a step of inferring and generating one or more problem-solving means corresponding to the extracted one or more pieces of second causal structure information, and presenting the generated one or more problem-solving means; receiving a selection from the user of one or more of the presented problem-solving means; a step of executing a first simulation in which the received one or more problem-solving means are applied to the problem, and a second simulation in which the presented one or more problem-solving means are not applied to the problem, and estimating an effect by comparing a predicted result of the first simulation with a predicted result of the second simulation; presenting the estimated result; A program that executes the following.

2. the first causal structure information is extracted from the received information about the problem, includes a plurality of elements including the problem and the cause, and is an effective acyclic graph or text data indicating a causal relationship between the plurality of elements; The program according to claim 1.

3. The second causal structure information is generated by analyzing at least one case information of academic papers, patent documents, business cases, and historical cases. The program according to claim 1.

4. a step of storing the first causal structure information, the problem-solving means, and the estimated result in the database; The program according to claim 1 , which is executed by the processor.

5. In the extracting step, evaluating the agreement or similarity between the first causal structure information and the second causal structure information using a graph isomorphism determination, a subgraph isomorphism determination, or a graph kernel method; The program according to claim 1.

6. In the extracting step, presenting a score indicating a degree of agreement or a degree of similarity between the first causal structure information and the second causal structure information; The program according to claim 1.

7. In the estimating step, verifying the comparison multiple times and fine-tuning the parameters in each verification; The program according to claim 1.

8. A method implemented on a computer having a processor and a memory, wherein the processor performs all of the steps performed in the invention according to any one of claims 1 to 7.

9. 8. An information processing device comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of claims 1 to 7.

10. A system comprising means for executing all steps performed in the invention according to any one of claims 1 to 7.

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