system

Abstract

The system according to the embodiment aims to efficiently grasp the connection points and dependencies between systems. [Solution] A system according to an embodiment includes a connection point description unit, a configuration diagram generation unit, and a dependency relationship identification unit. The connection point description unit describes information about connection points. The configuration diagram generation unit automatically creates a logical configuration diagram using natural language processing based on the information described by the connection point description unit. The dependency relationship identification unit identifies systems that have dependencies based on the logical configuration diagram generated by the configuration diagram generation unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to efficiently grasp the connection points and dependencies between systems, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently grasp the connection points and dependencies between systems. [Means for solving the problem]

[0006] A system according to an embodiment includes a connection point description unit, a configuration diagram generation unit, and a dependency relationship identification unit. The connection point description unit describes information about connection points. The configuration diagram generation unit automatically creates a logical configuration diagram using natural language processing based on the information described by the connection point description unit. The dependency relationship identification unit identifies systems that have dependencies based on the logical configuration diagram generated by the configuration diagram generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the connection points and dependencies between systems. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses natural language processing to automatically create logical configuration diagrams of all systems in a company and to describe the connection points and points representing the connections between systems. This system first describes the connection points and points representing the connections between systems in each system's design document. Next, natural language processing is used to automatically create logical configuration diagrams of all systems in the company. In this process, the logical configuration diagram is connected from the connection points to the design documents of other systems. Furthermore, when a change is made to a system, dependent systems can be identified. For example, the connection points and points representing the connections between systems are described in each system's design document. In this process, the design description method and the granularity of the description content are defined. For example, connection point information includes the name of the connected system, connection protocol, data format, etc. Furthermore, connection details include the data flow and processing order. Next, natural language processing is used to automatically create logical configuration diagrams of all systems in the company. Natural language processing extracts connection point and connection information from each system's design document and generates a logical configuration diagram based on this information. For example, the connection relationships between systems are illustrated based on the connection point information. It also illustrates the flow of data and the order of processing based on the details of the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies. Natural language processing analyzes the design documents of the changed system and identifies systems with dependencies. For example, it can identify systems with dependencies based on the connection points and connection details of the changed system. This mechanism makes it possible to automatically create a logical configuration diagram of all systems across the company and to list the connection points between systems and the points that represent the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies, making it easier to manage system changes. This mechanism makes it possible to automatically create a logical configuration diagram of all systems across the company and to list the connection points between systems and the points that represent the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies, making it easier to manage system changes.

[0029] The system according to the embodiment includes a connection point description unit, a configuration diagram generation unit, and a dependency relationship identification unit. The connection point description unit describes information about the connection points. The connection point information includes, but is not limited to, the name of the destination system, the connection protocol, and the data format. The connection point description unit describes, for example, the name of the destination system. The name of the destination system includes the name and version information of the system. The connection point description unit also describes the connection protocol. Examples of the connection protocol include HTTP, FTP, and TCP / IP. The connection point description unit also describes the data format. Examples of the data format include JSON, XML, and CSV. The configuration diagram generation unit uses natural language processing to automatically create a logical configuration diagram based on the information described by the connection point description unit. The natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The configuration diagram generation unit uses, for example, morphological analysis to extract information about the connection points and connections from the design documents of each system. The configuration diagram generation unit can also analyze information about connection points and connections using grammar analysis. The configuration diagram generation unit can also understand information about connection points and connections using semantic analysis and generate a logical configuration diagram. For example, the configuration diagram generation unit illustrates the connection relationships between systems based on information about connection points. The configuration diagram generation unit also illustrates the data flow and processing order based on the content of the connections. The dependency identification unit identifies systems with dependent relationships based on the logical configuration diagram generated by the configuration diagram generation unit. The dependency identification unit identifies systems with dependent relationships based on, for example, the connection points of systems that have changed and the content of the connections. The dependency identification unit can also analyze, for example, the connection points of systems that have changed and identify systems with dependent relationships. The dependency identification unit can also analyze the content of the connections and identify systems with dependent relationships. This allows the system according to the embodiment to enter information about connection points, automatically create a logical configuration diagram, and identify systems with dependent relationships.

[0030] The connection point description section can describe the name of the destination system, the connection protocol, and the data format. The connection point description section describes, for example, the name of the destination system. The name of the destination system includes the name of the system, version information, etc. The connection point description section also describes the connection protocol. Examples of connection protocols include HTTP, FTP, TCP / IP, etc. The connection point description section also describes the data format. Examples of data formats include JSON, XML, CSV, etc. As a result, the connection point description section describes detailed connection information, making the connection between systems clear.

[0031] The configuration diagram generation unit can use natural language processing to extract information about connection points and connections from the design documents of each system and generate a logical configuration diagram. The configuration diagram generation unit can use, for example, morphological analysis to extract information about connection points and connections from the design documents of each system. Morphological analysis is a technology that divides a sentence into words and analyzes the part of speech of each word. The configuration diagram generation unit can also analyze information about connection points and connections using grammatical analysis. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies relationships such as between subject, predicate, and object. The configuration diagram generation unit can also use semantic analysis to understand information about connection points and connections and generate a logical configuration diagram. Semantic analysis is a technology that analyzes the meaning of a sentence and understands the context. For example, the configuration diagram generation unit illustrates the connection relationships between systems based on information about connection points. The configuration diagram generation unit also illustrates the data flow and processing order based on the content of the connections. In this way, the generation of logical configuration diagrams is automated using natural language processing.

[0032] The dependency identification unit can identify systems that have dependencies based on the connection points of the systems that have changed and the details of the linkages. The dependency identification unit, for example, analyzes the connection points of the systems that have changed and identifies systems that have dependencies. The analysis of the connection points includes information such as the name of the connected system, the connection protocol, and the data format. The dependency identification unit can also analyze the details of the linkages and identify systems that have dependencies. The details of the linkages include information such as the data flow and the order of processing. In this way, the dependency of the systems that have changed can be identified and the scope of the impact can be understood.

[0033] The connection point description section can adjust the level of detail of the description based on the importance of the system when describing the connection points. For example, for a system with high importance, the connection point description section describes detailed connection protocols and data formats. For a system with low importance, the connection point description section can also describe only basic connection information. The connection point description section can also gradually increase the detail of the description of the connection points depending on the importance. This allows for efficient description by adjusting the level of detail of the description depending on the importance of the system.

[0034] When describing a connection point, the connection point description section can apply different description formats depending on the system category. For example, in the case of a database system, the connection point description section describes the table name and query format of the connection destination. In the case of a web service, the connection point description section can also describe the API endpoint and request format. In the case of a network device, the connection point description section can also describe the IP address and port number. This improves consistency in the description by applying a description format according to the system category.

[0035] The connection point description section can adjust the description content taking into account the geographical distribution of systems when describing connection points. For example, the connection point description section describes connection points between geographically distant systems, taking communication delay and bandwidth into consideration. The connection point description section can also describe connection points between systems in the same region, taking into account local network characteristics. The connection point description section can also describe redundancy and backup information for connection points between systems spanning multiple regions. This makes it possible to describe appropriate connection points by adjusting the description content taking into account the geographical distribution of systems.

[0036] When describing connection points, the connection point description unit can improve the accuracy of the description by referring to related literature for the system. The connection point description unit, for example, refers to details of the connection protocol from the related literature to provide accurate description. The connection point description unit can also refer to data format standards from the related literature to provide unified description. The connection point description unit can also refer to past connection cases from the related literature to provide description based on actual results. In this way, by referring to related literature, the accuracy of the description of connection points is improved.

[0037] When generating a configuration diagram, the configuration diagram generation unit can adjust the level of detail of the generated configuration diagram based on the importance of the system. For example, for a system with high importance, the configuration diagram generation unit generates a configuration diagram that includes detailed connection points and data flows. For a system with low importance, the configuration diagram generation unit can also generate a configuration diagram that includes only basic connection information. The configuration diagram generation unit can also gradually adjust the level of detail of the configuration diagram based on the importance. This allows for efficient generation of configuration diagrams by adjusting the level of detail of the generated configuration diagram based on the importance of the system.

[0038] When generating a configuration diagram, the configuration diagram generator can apply different generation algorithms depending on the system category. For example, in the case of a database system, the configuration diagram generator generates a configuration diagram that emphasizes the relationships between tables. In addition, in the case of a web service, the configuration diagram generator can also generate a configuration diagram that emphasizes API endpoints and data flows. In addition, in the case of network devices, the configuration diagram generator can also generate a configuration diagram that emphasizes the network topology. In this way, by applying a generation algorithm depending on the system category, the accuracy of the configuration diagram is improved.

[0039] When generating a configuration diagram, the configuration diagram generation unit can adjust the content to be generated taking into account the geographical distribution of systems. For example, the configuration diagram generation unit generates a configuration diagram taking into account communication delays and bandwidth for connection points between geographically distant systems. The configuration diagram generation unit can also generate a configuration diagram taking into account the characteristics of a local network for connection points between systems in the same region. The configuration diagram generation unit can also generate a configuration diagram that includes redundancy and backup information for connection points between systems that span multiple regions. This makes it possible to generate an appropriate configuration diagram by adjusting the content to be generated taking into account the geographical distribution of systems.

[0040] When generating a configuration diagram, the configuration diagram generation unit can improve the accuracy of the generation by referring to related literature for the system. For example, the configuration diagram generation unit references connection protocol details from related literature to generate an accurate configuration diagram. The configuration diagram generation unit can also reference data format standards from related literature to generate a unified configuration diagram. The configuration diagram generation unit can also reference past connection examples from related literature to generate a configuration diagram based on past performance. In this way, by referring to related literature, the accuracy of the generation of the configuration diagram is improved.

[0041] The dependency relationship identifying unit can adjust the level of detail of the identified dependency relationships based on the importance of the systems when identifying the dependency relationships. For example, the dependency relationship identifying unit identifies detailed dependency relationships for systems with high importance. The dependency relationship identifying unit can also identify only basic dependency relationships for systems with low importance. The dependency relationship identifying unit can also adjust the level of detail of the dependency relationships in stages according to the importance. This allows for efficient identification of dependency relationships by adjusting the level of detail of the identified dependency relationships according to the importance of the systems.

[0042] When identifying dependencies, the dependency identification unit can apply different identification algorithms depending on the system category. For example, in the case of a database system, the dependency identification unit identifies dependencies based on the relationships between tables. In addition, in the case of a web service, the dependency identification unit can also identify dependencies based on API endpoints and data flows. In addition, in the case of network devices, the dependency identification unit can also identify dependencies based on network topology. In this way, by applying an identification algorithm depending on the system category, the accuracy of identifying dependencies is improved.

[0043] When identifying dependencies, the dependency identification unit can adjust the identification content taking into account the geographical distribution of systems. For example, the dependency identification unit identifies dependencies between geographically distant systems by taking into account communication delays and bandwidth. The dependency identification unit can also identify dependencies between systems in the same region by taking into account the characteristics of the local network. The dependency identification unit can also identify dependencies between systems across multiple regions, including information on redundancy and backups. As a result, by adjusting the identification content taking into account the geographical distribution of systems, it becomes possible to identify appropriate dependencies.

[0044] When identifying dependencies, the dependency identification unit can improve the accuracy of the identification by referring to related literature about the system. For example, the dependency identification unit can refer to details of connection protocols from related literature to identify accurate dependencies. The dependency identification unit can also refer to data format standards from related literature to identify unified dependencies. The dependency identification unit can also refer to past connection cases from related literature to identify dependencies based on performance. In this way, by referring to related literature, the accuracy of identifying dependencies is improved.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When entering connection point information, the connection point description unit can provide an auto-completion function by referring to the user's past input history. For example, when the user newly enters information, it presents candidates based on previously entered connection destination system names, connection protocols, and data formats. The connection point description unit can also learn the past input history and preferentially display frequently used connection information. Furthermore, the connection point description unit can reduce the effort required for input by automatically completing the connection point description based on information previously entered by the user. This makes it more efficient to enter connection points by utilizing the user's past input history.

[0047] The configuration diagram generation unit can provide an interface that allows the user to manually modify the generated logical configuration diagram. For example, the user can modify the connection points or data flow of the generated configuration diagram by dragging and dropping. The configuration diagram generation unit can also learn the modifications made manually by the user and reflect them when generating subsequent configuration diagrams. Furthermore, the configuration diagram generation unit can provide a function that saves the modifications made by the user as a history and allows the user to return to the original state at any time. This provides an interface that allows the user to flexibly modify the generated configuration diagram.

[0048] The dependency identification unit can provide a function to simulate the impact of a system change on other systems based on the identified dependencies. For example, it can visually display the extent of the impact on other systems based on the connection points and linkages of the changed systems. The dependency identification unit can also generate a list of affected systems based on the simulation results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function to save the simulation results and reuse them for future changes. This makes it possible to understand in advance the impact of a system change on other systems and take appropriate measures.

[0049] The dependency identification unit can provide a function for monitoring the impact of system changes on other systems in real time based on the identified dependencies. For example, it can monitor the connection points and linkages of systems that have been changed in real time and immediately notify other systems of the impact. The dependency identification unit can also generate a list of affected systems based on the monitoring results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function for saving the monitoring results and reusing them for future changes. This makes it possible to grasp the impact of system changes on other systems in real time and respond quickly.

[0050] The dependency identification unit can provide a function to predict the impact that a change in one system will have on other systems based on the identified dependencies. For example, it can predict the extent of the impact on other systems based on the connection points and linkage details of the changed system. The dependency identification unit can also generate a list of affected systems based on the prediction results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function to save the prediction results and reuse them for future changes. This makes it possible to predict the impact that a change in one system will have on other systems in advance and take appropriate measures.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The connection point description section describes the connection point information. The connection point information includes the destination system name, connection protocol, data format, etc. For example, the destination system name includes the system name and version information, the connection protocol includes HTTP, FTP, TCP / IP, etc., and the data format includes JSON, XML, CSV, etc. Step 2: The configuration diagram generator uses natural language processing to automatically create a logical configuration diagram based on the information described by the connection point description unit. Natural language processing uses techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, it uses morphological analysis to extract information about connection points and connections from the design documents of each system, uses grammatical analysis to analyze the information about connection points and connections, and uses semantic analysis to understand the information about connection points and connections, and then generates a logical configuration diagram. The configuration diagram generator illustrates the connection relationships between systems, the data flow, and the processing order based on the information about connection points. Step 3: The dependency identification unit identifies systems that have dependencies based on the logical configuration diagram generated by the configuration diagram generation unit. For example, it identifies systems that have dependencies based on the connection points of systems that have changed and the details of the connections between them. The dependency identification unit analyzes the connection points of systems that have changed and identifies systems that have dependencies. It can also analyze the details of the connections and identify systems that have dependencies.

[0053] (Example 2) A system according to an embodiment of the present invention uses natural language processing to automatically create logical configuration diagrams of all systems in a company and to describe the connection points and points representing the connections between systems. This system first describes the connection points and points representing the connections between systems in each system's design document. Next, natural language processing is used to automatically create logical configuration diagrams of all systems in the company. In this process, the logical configuration diagram is connected from the connection points to the design documents of other systems. Furthermore, when a change is made to a system, dependent systems can be identified. For example, the connection points and points representing the connections between systems are described in each system's design document. In this process, the design description method and the granularity of the description content are defined. For example, connection point information includes the name of the connected system, connection protocol, data format, etc. Furthermore, connection details include the data flow and processing order. Next, natural language processing is used to automatically create logical configuration diagrams of all systems in the company. Natural language processing extracts connection point and connection information from each system's design document and generates a logical configuration diagram based on this information. For example, the connection relationships between systems are illustrated based on the connection point information. It also illustrates the flow of data and the order of processing based on the details of the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies. Natural language processing analyzes the design documents of the changed system and identifies systems with dependencies. For example, it can identify systems with dependencies based on the connection points and connection details of the changed system. This mechanism makes it possible to automatically create a logical configuration diagram of all systems across the company and to list the connection points between systems and the points that represent the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies, making it easier to manage system changes. This mechanism makes it possible to automatically create a logical configuration diagram of all systems across the company and to list the connection points between systems and the points that represent the connections. Furthermore, when a change is made to a system, it can identify systems with dependencies, making it easier to manage system changes.

[0054] The system according to the embodiment includes a connection point description unit, a configuration diagram generation unit, and a dependency relationship identification unit. The connection point description unit describes information about the connection points. The connection point information includes, but is not limited to, the name of the destination system, the connection protocol, and the data format. The connection point description unit describes, for example, the name of the destination system. The name of the destination system includes the name and version information of the system. The connection point description unit also describes the connection protocol. Examples of the connection protocol include HTTP, FTP, and TCP / IP. The connection point description unit also describes the data format. Examples of the data format include JSON, XML, and CSV. The configuration diagram generation unit uses natural language processing to automatically create a logical configuration diagram based on the information described by the connection point description unit. The natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The configuration diagram generation unit uses, for example, morphological analysis to extract information about the connection points and connections from the design documents of each system. The configuration diagram generation unit can also analyze information about connection points and connections using grammar analysis. The configuration diagram generation unit can also understand information about connection points and connections using semantic analysis and generate a logical configuration diagram. For example, the configuration diagram generation unit illustrates the connection relationships between systems based on information about connection points. The configuration diagram generation unit also illustrates the data flow and processing order based on the content of the connections. The dependency identification unit identifies systems with dependent relationships based on the logical configuration diagram generated by the configuration diagram generation unit. The dependency identification unit identifies systems with dependent relationships based on, for example, the connection points of systems that have changed and the content of the connections. The dependency identification unit can also analyze, for example, the connection points of systems that have changed and identify systems with dependent relationships. The dependency identification unit can also analyze the content of the connections and identify systems with dependent relationships. This allows the system according to the embodiment to enter information about connection points, automatically create a logical configuration diagram, and identify systems with dependent relationships.

[0055] The connection point description section can describe the name of the destination system, the connection protocol, and the data format. The connection point description section describes, for example, the name of the destination system. The name of the destination system includes the name of the system, version information, etc. The connection point description section also describes the connection protocol. Examples of connection protocols include HTTP, FTP, TCP / IP, etc. The connection point description section also describes the data format. Examples of data formats include JSON, XML, CSV, etc. As a result, the connection point description section describes detailed connection information, making the connection between systems clear.

[0056] The configuration diagram generation unit can use natural language processing to extract information about connection points and connections from the design documents of each system and generate a logical configuration diagram. The configuration diagram generation unit can use, for example, morphological analysis to extract information about connection points and connections from the design documents of each system. Morphological analysis is a technology that divides a sentence into words and analyzes the part of speech of each word. The configuration diagram generation unit can also analyze information about connection points and connections using grammatical analysis. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies relationships such as between subject, predicate, and object. The configuration diagram generation unit can also use semantic analysis to understand information about connection points and connections and generate a logical configuration diagram. Semantic analysis is a technology that analyzes the meaning of a sentence and understands the context. For example, the configuration diagram generation unit illustrates the connection relationships between systems based on information about connection points. The configuration diagram generation unit also illustrates the data flow and processing order based on the content of the connections. In this way, the generation of logical configuration diagrams is automated using natural language processing.

[0057] The dependency identification unit can identify systems that have dependencies based on the connection points of the systems that have changed and the details of the linkages. The dependency identification unit, for example, analyzes the connection points of the systems that have changed and identifies systems that have dependencies. The analysis of the connection points includes information such as the name of the connected system, the connection protocol, and the data format. The dependency identification unit can also analyze the details of the linkages and identify systems that have dependencies. The details of the linkages include information such as the data flow and the order of processing. In this way, the dependency of the systems that have changed can be identified and the scope of the impact can be understood.

[0058] The connection point description unit can estimate the user's emotions and adjust the method for writing connection points based on the estimated user emotions. For example, if the user is feeling stressed, the connection point description unit can provide a simple interface and minimize the steps for writing connection points. Furthermore, if the user is relaxed, the connection point description unit can provide detailed writing options and suggest a customizable writing method. Furthermore, if the user is in a hurry, the connection point description unit can prioritize voice input and enable the user to quickly write connection points. This reduces the burden on the user by adjusting the method for writing connection points according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0059] The connection point description section can adjust the level of detail of the description based on the importance of the system when describing the connection points. For example, for a system with high importance, the connection point description section describes detailed connection protocols and data formats. For a system with low importance, the connection point description section can also describe only basic connection information. The connection point description section can also gradually increase the detail of the description of the connection points depending on the importance. This allows for efficient description by adjusting the level of detail of the description depending on the importance of the system.

[0060] When describing a connection point, the connection point description section can apply different description formats depending on the system category. For example, in the case of a database system, the connection point description section describes the table name and query format of the connection destination. In the case of a web service, the connection point description section can also describe the API endpoint and request format. In the case of a network device, the connection point description section can also describe the IP address and port number. This improves consistency in the description by applying a description format according to the system category.

[0061] The connection point description unit can estimate the user's emotions and determine the order in which connection points are written based on the estimated user's emotions. For example, if the user is nervous, the connection point description unit can list the connection points in order from the most important. The connection point description unit can also allow the user to write them in any order if the user is relaxed. The connection point description unit can also list the connection points in order from the easiest if the user is in a hurry. This reduces the burden on the user by determining the order in which connection points are written based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The connection point description section can adjust the description content taking into account the geographical distribution of systems when describing connection points. For example, the connection point description section describes connection points between geographically distant systems, taking communication delay and bandwidth into consideration. The connection point description section can also describe connection points between systems in the same region, taking into account local network characteristics. The connection point description section can also describe redundancy and backup information for connection points between systems spanning multiple regions. This makes it possible to describe appropriate connection points by adjusting the description content taking into account the geographical distribution of systems.

[0063] When describing connection points, the connection point description unit can improve the accuracy of the description by referring to related literature for the system. The connection point description unit, for example, refers to details of the connection protocol from the related literature to provide accurate description. The connection point description unit can also refer to data format standards from the related literature to provide unified description. The connection point description unit can also refer to past connection cases from the related literature to provide description based on actual results. In this way, by referring to related literature, the accuracy of the description of connection points is improved.

[0064] The configuration diagram generation unit can estimate the user's emotions and adjust the method for generating the logical configuration diagram based on the estimated user emotions. For example, the configuration diagram generation unit generates a detailed configuration diagram when the user is relaxed. The configuration diagram generation unit can also generate a simplified configuration diagram when the user is in a hurry. The configuration diagram generation unit can also generate a visually appealing configuration diagram when the user is excited. This reduces the burden on the user by adjusting the method for generating the logical configuration diagram according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] When generating a configuration diagram, the configuration diagram generation unit can adjust the level of detail of the generated configuration diagram based on the importance of the system. For example, for a system with high importance, the configuration diagram generation unit generates a configuration diagram that includes detailed connection points and data flows. For a system with low importance, the configuration diagram generation unit can also generate a configuration diagram that includes only basic connection information. The configuration diagram generation unit can also gradually adjust the level of detail of the configuration diagram based on the importance. This allows for efficient generation of configuration diagrams by adjusting the level of detail of the generated configuration diagram based on the importance of the system.

[0066] When generating a configuration diagram, the configuration diagram generator can apply different generation algorithms depending on the system category. For example, in the case of a database system, the configuration diagram generator generates a configuration diagram that emphasizes the relationships between tables. In addition, in the case of a web service, the configuration diagram generator can also generate a configuration diagram that emphasizes API endpoints and data flows. In addition, in the case of network devices, the configuration diagram generator can also generate a configuration diagram that emphasizes the network topology. In this way, by applying a generation algorithm depending on the system category, the accuracy of the configuration diagram is improved.

[0067] The configuration diagram generation unit can estimate the user's emotions and adjust the display method of the logical configuration diagram based on the estimated user emotions. For example, if the user is nervous, the configuration diagram generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the configuration diagram generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the configuration diagram generation unit can provide a display method that focuses on the main points. This reduces the burden on the user by adjusting the display method of the logical configuration diagram according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When generating a configuration diagram, the configuration diagram generation unit can adjust the content to be generated taking into account the geographical distribution of systems. For example, the configuration diagram generation unit generates a configuration diagram taking into account communication delays and bandwidth for connection points between geographically distant systems. The configuration diagram generation unit can also generate a configuration diagram taking into account the characteristics of a local network for connection points between systems in the same region. The configuration diagram generation unit can also generate a configuration diagram that includes redundancy and backup information for connection points between systems that span multiple regions. This makes it possible to generate an appropriate configuration diagram by adjusting the content to be generated taking into account the geographical distribution of systems.

[0069] When generating a configuration diagram, the configuration diagram generation unit can improve the accuracy of the generation by referring to related literature for the system. For example, the configuration diagram generation unit references connection protocol details from related literature to generate an accurate configuration diagram. The configuration diagram generation unit can also reference data format standards from related literature to generate a unified configuration diagram. The configuration diagram generation unit can also reference past connection examples from related literature to generate a configuration diagram based on past performance. In this way, by referring to related literature, the accuracy of the generation of the configuration diagram is improved.

[0070] The dependency relationship identification unit can estimate the user's emotions and adjust the dependency relationship identification method based on the estimated user emotions. For example, when the user is relaxed, the dependency relationship identification unit can identify detailed dependencies. When the user is in a hurry, the dependency relationship identification unit can also identify simplified dependencies. When the user is excited, the dependency relationship identification unit can also identify visually appealing dependencies. This reduces the burden on the user by adjusting the dependency relationship identification method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The dependency relationship identifying unit can adjust the level of detail of the identified dependency relationships based on the importance of the systems when identifying the dependency relationships. For example, the dependency relationship identifying unit identifies detailed dependency relationships for systems with high importance. The dependency relationship identifying unit can also identify only basic dependency relationships for systems with low importance. The dependency relationship identifying unit can also adjust the level of detail of the dependency relationships in stages according to the importance. This allows for efficient identification of dependency relationships by adjusting the level of detail of the identified dependency relationships according to the importance of the systems.

[0072] When identifying dependencies, the dependency identification unit can apply different identification algorithms depending on the system category. For example, in the case of a database system, the dependency identification unit identifies dependencies based on the relationships between tables. In addition, in the case of a web service, the dependency identification unit can also identify dependencies based on API endpoints and data flows. In addition, in the case of network devices, the dependency identification unit can also identify dependencies based on network topology. In this way, by applying an identification algorithm depending on the system category, the accuracy of identifying dependencies is improved.

[0073] The dependency relationship identification unit can estimate the user's emotions and adjust the display method of the dependencies based on the estimated user emotions. For example, if the user is nervous, the dependency relationship identification unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the dependency relationship identification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the dependency relationship identification unit can also provide a display method that focuses on the main points. This reduces the burden on the user by adjusting the display method of the dependencies according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] When identifying dependencies, the dependency identification unit can adjust the identification content taking into account the geographical distribution of systems. For example, the dependency identification unit identifies dependencies between geographically distant systems by taking into account communication delays and bandwidth. The dependency identification unit can also identify dependencies between systems in the same region by taking into account the characteristics of the local network. The dependency identification unit can also identify dependencies between systems across multiple regions, including information on redundancy and backups. As a result, by adjusting the identification content taking into account the geographical distribution of systems, it becomes possible to identify appropriate dependencies.

[0075] When identifying dependencies, the dependency identification unit can improve the accuracy of the identification by referring to related literature about the system. For example, the dependency identification unit can refer to details of connection protocols from related literature to identify accurate dependencies. The dependency identification unit can also refer to data format standards from related literature to identify unified dependencies. The dependency identification unit can also refer to past connection cases from related literature to identify dependencies based on performance. In this way, by referring to related literature, the accuracy of identifying dependencies is improved. === Hard Collateral 1-1 === Each of the multiple elements, including the connection point description unit, configuration diagram generation unit, and dependency identification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the connection point description unit is realized by the control unit 46A of the smart device 14 and describes information such as the name of the connected system, the connection protocol, and the data format. The configuration diagram generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and automatically creates a logical configuration diagram using natural language processing. The dependency identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies systems that have dependencies based on the connection points and linkage details of systems that have changed. === Hard Collateral 1-2 === Each of the multiple elements including the connection point description unit, configuration diagram generation unit, and dependency identification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the connection point description unit is realized by the control unit 46A of the smart glasses 214 and describes information such as the name of the connected system, the connection protocol, and the data format. The configuration diagram generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and automatically creates a logical configuration diagram using natural language processing. The dependency identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies systems that have dependencies based on the connection points and linkage details of systems that have changed. === Hard Collateral 1-3 === Each of the multiple elements including the connection point description unit, configuration diagram generation unit, and dependency identification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the connection point description unit is realized by the control unit 46A of the headset type terminal 314 and describes information such as the name of the connected system, the connection protocol, and the data format. The configuration diagram generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and automatically creates a logical configuration diagram using natural language processing. The dependency identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies systems that have dependencies based on the connection points and linkage details of systems that have changed. === Hard Collateral 1-4 === Each of the multiple elements including the connection point description unit, configuration diagram generation unit, and dependency identification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the connection point description unit is realized by the control unit 46A of the robot 414 and describes information such as the name of the connected system, the connection protocol, and the data format. The configuration diagram generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and automatically creates a logical configuration diagram using natural language processing. The dependency identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies systems that have dependencies based on the connection points and linkage details of systems that have changed.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] When entering connection point information, the connection point description unit can provide an auto-completion function by referring to the user's past input history. For example, when the user newly enters information, it presents candidates based on previously entered connection destination system names, connection protocols, and data formats. The connection point description unit can also learn the past input history and preferentially display frequently used connection information. Furthermore, the connection point description unit can reduce the effort required for input by automatically completing the connection point description based on information previously entered by the user. This makes it more efficient to enter connection points by utilizing the user's past input history.

[0078] The configuration diagram generation unit can provide an interface that allows the user to manually modify the generated logical configuration diagram. For example, the user can modify the connection points or data flow of the generated configuration diagram by dragging and dropping. The configuration diagram generation unit can also learn the modifications made manually by the user and reflect them when generating subsequent configuration diagrams. Furthermore, the configuration diagram generation unit can provide a function that saves the modifications made by the user as a history and allows the user to return to the original state at any time. This provides an interface that allows the user to flexibly modify the generated configuration diagram.

[0079] The dependency identification unit can provide a function to simulate the impact of a system change on other systems based on the identified dependencies. For example, it can visually display the extent of the impact on other systems based on the connection points and linkages of the changed systems. The dependency identification unit can also generate a list of affected systems based on the simulation results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function to save the simulation results and reuse them for future changes. This makes it possible to understand in advance the impact of a system change on other systems and take appropriate measures.

[0080] The connection point description unit can estimate the user's emotions and automatically simplify the description of the connection point based on the estimated user's emotions. For example, if the user is feeling stressed, the description of the connection point can be minimized, allowing the user to enter only the minimum necessary information. In addition, if the user is relaxed, the connection point description unit can provide detailed description options, allowing the user to freely customize the description. Furthermore, if the user is in a hurry, the connection point description unit can prioritize voice input, allowing the user to quickly enter the connection point. In this way, the burden on the user can be reduced by adjusting the description of the connection point according to the user's emotions.

[0081] The configuration diagram generation unit can estimate the user's emotions from the generated logical configuration diagram and automatically adjust the layout of the configuration diagram based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible layout is provided. The configuration diagram generation unit can also provide a layout including detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the configuration diagram generation unit can also provide a layout that focuses on the main points. In this way, the burden on the user can be reduced by adjusting the layout of the configuration diagram according to the user's emotions.

[0082] The dependency identification unit can provide a function for monitoring the impact of system changes on other systems in real time based on the identified dependencies. For example, it can monitor the connection points and linkages of systems that have been changed in real time and immediately notify other systems of the impact. The dependency identification unit can also generate a list of affected systems based on the monitoring results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function for saving the monitoring results and reusing them for future changes. This makes it possible to grasp the impact of system changes on other systems in real time and respond quickly.

[0083] The connection point description unit can estimate the user's emotions and automatically adjust the order in which connection points are written based on the estimated user's emotions. For example, if the user is nervous, the connection points are written in order starting with the most important ones. The connection point description unit can also allow the user to write the connection points in any order if the user is relaxed. Furthermore, if the user is in a hurry, the connection point description unit can also write the connection points in order starting with the easiest ones. This reduces the burden on the user by adjusting the order in which connection points are written based on the user's emotions.

[0084] The configuration diagram generation unit can estimate the user's emotions from the generated logical configuration diagram and automatically adjust the display method of the configuration diagram based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. The configuration diagram generation unit can also provide a display method including detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the configuration diagram generation unit can also provide a display method that focuses on the main points. In this way, the burden on the user can be reduced by adjusting the display method of the configuration diagram according to the user's emotions.

[0085] The dependency identification unit can provide a function to predict the impact that a change in one system will have on other systems based on the identified dependencies. For example, it can predict the extent of the impact on other systems based on the connection points and linkage details of the changed system. The dependency identification unit can also generate a list of affected systems based on the prediction results and identify systems that should be addressed as a priority. Furthermore, the dependency identification unit can provide a function to save the prediction results and reuse them for future changes. This makes it possible to predict the impact that a change in one system will have on other systems in advance and take appropriate measures.

[0086] The connection point description unit can estimate the user's emotions and automatically adjust the method for describing connection points based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the steps for describing connection points. In addition, if the user is relaxed, the connection point description unit can provide detailed description options and suggest a customizable description method. Furthermore, if the user is in a hurry, the connection point description unit can prioritize voice input to enable quick description of connection points. This reduces the burden on the user by adjusting the method for describing connection points according to the user's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The connection point description section describes the connection point information. The connection point information includes the destination system name, connection protocol, data format, etc. For example, the destination system name includes the system name and version information, the connection protocol includes HTTP, FTP, TCP / IP, etc., and the data format includes JSON, XML, CSV, etc. Step 2: The configuration diagram generator uses natural language processing to automatically create a logical configuration diagram based on the information described by the connection point description unit. Natural language processing uses techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, it uses morphological analysis to extract information about connection points and connections from the design documents of each system, uses grammatical analysis to analyze the information about connection points and connections, and uses semantic analysis to understand the information about connection points and connections, and then generates a logical configuration diagram. The configuration diagram generator illustrates the connection relationships between systems, the data flow, and the processing order based on the information about connection points. Step 3: The dependency identification unit identifies systems that have dependencies based on the logical configuration diagram generated by the configuration diagram generation unit. For example, it identifies systems that have dependencies based on the connection points of systems that have changed and the details of the connections between them. The dependency identification unit analyzes the connection points of systems that have changed and identifies systems that have dependencies. It can also analyze the details of the connections and identify systems that have dependencies.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a connection point description section for describing information about the connection points; a configuration diagram generation unit that automatically generates a logical configuration diagram using natural language processing based on the information described by the connection point description unit; a dependency specification unit that identifies systems that have dependencies based on the logical configuration diagram generated by the configuration diagram generation unit. A system characterized by:

2. The connection point description section Enter the name of the destination system, connection protocol, and data format. The system of claim 1 .

3. The configuration diagram generation unit Using natural language processing, information on connections and connections is extracted from each system's design documents, and a logical configuration diagram is generated. The system of claim 1 .

4. The dependency relationship identification unit Identify systems with dependencies based on the connection points and connections of systems that have changed The system of claim 1 .

5. The connection point description section The system estimates the user's emotions and adjusts the description of connection points based on the estimated user emotions. The system of claim 1 .

6. The connection point description section When describing connection points, adjust the level of detail in the description based on the system's importance. The system of claim 1 .

7. The connection point description section Apply different description formats to connection points depending on the system category The system of claim 1 .

8. The connection point description section The user's emotion is estimated, and the order of the connection points is determined based on the estimated user's emotion. The system of claim 1 .

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