system

Abstract

The system according to this embodiment aims to automate the development of the digital environment necessary for the widespread adoption of AI agents. [Solution] The system according to the embodiment comprises a generation unit, a design unit, and an execution unit. The generation unit automatically prepares APIs and documentation using generation AI. The design unit understands the service based on the APIs and documentation generated by the generation unit and designs the specifications for external publication. The execution unit edits and executes the code based on the specifications designed by the design unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the preparation of the digital environment necessary for the popularization of AI agents is not automated and manual preparation is required, which is inefficient and an obstacle to popularization.

[0005] The system according to the embodiment aims to automate the preparation of the digital environment necessary for the popularization of AI agents.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a generation unit, a design unit, and an execution unit. The generation unit automatically prepares APIs and documentation using generation AI. The design unit understands the service based on the APIs and documentation generated by the generation unit and designs the specifications for external publication. The execution unit edits and executes code based on the specifications designed by the design unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the preparation of the digital environment necessary for the widespread adoption of AI agents. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The digital environment setup system according to an embodiment of the present invention is a system that automatically sets up the recipient's digital environment in order to promote the widespread adoption of AI agents. This digital environment setup system automatically sets up the environment using generative AI, an LLM (Large-Scale Language Model) understands the service, designs specifications for external publication, and generates sample code. This system enables the AI ​​agent to understand natural language commands from humans, break them down into tasks, and automatically prepare the necessary APIs and documentation to issue commands to each system. First, the user inputs a command in natural language. For example, a command such as "I want to go from XX to XX" is input. This command is input to the generative AI. Next, the generative AI analyzes the input command and automatically prepares the necessary APIs and documentation. The generative AI generates the necessary APIs and documentation to issue commands to each system and designs specifications for external publication. For example, it generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The LLM understands the service based on the generated APIs and documentation and designs specifications for external publication. Based on the generated APIs and documentation, the LLM deepens its understanding of the service and designs specifications for external publication. For example, it creates service usage flows, design documents, requirements specifications, service materials, and system design documents. Finally, based on the generated APIs and documents, it edits and executes the code and begins implementation for external release. This promotes the spread of AI agents, advances the digitalization of companies, and improves Japan's competitiveness. In this way, the digital environment development system can promote the spread of AI agents and advance the digitalization of companies.

[0029] The digital environment setup system according to the embodiment comprises a generation unit, a design unit, and an execution unit. The generation unit automatically prepares APIs and documentation using generation AI. The generation unit generates, for example, API design documents, authentication designs, specification documents, security risk assessments, and sample code. The generation unit uses generation AI to generate the APIs and documentation necessary to issue commands to each system and performs specification design for external publication. The design unit understands the service based on the APIs and documentation generated by the generation unit and performs specification design for external publication. The design unit creates, for example, service usage flows, design documents, requirements definition documents, service materials, and system design documents. The design unit deepens its understanding of the service based on the generated APIs and documentation and performs specification design for external publication. The execution unit edits and executes code based on the specifications designed by the design unit. The execution unit edits and executes code based on the generated APIs and documentation and begins implementation toward external publication. The execution unit edits and executes code based on the generated APIs and documentation and begins implementation toward external publication. As a result, the digital environment setup system according to the embodiment can efficiently perform tasks such as automatic preparation of APIs and documentation, understanding of services, specification design, and editing and execution of code.

[0030] The generation unit automatically prepares APIs and documentation using a generation AI. For example, it generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. Specifically, the generation AI utilizes natural language processing technology to create API design documents based on user-provided requirements and specifications. These documents detail endpoint definitions, request and response formats, authentication methods, and error handling. The authentication design uses authentication protocols such as OAuth and JWT to establish a user authentication and authorization mechanism. The specification documents clarify the overall system operation and the role of each component, provided in a format easily understood by developers. In the security risk assessment, the generation AI evaluates system vulnerabilities and risks based on existing security best practices and proposes necessary countermeasures. Sample code is provided as code snippets, including concrete usage and implementation examples, to enable developers to quickly utilize the API. The generation unit centrally manages these generated materials and can update and modify them as needed. The generation AI can continuously improve the quality of the generated materials by incorporating user feedback. This allows the generation unit to efficiently and accurately automate the creation of APIs and documentation, preparing them for external release from the early stages of the development process.

[0031] The design department understands the service based on the APIs and documentation generated by the generation department and designs the specifications for external release. For example, the design department creates service usage flows, design documents, requirements definition documents, service documentation, and system design documents. Specifically, the design department reviews the generated API design documents in detail and designs the service usage flows. These flows include how users utilize the service, the necessary operations and inputs at each step, and the system's responses. The design documents illustrate the overall system architecture and the interactions of each component, allowing developers to grasp the system's overall picture. The requirements definition documents clarify user needs and business requirements, and specifically describe the functions and performance the system must meet. Service documentation is a document that explains the service's features, benefits, and usage, serving to communicate the service's value to users and stakeholders. The system design document contains detailed system design information and serves as a guideline for developers to begin concrete implementation. When creating these documents, the design department deepens its understanding of the service based on the information provided by the generation department and designs the specifications for external release. The design department can continuously review and improve the specifications, incorporating user feedback and market trends. This allows the design department to efficiently understand the service, design the specifications, and prepare for external release.

[0032] The execution unit edits and executes code based on the specifications designed by the design unit. Based on the generated APIs and documentation, the execution unit edits and executes code and begins implementation for external release. Specifically, the execution unit implements code based on the design documents and requirements definitions provided by the design unit. Developers implement necessary functions and configure API endpoints, referring to the generated sample code. An integrated development environment (IDE) is used for efficient coding. The execution unit conducts unit and integration tests to ensure code quality, detecting and correcting bugs and defects early. Automated test tools can streamline the testing process and improve quality. Based on the generated security risk assessment results, the execution unit implements security measures to ensure system security. This includes, for example, implementing authentication protocols, data encryption, and access control settings. The execution unit prepares the code execution environment and manages the deployment process. A cloud platform is used to build a scalable infrastructure and ensure service availability and performance. The execution unit performs system operation verification and performance testing, and makes final adjustments for external release. This allows the execution unit to efficiently edit and execute code based on the specifications designed by the design unit, and complete the implementation for external release.

[0033] The generation unit can generate API design documents, authentication designs, specification documents, security risk assessments, and sample code. For example, the generation unit can clarify the specific content and format of the API design document, including endpoints, methods, and parameters. The generation unit can also clarify the specific methods and criteria for authentication design, using authentication methods such as OAuth and JWT. The generation unit can also clarify the specific content and format of the specification documents, including functional and non-functional specifications. The generation unit can also clarify the specific methods and criteria for security risk assessment, using vulnerability scans and risk assessment criteria. The generation unit can also clarify the specific content and format of the sample code, including the programming language and framework to be used. As a result, the generation unit can automatically generate API design documents, authentication designs, specification documents, security risk assessments, and sample code. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to generate API design documents, perform authentication design, create specification documents, assess security risks, and generate sample code.

[0034] The design department can create service usage flows, design documents, requirements definition documents, service documentation, and system design documents. For example, the design department can clarify the specific content and format of the service usage flow, including user stories and sequence diagrams. The design department can also clarify the specific content and format of the design document, including architecture diagrams and data flow diagrams. The design department can also clarify the specific content and format of the requirements definition document, including functional requirements and non-functional requirements. The design department can also clarify the specific content and format of the service documentation, including service overviews and user guides. The design department can also clarify the specific content and format of the system design document, including system architecture and database design. This allows the design department to automatically create service usage flows, design documents, requirements definition documents, service documentation, and system design documents. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to create service usage flows, design documents, requirements definition documents, service documentation, and system design documents.

[0035] The execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. For example, the execution unit can edit and execute code based on the generated APIs and documentation. The execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. For example, the execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. This allows the execution unit to edit and execute code based on the generated APIs and documentation and begin implementation for external release. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can edit and execute code based on APIs and documentation generated using AI, and begin implementation for external release.

[0036] The generation unit can analyze the user's past command history during generation and select the optimal method for generating APIs and documents. For example, the generation unit can prioritize generating APIs that the user has frequently used in the past. The generation unit can also select the most efficient generation method from the user's past command history. The generation unit can also generate documents in a similar format, referencing the format of documents previously generated by the user. This allows the generation unit to analyze the user's past command history and select the optimal generation method. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to analyze the user's past command history and select the optimal method for generating APIs and documents.

[0037] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can prioritize generating APIs related to the user's current ongoing projects. The generation unit can also generate relevant documents based on the user's areas of interest. The generation unit can also generate necessary documents according to the progress of the user's projects. This allows the generation unit to customize the generated content based on the user's current projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to customize the generated content based on the user's current projects and areas of interest.

[0038] The generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will prioritize the generation of APIs related to that region. The generation unit can also generate relevant documents based on the user's geographical location information. If the user is on the move, the generation unit can also generate the necessary APIs and documents based on their current location. This allows the generation unit to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can use a generation AI to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information.

[0039] The generation unit can analyze a user's social media activity during generation and generate relevant APIs and documentation. For example, the generation unit can generate APIs related to topics mentioned by the user on social media. The generation unit can also generate documentation related to areas of interest from the user's social media activity. The generation unit can also generate necessary APIs and documentation based on information shared by the user on social media. In this way, the generation unit can analyze a user's social media activity and generate relevant APIs and documentation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to analyze a user's social media activity and generate relevant APIs and documentation.

[0040] The design department can select the optimal design method by referring to past design data during the design process. For example, the design department can select the optimal design method based on past successful design data. The design department can also select an efficient design method from past design data. The design department can also analyze past design data and select the most suitable design method. This allows the design department to select the optimal design method by referring to past design data. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can use AI to refer to past design data and select the optimal design method.

[0041] The design department can customize the design content based on the user's current projects and areas of interest during the design phase. For example, the design department may prioritize the design of specifications related to the user's current ongoing projects. The design department may also design relevant specifications based on the user's areas of interest. The design department may also design necessary specifications according to the progress of the user's projects. This allows the design department to customize the design content based on the user's current projects and areas of interest. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to customize the design content based on the user's current projects and areas of interest.

[0042] The design department can prioritize designing highly relevant specifications by considering the user's geographical location information during the design phase. For example, if the user is in a specific region, the design department will prioritize designing specifications related to that region. The design department can also design relevant specifications based on the user's geographical location information. If the user is on the move, the design department can also design the necessary specifications based on their current location. This allows the design department to prioritize designing highly relevant specifications by considering the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to prioritize designing highly relevant specifications by considering the user's geographical location information.

[0043] The design department can analyze users' social media activity during the design phase and design relevant specifications. For example, the design department can design specifications related to topics mentioned by users on social media. The design department can also design specifications related to areas of interest based on users' social media activity. The design department can also design necessary specifications based on information shared by users on social media. This allows the design department to analyze users' social media activity and design relevant specifications. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to analyze users' social media activity and design relevant specifications.

[0044] The execution unit can select the optimal execution method by referring to past execution data during execution. For example, the execution unit can select the optimal execution method based on past successful execution data. The execution unit can also select an efficient execution method from past execution data. The execution unit can also analyze past execution data and select the most suitable execution method. In this way, the execution unit can select the optimal execution method by referring to past execution data. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can use AI to refer to past execution data and select the optimal execution method.

[0045] The execution unit can customize its execution based on the user's current projects and areas of interest at runtime. For example, the execution unit prioritizes executing code related to the user's current project. The execution unit can also execute relevant code based on the user's areas of interest. The execution unit can also execute necessary code depending on the progress of the user's project. This allows the execution unit to customize its execution based on the user's current projects and areas of interest. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to customize its execution based on the user's current projects and areas of interest.

[0046] The execution unit can prioritize the execution of highly relevant code at runtime, taking into account the user's geographical location. For example, if the user is in a specific region, the execution unit will prioritize the execution of code related to that region. The execution unit can also execute relevant code based on the user's geographical location. If the user is on the move, the execution unit can also execute necessary code based on their current location. This allows the execution unit to prioritize the execution of highly relevant code, taking into account the user's geographical location. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to prioritize the execution of highly relevant code, taking into account the user's geographical location.

[0047] The execution unit can analyze the user's social media activity at runtime and execute relevant code. For example, the execution unit can execute code related to topics mentioned by the user on social media. The execution unit can also execute code related to areas of interest based on the user's social media activity. The execution unit can also execute necessary code based on information shared by the user on social media. This allows the execution unit to analyze the user's social media activity and execute relevant code. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze the user's social media activity and execute relevant code.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The generation unit can analyze the user's past command history during generation and select the optimal method for generating APIs and documents. For example, it can prioritize generating APIs that the user has frequently used in the past. It can also select the most efficient generation method from the user's past command history. It can also generate documents in a similar format, referencing the format of documents previously generated by the user. In this way, the generation unit can analyze the user's past command history and select the optimal generation method. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to analyze the user's past command history and select the optimal method for generating APIs and documents.

[0050] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, it can prioritize generating APIs related to the user's current project. It can also generate relevant documents based on the user's areas of interest. It can also generate necessary documents according to the progress of the user's project. In this way, the generation unit can customize the generated content based on the user's current projects and areas of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to customize the generated content based on the user's current projects and areas of interest.

[0051] The generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, it can prioritize the generation of APIs related to that region. It can also generate relevant documents based on the user's geographical location information. If the user is on the move, it can generate the necessary APIs and documents based on their current location. In this way, the generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information.

[0052] The design department can select the optimal design method by referring to past design data during the design process. For example, they can select the optimal design method based on past successful design data. They can also select an efficient design method from past design data. They can also analyze past design data and select the most suitable design method. In this way, the design department can select the optimal design method by referring to past design data. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can use AI to refer to past design data and select the optimal design method.

[0053] The design department can customize the design content based on the user's current projects and areas of interest during the design process. For example, it can prioritize designing specifications related to the user's current projects. It can also design relevant specifications based on the user's areas of interest. It can also design necessary specifications according to the progress of the user's projects. This allows the design department to customize the design content based on the user's current projects and areas of interest. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to customize the design content based on the user's current projects and areas of interest.

[0054] The design department can prioritize designing highly relevant specifications by considering the user's geographical location information during the design phase. For example, if the user is in a specific region, the design department can prioritize designing specifications related to that region. It can also design relevant specifications based on the user's geographical location information. If the user is on the move, it can also design the necessary specifications based on their current location. This allows the design department to prioritize designing highly relevant specifications by considering the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can use AI to prioritize designing highly relevant specifications by considering the user's geographical location information.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The generation unit automatically prepares APIs and documentation using generation AI. For example, the generation unit generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The generation unit uses generation AI to generate the APIs and documentation necessary to issue commands to each system and designs the specifications for external publication. Step 2: The design department understands the service based on the APIs and documentation generated by the generation department and designs the specifications for external release. For example, the design department creates service usage flows, design documents, requirements definition documents, service materials, and system design documents. The design department deepens its understanding of the service based on the generated APIs and documentation and designs the specifications for external release. Step 3: The execution unit edits and executes the code based on the specifications designed by the design unit. The execution unit edits and executes the code based on the generated APIs and documentation, and begins implementation for external release. For example, the execution unit edits and executes the code based on the generated APIs and documentation, and begins implementation for external release.

[0057] (Example of form 2) The digital environment setup system according to an embodiment of the present invention is a system that automatically sets up the recipient's digital environment in order to promote the widespread adoption of AI agents. This digital environment setup system automatically sets up the environment using generative AI, an LLM (Large-Scale Language Model) understands the service, designs specifications for external publication, and generates sample code. This system enables the AI ​​agent to understand natural language commands from humans, break them down into tasks, and automatically prepare the necessary APIs and documentation to issue commands to each system. First, the user inputs a command in natural language. For example, a command such as "I want to go from XX to XX" is input. This command is input to the generative AI. Next, the generative AI analyzes the input command and automatically prepares the necessary APIs and documentation. The generative AI generates the necessary APIs and documentation to issue commands to each system and designs specifications for external publication. For example, it generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The LLM understands the service based on the generated APIs and documentation and designs specifications for external publication. Based on the generated APIs and documentation, the LLM deepens its understanding of the service and designs specifications for external publication. For example, it creates service usage flows, design documents, requirements specifications, service materials, and system design documents. Finally, based on the generated APIs and documents, it edits and executes the code and begins implementation for external release. This promotes the spread of AI agents, advances the digitalization of companies, and improves Japan's competitiveness. In this way, the digital environment development system can promote the spread of AI agents and advance the digitalization of companies.

[0058] The digital environment setup system according to the embodiment comprises a generation unit, a design unit, and an execution unit. The generation unit automatically prepares APIs and documentation using generation AI. The generation unit generates, for example, API design documents, authentication designs, specification documents, security risk assessments, and sample code. The generation unit uses generation AI to generate the APIs and documentation necessary to issue commands to each system and performs specification design for external publication. The design unit understands the service based on the APIs and documentation generated by the generation unit and performs specification design for external publication. The design unit creates, for example, service usage flows, design documents, requirements definition documents, service materials, and system design documents. The design unit deepens its understanding of the service based on the generated APIs and documentation and performs specification design for external publication. The execution unit edits and executes code based on the specifications designed by the design unit. The execution unit edits and executes code based on the generated APIs and documentation and begins implementation toward external publication. The execution unit edits and executes code based on the generated APIs and documentation and begins implementation toward external publication. As a result, the digital environment setup system according to the embodiment can efficiently perform tasks such as automatic preparation of APIs and documentation, understanding of services, specification design, and editing and execution of code.

[0059] The generation unit automatically prepares APIs and documentation using a generation AI. For example, it generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. Specifically, the generation AI utilizes natural language processing technology to create API design documents based on user-provided requirements and specifications. These documents detail endpoint definitions, request and response formats, authentication methods, and error handling. The authentication design uses authentication protocols such as OAuth and JWT to establish a user authentication and authorization mechanism. The specification documents clarify the overall system operation and the role of each component, provided in a format easily understood by developers. In the security risk assessment, the generation AI evaluates system vulnerabilities and risks based on existing security best practices and proposes necessary countermeasures. Sample code is provided as code snippets, including concrete usage and implementation examples, to enable developers to quickly utilize the API. The generation unit centrally manages these generated materials and can update and modify them as needed. The generation AI can continuously improve the quality of the generated materials by incorporating user feedback. This allows the generation unit to efficiently and accurately automate the creation of APIs and documentation, preparing them for external release from the early stages of the development process.

[0060] The design department understands the service based on the APIs and documentation generated by the generation department and designs the specifications for external release. For example, the design department creates service usage flows, design documents, requirements definition documents, service documentation, and system design documents. Specifically, the design department reviews the generated API design documents in detail and designs the service usage flows. These flows include how users utilize the service, the necessary operations and inputs at each step, and the system's responses. The design documents illustrate the overall system architecture and the interactions of each component, allowing developers to grasp the system's overall picture. The requirements definition documents clarify user needs and business requirements, and specifically describe the functions and performance the system must meet. Service documentation is a document that explains the service's features, benefits, and usage, serving to communicate the service's value to users and stakeholders. The system design document contains detailed system design information and serves as a guideline for developers to begin concrete implementation. When creating these documents, the design department deepens its understanding of the service based on the information provided by the generation department and designs the specifications for external release. The design department can continuously review and improve the specifications, incorporating user feedback and market trends. This allows the design department to efficiently understand the service, design the specifications, and prepare for external release.

[0061] The execution unit edits and executes code based on the specifications designed by the design unit. Based on the generated APIs and documentation, the execution unit edits and executes code and begins implementation for external release. Specifically, the execution unit implements code based on the design documents and requirements definitions provided by the design unit. Developers implement necessary functions and configure API endpoints, referring to the generated sample code. An integrated development environment (IDE) is used for efficient coding. The execution unit conducts unit and integration tests to ensure code quality, detecting and correcting bugs and defects early. Automated test tools can streamline the testing process and improve quality. Based on the generated security risk assessment results, the execution unit implements security measures to ensure system security. This includes, for example, implementing authentication protocols, data encryption, and access control settings. The execution unit prepares the code execution environment and manages the deployment process. A cloud platform is used to build a scalable infrastructure and ensure service availability and performance. The execution unit performs system operation verification and performance testing, and makes final adjustments for external release. This allows the execution unit to efficiently edit and execute code based on the specifications designed by the design unit, and complete the implementation for external release.

[0062] The generation unit can generate API design documents, authentication designs, specification documents, security risk assessments, and sample code. For example, the generation unit can clarify the specific content and format of the API design document, including endpoints, methods, and parameters. The generation unit can also clarify the specific methods and criteria for authentication design, using authentication methods such as OAuth and JWT. The generation unit can also clarify the specific content and format of the specification documents, including functional and non-functional specifications. The generation unit can also clarify the specific methods and criteria for security risk assessment, using vulnerability scans and risk assessment criteria. The generation unit can also clarify the specific content and format of the sample code, including the programming language and framework to be used. As a result, the generation unit can automatically generate API design documents, authentication designs, specification documents, security risk assessments, and sample code. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to generate API design documents, perform authentication design, create specification documents, assess security risks, and generate sample code.

[0063] The design department can create service usage flows, design documents, requirements definition documents, service documentation, and system design documents. For example, the design department can clarify the specific content and format of the service usage flow, including user stories and sequence diagrams. The design department can also clarify the specific content and format of the design document, including architecture diagrams and data flow diagrams. The design department can also clarify the specific content and format of the requirements definition document, including functional requirements and non-functional requirements. The design department can also clarify the specific content and format of the service documentation, including service overviews and user guides. The design department can also clarify the specific content and format of the system design document, including system architecture and database design. This allows the design department to automatically create service usage flows, design documents, requirements definition documents, service documentation, and system design documents. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to create service usage flows, design documents, requirements definition documents, service documentation, and system design documents.

[0064] The execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. For example, the execution unit can edit and execute code based on the generated APIs and documentation. The execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. For example, the execution unit can edit and execute code based on the generated APIs and documentation, and begin implementation for external release. This allows the execution unit to edit and execute code based on the generated APIs and documentation and begin implementation for external release. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can edit and execute code based on APIs and documentation generated using AI, and begin implementation for external release.

[0065] The generation unit can estimate the user's emotions and determine the priority of APIs and documents to generate based on the estimated emotions. For example, if the user is in a hurry, the generation unit will prioritize generating the most important APIs and documents. If the user is relaxed, the generation unit may also prioritize generating detailed documents. If the user is feeling anxious, the generation unit may also prioritize generating security-related documents. This allows the generation unit to prioritize APIs and documents based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without generative AI. For example, the generation unit can use generative AI to estimate the user's emotions and determine the priority of APIs and documents to generate based on the estimated emotions.

[0066] The generation unit can analyze the user's past command history during generation and select the optimal method for generating APIs and documents. For example, the generation unit can prioritize generating APIs that the user has frequently used in the past. The generation unit can also select the most efficient generation method from the user's past command history. The generation unit can also generate documents in a similar format, referencing the format of documents previously generated by the user. This allows the generation unit to analyze the user's past command history and select the optimal generation method. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to analyze the user's past command history and select the optimal method for generating APIs and documents.

[0067] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can prioritize generating APIs related to the user's current ongoing projects. The generation unit can also generate relevant documents based on the user's areas of interest. The generation unit can also generate necessary documents according to the progress of the user's projects. This allows the generation unit to customize the generated content based on the user's current projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to customize the generated content based on the user's current projects and areas of interest.

[0068] The generation unit can estimate the user's emotions and adjust the way APIs and documents are presented based on the estimated emotions. For example, if the user is nervous, the generation unit will use a simple and easy-to-understand presentation. If the user is relaxed, the generation unit may also use a presentation that includes detailed explanations. If the user is in a hurry, the generation unit may also use a concise presentation that gets straight to the point. This allows the generation unit to adjust the way APIs and documents are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can use a generative AI to estimate the user's emotions and adjust the way APIs and documents are presented based on the estimated emotions.

[0069] The generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will prioritize the generation of APIs related to that region. The generation unit can also generate relevant documents based on the user's geographical location information. If the user is on the move, the generation unit can also generate the necessary APIs and documents based on their current location. This allows the generation unit to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can use a generation AI to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information.

[0070] The generation unit can analyze a user's social media activity during generation and generate relevant APIs and documentation. For example, the generation unit can generate APIs related to topics mentioned by the user on social media. The generation unit can also generate documentation related to areas of interest from the user's social media activity. The generation unit can also generate necessary APIs and documentation based on information shared by the user on social media. In this way, the generation unit can analyze a user's social media activity and generate relevant APIs and documentation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to analyze a user's social media activity and generate relevant APIs and documentation.

[0071] The design department can estimate the user's emotions and determine the priority of the specifications to be designed based on those estimated emotions. For example, if the user is in a hurry, the design department will prioritize the most important specifications. If the user is relaxed, the design department may also prioritize detailed specifications. If the user is feeling anxious, the design department may also prioritize security-related specifications. This allows the design department to determine the priority of the specifications to be designed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can use AI to estimate the user's emotions and determine the priority of the specifications to be designed based on those estimated emotions.

[0072] The design department can select the optimal design method by referring to past design data during the design process. For example, the design department can select the optimal design method based on past successful design data. The design department can also select an efficient design method from past design data. The design department can also analyze past design data and select the most suitable design method. This allows the design department to select the optimal design method by referring to past design data. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can use AI to refer to past design data and select the optimal design method.

[0073] The design department can customize the design content based on the user's current projects and areas of interest during the design phase. For example, the design department may prioritize the design of specifications related to the user's current ongoing projects. The design department may also design relevant specifications based on the user's areas of interest. The design department may also design necessary specifications according to the progress of the user's projects. This allows the design department to customize the design content based on the user's current projects and areas of interest. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to customize the design content based on the user's current projects and areas of interest.

[0074] The design department can estimate the user's emotions and adjust the way the specifications are expressed based on those estimated emotions. For example, if the user is nervous, the design department might use a simple and easy-to-understand expression. If the user is relaxed, the design department might use an expression that includes detailed explanations. If the user is in a hurry, the design department might use a concise expression that gets straight to the point. This allows the design department to adjust the way the specifications are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can use AI to estimate the user's emotions and adjust the way the specifications are expressed based on those estimated emotions.

[0075] The design department can prioritize designing highly relevant specifications by considering the user's geographical location information during the design phase. For example, if the user is in a specific region, the design department will prioritize designing specifications related to that region. The design department can also design relevant specifications based on the user's geographical location information. If the user is on the move, the design department can also design the necessary specifications based on their current location. This allows the design department to prioritize designing highly relevant specifications by considering the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to prioritize designing highly relevant specifications by considering the user's geographical location information.

[0076] The design department can analyze users' social media activity during the design phase and design relevant specifications. For example, the design department can design specifications related to topics mentioned by users on social media. The design department can also design specifications related to areas of interest based on users' social media activity. The design department can also design necessary specifications based on information shared by users on social media. This allows the design department to analyze users' social media activity and design relevant specifications. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to analyze users' social media activity and design relevant specifications.

[0077] The execution unit can estimate the user's emotions and determine the priority of the code to execute based on the estimated emotions. For example, if the user is in a hurry, the execution unit will prioritize executing the most important code. If the user is relaxed, the execution unit may also prioritize executing detailed code. If the user is feeling anxious, the execution unit may also prioritize executing security-related code. This allows the execution unit to determine the priority of the code to execute based on the user's emotions. Emotion estimation is achieved using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to estimate the user's emotions and determine the priority of the code to execute based on the estimated emotions.

[0078] The execution unit can select the optimal execution method by referring to past execution data during execution. For example, the execution unit can select the optimal execution method based on past successful execution data. The execution unit can also select an efficient execution method from past execution data. The execution unit can also analyze past execution data and select the most suitable execution method. In this way, the execution unit can select the optimal execution method by referring to past execution data. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can use AI to refer to past execution data and select the optimal execution method.

[0079] The execution unit can customize its execution based on the user's current projects and areas of interest at runtime. For example, the execution unit prioritizes executing code related to the user's current project. The execution unit can also execute relevant code based on the user's areas of interest. The execution unit can also execute necessary code depending on the progress of the user's project. This allows the execution unit to customize its execution based on the user's current projects and areas of interest. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to customize its execution based on the user's current projects and areas of interest.

[0080] The execution unit can estimate the user's emotions and adjust the way the code is expressed based on the estimated emotions. For example, if the user is nervous, the execution unit may use a simple and easy-to-understand expression. If the user is relaxed, the execution unit may also use an expression that includes detailed explanations. If the user is in a hurry, the execution unit may also use a concise expression that gets straight to the point. This allows the execution unit to adjust the way the code is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can use AI to estimate the user's emotions and adjust the way the code is expressed based on the estimated emotions.

[0081] The execution unit can prioritize the execution of highly relevant code at runtime, taking into account the user's geographical location. For example, if the user is in a specific region, the execution unit will prioritize the execution of code related to that region. The execution unit can also execute relevant code based on the user's geographical location. If the user is on the move, the execution unit can also execute necessary code based on their current location. This allows the execution unit to prioritize the execution of highly relevant code, taking into account the user's geographical location. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to prioritize the execution of highly relevant code, taking into account the user's geographical location.

[0082] The execution unit can analyze the user's social media activity at runtime and execute relevant code. For example, the execution unit can execute code related to topics mentioned by the user on social media. The execution unit can also execute code related to areas of interest based on the user's social media activity. The execution unit can also execute necessary code based on information shared by the user on social media. This allows the execution unit to analyze the user's social media activity and execute relevant code. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze the user's social media activity and execute relevant code.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The generation unit can estimate the user's emotions and determine the priority of APIs and documents to generate based on the estimated emotions. For example, if the user is in a hurry, the most important APIs and documents can be generated first. If the user is relaxed, detailed documents can be generated first. If the user is anxious, security-related documents can be generated first. In this way, the generation unit can determine the priority of APIs and documents based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can use a generative AI to estimate the user's emotions and determine the priority of APIs and documents to generate based on the estimated emotions.

[0085] The generation unit can analyze the user's past command history during generation and select the optimal method for generating APIs and documents. For example, it can prioritize generating APIs that the user has frequently used in the past. It can also select the most efficient generation method from the user's past command history. It can also generate documents in a similar format, referencing the format of documents previously generated by the user. In this way, the generation unit can analyze the user's past command history and select the optimal generation method. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to analyze the user's past command history and select the optimal method for generating APIs and documents.

[0086] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, it can prioritize generating APIs related to the user's current project. It can also generate relevant documents based on the user's areas of interest. It can also generate necessary documents according to the progress of the user's project. In this way, the generation unit can customize the generated content based on the user's current projects and areas of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to customize the generated content based on the user's current projects and areas of interest.

[0087] The generation unit can estimate the user's emotions and adjust the way APIs and documents are presented based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand presentation can be used. If the user is relaxed, a presentation with detailed explanations can be used. If the user is in a hurry, a concise presentation that gets straight to the point can be used. In this way, the generation unit can adjust the way APIs and documents are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can estimate the user's emotions using a generative AI and adjust the way APIs and documents are presented based on the estimated emotions.

[0088] The generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, it can prioritize the generation of APIs related to that region. It can also generate relevant documents based on the user's geographical location information. If the user is on the move, it can generate the necessary APIs and documents based on their current location. In this way, the generation unit can prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can use a generation AI to prioritize the generation of highly relevant APIs and documents by considering the user's geographical location information.

[0089] The design department can estimate the user's emotions and prioritize the specifications to be designed based on those estimated emotions. For example, if the user is in a hurry, the most important specifications can be prioritized. If the user is relaxed, detailed specifications can be prioritized. If the user is anxious, security-related specifications can be prioritized. This allows the design department to prioritize the specifications to be designed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can use AI to estimate the user's emotions and prioritize the specifications to be designed based on those estimated emotions.

[0090] The design department can select the optimal design method by referring to past design data during the design process. For example, they can select the optimal design method based on past successful design data. They can also select an efficient design method from past design data. They can also analyze past design data and select the most suitable design method. In this way, the design department can select the optimal design method by referring to past design data. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can use AI to refer to past design data and select the optimal design method.

[0091] The design department can customize the design content based on the user's current projects and areas of interest during the design process. For example, it can prioritize designing specifications related to the user's current projects. It can also design relevant specifications based on the user's areas of interest. It can also design necessary specifications according to the progress of the user's projects. This allows the design department to customize the design content based on the user's current projects and areas of interest. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can use AI to customize the design content based on the user's current projects and areas of interest.

[0092] The design department can estimate the user's emotions and adjust the way the specifications are expressed based on those estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression can be used. If the user is relaxed, an expression that includes detailed explanations can be used. If the user is in a hurry, a concise expression that gets straight to the point can be used. This allows the design department to adjust the way the specifications are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can use AI to estimate the user's emotions and adjust the way the specifications are expressed based on those estimated emotions.

[0093] The design department can prioritize designing highly relevant specifications by considering the user's geographical location information during the design phase. For example, if the user is in a specific region, the design department can prioritize designing specifications related to that region. It can also design relevant specifications based on the user's geographical location information. If the user is on the move, it can also design the necessary specifications based on their current location. This allows the design department to prioritize designing highly relevant specifications by considering the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can use AI to prioritize designing highly relevant specifications by considering the user's geographical location information.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The generation unit automatically prepares APIs and documentation using generation AI. For example, the generation unit generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The generation unit uses generation AI to generate the APIs and documentation necessary to issue commands to each system and designs the specifications for external publication. Step 2: The design department understands the service based on the APIs and documentation generated by the generation department and designs the specifications for external release. For example, the design department creates service usage flows, design documents, requirements definition documents, service materials, and system design documents. The design department deepens its understanding of the service based on the generated APIs and documentation and designs the specifications for external release. Step 3: The execution unit edits and executes the code based on the specifications designed by the design unit. The execution unit edits and executes the code based on the generated APIs and documentation, and begins implementation for external release. For example, the execution unit edits and executes the code based on the generated APIs and documentation, and begins implementation for external release.

[0096] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0099] Each of the multiple elements described above, including the generation unit, design unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and automatically prepares APIs and documents using generation AI. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the service based on the generated APIs and documents and designs the specifications for external publication. The execution unit is implemented by the control unit 46A of the smart device 14 and edits and executes the code based on the designed specifications. The generation unit can, for example, estimate the user's emotions and determine the priority of APIs and documents to be generated based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the generation unit, design unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically prepares APIs and documents using generation AI. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the service based on the generated APIs and documents and designs the specifications for external publication. The execution unit is implemented by the control unit 46A of the smart glasses 214 and edits and executes the code based on the designed specifications. The generation unit can, for example, estimate the user's emotions and determine the priority of APIs and documents to be generated based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the generation unit, design unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically prepares APIs and documents using generation AI. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the service based on the generated APIs and documents and designs the specifications for external publication. The execution unit is implemented by the control unit 46A of the headset terminal 314 and edits and executes the code based on the designed specifications. The generation unit can, for example, estimate the user's emotions and determine the priority of APIs and documents to be generated based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the generation unit, design unit, and execution unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and automatically prepares APIs and documents using generation AI. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the service based on the generated APIs and documents and designs the specifications for external publication. The execution unit is implemented by the control unit 46A of the robot 414 and edits and executes the code based on the designed specifications. The generation unit can, for example, estimate the user's emotions and determine the priority of APIs and documents to be generated based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] 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.

[0159] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) A generation unit that automatically prepares APIs and documentation using generation AI, A design unit that understands the service based on the APIs and documents generated by the aforementioned generation unit and designs the specifications for external publication, The system comprises an execution unit that edits and executes code based on the specifications designed by the aforementioned design unit. A system characterized by the following features. (Note 2) The generating unit is Generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned design department, Create service usage flows, design documents, requirements specifications, service documentation, and system design documents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The execution unit is, Based on the generated APIs and documentation, edit and execute the code, and begin implementation for external release. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates user sentiment and determines the priority of APIs and documents to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is During generation, the system analyzes the user's past command history to select the optimal API and documentation generation method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is During generation, the generated content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We estimate user emotions and adjust the way APIs and documentation are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, the system prioritizes generating relevant APIs and documentation by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant APIs and documentation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned design department, Estimate user emotions and prioritize design specifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, During the design phase, the optimal design method is selected by referring to past design data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, During the design phase, the design is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, We estimate user emotions and adjust the way specifications are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, During the design phase, we prioritize designing specifications that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, During the design phase, we analyze users' social media activity and design the relevant specifications. The system described in Appendix 1, characterized by the features described herein. (Note 17) The execution unit is, It estimates the user's emotions and determines the priority of the code to execute based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The execution unit is, During execution, the system selects the optimal execution method by referring to past execution data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, At runtime, the execution is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, It estimates the user's emotions and adjusts how the code is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, At runtime, the system prioritizes the execution of relevant code, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, At runtime, the system analyzes the user's social media activity and executes relevant code. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that automatically prepares APIs and documentation using generation AI, A design unit that understands the service based on the APIs and documents generated by the aforementioned generation unit and designs the specifications for external publication, The system comprises an execution unit that edits and executes code based on the specifications designed by the aforementioned design unit. A system characterized by the following features.

2. The generating unit is Generates API design documents, authentication designs, specification documents, security risk assessments, and sample code. The system according to feature 1.

3. The aforementioned design department, Create service usage flows, design documents, requirements specifications, service documentation, and system design documents. The system according to feature 1.

4. The execution unit is, Based on the generated APIs and documentation, edit and execute the code, and begin implementation for external release. The system according to feature 1.

5. The generating unit is It estimates user sentiment and determines the priority of APIs and documents to generate based on the estimated user sentiment. The system according to feature 1.

6. The generating unit is During generation, the system analyzes the user's past command history to select the optimal API and documentation generation method. The system according to feature 1.

7. The generating unit is During generation, the generated content is customized based on the user's current projects and areas of interest. The system according to feature 1.

8. The generating unit is We estimate user emotions and adjust the way APIs and documentation are presented based on those estimated emotions. The system according to feature 1.

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