system

Abstract

An object of a system according to an embodiment is to automate a process from code generation to deployment.SOLUTION: A system according to an embodiment includes a generation AI and a CI tool. The generation AI includes a generation unit that receives an instruction from a user and generates a code based on the instruction. The CI tool includes a building unit that builds the code generated by the generating unit, a testing unit that tests the code built by the building unit, and a deploying unit that deploys the code tested by the testing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, the process from code generation to building, testing, and deployment was often done manually, which led to issues with inefficiency.

[0005] The system according to the embodiment aims to automate the process from code generation to deployment. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation AI and a CI tool. The generation AI includes a generation unit that receives instructions from a user and generates code based on the instructions. The CI tool includes a build unit that builds the code generated by the generation unit, a test unit that tests the code built by the build unit, and a deployment unit that deploys the code tested by the test unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process from code generation to deployment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A code generation system according to an embodiment of the present invention is a system that automatically creates working code using a generation AI and a CI tool. As a result, the code generation system allows the generation AI to receive instructions from a user, generate code based on the instructions, and automatically build, test, and deploy the code using the CI tool.

[0029] A code generation system according to an embodiment includes a generation AI, a CI tool, a generation unit, a build unit, a test unit, and a deployment unit. The generation unit receives instructions from a user and generates code based on the instructions. For example, if a user instructs the generation unit to "create a login function for a website," the generation unit analyzes the instructions and generates the necessary code. The generation unit can generate code based on the user's instructions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The generation AI receives prompts including the user's instructions and generates code that operates based on the prompts. The build unit builds the code generated by the generation unit. For example, the build unit compiles the code and converts it into an executable format. The build unit can automatically execute the build process using a CI tool. The test unit tests the code built by the build unit. For example, the test unit runs automated tests on the generated code to check the quality and operation of the code. The test unit can automatically execute the test process using a CI tool. The deployment unit deploys the code tested by the test unit. For example, the deployment unit deploys the generated code in a production environment so that the code actually runs. The deployment unit can also automatically execute the deployment process using a CI tool. This allows the code generation system according to the embodiment to automatically create running code using the generation AI and the CI tool.

[0030] The generation unit can learn the user's past code generation history and generate code that matches the user's preferences and style. For example, the generation AI analyzes the user's past code generation history and learns the user's preferences and style. For example, it prioritizes the use of specific coding styles and libraries. The generation unit also suggests optimal code based on the user's past project data. For example, it takes into account previously used frameworks and design patterns. The generation unit also improves the accuracy of code generation based on user feedback. For example, it reflects the user's preferred code writing style and commenting style. This makes it possible to generate code that matches the user's preferences and style.

[0031] The generation unit can analyze the user's voice instructions and generate code directly from the voice. For example, the generation AI analyzes the user's voice instructions in real time and generates code based on those instructions. For example, a user may say, "Create a login function." The generation unit also uses voice recognition technology to convert the user's voice instructions into text and generate code based on that text. For example, a user may say, "Set up a database connection." The generation AI also understands the context of the voice instructions and generates appropriate code. For example, when a user says, "Add a user authentication function," the necessary libraries and settings are automatically added. This makes it possible to generate code based on voice instructions.

[0032] The generation unit can simultaneously generate code in different programming languages ​​to achieve multi-platform compatibility. For example, the generation AI in the generation unit simultaneously generates code in different programming languages ​​based on user instructions. For example, it generates code in Python and JavaScript simultaneously. In addition, the generation unit generates code in different languages ​​to support multi-platforms. For example, it generates code compatible with both mobile apps and web apps. In addition, the generation AI in the generation unit takes into account the characteristics of different platforms to generate optimal code. For example, it generates code compatible with both iOS and Android. This enables multi-platform code generation.

[0033] The generation unit can analyze a user's handwritten sketches or diagrams and generate code based on them. For example, the generation unit uses image recognition technology to analyze a user's handwritten sketches using a generation AI, and generates code based on their content. For example, the generation unit analyzes a UI design sketch and generates code. The generation unit can also analyze a diagram drawn by the user and generate code based on that diagram. For example, the generation unit analyzes a flowchart and generates algorithm code. The generation unit can also analyze handwritten sketches or diagrams in real time, and the generation AI can instantly generate code. For example, a diagram drawn on a whiteboard can be photographed with a camera, and code can be generated based on the content of the photograph. This makes it possible to generate code based on handwritten sketches or diagrams.

[0034] CI tools can analyze and optimize code performance in real time during the build and test processes. CI tools can analyze and optimize code performance in real time during the build and test processes. For example, they can analyze code execution speed and memory usage. CI tools can also analyze code performance in real time and suggest optimizations. For example, they can identify areas where performance is degraded and suggest ways to improve them. CI tools can also monitor and optimize code performance in real time during the build and test processes. For example, they can identify performance bottlenecks and improve them. This makes it possible to analyze and optimize code performance in real time during the build and test processes.

[0035] CI tools can automatically create different test environments and run tests in multiple environments simultaneously. CI tools can, for example, automatically create different test environments and run tests in multiple environments simultaneously. For example, they can run tests in Windows, Linux, and macOS environments simultaneously. CI tools can also run tests in multiple environments simultaneously in automatically created test environments. For example, they can run tests in different versions of browsers simultaneously. CI tools can also create different test environments in real time and run tests in multiple environments simultaneously. For example, they can run tests in cloud and on-premise environments simultaneously. This allows different test environments to be automatically created and tests to be run in multiple environments simultaneously.

[0036] CI tools can analyze users' handwritten notes and comments during the build and test process and generate test cases based on them. For example, CI tools use image recognition technology to analyze users' handwritten notes and comments and generate test cases based on their content. For example, they can analyze handwritten test scenarios and generate them automatically. CI tools can also analyze notes and comments written by users and generate test cases based on them. For example, they can analyze handwritten bug reports and generate test cases. CI tools can also analyze handwritten notes and comments in real time and generate test cases instantly. For example, they can capture a test scenario written on a whiteboard with a camera and generate test cases based on the captured content. This makes it possible to generate test cases based on users' handwritten notes and comments.

[0037] CI tools can automatically check operation on different devices and platforms during the build and test processes. CI tools can automatically check operation on different devices and platforms during the build and test processes. For example, they can check operation on smartphones, tablets, and PCs. CI tools also build systems that automatically check operation on different devices and platforms. For example, they can check operation on both iOS and Android. CI tools can also check operation on different devices and platforms in real time during the build and test processes. For example, they can check operation in a cloud environment. This allows them to automatically check operation on different devices and platforms.

[0038] CI tools can check operation in a simulation environment before deployment and minimize risks. CI tools, for example, build a simulation environment before deployment and check operation. For example, they run tests in a simulation environment with the same settings as the production environment. CI tools also check operation in a simulation environment and minimize risks during deployment. For example, they determine whether deployment is possible based on the test results in the simulation environment. CI tools also check operation in a simulation environment in real time before deployment and minimize risks. For example, they run performance tests in a simulation environment. In this way, they can check operation in a simulation environment before deployment and minimize risks.

[0039] CI tools can collect post-deployment performance data in real time and reflect it in the next deployment. CI tools, for example, collect post-deployment performance data in real time and reflect it in the next deployment. For example, they monitor response times and error rates after deployment. CI tools also optimize the next deployment strategy based on the performance data. For example, they analyze post-deployment performance data and identify areas for improvement. CI tools also build systems that collect post-deployment performance data in real time and reflect it in the next deployment. For example, they automatically adjust settings based on post-deployment performance data. This makes it possible to collect post-deployment performance data in real time and reflect it in the next deployment.

[0040] CI tools can automatically adjust deployment strategies to suit different regions and time zones during the deployment process. For example, CI tools automatically adjust deployment strategies to suit different regions and time zones during the deployment process. For example, they may deploy during times that avoid peak hours in each region. CI tools also automatically adjust the timing of deployment to accommodate different time zones. For example, they may deploy during times when there are fewer users. CI tools also adjust deployment strategies to suit different regions and time zones in real time. For example, they may determine the timing of deployment based on user activity in each region. This makes it possible to automatically adjust deployment strategies to suit different regions and time zones.

[0041] During the deployment process, CI tools can analyze a user's handwritten sketches or diagrams and optimize the deployment procedures based on them. For example, CI tools use image recognition technology to analyze a user's handwritten sketches or diagrams and optimize the deployment procedures based on their content. For example, they analyze handwritten deployment flows and automate them. CI tools can also analyze diagrams of deployment procedures drawn by users and optimize the deployment procedures based on them. For example, they can analyze handwritten deployment procedures and suggest optimal procedures. CI tools can also analyze handwritten sketches or diagrams in real time and instantly optimize the deployment procedures. For example, they can capture deployment procedures drawn on a whiteboard with a camera and optimize the deployment procedures based on the captured content. This makes it possible to optimize the deployment procedures based on the user's handwritten sketches or diagrams.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The generator can learn from the user's past code generation history and generate code that matches the user's preferences and style. For example, the generation AI can analyze the user's past code generation history and learn the user's preferences and style. It can also prioritize the use of specific coding styles and libraries. The generator also suggests optimal code based on the user's past project data. It can also take into account previously used frameworks and design patterns. Furthermore, the generator AI can improve the accuracy of code generation based on user feedback. It can also reflect the user's preferred code writing style and commenting style. This makes it possible to generate code that matches the user's preferences and style.

[0044] The generation unit can analyze the user's voice instructions and generate code directly from the voice. For example, the generation AI can analyze the user's voice instructions in real time and generate code based on those instructions. The user can also give a voice instruction such as "Create a login function." The generation unit can also use voice recognition technology to convert the user's voice instructions into text and generate code based on that text. The user can also give an instruction such as "Set up a database connection." Furthermore, the generation AI in the generation unit can understand the context of the voice instruction and generate appropriate code. When the user instructs "Add a user authentication function," the necessary libraries and settings can also be automatically added. This makes it possible to generate code based on voice instructions.

[0045] The generation unit can simultaneously generate code in different programming languages, achieving multi-platform compatibility. For example, the generation AI generates code in different programming languages ​​simultaneously based on user instructions. It can also generate code in Python and JavaScript simultaneously. The generation unit also generates code in different languages ​​to achieve multi-platform compatibility. It can also generate code that is compatible with both mobile apps and web apps. Furthermore, the generation AI in the generation unit takes into account the characteristics of different platforms to generate optimal code. It can also generate code that is compatible with both iOS and Android. This makes it possible to generate code that is compatible with multiple platforms.

[0046] The generation unit can analyze a user's handwritten sketches or diagrams and generate code based on them. For example, the generation AI can analyze a user's handwritten sketches using image recognition technology and generate code based on their content. It can also analyze UI design sketches to generate code. The generation unit can also analyze diagrams drawn by the user and generate code based on those diagrams. It can also analyze flowcharts to generate algorithm code. Furthermore, the generation unit can analyze handwritten sketches and diagrams in real time, and the generation AI can instantly generate code. It can also take a photo of a diagram drawn on a whiteboard with a camera and generate code based on its content. This makes it possible to generate code based on handwritten sketches and diagrams.

[0047] CI tools can analyze and optimize code performance in real time during the build and test processes. For example, they can analyze and optimize code performance in real time during the build and test processes. They can also analyze code execution speed and memory usage. CI tools can also analyze code performance in real time and suggest optimizations. They can also identify areas where performance is degraded and suggest ways to improve them. Furthermore, CI tools can monitor and optimize code performance in real time during the build and test processes. They can also identify and improve performance bottlenecks. This makes it possible to analyze and optimize code performance in real time during the build and test processes.

[0048] CI tools can automatically create different test environments and run tests in multiple environments simultaneously. For example, they can automatically create different test environments and run tests in multiple environments simultaneously. Testing can also be done in Windows, Linux, and macOS environments simultaneously. CI tools can also run tests in multiple environments simultaneously in automatically created test environments. Testing can also be done in different versions of browsers simultaneously. CI tools can also create different test environments in real time and run tests in multiple environments simultaneously. Testing can also be done in cloud and on-premise environments simultaneously. This makes it possible to automatically create different test environments and run tests in multiple environments simultaneously.

[0049] CI tools can analyze users' handwritten notes and comments during the build and test process and generate test cases based on them. For example, they can use image recognition technology to analyze users' handwritten notes and comments and generate test cases based on their content. They can also analyze handwritten test scenarios and generate them automatically. CI tools can also analyze users' handwritten notes and comments and generate test cases based on them. They can also analyze handwritten bug reports and generate test cases. CI tools can also analyze handwritten notes and comments in real time and generate test cases instantly. They can also capture test scenarios written on a whiteboard with a camera and generate test cases based on the content. This makes it possible to generate test cases based on users' handwritten notes and comments.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The generator receives instructions from the user and generates code based on those instructions. For example, if the user instructs the generator to "create a login function for a website," the generator analyzes the instructions and generates the necessary code. The generator can also use a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate code based on the user's instructions. The generation AI receives a prompt containing the user's instructions and generates code that operates based on the prompt. Step 2: The build unit builds the code generated by the generation unit. For example, the build unit compiles the code and converts it into an executable format. The build unit can also automatically execute the build process using a CI tool. Step 3: The testing department tests the code built by the build department. For example, the testing department runs automated tests on the generated code to check the code quality and behavior. The testing department can also use CI tools to automatically execute the testing process. Step 4: The deployment department deploys the code that has been tested by the testing department. For example, the deployment department places the generated code in a production environment and makes it actually work. The deployment department can also use a CI tool to automatically execute the deployment process.

[0052] (Example 2) A code generation system according to an embodiment of the present invention is a system that automatically creates working code using a generation AI and a CI tool. As a result, the code generation system allows the generation AI to receive instructions from a user, generate code based on the instructions, and automatically build, test, and deploy the code using the CI tool.

[0053] A code generation system according to an embodiment includes a generation AI, a CI tool, a generation unit, a build unit, a test unit, and a deployment unit. The generation unit receives instructions from a user and generates code based on the instructions. For example, if a user instructs the generation unit to "create a login function for a website," the generation unit analyzes the instructions and generates the necessary code. The generation unit can generate code based on the user's instructions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The generation AI receives prompts including the user's instructions and generates code that operates based on the prompts. The build unit builds the code generated by the generation unit. For example, the build unit compiles the code and converts it into an executable format. The build unit can automatically execute the build process using a CI tool. The test unit tests the code built by the build unit. For example, the test unit runs automated tests on the generated code to check the quality and operation of the code. The test unit can automatically execute the test process using a CI tool. The deployment unit deploys the code tested by the test unit. For example, the deployment unit deploys the generated code in a production environment so that the code actually runs. The deployment unit can also automatically execute the deployment process using a CI tool. This allows the code generation system according to the embodiment to automatically create running code using the generation AI and the CI tool.

[0054] The generation unit can estimate the user's emotions and suggest code generation that elicits positive emotions. For example, the generation AI analyzes the user's input and estimates the user's emotional state. For example, if the user is feeling stressed, the generation AI suggests simple and intuitive code. The generation unit also monitors the user's emotions in real time and generates code that elicits positive emotions. For example, it suggests complex tasks when the user is relaxed. The generation unit also generates code that the user can enjoy working on based on the emotion estimation data. For example, it suggests code that incorporates game elements. This makes it possible to generate code that takes the user's emotions into consideration.

[0055] The generation unit can learn the user's past code generation history and generate code that matches the user's preferences and style. For example, the generation AI analyzes the user's past code generation history and learns the user's preferences and style. For example, it prioritizes the use of specific coding styles and libraries. The generation unit also suggests optimal code based on the user's past project data. For example, it takes into account previously used frameworks and design patterns. The generation unit also improves the accuracy of code generation based on user feedback. For example, it reflects the user's preferred code writing style and commenting style. This makes it possible to generate code that matches the user's preferences and style.

[0056] The generation unit can analyze the user's voice instructions and generate code directly from the voice. For example, the generation AI analyzes the user's voice instructions in real time and generates code based on those instructions. For example, a user may say, "Create a login function." The generation unit also uses voice recognition technology to convert the user's voice instructions into text and generate code based on that text. For example, a user may say, "Set up a database connection." The generation AI also understands the context of the voice instructions and generates appropriate code. For example, when a user says, "Add a user authentication function," the necessary libraries and settings are automatically added. This makes it possible to generate code based on voice instructions.

[0057] The generation unit can simultaneously generate code in different programming languages ​​to achieve multi-platform compatibility. For example, the generation AI in the generation unit simultaneously generates code in different programming languages ​​based on user instructions. For example, it generates code in Python and JavaScript simultaneously. In addition, the generation unit generates code in different languages ​​to support multi-platforms. For example, it generates code compatible with both mobile apps and web apps. In addition, the generation AI in the generation unit takes into account the characteristics of different platforms to generate optimal code. For example, it generates code compatible with both iOS and Android. This enables multi-platform code generation.

[0058] The generation unit can analyze a user's handwritten sketches or diagrams and generate code based on them. For example, the generation unit uses image recognition technology to analyze a user's handwritten sketches using a generation AI, and generates code based on their content. For example, the generation unit analyzes a UI design sketch and generates code. The generation unit can also analyze a diagram drawn by the user and generate code based on that diagram. For example, the generation unit analyzes a flowchart and generates algorithm code. The generation unit can also analyze handwritten sketches or diagrams in real time, and the generation AI can instantly generate code. For example, a diagram drawn on a whiteboard can be photographed with a camera, and code can be generated based on the content of the photograph. This makes it possible to generate code based on handwritten sketches or diagrams.

[0059] The generation unit can estimate the user's emotions and suggest the optimal timing for code generation when the user is relaxed. For example, the generation AI in the generation unit monitors the user's emotional state in real time and suggests code generation when the user is relaxed. For example, it sends a notification when the user is relaxed. The generation unit also identifies the timing when the user can work most efficiently based on the emotion estimation data and suggests code generation at that timing. For example, it makes suggestions when the user is concentrating. The generation unit also analyzes the user's emotional state and suggests code generation tasks that are optimal when the user is relaxed. For example, it suggests easy tasks when the user is relaxed. This makes it possible to suggest the optimal timing for code generation when the user is relaxed.

[0060] CI tools can infer user emotions based on test results and make improvement suggestions if negative results are obtained. CI tools, for example, analyze test results and infer user emotions if negative results are obtained. For example, they can infer that the user is feeling stressed when a test fails. CI tools can also infer user emotions based on test results and make improvement suggestions if negative results are obtained. For example, they can suggest improvements to error messages and debugging methods. CI tools can also analyze test results in real time, infer user emotions, and make improvement suggestions. For example, they can make suggestions to help the user relax when a test fails. This makes it possible to infer user emotions based on test results and make improvement suggestions if negative results are obtained.

[0061] CI tools can analyze and optimize code performance in real time during the build and test processes. CI tools can analyze and optimize code performance in real time during the build and test processes. For example, they can analyze code execution speed and memory usage. CI tools can also analyze code performance in real time and suggest optimizations. For example, they can identify areas where performance is degraded and suggest ways to improve them. CI tools can also monitor and optimize code performance in real time during the build and test processes. For example, they can identify performance bottlenecks and improve them. This makes it possible to analyze and optimize code performance in real time during the build and test processes.

[0062] CI tools can automatically create different test environments and run tests in multiple environments simultaneously. CI tools can, for example, automatically create different test environments and run tests in multiple environments simultaneously. For example, they can run tests in Windows, Linux, and macOS environments simultaneously. CI tools can also run tests in multiple environments simultaneously in automatically created test environments. For example, they can run tests in different versions of browsers simultaneously. CI tools can also create different test environments in real time and run tests in multiple environments simultaneously. For example, they can run tests in cloud and on-premise environments simultaneously. This allows different test environments to be automatically created and tests to be run in multiple environments simultaneously.

[0063] CI tools can analyze users' handwritten notes and comments during the build and test process and generate test cases based on them. For example, CI tools use image recognition technology to analyze users' handwritten notes and comments and generate test cases based on their content. For example, they can analyze handwritten test scenarios and generate them automatically. CI tools can also analyze notes and comments written by users and generate test cases based on them. For example, they can analyze handwritten bug reports and generate test cases. CI tools can also analyze handwritten notes and comments in real time and generate test cases instantly. For example, they can capture a test scenario written on a whiteboard with a camera and generate test cases based on the captured content. This makes it possible to generate test cases based on users' handwritten notes and comments.

[0064] CI tools can automatically check operation on different devices and platforms during the build and test processes. CI tools can automatically check operation on different devices and platforms during the build and test processes. For example, they can check operation on smartphones, tablets, and PCs. CI tools also build systems that automatically check operation on different devices and platforms. For example, they can check operation on both iOS and Android. CI tools can also check operation on different devices and platforms in real time during the build and test processes. For example, they can check operation in a cloud environment. This allows them to automatically check operation on different devices and platforms.

[0065] CI tools can improve user motivation by inferring user emotions based on test results and providing positive feedback. For example, CI tools analyze test results, infer user emotions, and provide positive feedback. For example, they display an encouraging message when a test is successful. CI tools also build systems that infer user emotions based on test results and provide positive feedback. For example, they award badges or points when a test is successful. CI tools also analyze test results in real time, infer user emotions, and provide positive feedback. For example, they introduce success stories when a test is successful. In this way, it is possible to infer user emotions based on test results and provide positive feedback, thereby improving user motivation.

[0066] CI tools can estimate user emotions after deployment and propose deployment strategies that elicit positive emotions. For example, CI tools can monitor user emotions after deployment in real time and propose deployment strategies that elicit positive emotions. For example, they can adjust the timing of deployment. CI tools can also propose deployment strategies that will elicit the most positive emotions from users based on emotion estimation data. For example, they can deploy when the user is relaxed. CI tools can also analyze user emotions after deployment and propose deployment strategies that will elicit positive emotions. For example, they can display a success message after deployment. This makes it possible to estimate user emotions after deployment and propose deployment strategies that will elicit positive emotions.

[0067] CI tools can check operation in a simulation environment before deployment and minimize risks. CI tools, for example, build a simulation environment before deployment and check operation. For example, they run tests in a simulation environment with the same settings as the production environment. CI tools also check operation in a simulation environment and minimize risks during deployment. For example, they determine whether deployment is possible based on the test results in the simulation environment. CI tools also check operation in a simulation environment in real time before deployment and minimize risks. For example, they run performance tests in a simulation environment. In this way, they can check operation in a simulation environment before deployment and minimize risks.

[0068] CI tools can collect post-deployment performance data in real time and reflect it in the next deployment. CI tools, for example, collect post-deployment performance data in real time and reflect it in the next deployment. For example, they monitor response times and error rates after deployment. CI tools also optimize the next deployment strategy based on the performance data. For example, they analyze post-deployment performance data and identify areas for improvement. CI tools also build systems that collect post-deployment performance data in real time and reflect it in the next deployment. For example, they automatically adjust settings based on post-deployment performance data. This makes it possible to collect post-deployment performance data in real time and reflect it in the next deployment.

[0069] CI tools can automatically adjust deployment strategies to suit different regions and time zones during the deployment process. For example, CI tools automatically adjust deployment strategies to suit different regions and time zones during the deployment process. For example, they may deploy during times that avoid peak hours in each region. CI tools also automatically adjust the timing of deployment to accommodate different time zones. For example, they may deploy during times when there are fewer users. CI tools also adjust deployment strategies to suit different regions and time zones in real time. For example, they may determine the timing of deployment based on user activity in each region. This makes it possible to automatically adjust deployment strategies to suit different regions and time zones.

[0070] During the deployment process, CI tools can analyze a user's handwritten sketches or diagrams and optimize the deployment procedures based on them. For example, CI tools use image recognition technology to analyze a user's handwritten sketches or diagrams and optimize the deployment procedures based on their content. For example, they analyze handwritten deployment flows and automate them. CI tools can also analyze diagrams of deployment procedures drawn by users and optimize the deployment procedures based on them. For example, they can analyze handwritten deployment procedures and suggest optimal procedures. CI tools can also analyze handwritten sketches or diagrams in real time and instantly optimize the deployment procedures. For example, they can capture deployment procedures drawn on a whiteboard with a camera and optimize the deployment procedures based on the captured content. This makes it possible to optimize the deployment procedures based on the user's handwritten sketches or diagrams.

[0071] CI tools can improve user satisfaction by estimating user emotions after deployment and providing positive feedback. For example, CI tools monitor user emotions after deployment in real time and provide positive feedback. For example, they display an encouraging message when deployment is successful. CI tools also provide feedback that evokes the most positive emotions in users based on emotion estimation data. For example, they award badges or points when deployment is successful. CI tools also build systems that analyze user emotions after deployment and provide positive feedback. For example, they introduce success stories when deployment is successful. This makes it possible to estimate user emotions after deployment and provide positive feedback, thereby improving user satisfaction.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The generation unit can estimate the user's emotions and suggest the optimal timing for code generation when the user is relaxed. For example, the generation AI can monitor the user's emotional state in real time and suggest code generation when the user is relaxed. It can also send notifications when the user is relaxed. The generation unit also identifies the timing when the user can work most efficiently based on the emotion estimation data and suggests code generation at that timing. For example, it can make suggestions when the user is concentrating. Furthermore, the generation AI can analyze the user's emotional state and suggest code generation tasks that are optimal when the user is relaxed. For example, it can suggest easy tasks when the user is relaxed. This makes it possible to suggest the optimal timing for code generation when the user is relaxed.

[0074] The generator can learn from the user's past code generation history and generate code that matches the user's preferences and style. For example, the generation AI can analyze the user's past code generation history and learn the user's preferences and style. It can also prioritize the use of specific coding styles and libraries. The generator also suggests optimal code based on the user's past project data. It can also take into account previously used frameworks and design patterns. Furthermore, the generator AI can improve the accuracy of code generation based on user feedback. It can also reflect the user's preferred code writing style and commenting style. This makes it possible to generate code that matches the user's preferences and style.

[0075] The generation unit can analyze the user's voice instructions and generate code directly from the voice. For example, the generation AI can analyze the user's voice instructions in real time and generate code based on those instructions. The user can also give a voice instruction such as "Create a login function." The generation unit can also use voice recognition technology to convert the user's voice instructions into text and generate code based on that text. The user can also give an instruction such as "Set up a database connection." Furthermore, the generation AI in the generation unit can understand the context of the voice instruction and generate appropriate code. When the user instructs "Add a user authentication function," the necessary libraries and settings can also be automatically added. This makes it possible to generate code based on voice instructions.

[0076] The generation unit can simultaneously generate code in different programming languages, achieving multi-platform compatibility. For example, the generation AI generates code in different programming languages ​​simultaneously based on user instructions. It can also generate code in Python and JavaScript simultaneously. The generation unit also generates code in different languages ​​to achieve multi-platform compatibility. It can also generate code that is compatible with both mobile apps and web apps. Furthermore, the generation AI in the generation unit takes into account the characteristics of different platforms to generate optimal code. It can also generate code that is compatible with both iOS and Android. This makes it possible to generate code that is compatible with multiple platforms.

[0077] The generation unit can analyze a user's handwritten sketches or diagrams and generate code based on them. For example, the generation AI can analyze a user's handwritten sketches using image recognition technology and generate code based on their content. It can also analyze UI design sketches to generate code. The generation unit can also analyze diagrams drawn by the user and generate code based on those diagrams. It can also analyze flowcharts to generate algorithm code. Furthermore, the generation unit can analyze handwritten sketches and diagrams in real time, and the generation AI can instantly generate code. It can also take a photo of a diagram drawn on a whiteboard with a camera and generate code based on its content. This makes it possible to generate code based on handwritten sketches and diagrams.

[0078] CI tools can infer user emotions based on test results and make improvement suggestions if a negative result is obtained. For example, they can analyze test results and infer user emotions if a negative result is obtained. They can also infer that the user is feeling stressed if a test fails. CI tools can also infer user emotions based on test results and make improvement suggestions if a negative result is obtained. They can also make suggestions for improving error messages and debugging methods. CI tools can also analyze test results in real time, infer user emotions, and make improvement suggestions. They can also make suggestions to help the user relax if a test fails. This makes it possible to infer user emotions based on test results and make improvement suggestions if a negative result is obtained.

[0079] CI tools can analyze and optimize code performance in real time during the build and test processes. For example, they can analyze and optimize code performance in real time during the build and test processes. They can also analyze code execution speed and memory usage. CI tools can also analyze code performance in real time and suggest optimizations. They can also identify areas where performance is degraded and suggest ways to improve them. Furthermore, CI tools can monitor and optimize code performance in real time during the build and test processes. They can also identify and improve performance bottlenecks. This makes it possible to analyze and optimize code performance in real time during the build and test processes.

[0080] CI tools can automatically create different test environments and run tests in multiple environments simultaneously. For example, they can automatically create different test environments and run tests in multiple environments simultaneously. Testing can also be done in Windows, Linux, and macOS environments simultaneously. CI tools can also run tests in multiple environments simultaneously in automatically created test environments. Testing can also be done in different versions of browsers simultaneously. CI tools can also create different test environments in real time and run tests in multiple environments simultaneously. Testing can also be done in cloud and on-premise environments simultaneously. This makes it possible to automatically create different test environments and run tests in multiple environments simultaneously.

[0081] CI tools can analyze users' handwritten notes and comments during the build and test process and generate test cases based on them. For example, they can use image recognition technology to analyze users' handwritten notes and comments and generate test cases based on their content. They can also analyze handwritten test scenarios and generate them automatically. CI tools can also analyze users' handwritten notes and comments and generate test cases based on them. They can also analyze handwritten bug reports and generate test cases. CI tools can also analyze handwritten notes and comments in real time and generate test cases instantly. They can also capture test scenarios written on a whiteboard with a camera and generate test cases based on the content. This makes it possible to generate test cases based on users' handwritten notes and comments.

[0082] CI tools can improve user satisfaction by estimating user emotions after deployment and providing positive feedback. For example, they can monitor user emotions after deployment in real time and provide positive feedback. They can also display an encouraging message when deployment is successful. CI tools can also provide feedback that evokes the most positive emotions from users based on emotion estimation data. They can also award badges and points when deployment is successful. CI tools can also build a system that analyzes user emotions after deployment and provides positive feedback. They can also introduce success stories when deployment is successful. This makes it possible to estimate user emotions after deployment and provide positive feedback, thereby improving user satisfaction.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The generator receives instructions from the user and generates code based on those instructions. For example, if the user instructs the generator to "create a login function for a website," the generator analyzes the instructions and generates the necessary code. The generator can also use a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate code based on the user's instructions. The generation AI receives a prompt containing the user's instructions and generates code that operates based on the prompt. Step 2: The build unit builds the code generated by the generation unit. For example, the build unit compiles the code and converts it into an executable format. The build unit can also automatically execute the build process using a CI tool. Step 3: The testing department tests the code built by the build department. For example, the testing department runs automated tests on the generated code to check the code quality and behavior. The testing department can also use CI tools to automatically execute the testing process. Step 4: The deployment department deploys the code that has been tested by the testing department. For example, the deployment department places the generated code in a production environment and makes it actually work. The deployment department can also use a CI tool to automatically execute the deployment process.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Generative AI and CI tools and The generated AI is a generator that receives instructions from a user and generates code based on the instructions; The CI tool is a build unit that builds the code generated by the generation unit; a test unit that tests the code built by the build unit; a deployment unit that deploys the code tested by the test unit. A system characterized by:

2. The generation unit The system estimates the user's emotions and proposes code generation that elicits positive emotions.

2. The system of claim 1.

3. The generation unit Learns the user's past code generation history and generates code that matches the user's preferences and style 2. The system of claim 1.

4. The generation unit Analyzing the user's voice instructions and generating code directly from the voice 2. The system of claim 1.

5. The generation unit Generate code in different programming languages ​​simultaneously to achieve multi-platform compatibility 2. The system of claim 1.

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