System

Abstract

An object of a system according to an embodiment is to speed up a process from initial creation to finishing of a program and to eliminate a shortage of manpower.SOLUTION: A system includes a generation unit, a test unit, and a finishing unit. The generation unit performs initial creation of a program using the generation AI. The test unit tests the program created by the generation unit. The finishing unit finishes the program 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 technology, the process from initial program creation to testing and finalization took time, and there was a problem of labor shortages.

[0005] The system according to the embodiment aims to speed up the process from initial program creation to completion, thereby resolving the labor shortage. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a test unit, and a finishing unit. The generation unit performs initial program creation using a generation AI. The test unit tests the program created by the generation unit. The finishing unit finishes the program tested by the test unit. [Effects of the Invention]

[0007] The system according to the embodiment can speed up the process from initial program creation to completion, thereby resolving manpower shortages. [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) In the programming system according to the embodiment of the present invention, a generation AI creates the initial program, and then a human tests and refines it. This allows the programming system to speed up programming and alleviate labor shortages.

[0029] A program creation system according to an embodiment includes a generation unit, a testing unit, and a finishing unit. The generation unit uses a generation AI to initially create a program. For example, the generation AI creates a program based on requirements and specifications input by a user. The generation AI can analyze a user's instruction, such as "I want you to create a login function for a website," and generate the necessary code. The generation AI can generate a program based on a prompt. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and generates a program based on the prompt. The testing unit tests the program created by the generation unit. For example, a programmer tests the code for the login function created by the generation AI to check for bugs and malfunctions. The testing unit can also perform unit testing and integration testing. The testing unit can also perform system testing. For example, the testing unit individually tests each function of the program created by the generation AI to check the overall operation. The finishing unit finishes the program tested by the testing unit. For example, a programmer improves the user interface and implements security measures. The finishing unit can also optimize the code. The finishing unit can also create documentation. For example, the finishing section optimizes the code of the program created by the generation AI and creates documentation. As a result, the program creation system according to the embodiment can speed up program creation and alleviate labor shortages. For example, by having the generation AI perform the initial creation of the program, the overall creation time is shortened. Furthermore, by having the generation AI take charge of the initial creation, even a small team can create a program efficiently.

[0030] The generator can learn the user's past programming style and preferences and customize code based on that. For example, the generator AI analyzes the user's past programming projects and learns their specific coding style and library usage trends. For example, it automatically applies specific naming conventions and code formats. The generator also learns patterns of code created by the user in the past and generates new programs based on that. For example, it prioritizes the use of specific algorithms and design patterns. The generator AI also customizes code comments and documentation to suit the user's preferences and style. For example, if a user prefers detailed comments, the generator AI automatically adds detailed comments. This makes it possible to generate code that suits the user's preferences.

[0031] The generation unit receives feedback from the user on the generated code in real time and can instantly modify the code based on that feedback. For example, when the generation AI generates code, the generation unit provides an interface that allows the user to provide feedback in real time. For example, if the user modifies part of the code, the modification is immediately reflected. In addition, when the user makes comments or suggests modifications to the generated code, the generation AI analyzes that feedback and automatically modifies the code. For example, it automatically fixes bugs pointed out by the user. In addition, the generation unit allows the generation AI to optimize and improve the code based on user feedback. For example, if a user points out a performance problem, the generation AI automatically optimizes it. This makes it possible to modify code in real time.

[0032] The generation unit can simultaneously generate code in different programming languages, allowing the user to select one. For example, the generation AI simultaneously generates code in multiple programming languages ​​based on the user's instructions. For example, the generation unit simultaneously generates code in Python, JavaScript, and Java, allowing the user to select one. The generation unit also provides optimal code depending on the programming language selected by the user. For example, if the user selects Python, the generation AI generates code that follows Python best practices. The generation unit also compares code generated by the generation AI in different programming languages, allowing the user to select the most suitable language. For example, the performance and readability of each language are evaluated. This makes it possible to generate code in multiple programming languages.

[0033] The generation unit can automatically incorporate security and performance best practices into the code it generates. For example, the generation unit automatically incorporates security best practices when the generation AI generates code. For example, it automatically adds input validation and error handling. The generation unit also generates code so that the generation AI takes performance best practices into consideration. For example, it uses efficient algorithms and data structures. The generation unit also generates code so that the generation AI takes both security and performance into consideration. For example, it uses secure communication protocols while achieving high-speed data processing. This makes it possible to automatically incorporate security and performance best practices.

[0034] In the testing section, the generative AI analyzes test results in real time and can suggest the cause of the bug and how to fix it. For example, when a programmer runs a test, the generative AI analyzes the test results in real time and identifies the cause of the bug. For example, it analyzes the error log to identify the problem area. In the testing section, the generative AI also suggests how to fix the bug based on the test results. For example, it provides specific steps for correcting a specific line of code. In the testing section, the generative AI also provides feedback in real time when the programmer performs a test, helping to fix the bug. For example, it highlights the areas that need to be fixed. This makes it possible to identify the cause of the bug and suggest how to fix it in real time.

[0035] The testing unit allows the generation AI to automatically generate test cases, helping programmers to perform testing efficiently. For example, the generation AI analyzes program specifications and code and automatically generates test cases. For example, it automatically generates unit tests for each function. The testing unit also provides the test cases automatically generated by the generation AI when programmers perform tests, helping them to perform tests efficiently. For example, it displays the results of test case execution in real time. The testing unit also allows the generation AI to automatically update test cases in response to changes in the program. For example, if a new function is added, it automatically generates test cases for that function. This enables efficient testing with automatically generated test cases.

[0036] The testing department can refer to similar bugs and fixes in other projects and suggest the optimal fix method. For example, the generation AI in the testing department analyzes similar bugs in other projects and refers to those fix methods to suggest them to the programmer. For example, it provides the optimal fix method based on the fix history in past projects. In addition, when a programmer performs testing, the generation AI in the testing department suggests fix methods for similar bugs in real time. For example, it provides fix procedures for specific bugs. In addition, the generation AI in the testing department learns fix methods in other projects and, based on that, suggests the optimal fix method to the programmer. For example, it provides fix patterns for the same type of bug. This makes it possible to utilize knowledge from past projects to fix bugs efficiently.

[0037] In the testing department, the generative AI evaluates the performance and security of the program and can suggest areas for improvement. In the testing department, for example, the generative AI evaluates the performance of the program based on the test results and suggests areas for improvement. For example, it identifies bottlenecks in processing speed and provides methods for optimization. In the testing department, the generative AI also evaluates the security of the program, identifies vulnerabilities, and suggests areas for improvement. For example, it detects the risk of SQL injection and provides countermeasures. In the testing department, the generative AI also evaluates the performance and security in real time when the programmer is testing and suggests areas for improvement. For example, it provides methods for optimizing memory usage. This makes it possible to evaluate and improve the performance and security of the program.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The generation unit's AI can learn a user's past programming style and preferences and customize code based on that. For example, the AI ​​can analyze a user's past programming projects and learn their specific coding style and library usage habits. For example, it can automatically apply specific naming conventions and code formats. The generation unit can also learn patterns in code created by the user in the past and generate new programs based on that. For example, it can prioritize the use of specific algorithms and design patterns. The generation unit can also customize code comments and documentation to suit the user's preferences and style. For example, if a user prefers detailed comments, the AI ​​can automatically add detailed comments. This makes it possible to generate code that suits the user's preferences.

[0040] The generation unit receives feedback from the user on the generated code in real time and can instantly modify the code based on that feedback. For example, when the generation AI generates code, it provides an interface that allows the user to provide feedback in real time. For example, if the user modifies part of the code, the modification is immediately reflected. In addition, when the user makes comments or suggestions for modifications to the generated code, the generation AI analyzes that feedback and automatically modifies the code. For example, it automatically fixes bugs pointed out by the user. In addition, the generation unit allows the generation AI to optimize and improve the code based on user feedback. For example, if a user points out a performance problem, the generation AI automatically optimizes it. This makes it possible to modify code in real time.

[0041] The generation unit can simultaneously generate code in different programming languages, allowing the user to select one. For example, the generation AI generates code in multiple programming languages ​​simultaneously based on the user's instructions. For example, it simultaneously generates code in Python, JavaScript, and Java, allowing the user to select one. The generation unit also provides optimal code depending on the programming language selected by the user. For example, if the user selects Python, it generates code that follows Python best practices. The generation unit also compares code generated by the generation AI in different programming languages, allowing the user to select the most suitable language. For example, it evaluates the performance and readability of each language. This makes it possible to generate code in multiple programming languages.

[0042] The generation unit can automatically incorporate security and performance best practices into the code it generates. For example, when the generation AI generates code, it automatically incorporates security best practices. For example, it automatically adds input validation and error handling. The generation unit also generates code so that the generation AI takes performance best practices into consideration. For example, it uses efficient algorithms and data structures. The generation unit also generates code so that the generation AI takes both security and performance into consideration. For example, it uses secure communication protocols while achieving high-speed data processing. This makes it possible to automatically incorporate security and performance best practices.

[0043] In the testing section, the generative AI analyzes test results in real time and can suggest the cause of the bug and how to fix it. For example, when a programmer runs a test, the generative AI analyzes the test results in real time and identifies the cause of the bug. For example, it analyzes the error log to identify the problem area. In the testing section, the generative AI also suggests how to fix the bug based on the test results. For example, it could provide specific steps for correcting a specific line of code. In the testing section, the generative AI also provides feedback in real time as the programmer tests, helping them fix the bug. For example, it could highlight areas that need to be fixed. This makes it possible to identify the cause of the bug and suggest how to fix it in real time.

[0044] The test unit allows the generation AI to automatically generate test cases, helping programmers to perform testing efficiently. For example, the generation AI analyzes program specifications and code and automatically generates test cases. For example, it automatically generates unit tests for each function. The test unit also provides the test cases automatically generated by the generation AI when programmers perform testing, helping them to perform testing efficiently. For example, it displays the results of test case execution in real time. The test unit also allows the generation AI to automatically update test cases in response to changes in the program. For example, if a new function is added, it automatically generates test cases for that function. This enables efficient testing with automatically generated test cases.

[0045] The testing department can refer to similar bugs and fixes in other projects and suggest the optimal fix method. For example, the generation AI analyzes similar bugs in other projects and refers to those fix methods to suggest them to the programmer. For example, it provides the optimal fix method based on the fix history of past projects. In addition, when a programmer performs testing, the generation AI in the testing department suggests fix methods for similar bugs in real time. For example, it provides fix procedures for specific bugs. In addition, the generation AI in the testing department learns fix methods in other projects and, based on that, suggests the optimal fix method to the programmer. For example, it provides fix patterns for the same type of bug. This makes it possible to utilize knowledge from past projects to fix bugs efficiently.

[0046] In the testing department, the generative AI evaluates the performance and security of the program and can suggest areas for improvement. For example, the generative AI evaluates the performance of the program based on the test results and suggests areas for improvement. For example, it identifies bottlenecks in processing speed and provides methods for optimization. In the testing department, the generative AI also evaluates the security of the program, identifies vulnerabilities, and suggests areas for improvement. For example, it detects the risk of SQL injection and provides countermeasures. In the testing department, the generative AI also evaluates the performance and security in real time when the programmer is performing tests and suggests areas for improvement. For example, it provides methods for optimizing memory usage. This makes it possible to evaluate and improve the performance and security of the program.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The generation unit uses the generation AI to initially create the program. For example, the generation AI creates a program based on requirements and specifications entered by the user. The generation AI can also analyze a user's instruction, such as "Please create a login function for a website," and generate the necessary code. Furthermore, the generation AI can generate a program based on a prompt. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and generates a program based on that prompt. Step 2: The testing department tests the program created by the generation department. For example, a programmer tests the code for the login function created by the generation AI to check for bugs and malfunctions. The testing department can also perform unit testing, integration testing, and system testing. For example, the testing department tests each function of the program created by the generation AI individually to check its overall operation. Step 3: The finishing department finalizes the program tested by the testing department. For example, a programmer may improve the user interface or implement security measures. The finishing department may also optimize the code and create documentation. For example, the finishing department may optimize the code of the program created by the generative AI and create documentation.

[0049] (Example 2) In the program creation system according to the embodiment of the present invention, a generation AI creates the initial program, and then a human tests and refines it. This allows the program creation system to speed up program creation and alleviate labor shortages.

[0050] A program creation system according to an embodiment includes a generation unit, a testing unit, and a finishing unit. The generation unit uses a generation AI to initially create a program. For example, the generation AI creates a program based on requirements and specifications input by a user. The generation AI can analyze a user's instruction, such as "I want you to create a login function for a website," and generate the necessary code. The generation AI can generate a program based on a prompt. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and generates a program based on the prompt. The testing unit tests the program created by the generation unit. For example, a programmer tests the code for the login function created by the generation AI to check for bugs and malfunctions. The testing unit can also perform unit testing and integration testing. The testing unit can also perform system testing. For example, the testing unit individually tests each function of the program created by the generation AI to check the overall operation. The finishing unit finishes the program tested by the testing unit. For example, a programmer improves the user interface and implements security measures. The finishing unit can also optimize the code. The finishing unit can also create documentation. For example, the finishing section optimizes the code of the program created by the generation AI and creates documentation. As a result, the program creation system according to the embodiment can speed up program creation and alleviate labor shortages. For example, by having the generation AI perform the initial creation of the program, the overall creation time is shortened. Furthermore, by having the generation AI take charge of the initial creation, even a small team can create a program efficiently.

[0051] The generator can learn the user's past programming style and preferences and customize code based on that. For example, the generator AI analyzes the user's past programming projects and learns their specific coding style and library usage trends. For example, it automatically applies specific naming conventions and code formats. The generator also learns patterns of code created by the user in the past and generates new programs based on that. For example, it prioritizes the use of specific algorithms and design patterns. The generator AI also customizes code comments and documentation to suit the user's preferences and style. For example, if a user prefers detailed comments, the generator AI automatically adds detailed comments. This makes it possible to generate code that suits the user's preferences.

[0052] The generation unit receives feedback from the user on the generated code in real time and can instantly modify the code based on that feedback. For example, when the generation AI generates code, the generation unit provides an interface that allows the user to provide feedback in real time. For example, if the user modifies part of the code, the modification is immediately reflected. In addition, when the user makes comments or suggests modifications to the generated code, the generation AI analyzes that feedback and automatically modifies the code. For example, it automatically fixes bugs pointed out by the user. In addition, the generation unit allows the generation AI to optimize and improve the code based on user feedback. For example, if a user points out a performance problem, the generation AI automatically optimizes it. This makes it possible to modify code in real time.

[0053] The generation unit uses the emotion estimation function to detect stress or dissatisfaction the user feels during the program generation process, allowing the generation AI to adjust the code generation method accordingly. For example, the generation unit analyzes the user's facial expressions and voice to detect stress or dissatisfaction. For example, if the user is dissatisfied, the generation AI changes the code generation method. The generation unit also uses the emotion estimation function to adjust the code generation speed when the user is feeling stressed. For example, if the user is impatient, the generation AI pauses code generation. The generation unit also optimizes the code generation method based on the user's emotional data. For example, if the user is relaxed, the generation AI generates more complex code. This makes it possible to generate code that corresponds to the user's emotions.

[0054] The generation unit can simultaneously generate code in different programming languages, allowing the user to select one. For example, the generation AI simultaneously generates code in multiple programming languages ​​based on the user's instructions. For example, the generation unit simultaneously generates code in Python, JavaScript, and Java, allowing the user to select one. The generation unit also provides optimal code depending on the programming language selected by the user. For example, if the user selects Python, the generation AI generates code that follows Python best practices. The generation unit also compares code generated by the generation AI in different programming languages, allowing the user to select the most suitable language. For example, the performance and readability of each language are evaluated. This makes it possible to generate code in multiple programming languages.

[0055] The generation unit can automatically incorporate security and performance best practices into the code it generates. For example, the generation unit automatically incorporates security best practices when the generation AI generates code. For example, it automatically adds input validation and error handling. The generation unit also generates code so that the generation AI takes performance best practices into consideration. For example, it uses efficient algorithms and data structures. The generation unit also generates code so that the generation AI takes both security and performance into consideration. For example, it uses secure communication protocols while achieving high-speed data processing. This makes it possible to automatically incorporate security and performance best practices.

[0056] The generation unit uses the emotion estimation function to understand the emotional nuances of the instructions the user gives to the generation AI and optimizes code generation based on that. For example, the generation unit allows the generation AI to analyze the emotional nuances of the user's voice or text and optimize code generation based on that. For example, if the user is in a hurry, the generation AI will generate code quickly. The generation unit also uses the emotion estimation function to understand the emotional nuances of the user's instructions and the generation AI will adjust how it generates code. For example, if the user is relaxed, the generation AI will add detailed comments. The generation unit also allows the generation AI to optimize code generation based on the user's emotional data. For example, if the user is feeling anxious, the generation AI will strengthen error handling. This allows code generation to be optimized based on the user's emotional nuances.

[0057] In the testing section, the generative AI analyzes test results in real time and can suggest the cause of the bug and how to fix it. For example, when a programmer runs a test, the generative AI analyzes the test results in real time and identifies the cause of the bug. For example, it analyzes the error log to identify the problem area. In the testing section, the generative AI also suggests how to fix the bug based on the test results. For example, it provides specific steps for correcting a specific line of code. In the testing section, the generative AI also provides feedback in real time when the programmer performs a test, helping to fix the bug. For example, it highlights the areas that need to be fixed. This makes it possible to identify the cause of the bug and suggest how to fix it in real time.

[0058] The testing unit allows the generation AI to automatically generate test cases, helping programmers to perform testing efficiently. For example, the generation AI analyzes program specifications and code and automatically generates test cases. For example, it automatically generates unit tests for each function. The testing unit also provides the test cases automatically generated by the generation AI when programmers perform tests, helping them to perform tests efficiently. For example, it displays the results of test case execution in real time. The testing unit also allows the generation AI to automatically update test cases in response to changes in the program. For example, if a new function is added, it automatically generates test cases for that function. This enables efficient testing with automatically generated test cases.

[0059] The testing department can use the emotion estimation function to detect frustration felt by programmers during testing, identify the cause, and propose solutions. For example, the generation AI in the testing department analyzes the programmer's facial expressions and voice to detect frustration felt during testing. For example, if a programmer is irritated, the cause is identified. The testing department also uses the emotion estimation function to identify the parts of the program where the programmer is feeling frustrated, and the generation AI proposes solutions. For example, it provides specific steps to fix a specific line of code. The testing department also uses the program's emotion data to optimize the testing process. For example, it prioritizes solving test cases that are likely to cause frustration. This reduces programmer frustration and enables more efficient testing.

[0060] The testing department can refer to similar bugs and fixes in other projects and suggest the optimal fix method. For example, the generation AI in the testing department analyzes similar bugs in other projects and refers to those fix methods to suggest them to the programmer. For example, it provides the optimal fix method based on the fix history in past projects. In addition, when a programmer performs testing, the generation AI in the testing department suggests fix methods for similar bugs in real time. For example, it provides fix procedures for specific bugs. In addition, the generation AI in the testing department learns fix methods in other projects and, based on that, suggests the optimal fix method to the programmer. For example, it provides fix patterns for the same type of bug. This makes it possible to utilize knowledge from past projects to fix bugs efficiently.

[0061] In the testing department, the generative AI evaluates the performance and security of the program and can suggest areas for improvement. In the testing department, for example, the generative AI evaluates the performance of the program based on the test results and suggests areas for improvement. For example, it identifies bottlenecks in processing speed and provides methods for optimization. In the testing department, the generative AI also evaluates the security of the program, identifies vulnerabilities, and suggests areas for improvement. For example, it detects the risk of SQL injection and provides countermeasures. In the testing department, the generative AI also evaluates the performance and security in real time when the programmer is testing and suggests areas for improvement. For example, it provides methods for optimizing memory usage. This makes it possible to evaluate and improve the performance and security of the program.

[0062] The testing unit uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by programmers during testing, thereby improving their motivation. For example, the generation AI in the testing unit analyzes the programmer's facial expressions and voice to detect positive emotions felt during testing. For example, if the programmer is feeling joyful, it provides feedback to reinforce that emotion. The testing unit also uses the emotion estimation function to identify areas where the programmer is feeling positive emotion, and the generation AI provides feedback to reinforce that emotion. For example, it displays words of praise for successful test cases. Furthermore, the testing unit uses the generation AI to optimize the testing process based on the programmer's emotion data and reinforce positive emotions. For example, it prioritizes tasks that give the programmer a sense of accomplishment. This improves the programmer's motivation.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The generation unit's AI can learn a user's past programming style and preferences and customize code based on that. For example, the AI ​​can analyze a user's past programming projects and learn their specific coding style and library usage habits. For example, it can automatically apply specific naming conventions and code formats. The generation unit can also learn patterns in code created by the user in the past and generate new programs based on that. For example, it can prioritize the use of specific algorithms and design patterns. The generation unit can also customize code comments and documentation to suit the user's preferences and style. For example, if a user prefers detailed comments, the AI ​​can automatically add detailed comments. This makes it possible to generate code that suits the user's preferences.

[0065] The generation unit receives feedback from the user on the generated code in real time and can instantly modify the code based on that feedback. For example, when the generation AI generates code, it provides an interface that allows the user to provide feedback in real time. For example, if the user modifies part of the code, the modification is immediately reflected. In addition, when the user makes comments or suggestions for modifications to the generated code, the generation AI analyzes that feedback and automatically modifies the code. For example, it automatically fixes bugs pointed out by the user. In addition, the generation unit allows the generation AI to optimize and improve the code based on user feedback. For example, if a user points out a performance problem, the generation AI automatically optimizes it. This makes it possible to modify code in real time.

[0066] The generation unit uses the emotion estimation function to detect stress or dissatisfaction the user feels during the program generation process, allowing the generation AI to adjust the code generation method accordingly. For example, the generation AI analyzes the user's facial expressions and voice to detect stress or dissatisfaction. For example, if the user is dissatisfied, the generation AI changes the code generation method. The generation unit also uses the emotion estimation function to allow the generation AI to adjust the code generation speed when the user is feeling stressed. For example, if the user is impatient, the generation AI pauses code generation. The generation unit also optimizes the code generation method based on the user's emotional data. For example, if the user is relaxed, the generation AI generates more complex code. This makes it possible to generate code that responds to the user's emotions.

[0067] The generation unit can simultaneously generate code in different programming languages, allowing the user to select one. For example, the generation AI generates code in multiple programming languages ​​simultaneously based on the user's instructions. For example, it simultaneously generates code in Python, JavaScript, and Java, allowing the user to select one. The generation unit also provides optimal code depending on the programming language selected by the user. For example, if the user selects Python, it generates code that follows Python best practices. The generation unit also compares code generated by the generation AI in different programming languages, allowing the user to select the most suitable language. For example, it evaluates the performance and readability of each language. This makes it possible to generate code in multiple programming languages.

[0068] The generation unit can automatically incorporate security and performance best practices into the code it generates. For example, when the generation AI generates code, it automatically incorporates security best practices. For example, it automatically adds input validation and error handling. The generation unit also generates code so that the generation AI takes performance best practices into consideration. For example, it uses efficient algorithms and data structures. The generation unit also generates code so that the generation AI takes both security and performance into consideration. For example, it uses secure communication protocols while achieving high-speed data processing. This makes it possible to automatically incorporate security and performance best practices.

[0069] The generation unit uses the emotion estimation function to understand the emotional nuances in the instructions the user gives to the generation AI and optimize code generation based on that. For example, the generation AI analyzes the emotional nuances in the user's voice or text and optimizes code generation based on that. For example, if the user is in a hurry, the generation AI generates code quickly. The generation unit also uses the emotion estimation function to understand the emotional nuances in the user's instructions and the generation AI adjusts how it generates code. For example, if the user is relaxed, the generation AI adds detailed comments. The generation unit also optimizes code generation based on the user's emotional data. For example, if the user is feeling anxious, the generation AI strengthens error handling. This allows code generation to be optimized based on the user's emotional nuances.

[0070] In the testing section, the generative AI analyzes test results in real time and can suggest the cause of the bug and how to fix it. For example, when a programmer runs a test, the generative AI analyzes the test results in real time and identifies the cause of the bug. For example, it analyzes the error log to identify the problem area. In the testing section, the generative AI also suggests how to fix the bug based on the test results. For example, it could provide specific steps for correcting a specific line of code. In the testing section, the generative AI also provides feedback in real time as the programmer tests, helping them fix the bug. For example, it could highlight areas that need to be fixed. This makes it possible to identify the cause of the bug and suggest how to fix it in real time.

[0071] The test unit allows the generation AI to automatically generate test cases, helping programmers to perform testing efficiently. For example, the generation AI analyzes program specifications and code and automatically generates test cases. For example, it automatically generates unit tests for each function. The test unit also provides the test cases automatically generated by the generation AI when programmers perform testing, helping them to perform testing efficiently. For example, it displays the results of test case execution in real time. The test unit also allows the generation AI to automatically update test cases in response to changes in the program. For example, if a new function is added, it automatically generates test cases for that function. This enables efficient testing with automatically generated test cases.

[0072] The testing department can use the emotion estimation function to detect frustration felt by programmers during testing, identify the cause, and propose solutions. For example, the generation AI analyzes the programmer's facial expressions and voice to detect frustration felt during testing. For example, if a programmer is irritated, the generation AI identifies the cause. The testing department can also use the emotion estimation function to identify the parts of the program where the programmer is feeling frustrated, and the generation AI proposes solutions. For example, it can provide specific steps to fix a specific line of code. The testing department can also use the generation AI to optimize the testing process based on the programmer's emotion data. For example, it can prioritize solving test cases that are likely to cause frustration. This reduces programmer frustration and enables more efficient testing.

[0073] The testing department can refer to similar bugs and fixes in other projects and suggest the optimal fix method. For example, the generation AI analyzes similar bugs in other projects and refers to those fix methods to suggest them to the programmer. For example, it provides the optimal fix method based on the fix history of past projects. In addition, when a programmer performs testing, the generation AI in the testing department suggests fix methods for similar bugs in real time. For example, it provides fix procedures for specific bugs. In addition, the generation AI in the testing department learns fix methods in other projects and, based on that, suggests the optimal fix method to the programmer. For example, it provides fix patterns for the same type of bug. This makes it possible to utilize knowledge from past projects to fix bugs efficiently.

[0074] In the testing department, the generative AI evaluates the performance and security of the program and can suggest areas for improvement. For example, the generative AI evaluates the performance of the program based on the test results and suggests areas for improvement. For example, it identifies bottlenecks in processing speed and provides methods for optimization. In the testing department, the generative AI also evaluates the security of the program, identifies vulnerabilities, and suggests areas for improvement. For example, it detects the risk of SQL injection and provides countermeasures. In the testing department, the generative AI also evaluates the performance and security in real time when the programmer is performing tests and suggests areas for improvement. For example, it provides methods for optimizing memory usage. This makes it possible to evaluate and improve the performance and security of the program.

[0075] The testing department can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by programmers during testing, thereby improving their motivation. For example, the generation AI analyzes the programmer's facial expressions and voice to detect positive emotions felt during testing. For example, if the programmer is feeling joyful, it provides feedback to reinforce that emotion. The testing department can also use the emotion estimation function to identify areas where the programmer is feeling positive emotion, and the generation AI provides feedback to reinforce that emotion. For example, it displays words of praise for successful test cases. Furthermore, the testing department can use the generation AI to optimize the testing process based on the programmer's emotion data, reinforcing positive emotions. For example, it prioritizes tasks that give the programmer a sense of accomplishment. This can improve programmer motivation.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The generation unit uses the generation AI to initially create the program. For example, the generation AI creates a program based on requirements and specifications entered by the user. The generation AI can also analyze a user's instruction, such as "Please create a login function for a website," and generate the necessary code. Furthermore, the generation AI can generate a program based on a prompt. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and generates a program based on that prompt. Step 2: The testing department tests the program created by the generation department. For example, a programmer tests the code for the login function created by the generation AI to check for bugs and malfunctions. The testing department can also perform unit testing, integration testing, and system testing. For example, the testing department tests each function of the program created by the generation AI individually to check its overall operation. Step 3: The finishing department finalizes the program tested by the testing department. For example, a programmer may improve the user interface or implement security measures. The finishing department may also optimize the code and create documentation. For example, the finishing department may optimize the code of the program created by the generative AI and create documentation.

[0078] 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.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit that uses generation AI to initially create a program; a test unit that tests the program created by the generation unit; a finishing unit that finishes the program tested by the testing unit. A system characterized by:

2. The generation unit Learns your past programming style and preferences and customizes your code accordingly 2. The system of claim 1.

3. The generation unit Receive real-time user feedback on the generated code and instantly modify the code based on the feedback.

2. The system of claim 1.

4. The generation unit Detects the stress and frustration felt by the user during the program generation process, and the AI ​​adjusts the code generation method accordingly.

2. The system of claim 1.

5. The generation unit Generate code in different programming languages ​​simultaneously, allowing the user to choose 2. The system of claim 1.

6. The generation unit Automatically incorporate security and performance best practices into the generated code 2. The system of claim 1.

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