system

Abstract

To provide a system that automatically generates program statements based on instructions in Japanese.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives instructions in Japanese from a user. The analysis unit analyzes the instructions in Japanese received by the reception unit. The generation unit generates program statements based on the instructions analyzed by the analysis unit. The display unit displays the program statements generated by the generation unit.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a large amount of manual programming input is required and it is difficult to generate an efficient program.

[0005] The system according to the embodiment aims to automatically generate program sentences based on Japanese instructions.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives a Japanese instruction from a user. The analysis unit analyzes the Japanese instruction received by the reception unit. The generation unit generates program sentences based on the instruction analyzed by the analysis unit. The display unit displays the program sentences generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate program statements based on instructions in Japanese. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The programming automation system according to an embodiment of the present invention is a system in which a user inputs in Japanese what kind of processing they want done, a generating AI analyzes the input, and outputs the desired program statement. The programming automation system can reduce manual input and improve programming efficiency. For example, a user inputs the processing content in Japanese. For example, they input "Create a program that sorts the elements of a list." This input is sent to the generating AI. Next, the generating AI analyzes the input Japanese instruction. The generating AI uses natural language processing technology to understand the user's instruction and generates an appropriate program statement. For example, in response to the instruction "Create a program that sorts the elements of a list," it generates a program statement using Python's sorting algorithm. The generated program statement is displayed in the programming editor. The user can check the generated program statement and make modifications as needed. This reduces manual input and improves programming efficiency. This mechanism makes it easy for even programming beginners to create programs. Furthermore, even experienced programmers can create programs more efficiently by saving the effort of manual input. For example, if a user inputs "Create a program that reads a file and displays its contents," the generating AI analyzes the instruction and generates a Python program statement related to file operations. The generated program statement includes the process of opening a file and displaying its contents. In this way, by using generation AI, program statements can be automatically generated based on the user's Japanese instructions, improving programming efficiency. Thus, the automated programming system can automatically generate program statements based on the user's Japanese instructions, improving programming efficiency.

[0029] The programming automation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives instructions from the user in Japanese. The user's Japanese instructions include, for example, natural language sentences or specific command formats, but are not limited to such examples. The reception unit receives instructions when, for example, the user inputs "Create a program that sorts the elements of a list." The analysis unit analyzes the Japanese instructions received by the reception unit using natural language processing techniques. The analysis unit analyzes the user's instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks down the user's instructions into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. The generation unit generates program statements based on the instructions analyzed by the analysis unit using a generation AI. The generation unit generates program statements using, for example, a text generation AI (e.g., LLM). The generation unit can also generate program statements using a Python sorting algorithm using a generation AI. For example, the generation unit generates program statements using algorithms such as quicksort or mergesort based on user instructions. The display unit displays the program statements generated by the generation unit in a programming editor. The display unit displays the generated program statements in a programming editor so that the user can verify them. As a result, the programming automation system according to the embodiment can automatically generate program statements based on the user's Japanese instructions and improve programming efficiency. For example, the generation unit can generate program statements using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements. As a result, the accuracy of program statement generation is improved by using a generation AI.Furthermore, the display unit makes it easier for the user to verify the generated program statements by displaying them in the programming editor. For example, the display unit displays the generated program statements in the programming editor so that the user can verify them. This makes it easier for the user to verify the generated program statements by displaying them in the programming editor.

[0030] The reception unit receives user instructions in Japanese. These instructions may include, but are not limited to, natural language sentences or specific command formats. For example, the reception unit might receive instructions if the user types "Create a program that sorts the elements of a list." Specifically, the reception unit receives the user's Japanese instructions in text format and sends the content to the analysis unit. The reception unit can provide various interfaces for user input, such as web browser forms, dedicated desktop applications, and even interfaces supporting voice input. In the case of voice input, speech recognition technology is used to convert the speech to text, which is then sent to the analysis unit. Furthermore, the reception unit can also store a history of user-entered instructions and manage them in a reusable format. This allows users to easily recall and reuse previously entered instructions, enabling efficient operation. The reception unit can also provide real-time feedback on user-entered instructions. For example, if there are errors or unclear parts in the input, it can immediately notify the user and prompt correction. This allows the reception desk to receive user instructions accurately and efficiently, improving the overall usability of the system.

[0031] The analysis unit uses natural language processing technology to analyze Japanese instructions received by the reception unit. The analysis unit analyzes user instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, the analysis unit first performs morphological analysis to break down the user's instructions into individual words. Next, it performs grammatical analysis to analyze the sentence structure. For example, it identifies grammatical elements such as subject, predicate, and object, and clarifies their relationships. Furthermore, it performs semantic analysis to understand the meaning of the instructions. In semantic analysis, the meaning of words and context are considered to accurately grasp the user's intent. For example, in response to the instruction "Create a program that sorts the elements of a list," the analysis unit extracts the keywords "list," "elements," "sort," "program," and "create," and analyzes their relationships. Based on these analysis results, the analysis unit converts the user's instructions into information for generating a specific program. Furthermore, the analysis unit can learn from past analysis results and user feedback to continuously improve its analysis accuracy. For example, if a user modifies the program statement generated in response to a specific instruction, the system learns the modification and incorporates it into subsequent analyses. This allows the analysis unit to accurately and efficiently analyze the user's instructions and provide appropriate information to the generation unit.

[0032] The generation unit uses a generation AI to generate program statements based on instructions analyzed by the analysis unit. For example, the generation unit can use a text generation AI (e.g., LLM) to generate program statements. The generation unit can also use the generation AI to generate program statements using Python sorting algorithms. Specifically, the generation unit generates appropriate program statements based on information provided by the analysis unit. For example, in response to the instruction "Create a program to sort the elements of a list," the generation unit will generate Python program statements using algorithms such as quicksort or mergesort. The generation AI has learned a large amount of program code in advance and can generate the optimal code based on user instructions. The generation unit can also perform internal tests to verify the accuracy of the generated program statements. For example, it can execute the generated program statements to check whether the expected results are obtained. Furthermore, the generation unit can adjust the style and format of the generated program statements, taking into account user preferences and past feedback. This allows the generation unit to quickly generate high-quality program statements that meet user needs.

[0033] The display unit displays the program statements generated by the generation unit in the programming editor. For example, the display unit displays the generated program statements in the programming editor for user review. Specifically, the display unit displays the generated program statements in the programming editor in an appropriate format, making them easily readable by the user. The programming editor includes syntax highlighting and auto-completion features, enabling users to efficiently review and modify the generated program statements. Furthermore, the display unit can also collect user feedback on the generated program statements and provide this feedback to the generation and analysis units. For example, if a user makes modifications to the generated program statements, the display unit records these modifications and incorporates them into subsequent generation. This allows the display unit to provide generated program statements in a user-friendly format and improve the overall system accuracy and efficiency based on user feedback. Additionally, the display unit can integrate the generated program statements with other development tools and version control systems. For example, by providing a function to directly commit the generated program statements to a Git repository, users can immediately integrate the generated code into their projects. This allows the display unit to provide diverse functions for efficiently managing and utilizing the generated program statements, significantly streamlining the user's development process.

[0034] The generation unit can generate program statements using a generation AI. For example, the generation unit generates program statements using a text generation AI (e.g., LLM). The generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. For example, the generation unit generates program statements using Python sorting algorithms based on user instructions. The generation unit can generate program statements that sort the elements of a list using algorithms such as quicksort or mergesort. This improves the accuracy of program statement generation by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements.

[0035] The analysis unit can analyze user instructions using natural language processing techniques. For example, the analysis unit analyzes user instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technique that breaks down a sentence into individual words and analyzes the part of speech and meaning of each word. Grammatical analysis is a technique that analyzes the structure of a sentence and clarifies the relationships between its constituent elements. Semantic analysis is a technique that understands the meaning of a sentence and provides an appropriate interpretation based on the context. For example, the analysis unit breaks down user instructions into individual words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. This improves the accuracy of the analysis of user instructions by using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can analyze instructions using an AI model that takes user instructions as input and outputs analysis results.

[0036] The display unit can display the generated program statements in a programming editor. For example, the display unit can display the generated program statements in a programming editor so that the user can verify them. Programming editors include, but are not limited to, Visual Studio Code and PyCharm. The display unit makes it easier for the user to verify the generated program statements by displaying them in a programming editor. For example, the display unit can display the generated program statements in a programming editor so that the user can verify them. This makes it easier for the user to verify the generated program statements by displaying them in a programming editor. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can display the program statements using an AI model that takes the generated program statements as input and displays them in a programming editor.

[0037] The generation unit can generate program statements using Python sorting algorithms. For example, the generation unit generates program statements that sort the elements of a list using algorithms such as quicksort or mergesort. Quicksort is a sorting algorithm based on divide and conquer, and can sort quickly. Mergesort is an algorithm that sorts by recursively dividing a list and merging the divided lists. For example, the generation unit generates program statements using algorithms such as quicksort or mergesort based on user instructions. This allows for the efficient generation of sorting program statements using Python sorting algorithms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements using sorting algorithms.

[0038] The generation unit can generate Python program statements related to file operations. For example, the generation unit generates program statements that perform operations such as reading, writing, and deleting files. File reading and writing includes operations such as opening a file and reading its contents, and writing data to a file. File deletion includes operations such as deleting a specified file. For example, the generation unit generates program statements that perform operations such as reading, writing, and deleting files based on user instructions. This makes it possible to automate file operations by generating Python program statements related to file operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements related to file operations.

[0039] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk may prioritize receiving instructions using methods the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time period based on the user's past instruction history. Furthermore, the reception desk can analyze the user's past instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may use an AI model that takes the user's past instruction history as input to select the optimal reception method.

[0040] The reception unit can filter instructions received in Japanese based on the user's current projects and areas of interest. For example, the reception unit will prioritize receiving instructions related to the project the user is currently working on. The reception unit can also filter relevant instructions based on the user's areas of interest. Furthermore, the reception unit can refer to the user's project history to receive the most relevant instructions. This allows the reception unit to prioritize receiving highly relevant instructions by filtering them based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's project data as input and filters out relevant instructions.

[0041] The reception unit can prioritize receiving instructions that are highly relevant based on the user's geographical location when receiving instructions in Japanese. For example, if the user is in a specific region, the reception unit will prioritize receiving instructions related to that region. The reception unit can also suggest the most appropriate instructions based on the user's current location. Furthermore, the reception unit can refer to the user's location information and filter the instructions to be highly relevant. This allows the reception unit to receive more appropriate instructions by prioritizing instructions that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's location information as input and filters out relevant instructions.

[0042] The reception unit can analyze the user's social media activity when receiving instructions in Japanese and receive relevant instructions. For example, the reception unit can prioritize receiving relevant instructions based on the content of the user's social media activity. The reception unit can also analyze the content of the user's social media posts and suggest the most appropriate instructions. Furthermore, the reception unit can refer to the activity of the user's social media followers and friends and filter relevant instructions. This allows the reception unit to prioritize receiving relevant instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's social media data as input and filters out relevant instructions.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions during the analysis. For example, the analysis unit performs a detailed analysis for instructions with high importance. It can also perform a concise analysis for instructions with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the instructions. This allows for efficient analysis by adjusting the level of detail according to the importance of the instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes instruction importance data as input.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, the analysis unit can apply a data analysis algorithm to instructions related to data processing. It can also apply an image analysis algorithm to instructions related to image processing. Furthermore, it can apply a natural language processing algorithm to instructions related to text processing. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category data of the instruction as input and applies an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analyses based on the timing of instruction submissions during the analysis process. For example, the analysis unit will prioritize analyses for urgent instructions. It can also prioritize analyses for instructions submitted early. Furthermore, the analysis unit can adjust the priority of analyses based on the submission timing. This allows for a quick response to urgent instructions by determining the priority of analyses based on the submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes instruction submission timing data as input to determine the priority of analyses.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the instructions during the analysis. For example, the analysis unit will prioritize the analysis of instructions that are highly relevant. It can also postpone the analysis of instructions that are less relevant. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the instructions. This allows for the prioritization of analysis of instructions that are highly relevant. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes instruction relevance data as input and adjusts the order of analysis.

[0047] The generation unit can adjust the level of detail of the generated code based on the importance of the program statement during generation. For example, the generation unit generates detailed code for program statements with high importance. It can also generate concise code for program statements with low importance. Furthermore, the generation unit can adjust the level of detail of the generated code according to the importance of the program statement. This allows for efficient generation of program statements by adjusting the level of detail of the generated code according to the importance of the program statement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take program statement importance data as input and adjust the level of detail of the generated code using an AI model.

[0048] The generation unit can apply different generation algorithms depending on the category of the program statement during generation. For example, the generation unit can apply a data analysis algorithm to program statements related to data processing. It can also apply an image analysis algorithm to program statements related to image processing. Furthermore, it can apply a natural language processing algorithm to program statements related to text processing. By applying an appropriate generation algorithm according to the category of the program statement, the generation accuracy is improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes program statement category data as input and applies an appropriate generation algorithm.

[0049] The generation unit can determine the generation priority based on the submission timing of program statements during generation. For example, the generation unit will prioritize the generation of urgent program statements. It can also prioritize the generation of program statements that have been submitted earlier. Furthermore, the generation unit can adjust the generation priority based on the submission timing. This allows for a quick response to urgent program statements by determining the generation priority based on the submission timing of the program statements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take program statement submission timing data as input and determine the generation priority using an AI model that determines the generation priority.

[0050] The generation unit can adjust the generation order based on the relationships between program statements during generation. For example, the generation unit prioritizes the generation of highly relevant program statements. It can also postpone the generation of less relevant program statements. Furthermore, the generation unit can adjust the generation order based on the relationships between program statements. This allows for the priority generation of highly relevant program statements by adjusting the generation order based on the relationships between program statements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the generation order using an AI model that takes program statement relationship data as input and adjusts the generation order.

[0051] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's operation history and select the most efficient display method. This allows the display unit to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may select a display method using an AI model that takes user operation history data as input and selects the optimal display method.

[0052] The display unit can customize the displayed content based on the user's current projects and areas of interest. For example, the display unit prioritizes displaying information related to the project the user is currently working on. The display unit can also display relevant information based on the user's areas of interest. Furthermore, the display unit can refer to the user's project history to provide optimal display content. This allows for the provision of highly relevant information by customizing the displayed content based on the user's projects and areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes the user's project data as input and customizes the displayed content.

[0053] The display unit can select the optimal display method based on the user's device information when displaying information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the display unit can provide a display method that includes detailed information. This allows for a more appropriate display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the user's device information as input and selects the optimal display method.

[0054] The display unit can provide a multilingual display according to the user's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, the display unit can provide the display in a specific language if the user selects that language. This enables a more appropriate display by providing a multilingual display according to the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes the user's language setting data as input and provides a multilingual display to provide the display.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception unit can also receive voice commands from users. For example, if a user gives a voice command such as, "Create a program that sorts the elements of a list," the reception unit converts the voice into text and sends it to the analysis unit. This further reduces the effort required for manual input and improves programming efficiency by using voice input. A speech recognition engine can also be used to improve the accuracy of voice command recognition. Furthermore, it is possible to save a history of voice commands and reuse past voice commands.

[0057] The generation unit can customize program statements based on the user's programming style. For example, it can learn the user's preferred coding style (number of spaces for indentation, variable naming conventions, etc.) and generate program statements based on that. This reduces the effort the user has to make corrections later and allows for more efficient program creation. The generation unit can also analyze the user's past program statements and suggest the optimal coding style. Furthermore, the generation unit can receive user feedback and improve its generation algorithm.

[0058] The display unit can visually highlight the generated program statements. For example, it can color-code important parts of the generated program statements. It can also highlight errors and warnings if they are present. Furthermore, it can display tooltips for each part of the generated program statements, providing detailed explanations. This makes the generated program statements easier for the user to understand, facilitating corrections and improvements. The display unit can also customize the highlighting style according to the user's preferences.

[0059] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, it can prioritize the instruction method the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time period based on the user's past instruction history. Furthermore, the reception desk can analyze the user's past instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can use an AI model that takes the user's past instruction history as input to select the optimal reception method.

[0060] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, a data analysis algorithm can be applied to instructions related to data processing. The analysis unit can also apply an image analysis algorithm to instructions related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to instructions related to text processing. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the instruction category data as input and applies an appropriate analysis algorithm.

[0061] The generation unit can apply different generation algorithms depending on the category of the program statement during generation. For example, a data analysis algorithm can be applied to program statements related to data processing. The generation unit can also apply an image analysis algorithm to program statements related to image processing. Furthermore, the generation unit can apply a natural language processing algorithm to program statements related to text processing. By applying an appropriate generation algorithm according to the category of the program statement, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes program statement category data as input and applies an appropriate generation algorithm.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives instructions from the user in Japanese. These instructions can include natural language sentences or specific command formats. For example, the desk will accept instructions such as "Create a program that sorts the elements of a list." Step 2: The analysis unit analyzes the Japanese instructions received by the reception unit using natural language processing technology. The analysis unit analyzes the user's instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, it breaks down the user's instructions into individual words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. Step 3: The generation unit uses a generation AI to generate program statements based on the instructions analyzed by the analysis unit. The generation unit generates program statements using a text generation AI (e.g., LLM). For example, it generates program statements using algorithms such as quicksort or mergesort based on user instructions. Step 4: The display unit displays the program statements generated by the generation unit in the programming editor. This allows the user to verify the generated program statements.

[0064] (Example of form 2) The programming automation system according to an embodiment of the present invention is a system in which a user inputs in Japanese what kind of processing they want done, a generating AI analyzes the input, and outputs the desired program statement. The programming automation system can reduce manual input and improve programming efficiency. For example, a user inputs the processing content in Japanese. For example, they input "Create a program that sorts the elements of a list." This input is sent to the generating AI. Next, the generating AI analyzes the input Japanese instruction. The generating AI uses natural language processing technology to understand the user's instruction and generates an appropriate program statement. For example, in response to the instruction "Create a program that sorts the elements of a list," it generates a program statement using Python's sorting algorithm. The generated program statement is displayed in the programming editor. The user can check the generated program statement and make modifications as needed. This reduces manual input and improves programming efficiency. This mechanism makes it easy for even programming beginners to create programs. Furthermore, even experienced programmers can create programs more efficiently by saving the effort of manual input. For example, if a user inputs "Create a program that reads a file and displays its contents," the generating AI analyzes the instruction and generates a Python program statement related to file operations. The generated program statement includes the process of opening a file and displaying its contents. In this way, by using generation AI, program statements can be automatically generated based on the user's Japanese instructions, improving programming efficiency. Thus, the automated programming system can automatically generate program statements based on the user's Japanese instructions, improving programming efficiency.

[0065] The programming automation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives instructions from the user in Japanese. The user's Japanese instructions include, for example, natural language sentences or specific command formats, but are not limited to such examples. The reception unit receives instructions when, for example, the user inputs "Create a program that sorts the elements of a list." The analysis unit analyzes the Japanese instructions received by the reception unit using natural language processing techniques. The analysis unit analyzes the user's instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks down the user's instructions into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. The generation unit generates program statements based on the instructions analyzed by the analysis unit using a generation AI. The generation unit generates program statements using, for example, a text generation AI (e.g., LLM). The generation unit can also generate program statements using a Python sorting algorithm using a generation AI. For example, the generation unit generates program statements using algorithms such as quicksort or mergesort based on user instructions. The display unit displays the program statements generated by the generation unit in a programming editor. The display unit displays the generated program statements in a programming editor so that the user can verify them. As a result, the programming automation system according to the embodiment can automatically generate program statements based on the user's Japanese instructions and improve programming efficiency. For example, the generation unit can generate program statements using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements. As a result, the accuracy of program statement generation is improved by using a generation AI.Furthermore, the display unit makes it easier for the user to verify the generated program statements by displaying them in the programming editor. For example, the display unit displays the generated program statements in the programming editor so that the user can verify them. This makes it easier for the user to verify the generated program statements by displaying them in the programming editor.

[0066] The reception unit receives user instructions in Japanese. These instructions may include, but are not limited to, natural language sentences or specific command formats. For example, the reception unit might receive instructions if the user types "Create a program that sorts the elements of a list." Specifically, the reception unit receives the user's Japanese instructions in text format and sends the content to the analysis unit. The reception unit can provide various interfaces for user input, such as web browser forms, dedicated desktop applications, and even interfaces supporting voice input. In the case of voice input, speech recognition technology is used to convert the speech to text, which is then sent to the analysis unit. Furthermore, the reception unit can also store a history of user-entered instructions and manage them in a reusable format. This allows users to easily recall and reuse previously entered instructions, enabling efficient operation. The reception unit can also provide real-time feedback on user-entered instructions. For example, if there are errors or unclear parts in the input, it can immediately notify the user and prompt correction. This allows the reception desk to receive user instructions accurately and efficiently, improving the overall usability of the system.

[0067] The analysis unit uses natural language processing technology to analyze Japanese instructions received by the reception unit. The analysis unit analyzes user instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, the analysis unit first performs morphological analysis to break down the user's instructions into individual words. Next, it performs grammatical analysis to analyze the sentence structure. For example, it identifies grammatical elements such as subject, predicate, and object, and clarifies their relationships. Furthermore, it performs semantic analysis to understand the meaning of the instructions. In semantic analysis, the meaning of words and context are considered to accurately grasp the user's intent. For example, in response to the instruction "Create a program that sorts the elements of a list," the analysis unit extracts the keywords "list," "elements," "sort," "program," and "create," and analyzes their relationships. Based on these analysis results, the analysis unit converts the user's instructions into information for generating a specific program. Furthermore, the analysis unit can learn from past analysis results and user feedback to continuously improve its analysis accuracy. For example, if a user modifies the program statement generated in response to a specific instruction, the system learns the modification and incorporates it into subsequent analyses. This allows the analysis unit to accurately and efficiently analyze the user's instructions and provide appropriate information to the generation unit.

[0068] The generation unit uses a generation AI to generate program statements based on instructions analyzed by the analysis unit. For example, the generation unit can use a text generation AI (e.g., LLM) to generate program statements. The generation unit can also use the generation AI to generate program statements using Python sorting algorithms. Specifically, the generation unit generates appropriate program statements based on information provided by the analysis unit. For example, in response to the instruction "Create a program to sort the elements of a list," the generation unit will generate Python program statements using algorithms such as quicksort or mergesort. The generation AI has learned a large amount of program code in advance and can generate the optimal code based on user instructions. The generation unit can also perform internal tests to verify the accuracy of the generated program statements. For example, it can execute the generated program statements to check whether the expected results are obtained. Furthermore, the generation unit can adjust the style and format of the generated program statements, taking into account user preferences and past feedback. This allows the generation unit to quickly generate high-quality program statements that meet user needs.

[0069] The display unit displays the program statements generated by the generation unit in the programming editor. For example, the display unit displays the generated program statements in the programming editor for user review. Specifically, the display unit displays the generated program statements in the programming editor in an appropriate format, making them easily readable by the user. The programming editor includes syntax highlighting and auto-completion features, enabling users to efficiently review and modify the generated program statements. Furthermore, the display unit can also collect user feedback on the generated program statements and provide this feedback to the generation and analysis units. For example, if a user makes modifications to the generated program statements, the display unit records these modifications and incorporates them into subsequent generation. This allows the display unit to provide generated program statements in a user-friendly format and improve the overall system accuracy and efficiency based on user feedback. Additionally, the display unit can integrate the generated program statements with other development tools and version control systems. For example, by providing a function to directly commit the generated program statements to a Git repository, users can immediately integrate the generated code into their projects. This allows the display unit to provide diverse functions for efficiently managing and utilizing the generated program statements, significantly streamlining the user's development process.

[0070] The generation unit can generate program statements using a generation AI. For example, the generation unit generates program statements using a text generation AI (e.g., LLM). The generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. For example, the generation unit generates program statements using Python sorting algorithms based on user instructions. The generation unit can generate program statements that sort the elements of a list using algorithms such as quicksort or mergesort. This improves the accuracy of program statement generation by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements.

[0071] The analysis unit can analyze user instructions using natural language processing techniques. For example, the analysis unit analyzes user instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technique that breaks down a sentence into individual words and analyzes the part of speech and meaning of each word. Grammatical analysis is a technique that analyzes the structure of a sentence and clarifies the relationships between its constituent elements. Semantic analysis is a technique that understands the meaning of a sentence and provides an appropriate interpretation based on the context. For example, the analysis unit breaks down user instructions into individual words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. This improves the accuracy of the analysis of user instructions by using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can analyze instructions using an AI model that takes user instructions as input and outputs analysis results.

[0072] The display unit can display the generated program statements in a programming editor. For example, the display unit can display the generated program statements in a programming editor so that the user can verify them. Programming editors include, but are not limited to, Visual Studio Code and PyCharm. The display unit makes it easier for the user to verify the generated program statements by displaying them in a programming editor. For example, the display unit can display the generated program statements in a programming editor so that the user can verify them. This makes it easier for the user to verify the generated program statements by displaying them in a programming editor. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can display the program statements using an AI model that takes the generated program statements as input and displays them in a programming editor.

[0073] The generation unit can generate program statements using Python sorting algorithms. For example, the generation unit generates program statements that sort the elements of a list using algorithms such as quicksort or mergesort. Quicksort is a sorting algorithm based on divide and conquer, and can sort quickly. Mergesort is an algorithm that sorts by recursively dividing a list and merging the divided lists. For example, the generation unit generates program statements using algorithms such as quicksort or mergesort based on user instructions. This allows for the efficient generation of sorting program statements using Python sorting algorithms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements using sorting algorithms.

[0074] The generation unit can generate Python program statements related to file operations. For example, the generation unit generates program statements that perform operations such as reading, writing, and deleting files. File reading and writing includes operations such as opening a file and reading its contents, and writing data to a file. File deletion includes operations such as deleting a specified file. For example, the generation unit generates program statements that perform operations such as reading, writing, and deleting files based on user instructions. This makes it possible to automate file operations by generating Python program statements related to file operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes user instructions as input and outputs program statements related to file operations.

[0075] The reception unit can estimate the user's emotions and adjust the timing of receiving instructions in Japanese based on the emotion data. For example, if the user is stressed, the reception unit can delay the reception timing to help them relax. Conversely, if the user is relaxed, the reception unit can also accept instructions immediately. Furthermore, if the user is in a hurry, the reception unit can speed up the reception timing for a quicker response. By adjusting the reception timing according to the user's emotions, instructions can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can take the user's emotion data as input and adjust the reception timing using an AI model that adjusts the reception timing.

[0076] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk may prioritize receiving instructions using methods the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time period based on the user's past instruction history. Furthermore, the reception desk can analyze the user's past instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may use an AI model that takes the user's past instruction history as input to select the optimal reception method.

[0077] The reception unit can filter instructions received in Japanese based on the user's current projects and areas of interest. For example, the reception unit will prioritize receiving instructions related to the project the user is currently working on. The reception unit can also filter relevant instructions based on the user's areas of interest. Furthermore, the reception unit can refer to the user's project history to receive the most relevant instructions. This allows the reception unit to prioritize receiving highly relevant instructions by filtering them based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's project data as input and filters out relevant instructions.

[0078] The reception desk can estimate the user's emotions and determine the priority of instructions to be received based on the emotion data. For example, if the user is stressed, the reception desk may postpone less important instructions. Conversely, if the user is relaxed, the reception desk may prioritize highly important instructions. Furthermore, if the user is in a hurry, the reception desk may prioritize urgent instructions. In this way, by prioritizing instructions according to the user's emotions, more appropriate instructions can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may take the user's emotion data as input and determine the priority of instructions using an AI model that determines the priority of instructions.

[0079] The reception unit can prioritize receiving instructions that are highly relevant based on the user's geographical location when receiving instructions in Japanese. For example, if the user is in a specific region, the reception unit will prioritize receiving instructions related to that region. The reception unit can also suggest the most appropriate instructions based on the user's current location. Furthermore, the reception unit can refer to the user's location information and filter the instructions to be highly relevant. This allows the reception unit to receive more appropriate instructions by prioritizing instructions that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's location information as input and filters out relevant instructions.

[0080] The reception unit can analyze the user's social media activity when receiving instructions in Japanese and receive relevant instructions. For example, the reception unit can prioritize receiving relevant instructions based on the content of the user's social media activity. The reception unit can also analyze the content of the user's social media posts and suggest the most appropriate instructions. Furthermore, the reception unit can refer to the activity of the user's social media followers and friends and filter relevant instructions. This allows the reception unit to prioritize receiving relevant instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can filter instructions using an AI model that takes the user's social media data as input and filters out relevant instructions.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the emotion data. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the user's emotion data as input and adjust the presentation of the analysis using an AI model that adjusts the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions during the analysis. For example, the analysis unit performs a detailed analysis for instructions with high importance. It can also perform a concise analysis for instructions with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the instructions. This allows for efficient analysis by adjusting the level of detail according to the importance of the instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes instruction importance data as input.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, the analysis unit can apply a data analysis algorithm to instructions related to data processing. It can also apply an image analysis algorithm to instructions related to image processing. Furthermore, it can apply a natural language processing algorithm to instructions related to text processing. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category data of the instruction as input and applies an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotion data. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the user's emotion data as input and adjust the length of the analysis using an AI model that adjusts the length of the analysis.

[0085] The analysis unit can determine the priority of analyses based on the timing of instruction submissions during the analysis process. For example, the analysis unit will prioritize analyses for urgent instructions. It can also prioritize analyses for instructions submitted early. Furthermore, the analysis unit can adjust the priority of analyses based on the submission timing. This allows for a quick response to urgent instructions by determining the priority of analyses based on the submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes instruction submission timing data as input to determine the priority of analyses.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the instructions during the analysis. For example, the analysis unit will prioritize the analysis of instructions that are highly relevant. It can also postpone the analysis of instructions that are less relevant. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the instructions. This allows for the prioritization of analysis of instructions that are highly relevant. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes instruction relevance data as input and adjusts the order of analysis.

[0087] The generation unit can estimate the user's emotions and adjust the way the generated program statements are expressed based on the emotion data. For example, if the user is relaxed, the generation unit can generate program statements with detailed comments. It can also generate concise program statements if the user is in a hurry. Furthermore, if the user is stressed, the generation unit can generate visually easy-to-understand program statements. This allows for the generation of more appropriate program statements by adjusting the expression of the program statements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can take user emotion data as input and adjust the expression of the program statements using an AI model that adjusts the expression of the program statements.

[0088] The generation unit can adjust the level of detail of the generated code based on the importance of the program statement during generation. For example, the generation unit generates detailed code for program statements with high importance. It can also generate concise code for program statements with low importance. Furthermore, the generation unit can adjust the level of detail of the generated code according to the importance of the program statement. This allows for efficient generation of program statements by adjusting the level of detail of the generated code according to the importance of the program statement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take program statement importance data as input and adjust the level of detail of the generated code using an AI model.

[0089] The generation unit can apply different generation algorithms depending on the category of the program statement during generation. For example, the generation unit can apply a data analysis algorithm to program statements related to data processing. It can also apply an image analysis algorithm to program statements related to image processing. Furthermore, it can apply a natural language processing algorithm to program statements related to text processing. By applying an appropriate generation algorithm according to the category of the program statement, the generation accuracy is improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes program statement category data as input and applies an appropriate generation algorithm.

[0090] The generation unit can estimate the user's emotions and adjust the length of the program statements it generates based on the emotion data. For example, if the user is in a hurry, the generation unit can generate short, concise program statements. If the user is relaxed, the generation unit can also generate longer program statements that include detailed explanations. Furthermore, if the user is stressed, the generation unit can generate visually easy-to-understand program statements. By adjusting the length of the program statements according to the user's emotions, more appropriate program statements can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the user's emotion data as input and adjust the length of the program statements using an AI model that adjusts the length of the program statements.

[0091] The generation unit can determine the generation priority based on the submission timing of program statements during generation. For example, the generation unit will prioritize the generation of urgent program statements. It can also prioritize the generation of program statements that have been submitted earlier. Furthermore, the generation unit can adjust the generation priority based on the submission timing. This allows for a quick response to urgent program statements by determining the generation priority based on the submission timing of the program statements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take program statement submission timing data as input and determine the generation priority using an AI model that determines the generation priority.

[0092] The generation unit can adjust the generation order based on the relationships between program statements during generation. For example, the generation unit prioritizes the generation of highly relevant program statements. It can also postpone the generation of less relevant program statements. Furthermore, the generation unit can adjust the generation order based on the relationships between program statements. This allows for the priority generation of highly relevant program statements by adjusting the generation order based on the relationships between program statements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the generation order using an AI model that takes program statement relationship data as input and adjusts the generation order.

[0093] The display unit can estimate the user's emotions and adjust the display method based on the emotion data. For example, if the user is tense, the display unit can provide an interface with calm colors. It can also provide an interface with bright colors if the user is enjoying themselves. Furthermore, if the user is tired, the display unit can provide a simple and highly visible interface. This allows for a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can adjust the display method using an AI model that takes user emotion data as input and adjusts the display method.

[0094] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's operation history and select the most efficient display method. This allows the display unit to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may select a display method using an AI model that takes user operation history data as input and selects the optimal display method.

[0095] The display unit can customize the displayed content based on the user's current projects and areas of interest. For example, the display unit prioritizes displaying information related to the project the user is currently working on. The display unit can also display relevant information based on the user's areas of interest. Furthermore, the display unit can refer to the user's project history to provide optimal display content. This allows for the provision of highly relevant information by customizing the displayed content based on the user's projects and areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes the user's project data as input and customizes the displayed content.

[0096] The display unit can estimate the user's emotions and adjust the display's operation procedures based on the emotion data. For example, if the user is tense, the display unit can provide simple and intuitive operation procedures. It can also provide detailed operation procedures if the user is enjoying themselves. Furthermore, if the user is tired, the display unit can provide minimal operation procedures. This allows for more appropriate operation procedures to be provided by adjusting the display's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or not. For example, the display unit can adjust the operation procedures using an AI model that takes user emotion data as input.

[0097] The display unit can select the optimal display method based on the user's device information when displaying information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the display unit can provide a display method that includes detailed information. This allows for a more appropriate display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the user's device information as input and selects the optimal display method.

[0098] The display unit can provide a multilingual display according to the user's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, the display unit can provide the display in a specific language if the user selects that language. This enables a more appropriate display by providing a multilingual display according to the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes the user's language setting data as input and provides a multilingual display to provide the display.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception unit can also receive voice commands from users. For example, if a user gives a voice command such as, "Create a program that sorts the elements of a list," the reception unit converts the voice into text and sends it to the analysis unit. This further reduces the effort required for manual input and improves programming efficiency by using voice input. A speech recognition engine can also be used to improve the accuracy of voice command recognition. Furthermore, it is possible to save a history of voice commands and reuse past voice commands.

[0101] The generation unit can customize program statements based on the user's programming style. For example, it can learn the user's preferred coding style (number of spaces for indentation, variable naming conventions, etc.) and generate program statements based on that. This reduces the effort the user has to make corrections later and allows for more efficient program creation. The generation unit can also analyze the user's past program statements and suggest the optimal coding style. Furthermore, the generation unit can receive user feedback and improve its generation algorithm.

[0102] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the emotion data. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is stressed, it can provide visually easy-to-understand analysis results. In this way, by adjusting the level of detail of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0103] The display unit can visually highlight the generated program statements. For example, it can color-code important parts of the generated program statements. It can also highlight errors and warnings if they are present. Furthermore, it can display tooltips for each part of the generated program statements, providing detailed explanations. This makes the generated program statements easier for the user to understand, facilitating corrections and improvements. The display unit can also customize the highlighting style according to the user's preferences.

[0104] The generation unit can estimate the user's emotions when generating program statements and adjust the way the generated program statements are expressed based on the emotion data. For example, if the user is relaxed, it can generate program statements with detailed comments. If the user is in a hurry, it can generate concise program statements. Furthermore, if the user is stressed, it can generate program statements that are visually easy to understand. In this way, by adjusting the way the program statements are expressed according to the user's emotions, more appropriate program statements can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, it can prioritize the instruction method the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time period based on the user's past instruction history. Furthermore, the reception desk can analyze the user's past instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can use an AI model that takes the user's past instruction history as input to select the optimal reception method.

[0106] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, a data analysis algorithm can be applied to instructions related to data processing. The analysis unit can also apply an image analysis algorithm to instructions related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to instructions related to text processing. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the instruction category data as input and applies an appropriate analysis algorithm.

[0107] The generation unit can apply different generation algorithms depending on the category of the program statement during generation. For example, a data analysis algorithm can be applied to program statements related to data processing. The generation unit can also apply an image analysis algorithm to program statements related to image processing. Furthermore, the generation unit can apply a natural language processing algorithm to program statements related to text processing. By applying an appropriate generation algorithm according to the category of the program statement, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate program statements using an AI model that takes program statement category data as input and applies an appropriate generation algorithm.

[0108] The reception desk can estimate the user's emotions and determine the priority of instructions to accept based on that emotion data. For example, if the user is stressed, it will postpone less important instructions. Conversely, if the user is relaxed, the reception desk can prioritize highly important instructions. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent instructions. This allows for the prioritization of instructions according to the user's emotions, ensuring that more appropriate instructions are received. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The display unit can estimate the user's emotions and adjust the display method based on the emotion data. For example, if the user is tense, it can provide an interface with calm colors. The display unit can also provide an interface with bright colors if the user is enjoying themselves. Furthermore, if the user is tired, it can provide a simple and highly visible interface. This allows for a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives instructions from the user in Japanese. These instructions can include natural language sentences or specific command formats. For example, the desk will accept instructions such as "Create a program that sorts the elements of a list." Step 2: The analysis unit analyzes the Japanese instructions received by the reception unit using natural language processing technology. The analysis unit analyzes the user's instructions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, it breaks down the user's instructions into individual words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instructions using semantic analysis. Step 3: The generation unit uses a generation AI to generate program statements based on the instructions analyzed by the analysis unit. The generation unit generates program statements using a text generation AI (e.g., LLM). For example, it generates program statements using algorithms such as quicksort or mergesort based on user instructions. Step 4: The display unit displays the program statements generated by the generation unit in the programming editor. This allows the user to verify the generated program statements.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives instructions from the user in Japanese. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program statements using generation AI. The display unit is implemented by the output device 40 of the smart device 14 and displays the generated program statements in a programming editor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's instructions in Japanese. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program statements using generation AI. The display unit is implemented by the speaker 240 of the smart glasses 214 and displays the generated program statements in a programming editor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives instructions from the user in Japanese. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program statements using generation AI. The display unit is implemented by the display 343 of the headset terminal 314 and displays the generated program statements in a programming editor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives instructions from the user in Japanese. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program statements using generation AI. The display unit is implemented by the speaker 240 of the robot 414 and displays the generated program statements in a programming editor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception desk that accepts user instructions in Japanese, An analysis unit that analyzes the Japanese instructions received by the reception unit, A generation unit that generates program statements based on instructions analyzed by the analysis unit, The system includes a display unit that displays the program statements generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate program statements using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze user instructions using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display the generated program statement in the programming editor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generates program statements using Python's sorting algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate Python program statements for file operations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is This document describes how to estimate the user's emotions and how to specifically adjust the timing of receiving instructions in Japanese based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past instruction history and select the appropriate method of receiving the request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions in Japanese, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is This document describes how to estimate user emotions and how to specifically determine the priority of instructions to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions in Japanese, the system prioritizes receiving instructions that are most relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions in Japanese, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, This document describes how to estimate user emotions and how to specifically adjust the representation of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the instruction. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, This document describes how to estimate user sentiment and how to specifically adjust the length of the analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is This document describes how to estimate user emotions and how to specifically adjust the expression of program statements generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail is adjusted based on the importance of the program statements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the program statement. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is This document describes how to estimate user emotions and how to specifically adjust the length of program statements generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined based on when the program statements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation order is adjusted based on the relevance of the program statements. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is This document describes how to estimate user sentiment and how to specifically adjust the display method based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying information, the system selects the appropriate display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displayed, the content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is This document describes how to estimate user emotions and how to specifically adjust the display operation procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying content, the system selects the appropriate display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displayed, the system provides multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user instructions in Japanese, An analysis unit that analyzes the Japanese instructions received by the reception unit, A generation unit that generates program statements based on instructions analyzed by the analysis unit, The system includes a display unit that displays the program statements generated by the generation unit, The reception unit estimates the user's emotion, and if the estimated emotion category is relaxed, it immediately accepts the instruction; if the estimated emotion category is stressed, it delays the timing of accepting the instruction until the emotion category becomes relaxed. A system characterized by the following features.

2. The generating unit is Generate program statements using a generation AI. The system according to feature 1.

3. The aforementioned analysis unit, Analyze user instructions using natural language processing technology. The system according to feature 1.

4. The aforementioned display unit is Display the generated program statement in the programming editor. The system according to feature 1.

5. The generating unit is Generates program statements using Python's sorting algorithm. The system according to feature 1.

6. The generating unit is Generate Python program statements for file operations. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past instruction history and select the appropriate method of receiving the request. The system according to feature 1.

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