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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that programming requires a lot of manual input and makes it difficult to generate programs efficiently.

[0005] The system according to the embodiment aims to automatically generate program statements based on instructions in Japanese. [Means for solving the problem]

[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 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. Effect of the Invention

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

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) In the programming automation system according to the embodiment of the present invention, when a user inputs what kind of processing he / she wants to perform in Japanese, a generation 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, the user inputs "Create a program to sort the elements of a list." This input is sent to the generation AI. The generation AI then analyzes the input Japanese instruction. The generation AI uses natural language processing technology to understand the user's instruction and generate appropriate program statements. For example, in response to the instruction "Create a program to sort the elements of a list," a program statement using a Python sorting algorithm is generated. The generated program statement is displayed in a programming editor. The user can check the generated program statement and make corrections as necessary. This reduces manual input and improves programming efficiency. With this mechanism, even beginners in programming can easily create programs. Also, even experienced programmers can create programs more efficiently by eliminating the need for manual input. For example, when a user inputs "Create a program to read a file and display its contents," the generation AI analyzes the instruction and generates a program statement related to Python file operations. The generated program statements include a process to open a file and display its contents. In this way, by using a generation AI, it is possible to automatically generate program statements based on the user's instructions in Japanese, improving programming efficiency. In this way, the programming automation system can automatically generate program statements based on the user's instructions in Japanese, improving programming efficiency.

[0029] The programming automation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives a user's instruction in Japanese. The user's instruction in Japanese includes, but is not limited to, a sentence in a natural language and a specific command format. For example, the user inputs "Create a program to sort elements of a list" to receive the instruction. The analysis unit uses natural language processing technology to analyze the Japanese instruction received by the reception unit. The analysis unit analyzes the user's instruction using, for example, morphological analysis, grammatical analysis, semantic analysis, or other technology. For example, the analysis unit breaks down the user's instruction into words by morphological analysis, analyzes the structure of the sentence by grammatical analysis, and understands the meaning of the instruction by semantic analysis. The generation unit uses a generation AI to generate program statements based on the instruction analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (for example, LLM) to generate the program statements. The generation unit can also use the generation AI to generate program statements using a Python sorting algorithm. For example, the generation unit generates program statements using an algorithm such as quick sort or merge sort based on a user's instruction. 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, for example, so that the user can confirm them. As a result, the programming automation system according to the embodiment can automatically generate program statements based on a user's instruction in Japanese, improving the efficiency of programming. For example, the generation unit can generate program statements using a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can generate program statements using an AI model that takes a user's instruction as input and outputs program statements. As a result, the generation accuracy of program statements is improved by using the generation AI.Furthermore, the display unit displays the generated program statements in a programming editor, making it easier for the user to check them. For example, the display unit displays the generated program statements in a programming editor so that the user can check them. This makes it easier for the user to check the generated program statements by displaying them in the programming editor.

[0030] The reception unit receives the user's instructions in Japanese. The user's instructions in Japanese include, but are not limited to, natural language sentences and specific command formats. For example, the reception unit receives the instructions by the user inputting "create a program to sort the elements of a list". Specifically, the reception unit receives the instructions in Japanese input by the user in text format and transmits the contents to the analysis unit. The reception unit can provide an interface for the user to input, such as a form on a web browser, a dedicated desktop application, or even an interface that supports voice input. In the case of voice input, voice recognition technology is used to convert voice to text, and the text is transmitted to the analysis unit. Furthermore, the reception unit can also have a function of saving the history of instructions input by the user and managing it in a reusable form. This allows the user to easily call up and reuse instructions input in the past, enabling efficient operation. The reception unit can also provide feedback in real time to the instructions input by the user. For example, if there is an error in the input content or an unclear part, the user can be immediately notified and prompted to correct it. This enables the reception unit to receive user instructions accurately and efficiently, thereby improving the operability of the entire system.

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

[0032] 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, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate program statements using a Python sorting algorithm. Specifically, the generation unit generates appropriate program statements based on information provided by the analysis unit. For example, in response to an instruction to "create a program to sort the elements of a list," the generation unit generates Python program statements using algorithms such as quick sort and merge sort. The generation AI has learned a large amount of program code in advance and can generate optimal code based on the user's instructions. The generation unit can also perform internal testing to ensure that the generated program statements are accurate. For example, the generated program statements are executed 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 the user's preferences and past feedback. This allows the generation unit to quickly generate high-quality program statements that meet the user's 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 so that the user can check them. Specifically, the display unit displays the generated program statements in the programming editor in an appropriate format so that the user can easily read them. The programming editor has a syntax highlighting and auto-completion function so that the user can efficiently check and correct the generated program statements. Furthermore, the display unit can also have a function to collect user feedback on the generated program statements and provide the feedback to the generation unit and the analysis unit. For example, when a user makes corrections to the generated program statements, the corrections are recorded and reflected in the next and subsequent generation. In this way, the display unit can provide the generated program statements in a form that is easy for the user to check, and can improve the accuracy and efficiency of the entire system based on the user's feedback. In addition, the display unit can link 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, the user can instantly integrate the generated code into the project. In this way, the display unit can provide a variety of functions for efficiently managing and utilizing the generated program statements, and can significantly streamline the user's development process.

[0034] The generation unit can generate program statements using a generation AI. The generation unit generates program statements using, for example, a text generation AI (for example, LLM). The generation AI has learned a large amount of text data and has advanced natural language processing capabilities. For example, the generation unit generates program statements using a Python sorting algorithm based on a user's instruction. The generation unit can generate program statements that sort elements of a list using an algorithm such as quick sort or merge sort. This improves the accuracy of program statement generation by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can generate program statements using an AI model that takes a user's instruction as input and outputs program statements.

[0035] The analysis unit can analyze the user's instruction using natural language processing technology. The analysis unit analyzes the user's instruction using, for example, technologies such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technology that breaks down a sentence into words and analyzes the part of speech and meaning of each word. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies the relationship between the components of the sentence. Semantic analysis is a technology that understands the meaning of a sentence and performs an appropriate interpretation based on the context. For example, the analysis unit breaks down the user's instruction into words by morphological analysis, analyzes the structure of the sentence by grammatical analysis, and understands the meaning of the instruction by semantic analysis. As a result, the analysis accuracy of the user's instruction is improved by using natural language processing technology. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the instruction using an AI model that inputs the user's instruction and outputs the analysis result.

[0036] The display unit can display the generated program statements in a programming editor. The display unit, for example, displays the generated program statements in the programming editor so that the user can confirm them. Programming editors include, for example, Visual Studio Code and PyCharm, but are not limited to such examples. The display unit displays the generated program statements in the programming editor, making it easier for the user to confirm them. For example, the display unit displays the generated program statements in the programming editor so that the user can confirm them. As a result, the display unit displays the generated program statements in the programming editor so that the user can confirm them. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display the program statements using an AI model that uses the generated program statements as input and displays them in the programming editor.

[0037] The generation unit can generate program statements using a Python sorting algorithm. The generation unit generates program statements for sorting elements of a list using an algorithm such as quick sort or merge sort. Quick sort is a sorting algorithm based on a divide-and-conquer method, and can perform sorting at high speed. Merge sort is an algorithm that recursively divides a list and merges the divided lists to perform sorting. For example, the generation unit generates program statements using an algorithm such as quick sort or merge sort based on a user's instruction. As a result, by using a Python sorting algorithm, program statements for sorting processing can be efficiently generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate program statements using an AI model that receives a user's instruction as input and outputs program statements using a sorting algorithm.

[0038] The generation unit can generate program statements related to Python file operations. The generation unit generates program statements for performing operations such as reading and writing files and deleting files, for example. Reading and writing files includes an operation of opening a file and reading the contents, and an operation of writing data to a file. Deleting a file includes an operation of deleting a specified file. For example, the generation unit generates program statements for performing operations such as reading and writing files and deleting files based on a user's instruction. This makes it possible to automate file operations by generating program statements related to Python file operations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate program statements using an AI model that receives a user's instruction as input and outputs program statements related to file operations.

[0039] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially receives an instruction method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past instruction history. Furthermore, the reception unit 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 the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past instruction history and selects the optimal reception method.

[0040] The reception unit can filter instructions based on the user's current project or field of interest when receiving instructions in Japanese. For example, the reception unit preferentially receives instructions related to a project that the user is currently working on. The reception unit can also filter related instructions based on the user's field of interest. Furthermore, the reception unit can refer to the user's project history and receive optimal instructions. This makes it possible to preferentially receive highly relevant instructions by filtering instructions based on the user's project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that inputs the user's project data and filters related instructions.

[0041] The reception unit can preferentially receive highly relevant instructions based on the geographical location information of the user when receiving instructions in Japanese. For example, when the user is in a specific area, the reception unit preferentially receives instructions related to the area. The reception unit can also suggest optimal instructions based on the user's current location. Furthermore, the reception unit can also filter highly relevant instructions by referring to the user's location information. As a result, more appropriate instructions can be received by preferentially receiving highly relevant instructions based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that uses the user's location information as an input and filters related instructions.

[0042] The reception unit can analyze the user's social media activity and receive related instructions when receiving instructions in Japanese. The reception unit preferentially receives related instructions based on, for example, the user's social media activity. The reception unit can also analyze the user's social media posting content and suggest optimal instructions. Furthermore, the reception unit can refer to the activities of the user's social media followers and friends and filter related instructions. In this way, by analyzing the user's social media activity, related instructions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that inputs the user's social media data and filters related instructions.

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

[0044] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, the analysis unit applies a data analysis algorithm to an instruction related to data processing. The analysis unit can also apply an image analysis algorithm to an instruction related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to an instruction related to text processing. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs category data of the instruction and applies an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission time of the instruction during the analysis. For example, the analysis unit prioritizes analysis for an urgent instruction. The analysis unit can also prioritize analysis for an instruction submitted early. Furthermore, the analysis unit can adjust the priority of the analysis based on the submission time. This allows for a rapid response to an urgent instruction by determining the priority of the analysis based on the submission time of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that inputs the submission time data of the instruction and determines the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the instructions during analysis. For example, the analysis unit prioritizes analysis of highly relevant instructions. The analysis unit can also postpone analysis of less relevant instructions. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the instructions. In this way, by adjusting the order of analysis based on the relevance of the instructions, highly relevant instructions can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that uses relevance data of instructions as input and adjusts the order of analysis.

[0047] The generation unit can adjust the level of detail of the generation based on the importance of the program statement at the time of generation. For example, the generation unit generates detailed code for a program statement with high importance. The generation unit can also generate concise code for a program statement with low importance. Furthermore, the generation unit can adjust the level of detail of the generation according to the importance of the program statement. This allows efficient generation of program statements by adjusting the level of detail of the generation according to the importance of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs importance data of the program statement and adjusts the level of detail of the generation.

[0048] The generation unit can apply different generation algorithms depending on the category of the program statement at the time of generation. For example, the generation unit applies a data analysis algorithm to a program statement related to data processing. The generation unit can also apply an image analysis algorithm to a program statement related to image processing. Furthermore, the generation unit can apply a natural language processing algorithm to a program statement related to text processing. This improves the generation accuracy by applying an appropriate generation algorithm depending on the category of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate the program statement using an AI model that receives category data of the program statement and applies an appropriate generation algorithm.

[0049] The generation unit can determine the priority of generation based on the submission time of the program statement at the time of generation. For example, the generation unit prioritizes generation for urgent program statements. The generation unit can also prioritize generation for program statements that are submitted early. Furthermore, the generation unit can adjust the priority of generation based on the submission time. This allows for a rapid response to urgent program statements by determining the priority of generation based on the submission time of the program statements. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of generation using an AI model that receives input data on the submission time of the program statements and determines the priority of generation.

[0050] The generation unit can adjust the order of generation based on the relevance of the program statements at the time of generation. For example, the generation unit prioritizes generation of highly relevant program statements. The generation unit can also postpone generation of less relevant program statements. Furthermore, the generation unit can adjust the order of generation based on the relevance of the program statements. In this way, by adjusting the order of generation based on the relevance of the program statements, highly relevant program statements can be generated preferentially. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can adjust the order of generation using an AI model that uses relevance data of program statements as input and adjusts the order of generation.

[0051] The display unit can select the optimal display method by referring to the user's past operation history at the time of display. For example, the display unit preferentially provides a display method that the user has used in the past. The display unit can also suggest the optimal display method from the user's past operation history. Furthermore, the display unit can analyze the user's operation history and select the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can select the display method using an AI model that inputs the user's operation history data and selects the optimal display method.

[0052] The display unit can customize the display contents based on the user's current project or field of interest at the time of display. For example, the display unit preferentially displays information related to the project the user is currently working on. The display unit can also display related information based on the user's field of interest. Furthermore, the display unit can also provide optimal display contents by referring to the user's project history. This makes it possible to provide highly relevant information by customizing the display contents based on the user's project or field of interest. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can customize the display contents using an AI model that uses the user's project data as input and customizes the display contents.

[0053] The display unit can select an optimal display method based on the user's device information at the time of display. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. This enables more appropriate display by selecting an optimal display method based on the user's device information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's device information and selects an optimal display method.

[0054] The display unit can provide a multilingual display according to the user's language setting at the time of display. The display unit automatically sets the display language based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can also provide a display in that language. This allows for a more appropriate display by providing a multilingual display according to the user's language setting. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can provide a display using an AI model that uses the user's language setting data as an input and provides a multilingual display.

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

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

[0057] The generator can customize program statements based on the user's programming style. For example, it can learn the coding style (number of indentation spaces, variable naming rules, etc.) that the user prefers, and generate program statements based on that. This reduces the effort required for the user to make subsequent corrections, and allows the user to create programs more efficiently. The generator can also analyze the user's past program statements and suggest optimal coding styles. Furthermore, the generator can receive user feedback and improve the generation algorithm.

[0058] The display unit can visually highlight the generated program statements. For example, important parts of the generated program statements can be displayed in different colors. If errors or warnings are included, they can also be highlighted. Furthermore, tooltips can be displayed for each part of the generated program statements to provide detailed explanations. This makes it easier for the user to understand the generated program statements and makes it easier to correct or improve them. The display unit can also customize the highlighting style according to the user's preferences.

[0059] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially accepts the instruction method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past instruction history. Furthermore, the reception unit 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 the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past instruction history and selects 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 is applied to an instruction related to data processing. The analysis unit can also apply an image analysis algorithm to an instruction related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to an instruction related to text processing. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs instruction category data and applies an appropriate analysis algorithm.

[0061] The generation unit can apply different generation algorithms depending on the category of the program statement at the time of generation. For example, a data analysis algorithm is applied to a program statement related to data processing. The generation unit can also apply an image analysis algorithm to a program statement related to image processing. The generation unit can also apply a natural language processing algorithm to a program statement related to text processing. This improves the generation accuracy by applying an appropriate generation algorithm depending on the category of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate the program statement using an AI model that receives category data of the program statement and applies an appropriate generation algorithm.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit receives the user's instructions in Japanese. The user's instructions in Japanese include natural language sentences and specific command formats. For example, the user may input "Create a program that sorts the elements of a list," and the instruction is received. Step 2: The analysis unit uses natural language processing technology to analyze the Japanese instruction received by the reception unit. The analysis unit analyzes the user's instruction using technology such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks down the user's instruction into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instruction using semantic analysis. Step 3: The generator uses a generation AI to generate program statements based on the instructions analyzed by the analyzer. The generator uses a text generation AI (e.g., LLM) to generate program statements. For example, the generator generates program statements using algorithms such as quick sort and merge sort based on user instructions. Step 4: The display unit displays the program statements generated by the generator in the programming editor, thereby allowing the user to check the generated program statements.

[0064] (Example 2) In the programming automation system according to the embodiment of the present invention, when a user inputs what kind of processing he / she wants to perform in Japanese, a generation 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, the user inputs "Create a program to sort the elements of a list." This input is sent to the generation AI. The generation AI then analyzes the input Japanese instruction. The generation AI uses natural language processing technology to understand the user's instruction and generate appropriate program statements. For example, in response to the instruction "Create a program to sort the elements of a list," a program statement using a Python sorting algorithm is generated. The generated program statement is displayed in a programming editor. The user can check the generated program statement and make corrections as necessary. This reduces manual input and improves programming efficiency. With this mechanism, even beginners in programming can easily create programs. Also, even experienced programmers can create programs more efficiently by eliminating the need for manual input. For example, when a user inputs "Create a program to read a file and display its contents," the generation AI analyzes the instruction and generates a program statement related to Python file operations. The generated program statements include a process to open a file and display its contents. In this way, by using a generation AI, it is possible to automatically generate program statements based on the user's instructions in Japanese, improving programming efficiency. In this way, the programming automation system can automatically generate program statements based on the user's instructions in Japanese, improving programming efficiency.

[0065] The programming automation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives a user's instruction in Japanese. The user's instruction in Japanese includes, but is not limited to, a sentence in a natural language and a specific command format. For example, the user inputs "Create a program to sort elements of a list" to receive the instruction. The analysis unit uses natural language processing technology to analyze the Japanese instruction received by the reception unit. The analysis unit analyzes the user's instruction using, for example, morphological analysis, grammatical analysis, semantic analysis, or other technology. For example, the analysis unit breaks down the user's instruction into words by morphological analysis, analyzes the structure of the sentence by grammatical analysis, and understands the meaning of the instruction by semantic analysis. The generation unit uses a generation AI to generate program statements based on the instruction analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (for example, LLM) to generate the program statements. The generation unit can also use the generation AI to generate program statements using a Python sorting algorithm. For example, the generation unit generates program statements using an algorithm such as quick sort or merge sort based on a user's instruction. 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, for example, so that the user can confirm them. As a result, the programming automation system according to the embodiment can automatically generate program statements based on a user's instruction in Japanese, improving the efficiency of programming. For example, the generation unit can generate program statements using a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can generate program statements using an AI model that takes a user's instruction as input and outputs program statements. As a result, the generation accuracy of program statements is improved by using the generation AI.Furthermore, the display unit displays the generated program statements in a programming editor, making it easier for the user to check them. For example, the display unit displays the generated program statements in a programming editor so that the user can check them. This makes it easier for the user to check the generated program statements by displaying them in the programming editor.

[0066] The reception unit receives the user's instructions in Japanese. The user's instructions in Japanese include, but are not limited to, natural language sentences and specific command formats. For example, the reception unit receives the instructions by the user inputting "create a program to sort the elements of a list". Specifically, the reception unit receives the instructions in Japanese input by the user in text format and transmits the contents to the analysis unit. The reception unit can provide an interface for the user to input, such as a form on a web browser, a dedicated desktop application, or even an interface that supports voice input. In the case of voice input, voice recognition technology is used to convert voice to text, and the text is transmitted to the analysis unit. Furthermore, the reception unit can also have a function of saving the history of instructions input by the user and managing it in a reusable form. This allows the user to easily call up and reuse instructions input in the past, enabling efficient operation. The reception unit can also provide feedback in real time to the instructions input by the user. For example, if there is an error in the input content or an unclear part, the user can be immediately notified and prompted to correct it. This enables the reception unit to receive user instructions accurately and efficiently, thereby improving the operability of the entire system.

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

[0068] 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, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate program statements using a Python sorting algorithm. Specifically, the generation unit generates appropriate program statements based on information provided by the analysis unit. For example, in response to an instruction to "create a program to sort the elements of a list," the generation unit generates Python program statements using algorithms such as quick sort and merge sort. The generation AI has learned a large amount of program code in advance and can generate optimal code based on the user's instructions. The generation unit can also perform internal testing to ensure that the generated program statements are accurate. For example, the generated program statements are executed 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 the user's preferences and past feedback. This allows the generation unit to quickly generate high-quality program statements that meet the user's 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 so that the user can check them. Specifically, the display unit displays the generated program statements in the programming editor in an appropriate format so that the user can easily read them. The programming editor has a syntax highlighting and auto-completion function so that the user can efficiently check and correct the generated program statements. Furthermore, the display unit can also have a function to collect user feedback on the generated program statements and provide the feedback to the generation unit and the analysis unit. For example, when a user makes corrections to the generated program statements, the corrections are recorded and reflected in the next and subsequent generation. In this way, the display unit can provide the generated program statements in a form that is easy for the user to check, and can improve the accuracy and efficiency of the entire system based on the user's feedback. In addition, the display unit can link 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, the user can instantly integrate the generated code into the project. In this way, the display unit can provide a variety of functions for efficiently managing and utilizing the generated program statements, and can significantly streamline the user's development process.

[0070] The generation unit can generate program statements using a generation AI. The generation unit generates program statements using, for example, a text generation AI (for example, LLM). The generation AI has learned a large amount of text data and has advanced natural language processing capabilities. For example, the generation unit generates program statements using a Python sorting algorithm based on a user's instruction. The generation unit can generate program statements that sort elements of a list using an algorithm such as quick sort or merge sort. This improves the accuracy of program statement generation by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can generate program statements using an AI model that takes a user's instruction as input and outputs program statements.

[0071] The analysis unit can analyze the user's instruction using natural language processing technology. The analysis unit analyzes the user's instruction using, for example, technologies such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technology that breaks down a sentence into words and analyzes the part of speech and meaning of each word. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies the relationship between the components of the sentence. Semantic analysis is a technology that understands the meaning of a sentence and performs an appropriate interpretation based on the context. For example, the analysis unit breaks down the user's instruction into words by morphological analysis, analyzes the structure of the sentence by grammatical analysis, and understands the meaning of the instruction by semantic analysis. As a result, the analysis accuracy of the user's instruction is improved by using natural language processing technology. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the instruction using an AI model that inputs the user's instruction and outputs the analysis result.

[0072] The display unit can display the generated program statements in a programming editor. The display unit, for example, displays the generated program statements in the programming editor so that the user can confirm them. Programming editors include, for example, Visual Studio Code and PyCharm, but are not limited to such examples. The display unit displays the generated program statements in the programming editor, making it easier for the user to confirm them. For example, the display unit displays the generated program statements in the programming editor so that the user can confirm them. As a result, the display unit displays the generated program statements in the programming editor so that the user can confirm them. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display the program statements using an AI model that uses the generated program statements as input and displays them in the programming editor.

[0073] The generation unit can generate program statements using a Python sorting algorithm. The generation unit generates program statements for sorting elements of a list using an algorithm such as quick sort or merge sort. Quick sort is a sorting algorithm based on a divide-and-conquer method, and can perform sorting at high speed. Merge sort is an algorithm that recursively divides a list and merges the divided lists to perform sorting. For example, the generation unit generates program statements using an algorithm such as quick sort or merge sort based on a user's instruction. As a result, by using a Python sorting algorithm, program statements for sorting processing can be efficiently generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate program statements using an AI model that receives a user's instruction as input and outputs program statements using a sorting algorithm.

[0074] The generation unit can generate program statements related to Python file operations. The generation unit generates program statements for performing operations such as reading and writing files and deleting files, for example. Reading and writing files includes an operation of opening a file and reading the contents, and an operation of writing data to a file. Deleting a file includes an operation of deleting a specified file. For example, the generation unit generates program statements for performing operations such as reading and writing files and deleting files based on a user's instruction. This makes it possible to automate file operations by generating program statements related to Python file operations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate program statements using an AI model that receives a user's instruction as input and outputs program statements related to file operations.

[0075] The reception unit can estimate the user's emotion and adjust the timing of receiving instructions in Japanese based on the emotion data. For example, when the user is stressed, the reception unit delays the reception timing to relax the user. Also, when the user is relaxed, the reception unit can immediately receive instructions. Furthermore, when the user is in a hurry, the reception unit can advance the reception timing to quickly respond. This allows the instruction to be received at a more appropriate timing by adjusting the reception timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the reception timing using an AI model that inputs the user's emotion data and adjusts the reception timing.

[0076] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially receives an instruction method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past instruction history. Furthermore, the reception unit 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 the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past instruction history and selects the optimal reception method.

[0077] The reception unit can filter instructions based on the user's current project or field of interest when receiving instructions in Japanese. For example, the reception unit preferentially receives instructions related to a project that the user is currently working on. The reception unit can also filter related instructions based on the user's field of interest. Furthermore, the reception unit can refer to the user's project history and receive optimal instructions. This makes it possible to preferentially receive highly relevant instructions by filtering instructions based on the user's project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that inputs the user's project data and filters related instructions.

[0078] The reception unit can estimate the user's emotion and determine the priority of the received instructions based on the emotion data. For example, when the user is stressed, the reception unit postpones instructions with a low importance. Also, when the user is relaxed, the reception unit can preferentially receive instructions with a high importance. Furthermore, when the user is in a hurry, the reception unit can preferentially receive urgent instructions. In this way, by determining the priority of instructions according to the user's emotion, more appropriate instructions can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can determine the priority of instructions using an AI model that inputs the user's emotion data and determines the priority of instructions.

[0079] The reception unit can preferentially receive highly relevant instructions based on the geographical location information of the user when receiving instructions in Japanese. For example, when the user is in a specific area, the reception unit preferentially receives instructions related to the area. The reception unit can also suggest optimal instructions based on the user's current location. Furthermore, the reception unit can also filter highly relevant instructions by referring to the user's location information. As a result, more appropriate instructions can be received by preferentially receiving highly relevant instructions based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that uses the user's location information as an input and filters related instructions.

[0080] The reception unit can analyze the user's social media activity and receive related instructions when receiving instructions in Japanese. The reception unit preferentially receives related instructions based on, for example, the user's social media activity. The reception unit can also analyze the user's social media posting content and suggest optimal instructions. Furthermore, the reception unit can refer to the activities of the user's social media followers and friends and filter related instructions. In this way, by analyzing the user's social media activity, related instructions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can filter instructions using an AI model that inputs the user's social media data and filters related instructions.

[0081] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the emotion data. For example, the analysis unit provides a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is stressed. This allows a more appropriate analysis result to be provided by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized 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 (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can adjust the expression method of the analysis using an AI model that inputs the user's emotion data and adjusts the expression method of the analysis.

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

[0083] The analysis unit can apply different analysis algorithms depending on the category of the instruction during analysis. For example, the analysis unit applies a data analysis algorithm to an instruction related to data processing. The analysis unit can also apply an image analysis algorithm to an instruction related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to an instruction related to text processing. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs category data of the instruction 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, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This allows for a more appropriate analysis result to be provided by adjusting the length of the analysis according to the user's emotions. The emotion estimation is realized 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 (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can adjust the length of the analysis using an AI model that inputs the user's emotion data and adjusts the length of the analysis.

[0085] The analysis unit can determine the priority of the analysis based on the submission time of the instruction during the analysis. For example, the analysis unit prioritizes analysis for an urgent instruction. The analysis unit can also prioritize analysis for an instruction submitted early. Furthermore, the analysis unit can adjust the priority of the analysis based on the submission time. This allows for a rapid response to an urgent instruction by determining the priority of the analysis based on the submission time of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that inputs the submission time data of the instruction and determines the priority of the analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the instructions during analysis. For example, the analysis unit prioritizes analysis of highly relevant instructions. The analysis unit can also postpone analysis of less relevant instructions. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the instructions. In this way, by adjusting the order of analysis based on the relevance of the instructions, highly relevant instructions can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that uses relevance data of instructions as input and adjusts the order of analysis.

[0087] The generation unit can estimate the user's emotion and adjust the expression method of the program sentence to be generated based on the emotion data. For example, when the user is relaxed, the generation unit generates a program sentence with detailed comments. Furthermore, when the user is in a hurry, the generation unit can also generate a concise program sentence. Furthermore, when the user is stressed, the generation unit can also generate a visually easy-to-understand program sentence. In this way, by adjusting the expression method of the program sentence according to the user's emotion, a more appropriate program sentence can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can adjust the expression method of the program sentence using an AI model that inputs the user's emotion data and adjusts the expression method of the program sentence.

[0088] The generation unit can adjust the level of detail of the generation based on the importance of the program statement at the time of generation. For example, the generation unit generates detailed code for a program statement with high importance. The generation unit can also generate concise code for a program statement with low importance. Furthermore, the generation unit can adjust the level of detail of the generation according to the importance of the program statement. This allows efficient generation of program statements by adjusting the level of detail of the generation according to the importance of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs importance data of the program statement and adjusts the level of detail of the generation.

[0089] The generation unit can apply different generation algorithms depending on the category of the program statement at the time of generation. For example, the generation unit applies a data analysis algorithm to a program statement related to data processing. The generation unit can also apply an image analysis algorithm to a program statement related to image processing. Furthermore, the generation unit can apply a natural language processing algorithm to a program statement related to text processing. This improves the generation accuracy by applying an appropriate generation algorithm depending on the category of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate the program statement using an AI model that receives category data of the program statement and applies an appropriate generation algorithm.

[0090] The generation unit can estimate the user's emotion and adjust the length of the program sentence to be generated based on the emotion data. For example, when the user is in a hurry, the generation unit generates a short program sentence that is to the point. In addition, when the user is relaxed, the generation unit can also generate a longer program sentence that includes a detailed explanation. Furthermore, when the user is stressed, the generation unit can also generate a program sentence that is visually easy to understand. In this way, by adjusting the length of the program sentence according to the user's emotion, a more appropriate program sentence can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can adjust the length of the program sentence using an AI model that inputs the user's emotion data and adjusts the length of the program sentence.

[0091] The generation unit can determine the priority of generation based on the submission time of the program statement at the time of generation. For example, the generation unit prioritizes generation for urgent program statements. The generation unit can also prioritize generation for program statements that are submitted early. Furthermore, the generation unit can adjust the priority of generation based on the submission time. This allows for a rapid response to urgent program statements by determining the priority of generation based on the submission time of the program statements. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of generation using an AI model that receives input data on the submission time of the program statements and determines the priority of generation.

[0092] The generation unit can adjust the order of generation based on the relevance of the program statements at the time of generation. For example, the generation unit prioritizes generation of highly relevant program statements. The generation unit can also postpone generation of less relevant program statements. Furthermore, the generation unit can adjust the order of generation based on the relevance of the program statements. In this way, by adjusting the order of generation based on the relevance of the program statements, highly relevant program statements can be generated preferentially. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can adjust the order of generation using an AI model that uses relevance data of program statements as input and adjusts the order of generation.

[0093] The display unit can estimate the user's emotion and adjust the display method based on the emotion data. For example, when the user is nervous, the display unit provides an interface with a calm color scheme. Also, when the user is having fun, the display unit can provide an interface with a bright color scheme. Furthermore, when the user is tired, the display unit can provide a simple and highly visible interface. This allows for more appropriate display by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can adjust the display method using an AI model that uses the user's emotion data as an 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 at the time of display. For example, the display unit preferentially provides a display method that the user has used in the past. The display unit can also suggest the optimal display method from the user's past operation history. Furthermore, the display unit can analyze the user's operation history and select the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can select the display method using an AI model that inputs the user's operation history data and selects the optimal display method.

[0095] The display unit can customize the display contents based on the user's current project or field of interest at the time of display. For example, the display unit preferentially displays information related to the project the user is currently working on. The display unit can also display related information based on the user's field of interest. Furthermore, the display unit can also provide optimal display contents by referring to the user's project history. This makes it possible to provide highly relevant information by customizing the display contents based on the user's project or field of interest. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can customize the display contents using an AI model that uses the user's project data as input and customizes the display contents.

[0096] The display unit can estimate the user's emotion and adjust the displayed operation procedure based on the emotion data. For example, when the user is nervous, the display unit provides a simple and intuitive operation procedure. In addition, when the user is enjoying, the display unit can also provide a detailed operation procedure. Furthermore, when the user is tired, the display unit can also provide a minimum operation procedure. This allows the display operation procedure to be adjusted according to the user's emotion, thereby providing a more appropriate operation procedure. The emotion estimation is realized 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 (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the display unit can adjust the operation procedure using an AI model that inputs the user's emotion data and adjusts the operation procedure.

[0097] The display unit can select an optimal display method based on the user's device information at the time of display. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. This enables more appropriate display by selecting an optimal display method based on the user's device information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's device information and selects an optimal display method.

[0098] The display unit can provide a multilingual display according to the user's language setting at the time of display. The display unit automatically sets the display language based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can also provide a display in that language. This allows for a more appropriate display by providing a multilingual display according to the user's language setting. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can provide a display using an AI model that uses the user's language setting data as an input and provides a multilingual display.

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

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

[0101] The generator can customize program statements based on the user's programming style. For example, it can learn the coding style (number of indentation spaces, variable naming rules, etc.) that the user prefers, and generate program statements based on that. This reduces the effort required for the user to make subsequent corrections, and allows the user to create programs more efficiently. The generator can also analyze the user's past program statements and suggest optimal coding styles. Furthermore, the generator can receive user feedback and improve the 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, when the user is relaxed, a detailed analysis result can be provided. Also, when the user is in a hurry, a concise analysis result can be provided. Furthermore, when the user is stressed, a visually easy-to-understand analysis result can be provided. 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. The estimation of emotions is realized using an emotion estimation function using, for example, 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 such examples.

[0103] The display unit can visually highlight the generated program statements. For example, important parts of the generated program statements can be displayed in different colors. If errors or warnings are included, they can also be highlighted. Furthermore, tooltips can be displayed for each part of the generated program statements to provide detailed explanations. This makes it easier for the user to understand the generated program statements and makes it easier to correct or improve them. The display unit can also customize the highlighting style according to the user's preferences.

[0104] The generation unit can estimate the user's emotion when generating a program sentence, and adjust the expression method of the generated program sentence based on the emotion data. For example, when the user is relaxed, a program sentence with detailed comments can be generated. Also, when the user is in a hurry, a concise program sentence can be generated. Furthermore, when the user is stressed, a visually easy-to-understand program sentence can be generated. In this way, by adjusting the expression method of the program sentence according to the user's emotion, a more appropriate program sentence can be generated. The emotion estimation is realized using an emotion estimation function using, for example, 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 such examples.

[0105] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially accepts the instruction method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past instruction history. Furthermore, the reception unit 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 the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past instruction history and selects 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 is applied to an instruction related to data processing. The analysis unit can also apply an image analysis algorithm to an instruction related to image processing. Furthermore, the analysis unit can apply a natural language processing algorithm to an instruction related to text processing. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs instruction category data and applies an appropriate analysis algorithm.

[0107] The generation unit can apply different generation algorithms depending on the category of the program statement at the time of generation. For example, a data analysis algorithm is applied to a program statement related to data processing. The generation unit can also apply an image analysis algorithm to a program statement related to image processing. The generation unit can also apply a natural language processing algorithm to a program statement related to text processing. This improves the generation accuracy by applying an appropriate generation algorithm depending on the category of the program statement. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate the program statement using an AI model that receives category data of the program statement and applies an appropriate generation algorithm.

[0108] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the emotion data. For example, when the user is stressed, instructions with low importance are postponed. Also, when the user is relaxed, the reception unit can preferentially receive instructions with high importance. Furthermore, when the user is in a hurry, the reception unit can preferentially receive urgent instructions. In this way, by determining the priority of instructions according to the user's emotions, more appropriate instructions can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] The display unit can estimate the user's emotions and adjust the display method based on the emotion data. For example, when the user is nervous, the display unit provides an interface with calm colors. When the user is having fun, the display unit can also provide an interface with bright colors. When the user is tired, the display unit can also provide a simple and highly visible interface. This allows for more appropriate display by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, 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 such examples.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The reception unit receives the user's instructions in Japanese. The user's instructions in Japanese include natural language sentences and specific command formats. For example, the user may input "Create a program that sorts the elements of a list," and the instruction is received. Step 2: The analysis unit uses natural language processing technology to analyze the Japanese instruction received by the reception unit. The analysis unit analyzes the user's instruction using technology such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks down the user's instruction into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the instruction using semantic analysis. Step 3: The generator uses a generation AI to generate program statements based on the instructions analyzed by the analyzer. The generator uses a text generation AI (e.g., LLM) to generate program statements. For example, the generator generates program statements using algorithms such as quick sort and merge sort based on user instructions. Step 4: The display unit displays the program statements generated by the generator in the programming editor, thereby allowing the user to check 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's instruction in Japanese. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's instruction using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program statement using a generation AI. The display unit is realized, for example, by the output device 40 of the smart device 14 and displays the generated program statement on a programming editor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0118] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's instruction in Japanese. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's instruction using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program statement using a generation AI. The display unit is realized, for example, by the speaker 240 of the smart glasses 214 and displays the generated program statement on a programming editor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0134] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a user's instruction in Japanese. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's instruction using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program statement using a generation AI. The display unit is realized, for example, by the display 343 of the headset type terminal 314 and displays the generated program statement on a programming editor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0149] 7, a 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 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a user's instruction in Japanese. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's instruction using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a program statement using a generation AI. The display unit is realized, for example, by the speaker 240 of the robot 414 and displays the generated program statement in a programming editor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0174] In addition, 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 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) a reception unit that receives instructions in Japanese from a user; an analysis unit that analyzes the Japanese instruction received by the reception unit; a generation unit that generates a program statement based on the instruction analyzed by the analysis unit; A display unit that displays the program statements generated by the generation unit. A system characterized by: (Appendix 2) The generation unit is Generate program statements using generative AI 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze user instructions using natural language processing technology 2. The system of claim 1, (Appendix 4) The display unit is Display the generated program statements in the programming editor 2. The system of claim 1, (Appendix 5) The generation unit is Generates program statements using Python's sorting algorithm 2. The system of claim 1, (Appendix 6) The generation unit is Generates Python program statements for file operations 2. The system of claim 1, (Appendix 7) The reception unit is Describe a method for estimating a user's emotions and specifically adjusting the timing of accepting instructions in Japanese based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is Filter incoming Japanese instructions based on the user's current projects and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is A method for estimating a user's emotion and specifically determining the priority of instructions to be received based on the estimated user's emotion is described. 2. The system of claim 1, (Appendix 11) The reception unit is When receiving instructions in Japanese, prioritize instructions that are more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 12) The reception unit is When receiving instructions in Japanese, analyze the user's social media activity and receive related instructions. 2. The system of claim 1, (Appendix 13) The analysis unit is Describe a method for estimating a user's emotions and specifically adjusting the way the analysis is expressed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the instructions 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, apply different parsing algorithms depending on the category of the instruction 2. The system of claim 1, (Appendix 16) The analysis unit is Describe a method for estimating a user's emotion and specifically adjusting the length of the analysis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when instructions were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on directive relevance 2. The system of claim 1, (Appendix 19) The generation unit is A method is described for estimating a user's emotions and specifically adjusting the expression method of generated program statements based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The generation unit is At generation time, the level of detail of the generated code is adjusted based on the importance of each program statement. 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different generation algorithms are applied depending on the category of the program statement. 2. The system of claim 1, (Appendix 22) The generation unit is A method for estimating a user's emotion and specifically adjusting the length of a program sentence to be generated based on the estimated user's emotion is described. 2. The system of claim 1, (Appendix 23) The generation unit is At generation time, generation priority is determined based on the submission time of the program statement. 2. The system of claim 1, (Appendix 24) The generation unit is At generation time, the order of generation is adjusted based on the relevance of program statements. 2. The system of claim 1, (Appendix 25) The display unit is Describe a method for estimating a user's emotion and specifically adjusting the display method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The display unit is When displaying, the appropriate display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 27) The display unit is At display time, customize the content based on the user's current project or area of ​​interest 2. The system of claim 1, (Appendix 28) The display unit is Describe a method for estimating a user's emotion and specifically adjusting the display operation procedure based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The display unit is When displaying, select the appropriate display method based on the user's device information 2. The system of claim 1, (Appendix 30) The display unit is When displayed, it provides multilingual display according to the user's language settings. 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. a reception unit that receives instructions in Japanese from a user; an analysis unit that analyzes the instruction in Japanese received by the reception unit; a generation unit that generates a program statement based on the instruction analyzed by the analysis unit; A display unit that displays the program statements generated by the generation unit. A system characterized by:

2. The generation unit is Generate program statements using AI 2. The system of claim 1 .

3. The analysis unit is Analyze user instructions using natural language processing technology 2. The system of claim 1 .

4. The display unit is Display the generated program statements in the programming editor 2. The system of claim 1 .

5. The generation unit is Generates program statements using Python's sorting algorithm 2. The system of claim 1 .

6. The generation unit is Generate Python program statements for file operations 2. The system of claim 1 .

7. The reception unit is Describe a method for estimating a user's emotions and specifically adjusting the timing of accepting instructions in Japanese based on the estimated user's emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1 .

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