System

Abstract

To provide a system that generates an application creation script code for automatizing job execution without the need for programming skills.SOLUTION: A system according to an embodiment of the present invention comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of business content from a user. The generation unit analyzes the business content received by the reception unit and generates an application creation script code. The provision unit supplies the application creation script code generated by the generation unit to the user.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] With conventional technology, creating programs to automate business processes required programming skills and effort, which hindered widespread adoption.

[0005] The system according to the embodiment aims to generate application creation script code for business automation even if the user does not have programming skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of business content from a user. The generation unit analyzes the business content received by the reception unit and generates a corresponding application creation script code. The provision unit provides the application creation script code generated by the generation unit to the user. Effect of the Invention

[0007] The system according to the embodiment can generate application creation script code for business automation even if the user does not have programming skills. [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 the 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) The business automation system according to the embodiment of the present invention is a system that uses a generation AI to generate application creation script (for example, GAS (Google (registered trademark) Apps Script) or AppleScript, etc.) code, thereby reducing the man-hours required for developing a business automation program. In this business automation system, a user inputs the business content to be automated in natural language, the generation AI analyzes the input, generates the corresponding application creation script code, and provides the generated application creation script code in a form that the user can easily use. For example, when a user inputs a specific business content such as "open a specific spreadsheet at 9:00 every day and update data," this input is sent to the generation AI. The generation AI understands the input of natural language and generates the corresponding application creation script code. For example, in response to an input of "open a specific spreadsheet at 9:00 every day and update data," the generation AI generates the application creation script code for executing the business. The generated application creation script code is automatically inserted into the user's application creation script editor or sent by email. In this way, the user can use the generated code as it is. This mechanism allows a business automation program to be created even without programming skills, promoting the spread of business automation. For example, a program that previously took several hours to create can now be completed in just a few minutes. Furthermore, the generative AI generates optimal code based on user input, which improves the quality of business automation programs. For example, the generative AI can generate code that incorporates the latest application creation script functions and best practices. This allows users to easily create high-quality automation programs. As a result, the business automation system can reduce the development effort for business automation programs and promote the spread of business automation by inputting the user's business content in natural language and having the generative AI generate and provide application creation script code.

[0029] The business automation system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of business contents from a user. The input of business contents from a user includes, for example, accounting work, sales work, project management, and the like, but is not limited to such examples. The reception unit can analyze the user's input using, for example, natural language processing technology. The generation unit uses a generation AI to analyze the business contents received by the reception unit and generate a corresponding application creation script code. The generation AI uses, for example, deep learning or natural language processing technology to analyze the user's input and generate an optimal application creation script code. For example, the generation AI generates an application creation script code for executing the business in response to an input such as "open a specific spreadsheet at 9 o'clock every day and update data". The provision unit provides the application creation script code generated by the generation unit to a user. For example, the provision unit can automatically insert the generated application creation script code into the user's application creation script editor. The provision unit can also send the generated application creation script code to the user by email. This allows the user to easily use the generated application creation script code. For example, the providing unit automatically inserts the generated application creation script code into a user's application creation script editor, allowing the user to use it as is. Also, the providing unit transmits the generated application creation script code by email, allowing the user to use the code from the email received. In this way, the business automation system allows the user to input the content of the user's business in natural language, and the generation AI generates and provides the application creation script code, thereby reducing the man-hours required for developing business automation programs and promoting the spread of business automation.

[0030] The reception unit receives input of the business contents from the user. The input of the business contents from the user includes, but is not limited to, accounting, sales, and project management, for example. The reception unit can analyze the input of the user using, for example, natural language processing technology. Specifically, the text input by the user is tokenized and the meaning of each token is analyzed to accurately understand the business contents. For example, if the user inputs "Create a sales report at the end of each month and send it by email," the reception unit extracts keywords such as "end of each month," "sales report," "create," and "send by email," and analyzes the meaning of each. Furthermore, the reception unit can understand the input contents of the user based on the context and complement ambiguous expressions and omitted parts. For example, for an input such as "prepare for a meeting next week," the reception unit can infer the specific date of the meeting and the preparation contents and confirm them with the user. In addition, the reception unit can learn the user's past input history and business patterns to improve the accuracy of the input contents. As a result, the reception unit can accurately and quickly accept the user's business contents and smoothly hand them over to the next processing step.

[0031] The generation unit uses the generation AI to analyze the business content received by the reception unit and generate a corresponding application creation script code. The generation AI analyzes the user's input using, for example, deep learning or natural language processing technology, and generates an optimal application creation script code. Specifically, the generation AI analyzes the user's input content and identifies a series of operations and procedures corresponding to the business content. For example, for an input such as "open a specific spreadsheet at 9 o'clock every day and update the data," the generation AI analyzes the spreadsheet identification, data update method, execution timing, etc., and generates the application creation script code based on the analysis. The generation AI can also refer to a database of past business content and generated codes to suggest optimal codes for similar business content. Furthermore, the generation AI evaluates the quality of the generated code and makes corrections and optimizations as necessary. For example, it can evaluate the execution efficiency of the code and the appropriateness of error handling, and automatically reflect improvements. As a result, the generation unit can quickly and with high quality generate application creation script code that is optimal for the user's business content, maximizing the effect of business automation.

[0032] The providing unit provides the user with the application creation script code generated by the generating unit. For example, the providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit can also send the generated application creation script code to the user by email. Specifically, the providing unit directly inserts the generated application creation script code into the user's application creation script editor, allowing the user to execute the code as is. This allows the user to start business automation without any hassle. The providing unit also sends the generated application creation script code by email, allowing the user to copy and use the code from the received email. Furthermore, the providing unit can automatically generate a document including a method of using the generated application creation script code and points to note, and provide it to the user. For example, the providing unit generates a detailed document including an explanation of each part of the code, a setting method, a troubleshooting guide, and the like, to help the user correctly understand the code and use it effectively. This allows the providing unit to quickly and reliably provide the generated application creation script code to the user, and smoothly advance the introduction of business automation.

[0033] The generation unit can analyze the business content in natural language by the generation AI and generate a corresponding application creation script code. The generation unit can analyze the business content in natural language input by the user, for example, by using the generation AI. The generation AI can analyze the user's input by using, for example, deep learning or natural language processing technology, and generate an optimal application creation script code. For example, when the user inputs the business content of "open a specific spreadsheet at 9 o'clock every day and update data," the generation AI can generate an application creation script code for executing the business. The generation AI can analyze the user's input by using, for example, natural language processing technology, and generate a corresponding application creation script code. The generation AI can analyze the user's input by using, for example, deep learning, and generate an optimal application creation script code. As a result, the generation unit can analyze the business content in natural language by using the generation AI and generate a corresponding application creation script code.

[0034] The providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit, for example, automatically inserts the generated application creation script code into the user's application creation script editor. Specific methods for automatic insertion include, for example, the use of an API and an automatic insertion function that does not require user operation, but are not limited to such examples. For example, the providing unit automatically inserts the generated application creation script code into the user's application creation script editor so that the user can use it as is. The providing unit can automatically insert the generated application creation script code into the user's application creation script editor, for example, using an API. In addition, the providing unit has a function of automatically inserting the generated application creation script code without requiring user operation. As a result, the providing unit can automatically insert the generated application creation script code into the application creation script editor so that the user can easily use it.

[0035] The providing unit can transmit the generated application creation script code to the user by email. The providing unit, for example, transmits the generated application creation script code to the user by email. A specific method of transmitting by email includes, for example, using an SMTP protocol and setting a transmission timing, but is not limited to such examples. For example, the providing unit transmits the generated application creation script code by email, so that the user can use the code from the email received. The providing unit can transmit the generated application creation script code by email, for example, using the SMTP protocol. In addition, the providing unit has a function of setting a transmission timing and transmitting the generated application creation script code by email at an appropriate timing. As a result, the providing unit can transmit the generated application creation script code by email, so that the user can easily use it.

[0036] The generation unit can generate code incorporating the latest application creation script functions and recommended practices. The generation unit generates code incorporating the latest application creation script functions and best practices, for example, using a generation AI. The latest application creation script functions include, for example, new APIs and added functions, but are not limited to such examples. The generation unit can generate code incorporating the latest application creation script functions, for example, using a generation AI. The generation unit can also generate code incorporating recommended practices (best practices). For example, the generation unit can generate optimal application creation script code based on industry standards and best practices. As a result, the generation unit can improve the quality of the business automation program by generating code incorporating the latest application creation script functions and best practices.

[0037] The reception unit can analyze the user's past work content input history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past work content input history and suggest an optimal input method. The reception unit can, for example, use AI to analyze the user's past work content input history. For example, the reception unit automatically displays work content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest work content to be used in a specific time period from the user's past input history. As a result, the reception unit can propose an optimal input method to the user by analyzing the past input history, thereby improving input efficiency.

[0038] The reception unit can filter the input contents based on the user's current project and field of interest when inputting the business contents. For example, the reception unit filters the input contents based on the user's current project and field of interest when inputting the business contents. The reception unit can, for example, use AI to analyze the user's current project and field of interest and filter the input contents. For example, the reception unit preferentially displays business contents related to a project currently being carried out by the user. In addition, the reception unit can also suggest related business contents based on the user's field of interest. Furthermore, the reception unit can filter the input contents based on fields in which the user has shown interest in the past. As a result, the reception unit can preferentially input highly relevant business contents by filtering the input contents based on the current project and field of interest.

[0039] The reception unit can preferentially input highly relevant work contents in consideration of the geographical location information of the user when inputting work contents. For example, the reception unit preferentially inputs highly relevant work contents in consideration of the geographical location information of the user when inputting work contents. The reception unit can, for example, use AI to analyze the geographical location information of the user and preferentially input highly relevant work contents. For example, when the user is in a specific area, the reception unit preferentially displays work contents related to the area. In addition, when the user is on a business trip, the reception unit can also preferentially input work contents related to the business trip destination. Furthermore, when the user is at home, the reception unit can also preferentially input work contents to be performed at home. As a result, the reception unit can preferentially input highly relevant work contents by considering the geographical location information.

[0040] The reception unit can analyze the user's social media activity when inputting the work content, and suggest related work content. The reception unit can, for example, use AI to analyze the user's social media activity and suggest related work content. For example, the reception unit can automatically suggest work content mentioned by the user on social media. The reception unit can also predict and suggest work content of interest from the user's social media activity. Furthermore, the reception unit can also suggest work content related to accounts the user follows on social media. As a result, the reception unit can suggest related work content by analyzing social media activity, and improve input efficiency.

[0041] The generation unit can adjust the level of detail of the code based on the importance of the business content when generating the application creation script code. For example, the generation unit adjusts the level of detail of the code based on the importance of the business content when generating the application creation script code. The generation unit can evaluate the importance of the business content using, for example, a generation AI and generate optimal application creation script code. For example, the generation unit generates application creation script code including detailed error handling for important business content. In addition, the generation unit can also generate application creation script code including only basic functions for general business content. Furthermore, the generation unit can generate application creation script code that can be executed with minimal code for simple business content. As a result, the generation unit can generate an appropriate level of application creation script code by adjusting the level of detail of the code based on the importance of the business content.

[0042] The generating unit can apply different generation algorithms depending on the category of the business content when generating the application creation script code. For example, the generating unit applies different generation algorithms depending on the category of the business content when generating the application creation script code. The generating unit can analyze the category of the business content using, for example, a generation AI and apply an optimal generation algorithm. For example, the generating unit can generate application creation script code that performs efficient data manipulation for a data processing task. In addition, the generating unit can also generate application creation script code that includes an appropriate notification method for a notification task. Furthermore, the generating unit can generate application creation script code that includes a calendar operation for a schedule management task. As a result, the generating unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

[0043] The generation unit can determine the priority of the code based on the submission time of the business content when generating the application creation script code. For example, the generation unit determines the priority of the code based on the submission time of the business content when generating the application creation script code. The generation unit can analyze the submission time of the business content using, for example, a generation AI, and generate optimal application creation script code. For example, the generation unit generates application creation script code with the highest priority for urgent business content. In addition, the generation unit can also generate application creation script code based on a schedule for periodic business content. Furthermore, the generation unit can generate application creation script code in a planned manner for long-term business content. As a result, the generation unit can respond according to the urgency by determining the priority of the code based on the submission time of the business content.

[0044] The generation unit can adjust the order of the code based on the relevance of the business contents when generating the application creation script code. For example, the generation unit adjusts the order of the code based on the relevance of the business contents when generating the application creation script code. The generation unit can analyze the relevance of the business contents using, for example, a generation AI, and generate the application creation script code in an optimal order. For example, the generation unit generates application creation script code preferentially for business contents with high relevance. In addition, the generation unit can also generate application creation script code for business contents with low relevance at a later date. Furthermore, the generation unit can generate the application creation script code in an optimal order based on the relevance of the business contents. As a result, the generation unit adjusts the order of the code based on the relevance of the business contents, thereby enabling efficient code generation.

[0045] The providing unit can select the optimal providing method by referring to the user's past code usage history when providing the application creation script code. For example, the providing unit selects the optimal providing method by referring to the user's past code usage history when providing the application creation script code. The providing unit can, for example, use AI to analyze the user's past code usage history and select the optimal providing method. For example, the providing unit preferentially selects the providing method (email, editor insertion, etc.) that the user has used in the past. In addition, the providing unit can also suggest the optimal providing method from the user's past usage history. Furthermore, the providing unit can provide the application creation script code based on the providing method that the user has used preferentially in the past. As a result, the providing unit can select the optimal providing method for the user by referring to the past usage history, thereby improving convenience.

[0046] The providing unit can customize the provided content based on the user's current project status when providing the application creation script code. For example, the providing unit customizes the provided content based on the user's current project status when providing the application creation script code. The providing unit can analyze the user's current project status using, for example, AI and provide the optimal application creation script code. For example, the providing unit preferentially provides application creation script code related to a project currently in progress by the user. In addition, the providing unit can also suggest the optimal application creation script code based on the user's project status. Furthermore, the providing unit can customize the provided content of the application creation script code according to the project the user is currently working on. As a result, the providing unit can provide the optimal application creation script code for the user by customizing the provided content based on the current project status.

[0047] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the application creation script code. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing the application creation script code. The providing unit can analyze the geographical location information of the user using, for example, AI and select the optimal providing method. For example, when the user is in a specific area, the providing unit provides the application creation script code related to the area. In addition, when the user is on a business trip, the providing unit can also provide the application creation script code related to the business trip destination. Furthermore, when the user is at home, the providing unit can also provide the application creation script code related to the work performed at home. As a result, the providing unit can select the optimal providing method for the user by considering the geographical location information.

[0048] The providing unit can propose the content to be provided by analyzing the social media activity of the user when providing the application creation script code. For example, the providing unit proposes the content to be provided by analyzing the social media activity of the user when providing the application creation script code. The providing unit can, for example, use AI to analyze the social media activity of the user and provide the application creation script code based on the related business content. For example, the providing unit provides the application creation script code related to the business content mentioned by the user on social media. The providing unit can also propose the application creation script code related to the business content of interest from the user's social media activity. Furthermore, the providing unit can also provide the application creation script code related to the account the user follows on social media. As a result, the providing unit can provide the application creation script code based on the business content related to the user by analyzing the social media activity.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the user's past work content input history and suggest an appropriate input method. For example, the reception unit automatically displays work content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest work content to be used in a specific time period from the user's past input history. In this way, the reception unit can analyze the past input history to suggest the most suitable input method to the user and improve input efficiency.

[0051] The generation unit can adjust the level of detail of the code based on the importance of the business content when generating the application creation script code. For example, the generation unit can use a generation AI to evaluate the importance of the business content and generate optimal application creation script code. The generation unit generates application creation script code including detailed error handling for important business content. The generation unit can also generate application creation script code including only basic functions for general business content. Furthermore, the generation unit can generate application creation script code that can be executed with a minimum of code for simple business content. As a result, the generation unit can generate an appropriate level of application creation script code by adjusting the level of detail of the code based on the importance of the business content.

[0052] The reception unit can filter the input contents based on the user's current project and field of interest when inputting the work content. For example, the reception unit can use AI to analyze the user's current project and field of interest and filter the input contents. The reception unit preferentially displays work contents related to the project currently being carried out by the user. The reception unit can also suggest related work contents based on the user's field of interest. Furthermore, the reception unit can filter the input contents based on the field in which the user has shown interest in the past. As a result, the reception unit can preferentially input highly relevant work contents by filtering the input contents based on the current project and field of interest.

[0053] When providing the application creation script code, the providing unit can select the optimal providing method by referring to the user's past code usage history. For example, the providing unit can use AI to analyze the user's past code usage history and select the optimal providing method. The providing unit preferentially selects the providing method (email, editor insertion, etc.) that the user has used in the past. The providing unit can also suggest the optimal providing method from the user's past usage history. Furthermore, the providing unit can provide the application creation script code based on the providing method that the user has used preferably in the past. In this way, the providing unit can select the optimal providing method for the user by referring to the past usage history, thereby improving convenience.

[0054] The generation unit can apply different generation algorithms depending on the category of the business content when generating the application creation script code. For example, the generation unit can use a generation AI to analyze the category of the business content and apply an optimal generation algorithm. The generation unit generates application creation script code that performs efficient data manipulation for a data processing task. The generation unit can also generate application creation script code that includes an appropriate notification method for a notification task. Furthermore, the generation unit can generate application creation script code that includes calendar manipulation for a schedule management task. In this way, the generation unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

[0055] When inputting the work content, the reception unit can prioritize inputting highly relevant work content in consideration of the geographical location information of the user. For example, the reception unit can use AI to analyze the geographical location information of the user and prioritize inputting highly relevant work content. When the user is in a specific area, the reception unit can prioritize displaying work content related to the area. In addition, when the user is on a business trip, the reception unit can also prioritize inputting work content related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting work content to be performed at home. In this way, the reception unit can prioritize inputting highly relevant work content by considering the geographical location information.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives an input of business contents from a user. The input of business contents from a user includes, but is not limited to, accounting, sales, project management, etc. The reception unit can analyze the user's input using, for example, natural language processing technology. Step 2: The generation unit uses the generation AI to analyze the business content accepted by the acceptance unit and generate a corresponding application creation script code. The generation AI analyzes the user's input using, for example, deep learning or natural language processing technology, and generates an optimal application creation script code. For example, in response to an input such as "open a specific spreadsheet at 9:00 every day and update the data," the generation unit generates an application creation script code for executing the business. Step 3: The providing unit provides the application creation script code generated by the generating unit to the user. For example, the providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit can also send the generated application creation script code to the user by email. This allows the user to easily use the generated application creation script code.

[0058] (Example 2) The business automation system according to the embodiment of the present invention is a system that uses a generation AI to generate application creation script (for example, GAS (Google Apps Script) or AppleScript) code, thereby reducing the man-hours required for developing a business automation program. In this business automation system, a user inputs the business content to be automated in natural language, and the generation AI analyzes the input to generate a corresponding application creation script code, and provides the generated application creation script code in a form that the user can easily use. For example, when a user inputs a specific business content such as "open a specific spreadsheet at 9 o'clock every day and update data," this input is sent to the generation AI. The generation AI understands the input of natural language and generates a corresponding application creation script code. For example, in response to an input of "open a specific spreadsheet at 9 o'clock every day and update data," the generation AI generates an application creation script code for executing the business. The generated application creation script code is automatically inserted into the user's application creation script editor or sent by email. In this way, the user can use the generated code as it is. This mechanism allows a business automation program to be created even without programming skills, promoting the spread of business automation. For example, a program that previously took several hours to create can now be completed in just a few minutes. Furthermore, the generative AI generates optimal code based on user input, which improves the quality of business automation programs. For example, the generative AI can generate code that incorporates the latest application creation script functions and best practices. This allows users to easily create high-quality automation programs. As a result, the business automation system can reduce the development effort for business automation programs and promote the spread of business automation by inputting the user's business content in natural language and having the generative AI generate and provide application creation script code.

[0059] The business automation system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of business contents from a user. The input of business contents from a user includes, for example, accounting work, sales work, project management, and the like, but is not limited to such examples. The reception unit can analyze the user's input using, for example, natural language processing technology. The generation unit uses a generation AI to analyze the business contents received by the reception unit and generate a corresponding application creation script code. The generation AI uses, for example, deep learning or natural language processing technology to analyze the user's input and generate an optimal application creation script code. For example, the generation AI generates an application creation script code for executing the business in response to an input such as "open a specific spreadsheet at 9 o'clock every day and update data". The provision unit provides the application creation script code generated by the generation unit to a user. For example, the provision unit can automatically insert the generated application creation script code into the user's application creation script editor. The provision unit can also send the generated application creation script code to the user by email. This allows the user to easily use the generated application creation script code. For example, the providing unit automatically inserts the generated application creation script code into a user's application creation script editor, allowing the user to use it as is. Also, the providing unit transmits the generated application creation script code by email, allowing the user to use the code from the email received. In this way, the business automation system allows the user to input the content of the user's business in natural language, and the generation AI generates and provides the application creation script code, thereby reducing the man-hours required for developing business automation programs and promoting the spread of business automation.

[0060] The reception unit receives input of the business contents from the user. The input of the business contents from the user includes, but is not limited to, accounting, sales, and project management, for example. The reception unit can analyze the input of the user using, for example, natural language processing technology. Specifically, the text input by the user is tokenized and the meaning of each token is analyzed to accurately understand the business contents. For example, if the user inputs "Create a sales report at the end of each month and send it by email," the reception unit extracts keywords such as "end of each month," "sales report," "create," and "send by email," and analyzes the meaning of each. Furthermore, the reception unit can understand the input contents of the user based on the context and complement ambiguous expressions and omitted parts. For example, for an input such as "prepare for a meeting next week," the reception unit can infer the specific date of the meeting and the preparation contents and confirm them with the user. In addition, the reception unit can learn the user's past input history and business patterns to improve the accuracy of the input contents. As a result, the reception unit can accurately and quickly accept the user's business contents and smoothly hand them over to the next processing step.

[0061] The generation unit uses the generation AI to analyze the business content received by the reception unit and generate a corresponding application creation script code. The generation AI analyzes the user's input using, for example, deep learning or natural language processing technology, and generates an optimal application creation script code. Specifically, the generation AI analyzes the user's input content and identifies a series of operations and procedures corresponding to the business content. For example, for an input such as "open a specific spreadsheet at 9 o'clock every day and update the data," the generation AI analyzes the spreadsheet identification, data update method, execution timing, etc., and generates the application creation script code based on the analysis. The generation AI can also refer to a database of past business content and generated codes to suggest optimal codes for similar business content. Furthermore, the generation AI evaluates the quality of the generated code and makes corrections and optimizations as necessary. For example, it can evaluate the execution efficiency of the code and the appropriateness of error handling, and automatically reflect improvements. As a result, the generation unit can quickly and with high quality generate application creation script code that is optimal for the user's business content, maximizing the effect of business automation.

[0062] The providing unit provides the user with the application creation script code generated by the generating unit. For example, the providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit can also send the generated application creation script code to the user by email. Specifically, the providing unit directly inserts the generated application creation script code into the user's application creation script editor, allowing the user to execute the code as is. This allows the user to start business automation without any hassle. The providing unit also sends the generated application creation script code by email, allowing the user to copy and use the code from the received email. Furthermore, the providing unit can automatically generate a document including a method of using the generated application creation script code and points to note, and provide it to the user. For example, the providing unit generates a detailed document including an explanation of each part of the code, a setting method, a troubleshooting guide, and the like, to help the user correctly understand the code and use it effectively. This allows the providing unit to quickly and reliably provide the generated application creation script code to the user, and smoothly advance the introduction of business automation.

[0063] The generation unit can analyze the business content in natural language by the generation AI and generate a corresponding application creation script code. The generation unit can analyze the business content in natural language input by the user, for example, by using the generation AI. The generation AI can analyze the user's input by using, for example, deep learning or natural language processing technology, and generate an optimal application creation script code. For example, when the user inputs the business content of "open a specific spreadsheet at 9 o'clock every day and update data," the generation AI can generate an application creation script code for executing the business. The generation AI can analyze the user's input by using, for example, natural language processing technology, and generate a corresponding application creation script code. The generation AI can analyze the user's input by using, for example, deep learning, and generate an optimal application creation script code. As a result, the generation unit can analyze the business content in natural language by using the generation AI and generate a corresponding application creation script code.

[0064] The providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit, for example, automatically inserts the generated application creation script code into the user's application creation script editor. Specific methods for automatic insertion include, for example, the use of an API and an automatic insertion function that does not require user operation, but are not limited to such examples. For example, the providing unit automatically inserts the generated application creation script code into the user's application creation script editor so that the user can use it as is. The providing unit can automatically insert the generated application creation script code into the user's application creation script editor, for example, using an API. In addition, the providing unit has a function of automatically inserting the generated application creation script code without requiring user operation. As a result, the providing unit can automatically insert the generated application creation script code into the application creation script editor so that the user can easily use it.

[0065] The providing unit can transmit the generated application creation script code to the user by email. The providing unit, for example, transmits the generated application creation script code to the user by email. A specific method of transmitting by email includes, for example, using an SMTP protocol and setting a transmission timing, but is not limited to such examples. For example, the providing unit transmits the generated application creation script code by email, so that the user can use the code from the email received. The providing unit can transmit the generated application creation script code by email, for example, using the SMTP protocol. In addition, the providing unit has a function of setting a transmission timing and transmitting the generated application creation script code by email at an appropriate timing. As a result, the providing unit can transmit the generated application creation script code by email, so that the user can easily use it.

[0066] The generation unit can generate code incorporating the latest application creation script functions and recommended practices. The generation unit generates code incorporating the latest application creation script functions and best practices, for example, using a generation AI. The latest application creation script functions include, for example, new APIs and added functions, but are not limited to such examples. The generation unit can generate code incorporating the latest application creation script functions, for example, using a generation AI. The generation unit can also generate code incorporating recommended practices (best practices). For example, the generation unit can generate optimal application creation script code based on industry standards and best practices. As a result, the generation unit can improve the quality of the business automation program by generating code incorporating the latest application creation script functions and best practices.

[0067] The reception unit can estimate the user's emotion and adjust the input interface of the work content based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input interface of the work content based on the estimated user's emotion. The emotion estimation is realized by 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. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input the work content quickly. As a result, the reception unit adjusts the input interface according to the user's emotion, thereby reducing the user's stress and enabling efficient input.

[0068] The reception unit can analyze the user's past work content input history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past work content input history and suggest an optimal input method. The reception unit can, for example, use AI to analyze the user's past work content input history. For example, the reception unit automatically displays work content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest work content to be used in a specific time period from the user's past input history. As a result, the reception unit can propose an optimal input method to the user by analyzing the past input history, thereby improving input efficiency.

[0069] The reception unit can filter the input contents based on the user's current project and field of interest when inputting the business contents. For example, the reception unit filters the input contents based on the user's current project and field of interest when inputting the business contents. The reception unit can, for example, use AI to analyze the user's current project and field of interest and filter the input contents. For example, the reception unit preferentially displays business contents related to a project currently being carried out by the user. In addition, the reception unit can also suggest related business contents based on the user's field of interest. Furthermore, the reception unit can filter the input contents based on fields in which the user has shown interest in the past. As a result, the reception unit can preferentially input highly relevant business contents by filtering the input contents based on the current project and field of interest.

[0070] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input contents based on the estimated user's emotion. The emotion estimation is realized by 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. For example, when the user is feeling stressed, the reception unit allows the user to input important work contents with priority. In addition, when the user is relaxed, the reception unit can also allow the user to input detailed work contents with priority. Furthermore, when the user is in a hurry, the reception unit can also allow the user to input work contents that can be completed quickly with priority. In this way, the reception unit can input important work contents with priority by determining the priority of the input contents according to the user's emotion.

[0071] The reception unit can preferentially input highly relevant work contents in consideration of the geographical location information of the user when inputting work contents. For example, the reception unit preferentially inputs highly relevant work contents in consideration of the geographical location information of the user when inputting work contents. The reception unit can, for example, use AI to analyze the geographical location information of the user and preferentially input highly relevant work contents. For example, when the user is in a specific area, the reception unit preferentially displays work contents related to the area. In addition, when the user is on a business trip, the reception unit can also preferentially input work contents related to the business trip destination. Furthermore, when the user is at home, the reception unit can also preferentially input work contents to be performed at home. As a result, the reception unit can preferentially input highly relevant work contents by considering the geographical location information.

[0072] The reception unit can analyze the user's social media activity when inputting the work content, and suggest related work content. The reception unit can, for example, use AI to analyze the user's social media activity and suggest related work content. For example, the reception unit can automatically suggest work content mentioned by the user on social media. The reception unit can also predict and suggest work content of interest from the user's social media activity. Furthermore, the reception unit can also suggest work content related to accounts the user follows on social media. As a result, the reception unit can suggest related work content by analyzing social media activity, and improve input efficiency.

[0073] The generation unit can estimate the user's emotion and adjust the expression method of the application creation script code to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the application creation script code to be generated based on the estimated user's emotion. The emotion estimation is realized by 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. For example, the generation unit generates application creation script code with detailed comments when the user is relaxed. In addition, the generation unit can also generate concise and executable application creation script code when the user is in a hurry. Furthermore, the generation unit can generate simple and easy-to-understand application creation script code when the user is stressed. In this way, the generation unit can provide code that is easy for the user to understand by adjusting the expression method of the application creation script code according to the user's emotion.

[0074] The generation unit can adjust the level of detail of the code based on the importance of the business content when generating the application creation script code. For example, the generation unit adjusts the level of detail of the code based on the importance of the business content when generating the application creation script code. The generation unit can evaluate the importance of the business content using, for example, a generation AI and generate optimal application creation script code. For example, the generation unit generates application creation script code including detailed error handling for important business content. In addition, the generation unit can also generate application creation script code including only basic functions for general business content. Furthermore, the generation unit can generate application creation script code that can be executed with minimal code for simple business content. As a result, the generation unit can generate an appropriate level of application creation script code by adjusting the level of detail of the code based on the importance of the business content.

[0075] The generating unit can apply different generation algorithms depending on the category of the business content when generating the application creation script code. For example, the generating unit applies different generation algorithms depending on the category of the business content when generating the application creation script code. The generating unit can analyze the category of the business content using, for example, a generation AI and apply an optimal generation algorithm. For example, the generating unit can generate application creation script code that performs efficient data manipulation for a data processing task. In addition, the generating unit can also generate application creation script code that includes an appropriate notification method for a notification task. Furthermore, the generating unit can generate application creation script code that includes a calendar operation for a schedule management task. As a result, the generating unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

[0076] The generation unit can estimate the user's emotion and adjust the length of the application creation script code to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the application creation script code to be generated based on the estimated user's emotion. The emotion estimation is realized by 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. For example, when the user is in a hurry, the generation unit generates a short and to the point application creation script code. In addition, when the user is relaxed, the generation unit can also generate a longer application creation script code including detailed explanations. Furthermore, when the user is stressed, the generation unit can also generate a simple and easy-to-understand application creation script code. In this way, the generation unit can provide the optimal code for the user by adjusting the length of the application creation script code according to the user's emotion.

[0077] The generation unit can determine the priority of the code based on the submission time of the business content when generating the application creation script code. For example, the generation unit determines the priority of the code based on the submission time of the business content when generating the application creation script code. The generation unit can analyze the submission time of the business content using, for example, a generation AI, and generate optimal application creation script code. For example, the generation unit generates application creation script code with the highest priority for urgent business content. In addition, the generation unit can also generate application creation script code based on a schedule for periodic business content. Furthermore, the generation unit can generate application creation script code in a planned manner for long-term business content. As a result, the generation unit can respond according to the urgency by determining the priority of the code based on the submission time of the business content.

[0078] The generation unit can adjust the order of the code based on the relevance of the business contents when generating the application creation script code. For example, the generation unit adjusts the order of the code based on the relevance of the business contents when generating the application creation script code. The generation unit can analyze the relevance of the business contents using, for example, a generation AI, and generate the application creation script code in an optimal order. For example, the generation unit generates application creation script code preferentially for business contents with high relevance. In addition, the generation unit can also generate application creation script code for business contents with low relevance at a later date. Furthermore, the generation unit can generate the application creation script code in an optimal order based on the relevance of the business contents. As a result, the generation unit adjusts the order of the code based on the relevance of the business contents, thereby enabling efficient code generation.

[0079] The providing unit can estimate the user's emotion and adjust the method of providing the application creation script code based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the application creation script code based on the estimated user's emotion. The emotion estimation is realized by 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. For example, the providing unit provides the application creation script code with a detailed explanation when the user is relaxed. In addition, the providing unit can also provide the application creation script code with a concise explanation when the user is in a hurry. Furthermore, the providing unit can also provide the application creation script code in a simple and easy-to-understand form when the user is stressed. As a result, the providing unit can provide the application creation script code in an optimal form for the user by adjusting the provision method according to the user's emotion.

[0080] The providing unit can select the optimal providing method by referring to the user's past code usage history when providing the application creation script code. For example, the providing unit selects the optimal providing method by referring to the user's past code usage history when providing the application creation script code. The providing unit can, for example, use AI to analyze the user's past code usage history and select the optimal providing method. For example, the providing unit preferentially selects the providing method (email, editor insertion, etc.) that the user has used in the past. In addition, the providing unit can also suggest the optimal providing method from the user's past usage history. Furthermore, the providing unit can provide the application creation script code based on the providing method that the user has used preferentially in the past. As a result, the providing unit can select the optimal providing method for the user by referring to the past usage history, thereby improving convenience.

[0081] The providing unit can customize the provided content based on the user's current project status when providing the application creation script code. For example, the providing unit customizes the provided content based on the user's current project status when providing the application creation script code. The providing unit can analyze the user's current project status using, for example, AI and provide the optimal application creation script code. For example, the providing unit preferentially provides application creation script code related to a project currently in progress by the user. In addition, the providing unit can also suggest the optimal application creation script code based on the user's project status. Furthermore, the providing unit can customize the provided content of the application creation script code according to the project the user is currently working on. As a result, the providing unit can provide the optimal application creation script code for the user by customizing the provided content based on the current project status.

[0082] The providing unit can estimate the user's emotion and determine the priority order of providing the application creation script code based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority order of providing the application creation script code based on the estimated user's emotion. The emotion estimation is realized by 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. For example, when the user is in a hurry, the providing unit provides the application creation script code with the highest priority. In addition, when the user is relaxed, the providing unit can also provide the application creation script code with a detailed explanation. Furthermore, when the user is stressed, the providing unit can also provide the application creation script code in a simple and easy-to-understand form. In this way, the providing unit can provide the application creation script code at the optimal timing for the user by determining the priority order of providing according to the user's emotion.

[0083] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the application creation script code. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing the application creation script code. The providing unit can analyze the geographical location information of the user using, for example, AI and select the optimal providing method. For example, when the user is in a specific area, the providing unit provides the application creation script code related to the area. In addition, when the user is on a business trip, the providing unit can also provide the application creation script code related to the business trip destination. Furthermore, when the user is at home, the providing unit can also provide the application creation script code related to the work performed at home. As a result, the providing unit can select the optimal providing method for the user by considering the geographical location information.

[0084] The providing unit can propose the content to be provided by analyzing the social media activity of the user when providing the application creation script code. For example, the providing unit proposes the content to be provided by analyzing the social media activity of the user when providing the application creation script code. The providing unit can, for example, use AI to analyze the social media activity of the user and provide the application creation script code based on the related business content. For example, the providing unit provides the application creation script code related to the business content mentioned by the user on social media. The providing unit can also propose the application creation script code related to the business content of interest from the user's social media activity. Furthermore, the providing unit can also provide the application creation script code related to the account the user follows on social media. As a result, the providing unit can provide the application creation script code based on the business content related to the user by analyzing the social media activity.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can analyze the user's past work content input history and suggest an appropriate input method. For example, the reception unit automatically displays work content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest work content to be used in a specific time period from the user's past input history. In this way, the reception unit can analyze the past input history to suggest the most suitable input method to the user and improve input efficiency.

[0087] The generation unit can adjust the level of detail of the code based on the importance of the business content when generating the application creation script code. For example, the generation unit can use a generation AI to evaluate the importance of the business content and generate optimal application creation script code. The generation unit generates application creation script code including detailed error handling for important business content. The generation unit can also generate application creation script code including only basic functions for general business content. Furthermore, the generation unit can generate application creation script code that can be executed with a minimum of code for simple business content. As a result, the generation unit can generate an appropriate level of application creation script code by adjusting the level of detail of the code based on the importance of the business content.

[0088] The providing unit can estimate the user's emotion and adjust the method of providing the application creation script code based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides the application creation script code with a detailed explanation. When the user is in a hurry, the providing unit can also provide the application creation script code with a concise explanation. Furthermore, when the user is stressed, the providing unit can also provide the application creation script code in a simple and easy-to-understand form. In this way, the providing unit can provide the application creation script code in an optimal form for the user by adjusting the providing method according to the user's emotion.

[0089] The reception unit can filter the input contents based on the user's current project and field of interest when inputting the work content. For example, the reception unit can use AI to analyze the user's current project and field of interest and filter the input contents. The reception unit preferentially displays work contents related to the project currently being carried out by the user. The reception unit can also suggest related work contents based on the user's field of interest. Furthermore, the reception unit can filter the input contents based on the field in which the user has shown interest in the past. As a result, the reception unit can preferentially input highly relevant work contents by filtering the input contents based on the current project and field of interest.

[0090] The generating unit can estimate the user's emotion and adjust the expression method of the generated application creation script code based on the estimated user's emotion. For example, when the user is relaxed, the generating unit generates application creation script code with detailed comments. When the user is in a hurry, the generating unit can also generate concise and executable application creation script code. Furthermore, when the user is stressed, the generating unit can generate simple and easy-to-understand application creation script code. In this way, the generating unit can provide code that is easy for the user to understand by adjusting the expression method of the application creation script code according to the user's emotion.

[0091] When providing the application creation script code, the providing unit can select the optimal providing method by referring to the user's past code usage history. For example, the providing unit can use AI to analyze the user's past code usage history and select the optimal providing method. The providing unit preferentially selects the providing method (email, editor insertion, etc.) that the user has used in the past. The providing unit can also suggest the optimal providing method from the user's past usage history. Furthermore, the providing unit can provide the application creation script code based on the providing method that the user has used preferably in the past. In this way, the providing unit can select the optimal providing method for the user by referring to the past usage history, thereby improving convenience.

[0092] The reception unit can estimate the user's emotion and adjust the input interface of the work contents based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input procedures. When the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input the work contents quickly. In this way, the reception unit can reduce the user's stress and enable efficient input by adjusting the input interface according to the user's emotion.

[0093] The generation unit can apply different generation algorithms depending on the category of the business content when generating the application creation script code. For example, the generation unit can use a generation AI to analyze the category of the business content and apply an optimal generation algorithm. The generation unit generates application creation script code that performs efficient data manipulation for a data processing task. The generation unit can also generate application creation script code that includes an appropriate notification method for a notification task. Furthermore, the generation unit can generate application creation script code that includes calendar manipulation for a schedule management task. In this way, the generation unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

[0094] The providing unit can estimate the user's emotions and determine the priority order for providing the application creation script code based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit provides the application creation script code with the highest priority. Also, when the user is relaxed, the providing unit can provide the application creation script code with a detailed explanation. Furthermore, when the user is stressed, the providing unit can provide the application creation script code in a simple and easy-to-understand form. In this way, the providing unit can provide the application creation script code at the optimal timing for the user by determining the priority order for providing according to the user's emotions.

[0095] When inputting the work content, the reception unit can prioritize inputting highly relevant work content in consideration of the geographical location information of the user. For example, the reception unit can use AI to analyze the geographical location information of the user and prioritize inputting highly relevant work content. When the user is in a specific area, the reception unit can prioritize displaying work content related to the area. In addition, when the user is on a business trip, the reception unit can also prioritize inputting work content related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting work content to be performed at home. In this way, the reception unit can prioritize inputting highly relevant work content by considering the geographical location information.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The reception unit receives an input of business contents from a user. The input of business contents from a user includes, but is not limited to, accounting, sales, project management, etc. The reception unit can analyze the user's input using, for example, natural language processing technology. Step 2: The generation unit uses the generation AI to analyze the business content accepted by the acceptance unit and generate a corresponding application creation script code. The generation AI analyzes the user's input using, for example, deep learning or natural language processing technology, and generates an optimal application creation script code. For example, in response to an input such as "open a specific spreadsheet at 9:00 every day and update the data," the generation unit generates an application creation script code for executing the business. Step 3: The providing unit provides the application creation script code generated by the generating unit to the user. For example, the providing unit can automatically insert the generated application creation script code into the user's application creation script editor. The providing unit can also send the generated application creation script code to the user by email. This allows the user to easily use the generated application creation script code.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 control unit 46A of the smart device 14 and receives an input of the business contents from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the application creation script code using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 control unit 46A of the smart glasses 214 and receives an input of the business contents from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the application creation script code using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0121] 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.

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

[0123] 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).

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

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 control unit 46A of the headset type terminal 314, and receives an input of the business contents from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the application creation script code using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 control unit 46A of the robot 414, and receives an input of the business contents from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an application creation script code using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] These emotions are distributed in the 3 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.

[0154] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Appendix 1) A reception unit that receives input of business contents from a user; a generation unit that analyzes the business content received by the reception unit and generates a corresponding application creation script code; a providing unit that provides a user with the application creation script code generated by the generating unit; A system characterized by: (Appendix 2) The generation unit is Generative AI analyzes business content in natural language and generates corresponding application creation script code 2. The system of claim 1, (Appendix 3) The providing unit is Automatically inserting generated application creation script code into the user's application creation script editor 2. The system of claim 1, (Appendix 4) The providing unit is Email the generated application creation script code to the user 2. The system of claim 1, (Appendix 5) The generation unit is Generate code that incorporates modern application creation scripting features and recommended practices 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotion and adjust an input interface for business contents based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past work content input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 8) The reception unit is As you enter your job description, filter it based on your current projects and interests 2. The system of claim 1, (Appendix 9) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 10) The reception unit is When entering work content, the system takes into account the user's geographic location information and prioritizes the most relevant work content. 2. The system of claim 1, (Appendix 11) The reception unit is When entering a job description, the app analyzes the user's social media activity and suggests related job descriptions. 2. The system of claim 1, (Appendix 12) The generation unit is Infer a user's emotions and adjust the expression of the application creation script code to be generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The generation unit is When generating application creation script code, adjust the level of code detail based on the importance of the business content. 2. The system of claim 1, (Appendix 14) The generation unit is When generating application creation script code, different generation algorithms are applied depending on the business content category. 2. The system of claim 1, (Appendix 15) The generation unit is Infer a user's emotion and adjust the length of the application creation script code to be generated based on the estimated user emotion. 2. The system of claim 1, (Appendix 16) The generation unit is Application creation script code generation prioritizes code based on when the work is submitted 2. The system of claim 1, (Appendix 17) The generation unit is When generating application creation script code, adjust the order of code based on business relevance 2. The system of claim 1, (Appendix 18) The providing unit is Inferring a user's emotion and adjusting how application creation script code is provided based on the inferred user emotion 2. The system of claim 1, (Appendix 19) The providing unit is When providing application creation script code, the optimal method of providing the code is selected by referring to the user's past code usage history. 2. The system of claim 1, (Appendix 20) The providing unit is When providing application creation script code, customize the content based on the user's current project context 2. The system of claim 1, (Appendix 21) The providing unit is To estimate a user's emotion and determine a priority of providing application creation script code based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The providing unit is When providing application creation script code, the optimal method of providing it is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The providing unit is When providing script code for application creation, analyze the user's social media activity to suggest content to be provided 2. The system of claim 1, [Explanation of symbols]

[0170] 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 input of business contents from a user; a generation unit that analyzes the business content received by the reception unit and generates a corresponding application creation script code; a providing unit that provides a user with the application creation script code generated by the generating unit; A system characterized by:

2. The generation unit is Analyze business content in natural language using generative AI and generate corresponding application creation script code 2. The system of claim 1 .

3. The providing unit is The application creation script code generated by the generation unit is automatically inserted into a user's application creation script editor.

2. The system of claim 1 .

4. The providing unit is The application creation script code generated by the generation unit is sent to the user by email.

2. The system of claim 1 .

5. The generation unit is Generate code that incorporates modern application creation scripting features and recommended practices 2. The system of claim 1 .

6. The reception unit is To estimate a user's emotion and adjust an input interface for business contents based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past work content input history and suggest appropriate input methods 2. The system of claim 1 .

8. The reception unit is As you enter your job description, filter it based on your current projects and interests 2. The system of claim 1 .

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