System

Abstract

An object of a system according to an embodiment is to easily create a tool for automating troublesome work.SOLUTION: A system includes an input unit, an analysis unit, a collection unit, and a generation unit. The input unit inputs work that the user thinks troublesome. The analysis unit analyzes the work content input by the input unit. The collection unit automatically collects necessary data and documents based on the contents analyzed by the analysis unit. The generation unit generates an automatic creation tool based on the data and documents collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the process of creating tools to automate tedious tasks is complex and time-consuming.

[0005] The system according to the embodiment aims to easily create a tool that automates tedious tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a collection unit, and a generation unit. The input unit inputs tasks that a user finds troublesome. The analysis unit analyzes the task content input by the input unit. The collection unit automatically collects necessary data and documents based on the content analyzed by the analysis unit. The generation unit generates an automatic creation tool based on the data and documents collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy to create tools that automate tedious tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automation tool generation system according to the embodiment of the present invention is a system for generating tools for automating tasks that users find tedious, thereby enabling the automation tool generation system to efficiently automate tasks that users find tedious.

[0029] An automation tool generation system according to an embodiment includes an input unit, an analysis unit, a collection unit, and a generation unit. The input unit inputs tasks that a user finds troublesome. For example, the user inputs "creating documents to submit to government offices is a hassle." The input unit can also input "entering data every day is a hassle." The input unit can also input "creating periodic reports is a hassle." The analysis unit analyzes the task content input by the input unit. For example, the analysis unit analyzes the task content using natural language processing technology. The analysis unit can also analyze the task content using pattern recognition technology. The analysis unit can also analyze the task content using a machine learning algorithm. The collection unit automatically collects required data and documents based on the content analyzed by the analysis unit. For example, the collection unit collects required data using web scraping technology. The collection unit can also collect required data using API integration. The collection unit can also collect required data using a database query. The generation unit generates an automatic creation tool based on the data and documents collected by the collection unit. For example, the generation unit generates a report generation tool. The generation unit can also generate a data analysis tool. The generation unit can also generate a task management tool. As a result, the automation tool generation system according to the embodiment can efficiently automate tasks that users find tedious.

[0030] The analysis unit can refer to a database of similar past tasks and suggest the optimal automation method for the task content entered by the user. For example, if a user enters, "It's a hassle to prepare documents to submit to government offices," the generation AI will refer to data from users who have performed similar tasks in the past and suggest the optimal automation method. For example, it can suggest using a specific document template or automatically entering the required information. Similarly, if a user enters, "Entering data every day is a hassle," the generation AI will refer to the history of past data entry tasks and suggest the optimal automation method. For example, it can suggest automatic entry from a database or the use of a standard format. Similarly, if a user enters, "Preparing regular reports is a hassle," the analysis unit will refer to data from past report creation and suggest the optimal automation method. For example, it can suggest using a report template or automatically compiling data. This allows the generation AI to suggest the optimal automation method by referring to a database of similar past tasks.

[0031] The analysis unit learns the user's past input history and can predict and automatically complete future input. For example, when a user inputs, "Monthly expense settlement is a hassle," the generation AI learns past expense settlement data and predicts and automatically completes future input. For example, it automatically inputs regularly occurring expense items. Also, when a user inputs, "Preparing minutes for regular meetings is a hassle," the generation AI learns past minutes data and predicts and automatically completes future input. For example, it automatically inputs meeting participants and agenda items. Also, when a user inputs, "Daily task management is a hassle," the generation AI learns past task management data and predicts and automatically completes future input. For example, it automatically inputs regularly occurring tasks. In this way, by learning the user's past input history, it can predict and automatically complete future input.

[0032] The input unit allows users to describe their work through voice input, and the generation AI can analyze the voice to generate an automation tool. For example, if a user voice-inputs, "It's a hassle to prepare documents to submit to the government office," the input unit allows the generation AI to analyze the voice, identify the necessary data and documents, and generate an automation tool. For example, it provides an automatic document template input function. Also, if a user voice-inputs, "Entering data every day is a hassle," the generation AI analyzes the voice and generates a tool to automate data entry tasks. For example, it provides an automatic input function from a database. Also, if a user voice-inputs, "Preparing periodic reports is a hassle," the generation AI analyzes the voice and generates a tool to automate report creation tasks. For example, it provides an automatic data aggregation and graph creation function. This allows users to describe their work through voice input, and the generation AI analyzes the voice and generates an automation tool.

[0033] The input unit can add a function for sharing work content between different users, allowing them to refer to automation tools created by other users. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI references similar automation tools created by other users and suggests the most appropriate tool. For example, it provides a document template sharing function. Also, when a user inputs, "Entering data every day is a hassle," the generation AI references data entry automation tools created by other users and suggests the most appropriate tool. For example, it can share an automatic input function from a database. Also, when a user inputs, "Creating periodic reports is a hassle," the generation AI references report creation automation tools created by other users and suggests the most appropriate tool. For example, it can share functions for automatic data aggregation and graph creation. By adding a function for sharing work content between different users, automation tools created by other users can be used as a reference.

[0034] The collection unit can crawl public databases on the Internet and automatically collect the necessary data. For example, if a user inputs, "It's a hassle to prepare documents to submit to government offices," the generation AI will crawl public databases on the Internet and automatically collect the necessary document templates and related information. Alternatively, if a user inputs, "It's a hassle to enter data every day," the generation AI will crawl public databases on the Internet and automatically collect the necessary data. For example, it will collect the latest market data and statistical information. Alternatively, if a user inputs, "It's a hassle to prepare periodic reports," the generation AI will crawl public databases on the Internet and automatically collect the necessary data. For example, it will collect industry reports and research data. This allows the necessary data to be automatically collected by crawling public databases on the Internet.

[0035] The collection unit can automatically search and collect necessary documents from the user's email or cloud storage. For example, if a user inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI will search the user's email or cloud storage and automatically collect the necessary documents. For example, it will collect past application documents and related materials. Also, if a user inputs, "It's a hassle to enter data every day," the generation AI will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past data entry files and related materials. Also, if a user inputs, "It's a hassle to prepare regular reports," the generation AI will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past reports and related materials. This allows the generation AI to automatically search and collect necessary documents from the user's email or cloud storage.

[0036] The collection unit can convert data in different formats into a unified format and centrally manage the collected data. For example, when a user inputs, "It's a hassle to create documents to submit to government offices," the generation AI converts document templates in different formats into a unified format and centrally manages them. For example, it converts PDF and Word files into a unified format. Similarly, when a user inputs, "Entering data every day is a hassle," the generation AI converts data in different formats into a unified format and centrally manages them. For example, it converts Excel and CSV files into a unified format. Similarly, when a user inputs, "Creating periodic reports is a hassle," the generation AI converts data in different formats into a unified format and centrally manages them. For example, it converts text files and image files into a unified format. By converting data in different formats into a unified format and centrally managing it, data consistency and management become easier.

[0037] The collection unit can work with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, when a user inputs, "It's a hassle to prepare documents to submit to government offices," the generation AI works with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with a document creation tool. Also, when a user inputs, "It's a hassle to enter data every day," the collection unit can link the generation AI with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with an input automation tool. Also, when a user inputs, "It's a hassle to prepare periodic reports," the collection unit can link the generation AI with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with a report creation tool. This seamless integration of the process from data collection to tool generation enables efficient automation.

[0038] The generation unit can learn the user's workflow and customize and provide the optimal automation tool. For example, if a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal document creation tool. For example, it customizes specific document templates and auto-fill functions. Also, if a user inputs, "Daily data entry is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal data entry tool. For example, it customizes the use of automatic entry from a database or standard formats. Also, if a user inputs, "Creating regular reports is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal report creation tool. For example, it customizes automatic data aggregation and graph creation functions. In this way, the generation AI can learn the user's workflow and customize and provide the optimal automation tool, thereby improving the user's work efficiency.

[0039] The generation unit reflects user feedback in real time and can continuously improve the accuracy of the tool. For example, when a user inputs, "It's a hassle to create documents to submit to government offices," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the document creation tool. For example, the generation AI improves templates based on user feedback. Also, when a user inputs, "It's a hassle to enter data every day," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the data entry tool. For example, the generation AI improves the input automation function based on user feedback. Also, when a user inputs, "It's a hassle to create periodic reports," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the report creation tool. For example, the generation AI improves the automatic aggregation function based on user feedback. In this way, the tool's accuracy can be continuously improved by reflecting user feedback in real time.

[0040] The generation unit can generate automatic creation tools for different platforms. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI generates a document creation tool for a mobile app. For example, it provides an interface that is easy to operate on a smartphone. Also, when a user inputs, "Entering data every day is a hassle," the generation AI generates a data entry tool for a web app. For example, it provides an auto-fill function that runs on a browser. Also, when a user inputs, "Creating periodic reports is a hassle," the generation AI generates a report creation tool for a desktop app. For example, it provides an automatic aggregation function that runs on a PC. This allows for generating automatic creation tools for different platforms, improving user convenience.

[0041] The generation unit can refer to tools created by other users and suggest the most appropriate tool. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI references similar document creation tools created by other users and suggests the most appropriate tool. For example, it provides a document template sharing function. Also, when a user inputs, "Entering data every day is a hassle," the generation AI references data entry tools created by other users and suggests the most appropriate tool. For example, it shares an automatic input function from a database. Also, when a user inputs, "Creating regular reports is a hassle," the generation unit references report creation tools created by other users and suggests the most appropriate tool. For example, it shares automatic data aggregation and graph creation functions. In this way, the generation unit can refer to tools created by other users and suggest the most appropriate tool.

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

[0043] When analyzing the user's work content, the analysis unit learns the user's past work patterns and can predict and automate future work. For example, if a user inputs, "Monthly expense settlement is a hassle," the analysis unit learns past expense settlement data and predicts and automates future expense settlement. For example, it automatically inputs regularly occurring expense items. Also, if a user inputs, "Preparing minutes for regular meetings is a hassle," the analysis unit learns past minutes data and predicts and automates the creation of minutes for future meetings. For example, it automatically inputs meeting participants and agenda items. Also, if a user inputs, "Daily task management is a hassle," the analysis unit learns past task management data and predicts and automates future task management. For example, it automatically inputs regularly occurring tasks. In this way, by learning the user's past work patterns, it is possible to predict and automate future tasks.

[0044] The collection unit can automatically search for and collect necessary documents from the user's email or cloud storage. For example, if the user inputs, "It's a hassle to prepare documents to submit to the government office," the collection unit will search the user's email or cloud storage and automatically collect the necessary documents. For example, it will collect past application documents and related materials. Also, if the user inputs, "It's a hassle to enter data every day," the collection unit will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past data entry files and related materials. Also, if the user inputs, "It's a hassle to prepare regular reports," the collection unit will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past reports and related materials. This allows the collection unit to automatically search for and collect necessary documents from the user's email or cloud storage.

[0045] The generation unit can learn the user's workflow and customize and provide the optimal automation tool. For example, if a user inputs, "Creating documents to submit to government offices is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal document creation tool. For example, it customizes a specific document template or auto-fill function. Also, if a user inputs, "Entering data every day is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal data entry tool. For example, it customizes auto-entry from a database or the use of standard formats. Also, if a user inputs, "Creating regular reports is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal report creation tool. For example, it customizes automatic data aggregation and graph creation functions. In this way, the generation unit can learn the user's workflow and customize and provide the optimal automation tool, thereby improving the user's work efficiency.

[0046] The input unit allows users to describe their work through voice input, and the generation AI can analyze the voice to generate an automation tool. For example, if a user voice-inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI analyzes the voice, identifies the necessary data and documents, and generates an automation tool. For example, it provides an automatic document template entry function. Alternatively, if a user voice-inputs, "Entering data every day is a hassle," the generation AI analyzes the voice and generates a tool to automate data entry tasks. For example, it provides an automatic entry function from a database. Alternatively, if a user voice-inputs, "Preparing periodic reports is a hassle," the generation AI analyzes the voice and generates a tool to automate report creation tasks. For example, it provides an automatic data aggregation and graph creation function. This allows users to describe their work through voice input, and the generation AI analyzes the voice and generates an automation tool.

[0047] The collection unit can crawl public databases on the Internet and automatically collect required data. For example, if a user types "creating documents to submit to government offices is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required document templates and related information. Also, if a user types "entering data every day is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required data. For example, it will collect the latest market data and statistical information. Also, if a user types "creating regular reports is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required data. For example, it will collect industry reports and research data. In this way, required data can be automatically collected by crawling public databases on the Internet.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The input section allows the user to input tasks that they find troublesome. For example, the user can input "creating documents to submit to the government office is a hassle." The user can also input "entering data every day is a hassle." The user can also input "creating regular reports is a hassle." Step 2: The analysis unit analyzes the work content input by the input unit. For example, the analysis unit analyzes the work content using natural language processing technology, pattern recognition technology, and machine learning algorithms. Step 3: The collection department automatically collects the necessary data and documents based on the content analyzed by the analysis department. For example, the collection department may use web scraping technology, API integration, or database queries to collect the necessary data. Step 4: The generation unit generates automated tools based on the data and documents collected by the collection unit. For example, the generation unit generates report generation tools, data analysis tools, and task management tools.

[0050] (Example 2) The automation tool generation system according to the embodiment of the present invention is a system for generating tools for automating tasks that users find tedious, thereby enabling the automation tool generation system to efficiently automate tasks that users find tedious.

[0051] An automation tool generation system according to an embodiment includes an input unit, an analysis unit, a collection unit, and a generation unit. The input unit inputs tasks that a user finds troublesome. For example, the user inputs "creating documents to submit to government offices is a hassle." The input unit can also input "entering data every day is a hassle." The input unit can also input "creating periodic reports is a hassle." The analysis unit analyzes the task content input by the input unit. For example, the analysis unit analyzes the task content using natural language processing technology. The analysis unit can also analyze the task content using pattern recognition technology. The analysis unit can also analyze the task content using a machine learning algorithm. The collection unit automatically collects required data and documents based on the content analyzed by the analysis unit. For example, the collection unit collects required data using web scraping technology. The collection unit can also collect required data using API integration. The collection unit can also collect required data using a database query. The generation unit generates an automatic creation tool based on the data and documents collected by the collection unit. For example, the generation unit generates a report generation tool. The generation unit can also generate a data analysis tool. The generation unit can also generate a task management tool. As a result, the automation tool generation system according to the embodiment can efficiently automate tasks that users find tedious.

[0052] The analysis unit can refer to a database of similar past tasks and suggest the optimal automation method for the task content entered by the user. For example, if a user enters, "It's a hassle to prepare documents to submit to government offices," the generation AI will refer to data from users who have performed similar tasks in the past and suggest the optimal automation method. For example, it can suggest using a specific document template or automatically entering the required information. Similarly, if a user enters, "Entering data every day is a hassle," the generation AI will refer to the history of past data entry tasks and suggest the optimal automation method. For example, it can suggest automatic entry from a database or the use of a standard format. Similarly, if a user enters, "Preparing regular reports is a hassle," the analysis unit will refer to data from past report creation and suggest the optimal automation method. For example, it can suggest using a report template or automatically compiling data. This allows the generation AI to suggest the optimal automation method by referring to a database of similar past tasks.

[0053] The analysis unit learns the user's past input history and can predict and automatically complete future input. For example, when a user inputs, "Monthly expense settlement is a hassle," the generation AI learns past expense settlement data and predicts and automatically completes future input. For example, it automatically inputs regularly occurring expense items. Also, when a user inputs, "Preparing minutes for regular meetings is a hassle," the generation AI learns past minutes data and predicts and automatically completes future input. For example, it automatically inputs meeting participants and agenda items. Also, when a user inputs, "Daily task management is a hassle," the generation AI learns past task management data and predicts and automatically completes future input. For example, it automatically inputs regularly occurring tasks. In this way, by learning the user's past input history, it can predict and automatically complete future input.

[0054] The analysis unit uses the emotion estimation function to analyze the emotions of the user regarding the work content entered by the user and prioritizes automating the parts that are particularly stressful. For example, when a user enters, "Entering data every day is stressful," the generation AI uses the emotion estimation function to analyze the parts that are particularly stressful and prioritizes automating those parts. For example, automatic data entry and checking functions can be added. Also, when a user enters, "Preparing regular reports is stressful," the analysis unit uses the emotion estimation function to analyze the parts that are particularly stressful and prioritizes automating those parts. For example, automatic data aggregation and graph creation functions can be added. Also, when a user enters, "Reconciling monthly expenses is stressful," the analysis unit uses the emotion estimation function to analyze the parts that are particularly stressful and prioritizes automating those parts. For example, automatic entry of expense items and automatic receipt scanning functions can be added. This reduces the burden on the user by prioritizing automation of the parts that are particularly stressful for the user.

[0055] The input unit allows users to describe their work through voice input, and the generation AI can analyze the voice to generate an automation tool. For example, if a user voice-inputs, "It's a hassle to prepare documents to submit to the government office," the input unit allows the generation AI to analyze the voice, identify the necessary data and documents, and generate an automation tool. For example, it provides an automatic document template input function. Also, if a user voice-inputs, "Entering data every day is a hassle," the generation AI analyzes the voice and generates a tool to automate data entry tasks. For example, it provides an automatic input function from a database. Also, if a user voice-inputs, "Preparing periodic reports is a hassle," the generation AI analyzes the voice and generates a tool to automate report creation tasks. For example, it provides an automatic data aggregation and graph creation function. This allows users to describe their work through voice input, and the generation AI analyzes the voice and generates an automation tool.

[0056] The input unit can add a function for sharing work content between different users, allowing them to refer to automation tools created by other users. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI references similar automation tools created by other users and suggests the most appropriate tool. For example, it provides a document template sharing function. Also, when a user inputs, "Entering data every day is a hassle," the generation AI references data entry automation tools created by other users and suggests the most appropriate tool. For example, it can share an automatic input function from a database. Also, when a user inputs, "Creating periodic reports is a hassle," the generation AI references report creation automation tools created by other users and suggests the most appropriate tool. For example, it can share functions for automatic data aggregation and graph creation. By adding a function for sharing work content between different users, automation tools created by other users can be used as a reference.

[0057] The input unit can use an emotion estimation function to analyze the user's emotions in real time when entering data and provide an interface for eliciting positive emotions. For example, when a user enters, "Entering data every day is a hassle," the generation AI can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can display encouraging messages or success stories. When a user enters, "Preparing periodic reports is a hassle," the generation AI can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can visually display progress. When a user enters, "Reconciling monthly expenses is a hassle," the generation AI can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can display a graph of the expense settlement progress. This allows the generation AI to analyze the user's emotions in real time when entering data and provide an interface for eliciting positive emotions.

[0058] The collection unit can crawl public databases on the Internet and automatically collect the necessary data. For example, if a user inputs, "It's a hassle to prepare documents to submit to government offices," the generation AI will crawl public databases on the Internet and automatically collect the necessary document templates and related information. Alternatively, if a user inputs, "It's a hassle to enter data every day," the generation AI will crawl public databases on the Internet and automatically collect the necessary data. For example, it will collect the latest market data and statistical information. Alternatively, if a user inputs, "It's a hassle to prepare periodic reports," the generation AI will crawl public databases on the Internet and automatically collect the necessary data. For example, it will collect industry reports and research data. This allows the necessary data to be automatically collected by crawling public databases on the Internet.

[0059] The collection unit can automatically search and collect necessary documents from the user's email or cloud storage. For example, if a user inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI will search the user's email or cloud storage and automatically collect the necessary documents. For example, it will collect past application documents and related materials. Also, if a user inputs, "It's a hassle to enter data every day," the generation AI will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past data entry files and related materials. Also, if a user inputs, "It's a hassle to prepare regular reports," the generation AI will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past reports and related materials. This allows the generation AI to automatically search and collect necessary documents from the user's email or cloud storage.

[0060] The collection unit can use the emotion estimation function to adjust the generation AI to prioritize collecting data that the user finds particularly important. For example, when a user inputs, "It's a hassle to prepare documents to submit to government offices," the collection unit causes the generation AI to use the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important document templates and related information. Also, when a user inputs, "It's a hassle to enter data every day," the collection unit causes the generation AI to use the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important market data and statistical information. Also, when a user inputs, "It's a hassle to prepare periodic reports," the collection unit causes the generation AI to use the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important industry reports and research data. This enables data collection that meets the user's needs by prioritizing the collection of data that the user finds particularly important.

[0061] The collection unit can convert data in different formats into a unified format and centrally manage the collected data. For example, when a user inputs, "It's a hassle to create documents to submit to government offices," the generation AI converts document templates in different formats into a unified format and centrally manages them. For example, it converts PDF and Word files into a unified format. Similarly, when a user inputs, "Entering data every day is a hassle," the generation AI converts data in different formats into a unified format and centrally manages them. For example, it converts Excel and CSV files into a unified format. Similarly, when a user inputs, "Creating periodic reports is a hassle," the generation AI converts data in different formats into a unified format and centrally manages them. For example, it converts text files and image files into a unified format. By converting data in different formats into a unified format and centrally managing it, data consistency and management become easier.

[0062] The collection unit can work with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, when a user inputs, "It's a hassle to prepare documents to submit to government offices," the generation AI works with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with a document creation tool. Also, when a user inputs, "It's a hassle to enter data every day," the collection unit can link the generation AI with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with an input automation tool. Also, when a user inputs, "It's a hassle to prepare periodic reports," the collection unit can link the generation AI with other automation tools to seamlessly integrate the process from data collection to tool generation. For example, it links a data collection tool with a report creation tool. This seamless integration of the process from data collection to tool generation enables efficient automation.

[0063] The collection unit can use the emotion estimation function to provide a feedback function to reduce the user's anxiety about the progress of data collection. For example, when the user inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI uses the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the progress can be visually displayed to provide a sense of security. Furthermore, when the user inputs, "Entering data every day is a hassle," the generation AI uses the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the generation AI can check the collected data and provide a progress report. Furthermore, when the user inputs, "It's a hassle to prepare periodic reports," the generation AI can use the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the generation AI can check the quality of the collected data and notify the user of the progress. This provides a feedback function to reduce the user's anxiety about the progress of data collection, thereby increasing the user's sense of security.

[0064] The generation unit can learn the user's workflow and customize and provide the optimal automation tool. For example, if a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal document creation tool. For example, it customizes specific document templates and auto-fill functions. Also, if a user inputs, "Daily data entry is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal data entry tool. For example, it customizes the use of automatic entry from a database or standard formats. Also, if a user inputs, "Creating regular reports is a hassle," the generation AI learns the user's workflow and customizes and provides the optimal report creation tool. For example, it customizes automatic data aggregation and graph creation functions. In this way, the generation AI can learn the user's workflow and customize and provide the optimal automation tool, thereby improving the user's work efficiency.

[0065] The generation unit reflects user feedback in real time and can continuously improve the accuracy of the tool. For example, when a user inputs, "It's a hassle to create documents to submit to government offices," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the document creation tool. For example, the generation AI improves templates based on user feedback. Also, when a user inputs, "It's a hassle to enter data every day," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the data entry tool. For example, the generation AI improves the input automation function based on user feedback. Also, when a user inputs, "It's a hassle to create periodic reports," the generation AI reflects the user's feedback in real time and continuously improves the accuracy of the report creation tool. For example, the generation AI improves the automatic aggregation function based on user feedback. In this way, the tool's accuracy can be continuously improved by reflecting user feedback in real time.

[0066] The generation unit can use the emotion estimation function to analyze the user's emotions when using the tool and provide an interface to improve usability. For example, when a user inputs, "Preparing documents to submit to government offices is a hassle," the generation AI uses the emotion estimation function to analyze the user's emotions when using the tool and provide an interface to improve usability. For example, it displays an intuitive operation guide. Also, when a user inputs, "Entering data every day is a hassle," the generation AI uses the emotion estimation function to analyze the user's emotions when using the tool and provides an interface to improve usability. For example, it simplifies the operation procedure. Also, when a user inputs, "Preparing periodic reports is a hassle," the generation AI uses the emotion estimation function to analyze the user's emotions when using the tool and provides an interface to improve usability. For example, it provides visual feedback. This allows the generation unit to analyze the user's emotions when using the tool and provide an interface to improve usability, thereby increasing user satisfaction.

[0067] The generation unit can generate automatic creation tools for different platforms. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI generates a document creation tool for a mobile app. For example, it provides an interface that is easy to operate on a smartphone. Also, when a user inputs, "Entering data every day is a hassle," the generation AI generates a data entry tool for a web app. For example, it provides an auto-fill function that runs on a browser. Also, when a user inputs, "Creating periodic reports is a hassle," the generation AI generates a report creation tool for a desktop app. For example, it provides an automatic aggregation function that runs on a PC. This allows for generating automatic creation tools for different platforms, improving user convenience.

[0068] The generation unit can refer to tools created by other users and suggest the most appropriate tool. For example, when a user inputs, "Creating documents to submit to government offices is a hassle," the generation AI references similar document creation tools created by other users and suggests the most appropriate tool. For example, it provides a document template sharing function. Also, when a user inputs, "Entering data every day is a hassle," the generation AI references data entry tools created by other users and suggests the most appropriate tool. For example, it shares an automatic input function from a database. Also, when a user inputs, "Creating regular reports is a hassle," the generation unit references report creation tools created by other users and suggests the most appropriate tool. For example, it shares automatic data aggregation and graph creation functions. In this way, the generation unit can refer to tools created by other users and suggest the most appropriate tool.

[0069] The generation unit uses the emotion estimation function to monitor the user's emotions in real time when using the tool and continuously provide the optimal tool. For example, when a user inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI uses the emotion estimation function to monitor the user's emotions in real time when using the tool and continuously provide the optimal document preparation tool. For example, the generation unit adjusts the operation guide according to the user's emotions. Also, when a user inputs, "It's a hassle to enter data every day," the generation AI uses the emotion estimation function to monitor the user's emotions in real time when using the tool and continuously provide the optimal data entry tool. For example, the generation unit adjusts the input procedure according to the user's emotions. Also, when a user inputs, "It's a hassle to prepare periodic reports," the generation unit uses the emotion estimation function to monitor the user's emotions in real time when using the tool and continuously provide the optimal report preparation tool. For example, the generation AI adjusts the visual feedback according to the user's emotions. This allows the generation unit to monitor the user's emotions in real time when using the tool and continuously provide the optimal tool, thereby increasing user satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When analyzing the user's work content, the analysis unit learns the user's past work patterns and can predict and automate future work. For example, if a user inputs, "Monthly expense settlement is a hassle," the analysis unit learns past expense settlement data and predicts and automates future expense settlement. For example, it automatically inputs regularly occurring expense items. Also, if a user inputs, "Preparing minutes for regular meetings is a hassle," the analysis unit learns past minutes data and predicts and automates the creation of minutes for future meetings. For example, it automatically inputs meeting participants and agenda items. Also, if a user inputs, "Daily task management is a hassle," the analysis unit learns past task management data and predicts and automates future task management. For example, it automatically inputs regularly occurring tasks. In this way, by learning the user's past work patterns, it is possible to predict and automate future tasks.

[0072] The collection unit can automatically search for and collect necessary documents from the user's email or cloud storage. For example, if the user inputs, "It's a hassle to prepare documents to submit to the government office," the collection unit will search the user's email or cloud storage and automatically collect the necessary documents. For example, it will collect past application documents and related materials. Also, if the user inputs, "It's a hassle to enter data every day," the collection unit will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past data entry files and related materials. Also, if the user inputs, "It's a hassle to prepare regular reports," the collection unit will search the user's email or cloud storage and automatically collect the necessary data. For example, it will collect past reports and related materials. This allows the collection unit to automatically search for and collect necessary documents from the user's email or cloud storage.

[0073] The generation unit can learn the user's workflow and customize and provide the optimal automation tool. For example, if a user inputs, "Creating documents to submit to government offices is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal document creation tool. For example, it customizes a specific document template or auto-fill function. Also, if a user inputs, "Entering data every day is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal data entry tool. For example, it customizes auto-entry from a database or the use of standard formats. Also, if a user inputs, "Creating regular reports is a hassle," the generation unit learns the user's workflow and customizes and provides the optimal report creation tool. For example, it customizes automatic data aggregation and graph creation functions. In this way, the generation unit can learn the user's workflow and customize and provide the optimal automation tool, thereby improving the user's work efficiency.

[0074] The input unit allows users to describe their work through voice input, and the generation AI can analyze the voice to generate an automation tool. For example, if a user voice-inputs, "It's a hassle to prepare documents to submit to the government office," the generation AI analyzes the voice, identifies the necessary data and documents, and generates an automation tool. For example, it provides an automatic document template entry function. Alternatively, if a user voice-inputs, "Entering data every day is a hassle," the generation AI analyzes the voice and generates a tool to automate data entry tasks. For example, it provides an automatic entry function from a database. Alternatively, if a user voice-inputs, "Preparing periodic reports is a hassle," the generation AI analyzes the voice and generates a tool to automate report creation tasks. For example, it provides an automatic data aggregation and graph creation function. This allows users to describe their work through voice input, and the generation AI analyzes the voice and generates an automation tool.

[0075] The collection unit can crawl public databases on the Internet and automatically collect required data. For example, if a user types "creating documents to submit to government offices is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required document templates and related information. Also, if a user types "entering data every day is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required data. For example, it will collect the latest market data and statistical information. Also, if a user types "creating regular reports is a hassle," the collection unit will crawl public databases on the Internet and automatically collect required data. For example, it will collect industry reports and research data. In this way, required data can be automatically collected by crawling public databases on the Internet.

[0076] The analysis unit uses the emotion estimation function to analyze the emotions of the user regarding the work content entered by the user, and can prioritize automating parts that are particularly stressful. For example, if a user enters "entering data every day is stressful," the analysis unit uses the emotion estimation function to analyze parts that are particularly stressful and prioritizes automating those parts. For example, an automatic data entry or checking function can be added. Also, if a user enters "creating regular reports is stressful," the analysis unit uses the emotion estimation function to analyze parts that are particularly stressful and prioritizes automating those parts. For example, an automatic data aggregation or graph creation function can be added. Also, if a user enters "redeeming monthly expenses is stressful," the analysis unit uses the emotion estimation function to analyze parts that are particularly stressful and prioritizes automating those parts. For example, an automatic expense item entry function or an automatic receipt scanning function can be added. This can reduce the burden on the user by prioritizing automation of parts that are particularly stressful for the user.

[0077] The input unit can use the emotion estimation function to analyze the user's emotions in real time when entering data, and provide an interface for eliciting positive emotions. For example, when a user enters "entering data every day is a hassle," the input unit can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can display encouraging messages or success stories. When a user enters "creating regular reports is a hassle," the input unit can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can visually display progress. When a user enters "redeeming monthly expenses is a hassle," the input unit can use the emotion estimation function to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can display the progress of expense reimbursement in a graph. This makes it possible to analyze the user's emotions in real time when entering data, and provide an interface for eliciting positive emotions.

[0078] The collection unit can use the emotion estimation function to adjust the generation AI to prioritize collecting data that the user finds particularly important. For example, if a user inputs, "It's a hassle to prepare documents to submit to government offices," the collection unit uses the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important document templates and related information. Also, if a user inputs, "It's a hassle to enter data every day," the collection unit uses the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important market data and statistical information. Also, if a user inputs, "It's a hassle to prepare periodic reports," the collection unit uses the emotion estimation function to prioritize collecting data that the user finds particularly important. For example, it prioritizes collecting important industry reports and research data. This enables data collection that meets the user's needs by prioritizing the collection of data that the user finds particularly important.

[0079] The collection unit can use the emotion estimation function to provide a feedback function to reduce the user's anxiety about the progress of data collection. For example, when a user inputs, "It's a hassle to prepare documents to submit to the government office," the collection unit uses the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the progress can be visually displayed to provide a sense of security. Furthermore, when a user inputs, "It's a hassle to enter data every day," the collection unit uses the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the collected data can be checked or a progress report can be provided. Furthermore, when a user inputs, "It's a hassle to prepare periodic reports," the collection unit uses the emotion estimation function to provide feedback to reduce the user's anxiety about the progress of data collection. For example, the collection unit checks the quality of the collected data or notifies the user of the progress. In this way, by providing a feedback function to reduce the user's anxiety about the progress of data collection, the user's sense of security can be increased.

[0080] The generation unit can use the emotion estimation function to analyze the emotion a user feels when using the tool and provide an interface for improving usability. For example, when a user inputs, "It's a hassle to prepare documents to submit to the government office," the generation unit can use the emotion estimation function to analyze the emotion a user feels when using the tool and provide an interface for improving usability. For example, the generation unit can display an intuitive operation guide. Also, when a user inputs, "It's a hassle to enter data every day," the generation unit can use the emotion estimation function to analyze the emotion a user feels when using the tool and provide an interface for improving usability. For example, the generation unit can simplify the operation procedure. Also, when a user inputs, "It's a hassle to prepare periodic reports," the generation unit can use the emotion estimation function to analyze the emotion a user feels when using the tool and provide an interface for improving usability. For example, the generation unit can provide visual feedback. In this way, the generation unit can analyze the emotion a user feels when using the tool and provide an interface for improving usability, thereby increasing user satisfaction.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The input section allows the user to input tasks that they find troublesome. For example, the user can input "creating documents to submit to the government office is a hassle." The user can also input "entering data every day is a hassle." The user can also input "creating regular reports is a hassle." Step 2: The analysis unit analyzes the work content input by the input unit. For example, the analysis unit analyzes the work content using natural language processing technology, pattern recognition technology, and machine learning algorithms. Step 3: The collection department automatically collects the necessary data and documents based on the content analyzed by the analysis department. For example, the collection department may use web scraping technology, API integration, or database queries to collect the necessary data. Step 4: The generation unit generates automated tools based on the data and documents collected by the collection unit. For example, the generation unit generates report generation tools, data analysis tools, and task management tools.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0119] 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] 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.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. an input unit for inputting tasks that the user finds troublesome; an analysis unit that analyzes the work content input by the input unit; a collection unit that automatically collects necessary data and documents based on the content analyzed by the analysis unit; a generation unit that generates an automatic creation tool based on the data and documents collected by the collection unit. A system characterized by:

2. The analysis unit For the work content entered by the user, a database of similar past work is referenced to propose the optimal automation method 2. The system of claim 1.

3. The analysis unit Learns the user's past input history and predicts and automatically completes input from the next time onwards 2. The system of claim 1.

4. The analysis unit The emotions of the user regarding the work content input by the user are analyzed, and parts that cause particular stress are prioritized for automation.

2. The system of claim 1.

5. The input unit The user can explain the work content by voice input, and the generation AI analyzes the voice and generates an automation tool.

2. The system of claim 1.

6. The input unit Add a function for sharing the work content between different users, allowing them to refer to automation tools created by other users.

2. The system of claim 1.

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