System

Abstract

An object of the system according to the embodiment is to allow an IT beginner to smoothly improve the efficiency of work and grow into an IT personnel.SOLUTION: A system includes a work content input part, an analysis part, a tool presentation part, and a training program presentation part. A business content input part inputs the content of business desired to be made efficient by a user. The analysis unit analyzes the work content input by the work content input unit. The tool presentation unit presents an appropriate tool or programming language based on the work content analyzed by the analysis unit. The training program presentation unit presents a basic training program of the tool or the programming language presented by the tool presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to select the tools and programming languages ​​necessary to improve work efficiency and to acquire the basic knowledge, making it difficult for IT novices to smoothly develop into IT professionals.

[0005] The system according to the embodiment aims to enable IT novices to smoothly improve work efficiency and develop into IT professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a business content input unit, an analysis unit, a tool presentation unit, and a training program presentation unit. The business content input unit inputs the content of the business that the user wants to improve the efficiency of. The analysis unit analyzes the business content input by the business content input unit. The tool presentation unit presents appropriate tools and programming languages ​​based on the business content analyzed by the analysis unit. The training program presentation unit presents basic training programs for the tools and programming languages ​​presented by the tool presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows IT novices to smoothly improve work efficiency and develop into IT professionals. [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 business efficiency support system according to the embodiment of the present invention is a system in which a user inputs the details of the business they want to improve the efficiency of into a generation AI, and the generation AI then suggests appropriate tools, programming languages, and efficiency methods based on the details, and also suggests basic training programs. This allows the business efficiency support system to help IT novices smoothly develop into IT professionals.

[0029] A business efficiency improvement support system according to an embodiment includes a business content input unit, an analysis unit, a tool presentation unit, and a training program presentation unit. The business content input unit inputs the content of a business that a user wants to improve the efficiency of. For example, the user inputs the business content in text format. The business content input unit can also support voice input and handwritten input. The analysis unit analyzes the business content input by the business content input unit. For example, a generation AI analyzes the business content using text analysis technology. The analysis unit can also refer to the user's past business history to perform a more accurate analysis. The tool presentation unit presents appropriate tools and programming languages ​​based on the business content analyzed by the analysis unit. For example, the generation AI suggests tools such as Python and Excel macros. The tool presentation unit can also select appropriate tools and programming languages ​​taking into account the user's past learning history and skill level. The training program presentation unit presents basic training programs for the tools and programming languages ​​presented by the tool presentation unit. For example, the generation AI suggests a basic Python course or a basic Excel macro operation course. The training program presentation section can also be customized to suit the user's learning style and pace. This allows the business efficiency support system to help IT novices smoothly develop into IT professionals. For example, users can improve their work efficiency by learning scripts to automate data entry tasks and applying them to their actual work. Furthermore, the generation AI presents appropriate tools and efficiency methods, allowing users to improve efficiency without hesitation.

[0030] The work content input unit can perform more accurate analysis by referring to the user's past work history. For example, when a user inputs work content into the generation AI, the work content input unit automatically refers to past work history data to identify similar work content. For example, it analyzes the current work content based on the history of past data entry work. The generation AI also analyzes the user's past work history to extract frequently performed tasks and specific patterns. This improves the accuracy of the work content input by the user. Furthermore, when a user inputs work content, the generation AI provides an auto-completion function based on the past work history. For example, it automatically completes the current work content by referring to content entered in the past. This improves the accuracy of the user's work content.

[0031] The work content input unit can automatically categorize the input work content and present examples of other users with similar work content. In the work content input unit, for example, the generation AI analyzes the input work content and automatically categorizes it. For example, it classifies it into categories such as data entry, report creation, and email management. Based on the categorized work content, the generation AI then presents similar examples from other users. For example, it displays examples of how work in the same category has been made more efficient. The generation AI also analyzes the input work content and presents success stories and efficiency methods from other users with similar work content. For example, it introduces scripts and tools that automate the same work. This allows users to refer to other users' success stories.

[0032] The work content input unit also supports voice input and handwriting input, improving user convenience. For example, the generation AI uses voice recognition technology to allow users to input work content by voice. For example, the work content can be automatically converted into text by the user simply speaking into a microphone. To support handwriting input, the generation AI also employs handwriting recognition technology. For example, when a user enters work content by hand into a tablet or smartphone, it is automatically converted into digital text. The unit also provides an interface that supports both voice input and handwriting input, allowing users to input work content in the way that is most convenient for them. For example, it is possible to use a combination of voice input and handwriting input. This allows users to input work content in the way that is most convenient for them.

[0033] The business content input unit can automatically translate the input business content into other languages ​​and analyze it from a global perspective. For example, the generation AI automatically translates the input business content into other languages ​​and analyzes it from a global perspective. For example, business content input in Japanese is translated into English or Chinese. Furthermore, based on the translated business content, the generation AI presents similar cases and efficiency methods in other language-speaking countries. For example, successful cases in English-speaking countries are translated and displayed in Japanese. The generation AI also provides a multilingual interface, allowing users to input business content in any language. For example, it is automatically translated according to the language selected by the user. This makes it possible to analyze business content from a global perspective.

[0034] The tool suggestion unit can select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. In the tool suggestion unit, for example, the generation AI analyzes the user's past learning history and selects appropriate tools and programming languages. For example, for a user who has previously studied Python, it suggests efficiency methods using Python. The generation AI also suggests appropriate tools and programming languages ​​by taking into account the user's skill level. For example, it suggests simple tools and languages ​​for beginners and advanced tools and languages ​​for advanced users. The generation AI also selects the optimal tools and programming languages ​​based on the user's learning history and skill level. For example, it refers to what has been learned in the past and the results of skill tests. This makes it possible to provide the optimal tools and programming languages ​​according to the user's skill level.

[0035] The tool presentation unit can deepen the user's understanding by explaining in detail the reasons for selecting the presented tools and programming languages. For example, the tool presentation unit can explain in detail the reasons for selecting the tools and programming languages ​​presented by the generation AI. For example, it can explain why Python is suitable for data processing or why Excel macros are useful for automation. Furthermore, to make it easier for the user to understand the presented tools and programming languages, the generation AI can explain the reasons for their selection using specific examples. For example, it can introduce past success stories and specific use cases. Furthermore, the generation AI can deepen the user's understanding by explaining in detail the reasons for selecting the tools and programming languages. For example, it can explain the reasons for selection step by step to help the user understand. This makes it easier for the user to understand the presented tools and programming languages.

[0036] The tool presentation unit can collect user feedback on the presented tools and programming languages ​​and improve the selection algorithm based on that data. The tool presentation unit, for example, collects user feedback on the tools and programming languages ​​presented by the generation AI and improves the selection algorithm based on that data. For example, it analyzes user ratings and comments. Furthermore, based on user feedback, the generation AI continuously improves the selection algorithm for tools and programming languages. For example, it refers to user satisfaction and frequency of use. Furthermore, the generation AI collects user feedback and optimizes the selection algorithm based on that data. For example, it analyzes the feedback data and adjusts the selection criteria. This allows the selection algorithm to continuously improve based on user feedback.

[0037] The tool presentation unit can optimize the presentation of tools and programming languages ​​to the user's work environment and device. In the tool presentation unit, for example, a generation AI analyzes the user's work environment and device and presents the optimal tools and programming languages ​​for them. For example, for a user using a desktop PC, it suggests desktop tools. The generation AI also selects the optimal tools and programming languages ​​according to the user's work environment. For example, it suggests cloud-based tools that are suitable for a remote work environment. The generation AI also analyzes the device the user uses and presents the optimal tools and programming languages ​​for that device. For example, it suggests mobile tools for a user using a smartphone. This makes it possible to provide the optimal tools and programming languages ​​for the user's work environment and device.

[0038] The efficiency method suggestion unit can analyze the user's entire workflow and suggest optimal efficiency measures. In the efficiency method suggestion unit, for example, the generation AI analyzes the user's entire workflow and suggests efficiency measures for each step. For example, it optimizes the entire process from data entry to report creation. It can also analyze the user's workflow in detail, identify bottlenecks, and suggest efficiency measures for those parts. For example, it can suggest methods to automate tasks that are currently done manually. The generation AI can also analyze the user's entire workflow and suggest optimal efficiency measures. For example, it can suggest methods to automate tasks by linking multiple tools. This makes it possible to optimize the user's entire workflow.

[0039] The efficiency method presentation unit can explain in detail the advantages and disadvantages of efficiency methods to promote user understanding. For example, the efficiency method presentation unit provides a detailed explanation of the advantages and disadvantages of the efficiency methods presented by the generation AI. For example, it provides a specific explanation of the advantages and disadvantages of automation using Python. Furthermore, to make it easier for users to understand the efficiency methods, the generation AI explains the advantages and disadvantages using specific examples. For example, it introduces past success stories and failure stories. Furthermore, the generation AI promotes user understanding by providing a detailed explanation of the advantages and disadvantages of the efficiency methods. For example, it provides a step-by-step explanation of the reasons for selecting an efficiency method and the implementation procedure. This makes it easier for users to understand the advantages and disadvantages of the efficiency methods.

[0040] The efficiency method presentation unit can compare the presented efficiency method with success cases of other users and feed back the results. For example, the generation AI compares the presented efficiency method with success cases of other users and feeds back the results. For example, the generation AI presents the success rate and effectiveness of other users who used the same method. Furthermore, the generation AI evaluates the effectiveness of the presented efficiency method based on the success cases of other users and feeds back the results to the user. For example, the generation AI presents specific data and results of the success cases. Furthermore, the generation AI compares the presented efficiency method with success cases of other users and suggests the optimal method based on the results. For example, the generation AI preferentially suggests methods with a large number of success cases. This allows the user to refer to the success cases of other users.

[0041] The efficiency method presentation unit can optimize the presentation of efficiency methods to the user's work environment and device used. In the efficiency method presentation unit, for example, the generation AI analyzes the user's work environment and device used and presents the optimal efficiency method for that. For example, for a user who uses a desktop PC, it proposes efficiency methods for desktops. The generation AI also selects the optimal efficiency method according to the user's work environment. For example, it proposes cloud-based efficiency methods that are suitable for remote work environments. The generation AI also analyzes the device used by the user and presents the optimal efficiency method for that. For example, for a user who uses a smartphone, it proposes efficiency methods for mobile devices. This makes it possible to provide the optimal efficiency method for the user's work environment and device used.

[0042] The training program presentation unit can customize the program to suit the user's learning style and pace. For example, the generation AI analyzes the user's learning style and presents a training program that matches it. For example, it provides video courses to users who prefer visual learning, and text materials to users who prefer text-based learning. The generation AI also customizes the training program to suit the user's learning pace. For example, it adjusts the learning content according to the user's progress, allowing the user to study at a comfortable pace. The generation AI also analyzes the user's learning style and pace and presents a training program that is optimal for that. For example, it incorporates interactive quizzes and practical exercises. This allows the user to study at a comfortable pace.

[0043] The training program presentation unit can compare the presented training program with the learning histories of other users and provide feedback on the results. For example, the training program presentation unit compares the presented training program with the learning histories of other users and provides feedback on the results. For example, the generation AI presents the learning outcomes and progress of other users who have taken the same program. Furthermore, the generation AI evaluates the effectiveness of the presented training program based on the learning histories of other users and provides feedback on the results to the user. For example, specific data and results of learning outcomes are presented. Furthermore, the generation AI compares the presented training program with the learning histories of other users and suggests the optimal program based on the results. For example, programs with high learning outcomes are suggested preferentially. This allows the user to refer to the learning histories of other users.

[0044] The training program presentation unit can optimize the presentation of the training program to the user's work environment and device used. For example, the generation AI in the training program presentation unit analyzes the user's work environment and device used, and presents the optimal training program for that. For example, it proposes a desktop training program for a user who uses a desktop PC. The generation AI also selects the optimal training program according to the user's work environment. For example, it proposes an online training program suitable for a remote work environment. The generation AI also analyzes the device used by the user and presents the optimal training program for that. For example, it proposes a mobile training program for a user who uses a smartphone. This makes it possible to provide the optimal training program for the user's work environment and device used.

[0045] The basic knowledge acquisition unit can monitor the user's learning progress in real time and provide appropriate feedback. In the basic knowledge acquisition unit, for example, the generation AI monitors the user's learning progress in real time and provides appropriate feedback. For example, it suggests what to learn next based on the learning progress. In addition, when the user acquires basic knowledge, the generation AI monitors the progress in real time and provides feedback based on the learning pace and level of understanding. For example, it provides supplementary explanations for parts that are not fully understood. In addition, the generation AI monitors the user's learning progress in real time and provides appropriate feedback to maximize the user's learning effectiveness. For example, it displays praise and encouraging messages based on the learning progress. This maximizes the user's learning effectiveness.

[0046] After acquiring the basic knowledge, the basic knowledge acquisition unit can evaluate the results of the user's work efficiency improvement and suggest the next step. In the basic knowledge acquisition unit, for example, the generation AI evaluates the results of the work efficiency improvement after the user has acquired the basic knowledge and suggests the next step. For example, if automation of data entry work is successful, it will suggest automating report creation next. In addition, to evaluate the results of the user's work efficiency improvement, the generation AI uses specific indicators to evaluate. For example, it may evaluate based on the reduction in work time or error rate. In addition, the generation AI evaluates the results of the user's work efficiency improvement and suggests the next step based on the results. For example, it may suggest a new work efficiency method that applies the basic knowledge. This allows the user to receive appropriate suggestions to move on to the next step.

[0047] The basic knowledge acquisition unit can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and provide feedback on the results. For example, the generation AI can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and provide feedback on the results. For example, it can present the success rate and effectiveness of other users who used the same method. Furthermore, based on the success cases of other users, the generation AI can evaluate the effectiveness of the process of acquiring basic knowledge and improving work efficiency and provide feedback on the results to the user. For example, it can present specific data and results of the success cases. Furthermore, the generation AI can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and propose the optimal method based on the results. For example, it can prioritize the proposal of methods with a large number of success cases. This allows the user to refer to the success cases of other users.

[0048] The basic knowledge acquisition unit can optimize the process of acquiring basic knowledge and improving work efficiency to the user's work environment and device used. For example, the generation AI analyzes the user's work environment and device used, and presents the optimal process for acquiring basic knowledge and improving work efficiency. For example, it proposes a desktop process for a user who uses a desktop PC. The generation AI also selects the optimal process for acquiring basic knowledge and improving work efficiency according to the user's work environment. For example, it proposes a cloud-based process that is suitable for a remote work environment. The generation AI also analyzes the device used by the user and presents the optimal process for acquiring basic knowledge and improving work efficiency. For example, it proposes a mobile process for a user who uses a smartphone. This makes it possible to provide the optimal process for the user's work environment and device used.

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

[0050] The work content input unit can automatically categorize the input work content and present examples of other users with similar work content. For example, the generation AI can analyze the input work content and automatically categorize it. Categories such as data entry, report creation, and email management are possible. Based on the categorized work content, the generation AI can also present similar examples from other users. For example, it can display examples of how work in the same category has been streamlined. Furthermore, the generation AI can analyze the input work content and present success stories and efficiency methods from other users with similar work content. For example, it can introduce scripts and tools that automate the same work. This allows users to refer to other users' success stories.

[0051] The work content input unit can also support voice input and handwriting input, improving user convenience. For example, the generation AI can use voice recognition technology to allow users to input work content by voice. The user simply speaks into the microphone, and the work content is automatically converted into text. To support handwriting input, the generation AI can also incorporate handwriting recognition technology. When a user enters work content by handwriting into a tablet or smartphone, it is automatically converted into digital text. Furthermore, an interface that supports both voice input and handwriting input can be provided, allowing users to input work content in the way that is most convenient for them. For example, it is possible to use a combination of voice input and handwriting input. This allows users to input work content in the way that is most convenient for them.

[0052] The business content input unit can automatically translate the input business content into other languages ​​and analyze it from a global perspective. For example, the generation AI can automatically translate the input business content into other languages ​​and analyze it from a global perspective. Business content input in Japanese can be translated into English or Chinese. Furthermore, based on the translated business content, the generation AI can present similar cases and efficiency methods in other language-speaking countries. For example, successful cases in English-speaking countries can be translated and displayed in Japanese. Furthermore, the generation AI can provide a multilingual interface, allowing users to input business content in any language. For example, it is automatically translated according to the language selected by the user. This makes it possible to analyze business content from a global perspective.

[0053] The tool suggestion unit can select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. For example, the generation AI can analyze the user's past learning history and select appropriate tools and programming languages. For a user who has previously studied Python, it can suggest efficient methods using Python. The generation AI can also suggest appropriate tools and programming languages ​​by taking into account the user's skill level. It can suggest simple tools and languages ​​for beginners and advanced tools and languages ​​for advanced users. Furthermore, the generation AI can select the optimal tools and programming languages ​​based on the user's learning history and skill level. It can refer to past learning content and skill test results. This makes it possible to provide the optimal tools and programming languages ​​according to the user's skill level.

[0054] The tool presentation unit can provide detailed explanations of the reasons for selecting the presented tools and programming languages, deepening the user's understanding. For example, the generation AI can provide detailed explanations of the reasons for selecting the presented tools and programming languages. It can explain why Python is suitable for data processing or why Excel macros are useful for automation. Furthermore, to help users understand the presented tools and programming languages, the generation AI can explain the reasons for their selection with specific examples. It can introduce past success stories and specific use cases. Furthermore, the generation AI can provide detailed explanations of the reasons for selecting tools and programming languages, deepening the user's understanding. It can explain the reasons for selection step by step to help the user understand. This makes it easier for users to understand the presented tools and programming languages.

[0055] The efficiency method suggestion unit can analyze the user's entire workflow and suggest optimal efficiency measures. For example, the generation AI can analyze the user's entire workflow and suggest efficiency measures for each step. It is possible to optimize the entire process from data entry to report creation. It can also analyze the user's workflow in detail, identify bottlenecks, and suggest efficiency measures for those parts. It can suggest methods to automate tasks that are currently done manually. Furthermore, the generation AI can analyze the user's entire workflow and suggest optimal efficiency measures. It can suggest methods to automate tasks by linking multiple tools. This makes it possible to optimize the user's entire workflow.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The work content input unit inputs the details of the work the user wants to improve the efficiency of. For example, the user can input the work content in text format. The work content input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the work content input by the work content input unit. For example, the generation AI analyzes the work content using text analysis technology. The analysis unit can also refer to the user's past work history to perform a more accurate analysis. Step 3: The tool suggestion unit suggests appropriate tools and programming languages ​​based on the work content analyzed by the analysis unit. For example, the generation AI suggests tools such as Python or Excel macros. The tool suggestion unit can also select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. Step 4: The training program presentation unit presents basic training programs for the tools and programming languages ​​presented by the tool presentation unit. For example, the generation AI suggests basic Python courses or courses on basic Excel macro operations. The training program presentation unit can also customize the programs to suit the user's learning style and pace.

[0058] (Example 2) The business efficiency support system according to the embodiment of the present invention is a system in which a user inputs the details of the business they want to improve the efficiency of into a generation AI, and the generation AI then suggests appropriate tools, programming languages, and efficiency methods based on the details, and also suggests basic training programs. This allows the business efficiency support system to help IT novices smoothly develop into IT professionals.

[0059] A business efficiency improvement support system according to an embodiment includes a business content input unit, an analysis unit, a tool presentation unit, and a training program presentation unit. The business content input unit inputs the content of a business that a user wants to improve the efficiency of. For example, the user inputs the business content in text format. The business content input unit can also support voice input and handwritten input. The analysis unit analyzes the business content input by the business content input unit. For example, a generation AI analyzes the business content using text analysis technology. The analysis unit can also refer to the user's past business history to perform a more accurate analysis. The tool presentation unit presents appropriate tools and programming languages ​​based on the business content analyzed by the analysis unit. For example, the generation AI suggests tools such as Python and Excel macros. The tool presentation unit can also select appropriate tools and programming languages ​​taking into account the user's past learning history and skill level. The training program presentation unit presents basic training programs for the tools and programming languages ​​presented by the tool presentation unit. For example, the generation AI suggests a basic Python course or a basic Excel macro operation course. The training program presentation section can also be customized to suit the user's learning style and pace. This allows the business efficiency support system to help IT novices smoothly develop into IT professionals. For example, users can improve their work efficiency by learning scripts to automate data entry tasks and applying them to their actual work. Furthermore, the generation AI presents appropriate tools and efficiency methods, allowing users to improve efficiency without hesitation.

[0060] The work content input unit can perform more accurate analysis by referring to the user's past work history. For example, when a user inputs work content into the generation AI, the work content input unit automatically refers to past work history data to identify similar work content. For example, it analyzes the current work content based on the history of past data entry work. The generation AI also analyzes the user's past work history to extract frequently performed tasks and specific patterns. This improves the accuracy of the work content input by the user. Furthermore, when a user inputs work content, the generation AI provides an auto-completion function based on the past work history. For example, it automatically completes the current work content by referring to content entered in the past. This improves the accuracy of the user's work content.

[0061] The task content input unit can estimate the user's emotional state and provide a less stressful interface. For example, when a user inputs task content, the generation AI uses facial recognition technology to analyze the user's facial expression and estimate their emotional state. For example, if the user is feeling stressed, the interface is simplified. The generation AI also analyzes the user's tone of voice to estimate their emotional state. For example, if the user is tired, the interface is changed to one that is more intuitive and easy to use. Furthermore, when the user inputs task content, the generation AI monitors the user's emotional state in real time and provides a less stressful interface. For example, it adopts colors and designs that help the user relax. This reduces user stress and provides an easy-to-use interface.

[0062] The work content input unit can automatically categorize the input work content and present examples of other users with similar work content. In the work content input unit, for example, the generation AI analyzes the input work content and automatically categorizes it. For example, it classifies it into categories such as data entry, report creation, and email management. Based on the categorized work content, the generation AI then presents similar examples from other users. For example, it displays examples of how work in the same category has been made more efficient. The generation AI also analyzes the input work content and presents success stories and efficiency methods from other users with similar work content. For example, it introduces scripts and tools that automate the same work. This allows users to refer to other users' success stories.

[0063] The work content input unit also supports voice input and handwriting input, improving user convenience. For example, the generation AI uses voice recognition technology to allow users to input work content by voice. For example, the work content can be automatically converted into text by the user simply speaking into a microphone. To support handwriting input, the generation AI also employs handwriting recognition technology. For example, when a user enters work content by hand into a tablet or smartphone, it is automatically converted into digital text. The unit also provides an interface that supports both voice input and handwriting input, allowing users to input work content in the way that is most convenient for them. For example, it is possible to use a combination of voice input and handwriting input. This allows users to input work content in the way that is most convenient for them.

[0064] The business content input unit can automatically translate the input business content into other languages ​​and analyze it from a global perspective. For example, the generation AI automatically translates the input business content into other languages ​​and analyzes it from a global perspective. For example, business content input in Japanese is translated into English or Chinese. Furthermore, based on the translated business content, the generation AI presents similar cases and efficiency methods in other language-speaking countries. For example, successful cases in English-speaking countries are translated and displayed in Japanese. The generation AI also provides a multilingual interface, allowing users to input business content in any language. For example, it is automatically translated according to the language selected by the user. This makes it possible to analyze business content from a global perspective.

[0065] The task content input unit uses an emotion estimation function to analyze the user's emotions in real time when inputting task content and can provide positive feedback. In the task content input unit, for example, the generation AI analyzes the user's facial expressions and voice and estimates emotions in real time. For example, if the user has positive emotions when inputting task content, an encouraging message is displayed. In addition, the emotion estimation function is used to make suggestions to help the user relax if the user has negative emotions when inputting task content. For example, a message encouraging deep breathing is displayed. In addition, the generation AI provides positive feedback according to the user's emotional state. For example, if the user accurately inputs task content, a compliment or encouraging message is displayed. This allows the user to input task content with positive emotions.

[0066] The tool suggestion unit can select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. In the tool suggestion unit, for example, the generation AI analyzes the user's past learning history and selects appropriate tools and programming languages. For example, for a user who has previously studied Python, it suggests efficiency methods using Python. The generation AI also suggests appropriate tools and programming languages ​​by taking into account the user's skill level. For example, it suggests simple tools and languages ​​for beginners and advanced tools and languages ​​for advanced users. The generation AI also selects the optimal tools and programming languages ​​based on the user's learning history and skill level. For example, it refers to what has been learned in the past and the results of skill tests. This makes it possible to provide the optimal tools and programming languages ​​according to the user's skill level.

[0067] The tool presentation unit can deepen the user's understanding by explaining in detail the reasons for selecting the presented tools and programming languages. For example, the tool presentation unit can explain in detail the reasons for selecting the tools and programming languages ​​presented by the generation AI. For example, it can explain why Python is suitable for data processing or why Excel macros are useful for automation. Furthermore, to make it easier for the user to understand the presented tools and programming languages, the generation AI can explain the reasons for their selection using specific examples. For example, it can introduce past success stories and specific use cases. Furthermore, the generation AI can deepen the user's understanding by explaining in detail the reasons for selecting the tools and programming languages. For example, it can explain the reasons for selection step by step to help the user understand. This makes it easier for the user to understand the presented tools and programming languages.

[0068] The tool presentation unit can collect user feedback on the presented tools and programming languages ​​and improve the selection algorithm based on that data. The tool presentation unit, for example, collects user feedback on the tools and programming languages ​​presented by the generation AI and improves the selection algorithm based on that data. For example, it analyzes user ratings and comments. Furthermore, based on user feedback, the generation AI continuously improves the selection algorithm for tools and programming languages. For example, it refers to user satisfaction and frequency of use. Furthermore, the generation AI collects user feedback and optimizes the selection algorithm based on that data. For example, it analyzes the feedback data and adjusts the selection criteria. This allows the selection algorithm to continuously improve based on user feedback.

[0069] The tool presentation unit can optimize the presentation of tools and programming languages ​​to the user's work environment and device. In the tool presentation unit, for example, a generation AI analyzes the user's work environment and device and presents the optimal tools and programming languages ​​for them. For example, for a user using a desktop PC, it suggests desktop tools. The generation AI also selects the optimal tools and programming languages ​​according to the user's work environment. For example, it suggests cloud-based tools that are suitable for a remote work environment. The generation AI also analyzes the device the user uses and presents the optimal tools and programming languages ​​for that device. For example, it suggests mobile tools for a user using a smartphone. This makes it possible to provide the optimal tools and programming languages ​​for the user's work environment and device.

[0070] The tool presentation unit can use the emotion estimation function to adjust the interface so that the user feels positive emotions toward the presented tool or language. For example, the tool presentation unit uses a generation AI to analyze the user's emotional state and adjust the interface so that the user feels positive emotions toward the presented tool or programming language. For example, it may use colors and designs that relax the user. The emotion estimation function may also be used to customize the interface so that the user feels positive emotions toward the presented tool or language. For example, it may provide a layout and font that matches the user's preferences. The generation AI may also adjust the interface according to the user's emotional state to elicit positive emotions. For example, if the user is feeling stressed, it may provide a simple and intuitive interface. This allows the user to use the tool or programming language while feeling positive emotions.

[0071] The efficiency method suggestion unit can analyze the user's entire workflow and suggest optimal efficiency measures. In the efficiency method suggestion unit, for example, the generation AI analyzes the user's entire workflow and suggests efficiency measures for each step. For example, it optimizes the entire process from data entry to report creation. It can also analyze the user's workflow in detail, identify bottlenecks, and suggest efficiency measures for those parts. For example, it can suggest methods to automate tasks that are currently done manually. The generation AI can also analyze the user's entire workflow and suggest optimal efficiency measures. For example, it can suggest methods to automate tasks by linking multiple tools. This makes it possible to optimize the user's entire workflow.

[0072] The efficiency method presentation unit can explain in detail the advantages and disadvantages of efficiency methods to promote user understanding. For example, the efficiency method presentation unit provides a detailed explanation of the advantages and disadvantages of the efficiency methods presented by the generation AI. For example, it provides a specific explanation of the advantages and disadvantages of automation using Python. Furthermore, to make it easier for users to understand the efficiency methods, the generation AI explains the advantages and disadvantages using specific examples. For example, it introduces past success stories and failure stories. Furthermore, the generation AI promotes user understanding by providing a detailed explanation of the advantages and disadvantages of the efficiency methods. For example, it provides a step-by-step explanation of the reasons for selecting an efficiency method and the implementation procedure. This makes it easier for users to understand the advantages and disadvantages of the efficiency methods.

[0073] The efficiency method presentation unit can use the emotion estimation function to analyze the emotions the user has toward the presented efficiency methods and provide the most appropriate method. For example, the generation AI in the efficiency method presentation unit analyzes the user's facial expressions and voice to estimate the user's emotions toward the presented efficiency methods. For example, if the user is feeling anxious, the unit will suggest a simpler efficiency method. Furthermore, if the emotion estimation function is used and the user has positive emotions toward the presented efficiency methods, the unit will preferentially provide those methods. For example, it will suggest a method that the user is interested in. Furthermore, the generation AI selects the most appropriate efficiency method according to the user's emotional state. For example, if the user is relaxed, the unit will suggest a more advanced efficiency method. This allows the user to select an efficiency method while maintaining positive emotions.

[0074] The efficiency method presentation unit can compare the presented efficiency method with success cases of other users and feed back the results. For example, the generation AI compares the presented efficiency method with success cases of other users and feeds back the results. For example, the generation AI presents the success rate and effectiveness of other users who used the same method. Furthermore, the generation AI evaluates the effectiveness of the presented efficiency method based on the success cases of other users and feeds back the results to the user. For example, the generation AI presents specific data and results of the success cases. Furthermore, the generation AI compares the presented efficiency method with success cases of other users and suggests the optimal method based on the results. For example, the generation AI preferentially suggests methods with a large number of success cases. This allows the user to refer to the success cases of other users.

[0075] The efficiency method presentation unit can optimize the presentation of efficiency methods to the user's work environment and device used. In the efficiency method presentation unit, for example, the generation AI analyzes the user's work environment and device used and presents the optimal efficiency method for that. For example, for a user who uses a desktop PC, it proposes efficiency methods for desktops. The generation AI also selects the optimal efficiency method according to the user's work environment. For example, it proposes cloud-based efficiency methods that are suitable for remote work environments. The generation AI also analyzes the device used by the user and presents the optimal efficiency method for that. For example, for a user who uses a smartphone, it proposes efficiency methods for mobile devices. This makes it possible to provide the optimal efficiency method for the user's work environment and device used.

[0076] The efficiency method presentation unit can use the emotion estimation function to adjust the interface so that the user feels positive about the presented efficiency methods. For example, the generation AI analyzes the user's emotional state and adjusts the interface so that the user feels positive about the presented efficiency methods. For example, it employs colors and designs that relax the user. The emotion estimation function is also used to customize the interface so that the user feels positive about the presented efficiency methods. For example, it provides a layout and fonts that match the user's preferences. The generation AI also adjusts the interface according to the user's emotional state to elicit positive emotions. For example, if the user is feeling stressed, it provides a simple and intuitive interface. This allows the user to use the efficiency methods while feeling positive emotions.

[0077] The training program presentation unit can customize the program to suit the user's learning style and pace. For example, the generation AI analyzes the user's learning style and presents a training program that matches it. For example, it provides video courses to users who prefer visual learning, and text materials to users who prefer text-based learning. The generation AI also customizes the training program to suit the user's learning pace. For example, it adjusts the learning content according to the user's progress, allowing the user to study at a comfortable pace. The generation AI also analyzes the user's learning style and pace and presents a training program that is optimal for that. For example, it incorporates interactive quizzes and practical exercises. This allows the user to study at a comfortable pace.

[0078] The training program presentation unit can use the emotion estimation function to analyze the emotions the user has toward the training program and provide the most appropriate program. In the training program presentation unit, for example, the generation AI analyzes the user's facial expressions and voice to estimate the user's emotions toward the training program. For example, if the user is feeling anxious, an easier program is suggested. Also, using the emotion estimation function, if the user has positive emotions toward the training program, that program is preferentially provided. For example, a program that the user is interested in is suggested. Also, the generation AI selects the most appropriate training program according to the user's emotional state. For example, if the user is relaxed, a more advanced program is suggested. This allows the user to take the training program with positive emotions.

[0079] The training program presentation unit can compare the presented training program with the learning histories of other users and provide feedback on the results. For example, the training program presentation unit compares the presented training program with the learning histories of other users and provides feedback on the results. For example, the generation AI presents the learning outcomes and progress of other users who have taken the same program. Furthermore, the generation AI evaluates the effectiveness of the presented training program based on the learning histories of other users and provides feedback on the results to the user. For example, specific data and results of learning outcomes are presented. Furthermore, the generation AI compares the presented training program with the learning histories of other users and suggests the optimal program based on the results. For example, programs with high learning outcomes are suggested preferentially. This allows the user to refer to the learning histories of other users.

[0080] The training program presentation unit can optimize the presentation of the training program to the user's work environment and device used. For example, the generation AI in the training program presentation unit analyzes the user's work environment and device used, and presents the optimal training program for that. For example, it proposes a desktop training program for a user who uses a desktop PC. The generation AI also selects the optimal training program according to the user's work environment. For example, it proposes an online training program suitable for a remote work environment. The generation AI also analyzes the device used by the user and presents the optimal training program for that. For example, it proposes a mobile training program for a user who uses a smartphone. This makes it possible to provide the optimal training program for the user's work environment and device used.

[0081] The training program presentation unit can use the emotion estimation function to adjust the interface so that the user feels positive emotions toward the training program. For example, the generation AI analyzes the user's emotional state and adjusts the interface so that the user feels positive emotions toward the training program. For example, it employs colors and designs that relax the user. The emotion estimation function also customizes the interface so that the user feels positive emotions toward the training program. For example, it provides a layout and fonts that match the user's preferences. The generation AI also adjusts the interface according to the user's emotional state to elicit positive emotions. For example, if the user is feeling stressed, it provides a simple and intuitive interface. This allows the user to take the training program while feeling positive emotions.

[0082] The basic knowledge acquisition unit can monitor the user's learning progress in real time and provide appropriate feedback. In the basic knowledge acquisition unit, for example, the generation AI monitors the user's learning progress in real time and provides appropriate feedback. For example, it suggests what to learn next based on the learning progress. In addition, when the user acquires basic knowledge, the generation AI monitors the progress in real time and provides feedback based on the learning pace and level of understanding. For example, it provides supplementary explanations for parts that are not fully understood. In addition, the generation AI monitors the user's learning progress in real time and provides appropriate feedback to maximize the user's learning effectiveness. For example, it displays praise and encouraging messages based on the learning progress. This maximizes the user's learning effectiveness.

[0083] After acquiring the basic knowledge, the basic knowledge acquisition unit can evaluate the results of the user's work efficiency improvement and suggest the next step. In the basic knowledge acquisition unit, for example, the generation AI evaluates the results of the work efficiency improvement after the user has acquired the basic knowledge and suggests the next step. For example, if automation of data entry work is successful, it will suggest automating report creation next. In addition, to evaluate the results of the user's work efficiency improvement, the generation AI uses specific indicators to evaluate. For example, it may evaluate based on the reduction in work time or error rate. In addition, the generation AI evaluates the results of the user's work efficiency improvement and suggests the next step based on the results. For example, it may suggest a new work efficiency method that applies the basic knowledge. This allows the user to receive appropriate suggestions to move on to the next step.

[0084] The basic knowledge acquisition unit uses the emotion estimation function to analyze the user's emotions regarding the acquisition of basic knowledge and work efficiency, and can provide optimal support. In the basic knowledge acquisition unit, for example, the generation AI analyzes the user's facial expressions and voice to estimate the user's emotions regarding the acquisition of basic knowledge and work efficiency. For example, if the user is feeling anxious, more attentive support is provided. Furthermore, if the user has positive emotions regarding the acquisition of basic knowledge and work efficiency using the emotion estimation function, support to maintain those emotions is provided. For example, compliments and encouraging messages are displayed. Furthermore, the generation AI provides optimal support according to the user's emotional state. For example, if the user is feeling stressed, suggestions to help them relax are made. This allows the user to acquire basic knowledge while maintaining positive emotions and improve work efficiency.

[0085] The basic knowledge acquisition unit can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and provide feedback on the results. For example, the generation AI can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and provide feedback on the results. For example, it can present the success rate and effectiveness of other users who used the same method. Furthermore, based on the success cases of other users, the generation AI can evaluate the effectiveness of the process of acquiring basic knowledge and improving work efficiency and provide feedback on the results to the user. For example, it can present specific data and results of the success cases. Furthermore, the generation AI can compare the process of acquiring basic knowledge and improving work efficiency with the success cases of other users and propose the optimal method based on the results. For example, it can prioritize the proposal of methods with a large number of success cases. This allows the user to refer to the success cases of other users.

[0086] The basic knowledge acquisition unit can optimize the process of acquiring basic knowledge and improving work efficiency to the user's work environment and device used. For example, the generation AI analyzes the user's work environment and device used, and presents the optimal process for acquiring basic knowledge and improving work efficiency. For example, it proposes a desktop process for a user who uses a desktop PC. The generation AI also selects the optimal process for acquiring basic knowledge and improving work efficiency according to the user's work environment. For example, it proposes a cloud-based process that is suitable for a remote work environment. The generation AI also analyzes the device used by the user and presents the optimal process for acquiring basic knowledge and improving work efficiency. For example, it proposes a mobile process for a user who uses a smartphone. This makes it possible to provide the optimal process for the user's work environment and device used.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The task content input unit can estimate the user's emotional state and provide a less stressful interface. For example, when a user inputs task content, the generation AI uses facial recognition technology to analyze the user's facial expression and estimate their emotional state. If the user is feeling stressed, the interface can be simplified. The generation AI can also analyze the user's tone of voice to estimate their emotional state. If the user is tired, the interface can be changed to one that is more intuitive and easy to use. Furthermore, when the user inputs task content, the generation AI can monitor the user's emotional state in real time and provide a less stressful interface. For example, it can adopt colors and designs that help the user relax. This reduces user stress and provides an easy-to-use interface.

[0089] The work content input unit can automatically categorize the input work content and present examples of other users with similar work content. For example, the generation AI can analyze the input work content and automatically categorize it. Categories such as data entry, report creation, and email management are possible. Based on the categorized work content, the generation AI can also present similar examples from other users. For example, it can display examples of how work in the same category has been streamlined. Furthermore, the generation AI can analyze the input work content and present success stories and efficiency methods from other users with similar work content. For example, it can introduce scripts and tools that automate the same work. This allows users to refer to other users' success stories.

[0090] The work content input unit can also support voice input and handwriting input, improving user convenience. For example, the generation AI can use voice recognition technology to allow users to input work content by voice. The user simply speaks into the microphone, and the work content is automatically converted into text. To support handwriting input, the generation AI can also incorporate handwriting recognition technology. When a user enters work content by handwriting into a tablet or smartphone, it is automatically converted into digital text. Furthermore, an interface that supports both voice input and handwriting input can be provided, allowing users to input work content in the way that is most convenient for them. For example, it is possible to use a combination of voice input and handwriting input. This allows users to input work content in the way that is most convenient for them.

[0091] The business content input unit can automatically translate the input business content into other languages ​​and analyze it from a global perspective. For example, the generation AI can automatically translate the input business content into other languages ​​and analyze it from a global perspective. Business content input in Japanese can be translated into English or Chinese. Furthermore, based on the translated business content, the generation AI can present similar cases and efficiency methods in other language-speaking countries. For example, successful cases in English-speaking countries can be translated and displayed in Japanese. Furthermore, the generation AI can provide a multilingual interface, allowing users to input business content in any language. For example, it is automatically translated according to the language selected by the user. This makes it possible to analyze business content from a global perspective.

[0092] The task content input unit uses an emotion estimation function to analyze the user's emotions in real time when inputting task content and provide positive feedback. For example, the generation AI can analyze the user's facial expressions and voice to estimate emotions in real time. If the user has positive emotions when inputting task content, an encouraging message can be displayed. Furthermore, if the user has negative emotions when inputting task content, the emotion estimation function can be used to make suggestions to help them relax. For example, a message encouraging them to take a deep breath can be displayed. Furthermore, the generation AI can provide positive feedback depending on the user's emotional state. If the user accurately inputs task content, a compliment or encouraging message can be displayed. This allows the user to input task content with positive emotions.

[0093] The tool suggestion unit can select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. For example, the generation AI can analyze the user's past learning history and select appropriate tools and programming languages. For a user who has previously studied Python, it can suggest efficient methods using Python. The generation AI can also suggest appropriate tools and programming languages ​​by taking into account the user's skill level. It can suggest simple tools and languages ​​for beginners and advanced tools and languages ​​for advanced users. Furthermore, the generation AI can select the optimal tools and programming languages ​​based on the user's learning history and skill level. It can refer to past learning content and skill test results. This makes it possible to provide the optimal tools and programming languages ​​according to the user's skill level.

[0094] The tool presentation unit can provide detailed explanations of the reasons for selecting the presented tools and programming languages, deepening the user's understanding. For example, the generation AI can provide detailed explanations of the reasons for selecting the presented tools and programming languages. It can explain why Python is suitable for data processing or why Excel macros are useful for automation. Furthermore, to help users understand the presented tools and programming languages, the generation AI can explain the reasons for their selection with specific examples. It can introduce past success stories and specific use cases. Furthermore, the generation AI can provide detailed explanations of the reasons for selecting tools and programming languages, deepening the user's understanding. It can explain the reasons for selection step by step to help the user understand. This makes it easier for users to understand the presented tools and programming languages.

[0095] The tool presentation unit can use the emotion estimation function to adjust the interface so that the user feels positive emotions toward the presented tools and languages. For example, the generation AI can analyze the user's emotional state and adjust the interface so that the user feels positive emotions toward the presented tools and programming languages. It is possible to adopt colors and designs that relax the user. The emotion estimation function can also be used to customize the interface so that the user feels positive emotions toward the presented tools and languages. It can provide layouts and fonts that match the user's preferences. Furthermore, the generation AI can adjust the interface according to the user's emotional state to elicit positive emotions. If the user is feeling stressed, it can provide a simple and intuitive interface. This allows the user to use the tools and programming languages ​​while feeling positive emotions.

[0096] The efficiency method suggestion unit can analyze the user's entire workflow and suggest optimal efficiency measures. For example, the generation AI can analyze the user's entire workflow and suggest efficiency measures for each step. It is possible to optimize the entire process from data entry to report creation. It can also analyze the user's workflow in detail, identify bottlenecks, and suggest efficiency measures for those parts. It can suggest methods to automate tasks that are currently done manually. Furthermore, the generation AI can analyze the user's entire workflow and suggest optimal efficiency measures. It can suggest methods to automate tasks by linking multiple tools. This makes it possible to optimize the user's entire workflow.

[0097] The efficiency method presentation unit can use the emotion estimation function to analyze the user's emotions toward the presented efficiency methods and provide the most appropriate method. For example, the generation AI can analyze the user's facial expressions and voice to estimate their emotions toward the presented efficiency methods. If the user is feeling anxious, a simpler efficiency method can be suggested. Furthermore, if the emotion estimation function is used and the user has positive emotions toward the presented efficiency methods, that method can be provided preferentially. It is possible to suggest methods that the user is interested in. Furthermore, the generation AI can select the most appropriate efficiency method according to the user's emotional state. If the user is relaxed, a more advanced efficiency method can be suggested. This allows the user to select an efficiency method while maintaining positive emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The work content input unit inputs the details of the work the user wants to improve the efficiency of. For example, the user can input the work content in text format. The work content input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the work content input by the work content input unit. For example, the generation AI analyzes the work content using text analysis technology. The analysis unit can also refer to the user's past work history to perform a more accurate analysis. Step 3: The tool suggestion unit suggests appropriate tools and programming languages ​​based on the work content analyzed by the analysis unit. For example, the generation AI suggests tools such as Python or Excel macros. The tool suggestion unit can also select appropriate tools and programming languages ​​by taking into account the user's past learning history and skill level. Step 4: The training program presentation unit presents basic training programs for the tools and programming languages ​​presented by the tool presentation unit. For example, the generation AI suggests basic Python courses or courses on basic Excel macro operations. The training program presentation unit can also customize the programs to suit the user's learning style and pace.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

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

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0123] 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).

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

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0128] 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A work content input section where users input the details of the work they want to improve the efficiency of; an analysis unit that analyzes the business content input by the business content input unit; a tool suggestion unit that suggests appropriate tools and programming languages ​​based on the business content analyzed by the analysis unit; a training program presentation unit that presents a basic training program for the tool or programming language presented by the tool presentation unit. A system characterized by:

2. The business content input unit Refer to the user's past work history to perform more accurate analysis 2. The system of claim 1.

3. The business content input unit Estimating the user's emotional state and providing a stress-free interface 2. The system of claim 1.

4. The business content input unit Automatically categorizes input work content and presents examples of other users with similar work content 2. The system of claim 1.

5. The business content input unit Supports voice input and handwriting input, improving user convenience 2. The system of claim 1.

6. The business content input unit Automatically translates input business content into other languages ​​and analyzes it from a global perspective 2. The system of claim 1.

7. The business content input unit Analyzes users' emotions in real time when they input their work details and provides positive feedback 2. The system of claim 1.

8. The tool presenting unit Select the appropriate tools and programming languages, taking into account the user's learning history and skill level.

2. The system of claim 1.

Images