System

Abstract

An object of the system according to the embodiment is to improve the learning efficiency of employees by effectively utilizing successful cases in a company as learning materials.SOLUTION: A system includes a success case report collection part, an analysis part, a teaching material generation part, and a provision part. A success case report collection part collects the latest success case report in the company. The analysis unit analyzes the success case report collected by the success case report collection unit. A teaching material generation part generates a role playing teaching material on the basis of the contents of the successful case analyzed by the analysis part. The providing part provides the role playing teaching material generated by the teaching material generation part to the employee in a game form.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 effectively use success stories within the company as learning materials, resulting in low employee learning efficiency.

[0005] The system according to the embodiment aims to improve the learning efficiency of employees by effectively utilizing success stories within the company as learning materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a success story report collection unit, an analysis unit, a teaching material generation unit, and a provision unit. The success story report collection unit collects the most recent success story reports from within the company. The analysis unit analyzes the success story reports collected by the success story report collection unit. The teaching material generation unit generates role-playing teaching materials based on the content of the success stories analyzed by the analysis unit. The provision unit provides the role-playing teaching materials generated by the teaching material generation unit to employees in the form of a game. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize success stories within the company as learning materials, thereby improving the learning efficiency of employees. [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 role-playing teaching material generation system according to an embodiment of the present invention is a system in which a generation AI instantly creates role-playing teaching materials based on the most recent success case reports from within the company, allowing employees to learn the know-how of the success cases through experiential learning, such as playing a game. As a result, the role-playing teaching material generation system allows employees to receive experiential education based on the latest success cases, thereby improving learning effectiveness.

[0029] A role-playing teaching material generation system according to an embodiment includes a success case report collection unit, an analysis unit, a teaching material generation unit, and a provision unit. The success case report collection unit collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection unit can also automatically collect success case reports from an internal database. The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI analyzes the content of the success case reports using a text generation AI (e.g., LLM). The generation AI can also extract key points of success cases using natural language processing technology. The generation AI can also analyze patterns of success cases using data mining technology. The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use a multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also generate teaching materials that set branching points in the scenario, allowing users to try different options. The provision unit provides the role-playing teaching materials generated by the teaching material generation unit to employees in the form of a game. For example, the provision unit provides the teaching materials through a web application or a mobile application. The provision unit can also use VR or AR technology to provide a more immersive experience. The provision unit can also provide experiential learning in the form of a game that incorporates a competitive element in which employees compete against each other for the highest score. This allows the role-playing teaching material generation system according to the embodiment to provide employees with experiential education based on the latest success stories, thereby improving learning effectiveness. For example, employees can engage in experiential learning as if they were playing a game, effectively acquiring know-how from success stories. Employees can also obtain a more realistic learning experience through an immersive experience using VR or AR technology. Furthermore, employees competing against each other for the highest score can increase their motivation to learn.

[0030] When analyzing the content of a success case report, the analysis unit analyzes not only the context of the report but also the content of related internal chats and emails, thereby extracting more detailed background information. For example, when analyzing the content of a success case report, the analysis unit simultaneously analyzes the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information. The analysis unit also builds a system that analyzes not only the content of the report but also the content of related internal chats and emails to extract background information of success cases. For example, it extracts keywords from chat logs and emails and associates them with the report. Furthermore, when analyzing a success case report, the analysis unit automatically collects the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information of the success case. This complements the context of the report and extracts more detailed background information.

[0031] The analysis unit can cluster the analysis results of the success case reports, taking into account their relevance to other success cases, and identify common success factors. The analysis unit, for example, clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, it classifies cases with similar success factors into the same cluster. The analysis unit also uses a clustering algorithm to associate the analysis results of the success case reports with other success cases and identify common success factors. Specifically, it performs clustering based on keywords for success factors. The analysis unit also builds a system that clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, it analyzes patterns of success factors and extracts commonalities. By identifying common success factors, it is possible to systematize the know-how of success cases.

[0032] The success case report collection department can collect success cases not only within the company but also across the industry, thereby collecting a wider range of know-how. For example, the success case report collection department can expand the scope of success case report collection from within the company to the entire industry, and collect success cases from other companies. For example, it can refer to an industry success case database to collect know-how. In addition, to collect success cases from the entire industry, the success case report collection department can build a system that automatically collects data from industry associations and specialized magazines and integrates it with success cases from within the company. In addition, the success case report collection department can expand the scope of success case report collection to the entire industry, and collect success cases from other companies. For example, it can collect success cases presented at industry conferences and seminars and store them in a database. In this way, by collecting success cases from the entire industry, a wider range of know-how can be obtained.

[0033] The success case report collection unit can also collect presentation materials and videos used by the reporter and perform multimedia analysis. For example, when collecting success case reports, the success case report collection unit also collects presentation materials and videos used by the reporter and performs multimedia analysis. For example, it analyzes the content of slides and the audio of videos. The success case report collection unit also collects presentation materials and videos and builds a system that performs multimedia analysis. For example, it automatically analyzes the text of slides and the audio of videos to extract success factors. The success case report collection unit also collects presentation materials and videos used by the reporter when collecting success case reports and performs multimedia analysis. For example, it converts the audio of videos into text and analyzes it. In this way, more detailed success factors can be extracted by analyzing the presentation materials and videos.

[0034] The teaching material generation unit can provide a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the teaching material generation unit reflects the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the culture and values ​​of the organization are incorporated into the scenario. The teaching material generation unit also generates a scenario that reflects the cultural elements and organizational characteristics behind the success stories to provide a more realistic experience. For example, the scenario is adjusted based on the characteristics of the organization. The teaching material generation unit also reflects the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the cultural background and organizational characteristics are incorporated into the scenario. In this way, a more realistic experience can be provided by reflecting the cultural elements and organizational characteristics.

[0035] The teaching material generation unit can provide many branching points in the scenario, allowing the user to try different options. For example, when generating role-playing teaching materials, the teaching material generation unit provides many branching points in the scenario, allowing the user to try different options. For example, multiple options are provided, allowing the user to choose freely. The teaching material generation unit also builds a system that provides many branching points in the scenario, allowing the user to try different options. For example, a different development is prepared for each branching point in the scenario. The teaching material generation unit also provides many branching points in the scenario, allowing the user to try different options, when generating role-playing teaching materials. For example, a different outcome is prepared for each option. This allows the user to try different options, thereby broadening the scope of learning.

[0036] The teaching material generation unit can expand the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, the teaching material generation unit expands the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department. The teaching material generation unit also builds a system for generating role-playing teaching materials based on success stories from other departments. For example, scenarios are generated based on success stories from the human resources department. The teaching material generation unit also expands the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department and the human resources department. In this way, by expanding the target for which role-playing teaching materials are generated, learning can be conducted in a wider range of departments.

[0037] The teaching material generation unit can combine virtual reality (VR) and augmented reality (AR) technology with role-playing teaching materials to provide a more immersive experience. For example, the teaching material generation unit combines virtual reality (VR) technology with role-playing teaching materials to enable users to engage in experiential learning in a virtual space. For example, it provides a scenario in which users interact with a virtual customer using a VR headset. The teaching material generation unit also combines real-world elements with role-playing teaching materials using augmented reality (AR) technology. For example, it uses AR glasses to display a virtual customer in a real office environment and provides a scenario in which users can interact with the customer. The teaching material generation unit also combines VR and AR technology with role-playing teaching materials to build a system that provides a more immersive experience. For example, it uses VR and AR to generate realistic scenarios that users can actually experience. In this way, combining VR and AR technology can provide a more immersive experience.

[0038] The provision department can incorporate a competitive element into game-style experiential learning, allowing employees to compete against each other for scores, thereby enhancing learning effectiveness. The provision department, for example, builds a system that incorporates a competitive element into game-style experiential learning, allowing employees to compete against each other for scores. For example, scores are awarded according to learning outcomes, and a ranking is displayed. The provision department also provides game-style experiential learning that incorporates a competitive element, allowing employees to compete against each other for scores, thereby enhancing learning effectiveness. For example, a score competition is held between teams. The provision department also builds a system that incorporates a competitive element into game-style experiential learning, allowing employees to compete against each other for scores. For example, scores are awarded each time a specific challenge is cleared, encouraging competition. In this way, the introduction of a competitive element can enhance learning effectiveness.

[0039] The provision unit can reflect actual business data in the game-style experiential learning to provide a more realistic scenario. The provision unit, for example, builds a system that reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, actual customer data or project data is incorporated into the scenario. The provision unit also provides a scenario based on actual business data to make the game-style experiential learning more realistic. For example, actual sales data is used to generate a dialogue scenario with a customer. The provision unit also reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a scenario based on actual project data is generated. In this way, by reflecting actual business data, a more realistic scenario can be provided.

[0040] The provision unit allows game-style experiential learning to be conducted not only by individuals but also by teams, and can incorporate team-building elements. The provision unit, for example, constructs a system that allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it sets challenges for teams to compete against each other. The provision unit also provides game-style experiential learning that can be conducted by teams, and incorporates team-building elements. For example, it provides a scenario in which teams work together to complete a challenge. The provision unit also allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it provides a scenario that promotes team communication. In this way, experiential learning can be conducted by teams, thereby enhancing the effectiveness of team building.

[0041] The provision department can incorporate success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. The provision department, for example, builds a system that incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, it provides scenarios based on success stories from different industries. The provision department also provides scenarios based on success stories from different industries, allowing employees to learn from a variety of perspectives. For example, it generates role-playing teaching materials based on customer service cases from different industries. The provision department also incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, it provides scenarios based on project management cases from different industries. In this way, by incorporating success stories from different industries, it is possible to learn from a variety of perspectives.

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

[0043] The success case report collection unit collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection unit can also automatically collect success case reports from an internal database. The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI analyzes the content of the success case reports using a text generation AI (e.g., LLM). The generation AI can also extract the key points of the success cases using natural language processing technology. The generation AI can also analyze the patterns of success cases using data mining technology. The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of the success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use a multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also create teaching materials that include branching points in the scenario and allow users to try different options. The provision unit provides employees with the role-playing learning materials generated by the learning material generation unit in a game format. For example, the provision unit provides the learning materials through a web application or a mobile application. The provision unit can also provide a more immersive experience using VR or AR technology. The provision unit can also provide experiential learning in a game format that incorporates a competitive element in which employees compete against each other for the highest score. As a result, the role-playing learning material generation system according to the embodiment allows employees to receive experiential education based on the latest success stories, thereby improving learning effectiveness. For example, employees can engage in experiential learning as if they were playing a game, and can effectively acquire know-how from success stories. Furthermore, employees can obtain a more realistic learning experience through an immersive experience using VR or AR technology. Furthermore, employees competing against each other for the highest score can increase their motivation to learn.

[0044] When analyzing the content of a success case report, the analysis unit analyzes not only the context of the report but also the content of related internal chats and emails, thereby extracting more detailed background information. For example, when analyzing the content of a success case report, the analysis unit simultaneously analyzes the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information. The analysis unit also builds a system that analyzes not only the content of the report but also the content of related internal chats and emails to extract background information of success cases. For example, it extracts keywords from chat logs and emails and associates them with the report. Furthermore, when analyzing a success case report, the analysis unit automatically collects the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information of the success case. This complements the context of the report and extracts more detailed background information.

[0045] The analysis unit can cluster the analysis results of the success case reports, taking into account their relevance to other success cases, and identify common success factors. For example, the analysis results of the success case reports can be clustered, and common success factors can be identified, taking into account their relevance to other success cases. For example, cases with similar success factors can be classified into the same cluster. The analysis unit can also use a clustering algorithm to associate the analysis results of the success case reports with other success cases and identify common success factors. Specifically, the analysis unit performs clustering based on keywords for success factors. The analysis unit can also build a system that clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, the analysis unit can analyze patterns of success factors and extract commonalities. By identifying common success factors, the know-how of success cases can be systematized.

[0046] The success case report collection department can collect success cases not only from within the company but also from the entire industry, thereby collecting a wider range of know-how. For example, the scope of success case report collection can be expanded from within the company to the entire industry, and success cases from other companies can also be collected. For example, know-how can be collected by referring to a database of success cases in the industry. In addition, to collect success cases from the entire industry, the success case report collection department can build a system that automatically collects data from industry associations and specialist magazines and integrates it with success cases from within the company. In addition, the success case report collection department can expand the scope of success case report collection to the entire industry, and collect success cases from other companies. For example, success cases presented at industry conferences and seminars can be collected and stored in a database. In this way, by collecting success cases from the entire industry, a wider range of know-how can be obtained.

[0047] The success case report collection unit can also collect presentation materials and videos used by reporters and perform multimedia analysis. For example, when collecting success case reports, the unit can also collect presentation materials and videos used by reporters and perform multimedia analysis. For example, it can analyze the content of slides and the audio of videos. The success case report collection unit can also build a system that collects presentation materials and videos and performs multimedia analysis. For example, it can automatically analyze the text on slides and the audio of videos to extract success factors. The success case report collection unit can also collect presentation materials and videos used by reporters when collecting success case reports and perform multimedia analysis. For example, it can convert the audio of videos into text and analyze it. In this way, it can extract more detailed success factors by analyzing the presentation materials and videos.

[0048] The teaching material generation unit can provide a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the cultural elements and organizational characteristics behind the success stories can be reflected in the scenario of the role-playing teaching material. For example, the culture and values ​​of the organization can be incorporated into the scenario. The teaching material generation unit can also generate a scenario that provides a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the scenario can be adjusted based on the characteristics of the organization. The teaching material generation unit can also reflect the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the cultural background and organizational characteristics can be incorporated into the scenario. In this way, a more realistic experience can be provided by reflecting the cultural elements and organizational characteristics.

[0049] The teaching material generation unit can provide many branching points in the scenario, allowing the user to try different options. For example, when generating role-playing teaching materials, the unit provides many branching points in the scenario, allowing the user to try different options. For example, multiple options are provided, allowing the user to choose freely. The teaching material generation unit also builds a system that provides many branching points in the scenario, allowing the user to try different options. For example, a different development is prepared for each branching point in the scenario. The teaching material generation unit also provides many branching points in the scenario, allowing the user to try different options. For example, a different outcome is prepared for each option. This allows the user to try different options, broadening the scope of learning.

[0050] The teaching material generation unit can expand the target for which role-playing teaching materials are generated, not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, the target for which role-playing teaching materials are generated is expanded not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, teaching materials based on success stories from the technical department are generated. The teaching material generation unit also builds a system for generating role-playing teaching materials based on success stories from other departments. For example, a scenario is generated based on a success story from the human resources department. The teaching material generation unit also expands the target for which role-playing teaching materials are generated, not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department and the human resources department. In this way, by expanding the target for which role-playing teaching materials are generated, learning can be conducted in a wider range of departments.

[0051] The teaching material generation unit can combine virtual reality (VR) and augmented reality (AR) technology with role-playing teaching materials to provide a more immersive experience. For example, virtual reality (VR) technology can be combined with role-playing teaching materials to enable users to engage in experiential learning in a virtual space. For example, a scenario in which users interact with a virtual customer is provided using a VR headset. The teaching material generation unit also combines real-world elements with role-playing teaching materials using augmented reality (AR) technology. For example, a scenario in which virtual customers are displayed in a real office environment using AR glasses and an interaction is provided. The teaching material generation unit also combines VR and AR technology with role-playing teaching materials to build a system that provides a more immersive experience. For example, a realistic scenario can be generated using VR or AR, allowing users to actually experience it. In this way, a more immersive experience can be provided by combining VR and AR technology.

[0052] The provision department can enhance learning effectiveness by incorporating a competitive element into game-style experiential learning and having employees compete with each other for scores. For example, a system can be constructed in which a competitive element is incorporated into game-style experiential learning and employees compete with each other for scores. For example, scores can be awarded according to learning outcomes and a ranking can be displayed. The provision department can also enhance learning effectiveness by providing game-style experiential learning that incorporates a competitive element and having employees compete with each other for scores. For example, a score competition can be held between teams. The provision department can also enhance learning effectiveness by incorporating a competitive element into game-style experiential learning and having employees compete with each other for scores. For example, scores can be awarded each time a specific challenge is cleared, encouraging competition. In this way, the introduction of a competitive element can enhance learning effectiveness.

[0053] The provision unit can reflect actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a system is constructed that reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, actual customer data or project data is incorporated into the scenario. The provision unit also provides a scenario based on actual business data to make the game-style experiential learning more realistic. For example, actual sales data is used to generate a customer dialogue scenario. The provision unit also reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a scenario based on actual project data is generated. In this way, by reflecting actual business data, a more realistic scenario can be provided.

[0054] The provision unit allows game-style experiential learning to be conducted not only by individuals but also by teams, and can incorporate team-building elements. For example, a system is constructed that allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, a team challenge is set. The provision unit also provides game-style experiential learning that can be conducted by teams, and incorporates team-building elements. For example, it provides a scenario in which teams work together to complete a challenge. The provision unit also allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it provides a scenario that promotes team communication. In this way, the effectiveness of team building can be enhanced by conducting experiential learning on a team basis.

[0055] The provision department can incorporate success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, a system is constructed that incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, scenarios based on success stories from different industries are provided. The provision department also provides scenarios based on success stories from different industries, allowing employees to learn from a variety of perspectives. For example, role-playing teaching materials are generated based on customer service cases from different industries. The provision department also incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, scenarios based on project management cases from different industries are provided. In this way, incorporating success stories from different industries allows employees to learn from a variety of perspectives.

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

[0057] Step 1: The success case report collection department collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection department can also automatically collect success case reports from an internal database. Step 2: The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the content of the success case reports. The generation AI may also use natural language processing technology to extract key points from the success cases. The generation AI may also use data mining technology to analyze patterns in the success cases. Step 3: The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of the success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also set branching points in the scenario and generate teaching materials that allow users to try different options. Step 4: The provision department provides the role-playing learning materials generated by the learning material generation department to employees in a game format. For example, the provision department provides the learning materials through a web application or a mobile application. The provision department can also use VR or AR technology to provide a more immersive experience. The provision department can also provide experiential learning in a game format that incorporates a competitive element in which employees compete against each other for the highest score.

[0058] (Example 2) The role-playing teaching material generation system according to an embodiment of the present invention is a system in which a generation AI instantly creates role-playing teaching materials based on the most recent success case reports from within the company, allowing employees to learn the know-how of the success cases through experiential learning, such as playing a game. As a result, the role-playing teaching material generation system allows employees to receive experiential education based on the latest success cases, thereby improving learning effectiveness.

[0059] A role-playing teaching material generation system according to an embodiment includes a success case report collection unit, an analysis unit, a teaching material generation unit, and a provision unit. The success case report collection unit collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection unit can also automatically collect success case reports from an internal database. The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI analyzes the content of the success case reports using a text generation AI (e.g., LLM). The generation AI can also extract key points of success cases using natural language processing technology. The generation AI can also analyze patterns of success cases using data mining technology. The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use a multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also generate teaching materials that set branching points in the scenario, allowing users to try different options. The provision unit provides the role-playing teaching materials generated by the teaching material generation unit to employees in the form of a game. For example, the provision unit provides the teaching materials through a web application or a mobile application. The provision unit can also use VR or AR technology to provide a more immersive experience. The provision unit can also provide experiential learning in the form of a game that incorporates a competitive element in which employees compete against each other for the highest score. This allows the role-playing teaching material generation system according to the embodiment to provide employees with experiential education based on the latest success stories, thereby improving learning effectiveness. For example, employees can engage in experiential learning as if they were playing a game, effectively acquiring know-how from success stories. Employees can also obtain a more realistic learning experience through an immersive experience using VR or AR technology. Furthermore, employees competing against each other for the highest score can increase their motivation to learn.

[0060] The success case report collection unit can analyze the reporter's emotional state using an emotion estimation function and prioritize collecting cases with strong positive emotions. For example, when collecting success case reports, the success case report collection unit analyzes the reporter's facial expressions and voice and calculates an emotion score. Reports with high positive emotion scores are preferentially stored in a database. The success case report collection unit also uses the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases with strong positive emotions. This makes it possible to prioritize collecting cases with high emotional value. The success case report collection unit also builds a system that analyzes the reporter's emotional state and prioritizes collecting cases with strong positive emotions. For example, if the reporter's emotion score is above a certain level, the report is automatically collected. This makes it possible to prioritize collecting cases with strong positive emotions and collect cases with high emotional value.

[0061] When analyzing the content of a success case report, the analysis unit analyzes not only the context of the report but also the content of related internal chats and emails, thereby extracting more detailed background information. For example, when analyzing the content of a success case report, the analysis unit simultaneously analyzes the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information. The analysis unit also builds a system that analyzes not only the content of the report but also the content of related internal chats and emails to extract background information of success cases. For example, it extracts keywords from chat logs and emails and associates them with the report. Furthermore, when analyzing a success case report, the analysis unit automatically collects the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information of the success case. This complements the context of the report and extracts more detailed background information.

[0062] The analysis unit can cluster the analysis results of the success case reports, taking into account their relevance to other success cases, and identify common success factors. The analysis unit, for example, clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, it classifies cases with similar success factors into the same cluster. The analysis unit also uses a clustering algorithm to associate the analysis results of the success case reports with other success cases and identify common success factors. Specifically, it performs clustering based on keywords for success factors. The analysis unit also builds a system that clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, it analyzes patterns of success factors and extracts commonalities. By identifying common success factors, it is possible to systematize the know-how of success cases.

[0063] The success case report collection department can collect success cases not only within the company but also across the industry, thereby collecting a wider range of know-how. For example, the success case report collection department can expand the scope of success case report collection from within the company to the entire industry, and collect success cases from other companies. For example, it can refer to an industry success case database to collect know-how. In addition, to collect success cases from the entire industry, the success case report collection department can build a system that automatically collects data from industry associations and specialized magazines and integrates it with success cases from within the company. In addition, the success case report collection department can expand the scope of success case report collection to the entire industry, and collect success cases from other companies. For example, it can collect success cases presented at industry conferences and seminars and store them in a database. In this way, by collecting success cases from the entire industry, a wider range of know-how can be obtained.

[0064] The success case report collection unit can also collect presentation materials and videos used by the reporter and perform multimedia analysis. For example, when collecting success case reports, the success case report collection unit also collects presentation materials and videos used by the reporter and performs multimedia analysis. For example, it analyzes the content of slides and the audio of videos. The success case report collection unit also collects presentation materials and videos and builds a system that performs multimedia analysis. For example, it automatically analyzes the text of slides and the audio of videos to extract success factors. The success case report collection unit also collects presentation materials and videos used by the reporter when collecting success case reports and performs multimedia analysis. For example, it converts the audio of videos into text and analyzes it. In this way, more detailed success factors can be extracted by analyzing the presentation materials and videos.

[0065] The success case report collection unit can use the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. The success case report collection unit, for example, uses the emotion estimation function to analyze the reporter's emotional state in real time and build a system that automatically highlights cases where positive emotions are strong. For example, it analyzes the reporter's facial expressions and voice. The success case report collection unit also analyzes the reporter's emotional state in real time and automatically highlights cases where positive emotions are strong. For example, if the emotion score is above a certain level, the report is automatically highlighted. The success case report collection unit also uses the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. For example, it highlights based on the reporter's emotion score. In this way, by automatically highlighting cases where positive emotions are strong, it is possible to collect cases with high emotional value.

[0066] The teaching material generation unit can use the emotion estimation function to generate a scenario that the user can most emotionally empathize with. For example, when generating role-playing teaching materials, the teaching material generation unit uses the emotion estimation function to generate a scenario that the user can most emotionally empathize with. For example, the scenario is adjusted based on an emotion score. The teaching material generation unit also uses the emotion estimation function to build a system that generates a scenario that the user can most emotionally empathize with. For example, the emotional reaction of the user is analyzed in real time and the scenario is adjusted. The teaching material generation unit also uses the emotion estimation function to generate a scenario that the user can most emotionally empathize with when generating role-playing teaching materials. For example, scenarios with high emotion scores are preferentially generated. This allows for the generation of a scenario that the user can most emotionally empathize with, thereby improving the learning effect.

[0067] The teaching material generation unit can provide a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the teaching material generation unit reflects the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the culture and values ​​of the organization are incorporated into the scenario. The teaching material generation unit also generates a scenario that reflects the cultural elements and organizational characteristics behind the success stories to provide a more realistic experience. For example, the scenario is adjusted based on the characteristics of the organization. The teaching material generation unit also reflects the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the cultural background and organizational characteristics are incorporated into the scenario. In this way, a more realistic experience can be provided by reflecting the cultural elements and organizational characteristics.

[0068] The teaching material generation unit can provide many branching points in the scenario, allowing the user to try different options. For example, when generating role-playing teaching materials, the teaching material generation unit provides many branching points in the scenario, allowing the user to try different options. For example, multiple options are provided, allowing the user to choose freely. The teaching material generation unit also builds a system that provides many branching points in the scenario, allowing the user to try different options. For example, a different development is prepared for each branching point in the scenario. The teaching material generation unit also provides many branching points in the scenario, allowing the user to try different options, when generating role-playing teaching materials. For example, a different outcome is prepared for each option. This allows the user to try different options, thereby broadening the scope of learning.

[0069] The teaching material generation unit can expand the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, the teaching material generation unit expands the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department. The teaching material generation unit also builds a system for generating role-playing teaching materials based on success stories from other departments. For example, scenarios are generated based on success stories from the human resources department. The teaching material generation unit also expands the target for which role-playing teaching materials are generated not only for the sales department but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department and the human resources department. In this way, by expanding the target for which role-playing teaching materials are generated, learning can be conducted in a wider range of departments.

[0070] The teaching material generation unit can combine virtual reality (VR) and augmented reality (AR) technology with role-playing teaching materials to provide a more immersive experience. For example, the teaching material generation unit combines virtual reality (VR) technology with role-playing teaching materials to enable users to engage in experiential learning in a virtual space. For example, it provides a scenario in which users interact with a virtual customer using a VR headset. The teaching material generation unit also combines real-world elements with role-playing teaching materials using augmented reality (AR) technology. For example, it uses AR glasses to display a virtual customer in a real office environment and provides a scenario in which users can interact with the customer. The teaching material generation unit also combines VR and AR technology with role-playing teaching materials to build a system that provides a more immersive experience. For example, it uses VR and AR to generate realistic scenarios that users can actually experience. In this way, combining VR and AR technology can provide a more immersive experience.

[0071] The learning material generation unit can use the emotion estimation function to analyze the emotional reactions of the user as they progress through a scenario in real time and dynamically adjust the difficulty level of the scenario. The learning material generation unit, for example, uses the emotion estimation function to analyze the emotional reactions of the user as they progress through a scenario in real time and build a system that dynamically adjusts the difficulty level of the scenario. For example, the difficulty level is adjusted based on the user's emotion score. The learning material generation unit also analyzes the user's emotional reactions in real time and dynamically adjusts the difficulty level of the scenario. For example, if the emotion score is low, the difficulty level of the scenario is lowered. The learning material generation unit also uses the emotion estimation function to analyze the user's emotional reactions in real time and dynamically adjust the difficulty level of the scenario. For example, if the emotion score is high, the difficulty level of the scenario is increased. In this way, the learning effect can be improved by dynamically adjusting the difficulty level of the scenario according to the user's emotional reactions.

[0072] The providing unit can use the emotion estimation function to provide feedback according to the user's emotional state in a game-style experiential learning. For example, in a game-style experiential learning, the providing unit uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback according to the emotional state. For example, if the user has a strong positive emotion, a compliment is displayed. The providing unit also uses the emotion estimation function to build a system that provides feedback according to the user's emotional state. For example, if the user has a strong negative emotion, an encouraging message is displayed. The providing unit also uses the emotion estimation function to analyze the user's emotional state in real time in a game-style experiential learning and provide feedback according to the emotional state. For example, the feedback is adjusted based on an emotion score. This allows the learning effect to be improved by providing feedback according to the user's emotional state.

[0073] The provision department can incorporate a competitive element into game-style experiential learning, allowing employees to compete against each other for scores, thereby enhancing learning effectiveness. The provision department, for example, builds a system that incorporates a competitive element into game-style experiential learning, allowing employees to compete against each other for scores. For example, scores are awarded according to learning outcomes, and a ranking is displayed. The provision department also provides game-style experiential learning that incorporates a competitive element, allowing employees to compete against each other for scores, thereby enhancing learning effectiveness. For example, a score competition is held between teams. The provision department also builds a system that incorporates a competitive element into game-style experiential learning, allowing employees to compete against each other for scores. For example, scores are awarded each time a specific challenge is cleared, encouraging competition. In this way, the introduction of a competitive element can enhance learning effectiveness.

[0074] The provision unit can reflect actual business data in the game-style experiential learning to provide a more realistic scenario. The provision unit, for example, builds a system that reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, actual customer data or project data is incorporated into the scenario. The provision unit also provides a scenario based on actual business data to make the game-style experiential learning more realistic. For example, actual sales data is used to generate a dialogue scenario with a customer. The provision unit also reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a scenario based on actual project data is generated. In this way, by reflecting actual business data, a more realistic scenario can be provided.

[0075] The provision unit allows game-style experiential learning to be conducted not only by individuals but also by teams, and can incorporate team-building elements. The provision unit, for example, constructs a system that allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it sets challenges for teams to compete against each other. The provision unit also provides game-style experiential learning that can be conducted by teams, and incorporates team-building elements. For example, it provides a scenario in which teams work together to complete a challenge. The provision unit also allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it provides a scenario that promotes team communication. In this way, experiential learning can be conducted by teams, thereby enhancing the effectiveness of team building.

[0076] The provision department can incorporate success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. The provision department, for example, builds a system that incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, it provides scenarios based on success stories from different industries. The provision department also provides scenarios based on success stories from different industries, allowing employees to learn from a variety of perspectives. For example, it generates role-playing teaching materials based on customer service cases from different industries. The provision department also incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, it provides scenarios based on project management cases from different industries. In this way, by incorporating success stories from different industries, it is possible to learn from a variety of perspectives.

[0077] The providing unit can use the emotion estimation function to analyze the user's emotional response during game-style experiential learning and automatically generate a scenario that elicits positive emotions. The providing unit, for example, uses the emotion estimation function to analyze the user's emotional response during game-style experiential learning in real time and build a system that automatically generates a scenario that elicits positive emotions. For example, the providing unit adjusts the scenario based on the emotion score. The providing unit also analyzes the user's emotional response in real time and automatically generates a scenario that elicits positive emotions. For example, if the emotion score is low, it adds positive elements. The providing unit also uses the emotion estimation function to analyze the user's emotional response during game-style experiential learning and automatically generates a scenario that elicits positive emotions. For example, if the emotion score is high, it strengthens the scenario. In this way, the learning effect can be improved by analyzing the user's emotional response and automatically generating a scenario that elicits positive emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The success case report collection unit collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection unit can also automatically collect success case reports from an internal database. The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI analyzes the content of the success case reports using a text generation AI (e.g., LLM). The generation AI can also extract the key points of the success cases using natural language processing technology. The generation AI can also analyze the patterns of success cases using data mining technology. The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of the success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use a multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also create teaching materials that include branching points in the scenario and allow users to try different options. The provision unit provides employees with the role-playing learning materials generated by the learning material generation unit in a game format. For example, the provision unit provides the learning materials through a web application or a mobile application. The provision unit can also provide a more immersive experience using VR or AR technology. The provision unit can also provide experiential learning in a game format that incorporates a competitive element in which employees compete against each other for the highest score. As a result, the role-playing learning material generation system according to the embodiment allows employees to receive experiential education based on the latest success stories, thereby improving learning effectiveness. For example, employees can engage in experiential learning as if they were playing a game, and can effectively acquire know-how from success stories. Furthermore, employees can obtain a more realistic learning experience through an immersive experience using VR or AR technology. Furthermore, employees competing against each other for the highest score can increase their motivation to learn.

[0080] The success case report collection unit can analyze the reporter's emotional state using an emotion estimation function and prioritize collecting cases with strong positive emotions. For example, when collecting success case reports, the reporter's facial expressions and voice are analyzed to calculate an emotion score. Reports with high positive emotion scores are preferentially stored in the database. The success case report collection unit also uses the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases with strong positive emotions. This makes it possible to prioritize collecting cases with high emotional value. The success case report collection unit also builds a system that analyzes the reporter's emotional state and prioritizes collecting cases with strong positive emotions. For example, if the reporter's emotion score is above a certain level, the report is automatically collected. This makes it possible to prioritize collecting cases with strong positive emotions and collect cases with high emotional value.

[0081] When analyzing the content of a success case report, the analysis unit analyzes not only the context of the report but also the content of related internal chats and emails, thereby extracting more detailed background information. For example, when analyzing the content of a success case report, the analysis unit simultaneously analyzes the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information. The analysis unit also builds a system that analyzes not only the content of the report but also the content of related internal chats and emails to extract background information of success cases. For example, it extracts keywords from chat logs and emails and associates them with the report. Furthermore, when analyzing a success case report, the analysis unit automatically collects the content of related internal chats and emails to complement the context of the report. This makes it possible to extract more detailed background information of the success case. This complements the context of the report and extracts more detailed background information.

[0082] The analysis unit can cluster the analysis results of the success case reports, taking into account their relevance to other success cases, and identify common success factors. For example, the analysis results of the success case reports can be clustered, and common success factors can be identified, taking into account their relevance to other success cases. For example, cases with similar success factors can be classified into the same cluster. The analysis unit can also use a clustering algorithm to associate the analysis results of the success case reports with other success cases and identify common success factors. Specifically, the analysis unit performs clustering based on keywords for success factors. The analysis unit can also build a system that clusters the analysis results of the success case reports and identifies common success factors, taking into account their relevance to other success cases. For example, the analysis unit can analyze patterns of success factors and extract commonalities. By identifying common success factors, the know-how of success cases can be systematized.

[0083] The success case report collection department can collect success cases not only from within the company but also from the entire industry, thereby collecting a wider range of know-how. For example, the scope of success case report collection can be expanded from within the company to the entire industry, and success cases from other companies can also be collected. For example, know-how can be collected by referring to a database of success cases in the industry. In addition, to collect success cases from the entire industry, the success case report collection department can build a system that automatically collects data from industry associations and specialist magazines and integrates it with success cases from within the company. In addition, the success case report collection department can expand the scope of success case report collection to the entire industry, and collect success cases from other companies. For example, success cases presented at industry conferences and seminars can be collected and stored in a database. In this way, by collecting success cases from the entire industry, a wider range of know-how can be obtained.

[0084] The success case report collection unit can also collect presentation materials and videos used by reporters and perform multimedia analysis. For example, when collecting success case reports, the unit can also collect presentation materials and videos used by reporters and perform multimedia analysis. For example, it can analyze the content of slides and the audio of videos. The success case report collection unit can also build a system that collects presentation materials and videos and performs multimedia analysis. For example, it can automatically analyze the text on slides and the audio of videos to extract success factors. The success case report collection unit can also collect presentation materials and videos used by reporters when collecting success case reports and perform multimedia analysis. For example, it can convert the audio of videos into text and analyze it. In this way, it can extract more detailed success factors by analyzing the presentation materials and videos.

[0085] The success story report collection unit can use the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. For example, a system can be constructed that uses the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. For example, the reporter's facial expressions and voice can be analyzed. The success story report collection unit can also analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. For example, if the emotion score is above a certain level, the report can be automatically highlighted. The success story report collection unit can also use the emotion estimation function to analyze the reporter's emotional state in real time and automatically highlight cases where positive emotions are strong. For example, highlighting can be done based on the reporter's emotion score. In this way, cases where positive emotions are strong can be automatically highlighted, thereby collecting cases with high emotional value.

[0086] The teaching material generation unit can use the emotion estimation function to generate a scenario that the user can most emotionally empathize with. For example, when generating role-playing teaching materials, the emotion estimation function is used to generate a scenario that the user can most emotionally empathize with. For example, the scenario is adjusted based on an emotion score. The teaching material generation unit also uses the emotion estimation function to build a system that generates a scenario that the user can most emotionally empathize with. For example, the emotional reaction of the user is analyzed in real time and the scenario is adjusted. The teaching material generation unit also uses the emotion estimation function to generate a scenario that the user can most emotionally empathize with when generating role-playing teaching materials. For example, scenarios with high emotion scores are preferentially generated. This allows for the generation of a scenario that the user can most emotionally empathize with, thereby improving the learning effect.

[0087] The teaching material generation unit can provide a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the cultural elements and organizational characteristics behind the success stories can be reflected in the scenario of the role-playing teaching material. For example, the culture and values ​​of the organization can be incorporated into the scenario. The teaching material generation unit can also generate a scenario that provides a more realistic experience by reflecting the cultural elements and organizational characteristics behind the success stories. For example, the scenario can be adjusted based on the characteristics of the organization. The teaching material generation unit can also reflect the cultural elements and organizational characteristics behind the success stories in the scenario of the role-playing teaching material. For example, the cultural background and organizational characteristics can be incorporated into the scenario. In this way, a more realistic experience can be provided by reflecting the cultural elements and organizational characteristics.

[0088] The teaching material generation unit can provide many branching points in the scenario, allowing the user to try different options. For example, when generating role-playing teaching materials, the unit provides many branching points in the scenario, allowing the user to try different options. For example, multiple options are provided, allowing the user to choose freely. The teaching material generation unit also builds a system that provides many branching points in the scenario, allowing the user to try different options. For example, a different development is prepared for each branching point in the scenario. The teaching material generation unit also provides many branching points in the scenario, allowing the user to try different options. For example, a different outcome is prepared for each option. This allows the user to try different options, broadening the scope of learning.

[0089] The teaching material generation unit can expand the target for which role-playing teaching materials are generated, not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, the target for which role-playing teaching materials are generated is expanded not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, teaching materials based on success stories from the technical department are generated. The teaching material generation unit also builds a system for generating role-playing teaching materials based on success stories from other departments. For example, a scenario is generated based on a success story from the human resources department. The teaching material generation unit also expands the target for which role-playing teaching materials are generated, not only for the sales department, but also for other departments such as the technical department and the human resources department. For example, teaching materials are generated based on success stories from the technical department and the human resources department. In this way, by expanding the target for which role-playing teaching materials are generated, learning can be conducted in a wider range of departments.

[0090] The teaching material generation unit can combine virtual reality (VR) and augmented reality (AR) technology with role-playing teaching materials to provide a more immersive experience. For example, virtual reality (VR) technology can be combined with role-playing teaching materials to enable users to engage in experiential learning in a virtual space. For example, a scenario in which users interact with a virtual customer is provided using a VR headset. The teaching material generation unit also combines real-world elements with role-playing teaching materials using augmented reality (AR) technology. For example, a scenario in which virtual customers are displayed in a real office environment using AR glasses and an interaction is provided. The teaching material generation unit also combines VR and AR technology with role-playing teaching materials to build a system that provides a more immersive experience. For example, a realistic scenario can be generated using VR or AR, allowing users to actually experience it. In this way, a more immersive experience can be provided by combining VR and AR technology.

[0091] The learning material generation unit can use the emotion estimation function to analyze the emotional reactions of the user as they progress through a scenario in real time and dynamically adjust the difficulty level of the scenario. For example, a system is constructed that uses the emotion estimation function to analyze the emotional reactions of the user as they progress through a scenario in real time and dynamically adjust the difficulty level of the scenario. For example, the difficulty level is adjusted based on the user's emotion score. The learning material generation unit also analyzes the user's emotional reactions in real time and dynamically adjusts the difficulty level of the scenario. For example, if the emotion score is low, the difficulty level of the scenario is lowered. The learning material generation unit also uses the emotion estimation function to analyze the user's emotional reactions in real time and dynamically adjust the difficulty level of the scenario. For example, if the emotion score is high, the difficulty level of the scenario is increased. In this way, the learning effect can be improved by dynamically adjusting the difficulty level of the scenario according to the user's emotional reactions.

[0092] The providing unit can use the emotion estimation function to provide feedback according to the user's emotional state in a game-style experiential learning. For example, in a game-style experiential learning, the emotion estimation function is used to analyze the user's emotional state in real time and provide feedback according to the emotional state. For example, if the user has a strong positive emotion, a compliment is displayed. The providing unit also uses the emotion estimation function to build a system that provides feedback according to the user's emotional state. For example, if the user has a strong negative emotion, an encouraging message is displayed. The providing unit also uses the emotion estimation function to analyze the user's emotional state in real time in a game-style experiential learning and provide feedback according to the emotional state. For example, the feedback is adjusted based on the emotion score. This makes it possible to improve the learning effect by providing feedback according to the user's emotional state.

[0093] The provision department can enhance learning effectiveness by incorporating a competitive element into game-style experiential learning and having employees compete with each other for scores. For example, a system can be constructed in which a competitive element is incorporated into game-style experiential learning and employees compete with each other for scores. For example, scores can be awarded according to learning outcomes and a ranking can be displayed. The provision department can also enhance learning effectiveness by providing game-style experiential learning that incorporates a competitive element and having employees compete with each other for scores. For example, a score competition can be held between teams. The provision department can also enhance learning effectiveness by incorporating a competitive element into game-style experiential learning and having employees compete with each other for scores. For example, scores can be awarded each time a specific challenge is cleared, encouraging competition. In this way, the introduction of a competitive element can enhance learning effectiveness.

[0094] The provision unit can reflect actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a system is constructed that reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, actual customer data or project data is incorporated into the scenario. The provision unit also provides a scenario based on actual business data to make the game-style experiential learning more realistic. For example, actual sales data is used to generate a customer dialogue scenario. The provision unit also reflects actual business data in the game-style experiential learning to provide a more realistic scenario. For example, a scenario based on actual project data is generated. In this way, by reflecting actual business data, a more realistic scenario can be provided.

[0095] The provision unit allows game-style experiential learning to be conducted not only by individuals but also by teams, and can incorporate team-building elements. For example, a system is constructed that allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, a team challenge is set. The provision unit also provides game-style experiential learning that can be conducted by teams, and incorporates team-building elements. For example, it provides a scenario in which teams work together to complete a challenge. The provision unit also allows game-style experiential learning to be conducted not only by individuals but also by teams, and incorporates team-building elements. For example, it provides a scenario that promotes team communication. In this way, the effectiveness of team building can be enhanced by conducting experiential learning on a team basis.

[0096] The provision department can incorporate success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, a system is constructed that incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, scenarios based on success stories from different industries are provided. The provision department also provides scenarios based on success stories from different industries, allowing employees to learn from a variety of perspectives. For example, role-playing teaching materials are generated based on customer service cases from different industries. The provision department also incorporates success stories from different industries into the game-style experiential learning, allowing employees to learn from a variety of perspectives. For example, scenarios based on project management cases from different industries are provided. In this way, incorporating success stories from different industries allows employees to learn from a variety of perspectives.

[0097] The providing unit can use the emotion estimation function to analyze the user's emotional response during game-style experiential learning and automatically generate a scenario that elicits positive emotions. For example, the emotion estimation function is used to build a system that analyzes the user's emotional response during game-style experiential learning in real time and automatically generates a scenario that elicits positive emotions. For example, the scenario is adjusted based on the emotion score. The providing unit also analyzes the user's emotional response in real time and automatically generates a scenario that elicits positive emotions. For example, if the emotion score is low, positive elements are added. The providing unit also uses the emotion estimation function to analyze the user's emotional response during game-style experiential learning and automatically generate a scenario that elicits positive emotions. For example, if the emotion score is high, the scenario is strengthened. In this way, the learning effect can be improved by analyzing the user's emotional response and automatically generating a scenario that elicits positive emotions.

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

[0099] Step 1: The success case report collection department collects the most recent success case reports from within the company. For example, it collects success case reports from the sales department and project completion reports. The success case report collection department can also automatically collect success case reports from an internal database. Step 2: The analysis unit analyzes the success case reports collected by the success case report collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the content of the success case reports. The generation AI may also use natural language processing technology to extract key points from the success cases. The generation AI may also use data mining technology to analyze patterns in the success cases. Step 3: The teaching material generation unit generates role-playing teaching materials based on the content of the success cases analyzed by the analysis unit. For example, the generation AI converts the key points of the success cases into a scenario format and creates teaching materials that employees can actually experience. The generation AI can also use multimodal generation AI to generate teaching materials that include not only text but also images and audio. The generation AI can also set branching points in the scenario and generate teaching materials that allow users to try different options. Step 4: The provision department provides the role-playing learning materials generated by the learning material generation department to employees in a game format. For example, the provision department provides the learning materials through a web application or a mobile application. The provision department can also use VR or AR technology to provide a more immersive experience. The provision department can also provide experiential learning in a game format that incorporates a competitive element in which employees compete against each other for the highest score.

[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. The Success Case Report Collection Department collects the most recent success case reports from within the company, an analysis unit that analyzes the success case reports collected by the success case report collection unit; a teaching material generation unit that generates role-playing teaching materials based on the contents of the successful cases analyzed by the analysis unit; a providing unit that provides role-playing teaching materials generated by the teaching material generating unit to employees in a game format. A system characterized by:

2. The success case report collection unit Analyze the reporter's emotional state using an emotion estimation function, and prioritize collecting cases with strong positive emotions.

2. The system of claim 1.

3. The analysis unit When analyzing the content of the success case reports, we analyze not only the context of the report but also the content of related internal chats and emails to extract more detailed background information.

2. The system of claim 1.

4. The analysis unit The analysis results of the success case reports are clustered in consideration of their relevance to other success cases, and common success factors are identified.

2. The system of claim 1.

5. The success case report collection unit Collect success stories not only within the company but across the industry to gather a broader range of know-how 2. The system of claim 1.

6. The success case report collection unit We will also collect presentation materials and videos used by presenters and conduct multimedia analysis.

2. The system of claim 1.

7. The success case report collection unit The emotional state of the reporter is analyzed in real time using an emotion estimation function, and cases where positive emotions are strong are automatically highlighted.

2. The system of claim 1.

8. The teaching material generation unit Generate scenarios that users can most easily relate to emotionally 2. The system of claim 1.

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