system

Abstract

The system according to this embodiment aims to improve hospitality, interpersonal skills, and team cohesion by generating and providing exercises based on the user's scenarios and circumstances. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of scenarios and situations from the user. The generation unit analyzes the scenarios and situations received by the reception unit and generates exercises. The provision unit provides the exercises generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there is a lack of effective training means for improving hospitality, human power, and team unity during business operations.

[0005] The system according to the embodiment aims to improve hospitality, human power, and team unity by generating and providing exercises based on the user's scenario and situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of scenarios and situations from the user. The generation unit analyzes the scenarios and situations received by the reception unit and generates exercises. The provision unit provides the exercises generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can improve hospitality, interpersonal skills, and team cohesion by generating and providing exercises based on the user's scenarios and circumstances. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Human Skills AI Exercise System according to an embodiment of the present invention is a novel tool that utilizes generative AI to support the mental growth and training of employees. This system allows users to input specific scenarios or situations, and the generative AI generates exercises based on those scenarios to improve hospitality, interpersonal skills, and team cohesion. By actually experiencing these exercises, users can promote mental growth and training. For example, if a user inputs a "customer service scenario" or a "team building scenario," the generative AI analyzes the scenario and proposes the most suitable exercises. By performing the generated exercises, users can improve their customer service skills or team building skills. This mechanism allows for not only technical skill improvement but also simultaneous mental growth and training. This is expected to improve employees' hospitality, interpersonal skills, and team cohesion, leading to an improvement in work quality. Thus, the Human Skills AI Exercise System can support the mental growth and training of employees and improve work quality.

[0029] The Human Skills AI Exercise System according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of scenarios and situations from the user. For example, the reception unit allows the user to input a "customer service scenario" or a "team building scenario." The reception unit transmits the scenario or situation entered by the user to the generation AI. The generation unit uses the generation AI to analyze the scenario or situation received by the reception unit and generate exercises. The generation unit proposes the most suitable exercises based on past data and case studies, for example. The generation unit can generate exercises based on a "customer service scenario" to enable the generation AI to understand customer needs and provide appropriate responses. The generation unit can also generate exercises based on a "team building scenario" to improve team cohesion. The provision unit provides the exercises generated by the generation unit to the user. The provision unit can provide the generated exercises in video format, for example. The provision unit can also provide the generated exercises in text format. Thus, the Human Skills AI Exercise System according to this embodiment can support mental growth and training by generating and providing exercises based on the user's scenarios and situations.

[0030] The reception desk receives input of scenarios and situations from users. For example, the reception desk allows users to input "customer service scenarios" or "team building scenarios." Specifically, the reception desk provides an interface that accepts user-inputted scenarios and situations in text format. Users can input scenarios and situations through a dedicated application or website. The reception desk analyzes the user-inputted scenarios and situations in real time and converts them into an appropriate format. For example, if a user inputs "handling a complaint" as a "customer service scenario," the reception desk converts this input into a format that the generation AI can easily understand and sends it to the generation department. The reception desk can also ask additional questions about the scenarios and situations entered by the user. For example, for a "handling a complaint" scenario, it can ask for specific complaint details and customer background information to gather more detailed information. This allows the reception desk to accurately understand the user's needs and situations and provide appropriate information to the generation department. Furthermore, the reception desk records the user's past input history and preferences, which can be used as a reference for future inputs. This allows users to input scenarios and situations more smoothly and improves the overall usability of the system.

[0031] The generation unit uses a generation AI to analyze scenarios and situations received by the reception unit and generate exercises. For example, the generation unit proposes the most suitable exercise based on past data and case studies. Specifically, the generation AI uses natural language processing technology to analyze the scenarios and situations entered by the user. For example, when generating exercises to understand customer needs and provide appropriate responses based on a "customer service scenario," the generation AI refers to past customer service cases and success stories to propose the most suitable response method. The generation AI uses machine learning algorithms to understand the context and intent of the scenario entered by the user and generate appropriate exercises. For example, if the user enters a "complaint handling" scenario, the generation AI will propose specific steps and communication methods to resolve customer dissatisfaction based on past complaint handling data. Furthermore, the generation AI can adjust the difficulty and content of the exercises according to the user's skill level and experience. For example, it will propose basic response methods for beginners and generate exercises including advanced response techniques for experienced users. This allows the generation unit to generate the most suitable exercises according to the user's needs and situation and send them to the delivery unit.

[0032] The service provider delivers the exercises generated by the generation unit to the user. Specifically, the generated exercises can be provided in video format. For example, the service provider can create video content from the generated exercises and make it available for users to watch. The video content includes specific steps and methods for performing the exercises, allowing users to learn the exercises in a visually easy-to-understand format. The service provider can also provide the generated exercises in text format. Text-based exercises include detailed instructions and points to note, allowing users to perform the exercises at their own pace. Furthermore, the service provider can record the progress and results of the exercises and provide feedback to the user. For example, after a user completes an exercise, the service provider evaluates the user's performance and suggests areas for improvement and the next steps. This allows users to continue performing the exercises while monitoring their growth and progress. The service provider can also collect user feedback and use it as data to improve the content and delivery methods of the exercises. This allows the service provider to provide users with the most suitable exercises and support their mental growth and training.

[0033] The generation unit can generate exercises based on past data and case studies. For example, it can analyze past training data and success stories and propose the most suitable exercises based on that analysis. By using past data, the generation unit can generate more effective exercises. For example, it can generate exercises to improve customer service skills based on past success stories of customer service. It can also generate exercises to improve team cohesion based on past success stories of team building. In this way, more effective exercises can be generated by basing them on past data and case studies.

[0034] The provider can provide the generated exercises in video format. For example, the provider can provide the generated exercises as streaming videos. The provider can also provide the generated exercises as downloadable videos. For example, the provider can provide the exercises in video format so that users can easily understand them visually. By providing the exercises in video format, they become easier to understand visually.

[0035] The provider can provide the generated exercises in text format. For example, the provider can provide the generated exercises as a PDF document. The provider can also provide the generated exercises as a web page. For example, the provider can provide detailed information about the exercises in text format so that users can view it. This allows for the provision of detailed information by providing the exercises in text format.

[0036] The generation unit can generate exercises based on customer service scenarios. For example, the generation unit can analyze customer service scenarios, understand customer needs, and generate exercises to provide appropriate responses. Based on customer service scenarios, the generation unit proposes exercises to improve customer service skills. For example, based on a complaint handling scenario, the generation unit generates exercises to improve complaint handling skills. The generation unit can also generate exercises to improve sales promotion skills based on a sales promotion scenario. In this way, customer service skills can be improved by generating exercises based on customer service scenarios.

[0037] The generation unit can generate exercises based on team-building scenarios. For example, the generation unit can analyze a team-building scenario and generate exercises to improve team cohesion. Based on the team-building scenario, the generation unit proposes exercises to improve team-building skills. For example, based on a scenario for improving teamwork, the generation unit generates exercises to improve teamwork skills. The generation unit can also generate exercises to improve leadership skills based on a leadership development scenario. In this way, by generating exercises based on team-building scenarios, team cohesion can be improved.

[0038] The reception desk can analyze the user's past scenario input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in scenarios the user has previously entered and suggest similar scenarios. From the user's past input history, the reception desk can analyze their tendency to input at specific times and prompt them to input during those times. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The reception desk can filter scenarios and situations based on the user's current work situation and areas of interest when they are entered. For example, the reception desk will prioritize suggesting scenarios related to the project the user is currently working on. The reception desk can also filter and display relevant scenarios based on the user's areas of interest. The reception desk can also suggest appropriate scenarios according to the user's work situation. This allows for the suggestion of appropriate scenarios based on the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0040] The reception desk can prioritize the input of highly relevant scenarios by considering the user's geographical location when inputting scenarios and situations. For example, if the user is in a specific region, the reception desk will prioritize suggesting scenarios related to that region. If the user is on a business trip, the reception desk can also prioritize suggesting scenarios related to the destination of the business trip. If the user is at home, the reception desk can also prioritize suggesting scenarios that can be performed at home. This allows the reception desk to suggest appropriate scenarios based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0041] The reception desk can analyze the user's social media activity and input relevant scenarios when scenarios or situations are entered. For example, the reception desk can suggest scenarios related to topics the user has shown interest in on social media. The reception desk can also suggest scenarios that take into account the opinions of experts the user follows on social media. The reception desk can also suggest scenarios related to articles the user has shared on social media. This allows for the suggestion of appropriate scenarios based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0042] The generation unit can adjust the level of detail of exercises based on the importance of the scenarios when generating exercises. For example, the generation unit generates detailed exercises for high-importance scenarios. The generation unit can also generate simplified exercises for low-importance scenarios. The generation unit can also adjust the number of steps in the exercises according to their importance. This allows for more effective exercises to be provided by adjusting the level of detail of the exercises according to the importance of the scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0043] The generation unit can apply different generation algorithms depending on the scenario category when generating exercises. For example, for customer service scenarios, the generation unit can apply an algorithm to understand customer needs. For team-building scenarios, the generation unit can also apply an algorithm to enhance team cohesion. For problem-solving scenarios, the generation unit can also apply an algorithm to improve problem-solving skills. By applying the appropriate generation algorithm according to the scenario category, more effective exercises can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0044] The generation unit can determine the priority of exercises based on the scenario submission dates when generating exercises. For example, the generation unit will prioritize generating exercises for scenarios with upcoming submission dates. The generation unit can also postpone generating exercises for scenarios with later submission dates. The generation unit can also adjust the order of exercise generation according to the submission dates. This allows for the provision of more effective exercises by prioritizing exercises based on the scenario submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0045] The generation unit can adjust the order of exercises based on the relevance of the scenarios when generating exercises. For example, the generation unit can prioritize generating exercises for highly relevant scenarios. The generation unit can also postpone generating exercises for less relevant scenarios. The generation unit can also adjust the order of exercise generation according to relevance. This allows for the provision of more effective exercises by adjusting the order of exercises based on the relevance of the scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0046] The service provider can select the optimal delivery method by referring to the user's past exercise history when providing exercises. For example, the service provider may prioritize suggesting delivery methods (video, text, etc.) that the user has preferred in the past. The service provider can also suggest effective delivery methods based on the user's past exercise history. The service provider can also analyze the user's past exercise history and select the optimal delivery method. This allows the service provider to suggest the most suitable delivery method to the user by referring to their past exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0047] The service provider can customize the method of delivering exercises based on the user's current work situation when providing exercises. For example, if the user is busy, the service provider can provide exercises that can be completed in a short time. If the user has time, the service provider can also provide more detailed exercises. The service provider can also provide exercises appropriate to the user's work situation. This ensures that appropriate exercises are provided according to the user's work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0048] The service provider can select the optimal method of providing exercises by considering the user's geographical location. For example, if the user is in a specific region, the service provider can prioritize providing exercises related to that region. If the user is on a business trip, the service provider can also prioritize providing exercises related to the destination of the business trip. If the user is at home, the service provider can also prioritize providing exercises that can be performed at home. This allows the service provider to provide appropriate exercises based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0049] The service provider can analyze the user's social media activity and suggest ways to provide exercises when providing exercises. For example, the service provider can suggest exercises related to topics the user has shown interest in on social media. The service provider can also suggest exercises that take into account the opinions of experts the user follows on social media. The service provider can also suggest exercises related to articles the user has shared on social media. This allows the service provider to provide appropriate exercises based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze a user's past exercise history and generate optimal exercises. For example, it can prioritize suggesting effective exercises based on data from exercises the user has performed in the past. The generation unit can also adjust the exercises to avoid exercises the user has previously found difficult. The generation unit can also identify specific patterns from the user's past exercise history and generate exercises based on those patterns. In this way, by analyzing past exercise history, it can provide the user with the most suitable exercises.

[0052] The service provider can adjust how exercises are delivered based on the user's current work situation. For example, if the user is busy, the service provider can provide exercises that can be completed in a short time. If the user has more time, the service provider can also provide more detailed exercises. The service provider can also provide exercises that are appropriate for the user's work situation. This ensures that appropriate exercises are provided according to the user's work situation.

[0053] The generation unit can apply different generation algorithms depending on the scenario category. For example, in a customer service scenario, the generation unit can apply an algorithm to understand customer needs. In a team-building scenario, the generation unit can also apply an algorithm to enhance team cohesion. In a problem-solving scenario, the generation unit can also apply an algorithm to improve problem-solving skills. By applying the appropriate generation algorithm according to the scenario category, more effective exercises can be provided.

[0054] The service provider can adjust how exercises are delivered, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide exercises relevant to that region. If the user is on a business trip, the service provider can also provide exercises relevant to the user's destination. If the user is at home, the service provider can also provide exercises that can be done at home. This allows the service provider to deliver appropriate exercises based on the user's geographical location.

[0055] The service provider can analyze a user's social media activity and suggest relevant exercises. For example, they can suggest exercises related to topics the user has shown interest in on social media. They can also suggest exercises that take into account the opinions of experts the user follows on social media. They can also suggest exercises related to articles the user has shared on social media. This allows them to provide appropriate exercises based on the user's social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives input from users regarding scenarios and situations. For example, users can input scenarios such as "customer service scenarios" or "team building scenarios." The reception desk then sends the user-inputted scenarios and situations to the AI ​​for generation. Step 2: The generation unit uses generation AI to analyze the scenarios and situations received by the reception unit and generate exercises. For example, it can suggest the most suitable exercises based on past data and case studies. Based on "customer service scenarios," the generation AI can generate exercises to understand customer needs and provide appropriate responses. It can also generate exercises to improve team cohesion based on "team building scenarios." Step 3: The providing unit provides the exercises generated by the generating unit to the user. For example, the generated exercises can be provided in video or text format.

[0058] (Example of form 2) The Human Skills AI Exercise System according to an embodiment of the present invention is a novel tool that utilizes generative AI to support the mental growth and training of employees. This system allows users to input specific scenarios or situations, and the generative AI generates exercises based on those scenarios to improve hospitality, interpersonal skills, and team cohesion. By actually experiencing these exercises, users can promote mental growth and training. For example, if a user inputs a "customer service scenario" or a "team building scenario," the generative AI analyzes the scenario and proposes the most suitable exercises. By performing the generated exercises, users can improve their customer service skills or team building skills. This mechanism allows for not only technical skill improvement but also simultaneous mental growth and training. This is expected to improve employees' hospitality, interpersonal skills, and team cohesion, leading to an improvement in work quality. Thus, the Human Skills AI Exercise System can support the mental growth and training of employees and improve work quality.

[0059] The Human Skills AI Exercise System according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of scenarios and situations from the user. For example, the reception unit allows the user to input a "customer service scenario" or a "team building scenario." The reception unit transmits the scenario or situation entered by the user to the generation AI. The generation unit uses the generation AI to analyze the scenario or situation received by the reception unit and generate exercises. The generation unit proposes the most suitable exercises based on past data and case studies, for example. The generation unit can generate exercises based on a "customer service scenario" to enable the generation AI to understand customer needs and provide appropriate responses. The generation unit can also generate exercises based on a "team building scenario" to improve team cohesion. The provision unit provides the exercises generated by the generation unit to the user. The provision unit can provide the generated exercises in video format, for example. The provision unit can also provide the generated exercises in text format. Thus, the Human Skills AI Exercise System according to this embodiment can support mental growth and training by generating and providing exercises based on the user's scenarios and situations.

[0060] The reception desk receives input of scenarios and situations from users. For example, the reception desk allows users to input "customer service scenarios" or "team building scenarios." Specifically, the reception desk provides an interface that accepts user-inputted scenarios and situations in text format. Users can input scenarios and situations through a dedicated application or website. The reception desk analyzes the user-inputted scenarios and situations in real time and converts them into an appropriate format. For example, if a user inputs "handling a complaint" as a "customer service scenario," the reception desk converts this input into a format that the generation AI can easily understand and sends it to the generation department. The reception desk can also ask additional questions about the scenarios and situations entered by the user. For example, for a "handling a complaint" scenario, it can ask for specific complaint details and customer background information to gather more detailed information. This allows the reception desk to accurately understand the user's needs and situations and provide appropriate information to the generation department. Furthermore, the reception desk records the user's past input history and preferences, which can be used as a reference for future inputs. This allows users to input scenarios and situations more smoothly and improves the overall usability of the system.

[0061] The generation unit uses a generation AI to analyze scenarios and situations received by the reception unit and generate exercises. For example, the generation unit proposes the most suitable exercise based on past data and case studies. Specifically, the generation AI uses natural language processing technology to analyze the scenarios and situations entered by the user. For example, when generating exercises to understand customer needs and provide appropriate responses based on a "customer service scenario," the generation AI refers to past customer service cases and success stories to propose the most suitable response method. The generation AI uses machine learning algorithms to understand the context and intent of the scenario entered by the user and generate appropriate exercises. For example, if the user enters a "complaint handling" scenario, the generation AI will propose specific steps and communication methods to resolve customer dissatisfaction based on past complaint handling data. Furthermore, the generation AI can adjust the difficulty and content of the exercises according to the user's skill level and experience. For example, it will propose basic response methods for beginners and generate exercises including advanced response techniques for experienced users. This allows the generation unit to generate the most suitable exercises according to the user's needs and situation and send them to the delivery unit.

[0062] The service provider delivers the exercises generated by the generation unit to the user. Specifically, the generated exercises can be provided in video format. For example, the service provider can create video content from the generated exercises and make it available for users to watch. The video content includes specific steps and methods for performing the exercises, allowing users to learn the exercises in a visually easy-to-understand format. The service provider can also provide the generated exercises in text format. Text-based exercises include detailed instructions and points to note, allowing users to perform the exercises at their own pace. Furthermore, the service provider can record the progress and results of the exercises and provide feedback to the user. For example, after a user completes an exercise, the service provider evaluates the user's performance and suggests areas for improvement and the next steps. This allows users to continue performing the exercises while monitoring their growth and progress. The service provider can also collect user feedback and use it as data to improve the content and delivery methods of the exercises. This allows the service provider to provide users with the most suitable exercises and support their mental growth and training.

[0063] The generation unit can generate exercises based on past data and case studies. For example, it can analyze past training data and success stories and propose the most suitable exercises based on that analysis. By using past data, the generation unit can generate more effective exercises. For example, it can generate exercises to improve customer service skills based on past success stories of customer service. It can also generate exercises to improve team cohesion based on past success stories of team building. In this way, more effective exercises can be generated by basing them on past data and case studies.

[0064] The provider can provide the generated exercises in video format. For example, the provider can provide the generated exercises as streaming videos. The provider can also provide the generated exercises as downloadable videos. For example, the provider can provide the exercises in video format so that users can easily understand them visually. By providing the exercises in video format, they become easier to understand visually.

[0065] The provider can provide the generated exercises in text format. For example, the provider can provide the generated exercises as a PDF document. The provider can also provide the generated exercises as a web page. For example, the provider can provide detailed information about the exercises in text format so that users can view it. This allows for the provision of detailed information by providing the exercises in text format.

[0066] The generation unit can generate exercises based on customer service scenarios. For example, the generation unit can analyze customer service scenarios, understand customer needs, and generate exercises to provide appropriate responses. Based on customer service scenarios, the generation unit proposes exercises to improve customer service skills. For example, based on a complaint handling scenario, the generation unit generates exercises to improve complaint handling skills. The generation unit can also generate exercises to improve sales promotion skills based on a sales promotion scenario. In this way, customer service skills can be improved by generating exercises based on customer service scenarios.

[0067] The generation unit can generate exercises based on team-building scenarios. For example, the generation unit can analyze a team-building scenario and generate exercises to improve team cohesion. Based on the team-building scenario, the generation unit proposes exercises to improve team-building skills. For example, based on a scenario for improving teamwork, the generation unit generates exercises to improve teamwork skills. The generation unit can also generate exercises to improve leadership skills based on a leadership development scenario. In this way, by generating exercises based on team-building scenarios, team cohesion can be improved.

[0068] The reception desk can estimate the user's emotions and adjust the timing of scenario and situation input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prompt them to input a scenario during a time when they can relax. If the user is concentrating, the reception desk can also prompt them to input a scenario at that time. If the user is tired, the reception desk can also prompt them to input a scenario after a break. By adjusting the input timing according to the user's emotions, more effective scenario input becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The reception desk can analyze the user's past scenario input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in scenarios the user has previously entered and suggest similar scenarios. From the user's past input history, the reception desk can analyze their tendency to input at specific times and prompt them to input during those times. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0070] The reception desk can filter scenarios and situations based on the user's current work situation and areas of interest when they are entered. For example, the reception desk will prioritize suggesting scenarios related to the project the user is currently working on. The reception desk can also filter and display relevant scenarios based on the user's areas of interest. The reception desk can also suggest appropriate scenarios according to the user's work situation. This allows for the suggestion of appropriate scenarios based on the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0071] The reception desk can estimate the user's emotions and determine the priority of input scenarios based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting relaxing scenarios. If the user is focused, the reception desk may also prioritize suggesting more difficult scenarios. If the user is tired, the reception desk may also prioritize suggesting easier scenarios. This allows for more effective scenario input by prioritizing scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The reception desk can prioritize the input of highly relevant scenarios by considering the user's geographical location when inputting scenarios and situations. For example, if the user is in a specific region, the reception desk will prioritize suggesting scenarios related to that region. If the user is on a business trip, the reception desk can also prioritize suggesting scenarios related to the destination of the business trip. If the user is at home, the reception desk can also prioritize suggesting scenarios that can be performed at home. This allows the reception desk to suggest appropriate scenarios based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0073] The reception desk can analyze the user's social media activity and input relevant scenarios when scenarios or situations are entered. For example, the reception desk can suggest scenarios related to topics the user has shown interest in on social media. The reception desk can also suggest scenarios that take into account the opinions of experts the user follows on social media. The reception desk can also suggest scenarios related to articles the user has shared on social media. This allows for the suggestion of appropriate scenarios based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0074] The generation unit can estimate the user's emotions and adjust the way the exercises are presented based on those emotions. For example, if the user is relaxed, the generation unit will generate exercises in a calm tone. If the user is tense, the generation unit can also generate exercises that include many encouraging words. If the user is excited, the generation unit can also generate exercises that make extensive use of energetic expressions. By adjusting the way the exercises are presented according to the user's emotions, more effective exercises can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The generation unit can adjust the level of detail of exercises based on the importance of the scenarios when generating exercises. For example, the generation unit generates detailed exercises for high-importance scenarios. The generation unit can also generate simplified exercises for low-importance scenarios. The generation unit can also adjust the number of steps in the exercises according to their importance. This allows for more effective exercises to be provided by adjusting the level of detail of the exercises according to the importance of the scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0076] The generation unit can apply different generation algorithms depending on the scenario category when generating exercises. For example, for customer service scenarios, the generation unit can apply an algorithm to understand customer needs. For team-building scenarios, the generation unit can also apply an algorithm to enhance team cohesion. For problem-solving scenarios, the generation unit can also apply an algorithm to improve problem-solving skills. By applying the appropriate generation algorithm according to the scenario category, more effective exercises can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0077] The generation unit can estimate the user's emotions and adjust the exercise length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate an exercise that can be completed in a short time. If the user is relaxed, the generation unit can also generate an exercise that takes a long time. If the user is focused, the generation unit can also generate an exercise of a moderate length. This allows for more effective exercise by adjusting the exercise length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The generation unit can determine the priority of exercises based on the scenario submission dates when generating exercises. For example, the generation unit will prioritize generating exercises for scenarios with upcoming submission dates. The generation unit can also postpone generating exercises for scenarios with later submission dates. The generation unit can also adjust the order of exercise generation according to the submission dates. This allows for the provision of more effective exercises by prioritizing exercises based on the scenario submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0079] The generation unit can adjust the order of exercises based on the relevance of the scenarios when generating exercises. For example, the generation unit can prioritize generating exercises for highly relevant scenarios. The generation unit can also postpone generating exercises for less relevant scenarios. The generation unit can also adjust the order of exercise generation according to relevance. This allows for the provision of more effective exercises by adjusting the order of exercises based on the relevance of the scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0080] The delivery unit can estimate the user's emotions and adjust the way the exercises are delivered based on those emotions. For example, if the user is relaxed, the delivery unit will deliver the exercises in a calm tone. If the user is tense, the delivery unit may also deliver exercises that include many encouraging words. If the user is excited, the delivery unit may also deliver exercises that make extensive use of energetic expressions. By adjusting the way the exercises are delivered according to the user's emotions, a more effective exercise can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The service provider can select the optimal delivery method by referring to the user's past exercise history when providing exercises. For example, the service provider may prioritize suggesting delivery methods (video, text, etc.) that the user has preferred in the past. The service provider can also suggest effective delivery methods based on the user's past exercise history. The service provider can also analyze the user's past exercise history and select the optimal delivery method. This allows the service provider to suggest the most suitable delivery method to the user by referring to their past exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0082] The service provider can customize the method of delivering exercises based on the user's current work situation when providing exercises. For example, if the user is busy, the service provider can provide exercises that can be completed in a short time. If the user has time, the service provider can also provide more detailed exercises. The service provider can also provide exercises appropriate to the user's work situation. This ensures that appropriate exercises are provided according to the user's work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0083] The service provider can estimate the user's emotions and determine the order in which exercises are offered based on those emotions. For example, if the user is relaxed, the service provider will prioritize calm exercises. If the user is tense, the service provider may also prioritize exercises that include many encouraging words. If the user is excited, the service provider may also prioritize energetic exercises. By determining the order of exercises according to the user's emotions, a more effective workout can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The service provider can select the optimal method of providing exercises by considering the user's geographical location. For example, if the user is in a specific region, the service provider can prioritize providing exercises related to that region. If the user is on a business trip, the service provider can also prioritize providing exercises related to the destination of the business trip. If the user is at home, the service provider can also prioritize providing exercises that can be performed at home. This allows the service provider to provide appropriate exercises based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0085] The service provider can analyze the user's social media activity and suggest ways to provide exercises when providing exercises. For example, the service provider can suggest exercises related to topics the user has shown interest in on social media. The service provider can also suggest exercises that take into account the opinions of experts the user follows on social media. The service provider can also suggest exercises related to articles the user has shared on social media. This allows the service provider to provide appropriate exercises based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The generation unit can estimate the user's emotions and adjust the difficulty of the exercises based on those emotions. For example, if the user is stressed, the generation unit will generate exercises of low difficulty. If the user is relaxed, the generation unit can also generate exercises of high difficulty. If the user is focused, the generation unit can also generate exercises of moderate difficulty. By adjusting the difficulty of the exercises according to the user's emotions, a more effective workout can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The service provider can estimate the user's emotions and adjust the timing of exercise delivery based on the estimated emotions. For example, if the user is feeling stressed, the service provider will deliver exercise during a time when the user can relax. The service provider can also deliver exercise when the user is concentrating. If the user is tired, the service provider can deliver exercise after a break. By adjusting the delivery timing according to the user's emotions, a more effective exercise can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The generation unit can estimate the user's emotions and adjust the exercise feedback method based on the estimated emotions. For example, if the user is relaxed, the generation unit will provide feedback in a calm tone. If the user is tense, the generation unit may also provide feedback that includes many words of encouragement. If the user is excited, the generation unit may also provide feedback that makes extensive use of energetic expressions. By adjusting the feedback method according to the user's emotions, a more effective exercise can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The delivery unit can estimate the user's emotions and adjust the format of the exercise delivery based on the estimated emotions. For example, if the user is relaxed, the delivery unit will deliver the exercise in a calm tone. If the user is tense, the delivery unit may also deliver an exercise that includes many encouraging words. If the user is excited, the delivery unit may also deliver an exercise that makes extensive use of energetic expressions. In this way, by adjusting the format of the exercise delivery according to the user's emotions, a more effective exercise can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit can estimate the user's emotions and adjust the exercise content based on those emotions. For example, if the user is relaxed, the generation unit will generate a calm exercise. If the user is tense, the generation unit can also generate an exercise with many encouraging words. If the user is excited, the generation unit can also generate an energetic exercise. By adjusting the exercise content according to the user's emotions, a more effective exercise can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The generation unit can analyze a user's past exercise history and generate optimal exercises. For example, it can prioritize suggesting effective exercises based on data from exercises the user has performed in the past. The generation unit can also adjust the exercises to avoid exercises the user has previously found difficult. The generation unit can also identify specific patterns from the user's past exercise history and generate exercises based on those patterns. In this way, by analyzing past exercise history, it can provide the user with the most suitable exercises.

[0093] The service provider can adjust how exercises are delivered based on the user's current work situation. For example, if the user is busy, the service provider can provide exercises that can be completed in a short time. If the user has more time, the service provider can also provide more detailed exercises. The service provider can also provide exercises that are appropriate for the user's work situation. This ensures that appropriate exercises are provided according to the user's work situation.

[0094] The generation unit can apply different generation algorithms depending on the scenario category. For example, in a customer service scenario, the generation unit can apply an algorithm to understand customer needs. In a team-building scenario, the generation unit can also apply an algorithm to enhance team cohesion. In a problem-solving scenario, the generation unit can also apply an algorithm to improve problem-solving skills. By applying the appropriate generation algorithm according to the scenario category, more effective exercises can be provided.

[0095] The service provider can adjust how exercises are delivered, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide exercises relevant to that region. If the user is on a business trip, the service provider can also provide exercises relevant to the user's destination. If the user is at home, the service provider can also provide exercises that can be done at home. This allows the service provider to deliver appropriate exercises based on the user's geographical location.

[0096] The service provider can analyze a user's social media activity and suggest relevant exercises. For example, they can suggest exercises related to topics the user has shown interest in on social media. They can also suggest exercises that take into account the opinions of experts the user follows on social media. They can also suggest exercises related to articles the user has shared on social media. This allows them to provide appropriate exercises based on the user's social media activity.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives input from users regarding scenarios and situations. For example, users can input scenarios such as "customer service scenarios" or "team building scenarios." The reception desk then sends the user-inputted scenarios and situations to the AI ​​for generation. Step 2: The generation unit uses generation AI to analyze the scenarios and situations received by the reception unit and generate exercises. For example, it can suggest the most suitable exercises based on past data and case studies. Based on "customer service scenarios," the generation AI can generate exercises to understand customer needs and provide appropriate responses. It can also generate exercises to improve team cohesion based on "team building scenarios." Step 3: The providing unit provides the exercises generated by the generating unit to the user. For example, the generated exercises can be provided in video or text format.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, enabling the user to input scenarios and situations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes scenarios and situations using generation AI and generates exercises. The provision unit is implemented, for example, by the output device 40 of the smart device 14, which provides the generated exercises to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

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

[0104] As shown in Figure 3, the data processing system 210 includes a 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, enabling the user to input scenarios and situations by voice. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes scenarios and situations using generation AI and generates exercises. The provision unit is implemented by the speaker 240 of the smart glasses 214, which provides the generated exercises to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, enabling the user to input scenarios and situations by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes scenarios and situations using generation AI and generates exercises. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which visually provides the generated exercises to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, enabling the user to input scenarios and situations by voice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes scenarios and situations using generation AI and generates exercises. The provision unit is implemented by the speaker 240 of the robot 414, which provides the generated exercises to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk that receives input of scenarios and situations from users, A generation unit analyzes the scenarios and situations received by the reception unit and generates exercises, The system includes a providing unit that provides the exercises generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is Generate exercises based on past data and case studies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated exercises are provided in video format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated exercises are provided in text format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate exercises based on customer interaction scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate exercises based on team-building scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of scenario and situation inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past scenario input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering scenarios or situations, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input scenarios based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting scenarios and situations, the system prioritizes inputting scenarios that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting scenarios and situations, the system analyzes the user's social media activity and inputs relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the way the exercises are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating exercises, adjust the level of detail of the exercises based on the importance of the scenario. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating exercises, different generation algorithms are applied depending on the scenario category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the exercise length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating exercises, prioritize them based on when the scenarios were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating exercises, adjust the order of exercises based on the relevance of the scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way exercises are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing exercises, the system selects the optimal method of delivery by referring to the user's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing exercises, customize the method of delivering the exercises based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which exercises are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing exercises, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing exercises, we analyze users' social media activity and suggest ways to deliver the exercises. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives input of scenarios and situations from users, A generation unit analyzes the scenarios and situations received by the reception unit and generates exercises, The system includes a providing unit that provides the exercises generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Generate exercises based on past data and case studies. The system according to feature 1.

3. The aforementioned supply unit is, The generated exercises are provided in video format. The system according to feature 1.

4. The aforementioned supply unit is, The generated exercises are provided in text format. The system according to feature 1.

5. The generating unit is Generate exercises based on customer interaction scenarios. The system according to feature 1.

6. The generating unit is Generate exercises based on team-building scenarios. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of scenario and situation inputs based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past scenario input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering scenarios or situations, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input scenarios based on the estimated user emotions. The system according to feature 1.

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