System

Abstract

An object of a system according to an embodiment is to support skill improvement individually optimized on the basis of a business type or a position of a user.SOLUTION: A system according to an embodiment includes a virtual scenario generation unit, an interaction unit, a response analysis unit, and a feedback provision unit. The virtual scenario generation unit generates a virtual scenario based on the type of business and the position of the user. The interactor performs interaction with the user on the basis of the virtual scenario generated by the virtual scenario generation unit. The answer analysis unit analyzes the user's answer obtained by the interaction unit. The feedback providing unit provides feedback based on the result analyzed by the answer analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to individually optimize the improvement of business people's skills, and there is room for improvement.

[0005] The system according to the embodiment aims to support skill improvement that is individually optimized based on the user's industry and position. [Means for solving the problem]

[0006] The system according to the embodiment includes a virtual scenario generation unit, an interaction unit, a response analysis unit, and a feedback provision unit. The virtual scenario generation unit generates a virtual scenario based on the user's industry and position. The interaction unit interacts with the user based on the virtual scenario generated by the virtual scenario generation unit. The response analysis unit analyzes the user's responses obtained by the interaction unit. The feedback provision unit provides feedback based on the results of the analysis by the response analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can support skill improvement that is individually optimized based on the user's industry and position. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A skill improvement support system according to an embodiment of the present invention uses a generative AI to generate virtual scenarios tailored to the user's industry and position, and provides practical learning opportunities through interaction with the user. This allows the skill improvement support system to analyze the user's responses and provide feedback and improvement suggestions optimized for each individual skill level.

[0029] A skill improvement support system according to an embodiment includes a virtual scenario generation unit, an interaction unit, a response analysis unit, and a feedback provision unit. The virtual scenario generation unit generates a virtual scenario based on a user's industry and position. For example, a sales user is provided with a business negotiation scenario with a customer, and a managerial user is provided with a team management scenario. The virtual scenario generation unit generates a virtual scenario based on prompts including information about the user's industry and position. The interaction unit interacts with the user based on the virtual scenario generated by the virtual scenario generation unit. For example, the virtual scenario may include a scene in which the user answers a customer's question or a scene in which the user communicates with team members. The interaction unit analyzes the user's response in real time and presents the next step. The response analysis unit analyzes the user's response obtained by the interaction unit. For example, if the user provides an appropriate response to a customer's question, the interaction unit highlights that point and provides specific advice if there is an area for improvement. The response analysis unit generates feedback based on the content of the user's response. The feedback provision unit provides feedback based on the results of the analysis by the response analysis unit. For example, the system may present the user with practice methods to improve their communication skills or assignments to hone their strategic thinking skills. The feedback providing unit generates improvement suggestions based on information about the user's skill level. As a result, the skill improvement support system according to the embodiment generates virtual scenarios based on the user's industry and position, provides practical learning opportunities through interactions, analyzes responses, and provides feedback, thereby supporting the improvement of overall business skills.

[0030] The virtual scenario generation unit can generate more personalized virtual scenarios based on the user's past work history and performance data. The virtual scenario generation unit, for example, analyzes the user's past work history and generates virtual scenarios based on success and failure cases in specific tasks. For example, it recreates a scenario of a successful business negotiation in the past to learn how the user would respond in a similar situation. The virtual scenario generation unit generates scenarios according to the user's skill level based on the user's performance data. For example, it provides a scenario for a task that the user found difficult in the past to encourage improvement. In this way, by generating more personalized virtual scenarios based on the user's past work history and performance data, it is possible to provide the user with optimal learning opportunities.

[0031] The virtual scenario generation unit can introduce interactive storytelling in which a virtual scenario branches depending on a user's choices. The virtual scenario generation unit introduces interactive storytelling in which a scenario branches depending on, for example, the answer selected by the user. For example, the development of a scenario changes when the user selects a different answer to a customer's question. The virtual scenario generation unit dynamically generates a scenario based on the user's choices, providing the user with a diverse learning experience. For example, the next scenario develops differently depending on the answer selected by the user. In this way, by introducing interactive storytelling in which a scenario branches depending on the user's choices, it is possible to provide the user with a diverse learning experience.

[0032] The virtual scenario generation unit crosses over scenarios from different industries and occupations, allowing the user to have a multifaceted perspective. For example, the virtual scenario generation unit combines scenarios from different industries to provide the user with a multifaceted perspective. For example, it combines a scenario from the medical industry with a scenario from the IT industry. The virtual scenario generation unit crosses over scenarios from different occupations to provide the user with a new perspective. For example, it combines scenarios from sales and marketing. In this way, by crossing over scenarios from different industries and occupations, the user can be provided with a multifaceted perspective and help improve their overall business skills.

[0033] The virtual scenario generation unit can incorporate audio and video into the virtual scenario to provide a more realistic experience. For example, the virtual scenario generation unit can incorporate audio into the virtual scenario to allow the user to experience an actual conversation. For example, actual audio can be used in a scenario of a business negotiation with a customer. The virtual scenario generation unit can incorporate video into the virtual scenario to allow the user to experience the scenario visually. For example, a video clip can be used in a scenario of a team meeting. In this way, by incorporating audio and video into the virtual scenario, a more realistic experience can be provided to the user, improving learning effectiveness.

[0034] The interaction unit can analyze the user's interaction history and optimize the interaction in the next scenario. The interaction unit, for example, analyzes the user's past interaction history and optimizes the interaction in the next scenario. For example, the next scenario is adjusted based on answers and actions selected in the past. The interaction unit dynamically adjusts the interaction in the next scenario based on the user's interaction history. For example, it recreates situations in which the user had difficulty in the past and encourages improvement. In this way, the learning effect can be improved by analyzing the user's interaction history and optimizing the interaction in the next scenario.

[0035] In the interaction section, the generation AI analyzes the user's non-verbal communication (facial expressions and gestures) during the interaction, enabling a more natural dialogue. For example, in the interaction section, the generation AI analyzes the user's facial expressions in real time and adjusts the content and tone of the dialogue. For example, if the user smiles, it will respond positively. In the interaction section, the generation AI analyzes the user's gestures and adjusts the flow of the dialogue. For example, if the user raises their hand, it will accept questions. In this way, by analyzing non-verbal communication, a more natural dialogue can be achieved, improving the user's learning experience.

[0036] The interaction unit can automatically translate the content of the interaction into different languages ​​to accommodate international users. The interaction unit, for example, builds a system that automatically translates the content of the interaction into different languages ​​in real time. For example, translating from English to French. The interaction unit automatically translates the content of the interaction based on the user's language setting. For example, if the user selects Japanese, it translates it into Japanese. In this way, by automatically translating the content of the interaction into different languages, it is possible to accommodate international users and provide learning opportunities.

[0037] The interaction section allows the generation AI to provide supplementary information and reference materials to the user in real time during the interaction. For example, the generation AI may provide supplementary information to the user in real time during the interaction. For example, it may present materials related to the content of a question asked by the user. The interaction section allows the generation AI to provide reference materials to the user. For example, it may present literature or web links that the user needs while studying. This makes it possible to support the user's learning by providing supplementary information and reference materials in real time during the interaction.

[0038] The answer analysis unit can analyze the user's answers in detail and identify the thought process behind the answers. The answer analysis unit, for example, builds a system that analyzes the user's answers in detail and identifies the thought process behind them. For example, it analyzes the logic by which the user arrived at the answer. The answer analysis unit identifies the thought process based on the content of the user's answers. For example, it analyzes the steps the user took to solve the problem. In this way, by analyzing the user's answers in detail and identifying the thought process, it is possible to understand the user's level of understanding and thought patterns and provide appropriate feedback.

[0039] The answer analysis unit can provide feedback that specifically indicates the good points and areas for improvement in the user's answer, encouraging the user to make improvements in the next scenario. The answer analysis unit, for example, analyzes the user's answer and provides feedback that specifically indicates the good points and areas for improvement. For example, if the user provides an appropriate answer, the unit specifically explains the reason for that. The answer analysis unit provides feedback that encourages the user to make improvements in the next scenario based on the content of the user's answer. For example, the unit specifically indicates areas where the user should improve and suggests how to address the issue in the next scenario. In this way, by specifically indicating the good points and areas for improvement in the user's answer, the user can be encouraged to make improvements in the next scenario, improving the learning effect.

[0040] The feedback providing unit can provide feedback in different formats (text, audio, video) to suit the user's learning style. For example, the feedback providing unit can provide feedback in text format so that the user can receive information in an easy-to-read format. For example, detailed explanations or specific advice can be provided in text. The feedback providing unit can provide feedback in audio format so that the user can receive information in an easy-to-listen format. For example, the feedback can be provided using narration or dialogue. The feedback providing unit can provide feedback in video format so that the user can receive information visually. For example, the feedback can be provided using video clips or animations. In this way, by providing feedback in different formats, it is possible to provide optimal feedback that suits the user's learning style.

[0041] The feedback providing unit can share the content of the feedback with other users and promote community-based learning. The feedback providing unit, for example, builds a system for sharing the content of the feedback with other users and promoting community-based learning. For example, it provides an online forum for sharing feedback. The feedback providing unit provides an environment in which users share feedback and learn from each other. For example, a user posts feedback, and other users provide comments or advice. In this way, by sharing the content of the feedback with other users, it is possible to promote community-based learning and provide an environment in which users learn from each other.

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

[0043] The skill improvement support system can further include a health management unit that monitors the user's health condition. The health management unit, for example, monitors the user's heart rate and stress level in real time and suggests taking a break at an appropriate time. If the user continues studying for a long period of time, the health management unit can send a notification urging the user to take a break. The health management unit can also optimize the study schedule based on the user's health data. This makes it possible to provide a learning environment that takes the user's health condition into consideration and support continuous learning.

[0044] The skill improvement support system can further include a progress management unit that visualizes the user's learning progress. The progress management unit displays, for example, the goals the user has achieved and the scenarios they have completed in graphs or charts, allowing the user to visually confirm which skills they have improved to what extent. The progress management unit can also suggest the next scenario or task to tackle based on the user's learning history. This makes it possible to visualize the user's learning progress and support effective learning while maintaining motivation.

[0045] The skill improvement support system can further include a customization unit that provides a customization function according to the user's learning style. For example, the customization unit adjusts the scenario based on the user's preferred learning method and pace. If the user prefers visual learning, the customization unit can provide a scenario that makes extensive use of graphics and videos. Also, if the user prefers to study in a short, concentrated period of time, the customization unit can provide a scenario that can be completed in a short session. This makes it possible to provide an optimal learning experience that matches the user's learning style.

[0046] The skill improvement support system can further include a planning unit that provides a personalized learning plan based on the user's learning data. The planning unit, for example, analyzes the user's past learning history and skill level to generate an optimal learning plan. If the user wants to improve a specific skill, it can also provide a learning plan specialized for that skill. The planning unit can also dynamically adjust the plan according to the user's learning progress to support effective learning. This makes it possible to provide the user with an optimal learning plan and support efficient skill improvement.

[0047] The skill improvement support system can further include an evaluation unit that evaluates the user's learning achievements and provides rewards. The evaluation unit, for example, evaluates the goals the user has achieved and the scenarios the user has completed, and awards points or badges. If the user improves a specific skill, it can provide special scenarios or content as a reward. The evaluation unit can also suggest the next assignment or scenario to tackle based on the user's learning achievements. In this way, by evaluating the user's learning achievements and providing rewards, it is possible to support effective learning while maintaining motivation.

[0048] The skill improvement support system can further include a community section that forms a learning community of users and provides an environment for mutual learning. The community section, for example, provides an online forum where users can share their learning content and results. Users can post questions and advice and receive feedback from other users. The community section can also provide opportunities for group learning where users work together to tackle assignments. This can improve learning effectiveness by forming a learning community of users and providing an environment for mutual learning.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The virtual scenario generator generates a virtual scenario based on the user's industry and position. For example, a sales user is provided with a scenario of business negotiations with a customer, and a managerial user is provided with a team management scenario. The virtual scenario generator generates a virtual scenario based on prompts containing information about the user's industry and position. Step 2: The interaction unit interacts with the user based on the virtual scenario generated by the virtual scenario generation unit. For example, it sets up a scene in the virtual scenario where the user answers a customer's question or communicates with team members. The interaction unit analyzes the user's responses in real time and suggests the next step. Step 3: The response analysis unit analyzes the user's responses obtained by the interaction unit. For example, if the user provides an appropriate response to a customer's question, it highlights that point and provides specific advice if there are areas for improvement. The response analysis unit generates feedback based on the user's responses. Step 4: The feedback provider provides feedback based on the results of the analysis by the response analyzer. For example, the feedback provider may suggest practice methods to improve the user's communication skills or tasks to hone strategic thinking skills. The feedback provider generates improvement suggestions based on information about the user's skill level.

[0051] (Example 2) A skill improvement support system according to an embodiment of the present invention uses a generative AI to generate virtual scenarios tailored to the user's industry and position, and provides practical learning opportunities through interaction with the user. This allows the skill improvement support system to analyze the user's responses and provide feedback and improvement suggestions optimized for each individual skill level.

[0052] A skill improvement support system according to an embodiment includes a virtual scenario generation unit, an interaction unit, a response analysis unit, and a feedback provision unit. The virtual scenario generation unit generates a virtual scenario based on a user's industry and position. For example, a sales user is provided with a business negotiation scenario with a customer, and a managerial user is provided with a team management scenario. The virtual scenario generation unit generates a virtual scenario based on prompts including information about the user's industry and position. The interaction unit interacts with the user based on the virtual scenario generated by the virtual scenario generation unit. For example, the virtual scenario may include a scene in which the user answers a customer's question or a scene in which the user communicates with team members. The interaction unit analyzes the user's response in real time and presents the next step. The response analysis unit analyzes the user's response obtained by the interaction unit. For example, if the user provides an appropriate response to a customer's question, the interaction unit highlights that point and provides specific advice if there is an area for improvement. The response analysis unit generates feedback based on the content of the user's response. The feedback provision unit provides feedback based on the results of the analysis by the response analysis unit. For example, the system may present the user with practice methods to improve their communication skills or assignments to hone their strategic thinking skills. The feedback providing unit generates improvement suggestions based on information about the user's skill level. As a result, the skill improvement support system according to the embodiment generates virtual scenarios based on the user's industry and position, provides practical learning opportunities through interactions, analyzes responses, and provides feedback, thereby supporting the improvement of overall business skills.

[0053] The virtual scenario generation unit can generate more personalized virtual scenarios based on the user's past work history and performance data. The virtual scenario generation unit, for example, analyzes the user's past work history and generates virtual scenarios based on success and failure cases in specific tasks. For example, it recreates a scenario of a successful business negotiation in the past to learn how the user would respond in a similar situation. The virtual scenario generation unit generates scenarios according to the user's skill level based on the user's performance data. For example, it provides a scenario for a task that the user found difficult in the past to encourage improvement. In this way, by generating more personalized virtual scenarios based on the user's past work history and performance data, it is possible to provide the user with optimal learning opportunities.

[0054] The virtual scenario generation unit can introduce interactive storytelling in which a virtual scenario branches depending on a user's choices. The virtual scenario generation unit introduces interactive storytelling in which a scenario branches depending on, for example, the answer selected by the user. For example, the development of a scenario changes when the user selects a different answer to a customer's question. The virtual scenario generation unit dynamically generates a scenario based on the user's choices, providing the user with a diverse learning experience. For example, the next scenario develops differently depending on the answer selected by the user. In this way, by introducing interactive storytelling in which a scenario branches depending on the user's choices, it is possible to provide the user with a diverse learning experience.

[0055] The virtual scenario generation unit can use the emotion estimation function to dynamically adjust the difficulty level and content of the virtual scenario according to the user's emotional state. The virtual scenario generation unit, for example, analyzes the user's emotional state in real time and adjusts the difficulty level of the scenario. For example, if the user is feeling stressed, the virtual scenario generation unit lowers the difficulty level of the scenario. The virtual scenario generation unit dynamically adjusts the content of the scenario based on the user's emotional state. For example, if the user is relaxed, the virtual scenario generation unit provides a more challenging scenario. In this way, by using the emotion estimation function to dynamically adjust the difficulty level and content of the scenario according to the user's emotional state, it is possible to provide an optimal learning experience.

[0056] The virtual scenario generation unit crosses over scenarios from different industries and occupations, allowing the user to have a multifaceted perspective. For example, the virtual scenario generation unit combines scenarios from different industries to provide the user with a multifaceted perspective. For example, it combines a scenario from the medical industry with a scenario from the IT industry. The virtual scenario generation unit crosses over scenarios from different occupations to provide the user with a new perspective. For example, it combines scenarios from sales and marketing. In this way, by crossing over scenarios from different industries and occupations, the user can be provided with a multifaceted perspective and help improve their overall business skills.

[0057] The virtual scenario generation unit can incorporate audio and video into the virtual scenario to provide a more realistic experience. For example, the virtual scenario generation unit can incorporate audio into the virtual scenario to allow the user to experience an actual conversation. For example, actual audio can be used in a scenario of a business negotiation with a customer. The virtual scenario generation unit can incorporate video into the virtual scenario to allow the user to experience the scenario visually. For example, a video clip can be used in a scenario of a team meeting. In this way, by incorporating audio and video into the virtual scenario, a more realistic experience can be provided to the user, improving learning effectiveness.

[0058] The virtual scenario generation unit can use the emotion estimation function to provide preferentially the virtual scenario that is most interesting to the user. The virtual scenario generation unit, for example, uses the emotion estimation function to identify the scenario that is most interesting to the user and provide that scenario preferentially. For example, the virtual scenario generation unit generates the next scenario based on the scenario in which the user has shown interest. The virtual scenario generation unit dynamically adjusts the scenario based on the user's interests to increase the user's motivation to learn. For example, it provides a scenario related to a topic that the user is interested in. In this way, the emotion estimation function can be used to preferentially provide the scenario that is most interesting to the user, thereby increasing the user's motivation to learn.

[0059] The interaction unit can analyze the user's interaction history and optimize the interaction in the next scenario. The interaction unit, for example, analyzes the user's past interaction history and optimizes the interaction in the next scenario. For example, the next scenario is adjusted based on answers and actions selected in the past. The interaction unit dynamically adjusts the interaction in the next scenario based on the user's interaction history. For example, it recreates situations in which the user had difficulty in the past and encourages improvement. In this way, the learning effect can be improved by analyzing the user's interaction history and optimizing the interaction in the next scenario.

[0060] In the interaction section, the generation AI analyzes the user's non-verbal communication (facial expressions and gestures) during the interaction, enabling a more natural dialogue. For example, in the interaction section, the generation AI analyzes the user's facial expressions in real time and adjusts the content and tone of the dialogue. For example, if the user smiles, it will respond positively. In the interaction section, the generation AI analyzes the user's gestures and adjusts the flow of the dialogue. For example, if the user raises their hand, it will accept questions. In this way, by analyzing non-verbal communication, a more natural dialogue can be achieved, improving the user's learning experience.

[0061] The interaction unit can use the emotion estimation function to adjust the content and tone of the interaction according to the emotional state of the user. The interaction unit, for example, uses the emotion estimation function to adjust the content of the interaction according to the emotional state of the user. For example, if the user is feeling stressed, the interaction unit provides content that will relax the user. The interaction unit adjusts the tone of the interaction based on the emotional state of the user. For example, if the user is relaxed, the interaction unit uses a friendly tone. In this way, by using the emotion estimation function to adjust the content and tone of the interaction according to the emotional state of the user, it is possible to provide the user with an optimal learning experience.

[0062] The interaction unit can automatically translate the content of the interaction into different languages ​​to accommodate international users. The interaction unit, for example, builds a system that automatically translates the content of the interaction into different languages ​​in real time. For example, translating from English to French. The interaction unit automatically translates the content of the interaction based on the user's language setting. For example, if the user selects Japanese, it translates it into Japanese. In this way, by automatically translating the content of the interaction into different languages, it is possible to accommodate international users and provide learning opportunities.

[0063] The interaction section allows the generation AI to provide supplementary information and reference materials to the user in real time during the interaction. For example, the generation AI may provide supplementary information to the user in real time during the interaction. For example, it may present materials related to the content of a question asked by the user. The interaction section allows the generation AI to provide reference materials to the user. For example, it may present literature or web links that the user needs while studying. This makes it possible to support the user's learning by providing supplementary information and reference materials in real time during the interaction.

[0064] The interaction unit can use the emotion estimation function to provide a user with an environment in which they can study in the most relaxed manner. The interaction unit, for example, uses the emotion estimation function to build a system that provides a user with an environment in which they can study in a relaxed manner. For example, the interaction unit adjusts the learning environment according to the user's emotional state. The interaction unit dynamically adjusts the learning environment based on the user's emotional state. For example, if the user is feeling stressed, relaxing music is provided. In this way, the emotion estimation function can be used to provide a user with an environment in which they can study in the most relaxed manner, thereby improving the learning effect.

[0065] The answer analysis unit can analyze the user's answers in detail and identify the thought process behind the answers. The answer analysis unit, for example, builds a system that analyzes the user's answers in detail and identifies the thought process behind them. For example, it analyzes the logic by which the user arrived at the answer. The answer analysis unit identifies the thought process based on the content of the user's answers. For example, it analyzes the steps the user took to solve the problem. In this way, by analyzing the user's answers in detail and identifying the thought process, it is possible to understand the user's level of understanding and thought patterns and provide appropriate feedback.

[0066] The answer analysis unit can provide feedback that specifically indicates the good points and areas for improvement in the user's answer, encouraging the user to make improvements in the next scenario. The answer analysis unit, for example, analyzes the user's answer and provides feedback that specifically indicates the good points and areas for improvement. For example, if the user provides an appropriate answer, the unit specifically explains the reason for that. The answer analysis unit provides feedback that encourages the user to make improvements in the next scenario based on the content of the user's answer. For example, the unit specifically indicates areas where the user should improve and suggests how to address the issue in the next scenario. In this way, by specifically indicating the good points and areas for improvement in the user's answer, the user can be encouraged to make improvements in the next scenario, improving the learning effect.

[0067] The answer analysis unit can use the emotion estimation function to adjust the content and tone of the feedback according to the emotional state of the user. The answer analysis unit, for example, uses the emotion estimation function to adjust the content of the feedback according to the emotional state of the user. For example, if the user is feeling stressed, the answer analysis unit provides feedback in a gentle tone. The answer analysis unit adjusts the tone of the feedback based on the emotional state of the user. For example, if the user is relaxed, the answer analysis unit provides feedback in a friendly tone. In this way, by using the emotion estimation function to adjust the content and tone of the feedback according to the emotional state of the user, it is possible to provide optimal feedback to the user.

[0068] The feedback providing unit can provide feedback in different formats (text, audio, video) to suit the user's learning style. For example, the feedback providing unit can provide feedback in text format so that the user can receive information in an easy-to-read format. For example, detailed explanations or specific advice can be provided in text. The feedback providing unit can provide feedback in audio format so that the user can receive information in an easy-to-listen format. For example, the feedback can be provided using narration or dialogue. The feedback providing unit can provide feedback in video format so that the user can receive information visually. For example, the feedback can be provided using video clips or animations. In this way, by providing feedback in different formats, it is possible to provide optimal feedback that suits the user's learning style.

[0069] The feedback providing unit can share the content of the feedback with other users and promote community-based learning. The feedback providing unit, for example, builds a system for sharing the content of the feedback with other users and promoting community-based learning. For example, it provides an online forum for sharing feedback. The feedback providing unit provides an environment in which users share feedback and learn from each other. For example, a user posts feedback, and other users provide comments or advice. In this way, by sharing the content of the feedback with other users, it is possible to promote community-based learning and provide an environment in which users learn from each other.

[0070] The feedback providing unit can use the emotion estimation function to provide the feedback format most acceptable to the user. For example, the feedback providing unit uses the emotion estimation function to identify the feedback format most acceptable to the user and provides the feedback in that format. For example, when the user is relaxed, the feedback providing unit provides the feedback in audio format. The feedback providing unit dynamically adjusts the feedback format based on the user's emotional state. For example, when the user is feeling stressed, the feedback is provided in text format. In this way, by using the emotion estimation function to provide the feedback format most acceptable to the user, the learning effect can be improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The skill improvement support system can further include a health management unit that monitors the user's health condition. The health management unit, for example, monitors the user's heart rate and stress level in real time and suggests taking a break at an appropriate time. If the user continues studying for a long period of time, the health management unit can send a notification urging the user to take a break. The health management unit can also optimize the study schedule based on the user's health data. This makes it possible to provide a learning environment that takes the user's health condition into consideration and support continuous learning.

[0073] The skill improvement support system can further include a progress management unit that visualizes the user's learning progress. The progress management unit displays, for example, the goals the user has achieved and the scenarios they have completed in graphs or charts, allowing the user to visually confirm which skills they have improved to what extent. The progress management unit can also suggest the next scenario or task to tackle based on the user's learning history. This makes it possible to visualize the user's learning progress and support effective learning while maintaining motivation.

[0074] The skill improvement support system can further include a customization unit that provides a customization function according to the user's learning style. For example, the customization unit adjusts the scenario based on the user's preferred learning method and pace. If the user prefers visual learning, the customization unit can provide a scenario that makes extensive use of graphics and videos. Also, if the user prefers to study in a short, concentrated period of time, the customization unit can provide a scenario that can be completed in a short session. This makes it possible to provide an optimal learning experience that matches the user's learning style.

[0075] The skill improvement support system can further include an emotion adaptation unit that estimates the user's emotion and adjusts the learning content based on the estimated emotion. For example, if the user is tired, the emotion adaptation unit can provide relaxing content or light tasks. If the user is excited, the emotion adaptation unit can also provide challenging tasks. The emotion adaptation unit can also adjust the learning progress according to the user's emotional state and provide feedback at the optimal timing. This makes it possible to provide learning content that suits the user's emotional state and support effective learning.

[0076] The skill improvement support system may further include a tone adjustment unit that estimates the user's emotions and adjusts the tone of the interaction based on the estimated emotions. For example, if the user is nervous, the tone adjustment unit may use a gentle tone when interacting with the user. If the user is relaxed, the tone adjustment unit may use a friendly tone when interacting with the user. The tone adjustment unit may also adjust the content of the interaction according to the user's emotional state and provide information in a form that is most acceptable to the user. This allows the system to provide interactions that are appropriate for the user's emotional state, thereby improving learning effectiveness.

[0077] The skill improvement support system may further include a feedback adaptation unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, the feedback adaptation unit may provide feedback in a gentle tone when the user is feeling stressed. If the user is relaxed, the feedback adaptation unit may also provide feedback in a friendly tone. The feedback adaptation unit may also adjust the content of the feedback according to the user's emotional state and provide information in a form that is most acceptable to the user. This allows the system to provide feedback according to the user's emotional state, thereby improving the learning effect.

[0078] The skill improvement support system may further include an environment adaptation unit that estimates the user's emotions and adjusts the learning environment based on the estimated emotions. For example, the environment adaptation unit may provide relaxing music or images when the user is feeling stressed. It may also provide a quiet environment when the user is concentrating. The environment adaptation unit may also dynamically adjust the learning environment according to the user's emotional state to provide an optimal learning experience. This may provide a learning environment that suits the user's emotional state, thereby improving learning effectiveness.

[0079] The skill improvement support system can further include a planning unit that provides a personalized learning plan based on the user's learning data. The planning unit, for example, analyzes the user's past learning history and skill level to generate an optimal learning plan. If the user wants to improve a specific skill, it can also provide a learning plan specialized for that skill. The planning unit can also dynamically adjust the plan according to the user's learning progress to support effective learning. This makes it possible to provide the user with an optimal learning plan and support efficient skill improvement.

[0080] The skill improvement support system can further include an evaluation unit that evaluates the user's learning achievements and provides rewards. The evaluation unit, for example, evaluates the goals the user has achieved and the scenarios the user has completed, and awards points or badges. If the user improves a specific skill, it can provide special scenarios or content as a reward. The evaluation unit can also suggest the next assignment or scenario to tackle based on the user's learning achievements. In this way, by evaluating the user's learning achievements and providing rewards, it is possible to support effective learning while maintaining motivation.

[0081] The skill improvement support system can further include a community section that forms a learning community of users and provides an environment for mutual learning. The community section, for example, provides an online forum where users can share their learning content and results. Users can post questions and advice and receive feedback from other users. The community section can also provide opportunities for group learning where users work together to tackle assignments. This can improve learning effectiveness by forming a learning community of users and providing an environment for mutual learning.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The virtual scenario generator generates a virtual scenario based on the user's industry and position. For example, a sales user is provided with a scenario of business negotiations with a customer, and a managerial user is provided with a team management scenario. The virtual scenario generator generates a virtual scenario based on prompts containing information about the user's industry and position. Step 2: The interaction unit interacts with the user based on the virtual scenario generated by the virtual scenario generation unit. For example, it sets up a scene in the virtual scenario where the user answers a customer's question or communicates with team members. The interaction unit analyzes the user's responses in real time and suggests the next step. Step 3: The response analysis unit analyzes the user's responses obtained by the interaction unit. For example, if the user provides an appropriate response to a customer's question, it highlights that point and provides specific advice if there are areas for improvement. The response analysis unit generates feedback based on the user's responses. Step 4: The feedback provider provides feedback based on the results of the analysis by the response analyzer. For example, the feedback provider may suggest practice methods to improve the user's communication skills or tasks to hone strategic thinking skills. The feedback provider generates improvement suggestions based on information about the user's skill level.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A unit for generating a virtual scenario based on the user's industry and position; an interaction unit that interacts with a user based on the virtual scenario generated by the virtual scenario generation unit; a response analysis unit that analyzes the user's responses obtained by the interaction unit; a feedback providing unit that provides feedback based on the results of the analysis by the response analysis unit. A system characterized by:

2. The virtual scenario generation unit Generate more personalized virtual scenarios based on users' past work history and performance data 2. The system of claim 1.

3. The interaction section is Analyze user interaction history to optimize interactions in the next scenario 2. The system of claim 1.

4. The response analysis department Deeply analyze user responses to identify the thought process behind the answers 2. The system of claim 1.

5. The feedback provider: Provide feedback in different formats (text, audio, video) to suit the user's learning style 2. The system of claim 1.

6. The virtual scenario generation unit Dynamically adjust the difficulty and content of virtual scenarios according to the user's emotional state 2. The system of claim 1.

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