system

Abstract

The system according to this embodiment aims to provide users with concrete experiences and learning opportunities to help them approach their desired role models. [Solution] The system according to the embodiment comprises a selection unit, an analysis unit, a generation unit, and a dialogue unit. The selection unit selects a role model that the user aspires to. The analysis unit analyzes the characteristics or behavioral patterns of the role model selected by the selection unit. The generation unit generates a simulation based on the information analyzed by the analysis unit. The dialogue unit interacts with the role model based on the simulation generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide specific experiences and learning for the user to approach the role model they aim for.

[0005] The system according to the embodiment aims to provide specific experiences and learning for the user to approach the role model they aim for.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, an analysis unit, a generation unit, and a dialogue unit. The selection unit selects a role model that the user aspires to. The analysis unit analyzes the characteristics or behavioral patterns of the role model selected by the selection unit. The generation unit generates a simulation based on the information analyzed by the analysis unit. The dialogue unit interacts with the role model based on the simulation generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with concrete experiences and learning opportunities to help them approach their desired role model. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The role model simulator according to an embodiment of the present invention is a system that utilizes generative AI to provide users with an experience that helps them approach their role models. This role model simulator allows the user to select a role model they aspire to, and the generative AI analyzes the characteristics and behavioral patterns of the selected role model to generate a virtual simulation. In this simulation, the user can interact with the role model, and the generative AI provides appropriate answers and advice based on the role model's knowledge and experience. Furthermore, the user learns the role model's behavior, and the generative AI analyzes the user's learning progress to provide appropriate feedback. The system also includes a function to track the user's progress, enabling the user to acquire the skills and knowledge necessary to approach their role model through the virtual experience. For example, when a user selects a role model they aspire to, the system recommends the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. Next, the generative AI analyzes the characteristics and behavioral patterns of the selected role model to generate a virtual simulation. In this simulation, the user can interact with the role model, asking questions and seeking advice. The generative AI provides appropriate answers based on the role model's knowledge and experience, and provides feedback according to the user's learning progress. Furthermore, by tracking user progress and adjusting learning content and pace, the system supports users in learning efficiently. This allows users to acquire the skills and knowledge to approach their role models through a virtual experience. In this way, the role model simulator can provide users with an experience that helps them become closer to their role models.

[0029] The role model simulator according to the embodiment comprises a selection unit, an analysis unit, a generation unit, and a dialogue unit. The selection unit selects a role model that the user aspires to. The role model that the user aspires to includes, but is not limited to, professional role models or personal role models. The selection unit selects, for example, a role model that the user aspires to from a list. The selection unit can also recommend the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. The analysis unit analyzes the characteristics and behavioral patterns of the role model selected by the selection unit. The characteristics and behavioral patterns include, for example, the frequency of behaviors and specific skills, but are not limited to, such examples. The analysis unit obtains, for example, the behavioral patterns of the role model from a database and analyzes them using an analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past data. The generation unit generates a simulation based on the information analyzed by the analysis unit. The simulation includes, for example, a virtual environment or a scenario-based simulation, but is not limited to, such examples. The generation unit generates a simulation using, for example, a generation AI. Furthermore, the generation unit can also generate optimal scenarios by referring to the user's emotions and past learning history. The dialogue unit interacts with the role model based on the simulation generated by the generation unit. The dialogue includes, but is not limited to, voice dialogue or text dialogue. The dialogue unit can, for example, allow the user to ask questions or seek advice from the role model. The dialogue unit can also provide optimal answers by referring to the user's emotions and past dialogue history. In this way, the role model simulator according to the embodiment can provide the user with an experience that helps them become closer to the role model.

[0030] A role model simulator includes a learning unit in which the user learns the behavior of a role model. The learning unit allows the user to learn the behavior of the role model. Learning includes, but is not limited to, repetitive practice and case studies. The learning unit provides, for example, practice for the user to imitate the behavior of the role model. The learning unit may also include a function to learn through specific scenarios and case studies. For example, the learning unit provides learning content based on real-world cases or hypothetical scenarios. This allows the user to learn the behavior of a role model. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can support learning using an AI model that analyzes the user's learning progress and provides appropriate feedback.

[0031] The learning unit may include a feedback unit that analyzes the user's learning progress and provides feedback. The feedback unit analyzes the user's learning progress and provides feedback. Learning progress includes, but is not limited to, test results and progress reports. The feedback unit may, for example, periodically evaluate the user's learning progress and provide appropriate feedback. The feedback unit may also adjust the learning content and pace according to the user's learning progress. For example, the feedback unit may evaluate whether the user understands the learning content and suggest the next learning step according to the level of understanding. This allows the user to learn efficiently. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit may support learning using an AI model that analyzes the user's learning progress and provides appropriate feedback.

[0032] The feedback unit may include a tracking unit that tracks the user's progress. The tracking unit tracks the user's progress. Progress includes, but is not limited to, task completion status and the passage of time. The tracking unit can, for example, monitor the user's learning progress in real time and visualize the progress. The tracking unit can also accumulate user progress data and analyze long-term learning trends. For example, the tracking unit can analyze the user's learning progress over time and evaluate the effectiveness of the learning. This allows the user to understand their learning progress and adjust their learning plan. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can support learning using an AI model that analyzes user progress data and provides appropriate feedback.

[0033] The generation unit may have the ability to learn through specific scenarios or case studies. Scenarios and case studies include, but are not limited to, actual cases or hypothetical scenarios. For example, the generation unit can generate scenarios for a user to deal with a specific situation and learn through those scenarios. The generation unit can also allow users to learn through case studies based on actual cases. For example, the generation unit can generate scenarios for a user to solve a specific problem and learn through those scenarios. This allows users to learn through specific scenarios and case studies. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can support learning by using a generative AI model that analyzes the user's learning progress and generates appropriate scenarios.

[0034] The dialogue unit may have functions to ask questions to a role model and to ask for advice. The dialogue unit may have functions to ask questions to a role model and to ask for advice. Questions may include, for example, open-ended questions and closed-ended questions. The dialogue unit may, for example, allow the user to ask a role model specific questions. The dialogue unit may also allow the user to ask a role model for advice. Advice may include, for example, specific action guidelines and general advice. The dialogue unit may, for example, allow the user to ask a role model for career advice. This allows the user to ask questions to a role model and to ask for advice. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit may support the dialogue using an AI model that provides appropriate answers to the user's questions.

[0035] The selection unit can analyze the user's past selection history and recommend the most suitable role model. The selection unit analyzes the user's past selection history and recommends the most suitable role model. The selection history includes, but is not limited to, past selection data and selection frequency. The selection unit can, for example, analyze the trends of role models the user has selected in the past and recommend similar role models. The selection unit can also prioritize displaying role models that the user has previously given high ratings to. Furthermore, the selection unit can recommend role models related to specific fields based on the user's past selection history. This allows the selection unit to recommend the most suitable role model based on the user's past selection history. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can present role model options using an AI model that analyzes the user's past selection history and recommends appropriate role models.

[0036] The selection unit can filter role models based on the user's current goals and areas of interest when selecting a role model. Goals include, but are not limited to, short-term and long-term goals. Areas of interest include, but are not limited to, hobbies and professional fields. The selection unit can, for example, prioritize displaying role models related to goals set by the user. The selection unit can also filter relevant role models based on the user's areas of interest. Furthermore, the selection unit can display role models related to projects the user is currently working on. This allows the user to select the most suitable role model based on their current goals and areas of interest. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's goals and areas of interest and recommends appropriate role models.

[0037] The selection unit can prioritize displaying highly relevant role models when selecting a role model, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data or IP addresses. For example, the selection unit can prioritize displaying role models close to the user's current location. It can also display role models relevant to the user's geographical location. Furthermore, it can prioritize displaying popular role models in the user's region. This allows the selection unit to display the most suitable role model based on the user's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's geographical location information and recommends appropriate role models.

[0038] The selection unit can analyze the user's social media activity and recommend relevant role models when selecting a role model. Social media activity includes, but is not limited to, examples such as post content and follower count. For example, the selection unit can recommend role models related to influencers the user follows. The selection unit can also recommend relevant role models based on the user's social media activity. Furthermore, the selection unit can recommend role models related to online communities the user participates in. This allows for the recommendation of the most suitable role model based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's social media activity and recommends appropriate role models.

[0039] The analysis unit can optimize its analysis algorithm by referring to past data when analyzing the characteristics and behavioral patterns of role models. Past data includes, but is not limited to, historical data and statistical data. For example, the analysis unit analyzes characteristics and behavioral patterns based on past role model data. The analysis unit can also optimize its analysis algorithm by referring to past user training data. Furthermore, the analysis unit can improve the accuracy of the analysis based on past feedback data. This allows for improved analysis accuracy by optimizing the analysis algorithm by referring to past data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes past data and provides appropriate analysis results.

[0040] The analysis unit can improve the accuracy of its analysis by combining different data sources when analyzing the characteristics and behavioral patterns of role models. These different data sources include, but are not limited to, social media data and academic paper data. For example, the analysis unit can combine social media data and academic paper data for analysis. It can also combine user feedback data and role model behavioral data for analysis. Furthermore, the analysis unit can combine data from different industries to analyze the characteristics of role models. This allows for improved accuracy of the analysis by combining different data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes different data sources and provides appropriate analysis results.

[0041] The analysis unit can perform analysis of the characteristics and behavioral patterns of role models while taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the analysis unit can analyze the characteristics of role models related to the user's region. Furthermore, the analysis unit can analyze the behavioral patterns of role models based on the user's geographical location. In addition, the analysis unit can analyze the characteristics of role models while considering trends in the user's region. This allows for optimal analysis based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes the user's geographical location information and provides appropriate analysis results.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and materials when analyzing the characteristics and behavioral patterns of role models. The analysis unit can improve the accuracy of its analysis by referring to relevant literature and materials when analyzing the characteristics and behavioral patterns of role models. Literature and materials include, but are not limited to, academic papers and technical reports. For example, the analysis unit can analyze the characteristics of role models by referring to academic papers. The analysis unit can also analyze the behavioral patterns of role models by referring to industry reports. Furthermore, the analysis unit can analyze the characteristics of role models by referring to relevant books. This allows for improved accuracy of the analysis by referring to relevant literature and materials. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes literature and materials and provides appropriate analysis results.

[0043] The generation unit can generate the optimal scenario by referring to the user's past learning history when generating a simulation. The generation unit generates the optimal scenario by referring to the user's past learning history when generating a simulation. Past learning history includes, but is not limited to, learning progress and test results. The generation unit generates the optimal scenario based on what the user has learned in the past, for example. The generation unit can also generate a scenario with high learning effectiveness from the user's past learning history. Furthermore, the generation unit can analyze the user's past learning data and generate the most effective scenario. This allows the generation of the optimal scenario based on the user's past learning history. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's past learning history and provides an appropriate scenario.

[0044] The generation unit can improve learning effectiveness by combining different scenarios and case studies when generating simulations. These different scenarios and case studies include, but are not limited to, real-world examples and hypothetical scenarios. For example, the generation unit can improve learning effectiveness by combining scenarios from different industries. Furthermore, the generation unit can provide practical learning by combining different case studies. In addition, the generation unit can conduct learning from multiple perspectives by combining different scenarios. This allows for improved learning effectiveness by combining different scenarios and case studies. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can support learning using a generative AI model that analyzes different scenarios and case studies and provides appropriate learning content.

[0045] The generation unit can generate highly relevant scenarios when generating simulations, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the generation unit generates scenarios related to the user's region. The generation unit can also generate highly relevant scenarios based on the user's geographical location. Furthermore, the generation unit can generate scenarios considering trends in the user's region. This allows for the generation of optimal scenarios based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can support simulations using a generative AI model that analyzes the user's geographical location information and provides appropriate scenarios.

[0046] The generation unit can analyze the user's social media activity and generate relevant scenarios when generating a simulation. Social media activity includes, but is not limited to, examples such as post content and follower count. For example, the generation unit can generate scenarios related to influencers the user follows. The generation unit can also generate relevant scenarios based on the user's social media activity. Furthermore, the generation unit can generate scenarios related to online communities the user participates in. This allows for the generation of optimal scenarios based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's social media activity and provides appropriate scenarios.

[0047] The dialogue unit can provide the most appropriate answer during a conversation by referring to the user's past conversation history. This past conversation history includes, but is not limited to, past questions and answers and conversation frequency. For example, the dialogue unit can provide the most appropriate answer based on questions the user has asked in the past. The dialogue unit can also provide relevant information from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation data to provide the most appropriate answer. This allows the dialogue unit to provide the most appropriate answer based on the user's past conversation history. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support conversations using an AI model that analyzes the user's past conversation history and provides appropriate answers.

[0048] The dialogue unit can apply different dialogue patterns during a conversation based on the role model's knowledge and experience. The role model's knowledge and experience include, but are not limited to, specialized knowledge and practical experience. For example, the dialogue unit can provide detailed answers to technical questions based on the role model's expertise. It can also provide career advice based on the role model's experience. Furthermore, it can provide dialogue about daily life based on the role model's behavioral patterns. This allows the dialogue unit to provide the most appropriate dialogue pattern based on the role model's knowledge and experience. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the role model's knowledge and experience and provides appropriate dialogue patterns.

[0049] The dialogue unit can provide highly relevant information during a conversation, taking into account the user's geographical location. This geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the dialogue unit can provide event information relevant to the user's current location. It can also provide local news based on the user's geographical location. Furthermore, it can provide trend information in the user's region. This allows the dialogue unit to provide optimal information based on the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the user's geographical location and provides appropriate information.

[0050] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant information. Social media activity includes, but is not limited to, posts and follower counts. For example, the dialogue unit can provide information related to influencers the user follows. It can also provide relevant news based on the user's social media activity. Furthermore, it can provide information related to online communities the user participates in. This allows the dialogue unit to provide optimal information based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the user's social media activity and provides appropriate information.

[0051] The learning unit can provide the optimal learning method by referring to the user's past learning history during learning. Past learning history includes, but is not limited to, learning progress and test results. For example, the learning unit provides the optimal learning method based on what the user has learned in the past. The learning unit can also provide highly effective learning methods based on the user's past learning history. Furthermore, the learning unit can analyze the user's past learning data to provide the most effective learning method. This allows the learning unit to provide the optimal learning method based on the user's past learning history. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes the user's past learning history and provides appropriate learning methods.

[0052] The learning unit can improve learning effectiveness by combining different learning methods during the learning process. These different learning methods include, but are not limited to, visual learning and text-based learning. For example, the learning unit can improve learning effectiveness by combining visual learning and text-based learning. Furthermore, the learning unit can improve learning effectiveness by combining practical exercises and theoretical learning. In addition, the learning unit can improve learning effectiveness by combining group learning and individual learning. This allows for the improvement of learning effectiveness by combining different learning methods. Some or all of the above-described processes in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes different learning methods and provides appropriate learning methods.

[0053] The learning unit can provide the optimal learning method during learning, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The learning unit can, for example, provide learning content relevant to the user's region. Furthermore, the learning unit can provide the optimal learning method based on the user's geographical location. In addition, the learning unit can provide learning content considering trends in the user's region. This allows the learning unit to provide the optimal learning method based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can support learning using an AI model that analyzes the user's geographical location information and provides an appropriate learning method.

[0054] The feedback unit can provide optimal feedback by referring to the user's past learning history when providing feedback. Past learning history includes, but is not limited to, learning progress and test results. The feedback unit provides optimal feedback based on what the user has learned in the past. Furthermore, the feedback unit can provide highly effective feedback based on the user's past learning history. In addition, the feedback unit can analyze the user's past learning data to provide the most effective feedback. This allows the feedback unit to provide optimal feedback based on the user's past learning history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's past learning history and provides appropriate feedback.

[0055] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. Geographical location information includes, but is not limited to, GPS data and IP addresses. The feedback unit can, for example, provide feedback relevant to the user's region. Furthermore, the feedback unit can provide optimal feedback based on the user's geographical location. In addition, the feedback unit can provide feedback by considering trends in the user's region. This allows for the provision of optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's geographical location information and provides appropriate feedback.

[0056] The tracking unit can provide the optimal tracking method by referring to the user's past learning history during tracking. Past learning history includes, but is not limited to, learning progress and test results. For example, the tracking unit provides the optimal tracking method based on what the user has learned in the past. The tracking unit can also provide a highly effective tracking method based on the user's past learning history. Furthermore, the tracking unit can analyze the user's past learning data to provide the most effective tracking method. This allows the tracking unit to provide the optimal tracking method based on the user's past learning history. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's past learning history and provides an appropriate tracking method.

[0057] The tracking unit can provide an optimal tracking method during tracking, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The tracking unit can, for example, provide a tracking method related to the user's region. Furthermore, the tracking unit can provide an optimal tracking method based on the user's geographical location. In addition, the tracking unit can provide a tracking method considering trends in the user's region. This allows for the provision of an optimal tracking method based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's geographical location information and provides an appropriate tracking method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] A role model simulator can include a learning style analysis unit that analyzes the user's learning style and provides the optimal learning method. The learning style analysis unit analyzes whether the user has a visual, auditory, or experiential learning style based on the user's past learning history and feedback. For example, it can provide a simulation that heavily utilizes visual content for visual learners, and a simulation that emphasizes audio guidance for auditory learners. It can also provide interactive scenarios for experiential learners. This allows for the provision of an optimal learning experience tailored to the user's learning style.

[0060] Role model simulators can incorporate gamification elements to maintain user motivation. For example, users can earn badges or points each time they achieve a specific learning goal. Leaderboards can also be implemented to encourage competition among users. Furthermore, bonus rewards can be offered for achieving goals within a specific timeframe. This can increase user motivation and encourage continuous learning.

[0061] A role model simulator can provide a customized learning plan based on the user's learning progress. For example, it can provide a step-by-step guide for the user to acquire a specific skill. It can also dynamically adjust the learning plan based on the user's progress and provide additional resources and support as needed. Furthermore, it can include features that allow the user to set a timeline for achieving their goals and track their progress. This enables the user to learn efficiently.

[0062] A role model simulator can include a prediction unit that predicts and suggests future learning content based on the user's learning history. The prediction unit analyzes the user's past learning and progress to suggest what they should learn next. For example, after a user has acquired a specific skill, it can suggest advanced learning content to apply that skill. It can also provide new learning content at appropriate times according to the user's learning pace. This allows the user to learn efficiently.

[0063] A role model simulator can provide region-specific learning content by taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide role models and case studies relevant to that region. It can also provide learning content based on local culture and customs. Furthermore, it can provide information related to local events and trends. This allows users to efficiently learn content relevant to their region.

[0064] The role model simulator can analyze a user's social media activity and provide relevant learning content. For example, it can provide role models and case studies related to influencers the user follows. It can also provide relevant news and trending information based on the user's social media activity. Furthermore, it can provide learning content related to the online communities the user participates in. This allows for the provision of optimal learning content based on the user's social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The selection section allows the user to choose a role model they aspire to. This role model can include professional or personal role models. The selection section allows the user to select a role model from a list. It can also recommend the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. Step 2: The analysis unit analyzes the characteristics and behavioral patterns of the role models selected by the selection unit. These characteristics and behavioral patterns include the frequency of behaviors and specific skills. The analysis unit retrieves the role model behavioral patterns from a database and analyzes them using an analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past data. Step 3: The generation unit generates simulations based on the information analyzed by the analysis unit. These simulations include virtual environments and scenario-based simulations. The generation unit generates simulations using generative AI. The generation unit can also generate optimal scenarios by referring to the user's emotions and past learning history. Step 4: The dialogue unit interacts with the role model based on the simulation generated by the generation unit. The dialogue can include voice dialogue, text dialogue, etc. The dialogue unit allows the user to ask questions of the role model and ask for advice. The dialogue unit can also provide the best possible answers by referring to the user's emotions and past dialogue history.

[0067] (Example of form 2) The role model simulator according to an embodiment of the present invention is a system that utilizes generative AI to provide users with an experience that helps them approach their role models. This role model simulator allows the user to select a role model they aspire to, and the generative AI analyzes the characteristics and behavioral patterns of the selected role model to generate a virtual simulation. In this simulation, the user can interact with the role model, and the generative AI provides appropriate answers and advice based on the role model's knowledge and experience. Furthermore, the user learns the role model's behavior, and the generative AI analyzes the user's learning progress to provide appropriate feedback. The system also includes a function to track the user's progress, enabling the user to acquire the skills and knowledge necessary to approach their role model through the virtual experience. For example, when a user selects a role model they aspire to, the system recommends the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. Next, the generative AI analyzes the characteristics and behavioral patterns of the selected role model to generate a virtual simulation. In this simulation, the user can interact with the role model, asking questions and seeking advice. The generative AI provides appropriate answers based on the role model's knowledge and experience, and provides feedback according to the user's learning progress. Furthermore, by tracking user progress and adjusting learning content and pace, the system supports users in learning efficiently. This allows users to acquire the skills and knowledge to approach their role models through a virtual experience. In this way, the role model simulator can provide users with an experience that helps them become closer to their role models.

[0068] The role model simulator according to the embodiment comprises a selection unit, an analysis unit, a generation unit, and a dialogue unit. The selection unit selects a role model that the user aspires to. The role model that the user aspires to includes, but is not limited to, professional role models or personal role models. The selection unit selects, for example, a role model that the user aspires to from a list. The selection unit can also recommend the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. The analysis unit analyzes the characteristics and behavioral patterns of the role model selected by the selection unit. The characteristics and behavioral patterns include, for example, the frequency of behaviors and specific skills, but are not limited to, such examples. The analysis unit obtains, for example, the behavioral patterns of the role model from a database and analyzes them using an analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past data. The generation unit generates a simulation based on the information analyzed by the analysis unit. The simulation includes, for example, a virtual environment or a scenario-based simulation, but is not limited to, such examples. The generation unit generates a simulation using, for example, a generation AI. Furthermore, the generation unit can also generate optimal scenarios by referring to the user's emotions and past learning history. The dialogue unit interacts with the role model based on the simulation generated by the generation unit. The dialogue includes, but is not limited to, voice dialogue or text dialogue. The dialogue unit can, for example, allow the user to ask questions or seek advice from the role model. The dialogue unit can also provide optimal answers by referring to the user's emotions and past dialogue history. In this way, the role model simulator according to the embodiment can provide the user with an experience that helps them become closer to the role model.

[0069] A role model simulator includes a learning unit in which the user learns the behavior of a role model. The learning unit allows the user to learn the behavior of the role model. Learning includes, but is not limited to, repetitive practice and case studies. The learning unit provides, for example, practice for the user to imitate the behavior of the role model. The learning unit may also include a function to learn through specific scenarios and case studies. For example, the learning unit provides learning content based on real-world cases or hypothetical scenarios. This allows the user to learn the behavior of a role model. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can support learning using an AI model that analyzes the user's learning progress and provides appropriate feedback.

[0070] The learning unit may include a feedback unit that analyzes the user's learning progress and provides feedback. The feedback unit analyzes the user's learning progress and provides feedback. Learning progress includes, but is not limited to, test results and progress reports. The feedback unit may, for example, periodically evaluate the user's learning progress and provide appropriate feedback. The feedback unit may also adjust the learning content and pace according to the user's learning progress. For example, the feedback unit may evaluate whether the user understands the learning content and suggest the next learning step according to the level of understanding. This allows the user to learn efficiently. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit may support learning using an AI model that analyzes the user's learning progress and provides appropriate feedback.

[0071] The feedback unit may include a tracking unit that tracks the user's progress. The tracking unit tracks the user's progress. Progress includes, but is not limited to, task completion status and the passage of time. The tracking unit can, for example, monitor the user's learning progress in real time and visualize the progress. The tracking unit can also accumulate user progress data and analyze long-term learning trends. For example, the tracking unit can analyze the user's learning progress over time and evaluate the effectiveness of the learning. This allows the user to understand their learning progress and adjust their learning plan. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can support learning using an AI model that analyzes user progress data and provides appropriate feedback.

[0072] The generation unit may have the ability to learn through specific scenarios or case studies. Scenarios and case studies include, but are not limited to, actual cases or hypothetical scenarios. For example, the generation unit can generate scenarios for a user to deal with a specific situation and learn through those scenarios. The generation unit can also allow users to learn through case studies based on actual cases. For example, the generation unit can generate scenarios for a user to solve a specific problem and learn through those scenarios. This allows users to learn through specific scenarios and case studies. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can support learning by using a generative AI model that analyzes the user's learning progress and generates appropriate scenarios.

[0073] The dialogue unit may have functions to ask questions to a role model and to ask for advice. The dialogue unit may have functions to ask questions to a role model and to ask for advice. Questions may include, for example, open-ended questions and closed-ended questions. The dialogue unit may, for example, allow the user to ask a role model specific questions. The dialogue unit may also allow the user to ask a role model for advice. Advice may include, for example, specific action guidelines and general advice. The dialogue unit may, for example, allow the user to ask a role model for career advice. This allows the user to ask questions to a role model and to ask for advice. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit may support the dialogue using an AI model that provides appropriate answers to the user's questions.

[0074] The selection unit can estimate the user's emotions and present role model options based on those estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is unmotivated, the selection unit may prioritize presenting inspiring role models. If the user is stressed, the selection unit may also present relaxing role models. Furthermore, if the user is excited, the selection unit may present challenging role models. This allows the selection unit to present the most suitable role model options based on the user's emotions. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit may present role model options using an AI model that analyzes the user's emotions and recommends appropriate role models.

[0075] The selection unit can analyze the user's past selection history and recommend the most suitable role model. The selection unit analyzes the user's past selection history and recommends the most suitable role model. The selection history includes, but is not limited to, past selection data and selection frequency. The selection unit can, for example, analyze the trends of role models the user has selected in the past and recommend similar role models. The selection unit can also prioritize displaying role models that the user has previously given high ratings to. Furthermore, the selection unit can recommend role models related to specific fields based on the user's past selection history. This allows the selection unit to recommend the most suitable role model based on the user's past selection history. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can present role model options using an AI model that analyzes the user's past selection history and recommends appropriate role models.

[0076] The selection unit can filter role models based on the user's current goals and areas of interest when selecting a role model. Goals include, but are not limited to, short-term and long-term goals. Areas of interest include, but are not limited to, hobbies and professional fields. The selection unit can, for example, prioritize displaying role models related to goals set by the user. The selection unit can also filter relevant role models based on the user's areas of interest. Furthermore, the selection unit can display role models related to projects the user is currently working on. This allows the user to select the most suitable role model based on their current goals and areas of interest. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's goals and areas of interest and recommends appropriate role models.

[0077] The selection unit can estimate the user's emotions and adjust the display order of the options based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the selection unit may display role models with detailed information at the top. If the user is in a hurry, the selection unit may also display role models with concise information at the top. Furthermore, if the user is excited, the selection unit may also display challenging role models at the top. This allows for the display of role models in the optimal order based on the user's emotions. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit may present role model options using an AI model that analyzes the user's emotions and recommends appropriate role models.

[0078] The selection unit can prioritize displaying highly relevant role models when selecting a role model, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data or IP addresses. For example, the selection unit can prioritize displaying role models close to the user's current location. It can also display role models relevant to the user's geographical location. Furthermore, it can prioritize displaying popular role models in the user's region. This allows the selection unit to display the most suitable role model based on the user's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's geographical location information and recommends appropriate role models.

[0079] The selection unit can analyze the user's social media activity and recommend relevant role models when selecting a role model. Social media activity includes, but is not limited to, examples such as post content and follower count. For example, the selection unit can recommend role models related to influencers the user follows. The selection unit can also recommend relevant role models based on the user's social media activity. Furthermore, the selection unit can recommend role models related to online communities the user participates in. This allows for the recommendation of the most suitable role model based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not. For example, the selection unit can present role model options using an AI model that analyzes the user's social media activity and recommends appropriate role models.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the analysis unit performs a detailed analysis when the user is relaxed. It can also perform a concise analysis when the user is in a hurry. Furthermore, it can provide visually stimulating analysis results when the user is excited. This allows the accuracy of the analysis to be adjusted based on the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes the user's emotions and provides appropriate analysis results.

[0081] The analysis unit can optimize its analysis algorithm by referring to past data when analyzing the characteristics and behavioral patterns of role models. Past data includes, but is not limited to, historical data and statistical data. For example, the analysis unit analyzes characteristics and behavioral patterns based on past role model data. The analysis unit can also optimize its analysis algorithm by referring to past user training data. Furthermore, the analysis unit can improve the accuracy of the analysis based on past feedback data. This allows for improved analysis accuracy by optimizing the analysis algorithm by referring to past data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes past data and provides appropriate analysis results.

[0082] The analysis unit can improve the accuracy of its analysis by combining different data sources when analyzing the characteristics and behavioral patterns of role models. These different data sources include, but are not limited to, social media data and academic paper data. For example, the analysis unit can combine social media data and academic paper data for analysis. It can also combine user feedback data and role model behavioral data for analysis. Furthermore, the analysis unit can combine data from different industries to analyze the characteristics of role models. This allows for improved accuracy of the analysis by combining different data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes different data sources and provides appropriate analysis results.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is in a hurry, the analysis unit can also display concise analysis results. Furthermore, if the user is excited, the analysis unit can display visually stimulating analysis results. This allows the display method of the analysis results to be adjusted based on the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes the user's emotions and provides appropriate analysis results.

[0084] The analysis unit can perform analysis of the characteristics and behavioral patterns of role models while taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the analysis unit can analyze the characteristics of role models related to the user's region. Furthermore, the analysis unit can analyze the behavioral patterns of role models based on the user's geographical location. In addition, the analysis unit can analyze the characteristics of role models while considering trends in the user's region. This allows for optimal analysis based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes the user's geographical location information and provides appropriate analysis results.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and materials when analyzing the characteristics and behavioral patterns of role models. The analysis unit can improve the accuracy of its analysis by referring to relevant literature and materials when analyzing the characteristics and behavioral patterns of role models. Literature and materials include, but are not limited to, academic papers and technical reports. For example, the analysis unit can analyze the characteristics of role models by referring to academic papers. The analysis unit can also analyze the behavioral patterns of role models by referring to industry reports. Furthermore, the analysis unit can analyze the characteristics of role models by referring to relevant books. This allows for improved accuracy of the analysis by referring to relevant literature and materials. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can support the analysis using a generative AI model that analyzes literature and materials and provides appropriate analysis results.

[0086] The generation unit can estimate the user's emotions and adjust the simulation content based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the generation unit can generate a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a simulation that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a simulation with visually stimulating effects. This allows the simulation content to be adjusted based on the user's emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's emotions and provides an appropriate simulation.

[0087] The generation unit can generate the optimal scenario by referring to the user's past learning history when generating a simulation. The generation unit generates the optimal scenario by referring to the user's past learning history when generating a simulation. Past learning history includes, but is not limited to, learning progress and test results. The generation unit generates the optimal scenario based on what the user has learned in the past, for example. The generation unit can also generate a scenario with high learning effectiveness from the user's past learning history. Furthermore, the generation unit can analyze the user's past learning data and generate the most effective scenario. This allows the generation of the optimal scenario based on the user's past learning history. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's past learning history and provides an appropriate scenario.

[0088] The generation unit can improve learning effectiveness by combining different scenarios and case studies when generating simulations. These different scenarios and case studies include, but are not limited to, real-world examples and hypothetical scenarios. For example, the generation unit can improve learning effectiveness by combining scenarios from different industries. Furthermore, the generation unit can provide practical learning by combining different case studies. In addition, the generation unit can conduct learning from multiple perspectives by combining different scenarios. This allows for improved learning effectiveness by combining different scenarios and case studies. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can support learning using a generative AI model that analyzes different scenarios and case studies and provides appropriate learning content.

[0089] The generation unit can estimate the user's emotions and adjust the difficulty of the simulation based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the generation unit can generate a low-difficulty simulation if the user is relaxed. It can also generate a high-difficulty simulation if the user is in a hurry. Furthermore, it can generate a high-difficulty simulation if the user is excited. This allows the simulation difficulty to be adjusted based on the user's emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's emotions and provides an appropriate simulation.

[0090] The generation unit can generate highly relevant scenarios when generating simulations, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the generation unit generates scenarios related to the user's region. The generation unit can also generate highly relevant scenarios based on the user's geographical location. Furthermore, the generation unit can generate scenarios considering trends in the user's region. This allows for the generation of optimal scenarios based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can support simulations using a generative AI model that analyzes the user's geographical location information and provides appropriate scenarios.

[0091] The generation unit can analyze the user's social media activity and generate relevant scenarios when generating a simulation. Social media activity includes, but is not limited to, examples such as post content and follower count. For example, the generation unit can generate scenarios related to influencers the user follows. The generation unit can also generate relevant scenarios based on the user's social media activity. Furthermore, the generation unit can generate scenarios related to online communities the user participates in. This allows for the generation of optimal scenarios based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can support the simulation using a generative AI model that analyzes the user's social media activity and provides appropriate scenarios.

[0092] The dialogue unit can estimate the user's emotions and adjust the content of the dialogue based on those emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the dialogue unit may provide a detailed explanation. If the user is in a hurry, the dialogue unit may provide a concise dialogue. Furthermore, if the user is excited, the dialogue unit may provide a visually stimulating dialogue. This allows the dialogue content to be adjusted based on the user's emotions. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the dialogue using an AI model that analyzes the user's emotions and provides appropriate dialogue content.

[0093] The dialogue unit can provide the most appropriate answer during a conversation by referring to the user's past conversation history. This past conversation history includes, but is not limited to, past questions and answers and conversation frequency. For example, the dialogue unit can provide the most appropriate answer based on questions the user has asked in the past. The dialogue unit can also provide relevant information from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation data to provide the most appropriate answer. This allows the dialogue unit to provide the most appropriate answer based on the user's past conversation history. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support conversations using an AI model that analyzes the user's past conversation history and provides appropriate answers.

[0094] The dialogue unit can apply different dialogue patterns during a conversation based on the role model's knowledge and experience. The role model's knowledge and experience include, but are not limited to, specialized knowledge and practical experience. For example, the dialogue unit can provide detailed answers to technical questions based on the role model's expertise. It can also provide career advice based on the role model's experience. Furthermore, it can provide dialogue about daily life based on the role model's behavioral patterns. This allows the dialogue unit to provide the most appropriate dialogue pattern based on the role model's knowledge and experience. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the role model's knowledge and experience and provides appropriate dialogue patterns.

[0095] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on those emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is nervous, the dialogue unit will engage in conversation in a calm tone. If the user is relaxed, the dialogue unit can engage in conversation in a bright tone. Furthermore, if the user is in a hurry, the dialogue unit can engage in conversation in a quick and concise tone. This allows the dialogue unit to adjust the tone of the dialogue based on the user's emotions. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the dialogue using an AI model that analyzes the user's emotions and provides an appropriate dialogue tone.

[0096] The dialogue unit can provide highly relevant information during a conversation, taking into account the user's geographical location. This geographical location information includes, but is not limited to, GPS data and IP addresses. For example, the dialogue unit can provide event information relevant to the user's current location. It can also provide local news based on the user's geographical location. Furthermore, it can provide trend information in the user's region. This allows the dialogue unit to provide optimal information based on the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the user's geographical location and provides appropriate information.

[0097] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant information. Social media activity includes, but is not limited to, posts and follower counts. For example, the dialogue unit can provide information related to influencers the user follows. It can also provide relevant news based on the user's social media activity. Furthermore, it can provide information related to online communities the user participates in. This allows the dialogue unit to provide optimal information based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can support the conversation using an AI model that analyzes the user's social media activity and provides appropriate information.

[0098] The learning unit can estimate the user's emotions and adjust the learning content based on those emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the learning unit can provide detailed learning content. If the user is in a hurry, the learning unit can provide concise learning content. Furthermore, if the user is excited, the learning unit can provide visually stimulating learning content. This allows the learning content to be adjusted based on the user's emotions. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes the user's emotions and provides appropriate learning content.

[0099] The learning unit can provide the optimal learning method by referring to the user's past learning history during learning. Past learning history includes, but is not limited to, learning progress and test results. For example, the learning unit provides the optimal learning method based on what the user has learned in the past. The learning unit can also provide highly effective learning methods based on the user's past learning history. Furthermore, the learning unit can analyze the user's past learning data to provide the most effective learning method. This allows the learning unit to provide the optimal learning method based on the user's past learning history. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes the user's past learning history and provides appropriate learning methods.

[0100] The learning unit can improve learning effectiveness by combining different learning methods during the learning process. These different learning methods include, but are not limited to, visual learning and text-based learning. For example, the learning unit can improve learning effectiveness by combining visual learning and text-based learning. Furthermore, the learning unit can improve learning effectiveness by combining practical exercises and theoretical learning. In addition, the learning unit can improve learning effectiveness by combining group learning and individual learning. This allows for the improvement of learning effectiveness by combining different learning methods. Some or all of the above-described processes in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes different learning methods and provides appropriate learning methods.

[0101] The learning unit can estimate the user's emotions and adjust the learning pace based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is relaxed, the learning unit can provide learning at a slow pace. If the user is in a hurry, the learning unit can provide learning at a rapid pace. Furthermore, if the user is excited, the learning unit can provide learning at a moderate pace. This allows the learning pace to be adjusted based on the user's emotions. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can support learning using an AI model that analyzes the user's emotions and provides an appropriate pace.

[0102] The learning unit can provide the optimal learning method during learning, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The learning unit can, for example, provide learning content relevant to the user's region. Furthermore, the learning unit can provide the optimal learning method based on the user's geographical location. In addition, the learning unit can provide learning content considering trends in the user's region. This allows the learning unit to provide the optimal learning method based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can support learning using an AI model that analyzes the user's geographical location information and provides an appropriate learning method.

[0103] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the feedback unit can provide detailed feedback when the user is relaxed. It can also provide concise feedback when the user is in a hurry. Furthermore, it can provide visually stimulating feedback when the user is excited. This allows the feedback content to be adjusted based on the user's emotions. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's emotions and provides appropriate feedback.

[0104] The feedback unit can provide optimal feedback by referring to the user's past learning history when providing feedback. Past learning history includes, but is not limited to, learning progress and test results. The feedback unit provides optimal feedback based on what the user has learned in the past. Furthermore, the feedback unit can provide highly effective feedback based on the user's past learning history. In addition, the feedback unit can analyze the user's past learning data to provide the most effective feedback. This allows the feedback unit to provide optimal feedback based on the user's past learning history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's past learning history and provides appropriate feedback.

[0105] The feedback unit can estimate the user's emotions and adjust the frequency of feedback based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the feedback unit provides frequent feedback when the user is relaxed. It can also provide minimal feedback when the user is in a hurry. Furthermore, it can provide feedback at a moderate frequency when the user is excited. This allows the feedback frequency to be adjusted based on the user's emotions. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's emotions and provides an appropriate feedback frequency.

[0106] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. Geographical location information includes, but is not limited to, GPS data and IP addresses. The feedback unit can, for example, provide feedback relevant to the user's region. Furthermore, the feedback unit can provide optimal feedback based on the user's geographical location. In addition, the feedback unit can provide feedback by considering trends in the user's region. This allows for the provision of optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can support feedback using an AI model that analyzes the user's geographical location information and provides appropriate feedback.

[0107] The tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the tracking unit can perform detailed tracking when the user is relaxed. It can also perform concise tracking when the user is in a hurry. Furthermore, it can perform visually stimulating tracking when the user is excited. This allows the tracking method to be adjusted based on the user's emotions. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's emotions and provides an appropriate tracking method.

[0108] The tracking unit can provide the optimal tracking method by referring to the user's past learning history during tracking. Past learning history includes, but is not limited to, learning progress and test results. For example, the tracking unit provides the optimal tracking method based on what the user has learned in the past. The tracking unit can also provide a highly effective tracking method based on the user's past learning history. Furthermore, the tracking unit can analyze the user's past learning data to provide the most effective tracking method. This allows the tracking unit to provide the optimal tracking method based on the user's past learning history. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's past learning history and provides an appropriate tracking method.

[0109] The tracking unit can estimate the user's emotions and adjust the tracking frequency based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the tracking unit tracks frequently when the user is relaxed. It can also perform minimal tracking when the user is in a hurry. Furthermore, it can track at a moderate frequency when the user is excited. This allows the tracking frequency to be adjusted based on the user's emotions. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's emotions and provides an appropriate tracking frequency.

[0110] The tracking unit can provide an optimal tracking method during tracking, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The tracking unit can, for example, provide a tracking method related to the user's region. Furthermore, the tracking unit can provide an optimal tracking method based on the user's geographical location. In addition, the tracking unit can provide a tracking method considering trends in the user's region. This allows for the provision of an optimal tracking method based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can support tracking using an AI model that analyzes the user's geographical location information and provides an appropriate tracking method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the selection unit, analysis unit, generation unit, dialogue unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, which allows the user to select a role model they aspire to from a list. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the characteristics and behavioral patterns of the selected role model. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a simulation based on the analyzed information. The dialogue unit is implemented by the control unit 46A of the smart device 14, which allows the user to interact with the role model. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows the user to learn the behavior of the role model. === Hard Collateral 1-2 === Each of the multiple elements described above, including the selection unit, analysis unit, generation unit, dialogue unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, which allows the user to select a role model they aspire to from a list. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the characteristics and behavioral patterns of the selected role model. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates a simulation based on the analyzed information. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, which allows the user to interact with the role model. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which allows the user to learn the behavior of the role model. === Hard Collateral 1-3 === Each of the multiple elements described above, including the selection unit, analysis unit, generation unit, dialogue unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, which allows the user to select a role model from a list. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the characteristics and behavioral patterns of the selected role model. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a simulation based on the analyzed information. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, which allows the user to interact with the role model. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows the user to learn the behavior of the role model. === Hard Collateral 1-4 === Each of the multiple elements described above, including the selection unit, analysis unit, generation unit, dialogue unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, which allows the user to select a role model from a list. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the characteristics and behavioral patterns of the selected role model. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a simulation based on the analyzed information. The dialogue unit is implemented by the control unit 46A of the robot 414, which allows the user to interact with the role model. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows the user to learn the behavior of the role model.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] A role model simulator can include a learning style analysis unit that analyzes the user's learning style and provides the optimal learning method. The learning style analysis unit analyzes whether the user has a visual, auditory, or experiential learning style based on the user's past learning history and feedback. For example, it can provide a simulation that heavily utilizes visual content for visual learners, and a simulation that emphasizes audio guidance for auditory learners. It can also provide interactive scenarios for experiential learners. This allows for the provision of an optimal learning experience tailored to the user's learning style.

[0113] Role model simulators can incorporate gamification elements to maintain user motivation. For example, users can earn badges or points each time they achieve a specific learning goal. Leaderboards can also be implemented to encourage competition among users. Furthermore, bonus rewards can be offered for achieving goals within a specific timeframe. This can increase user motivation and encourage continuous learning.

[0114] The role model simulator can estimate the user's emotions and adjust the learning environment based on those emotions. For example, if the user is feeling stressed, it can provide relaxing music or background music. If the user wants to improve their concentration, it can provide environment settings that support concentration. Furthermore, if the user is tired, it can display a notification prompting them to take a break. This allows for the provision of an optimal learning environment tailored to the user's emotions.

[0115] A role model simulator can provide a customized learning plan based on the user's learning progress. For example, it can provide a step-by-step guide for the user to acquire a specific skill. It can also dynamically adjust the learning plan based on the user's progress and provide additional resources and support as needed. Furthermore, it can include features that allow the user to set a timeline for achieving their goals and track their progress. This enables the user to learn efficiently.

[0116] The role model simulator can estimate the user's emotions and adjust the tone of feedback based on those emotions. For example, if the user is feeling down, it can provide feedback that includes words of encouragement. If the user is confident, it can provide challenging feedback. Furthermore, if the user is feeling anxious, it can provide calm and specific feedback. This allows for the provision of optimal feedback tailored to the user's emotions.

[0117] A role model simulator can include a prediction unit that predicts and suggests future learning content based on the user's learning history. The prediction unit analyzes the user's past learning and progress to suggest what they should learn next. For example, after a user has acquired a specific skill, it can suggest advanced learning content to apply that skill. It can also provide new learning content at appropriate times according to the user's learning pace. This allows the user to learn efficiently.

[0118] The role model simulator can estimate the user's emotions and adjust the learning pace based on those emotions. For example, if the user is relaxed, the learning can proceed at a slow pace. If the user is in a hurry, the learning can proceed at a faster pace. Furthermore, if the user is excited, the learning can proceed at a moderate pace. This allows the simulator to provide an optimal learning pace tailored to the user's emotions.

[0119] A role model simulator can provide region-specific learning content by taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide role models and case studies relevant to that region. It can also provide learning content based on local culture and customs. Furthermore, it can provide information related to local events and trends. This allows users to efficiently learn content relevant to their region.

[0120] The role model simulator can estimate the user's emotions and adjust the difficulty level of the learning content based on those emotions. For example, if the user is relaxed, it can provide more challenging learning content. If the user is stressed, it can provide easier learning content. Furthermore, if the user is excited, it can provide challenging learning content. This allows for the provision of optimal learning content tailored to the user's emotions.

[0121] The role model simulator can analyze a user's social media activity and provide relevant learning content. For example, it can provide role models and case studies related to influencers the user follows. It can also provide relevant news and trending information based on the user's social media activity. Furthermore, it can provide learning content related to the online communities the user participates in. This allows for the provision of optimal learning content based on the user's social media activity.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The selection section allows the user to choose a role model they aspire to. This role model can include professional or personal role models. The selection section allows the user to select a role model from a list. It can also recommend the most suitable role model by considering the user's emotions, past selection history, current goals, and areas of interest. Step 2: The analysis unit analyzes the characteristics and behavioral patterns of the role models selected by the selection unit. These characteristics and behavioral patterns include the frequency of behaviors and specific skills. The analysis unit retrieves the role model behavioral patterns from a database and analyzes them using an analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past data. Step 3: The generation unit generates simulations based on the information analyzed by the analysis unit. These simulations include virtual environments and scenario-based simulations. The generation unit generates simulations using generative AI. The generation unit can also generate optimal scenarios by referring to the user's emotions and past learning history. Step 4: The dialogue unit interacts with the role model based on the simulation generated by the generation unit. The dialogue can include voice dialogue, text dialogue, etc. The dialogue unit allows the user to ask questions of the role model and ask for advice. The dialogue unit can also provide the best possible answers by referring to the user's emotions and past dialogue history.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section where the user selects the role model they aspire to, An analysis unit analyzes the characteristics or behavioral patterns of the role model selected by the selection unit, A generation unit that generates a simulation based on the information analyzed by the analysis unit, The system includes an interaction unit that interacts with a role model based on the simulation generated by the generation unit. A system characterized by the following features.

2. It includes a learning section where users learn the behavior of role models. The system according to feature 1.

3. The aforementioned learning unit, It includes a feedback unit that analyzes the user's learning progress and provides feedback. The system according to feature 2.

4. The aforementioned feedback unit is It includes a tracking unit to track the user's progress. The system according to claim 3.

5. The generating unit is It features a function that allows learning through specific scenarios or case studies. The system according to feature 1.

6. The aforementioned dialogue unit, It includes features that allow you to ask questions to role models and seek their advice. The system according to feature 1.

7. The aforementioned selection unit is It estimates the user's emotions and presents role model options based on those estimated emotions. The system according to feature 1.

8. The aforementioned selection unit is Analyze the user's past selection history and recommend the most suitable role model. The system according to feature 1.

9. The aforementioned selection unit is When selecting a role model, filtering is performed based on the user's current goals and areas of interest. The system according to feature 1.

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