system

Abstract

The system according to this embodiment aims to cultivate problem-solving abilities through an interactive learning experience. [Solution] The system according to the embodiment comprises a generation unit, a dialogue unit, and a feedback unit. The generation unit generates a problem-solving scenario. The dialogue unit engages in dialogue based on the scenario generated by the generation unit. The feedback unit provides feedback on the content of the dialogue conducted by the dialogue 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] <00​​​​​​​​​​​​ [Effects of the Invention]

[0007] The system according to this embodiment can cultivate problem-solving abilities through an interactive learning experience. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An online platform according to an embodiment of the present invention is a system that uses generative AI to help students develop problem-solving skills. This system generates problem-solving scenarios, supports learning through dialogue, and provides feedback. For example, a user inputs a problem-solving scenario into the generative AI. For example, a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?" Next, the generative AI starts a dialogue based on this scenario. The student learns the problem-solving process through dialogue with the generative AI. For example, the generative AI suggests, "Let's organize the remaining work," and the student asks, "How would we organize the work?" The generative AI replies, "I would check with each member," and continues, "How would you summarize the confirmed work?" The student replies, "I would list them and write down how much time each one will take." The generative AI provides feedback on the dialogue to support the student's learning. For example, it provides feedback such as, "That's a great start," or "That's a good idea." This platform will enable students to develop practical problem-solving skills and contribute to reforming Japan's education system. Furthermore, advancements in generative AI technology will facilitate more efficient learning, contributing to the future improvement of Japan's economic strength. Thus, the online platform will allow students to cultivate problem-solving abilities using generative AI.

[0029] The online platform according to the embodiment comprises a generation unit, a dialogue unit, and a feedback unit. The generation unit generates a problem-solving scenario. The generation unit generates a problem-solving scenario based on information entered by the user, for example. The generation unit can generate a scenario using a generation AI. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How will you help the team?", the generation AI will start a dialogue based on this scenario. The dialogue unit conducts a dialogue based on the generated scenario. For example, the generation AI might suggest, "Let's organize the remaining work," and the student might ask, "How will we organize the work?" The generation AI might respond, "I will check with each member," and then continue, "How will you summarize the confirmed work?" The feedback unit provides feedback on the content of the dialogue. For example, the generation AI might give feedback such as, "That's a great start," or "That's a good idea." As a result, the online platform according to this embodiment can generate problem-solving scenarios using generative AI, support learning through dialogue, and provide feedback, thereby enabling the efficient development of problem-solving abilities.

[0030] The generation unit generates problem-solving scenarios. For example, it generates problem-solving scenarios based on information entered by the user. The generation unit can generate scenarios using generative AI. Specifically, the generative AI utilizes natural language processing technology to understand the context of the scenario entered by the user and generates an appropriate problem-solving scenario. For example, if a user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?", the generative AI will begin a dialogue based on this scenario. The generative AI first analyzes the background information of the scenario and identifies the core of the problem. Next, it refers to a database of similar past scenarios and solutions and proposes the best solution. For example, the generative AI might suggest, "Organize the remaining tasks," and then provide more specific steps and methods. The generative AI can dynamically respond to user input and generate new suggestions and advice as the scenario progresses. This allows the generation unit to provide flexible and effective solutions to the problems the user faces, improving the user's problem-solving abilities.

[0031] The dialogue unit conducts conversations based on generated scenarios. For example, the generating AI might suggest, "Let's organize the remaining tasks," and the student might ask, "How do we organize the tasks?" The generating AI might respond, "We'll check with each member," and then continue, "How do we summarize the confirmed tasks?" The dialogue unit plays a role in guiding the problem-solving process through the AI's interaction with the user. Specifically, the generating AI provides appropriate feedback to the user's questions and responses, ensuring the conversation flows smoothly. For example, if the user asks, "Please tell me a specific way to organize the tasks," the generating AI might provide specific advice such as, "First, list all the tasks and prioritize them. Then, assign tasks to each member." The dialogue unit provides real-time support for any questions or challenges the user faces during the problem-solving process, deepening the user's understanding. The dialogue unit can also monitor the user's reactions and progress, and adjust the content of the conversation as needed. This allows the dialogue unit to help users effectively acquire problem-solving skills and improve the quality of learning.

[0032] The feedback unit provides feedback on the content of the conversation. For example, the generating AI might say, "That's a great start," or "That's a good idea." The feedback unit plays a role in increasing the user's motivation to learn by providing appropriate evaluations and advice on the user's actions and responses. Specifically, the generating AI analyzes the content of the user's conversation and provides positive feedback at the appropriate time. For example, if the user reports, "I've listed all the tasks," the generating AI might suggest a specific next step, such as, "That's great. Now let's prioritize them." The feedback unit also makes appropriate suggestions if the user is heading in the wrong direction or if there are areas that need improvement. For example, if the user suggests, "I'll do all the tasks myself," the generating AI might advise, "It's important to work together as a team. Let's divide the roles among the members." In this way, the feedback unit can support the user in learning effectively and improving their problem-solving abilities. Furthermore, the feedback unit can record the user's progress and learning outcomes, which can be used to develop and improve long-term learning plans. In this way, the feedback unit can continuously support the user's growth and maximize the effectiveness of the entire online platform.

[0033] The generation unit can generate problem-solving scenarios based on information entered by the user. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?", the generation AI will start a dialogue based on this scenario. In this way, by generating scenarios based on the user's input information, it is possible to address individual learning needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input user-entered information into the generation AI and have the generation AI perform the scenario generation.

[0034] The dialogue unit can engage in conversations based on generated scenarios. For example, the AI ​​might suggest, "Let's organize the remaining tasks," and a student might ask, "How should we organize the tasks?" The AI ​​might respond, "I'll check with each member," and then continue, "How should we summarize the confirmed tasks?" In this way, engaging in conversations based on generated scenarios can cultivate practical problem-solving skills. Some or all of the above-described processes in the dialogue unit may be performed using the AI, or they may not. For example, the dialogue unit can input a generated scenario into the AI ​​and have the AI ​​perform the dialogue.

[0035] The feedback unit can provide feedback on the content of the dialogue. For example, the feedback unit's generating AI might provide feedback such as, "That's a very good start," or "That's a good idea." By providing feedback on the content of the dialogue, the learning effect can be enhanced. Some or all of the above-described processes in the feedback unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the feedback unit can input the dialogue content into the generating AI and have the generating AI generate the feedback.

[0036] The generation unit can analyze the user's past learning history and generate the optimal scenario. For example, the generation unit can adjust the difficulty level of the next scenario based on the success rate of scenarios the user has tackled in the past. The generation unit can also identify areas where the user has struggled in the past and provide scenarios related to those areas. Furthermore, the generation unit can provide scenarios that reinforce areas where the user has excelled in the past. In this way, by generating scenarios based on past learning history, individual learning needs can be addressed. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's past learning history into the generation AI and have the generation AI generate the optimal scenario.

[0037] The generation unit can customize the content of a scenario based on the user's current learning objectives when generating a scenario. For example, if the user has the learning objective of "strengthening teamwork," the generation AI will provide a scenario related to teamwork. Similarly, if the user has the learning objective of "developing leadership skills," the generation AI can provide a scenario related to leadership. Furthermore, if the user has the learning objective of "improving time management," the generation AI can provide a scenario related to time management. This allows for effective learning by customizing the scenario based on the user's current learning objectives. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input the user's current learning objectives into the generation AI and have the generation AI customize the scenario.

[0038] The generation unit can generate highly relevant scenarios by considering the user's geographical location information during scenario generation. For example, if the user is in an urban area, the generation AI can provide scenarios relevant to urban areas. Similarly, if the user is in a rural area, the generation AI can provide scenarios relevant to rural areas. Furthermore, if the user is in a specific region, the generation AI can provide scenarios related to the culture and history of that region. This allows for the provision of a highly relevant learning experience by generating scenarios based on geographical location information. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate highly relevant scenarios.

[0039] The generation unit can analyze a user's social media activity and generate relevant scenarios during scenario generation. For example, the generation unit can have the generating AI provide scenarios based on themes the user frequently discusses on social media. The generation unit can also have the generating AI provide scenarios based on the interests of accounts the user follows on social media. Furthermore, the generation unit can have the generating AI provide scenarios based on the activities of groups the user participates in on social media. This allows for the provision of a highly relevant learning experience by generating scenarios based on social media activity. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the generation unit can input the user's social media activity into the generating AI and have the generating AI generate relevant scenarios.

[0040] The dialogue unit can conduct the most appropriate dialogue by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can use the generative AI to provide relevant information based on questions the user has asked in the past. The dialogue unit can also use the generative AI to adjust the content of the dialogue based on the user's past interests. Furthermore, the dialogue unit can avoid topics that the user has previously found difficult, allowing the generative AI to guide the conversation. This allows for the response to individual learning needs by conducting conversations based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's past dialogue history into the generative AI and have the generative AI perform the most appropriate dialogue.

[0041] The dialogue unit can customize the content of the dialogue based on the user's current learning progress. For example, the dialogue unit can have the generative AI suggest the next step based on the user's current learning progress. Furthermore, if the user is lagging behind in a particular topic, the dialogue unit can have the generative AI conduct a dialogue related to that topic. Additionally, if the user is progressing quickly in a particular topic, the dialogue unit can have the generative AI provide a dialogue of the next difficulty level. This allows for effective learning by customizing the dialogue based on the current learning progress. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's current learning progress into the generative AI and have the generative AI customize the dialogue.

[0042] The dialogue unit can conduct highly relevant conversations by considering the user's geographical location information during the interaction. For example, if the user is in an urban area, the generative AI will conduct conversations related to urban areas. Similarly, if the user is in a rural area, the generative AI can conduct conversations related to rural areas. Furthermore, if the user is in a specific region, the generative AI can conduct conversations related to the culture and history of that region. This allows for a highly relevant learning experience by conducting conversations based on geographical location information. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's geographical location information into the generative AI and have the generative AI conduct highly relevant conversations.

[0043] The dialogue unit can analyze the user's social media activity during a conversation and conduct relevant dialogues. For example, the dialogue unit can use a generative AI to conduct a conversation based on the themes the user frequently discusses on social media. The dialogue unit can also use a generative AI to conduct a conversation based on the interests of the accounts the user follows on social media. Furthermore, the dialogue unit can use a generative AI to conduct a conversation based on the activities of the groups the user participates in on social media. This allows for a more relevant learning experience by conducting conversations based on social media activity. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI conduct relevant dialogues.

[0044] The feedback unit can provide optimal feedback by referring to the user's past learning achievements. For example, the feedback unit can use the generative AI to provide positive feedback based on scenarios in which the user has succeeded in the past. The feedback unit can also use the generative AI to point out areas for improvement based on scenarios in which the user has struggled in the past. Furthermore, the feedback unit can use the generative AI to provide feedback that reinforces areas in which the user has excelled in the past. This allows for individualized learning needs to be addressed by providing feedback based on past learning achievements. Some or all of the above processing in the feedback unit may be performed using the generative AI or not. For example, the feedback unit can input the user's past learning achievements into the generative AI and have the generative AI provide optimal feedback.

[0045] The feedback unit can customize the content of feedback based on the user's current learning goals. For example, if the user's learning goal is to "strengthen teamwork," the generating AI will provide feedback related to teamwork. Similarly, if the user's learning goal is to "develop leadership skills," the generating AI can provide feedback related to leadership. Furthermore, if the user's learning goal is to "improve time management," the generating AI can provide feedback related to time management. This allows for effective learning by customizing feedback based on current learning goals. Some or all of the above-described processes in the feedback unit may be performed using the generating AI, or they may not. For example, the feedback unit can input the user's current learning goals into the generating AI and have the generating AI customize the feedback.

[0046] The feedback unit can provide highly relevant feedback by considering the user's geographical location information. For example, if the user is in an urban area, the generating AI can provide feedback relevant to urban areas. Similarly, if the user is in a rural area, the generating AI can provide feedback relevant to rural areas. Furthermore, if the user is in a specific region, the generating AI can provide feedback related to the culture and history of that region. This allows for a highly relevant learning experience by providing feedback based on geographical location information. Some or all of the above processing in the feedback unit may be performed using the generating AI, or without it. For example, the feedback unit can input the user's geographical location information into the generating AI and have the generating AI provide highly relevant feedback.

[0047] The feedback unit can analyze the user's social media activity and provide relevant feedback during the feedback process. For example, the feedback unit can use a generative AI to provide feedback based on themes the user frequently discusses on social media. The feedback unit can also use a generative AI to provide feedback based on the interests of the accounts the user follows on social media. Furthermore, the feedback unit can use a generative AI to provide feedback based on the activities of groups the user participates in on social media. This allows for a more relevant learning experience by providing feedback based on social media activity. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without one. For example, the feedback unit can input the user's social media activity into a generative AI and have the generative AI provide relevant feedback.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The generation unit can analyze the user's learning style and generate the optimal scenario. For example, if the user is a visual learner, the generation AI can provide a scenario with many visual elements. If the user is an auditory learner, the generation AI can also provide a scenario that includes audio and music. Furthermore, if the user is an experiential learner, the generation AI can provide a scenario that simulates a real-world experience. This allows for the provision of an effective learning experience by generating scenarios according to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input user learning style data into the generation AI and have the generation AI generate the optimal scenario.

[0050] The dialogue unit can monitor the user's learning progress in real time and dynamically adjust the content of the dialogue. For example, if the user is stuck on a particular task, the generative AI can provide additional hints related to that task. Also, if the user is progressing well, the generative AI can suggest the next step. Furthermore, if the user shows interest in a particular topic, the generative AI can provide in-depth information related to that topic. This allows for effective learning support by providing dialogue that is tailored to the user's learning progress in real time. Some or all of the above processes in the dialogue unit may be performed using the generative AI or not. For example, the dialogue unit can input the user's learning progress data into the generative AI and have the generative AI adjust the content of the dialogue.

[0051] The generation unit can propose individualized learning plans based on the user's learning history. For example, the generation unit can analyze the success rate of scenarios the user has previously attempted, and the generating AI can propose the next learning steps. The generation unit can also identify areas where the user has struggled in the past, and the generating AI can provide scenarios related to those areas. Furthermore, based on areas where the user has excelled in the past, the generating AI can provide scenarios to strengthen those areas. This enables effective learning by proposing individualized learning plans based on past learning history. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input the user's learning history data into the generating AI and have the generating AI propose individualized learning plans.

[0052] The generation unit can generate a long-term learning plan based on the user's learning objectives. For example, if the user has the learning objective of "wanting to demonstrate leadership," the generation AI will provide a series of scenarios related to leadership. Similarly, if the user has the learning objective of "wanting to improve teamwork," the generation AI can provide a series of scenarios related to teamwork. Furthermore, if the user has the learning objective of "wanting to improve time management," the generation AI can provide a series of scenarios related to time management. This enables effective learning by generating a long-term learning plan based on the user's learning objectives. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input the user's learning objective data into the generation AI and have the generation AI generate a long-term learning plan.

[0053] The generation unit can generate region-specific problem-solving scenarios, taking into account the user's geographical location information. For example, if the user is in an urban area, the generation AI can provide problem-solving scenarios relevant to urban areas. Similarly, if the user is in a rural area, the generation AI can provide problem-solving scenarios relevant to rural areas. Furthermore, if the user is in a specific region, the generation AI can provide problem-solving scenarios related to the culture and history of that region. This allows for a highly relevant learning experience by generating scenarios based on geographical location information. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate region-specific scenarios.

[0054] The dialogue unit can analyze the user's social media activity and conduct relevant conversations. For example, the dialogue unit can use a generative AI to conduct conversations based on themes the user frequently discusses on social media. The dialogue unit can also use a generative AI to conduct conversations based on the interests of the accounts the user follows on social media. Furthermore, the dialogue unit can use a generative AI to conduct conversations based on the activities of groups the user participates in on social media. This allows for a more relevant learning experience by conducting conversations based on social media activity. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without one. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI conduct relevant conversations.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The generation unit generates a problem-solving scenario. The generation unit can generate a problem-solving scenario based on the information entered by the user, and can generate a scenario using a generation AI. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only 3 days left until the presentation. How would you help the team?", the generation AI will start a dialogue based on this scenario. Step 2: The dialogue team engages in a conversation based on the generated scenario. The AI ​​suggests, "Let's organize the remaining tasks," and the student asks, "How do we organize the tasks?" The AI ​​replies, "We check with each member," and continues, "How do we summarize the confirmed tasks?" Step 3: The feedback unit provides feedback on the conversation. The feedback unit's generating AI provides feedback such as "That's a great start" or "That's a good idea."

[0057] (Example of form 2) An online platform according to an embodiment of the present invention is a system that uses generative AI to help students develop problem-solving skills. This system generates problem-solving scenarios, supports learning through dialogue, and provides feedback. For example, a user inputs a problem-solving scenario into the generative AI. For example, a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?" Next, the generative AI starts a dialogue based on this scenario. The student learns the problem-solving process through dialogue with the generative AI. For example, the generative AI suggests, "Let's organize the remaining work," and the student asks, "How would we organize the work?" The generative AI replies, "I would check with each member," and continues, "How would you summarize the confirmed work?" The student replies, "I would list them and write down how much time each one will take." The generative AI provides feedback on the dialogue to support the student's learning. For example, it provides feedback such as, "That's a great start," or "That's a good idea." This platform will enable students to develop practical problem-solving skills and contribute to reforming Japan's education system. Furthermore, advancements in generative AI technology will facilitate more efficient learning, contributing to the future improvement of Japan's economic strength. Thus, the online platform will allow students to cultivate problem-solving abilities using generative AI.

[0058] The online platform according to the embodiment comprises a generation unit, a dialogue unit, and a feedback unit. The generation unit generates a problem-solving scenario. The generation unit generates a problem-solving scenario based on information entered by the user, for example. The generation unit can generate a scenario using a generation AI. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How will you help the team?", the generation AI will start a dialogue based on this scenario. The dialogue unit conducts a dialogue based on the generated scenario. For example, the generation AI might suggest, "Let's organize the remaining work," and the student might ask, "How will we organize the work?" The generation AI might respond, "I will check with each member," and then continue, "How will you summarize the confirmed work?" The feedback unit provides feedback on the content of the dialogue. For example, the generation AI might give feedback such as, "That's a great start," or "That's a good idea." As a result, the online platform according to this embodiment can generate problem-solving scenarios using generative AI, support learning through dialogue, and provide feedback, thereby enabling the efficient development of problem-solving abilities.

[0059] The generation unit generates problem-solving scenarios. For example, it generates problem-solving scenarios based on information entered by the user. The generation unit can generate scenarios using generative AI. Specifically, the generative AI utilizes natural language processing technology to understand the context of the scenario entered by the user and generates an appropriate problem-solving scenario. For example, if a user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?", the generative AI will begin a dialogue based on this scenario. The generative AI first analyzes the background information of the scenario and identifies the core of the problem. Next, it refers to a database of similar past scenarios and solutions and proposes the best solution. For example, the generative AI might suggest, "Organize the remaining tasks," and then provide more specific steps and methods. The generative AI can dynamically respond to user input and generate new suggestions and advice as the scenario progresses. This allows the generation unit to provide flexible and effective solutions to the problems the user faces, improving the user's problem-solving abilities.

[0060] The dialogue unit conducts conversations based on generated scenarios. For example, the generating AI might suggest, "Let's organize the remaining tasks," and the student might ask, "How do we organize the tasks?" The generating AI might respond, "We'll check with each member," and then continue, "How do we summarize the confirmed tasks?" The dialogue unit plays a role in guiding the problem-solving process through the AI's interaction with the user. Specifically, the generating AI provides appropriate feedback to the user's questions and responses, ensuring the conversation flows smoothly. For example, if the user asks, "Please tell me a specific way to organize the tasks," the generating AI might provide specific advice such as, "First, list all the tasks and prioritize them. Then, assign tasks to each member." The dialogue unit provides real-time support for any questions or challenges the user faces during the problem-solving process, deepening the user's understanding. The dialogue unit can also monitor the user's reactions and progress, and adjust the content of the conversation as needed. This allows the dialogue unit to help users effectively acquire problem-solving skills and improve the quality of learning.

[0061] The feedback unit provides feedback on the content of the conversation. For example, the generating AI might say, "That's a great start," or "That's a good idea." The feedback unit plays a role in increasing the user's motivation to learn by providing appropriate evaluations and advice on the user's actions and responses. Specifically, the generating AI analyzes the content of the user's conversation and provides positive feedback at the appropriate time. For example, if the user reports, "I've listed all the tasks," the generating AI might suggest a specific next step, such as, "That's great. Now let's prioritize them." The feedback unit also makes appropriate suggestions if the user is heading in the wrong direction or if there are areas that need improvement. For example, if the user suggests, "I'll do all the tasks myself," the generating AI might advise, "It's important to work together as a team. Let's divide the roles among the members." In this way, the feedback unit can support the user in learning effectively and improving their problem-solving abilities. Furthermore, the feedback unit can record the user's progress and learning outcomes, which can be used to develop and improve long-term learning plans. In this way, the feedback unit can continuously support the user's growth and maximize the effectiveness of the entire online platform.

[0062] The generation unit can generate problem-solving scenarios based on information entered by the user. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school's presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only three days left until the presentation. How would you help the team?", the generation AI will start a dialogue based on this scenario. In this way, by generating scenarios based on the user's input information, it is possible to address individual learning needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input user-entered information into the generation AI and have the generation AI perform the scenario generation.

[0063] The dialogue unit can engage in conversations based on generated scenarios. For example, the AI ​​might suggest, "Let's organize the remaining tasks," and a student might ask, "How should we organize the tasks?" The AI ​​might respond, "I'll check with each member," and then continue, "How should we summarize the confirmed tasks?" In this way, engaging in conversations based on generated scenarios can cultivate practical problem-solving skills. Some or all of the above-described processes in the dialogue unit may be performed using the AI, or they may not. For example, the dialogue unit can input a generated scenario into the AI ​​and have the AI ​​perform the dialogue.

[0064] The feedback unit can provide feedback on the content of the dialogue. For example, the feedback unit's generating AI might provide feedback such as, "That's a very good start," or "That's a good idea." By providing feedback on the content of the dialogue, the learning effect can be enhanced. Some or all of the above-described processes in the feedback unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the feedback unit can input the dialogue content into the generating AI and have the generating AI generate the feedback.

[0065] The generation unit can estimate the user's emotions and adjust the difficulty of the scenario based on the estimated emotions. For example, if the user is stressed, the generation AI can lower the difficulty of the scenario and provide an easy problem-solving scenario. Conversely, if the user is relaxed, the generation AI can increase the difficulty of the scenario and provide a more complex problem-solving scenario. Furthermore, if the user is excited, the generation AI can set the difficulty of the scenario to a moderate level and provide a moderate challenge. In this way, an appropriate learning experience can be provided by adjusting the difficulty of the scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the difficulty of the scenario.

[0066] The generation unit can analyze the user's past learning history and generate the optimal scenario. For example, the generation unit can adjust the difficulty level of the next scenario based on the success rate of scenarios the user has tackled in the past. The generation unit can also identify areas where the user has struggled in the past and provide scenarios related to those areas. Furthermore, the generation unit can provide scenarios that reinforce areas where the user has excelled in the past. In this way, by generating scenarios based on past learning history, individual learning needs can be addressed. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's past learning history into the generation AI and have the generation AI generate the optimal scenario.

[0067] The generation unit can customize the content of a scenario based on the user's current learning objectives when generating a scenario. For example, if the user has the learning objective of "strengthening teamwork," the generation AI will provide a scenario related to teamwork. Similarly, if the user has the learning objective of "developing leadership skills," the generation AI can provide a scenario related to leadership. Furthermore, if the user has the learning objective of "improving time management," the generation AI can provide a scenario related to time management. This allows for effective learning by customizing the scenario based on the user's current learning objectives. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input the user's current learning objectives into the generation AI and have the generation AI customize the scenario.

[0068] The generation unit can estimate the user's emotions and select a scenario theme based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a scenario with a relaxing theme using the generation AI. If the user is relaxed, the generation unit can also provide a scenario with a challenging theme using the generation AI. Furthermore, if the user is excited, the generation unit can provide a scenario with an interesting theme using the generation AI. This allows for the provision of an appropriate learning experience by selecting a scenario theme according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI select the scenario theme.

[0069] The generation unit can generate highly relevant scenarios by considering the user's geographical location information during scenario generation. For example, if the user is in an urban area, the generation AI can provide scenarios relevant to urban areas. Similarly, if the user is in a rural area, the generation AI can provide scenarios relevant to rural areas. Furthermore, if the user is in a specific region, the generation AI can provide scenarios related to the culture and history of that region. This allows for the provision of a highly relevant learning experience by generating scenarios based on geographical location information. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate highly relevant scenarios.

[0070] The generation unit can analyze a user's social media activity and generate relevant scenarios during scenario generation. For example, the generation unit can have the generating AI provide scenarios based on themes the user frequently discusses on social media. The generation unit can also have the generating AI provide scenarios based on the interests of accounts the user follows on social media. Furthermore, the generation unit can have the generating AI provide scenarios based on the activities of groups the user participates in on social media. This allows for the provision of a highly relevant learning experience by generating scenarios based on social media activity. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the generation unit can input the user's social media activity into the generating AI and have the generating AI generate relevant scenarios.

[0071] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue unit's generating AI can conduct the dialogue in a calm tone. If the user is relaxed, the dialogue unit's generating AI can conduct the dialogue in a bright tone. Furthermore, if the user is excited, the dialogue unit's generating AI can conduct the dialogue in an energetic tone. This allows for an appropriate dialogue experience by adjusting the tone of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using or without a generating AI. For example, the dialogue unit can input user emotion data into a generating AI and have the generating AI adjust the tone of the dialogue.

[0072] The dialogue unit can conduct the most appropriate dialogue by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can use the generative AI to provide relevant information based on questions the user has asked in the past. The dialogue unit can also use the generative AI to adjust the content of the dialogue based on the user's past interests. Furthermore, the dialogue unit can avoid topics that the user has previously found difficult, allowing the generative AI to guide the conversation. This allows for the response to individual learning needs by conducting conversations based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's past dialogue history into the generative AI and have the generative AI perform the most appropriate dialogue.

[0073] The dialogue unit can customize the content of the dialogue based on the user's current learning progress. For example, the dialogue unit can have the generative AI suggest the next step based on the user's current learning progress. Furthermore, if the user is lagging behind in a particular topic, the dialogue unit can have the generative AI conduct a dialogue related to that topic. Additionally, if the user is progressing quickly in a particular topic, the dialogue unit can have the generative AI provide a dialogue of the next difficulty level. This allows for effective learning by customizing the dialogue based on the current learning progress. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's current learning progress into the generative AI and have the generative AI customize the dialogue.

[0074] The dialogue unit can estimate the user's emotions and adjust the pace of the conversation based on the estimated emotions. For example, if the user is nervous, the dialogue unit's generative AI can conduct the conversation at a slow pace. If the user is relaxed, the dialogue unit can also have the generative AI conduct the conversation at a normal pace. Furthermore, if the user is excited, the dialogue unit can have the generative AI conduct the conversation at a fast pace. In this way, by adjusting the pace of the conversation according to the user's emotions, an appropriate conversational experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using the generative AI or not. For example, the dialogue unit can input user emotion data into the generative AI and have the generative AI adjust the pace of the conversation.

[0075] The dialogue unit can conduct highly relevant conversations by considering the user's geographical location information during the interaction. For example, if the user is in an urban area, the generative AI will conduct conversations related to urban areas. Similarly, if the user is in a rural area, the generative AI can conduct conversations related to rural areas. Furthermore, if the user is in a specific region, the generative AI can conduct conversations related to the culture and history of that region. This allows for a highly relevant learning experience by conducting conversations based on geographical location information. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the user's geographical location information into the generative AI and have the generative AI conduct highly relevant conversations.

[0076] The dialogue unit can analyze the user's social media activity during a conversation and conduct relevant dialogues. For example, the dialogue unit can use a generative AI to conduct a conversation based on the themes the user frequently discusses on social media. The dialogue unit can also use a generative AI to conduct a conversation based on the interests of the accounts the user follows on social media. Furthermore, the dialogue unit can use a generative AI to conduct a conversation based on the activities of the groups the user participates in on social media. This allows for a more relevant learning experience by conducting conversations based on social media activity. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI conduct relevant dialogues.

[0077] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is tense, the feedback unit can use a generative AI to provide gentle feedback. If the user is relaxed, the feedback unit can also use a generative AI to provide detailed feedback. Furthermore, if the user is excited, the feedback unit can use a generative AI to provide energetic feedback. In this way, appropriate feedback can be provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the feedback unit may be performed using a generative AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0078] The feedback unit can provide optimal feedback by referring to the user's past learning achievements. For example, the feedback unit can use the generative AI to provide positive feedback based on scenarios in which the user has succeeded in the past. The feedback unit can also use the generative AI to point out areas for improvement based on scenarios in which the user has struggled in the past. Furthermore, the feedback unit can use the generative AI to provide feedback that reinforces areas in which the user has excelled in the past. This allows for individualized learning needs to be addressed by providing feedback based on past learning achievements. Some or all of the above processing in the feedback unit may be performed using the generative AI or not. For example, the feedback unit can input the user's past learning achievements into the generative AI and have the generative AI provide optimal feedback.

[0079] The feedback unit can customize the content of feedback based on the user's current learning goals. For example, if the user's learning goal is to "strengthen teamwork," the generating AI will provide feedback related to teamwork. Similarly, if the user's learning goal is to "develop leadership skills," the generating AI can provide feedback related to leadership. Furthermore, if the user's learning goal is to "improve time management," the generating AI can provide feedback related to time management. This allows for effective learning by customizing feedback based on current learning goals. Some or all of the above-described processes in the feedback unit may be performed using the generating AI, or they may not. For example, the feedback unit can input the user's current learning goals into the generating AI and have the generating AI customize the feedback.

[0080] The feedback unit can estimate the user's emotions and adjust the timing of the feedback based on the estimated emotions. For example, if the user is tense, the feedback unit can have the generative AI provide feedback slowly. If the user is relaxed, the feedback unit can have the generative AI provide feedback at a normal timing. Furthermore, if the user is excited, the feedback unit can have the generative AI provide feedback quickly. This allows for the provision of appropriate feedback by adjusting the timing of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using the generative AI or not. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the feedback.

[0081] The feedback unit can provide highly relevant feedback by considering the user's geographical location information. For example, if the user is in an urban area, the generating AI can provide feedback relevant to urban areas. Similarly, if the user is in a rural area, the generating AI can provide feedback relevant to rural areas. Furthermore, if the user is in a specific region, the generating AI can provide feedback related to the culture and history of that region. This allows for a highly relevant learning experience by providing feedback based on geographical location information. Some or all of the above processing in the feedback unit may be performed using the generating AI, or without it. For example, the feedback unit can input the user's geographical location information into the generating AI and have the generating AI provide highly relevant feedback.

[0082] The feedback unit can analyze the user's social media activity and provide relevant feedback during the feedback process. For example, the feedback unit can use a generative AI to provide feedback based on themes the user frequently discusses on social media. The feedback unit can also use a generative AI to provide feedback based on the interests of the accounts the user follows on social media. Furthermore, the feedback unit can use a generative AI to provide feedback based on the activities of groups the user participates in on social media. This allows for a more relevant learning experience by providing feedback based on social media activity. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without one. For example, the feedback unit can input the user's social media activity into a generative AI and have the generative AI provide relevant feedback.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The generation unit can analyze the user's learning style and generate the optimal scenario. For example, if the user is a visual learner, the generation AI can provide a scenario with many visual elements. If the user is an auditory learner, the generation AI can also provide a scenario that includes audio and music. Furthermore, if the user is an experiential learner, the generation AI can provide a scenario that simulates a real-world experience. This allows for the provision of an effective learning experience by generating scenarios according to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input user learning style data into the generation AI and have the generation AI generate the optimal scenario.

[0085] The generation unit can estimate the user's emotions and adjust the scenario's feedback based on the estimated user emotions. For example, if the user is depressed, the generation unit's generating AI can provide feedback that includes words of encouragement. The generation unit can also provide challenging feedback if the user is confident. Furthermore, if the user is feeling anxious, the generation unit's generating AI can provide reassuring feedback. This allows for appropriate learning support by adjusting feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the feedback.

[0086] The dialogue unit can monitor the user's learning progress in real time and dynamically adjust the content of the dialogue. For example, if the user is stuck on a particular task, the generative AI can provide additional hints related to that task. Also, if the user is progressing well, the generative AI can suggest the next step. Furthermore, if the user shows interest in a particular topic, the generative AI can provide in-depth information related to that topic. This allows for effective learning support by providing dialogue that is tailored to the user's learning progress in real time. Some or all of the above processes in the dialogue unit may be performed using the generative AI or not. For example, the dialogue unit can input the user's learning progress data into the generative AI and have the generative AI adjust the content of the dialogue.

[0087] The feedback unit can estimate the user's emotions and adjust the format of the feedback based on the estimated emotions. For example, if the user is a visual learner, the feedback unit can use a generative AI to provide feedback using graphs and diagrams. If the user is an auditory learner, the feedback unit can also use a generative AI to provide audio feedback. Furthermore, if the user is an experiential learner, the feedback unit can use a generative AI to provide interactive feedback. This allows for effective learning support by adjusting the format of feedback according to the user's learning style. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using a generative AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the format of the feedback.

[0088] The generation unit can propose individualized learning plans based on the user's learning history. For example, the generation unit can analyze the success rate of scenarios the user has previously attempted, and the generating AI can propose the next learning steps. The generation unit can also identify areas where the user has struggled in the past, and the generating AI can provide scenarios related to those areas. Furthermore, based on areas where the user has excelled in the past, the generating AI can provide scenarios to strengthen those areas. This enables effective learning by proposing individualized learning plans based on past learning history. Some or all of the above processing in the generation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the generation unit can input the user's learning history data into the generating AI and have the generating AI propose individualized learning plans.

[0089] The dialogue unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit can use a generative AI to provide a relaxing dialogue. If the user is relaxed, the dialogue unit can use a generative AI to provide a challenging dialogue. Furthermore, if the user is excited, the dialogue unit can use a generative AI to provide an engaging dialogue. In this way, by adjusting the content of the dialogue according to the user's emotions, an appropriate dialogue experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using a generative AI or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI adjust the content of the dialogue.

[0090] The generation unit can generate a long-term learning plan based on the user's learning objectives. For example, if the user has the learning objective of "wanting to demonstrate leadership," the generation AI will provide a series of scenarios related to leadership. Similarly, if the user has the learning objective of "wanting to improve teamwork," the generation AI can provide a series of scenarios related to teamwork. Furthermore, if the user has the learning objective of "wanting to improve time management," the generation AI can provide a series of scenarios related to time management. This enables effective learning by generating a long-term learning plan based on the user's learning objectives. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input the user's learning objective data into the generation AI and have the generation AI generate a long-term learning plan.

[0091] The feedback unit can estimate the user's emotions and adjust the timing of the feedback based on the estimated emotions. For example, if the user is tense, the feedback unit can have the generative AI provide feedback slowly. If the user is relaxed, the feedback unit can have the generative AI provide feedback at a normal timing. Furthermore, if the user is excited, the feedback unit can have the generative AI provide feedback quickly. This allows for the provision of appropriate feedback by adjusting the timing of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using the generative AI or not. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the feedback.

[0092] The generation unit can generate region-specific problem-solving scenarios, taking into account the user's geographical location information. For example, if the user is in an urban area, the generation AI can provide problem-solving scenarios relevant to urban areas. Similarly, if the user is in a rural area, the generation AI can provide problem-solving scenarios relevant to rural areas. Furthermore, if the user is in a specific region, the generation AI can provide problem-solving scenarios related to the culture and history of that region. This allows for a highly relevant learning experience by generating scenarios based on geographical location information. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without it. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate region-specific scenarios.

[0093] The dialogue unit can analyze the user's social media activity and conduct relevant conversations. For example, the dialogue unit can use a generative AI to conduct conversations based on themes the user frequently discusses on social media. The dialogue unit can also use a generative AI to conduct conversations based on the interests of the accounts the user follows on social media. Furthermore, the dialogue unit can use a generative AI to conduct conversations based on the activities of groups the user participates in on social media. This allows for a more relevant learning experience by conducting conversations based on social media activity. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without one. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI conduct relevant conversations.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The generation unit generates a problem-solving scenario. The generation unit can generate a problem-solving scenario based on the information entered by the user, and can generate a scenario using a generation AI. For example, if the user enters a scenario such as, "Your class is scheduled to present a project at the school presentation. One of the team members is absent due to illness, and preparations for the presentation are behind schedule. There are only 3 days left until the presentation. How would you help the team?", the generation AI will start a dialogue based on this scenario. Step 2: The dialogue team engages in a conversation based on the generated scenario. The AI ​​suggests, "Let's organize the remaining tasks," and the student asks, "How do we organize the tasks?" The AI ​​replies, "We check with each member," and continues, "How do we summarize the confirmed tasks?" Step 3: The feedback unit provides feedback on the conversation. The feedback unit's generating AI provides feedback such as "That's a great start" or "That's a good idea."

[0096] 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.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] 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.

[0099] Each of the multiple elements described above, including the generation unit, dialogue unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a problem-solving scenario based on information input by the user. The dialogue unit is implemented by the control unit 46A of the smart device 14 and engages in dialogue based on the generated scenario. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback on the content of the dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] 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.

[0115] Each of the multiple elements described above, including the generation unit, dialogue unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a problem-solving scenario based on information input by the user. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and engages in dialogue based on the generated scenario. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback on the dialogue content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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.

[0131] Each of the multiple elements, including the generation unit, dialogue unit, and feedback unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a problem-solving scenario based on information input by the user. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and engages in dialogue based on the generated scenario. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback on the content of the dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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.

[0148] Each of the multiple elements described above, including the generation unit, dialogue unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a problem-solving scenario based on information input by the user. The dialogue unit is implemented by the control unit 46A of the robot 414 and engages in dialogue based on the generated scenario. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback on the content of the dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A generation unit that generates problem-solving scenarios, A dialogue unit that engages in dialogue based on the scenario generated by the generation unit, The system includes a feedback unit that provides feedback on the content of the dialogue conducted by the dialogue unit. A system characterized by the following features. (Note 2) The generating unit is Generate problem-solving scenarios based on information entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, Engage in dialogue based on the generated scenario. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide feedback on the content of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates the user's emotions and adjusts the difficulty of the scenario based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Analyze the user's past learning history and generate the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating a scenario, customize the scenario content based on the user's current learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates the user's emotions and selects a scenario theme based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating scenarios, the system considers the user's geographical location to generate highly relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During scenario generation, the system analyzes the user's social media activity and generates relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue unit, During conversations, the system refers to the user's past conversation history to perform the most appropriate dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the pace of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, During conversations, the system takes the user's geographical location into consideration to conduct more relevant discussions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and conducts relevant dialogues. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is When providing feedback, the system refers to the user's past learning achievements to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide more relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 generation unit that generates problem-solving scenarios, A dialogue unit that engages in dialogue based on the scenario generated by the generation unit, The system includes a feedback unit that provides feedback on the content of the dialogue conducted by the dialogue unit. A system characterized by the following features.

2. The generating unit is Generate problem-solving scenarios based on information entered by the user. The system according to feature 1.

3. The aforementioned dialogue unit, Engage in dialogue based on the generated scenario. The system according to feature 1.

4. The aforementioned feedback unit is Provide feedback on the content of the conversation. The system according to feature 1.

5. The generating unit is It estimates the user's emotions and adjusts the difficulty of the scenario based on those emotions. The system according to feature 1.

6. The generating unit is Analyze the user's past learning history and generate the optimal scenario. The system according to feature 1.

7. The generating unit is When generating a scenario, customize the scenario content based on the user's current learning objectives. The system according to feature 1.

8. The generating unit is The system estimates the user's emotions and selects a scenario theme based on those estimated emotions. The system according to feature 1.

9. The generating unit is When generating scenarios, the system considers the user's geographical location to generate highly relevant scenarios. The system according to feature 1.

10. The generating unit is During scenario generation, the system analyzes the user's social media activity and generates relevant scenarios. The system according to feature 1.

Images