System

Abstract

An object of the system according to the embodiment is to effectively follow children who take a long time off school.SOLUTION: A system according to an embodiment includes a robot, a generation AI, and a report creation unit. The robot is installed in a school. The production AI analyses the datum recorded by the robot. The report creation unit creates a report based on the AI analyzed by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide an effective support system for children who are absent from school for long periods of time, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively follow up on children who are absent from school for a long period of time. [Means for solving the problem]

[0006] The system according to the embodiment includes a robot, a generation AI, and a report creation unit. The robot is installed in a school. The generation AI analyzes data recorded by the robot. The report creation unit creates a report based on the data analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can effectively follow up on children who are absent from school for a long period of time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the support system according to an embodiment of the present invention, a robot installed in a school records what happens during lessons and recess, and a generation AI analyzes the data and creates a report. This allows the support system to support children who are absent from school for long periods of time and reduce the burden on teachers.

[0029] A support system according to an embodiment includes a robot, a generation AI, a recording unit, and a report creation unit. The robot is installed in a school and records what happens in classes and during recess. For example, the robot may be installed in a classroom and record what the teacher explains and what students say during class. The robot may also be installed in a hallway or schoolyard and record play and conversations during recess. The generation AI analyzes the data recorded by the robot. For example, the generation AI may convert audio data from classes into text and generate a report including key points and summaries. The generation AI may also analyze audio and video data from recess and generate a report including important events and conversations. The recording unit saves the data recorded by the robot. For example, the recording unit saves audio and video data from classes and converts them into a format that is easy for the generation AI to analyze. The report creation unit creates a report based on the data analyzed by the generation AI. For example, the report creation unit creates a report summarizing the content of classes and events during recess based on the text data generated by the generation AI. This allows the support system according to an embodiment to support children who are absent from school for long periods of time and reduce the burden on teachers.

[0030] The robot is equipped with an automatic movement function, allowing it to move freely inside and outside the classroom and record from various perspectives. For example, the robot is equipped with an automatic movement function and can move freely inside the classroom to record the lesson from multiple angles. For example, it can simultaneously record the teacher's explanation from the front and the students' reactions from the back. The automatic movement function can also be used to record areas outside the classroom, such as the hallway or schoolyard, during recess. For example, it can record students' play and interactions during recess from various perspectives and reflect this in reports. The robot can also move automatically and focus on specific students or groups to record. For example, it can track students who talk a lot or groups engaged in specific activities and record in detail. This allows the robot to move freely inside and outside the classroom and record, providing more multifaceted information.

[0031] The robot is equipped with a function that combines voice recognition and facial recognition, allowing it to focus on recording the remarks and actions of specific students. For example, the robot is equipped with a function that combines voice recognition and facial recognition to automatically record the remarks of specific students. For example, it can focus on recording the voices of students who talk a lot or ask questions, and reflect this in reports. It can also use facial recognition to track the actions of specific students and record their reactions during class and activities during breaks in detail. For example, it can record the activities of specific students in real time. It can also combine voice recognition and facial recognition to analyze the remarks and facial expressions of specific students and create reports that highlight important points. For example, it can simultaneously record the content of remarks and their facial expressions at the time, and reflect this in reports. This allows it to provide more detailed information by focusing on recording the remarks and actions of specific students.

[0032] Multiple robots can be installed to simultaneously record in different classrooms or locations and create an integrated report. For example, robots can be installed in multiple classrooms within a school and simultaneously record the content of lessons. For example, the content of lessons in each classroom can be recorded in real time, and the generation AI can create an integrated report. Robots can also be installed in locations outside the classroom to record recess from multiple angles. For example, activities in different locations such as the schoolyard or library can be simultaneously recorded and reflected in the integrated report. Multiple robots can also work together to record from different perspectives, and the generation AI can integrate the data to create a detailed report. For example, the same lesson can be recorded from different angles and compiled into an integrated report. This allows for simultaneous recording in multiple classrooms or locations and the creation of an integrated report, providing more comprehensive information.

[0033] The robot is equipped with a projector function and can record the lesson content while projecting it in real time. For example, a robot can be equipped with a projector function and record the lesson content while projecting it in real time. For example, the teacher's explanation can be projected using a projector to make it easier for students to understand visually. The projector function can also be used to record important points and slides from the lesson while projecting them. For example, in a math class, how to solve an equation can be projected to make it easier for students to understand. The lesson content can also be projected in real time and simultaneously recorded and reflected in a report. For example, the content projected by the projector can be recorded with a camera and a generation AI can create a report. In this way, the lesson content can be recorded while being projected in real time, making it easier to understand visually.

[0034] Generative AI can enhance natural language processing technology to analyze lesson content in more detail and add sections that provide simple explanations of technical terms and difficult parts. Generative AI, for example, enhances natural language processing technology to analyze lesson content in detail. For example, it can automatically extract technical terms and difficult parts and add sections that provide simple explanations. It can also analyze lesson content and generate sections that provide simple explanations of technical terms and difficult parts. For example, it can provide simple explanations of technical terms used in mathematics classes. It can also use natural language processing technology to analyze lesson content in detail and add sections that provide simple explanations of technical terms and difficult parts. For example, it can provide simple explanations of technical terms used in science classes. This allows it to analyze lesson content in more detail and provide simple explanations of technical terms and difficult parts, making it possible to provide reports that are easy to understand.

[0035] The generative AI has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time and reflect this in reports. The generative AI, for example, has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record what a teacher and students say during class and reflect this in reports. The generative AI has also improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can accurately record the contents of a group discussion and reflect this in reports. The generative AI has also improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record what a teacher and students say during class and reflect this in reports. This allows it to accurately record even when multiple speakers speak at the same time and reflect this in reports, providing more detailed information.

[0036] Generative AI can automatically generate multiple reports that match lesson content to different grades and levels. For example, generative AI can automatically generate multiple reports that match lesson content to different grades and levels. For example, it can create reports for elementary school students and junior high school students simultaneously. It can also analyze lesson content and automatically generate multiple reports that match different grades and levels. For example, it can create reports for beginners and advanced students simultaneously. Generative AI can also automatically generate multiple reports that match lesson content to different grades and levels. For example, it can create reports for elementary school students and junior high school students simultaneously. This allows it to meet a wider variety of needs by automatically generating multiple reports that match lesson content to different grades and levels.

[0037] The generative AI can add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, the generative AI can add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. The generative AI can also use the visual analysis function to convert the contents of slides used during class into text and include it in the report. For example, graphs and diagrams displayed on slides can be automatically converted into text. The generative AI can also add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. This allows the contents of the whiteboard and slides used during class to be converted into text and included in the report, providing more detailed information.

[0038] The generating AI can add a video analysis function to record play and activities during recess in detail, analyzing their movements and facial expressions and reflecting them in reports. The generating AI, for example, can add a video analysis function to record play and activities during recess in detail. For example, it can analyze students' movements and facial expressions and reflect important events in reports. The generating AI can also use the video analysis function to record play and activities during recess in detail. For example, it can analyze the content of the play and students' facial expressions and reflect these in reports. The generating AI can also add a video analysis function to record play and activities during recess in detail. For example, it can analyze students' movements and facial expressions and reflect these in reports. This allows for more detailed information to be provided by recording play and activities during recess in detail and analyzing their movements and facial expressions and reflecting these in reports.

[0039] The generative AI is equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. The generative AI is equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. For example, it can record in detail what activities a specific student is engaged in. The generative AI is also equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. For example, it can record in detail what activities a specific student is engaged in. By focusing on recording the behavior and conversations of specific students, more detailed information can be provided.

[0040] The generation AI can record events during recess from different perspectives and automatically generate multiple reports. For example, the generation AI records events during recess from different perspectives and automatically generates multiple reports. For example, it analyzes footage from different camera angles and creates multiple reports. Events during recess are also recorded from different perspectives and the generation AI automatically generates multiple reports. For example, it records events from the perspectives of different students and creates multiple reports. The generation AI can also be used to record events during recess from different perspectives and automatically generate multiple reports. For example, it can analyze footage from different camera angles and create multiple reports. This allows events during recess to be recorded from different perspectives and automatically generate multiple reports, making it possible to provide more diverse information.

[0041] The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function. The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function, for example. The generated report can be read aloud. The voice read-out function can also be used to accommodate the visually impaired and children who have difficulty reading and writing. For example, the generated report can be read aloud. The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function, for example. The generated report can be read aloud. This can accommodate the visually impaired and children who have difficulty reading and writing, making it possible to meet a wider variety of needs.

[0042] The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. The interactive function can also be used to allow recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. This allows recipients to add questions and comments in real time, making it possible to provide information in a more interactive manner.

[0043] Reports can be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, reports can be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, a report in PDF format can be distributed by email. Reports can also be distributed in different formats and provided in a format that meets the needs of the recipient. For example, a report in HTML format can be viewed in a dedicated app. Reports can also be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, a report in audio file format can be distributed. This allows reports to be distributed in different formats and provided in a format that meets the needs of the recipient, making it possible to meet a wider variety of needs.

[0044] Generative AI can automatically generate lesson plans and teaching materials to support teachers' lesson preparations. Generative AI, for example, automatically generates lesson plans and teaching materials to support teachers' lesson preparations. For example, teaching materials are automatically generated based on the theme of the lesson. Generative AI can also be used to automatically generate lesson plans and teaching materials. For example, teaching materials are automatically generated based on the theme of the lesson. Generative AI can also be used to automatically generate lesson plans and teaching materials to support teachers' lesson preparations. For example, teaching materials are automatically generated based on the theme of the lesson. In this way, by automatically generating lesson plans and teaching materials, it is possible to support teachers' lesson preparations and reduce their burden.

[0045] Robots can be equipped with functions to automatically check attendance during class and answer simple questions, thereby making teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. A simple question-and-answer function can be added to a robot to make teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. A simple question-and-answer function can be added to a robot to make teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. This can make teachers' work more efficient and reduce their burden by automatically checking attendance during class and answering simple questions.

[0046] The robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In addition, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In addition, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In this way, the quality of lessons can be improved by automatically collecting feedback after a lesson, which the generation AI analyzes and suggests areas for improvement.

[0047] The robot monitors students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. The robot, for example, monitors students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. The robot can also monitor students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. The robot can also monitor students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. In this way, the quality of lessons can be improved by monitoring students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations.

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

[0049] The robot is equipped with a combination of voice and facial recognition functions, allowing it to focus on recording the remarks and actions of specific students. For example, it can focus on recording the voices of students who talk a lot or ask questions, and reflect this in reports. It can also use facial recognition to track the actions of specific students, recording their reactions during class and their activities during breaks in detail. For example, it can record the activities of specific students in real time. It can also combine voice and facial recognition to analyze the remarks and facial expressions of specific students and create reports that highlight important points. For example, it can simultaneously record the content of what was said and the facial expression at the time, and reflect this in reports. This allows it to provide more detailed information by focusing on the remarks and actions of specific students.

[0050] The robot is equipped with an automatic movement function, allowing it to move freely inside and outside the classroom and record from various perspectives. For example, it can move freely inside the classroom and record the lesson from multiple angles. For example, it can simultaneously record the teacher's explanation from the front and the students' reactions from the back. The automatic movement function can also be used to record areas outside the classroom, such as the hallway or schoolyard, during breaks. For example, it can record students' play and interactions during breaks from various perspectives and reflect this in reports. The robot can also move automatically and focus on specific students or groups to record. For example, it can track students who talk a lot or groups engaged in specific activities and record in detail. This allows it to move freely inside and outside the classroom while recording, providing more multifaceted information.

[0051] The robot is equipped with a projector function, allowing it to record lesson content while projecting it in real time. For example, the robot could be equipped with a projector function and record lesson content while projecting it in real time. For example, the teacher's explanation could be projected using a projector to make it easier for students to understand visually. The projector function could also be used to record important points and slides from the lesson while projecting them. For example, in a math class, it could project how to solve equations to make it easier for students to understand. The robot could also project lesson content in real time and simultaneously record the content to be reflected in a report. For example, the content projected by the projector could be recorded with a camera, and a generation AI could create a report. This allows lesson content to be recorded while projecting it in real time, making it easier to understand visually.

[0052] Generative AI can enhance natural language processing technology to analyze lesson content in more detail and add sections that simply explain technical terms and difficult parts. For example, natural language processing technology can be enhanced to analyze lesson content in detail. For example, technical terms and difficult parts can be automatically extracted and sections that simply explain them can be added. The lesson content can also be analyzed to generate sections that simply explain technical terms and difficult parts. For example, technical terms used in mathematics classes can be simply explained. The lesson content can also be analyzed in detail using natural language processing technology to add sections that simply explain technical terms and difficult parts. For example, technical terms used in science classes can be simply explained. This allows for more detailed analysis of lesson content and simple explanations of technical terms and difficult parts, making it possible to provide reports that are easy to understand.

[0053] The generative AI has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time and reflect this in reports. For example, by improving speech recognition accuracy, it can accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record the remarks of a teacher and students during class and reflect this in reports. In addition, by improving speech recognition accuracy, it can accurately record even when multiple speakers speak at the same time. For example, it can accurately record the contents of a group discussion and reflect this in reports. In addition, by improving speech recognition accuracy in the generative AI, it can accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record the remarks of a teacher and students during class and reflect this in reports. This allows it to accurately record even when multiple speakers speak at the same time and reflect this in reports, providing more detailed information.

[0054] The generative AI can be added with a visual analysis function, which allows the contents of the whiteboard and slides used during class to be converted into text and included in the report. For example, by adding a visual analysis function, the contents of the whiteboard and slides used during class can be converted into text and included in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. The visual analysis function can also be used to convert the contents of slides used during class into text and included in the report. For example, graphs and diagrams displayed on slides can be automatically converted into text. The generative AI can also be added with a visual analysis function, which allows the contents of the whiteboard and slides used during class to be converted into text and included in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. This allows the contents of the whiteboard and slides used during class to be converted into text and included in the report, providing more detailed information.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The robot is installed in the school and records what happens during lessons and recess. For example, the robot can be installed in a classroom to record the teacher's explanations and students' comments during class. The robot can also be installed in the hallway or schoolyard to record play and conversations during recess. Step 2: The generative AI analyzes the data recorded by the robot. For example, the generative AI can convert audio data from a lesson into text and generate a report including key points and summaries. The generative AI can also analyze audio and video data from recess and generate a report including important events and conversations. Step 3: The report creation unit creates a report based on the data analyzed by the generation AI. For example, the report creation unit creates a report summarizing the contents of classes and events that occurred during recess based on the text data generated by the generation AI.

[0057] (Example 2) In the support system according to an embodiment of the present invention, a robot installed in a school records what happens during lessons and recess, and a generation AI analyzes the data and creates a report. This allows the support system to support children who are absent from school for long periods of time and reduce the burden on teachers.

[0058] A support system according to an embodiment includes a robot, a generation AI, a recording unit, and a report creation unit. The robot is installed in a school and records what happens in classes and during recess. For example, the robot may be installed in a classroom and record what the teacher explains and what students say during class. The robot may also be installed in a hallway or schoolyard and record play and conversations during recess. The generation AI analyzes the data recorded by the robot. For example, the generation AI may convert audio data from classes into text and generate a report including key points and summaries. The generation AI may also analyze audio and video data from recess and generate a report including important events and conversations. The recording unit saves the data recorded by the robot. For example, the recording unit saves audio and video data from classes and converts them into a format that is easy for the generation AI to analyze. The report creation unit creates a report based on the data analyzed by the generation AI. For example, the report creation unit creates a report summarizing the content of classes and events during recess based on the text data generated by the generation AI. This allows the support system according to an embodiment to support children who are absent from school for long periods of time and reduce the burden on teachers.

[0059] The robot is equipped with an emotion estimation function that can record students' emotions during class and breaks in real time and reflect them in reports. For example, the robot estimates emotions by analyzing students' facial expressions and tone of voice during class. For example, it uses a camera and microphone to record students' joy, excitement, and concentration in real time and reflect them in reports. It also uses the emotion estimation function to analyze students' emotions during breaks and create reports that highlight particularly enjoyable events and interactions with friends. For example, it detects smiles and laughter and records those moments in detail. It also monitors students' emotional changes during class in real time, and the generation AI uses that data to reflect the intensity and type of emotions in reports. For example, it records students' interest and level of understanding as the class progresses as emotional data. This allows students' emotions to be recorded in real time and reflected in reports, providing more detailed information.

[0060] The robot is equipped with an automatic movement function, allowing it to move freely inside and outside the classroom and record from various perspectives. For example, the robot is equipped with an automatic movement function and can move freely inside the classroom to record the lesson from multiple angles. For example, it can simultaneously record the teacher's explanation from the front and the students' reactions from the back. The automatic movement function can also be used to record areas outside the classroom, such as the hallway or schoolyard, during recess. For example, it can record students' play and interactions during recess from various perspectives and reflect this in reports. The robot can also move automatically and focus on specific students or groups to record. For example, it can track students who talk a lot or groups engaged in specific activities and record in detail. This allows the robot to move freely inside and outside the classroom and record, providing more multifaceted information.

[0061] The robot is equipped with a function that combines voice recognition and facial recognition, allowing it to focus on recording the remarks and actions of specific students. For example, the robot is equipped with a function that combines voice recognition and facial recognition to automatically record the remarks of specific students. For example, it can focus on recording the voices of students who talk a lot or ask questions, and reflect this in reports. It can also use facial recognition to track the actions of specific students and record their reactions during class and activities during breaks in detail. For example, it can record the activities of specific students in real time. It can also combine voice recognition and facial recognition to analyze the remarks and facial expressions of specific students and create reports that highlight important points. For example, it can simultaneously record the content of remarks and their facial expressions at the time, and reflect this in reports. This allows it to provide more detailed information by focusing on recording the remarks and actions of specific students.

[0062] Multiple robots can be installed to simultaneously record in different classrooms or locations and create an integrated report. For example, robots can be installed in multiple classrooms within a school and simultaneously record the content of lessons. For example, the content of lessons in each classroom can be recorded in real time, and the generation AI can create an integrated report. Robots can also be installed in locations outside the classroom to record recess from multiple angles. For example, activities in different locations such as the schoolyard or library can be simultaneously recorded and reflected in the integrated report. Multiple robots can also work together to record from different perspectives, and the generation AI can integrate the data to create a detailed report. For example, the same lesson can be recorded from different angles and compiled into an integrated report. This allows for simultaneous recording in multiple classrooms or locations and the creation of an integrated report, providing more comprehensive information.

[0063] The robot is equipped with a projector function and can record the lesson content while projecting it in real time. For example, a robot can be equipped with a projector function and record the lesson content while projecting it in real time. For example, the teacher's explanation can be projected using a projector to make it easier for students to understand visually. The projector function can also be used to record important points and slides from the lesson while projecting them. For example, in a math class, how to solve an equation can be projected to make it easier for students to understand. The lesson content can also be projected in real time and simultaneously recorded and reflected in a report. For example, the content projected by the projector can be recorded with a camera and a generation AI can create a report. In this way, the lesson content can be recorded while being projected in real time, making it easier to understand visually.

[0064] The robot is equipped with an emotion estimation function, and can provide real-time feedback on the emotions of absent children participating in classes remotely. For example, the robot is equipped with an emotion estimation function and can analyze the emotions of children participating in classes remotely in real time. For example, it can use a camera and microphone to analyze the facial expressions and tone of voice of remote participants to estimate their emotions. It can also provide real-time feedback on the emotions of children participating in classes remotely, which can be used to help progress the class. For example, it can use emotion data to check whether remote participants are understanding. It can also use the emotion estimation function to monitor changes in the emotions of remote participants in real time and adjust the content and progress of the class. For example, it can use emotion data to check whether remote participants are concentrating. This allows real-time feedback on the emotions of children participating in classes remotely, which can be used to help progress the class.

[0065] Generative AI can incorporate an emotion estimation function to create reports that emphasize important points based on students' reactions during class. For example, generative AI can incorporate an emotion estimation function and analyze students' facial expressions and tone of voice during class to estimate their emotions. For example, it can create a report that emphasizes parts of class that students found interesting based on emotional data. It can also analyze students' emotional reactions during class and generate a report that emphasizes parts that received particularly positive reactions. For example, it can record in detail the content of classes where students smiled or were excited. It can also use the emotion estimation function to monitor students' emotional changes during class in real time, and the generative AI can create a report that emphasizes important points based on that data. For example, it can reflect the intensity and type of emotion in the report. This makes it possible to provide more effective reports by emphasizing important points based on students' reactions during class.

[0066] Generative AI can enhance natural language processing technology to analyze lesson content in more detail and add sections that provide simple explanations of technical terms and difficult parts. Generative AI, for example, enhances natural language processing technology to analyze lesson content in detail. For example, it can automatically extract technical terms and difficult parts and add sections that provide simple explanations. It can also analyze lesson content and generate sections that provide simple explanations of technical terms and difficult parts. For example, it can provide simple explanations of technical terms used in mathematics classes. It can also use natural language processing technology to analyze lesson content in detail and add sections that provide simple explanations of technical terms and difficult parts. For example, it can provide simple explanations of technical terms used in science classes. This allows it to analyze lesson content in more detail and provide simple explanations of technical terms and difficult parts, making it possible to provide reports that are easy to understand.

[0067] The generative AI has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time and reflect this in reports. The generative AI, for example, has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record what a teacher and students say during class and reflect this in reports. The generative AI has also improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can accurately record the contents of a group discussion and reflect this in reports. The generative AI has also improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record what a teacher and students say during class and reflect this in reports. This allows it to accurately record even when multiple speakers speak at the same time and reflect this in reports, providing more detailed information.

[0068] Generative AI can automatically generate multiple reports that match lesson content to different grades and levels. For example, generative AI can automatically generate multiple reports that match lesson content to different grades and levels. For example, it can create reports for elementary school students and junior high school students simultaneously. It can also analyze lesson content and automatically generate multiple reports that match different grades and levels. For example, it can create reports for beginners and advanced students simultaneously. Generative AI can also automatically generate multiple reports that match lesson content to different grades and levels. For example, it can create reports for elementary school students and junior high school students simultaneously. This allows it to meet a wider variety of needs by automatically generating multiple reports that match lesson content to different grades and levels.

[0069] The generative AI can add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, the generative AI can add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. The generative AI can also use the visual analysis function to convert the contents of slides used during class into text and include it in the report. For example, graphs and diagrams displayed on slides can be automatically converted into text. The generative AI can also add a visual analysis function to convert the contents of the whiteboard and slides used during class into text and include it in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. This allows the contents of the whiteboard and slides used during class to be converted into text and included in the report, providing more detailed information.

[0070] The generation AI can use the emotion estimation function to analyze students' emotions during class and create a report that emphasizes parts that received a lot of positive responses. For example, the generation AI can use the emotion estimation function to analyze students' emotions during class and create a report that emphasizes parts that received a lot of positive responses. For example, it can record in detail the content of the class where there were a lot of smiles and excitement. It can also analyze students' emotional data during class and create a report that emphasizes parts that received a lot of positive responses. For example, it can record in detail parts with high emotion scores. It can also use the emotion estimation function to monitor students' emotional changes during class in real time, and the generation AI can use that data to create a report that emphasizes parts that received a lot of positive responses. For example, it can reflect the intensity and type of emotion in the report. This allows it to analyze students' emotions during class and emphasize parts that received a lot of positive responses, thereby providing more effective reports.

[0071] The generation AI can incorporate an emotion estimation function to analyze students' emotions during recess and create a report that highlights particularly enjoyable events. For example, the generation AI can incorporate an emotion estimation function and analyze students' facial expressions and tone of voice during recess to estimate their emotions. For example, it can create a report that highlights the parts of the class that students enjoyed based on the emotion data. It can also analyze students' emotional responses during recess and create a report that highlights events that elicited particularly positive responses. For example, it can record in detail events that elicited a lot of smiles and laughter. It can also use the emotion estimation function to monitor students' emotional changes during recess in real time, and the generation AI can use that data to create a report that highlights particularly enjoyable events. For example, it can reflect the intensity and type of emotion in the report. This allows it to provide more detailed information by analyzing students' emotions during recess and highlighting particularly enjoyable events.

[0072] The generating AI can add a video analysis function to record play and activities during recess in detail, analyzing their movements and facial expressions and reflecting them in reports. The generating AI, for example, can add a video analysis function to record play and activities during recess in detail. For example, it can analyze students' movements and facial expressions and reflect important events in reports. The generating AI can also use the video analysis function to record play and activities during recess in detail. For example, it can analyze the content of the play and students' facial expressions and reflect these in reports. The generating AI can also add a video analysis function to record play and activities during recess in detail. For example, it can analyze students' movements and facial expressions and reflect these in reports. This allows for more detailed information to be provided by recording play and activities during recess in detail and analyzing their movements and facial expressions and reflecting these in reports.

[0073] The generative AI is equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. The generative AI is equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. For example, it can record in detail what activities a specific student is engaged in. The generative AI is also equipped with a function that combines voice recognition and video analysis, and can focus on recording the behavior and conversations of specific students. For example, it can record in detail what activities a specific student is engaged in. By focusing on recording the behavior and conversations of specific students, more detailed information can be provided.

[0074] The generation AI can record events during recess from different perspectives and automatically generate multiple reports. For example, the generation AI records events during recess from different perspectives and automatically generates multiple reports. For example, it analyzes footage from different camera angles and creates multiple reports. Events during recess are also recorded from different perspectives and the generation AI automatically generates multiple reports. For example, it records events from the perspectives of different students and creates multiple reports. The generation AI can also be used to record events during recess from different perspectives and automatically generate multiple reports. For example, it can analyze footage from different camera angles and create multiple reports. This allows events during recess to be recorded from different perspectives and automatically generate multiple reports, making it possible to provide more diverse information.

[0075] The generative AI is equipped with an emotion estimation function, which allows it to analyze events during recess in real time and make suggestions that will elicit positive emotions. For example, the generative AI is equipped with an emotion estimation function and analyzes events during recess in real time. For example, it analyzes students' facial expressions and tone of voice and makes suggestions that will elicit positive emotions. The emotion estimation function can also be used to analyze events during recess in real time and make suggestions that will elicit positive emotions. For example, it can recommend activities that result in a lot of smiles and laughter. The generative AI is also equipped with an emotion estimation function, which allows it to analyze events during recess in real time and make suggestions that will elicit positive emotions. For example, it can analyze students' facial expressions and tone of voice and make suggestions that will elicit positive emotions. This allows it to analyze events during recess in real time and make suggestions that will elicit positive emotions, making it possible to provide a more enjoyable recess.

[0076] The generation AI can use the emotion estimation function to analyze students' emotions during recess and create a report that highlights events that elicited particularly positive responses. For example, the generation AI can use the emotion estimation function to analyze students' emotions during recess and create a report that highlights events that elicited particularly positive responses. For example, it can record in detail events that elicited a lot of smiles and laughter. It can also analyze students' emotional data during recess and create a report that highlights events that elicited particularly positive responses. For example, it can record in detail events with high emotion scores. It can also use the emotion estimation function to monitor students' emotional changes during recess in real time, and the generation AI can use that data to create a report that highlights events that elicited particularly positive responses. For example, it can reflect the intensity and type of emotion in the report. This allows it to provide more detailed information by analyzing students' emotions during recess and highlighting events that elicited particularly positive responses.

[0077] The report delivery system can incorporate an emotion estimation function to provide a customized report based on the emotions of the recipient. The report delivery system, for example, incorporates an emotion estimation function and provides a customized report by analyzing the emotions of the recipient. For example, it can emphasize content that the recipient has a positive emotion about. The emotion estimation function can also be used to provide a customized report based on the emotions of the recipient. For example, it can emphasize content that the recipient is likely to find interesting. The report delivery system can incorporate an emotion estimation function and provide a customized report by analyzing the emotions of the recipient. For example, it can emphasize content that the recipient has a positive emotion about. This makes it possible to provide information more effectively by providing a customized report based on the emotions of the recipient.

[0078] The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function. The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function, for example. The generated report can be read aloud. The voice read-out function can also be used to accommodate the visually impaired and children who have difficulty reading and writing. For example, the generated report can be read aloud. The report distribution system can be made to accommodate the visually impaired and children who have difficulty reading and writing by adding a voice read-out function, for example. The generated report can be read aloud. This can accommodate the visually impaired and children who have difficulty reading and writing, making it possible to meet a wider variety of needs.

[0079] The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. The interactive function can also be used to allow recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. The report delivery system can be added with an interactive function, allowing recipients to add questions and comments in real time. For example, by posting questions and comments on the contents of the report. This allows recipients to add questions and comments in real time, making it possible to provide information in a more interactive manner.

[0080] Reports can be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, reports can be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, a report in PDF format can be distributed by email. Reports can also be distributed in different formats and provided in a format that meets the needs of the recipient. For example, a report in HTML format can be viewed in a dedicated app. Reports can also be distributed in different formats (PDF, HTML, audio file, etc.) and provided in a format that meets the needs of the recipient. For example, a report in audio file format can be distributed. This allows reports to be distributed in different formats and provided in a format that meets the needs of the recipient, making it possible to meet a wider variety of needs.

[0081] The report delivery system is equipped with an emotion estimation function and can collect feedback in real time according to the emotions of the recipient. The report delivery system is, for example, equipped with an emotion estimation function and analyzes the emotions of the recipient to collect feedback in real time. For example, the emotion is estimated by analyzing the recipient's facial expression and tone of voice. The emotion estimation function is also used to collect feedback in real time according to the recipient's emotions. For example, the emotion while the recipient is reading the report is analyzed. The report delivery system is also equipped with an emotion estimation function and analyzes the emotions of the recipient to collect feedback in real time. For example, the emotion is estimated by analyzing the recipient's facial expression and tone of voice. This enables more effective information provision by collecting feedback in real time according to the recipient's emotions.

[0082] The report distribution system can use the emotion estimation function to analyze the receiver's emotional response to the report content and reflect it in the creation of the next report. The report distribution system, for example, uses the emotion estimation function to analyze the receiver's emotional response to the report content and reflect it in the creation of the next report. For example, content that received a lot of positive responses may be reflected in the next report. The report distribution system also analyzes the receiver's emotional response data and reflects it in the creation of the next report. For example, parts with high emotion scores may be reflected in the next report. The emotion estimation function also analyzes the receiver's emotional response to the report content and reflects it in the creation of the next report. For example, content that received a lot of positive responses may be reflected in the next report. In this way, by analyzing the receiver's emotional response to the report content and reflecting it in the creation of the next report, more effective information provision is possible.

[0083] Using the teacher's emotion estimation function, a system can be introduced that monitors stress levels during class and suggests breaks at appropriate times. Using the teacher's emotion estimation function, for example, stress levels during class can be monitored. For example, stress levels can be estimated by analyzing the teacher's facial expressions and tone of voice, and breaks can be suggested at appropriate times. In addition, the emotion estimation function can be used to monitor a teacher's stress level during class in real time and suggest breaks at appropriate times. For example, breaks can be suggested when stress levels rise. In addition, a system can be introduced that monitors stress levels during class based on teacher emotion estimation data and suggests breaks at appropriate times. For example, breaks can be suggested when stress levels rise. In this way, stress levels during class can be monitored and breaks can be suggested at appropriate times, thereby reducing the burden on teachers.

[0084] Generative AI can automatically generate lesson plans and teaching materials to support teachers' lesson preparations. Generative AI, for example, automatically generates lesson plans and teaching materials to support teachers' lesson preparations. For example, teaching materials are automatically generated based on the theme of the lesson. Generative AI can also be used to automatically generate lesson plans and teaching materials. For example, teaching materials are automatically generated based on the theme of the lesson. Generative AI can also be used to automatically generate lesson plans and teaching materials to support teachers' lesson preparations. For example, teaching materials are automatically generated based on the theme of the lesson. In this way, by automatically generating lesson plans and teaching materials, it is possible to support teachers' lesson preparations and reduce their burden.

[0085] Robots can be equipped with functions to automatically check attendance during class and answer simple questions, thereby making teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. A simple question-and-answer function can be added to a robot to make teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. A simple question-and-answer function can be added to a robot to make teachers' work more efficient. For example, a robot can recognize students' faces and automatically check attendance. This can make teachers' work more efficient and reduce their burden by automatically checking attendance during class and answering simple questions.

[0086] The robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In addition, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In addition, the robot can automatically collect feedback after a lesson, which the generation AI analyzes and suggests areas for improvement. For example, it can suggest areas for improvement to the lesson based on students' reactions and level of understanding. In this way, the quality of lessons can be improved by automatically collecting feedback after a lesson, which the generation AI analyzes and suggests areas for improvement.

[0087] The robot monitors students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. The robot, for example, monitors students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. The robot can also monitor students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. The robot can also monitor students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations. For example, it analyzes students' facial expressions and reactions to estimate their level of understanding and provide supplementary explanations. In this way, the quality of lessons can be improved by monitoring students' level of understanding during class in real time, and the generation AI can automatically provide supplementary explanations.

[0088] A system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. For example, a system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. For example, a system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. For example, a system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. For example, a system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. For example, a system can be introduced that uses the emotion estimation function to monitor a teacher's emotional state and suggest relaxation methods when stress levels rise. In this way, the burden on teachers can be reduced by monitoring their emotional state and suggesting relaxation methods when stress levels rise.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The robot is equipped with a combination of voice and facial recognition functions, allowing it to focus on recording the remarks and actions of specific students. For example, it can focus on recording the voices of students who talk a lot or ask questions, and reflect this in reports. It can also use facial recognition to track the actions of specific students, recording their reactions during class and their activities during breaks in detail. For example, it can record the activities of specific students in real time. It can also combine voice and facial recognition to analyze the remarks and facial expressions of specific students and create reports that highlight important points. For example, it can simultaneously record the content of what was said and the facial expression at the time, and reflect this in reports. This allows it to provide more detailed information by focusing on the remarks and actions of specific students.

[0091] The robot is equipped with an emotion estimation function that can record students' emotions during class and breaks in real time and reflect them in reports. For example, emotions can be estimated by analyzing students' facial expressions and tone of voice during class. For example, a camera and microphone can be used to record students' joy, excitement, and concentration in real time and reflect this in reports. The emotion estimation function can also be used to analyze students' emotions during breaks and create reports that highlight particularly enjoyable events and interactions with friends. For example, smiles and laughter can be detected and recorded in detail. The robot can also monitor students' emotional changes during class in real time, and the generation AI can use this data to reflect the intensity and type of emotions in reports. For example, students' interest and level of understanding as the class progresses can be recorded as emotional data. This allows students' emotions to be recorded in real time and reflected in reports, providing more detailed information.

[0092] The robot is equipped with an automatic movement function, allowing it to move freely inside and outside the classroom and record from various perspectives. For example, it can move freely inside the classroom and record the lesson from multiple angles. For example, it can simultaneously record the teacher's explanation from the front and the students' reactions from the back. The automatic movement function can also be used to record areas outside the classroom, such as the hallway or schoolyard, during breaks. For example, it can record students' play and interactions during breaks from various perspectives and reflect this in reports. The robot can also move automatically and focus on specific students or groups to record. For example, it can track students who talk a lot or groups engaged in specific activities and record in detail. This allows it to move freely inside and outside the classroom while recording, providing more multifaceted information.

[0093] The robot is equipped with a projector function, allowing it to record lesson content while projecting it in real time. For example, the robot could be equipped with a projector function and record lesson content while projecting it in real time. For example, the teacher's explanation could be projected using a projector to make it easier for students to understand visually. The projector function could also be used to record important points and slides from the lesson while projecting them. For example, in a math class, it could project how to solve equations to make it easier for students to understand. The robot could also project lesson content in real time and simultaneously record the content to be reflected in a report. For example, the content projected by the projector could be recorded with a camera, and a generation AI could create a report. This allows lesson content to be recorded while projecting it in real time, making it easier to understand visually.

[0094] The robot is equipped with an emotion estimation function, which can provide real-time feedback on the emotions of absent children participating in classes remotely. For example, the emotion estimation function can analyze the emotions of children participating in classes remotely in real time. For example, a camera and microphone can be used to analyze the facial expressions and tone of voice of remote participants to estimate their emotions. The robot can also provide real-time feedback on the emotions of children participating in classes remotely, which can be used to help with the progress of the class. For example, emotion data can be used to check whether remote participants are understanding. The emotion estimation function can also be used to monitor changes in the emotions of remote participants in real time and adjust the content and progress of the class. For example, emotion data can be used to check whether remote participants are concentrating. This allows real-time feedback on the emotions of children participating in classes remotely to help with the progress of the class.

[0095] Generative AI can incorporate an emotion estimation function to create reports that emphasize important points based on students' reactions during class. For example, by incorporating the emotion estimation function, it can analyze students' facial expressions and tone of voice during class to estimate their emotions. For example, it can create a report that emphasizes parts of class that students found interesting based on emotional data. It can also analyze students' emotional reactions during class and generate a report that emphasizes parts that received particularly positive reactions. For example, it can record in detail the content of classes where students smiled or were excited. It can also use the emotion estimation function to monitor students' emotional changes during class in real time, and the generative AI can create a report that emphasizes important points based on that data. For example, it can reflect the intensity and type of emotion in the report. This makes it possible to provide more effective reports by emphasizing important points based on students' reactions during class.

[0096] Generative AI can enhance natural language processing technology to analyze lesson content in more detail and add sections that simply explain technical terms and difficult parts. For example, natural language processing technology can be enhanced to analyze lesson content in detail. For example, technical terms and difficult parts can be automatically extracted and sections that simply explain them can be added. The lesson content can also be analyzed to generate sections that simply explain technical terms and difficult parts. For example, technical terms used in mathematics classes can be simply explained. The lesson content can also be analyzed in detail using natural language processing technology to add sections that simply explain technical terms and difficult parts. For example, technical terms used in science classes can be simply explained. This allows for more detailed analysis of lesson content and simple explanations of technical terms and difficult parts, making it possible to provide reports that are easy to understand.

[0097] The generative AI has improved speech recognition accuracy, allowing it to accurately record even when multiple speakers speak at the same time and reflect this in reports. For example, by improving speech recognition accuracy, it can accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record the remarks of a teacher and students during class and reflect this in reports. In addition, by improving speech recognition accuracy, it can accurately record even when multiple speakers speak at the same time. For example, it can accurately record the contents of a group discussion and reflect this in reports. In addition, by improving speech recognition accuracy in the generative AI, it can accurately record even when multiple speakers speak at the same time. For example, it can simultaneously record the remarks of a teacher and students during class and reflect this in reports. This allows it to accurately record even when multiple speakers speak at the same time and reflect this in reports, providing more detailed information.

[0098] The generative AI can be added with a visual analysis function, which allows the contents of the whiteboard and slides used during class to be converted into text and included in the report. For example, by adding a visual analysis function, the contents of the whiteboard and slides used during class can be converted into text and included in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. The visual analysis function can also be used to convert the contents of slides used during class into text and included in the report. For example, graphs and diagrams displayed on slides can be automatically converted into text. The generative AI can also be added with a visual analysis function, which allows the contents of the whiteboard and slides used during class to be converted into text and included in the report. For example, formulas and diagrams written on the whiteboard can be automatically converted into text. This allows the contents of the whiteboard and slides used during class to be converted into text and included in the report, providing more detailed information.

[0099] The generative AI can use the emotion estimation function to analyze students' emotions during class and create a report that highlights parts where there were many positive responses. For example, the emotion estimation function can be used to analyze students' emotions during class and create a report that highlights parts where there were many positive responses. For example, it can record in detail the content of the class where there were many smiles and excitement. It can also analyze students' emotional data during class and create a report that highlights parts where there were particularly many positive responses. For example, it can record in detail parts with high emotion scores. It can also use the emotion estimation function to monitor students' emotional changes during class in real time, and the generative AI can use that data to create a report that highlights parts where there were many positive responses. For example, it can reflect the intensity and type of emotion in the report. This makes it possible to provide more effective reports by analyzing students' emotions during class and highlighting parts where there were many positive responses.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The robot is installed in the school and records what happens during lessons and recess. For example, the robot can be installed in a classroom to record the teacher's explanations and students' comments during class. The robot can also be installed in the hallway or schoolyard to record play and conversations during recess. Step 2: The generative AI analyzes the data recorded by the robot. For example, the generative AI can convert audio data from a lesson into text and generate a report including key points and summaries. The generative AI can also analyze audio and video data from recess and generate a report including important events and conversations. Step 3: The report creation unit creates a report based on the data analyzed by the generation AI. For example, the report creation unit creates a report summarizing the contents of classes and events that occurred during recess based on the text data generated by the generation AI.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The robot installed in the school a generation AI that analyzes data recorded by the robot; a report creation unit that creates a report based on the data analyzed by the generation AI. A system characterized by:

2. The robot Equipped with an emotion estimation function, it records students' emotions in real time during class and breaks and reflects them in reports.

2. The system of claim 1.

3. The robot Equipped with an automatic movement function, it can move freely inside and outside the classroom and record from various viewpoints.

2. The system of claim 1.

4. The robot Equipped with a function that combines voice and facial recognition, it can record the remarks and actions of specific students.

2. The system of claim 1.

5. A plurality of the robots are installed, Record simultaneously across different classrooms and locations and create a consolidated report 2. The system of claim 1.

6. The robot Equipped with a projector function, it projects the lesson contents in real time while recording them.

2. The system of claim 1.

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