System

Abstract

An object of a system according to an embodiment is to propose an appropriate learning resource on the basis of characteristics and interests of an individual.SOLUTION: A system includes a characteristic table creation unit, an advice provision unit, and an interest suggestion unit. The characteristic table creation unit grasps the characteristics of an individual. The advice providing unit provides the individual advice based on the characteristics grasped by the characteristic table creation unit. The interest suggestion unit suggests an appropriate learning resource based on the interest or concern grasped by the characteristic table creation 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 technologies do not adequately suggest learning resources based on individual characteristics and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate learning resources based on individual characteristics and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a characteristic table creation unit, an advice providing unit, and an interest suggestion unit. The characteristic table creation unit grasps the characteristics of an individual. The advice providing unit provides individual advice based on the characteristics grasped by the characteristic table creation unit. The interest suggestion unit suggests appropriate learning resources based on the interests and concerns grasped by the characteristic table creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate learning resources based on individual characteristics and interests. [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) The individual learning support system according to an embodiment of the present invention is a system that understands individual characteristics and provides optimal advice based on them. In this system, a generative AI creates a table of individual characteristics and provides individual advice based on that table. This allows the individual learning support system to provide optimal learning methods based on each student's characteristics and interests, helping children pursue their own individuality.

[0029] An individual learning support system according to an embodiment includes a characteristic table creation unit, an advice providing unit, and an interest suggestion unit. The characteristic table creation unit identifies individual characteristics. For example, the characteristic table creation unit collects data including a student's learning history, areas of interest, strong and weak subjects, personality traits, etc. The characteristic table creation unit then analyzes this data using a generation AI to compile each student's characteristics in a table. For example, the generation AI receives and analyzes questions and questionnaire responses to identify the student's characteristics, learning history data, and other data. The advice providing unit provides individual advice based on the characteristics identified by the characteristic table creation unit. For example, the advice providing unit suggests specific study methods for further improving a student's strengths and approaches for overcoming weak subjects. The advice providing unit then generates individually customized advice using the generation AI. For example, the generation AI receives and analyzes a student's characteristic table and prompts for providing study methods and advice as input. The interest suggestion unit suggests appropriate learning resources based on the interests and concerns identified by the characteristic table creation unit. For example, the interest suggestion unit suggests science experiment kits and related online courses to students who are interested in science. The interest suggestion unit also uses a generation AI to analyze data related to students' interests and make appropriate suggestions. For example, the generation AI receives and analyzes data related to students' interests and concerns. This allows the individual learning support system according to the embodiment to provide optimal advice and learning resources based on individual characteristics. For example, it can suggest specific learning methods to further improve students' strengths in subjects and provide approaches to overcome weaker subjects. Furthermore, suggesting learning resources and activities related to students' fields of interest can increase students' motivation to learn.

[0030] The characteristic table creation unit can collect students' physical activity data and analyze the relationship between their physical condition during study and learning outcome. For example, the characteristic table creation unit uses a fitness tracker to measure the number of steps and amount of exercise to collect students' physical activity data. This allows the relationship between physical activity and learning outcome to be analyzed. The characteristic table creation unit also monitors students' heart rates and analyzes heart rate fluctuations during study. For example, the relationship between periods when heart rates are high and learning outcome is reflected in the characteristic table. The characteristic table creation unit also collects students' sleep data and analyzes the relationship with learning outcome. For example, the impact of sleep duration and sleep quality on learning outcome is reflected in the characteristic table. This allows the relationship between physical condition and learning outcome to be analyzed.

[0031] The characteristic table creation unit can analyze the student's social media activity and reflect changes in their interests and concerns in the characteristic table. The characteristic table creation unit, for example, analyzes the student's social media posts to identify changes in their interests and concerns. For example, it updates the characteristic table based on the content of the posts and hashtags. The characteristic table creation unit also analyzes the student's social media following and like history to identify changes in their interests. For example, it updates the characteristic table based on newly followed accounts and liked posts. The characteristic table creation unit also analyzes the student's comments and messages on social media to identify changes in their interests and concerns. For example, it updates the characteristic table based on the number and content of comments on a particular topic. In this way, changes in their interests and concerns based on their social media activity can be reflected in the characteristic table.

[0032] The characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby strengthening support at home and at school. The characteristic table creation unit can, for example, integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby strengthening support at home and at school. For example, it can provide a dedicated dashboard where the characteristic table can be viewed. It can also add a notification function to the portal site to notify parents and teachers when the student's characteristic table is updated. For example, it can share updated information about the characteristic table via email or app notification. It can also add a comment function to the portal site to enable parents and teachers to provide feedback on the characteristic table. For example, it can provide a function to post opinions and advice about the characteristic table. This can strengthen support at home and at school.

[0033] The characteristic table creation unit can propose optimal group learning combinations based on the characteristic table, maximizing the effectiveness of collaborative learning. The characteristic table creation unit, for example, proposes optimal group learning combinations based on the characteristic table, taking into account students' strengths and interests. For example, it combines students with different strengths. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account students' personality traits. For example, it combines students who can demonstrate leadership with students who can play a support role. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account students' learning styles. For example, it combines visual learners with auditory learners. This maximizes the effectiveness of group learning.

[0034] The advice providing unit can suggest the most suitable learning method based on the student's learning style (visual, auditory, tactile). For example, the advice providing unit analyzes the student's learning style and suggests visual learning materials to visual learners. For example, it may provide learning materials that make extensive use of diagrams and graphs. The advice providing unit may also suggest audio learning materials or podcasts to auditory learners. For example, it may provide recorded lectures or audiobooks. The advice providing unit may also suggest practical activities or hands-on learning methods to tactile learners. For example, it may provide experiment kits or craft projects. In this way, the advice providing unit can suggest the most suitable learning method based on learning style.

[0035] The advice providing unit can analyze a student's experiences of success and failure and provide advice to increase motivation based on that. For example, the advice providing unit analyzes a student's past experiences of success and provides advice to increase motivation based on that. For example, it looks back on successful experiences and suggests similar methods. The advice providing unit also analyzes a student's experiences of failure and suggests improvement measures based on that. For example, it identifies the cause of failure and provides specific countermeasures. The advice providing unit also analyzes a combination of successful and failed experiences and provides balanced advice. For example, it suggests ways to avoid failure while making use of successful experiences. In this way, it is possible to provide advice to increase motivation based on past experiences.

[0036] The advice providing unit can provide advice on a gamified learning platform, thereby increasing the enjoyment of learning. The advice providing unit can, for example, provide advice on a gamified learning platform, thereby increasing the enjoyment of learning. For example, the advice can be provided in the form of a quiz. The advice providing unit can also set goals to be achieved within the game, allowing students to earn points and rewards by completing the advice. For example, the advice can be provided in the form of a mission. The advice providing unit can also introduce a leaderboard into the learning platform, allowing students to compete with other students and increase their motivation to learn. For example, a system can be provided in which completing the advice can increase one's ranking. This can increase the enjoyment of learning.

[0037] The advice providing unit can share the student's advice with parents and teachers, thereby realizing consistent support at home and at school. The advice providing unit can, for example, share the student's advice with parents and teachers, thereby realizing consistent support at home and at school. For example, the advice content can be shared by email. The advice providing unit can also build a system that allows parents and teachers to provide feedback on the advice. For example, it can add a comment function for the advice. The advice providing unit can also provide a dashboard that allows parents and teachers to check the progress of the advice in real time. For example, it can display the degree of achievement of the advice in a graph. This makes it possible to realize consistent support at home and at school.

[0038] The interest suggestion unit can combine the learning history and interest data to suggest learning resources related to a future career path. For example, the interest suggestion unit analyzes a student's learning history and interest data to suggest learning resources related to a future career path. For example, it may introduce courses related to a specific occupation. The interest suggestion unit also suggests internships or volunteer activities related to a future career path based on the learning history and interest data. For example, it may provide work experience in a field of interest. The interest suggestion unit also combines the student's learning history and interest data to introduce experts and mentors related to a future career path. For example, it may provide opportunities to interact with professionals active in a field of interest. This makes it possible to suggest learning resources related to a future career path.

[0039] The interest suggestion unit can collect data on students' hobbies and extracurricular activities and customize learning resources based on the data. For example, the interest suggestion unit collects data on students' hobbies and extracurricular activities and customizes learning resources based on the data. For example, a student who likes music is provided with music theory materials. The interest suggestion unit also suggests related learning resources based on the data on hobbies and extracurricular activities. For example, a student who likes sports is introduced to a sports science course. The interest suggestion unit also analyzes the data on students' hobbies and extracurricular activities and suggests projects and assignments based on their interests. For example, an art project is provided for a student who likes art. This makes it possible to customize learning resources based on hobbies and extracurricular activities.

[0040] The interest suggestion unit can reflect the student's interest data in the school curriculum and provide an individualized learning program. For example, the interest suggestion unit can reflect the student's interest data in the school curriculum and provide an individualized learning program. For example, it can add elective courses related to the student's field of interest. The interest suggestion unit can also adjust the content of the curriculum based on the interest data and provide projects and assignments that match the student's interests. For example, it can suggest a research project based on the student's topic of interest. The interest suggestion unit can also reflect the student's interest data in the curriculum and create an individualized learning plan. For example, it can provide teaching materials and resources related to the student's field of interest. This can be reflected in the school curriculum and provide an individualized learning program.

[0041] The interest suggestion unit can build a social platform that connects students of different grades or classes based on the students' interests. For example, the interest suggestion unit builds a social platform that connects students of different grades or classes based on the students' interest data. For example, it provides an online community where students with common interests can interact. The interest suggestion unit also provides opportunities for students of different grades or classes to work on projects together based on the interest data. For example, it suggests group projects based on a common theme. The interest suggestion unit also provides a platform where students of different grades or classes can exchange information and give advice based on the students' interest data. For example, it adds an online forum or chat function. This makes it possible to build a social platform that connects students of different grades or classes.

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

[0043] The characteristic table creation unit can further collect data on the student's lifestyle habits and reflect this in the characteristic table in order to understand individual characteristics. For example, the characteristic table creation unit records the student's dietary content and meal times and analyzes the relationship between nutritional balance and learning effectiveness. The characteristic table creation unit also records the student's exercise habits and reflects the relationship between the amount of exercise and learning effectiveness in the characteristic table. Furthermore, the characteristic table creation unit records the student's sleep patterns and analyzes the relationship between sleep quality and learning effectiveness. This allows the relationship between lifestyle habits and learning effectiveness to be reflected in the characteristic table.

[0044] The characteristic table creation unit can collect students' physical activity data and analyze the relationship between their physical condition during study and learning outcome. For example, the characteristic table creation unit uses a fitness tracker to measure the number of steps and amount of exercise to collect students' physical activity data. This allows the relationship between physical activity and learning outcome to be analyzed. The characteristic table creation unit also monitors students' heart rates and analyzes heart rate fluctuations during study. For example, the relationship between periods when heart rates are high and learning outcome is reflected in the characteristic table. The characteristic table creation unit also collects students' sleep data and analyzes the relationship with learning outcome. For example, the impact of sleep duration and sleep quality on learning outcome is reflected in the characteristic table. This allows the relationship between physical condition and learning outcome to be analyzed.

[0045] The characteristic table creation unit can analyze students' social media activities and reflect changes in their interests and concerns in the characteristic table. For example, the characteristic table creation unit analyzes students' social media posts to identify changes in their interests and concerns. For example, the characteristic table creation unit updates the characteristic table based on the content of the posts and hashtags. The characteristic table creation unit also analyzes students' social media following and like history to identify changes in their interests. For example, the characteristic table creation unit updates the characteristic table based on newly followed accounts and liked posts. The characteristic table creation unit also analyzes students' comments and messages on social media to identify changes in their interests and concerns. For example, the characteristic table is updated based on the number and content of comments on a particular topic. In this way, changes in their interests and concerns based on their social media activities can be reflected in the characteristic table.

[0046] The characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby enhancing support at home and at school. For example, the characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby enhancing support at home and at school. For example, it can provide a dedicated dashboard where the characteristic table can be viewed. It can also add a notification function to the portal site to notify parents and teachers when a student's characteristic table is updated. For example, it can share updated information about the characteristic table via email or app notification. It can also add a comment function to the portal site to enable parents and teachers to provide feedback on the characteristic table. For example, it can provide a function to post opinions and advice about the characteristic table. This can enhance support at home and at school.

[0047] The characteristic table creation unit can propose optimal group learning combinations based on the characteristic table, maximizing the effectiveness of collaborative learning. For example, the characteristic table creation unit proposes optimal group learning combinations based on the characteristic table, taking into account the students' strengths and interests. For example, it may combine students with different strengths. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account the students' personality traits. For example, it may combine students who can demonstrate leadership with students who can play a support role. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account the students' learning styles. For example, it may combine visual learners with auditory learners. This maximizes the effectiveness of group learning.

[0048] The advice providing unit can suggest optimal learning methods based on a student's learning style (visual, auditory, tactile). For example, the advice providing unit analyzes a student's learning style and suggests visual learning materials to visual learners. For example, it may provide learning materials that make extensive use of diagrams and graphs. The advice providing unit may also suggest audio learning materials or podcasts to auditory learners. For example, it may provide lecture recordings or audiobooks. The advice providing unit may also suggest practical activities or hands-on learning methods to tactile learners. For example, it may provide experiment kits or craft projects. This makes it possible to suggest optimal learning methods based on learning styles.

[0049] The advice providing unit can analyze a student's experiences of success and failure and provide advice to increase motivation based on that. For example, the advice providing unit can analyze a student's past experiences of success and provide advice to increase motivation based on that. For example, it can look back on successful experiences and suggest similar methods. The advice providing unit can also analyze a student's experiences of failure and suggest improvement measures based on that. For example, it can identify the cause of failure and provide specific countermeasures. The advice providing unit can also analyze a combination of successful and failed experiences and provide balanced advice. For example, it can suggest ways to avoid failure while making use of successful experiences. In this way, it is possible to provide advice to increase motivation based on past experiences.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The characteristic table creation unit grasps individual characteristics. For example, the characteristic table creation unit collects data including the student's learning history, areas of interest, strong and weak subjects, personality traits, etc. The characteristic table creation unit then analyzes this data using a generation AI and compiles each student's characteristics in a table format. For example, the generation AI receives as input questions and questionnaire responses to grasp the student's characteristics, learning history data, etc., and analyzes them. Step 2: The advice providing unit provides individualized advice based on the characteristics identified by the characteristic table creation unit. For example, the advice providing unit suggests specific study methods to further improve strong subjects and approaches to overcome weak subjects. The advice providing unit also generates individually customized advice using a generation AI. For example, the generation AI receives as input the student's characteristic table and prompts for providing study methods and advice, and performs analysis. Step 3: The interest suggestion unit suggests appropriate learning resources based on the interests and concerns identified by the characteristic table creation unit. For example, the interest suggestion unit suggests science experiment kits and related online courses to a student who is interested in science. The interest suggestion unit also uses a generation AI to analyze data on the student's interests and concerns and make appropriate suggestions. For example, the generation AI receives data on the student's interests and concerns as input and analyzes it.

[0052] (Example 2) The individual learning support system according to an embodiment of the present invention is a system that understands individual characteristics and provides optimal advice based on them. In this system, a generative AI creates a table of individual characteristics and provides individual advice based on that table. This allows the individual learning support system to provide optimal learning methods based on each student's characteristics and interests, helping children pursue their own individuality.

[0053] An individual learning support system according to an embodiment includes a characteristic table creation unit, an advice providing unit, and an interest suggestion unit. The characteristic table creation unit identifies individual characteristics. For example, the characteristic table creation unit collects data including a student's learning history, areas of interest, strong and weak subjects, personality traits, etc. The characteristic table creation unit then analyzes this data using a generation AI to compile each student's characteristics in a table. For example, the generation AI receives and analyzes questions and questionnaire responses to identify the student's characteristics, learning history data, and other data. The advice providing unit provides individual advice based on the characteristics identified by the characteristic table creation unit. For example, the advice providing unit suggests specific study methods for further improving a student's strengths and approaches for overcoming weak subjects. The advice providing unit then generates individually customized advice using the generation AI. For example, the generation AI receives and analyzes a student's characteristic table and prompts for providing study methods and advice as input. The interest suggestion unit suggests appropriate learning resources based on the interests and concerns identified by the characteristic table creation unit. For example, the interest suggestion unit suggests science experiment kits and related online courses to students who are interested in science. The interest suggestion unit also uses a generation AI to analyze data related to students' interests and make appropriate suggestions. For example, the generation AI receives and analyzes data related to students' interests and concerns. This allows the individual learning support system according to the embodiment to provide optimal advice and learning resources based on individual characteristics. For example, it can suggest specific learning methods to further improve students' strengths in subjects and provide approaches to overcome weaker subjects. Furthermore, suggesting learning resources and activities related to students' fields of interest can increase students' motivation to learn.

[0054] The characteristic table creation unit can monitor the emotional state of students in real time and dynamically update the characteristic table based on emotional fluctuations. For example, to monitor the emotional state of students in real time, the characteristic table creation unit uses a wearable device to measure heart rate and electrodermal activity. This allows the level of stress and excitement to be understood and reflected in the characteristic table. The characteristic table creation unit also analyzes students' facial expressions using a camera to detect emotional fluctuations in real time. For example, the emotional state is identified based on facial changes such as smiling and furrowing the brow, and the characteristic table is updated. The characteristic table creation unit also analyzes students' vocal tones to detect emotional fluctuations. For example, the emotional state is identified based on changes in pitch and speed of voice, and the characteristic table is updated. This allows the characteristic table to be dynamically updated according to the emotional state of students.

[0055] The characteristic table creation unit can collect students' physical activity data and analyze the relationship between their physical condition during study and learning outcome. For example, the characteristic table creation unit uses a fitness tracker to measure the number of steps and amount of exercise to collect students' physical activity data. This allows the relationship between physical activity and learning outcome to be analyzed. The characteristic table creation unit also monitors students' heart rates and analyzes heart rate fluctuations during study. For example, the relationship between periods when heart rates are high and learning outcome is reflected in the characteristic table. The characteristic table creation unit also collects students' sleep data and analyzes the relationship with learning outcome. For example, the impact of sleep duration and sleep quality on learning outcome is reflected in the characteristic table. This allows the relationship between physical condition and learning outcome to be analyzed.

[0056] The characteristic table creation unit can analyze the student's social media activity and reflect changes in their interests and concerns in the characteristic table. The characteristic table creation unit, for example, analyzes the student's social media posts to identify changes in their interests and concerns. For example, it updates the characteristic table based on the content of the posts and hashtags. The characteristic table creation unit also analyzes the student's social media following and like history to identify changes in their interests. For example, it updates the characteristic table based on newly followed accounts and liked posts. The characteristic table creation unit also analyzes the student's comments and messages on social media to identify changes in their interests and concerns. For example, it updates the characteristic table based on the number and content of comments on a particular topic. In this way, changes in their interests and concerns based on their social media activity can be reflected in the characteristic table.

[0057] The characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby strengthening support at home and at school. The characteristic table creation unit can, for example, integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby strengthening support at home and at school. For example, it can provide a dedicated dashboard where the characteristic table can be viewed. It can also add a notification function to the portal site to notify parents and teachers when the student's characteristic table is updated. For example, it can share updated information about the characteristic table via email or app notification. It can also add a comment function to the portal site to enable parents and teachers to provide feedback on the characteristic table. For example, it can provide a function to post opinions and advice about the characteristic table. This can strengthen support at home and at school.

[0058] The characteristic table creation unit can propose optimal group learning combinations based on the characteristic table, maximizing the effectiveness of collaborative learning. The characteristic table creation unit, for example, proposes optimal group learning combinations based on the characteristic table, taking into account students' strengths and interests. For example, it combines students with different strengths. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account students' personality traits. For example, it combines students who can demonstrate leadership with students who can play a support role. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account students' learning styles. For example, it combines visual learners with auditory learners. This maximizes the effectiveness of group learning.

[0059] The characteristic table creation unit uses the emotion estimation function to customize the characteristic table according to the student's emotional state, thereby providing a positive learning environment. The characteristic table creation unit, for example, uses the emotion estimation function to customize the characteristic table according to the student's emotional state. For example, during times of high stress, it may suggest a relaxing learning method. The characteristic table creation unit also monitors the student's emotional state in real time and dynamically updates the characteristic table. For example, during times when the emotional state is positive, it may suggest a challenging assignment. The characteristic table creation unit also provides a learning environment according to the student's emotional state based on the emotion estimation data. For example, during times when the emotional state is negative, it may provide increased support. This allows for a positive learning environment.

[0060] The advice providing unit can combine the student's learning history and emotional data to provide advice at the emotionally optimal timing. The advice providing unit, for example, analyzes the student's learning history and emotional data to provide advice at the emotionally optimal timing. For example, it may suggest a new learning method when stress levels are low. The advice providing unit also adjusts the timing of advice based on the learning history and emotional data. For example, it may suggest a more difficult task when the student's emotional state is positive. The advice providing unit also monitors the emotional data in real time and provides advice based on the learning history. For example, it may introduce new learning resources when the student's emotional state is stable. This allows advice to be provided at the emotionally optimal timing.

[0061] The advice providing unit can suggest the most suitable learning method based on the student's learning style (visual, auditory, tactile). For example, the advice providing unit analyzes the student's learning style and suggests visual learning materials to visual learners. For example, it may provide learning materials that make extensive use of diagrams and graphs. The advice providing unit may also suggest audio learning materials or podcasts to auditory learners. For example, it may provide recorded lectures or audiobooks. The advice providing unit may also suggest practical activities or hands-on learning methods to tactile learners. For example, it may provide experiment kits or craft projects. In this way, the advice providing unit can suggest the most suitable learning method based on learning style.

[0062] The advice providing unit can analyze a student's experiences of success and failure and provide advice to increase motivation based on that. For example, the advice providing unit analyzes a student's past experiences of success and provides advice to increase motivation based on that. For example, it looks back on successful experiences and suggests similar methods. The advice providing unit also analyzes a student's experiences of failure and suggests improvement measures based on that. For example, it identifies the cause of failure and provides specific countermeasures. The advice providing unit also analyzes a combination of successful and failed experiences and provides balanced advice. For example, it suggests ways to avoid failure while making use of successful experiences. In this way, it is possible to provide advice to increase motivation based on past experiences.

[0063] The advice providing unit can provide advice on a gamified learning platform, thereby increasing the enjoyment of learning. The advice providing unit can, for example, provide advice on a gamified learning platform, thereby increasing the enjoyment of learning. For example, the advice can be provided in the form of a quiz. The advice providing unit can also set goals to be achieved within the game, allowing students to earn points and rewards by completing the advice. For example, the advice can be provided in the form of a mission. The advice providing unit can also introduce a leaderboard into the learning platform, allowing students to compete with other students and increase their motivation to learn. For example, a system can be provided in which completing the advice can increase one's ranking. This can increase the enjoyment of learning.

[0064] The advice providing unit can share the student's advice with parents and teachers, thereby realizing consistent support at home and at school. The advice providing unit can, for example, share the student's advice with parents and teachers, thereby realizing consistent support at home and at school. For example, the advice content can be shared by email. The advice providing unit can also build a system that allows parents and teachers to provide feedback on the advice. For example, it can add a comment function for the advice. The advice providing unit can also provide a dashboard that allows parents and teachers to check the progress of the advice in real time. For example, it can display the degree of achievement of the advice in a graph. This makes it possible to realize consistent support at home and at school.

[0065] The advice providing unit uses the emotion estimation function to adjust advice in real time according to the student's emotional state, thereby maintaining motivation to learn. The advice providing unit, for example, uses the emotion estimation function to adjust advice in real time according to the student's emotional state. For example, when the emotional state is negative, it provides an encouraging message. The advice providing unit also monitors the student's emotional state in real time and dynamically changes the content of the advice. For example, when the emotional state is positive, it suggests a challenging assignment. The advice providing unit also provides advice according to the student's emotional state based on the emotion estimation data. For example, when the emotional state is stable, it introduces new learning resources. This makes it possible to adjust advice in real time to maintain motivation to learn.

[0066] The interest suggestion unit can analyze the student's emotional data, identify moments when interest or concern increases, and make suggestions based on that. The interest suggestion unit, for example, analyzes the student's emotional data and identify moments when interest or concern increases. For example, it detects when the emotional score rises sharply. The interest suggestion unit also suggests learning resources related to moments when the student's interest or concern increases, based on the emotional data. For example, it introduces new learning materials when the emotional score is high. The interest suggestion unit also monitors the student's emotional data in real time and identifies moments when interest or concern increases. For example, it suggests online courses related to times when the emotional score is high. This makes it possible to identify moments when interest or concern increases and make suggestions based on that.

[0067] The interest suggestion unit can combine the learning history and interest data to suggest learning resources related to a future career path. For example, the interest suggestion unit analyzes a student's learning history and interest data to suggest learning resources related to a future career path. For example, it may introduce courses related to a specific occupation. The interest suggestion unit also suggests internships or volunteer activities related to a future career path based on the learning history and interest data. For example, it may provide work experience in a field of interest. The interest suggestion unit also combines the student's learning history and interest data to introduce experts and mentors related to a future career path. For example, it may provide opportunities to interact with professionals active in a field of interest. This makes it possible to suggest learning resources related to a future career path.

[0068] The interest suggestion unit can collect data on students' hobbies and extracurricular activities and customize learning resources based on the data. For example, the interest suggestion unit collects data on students' hobbies and extracurricular activities and customizes learning resources based on the data. For example, a student who likes music is provided with music theory materials. The interest suggestion unit also suggests related learning resources based on the data on hobbies and extracurricular activities. For example, a student who likes sports is introduced to a sports science course. The interest suggestion unit also analyzes the data on students' hobbies and extracurricular activities and suggests projects and assignments based on their interests. For example, an art project is provided for a student who likes art. This makes it possible to customize learning resources based on hobbies and extracurricular activities.

[0069] The interest suggestion unit can reflect the student's interest data in the school curriculum and provide an individualized learning program. For example, the interest suggestion unit can reflect the student's interest data in the school curriculum and provide an individualized learning program. For example, it can add elective courses related to the student's field of interest. The interest suggestion unit can also adjust the content of the curriculum based on the interest data and provide projects and assignments that match the student's interests. For example, it can suggest a research project based on the student's topic of interest. The interest suggestion unit can also reflect the student's interest data in the curriculum and create an individualized learning plan. For example, it can provide teaching materials and resources related to the student's field of interest. This can be reflected in the school curriculum and provide an individualized learning program.

[0070] The interest suggestion unit can build a social platform that connects students of different grades or classes based on the students' interests. For example, the interest suggestion unit builds a social platform that connects students of different grades or classes based on the students' interest data. For example, it provides an online community where students with common interests can interact. The interest suggestion unit also provides opportunities for students of different grades or classes to work on projects together based on the interest data. For example, it suggests group projects based on a common theme. The interest suggestion unit also provides a platform where students of different grades or classes can exchange information and give advice based on the students' interest data. For example, it adds an online forum or chat function. This makes it possible to build a social platform that connects students of different grades or classes.

[0071] The interest suggestion unit can use the emotion estimation function to detect moments when a student's interest or attention increases in real time, and instantly suggest learning resources based on that. The interest suggestion unit, for example, uses the emotion estimation function to detect moments when a student's interest or attention increases in real time. For example, it detects when the emotion score rises sharply. The interest suggestion unit also monitors the student's emotion data in real time, and instantly suggests learning resources related to the moments when interest or attention increases. For example, it introduces new learning materials when the emotion score is high. The interest suggestion unit also identifies moments when a student's interest or attention increases based on the emotion estimation data, and provides learning resources based on that in real time. For example, it suggests online courses related to times when the emotion score is high. In this way, it is possible to detect moments when interest or attention increases in real time, and instantly suggest learning resources based on that.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The characteristic table creation unit can further collect data on the student's lifestyle habits and reflect this in the characteristic table in order to understand individual characteristics. For example, the characteristic table creation unit records the student's dietary content and meal times and analyzes the relationship between nutritional balance and learning effectiveness. The characteristic table creation unit also records the student's exercise habits and reflects the relationship between the amount of exercise and learning effectiveness in the characteristic table. Furthermore, the characteristic table creation unit records the student's sleep patterns and analyzes the relationship between sleep quality and learning effectiveness. This allows the relationship between lifestyle habits and learning effectiveness to be reflected in the characteristic table.

[0074] The characteristic table creation unit can monitor the emotional state of students in real time and dynamically update the characteristic table based on emotional fluctuations. For example, the characteristic table creation unit uses a wearable device to measure heart rate and electrodermal activity to monitor the emotional state of students in real time. This allows the level of stress and excitement to be ascertained and reflected in the characteristic table. The characteristic table creation unit also analyzes students' facial expressions with a camera to detect emotional fluctuations in real time. For example, the emotional state is identified based on facial changes such as smiling and furrowing the brow, and the characteristic table is updated. The characteristic table creation unit also analyzes students' vocal tones to detect emotional fluctuations. For example, the emotional state is identified based on changes in voice pitch and speed, and the characteristic table is updated. This allows the characteristic table to be dynamically updated according to the emotional state of students.

[0075] The characteristic table creation unit can collect students' physical activity data and analyze the relationship between their physical condition during study and learning outcome. For example, the characteristic table creation unit uses a fitness tracker to measure the number of steps and amount of exercise to collect students' physical activity data. This allows the relationship between physical activity and learning outcome to be analyzed. The characteristic table creation unit also monitors students' heart rates and analyzes heart rate fluctuations during study. For example, the relationship between periods when heart rates are high and learning outcome is reflected in the characteristic table. The characteristic table creation unit also collects students' sleep data and analyzes the relationship with learning outcome. For example, the impact of sleep duration and sleep quality on learning outcome is reflected in the characteristic table. This allows the relationship between physical condition and learning outcome to be analyzed.

[0076] The characteristic table creation unit can analyze students' social media activities and reflect changes in their interests and concerns in the characteristic table. For example, the characteristic table creation unit analyzes students' social media posts to identify changes in their interests and concerns. For example, the characteristic table creation unit updates the characteristic table based on the content of the posts and hashtags. The characteristic table creation unit also analyzes students' social media following and like history to identify changes in their interests. For example, the characteristic table creation unit updates the characteristic table based on newly followed accounts and liked posts. The characteristic table creation unit also analyzes students' comments and messages on social media to identify changes in their interests and concerns. For example, the characteristic table is updated based on the number and content of comments on a particular topic. In this way, changes in their interests and concerns based on their social media activities can be reflected in the characteristic table.

[0077] The characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby enhancing support at home and at school. For example, the characteristic table creation unit can integrate the student's characteristic table into a portal site that can be accessed by parents and teachers, thereby enhancing support at home and at school. For example, it can provide a dedicated dashboard where the characteristic table can be viewed. It can also add a notification function to the portal site to notify parents and teachers when a student's characteristic table is updated. For example, it can share updated information about the characteristic table via email or app notification. It can also add a comment function to the portal site to enable parents and teachers to provide feedback on the characteristic table. For example, it can provide a function to post opinions and advice about the characteristic table. This can enhance support at home and at school.

[0078] The characteristic table creation unit can propose optimal group learning combinations based on the characteristic table, maximizing the effectiveness of collaborative learning. For example, the characteristic table creation unit proposes optimal group learning combinations based on the characteristic table, taking into account the students' strengths and interests. For example, it may combine students with different strengths. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account the students' personality traits. For example, it may combine students who can demonstrate leadership with students who can play a support role. The characteristic table creation unit also proposes optimal group learning combinations based on the characteristic table, taking into account the students' learning styles. For example, it may combine visual learners with auditory learners. This maximizes the effectiveness of group learning.

[0079] The characteristic table creation unit uses the emotion estimation function to customize the characteristic table according to the student's emotional state, thereby providing a positive learning environment. For example, the characteristic table creation unit uses the emotion estimation function to customize the characteristic table according to the student's emotional state. For example, during times of high stress, the characteristic table creation unit suggests a relaxing study method. The characteristic table creation unit also monitors the student's emotional state in real time and dynamically updates the characteristic table. For example, during times when the emotional state is positive, the characteristic table creation unit also provides a learning environment according to the student's emotional state based on the emotion estimation data. For example, during times when the emotional state is negative, the characteristic table creation unit provides increased support. This allows for a positive learning environment to be provided.

[0080] The advice providing unit can combine the student's learning history and emotional data to provide advice at the emotionally optimal timing. For example, the advice providing unit analyzes the student's learning history and emotional data to provide advice at the emotionally optimal timing. For example, it may suggest a new learning method when stress levels are low. The advice providing unit also adjusts the timing of advice based on the learning history and emotional data. For example, it may suggest a more difficult task when the student's emotional state is positive. The advice providing unit also monitors the emotional data in real time and provides advice based on the learning history. For example, it may introduce new learning resources when the student's emotional state is stable. This allows advice to be provided at the emotionally optimal timing.

[0081] The advice providing unit can suggest optimal learning methods based on a student's learning style (visual, auditory, tactile). For example, the advice providing unit analyzes a student's learning style and suggests visual learning materials to visual learners. For example, it may provide learning materials that make extensive use of diagrams and graphs. The advice providing unit may also suggest audio learning materials or podcasts to auditory learners. For example, it may provide lecture recordings or audiobooks. The advice providing unit may also suggest practical activities or hands-on learning methods to tactile learners. For example, it may provide experiment kits or craft projects. This makes it possible to suggest optimal learning methods based on learning styles.

[0082] The advice providing unit can analyze a student's experiences of success and failure and provide advice to increase motivation based on that. For example, the advice providing unit can analyze a student's past experiences of success and provide advice to increase motivation based on that. For example, it can look back on successful experiences and suggest similar methods. The advice providing unit can also analyze a student's experiences of failure and suggest improvement measures based on that. For example, it can identify the cause of failure and provide specific countermeasures. The advice providing unit can also analyze a combination of successful and failed experiences and provide balanced advice. For example, it can suggest ways to avoid failure while making use of successful experiences. In this way, it is possible to provide advice to increase motivation based on past experiences.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The characteristic table creation unit grasps individual characteristics. For example, the characteristic table creation unit collects data including the student's learning history, areas of interest, strong and weak subjects, personality traits, etc. The characteristic table creation unit then analyzes this data using a generation AI and compiles each student's characteristics in a table format. For example, the generation AI receives as input questions and questionnaire responses to grasp the student's characteristics, learning history data, etc., and analyzes them. Step 2: The advice providing unit provides individualized advice based on the characteristics identified by the characteristic table creation unit. For example, the advice providing unit suggests specific study methods to further improve strong subjects and approaches to overcome weak subjects. The advice providing unit also generates individually customized advice using a generation AI. For example, the generation AI receives as input the student's characteristic table and prompts for providing study methods and advice, and performs analysis. Step 3: The interest suggestion unit suggests appropriate learning resources based on the interests and concerns identified by the characteristic table creation unit. For example, the interest suggestion unit suggests science experiment kits and related online courses to a student who is interested in science. The interest suggestion unit also uses a generation AI to analyze data on the student's interests and concerns and make appropriate suggestions. For example, the generation AI receives data on the student's interests and concerns as input and analyzes it.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a characteristic table creation unit for grasping individual characteristics; an advice providing unit that provides individual advice based on the characteristics grasped by the characteristic table creating unit; an interest suggestion unit that suggests appropriate learning resources based on the interests and concerns grasped by the characteristic table creation unit; A system characterized by:

2. The characteristic table creation unit Monitor students' emotional states in real time and dynamically update the trait table based on emotional fluctuations.

2. The system of claim 1.

3. The characteristic table creation unit Collecting student physical activity data and analyzing the relationship between physical condition during learning and learning outcomes 2. The system of claim 1.

4. The characteristic table creation unit Analyze students' social media activity and reflect changes in their interests and concerns in a characteristic chart.

2. The system of claim 1.

5. The characteristic table creation unit Integrating student profile information into a parent-teacher accessible portal to enhance support at home and at school 2. The system of claim 1.

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