System

Abstract

An object of the system according to the embodiment is to personalize a study plan of each student and appropriately correct the study plan.SOLUTION: A system according to an embodiment includes an information collection part, a learning plan generation part, a data aggregation part, a gap analysis part, a plan correction part, and a motivation support part. The information collection unit collects information on a school of choice, a target score, a learning habit, and a personality of a student. The learning plan generation unit generates a personalized learning plan based on the information collected by the information collection unit. The data aggregation unit aggregates the learning history data from the wearable device and the learning app. The gap analysis unit analyzes a gap between the learning history data aggregated by the data aggregation unit and the plan. The plan modifying section appropriately modifies the plan based on the gap analyzed by the gap analyzing section. The motivation support unit provides encouragement in a manner suitable for the character of the student when the motivation decreases.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to personalize and appropriately adjust learning plans for each student.

[0005] The system according to the embodiment aims to personalize and appropriately modify each student's learning plan. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a study plan generation unit, a data aggregation unit, a gap analysis unit, a plan correction unit, and a motivation support unit. The information collection unit collects information such as a student's preferred school, target score, study habits, and personality. The study plan generation unit generates a personalized study plan based on the information collected by the information collection unit. The data aggregation unit aggregates study history data from wearable devices and study apps. The gap analysis unit analyzes the gap between the study history data aggregated by the data aggregation unit and the plan. The plan correction unit appropriately corrects the plan based on the gap analyzed by the gap analysis unit. The motivation support unit provides encouragement and motivation to a student in a manner suited to their personality when their motivation drops. [Effects of the Invention]

[0007] The system according to the embodiment can personalize and appropriately modify each student's learning plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention is a system that collects information such as each student's preferred school, target score, study habits, personality, etc., and provides a personalized learning plan. This allows the learning support system to provide optimal learning support to each student, maintaining their motivation to achieve their goals.

[0029] A learning support system according to an embodiment includes an information collection unit, a study plan generation unit, a data aggregation unit, a gap analysis unit, a plan correction unit, and a motivation support unit. The information collection unit collects information about a student's preferred school, target score, study habits, and personality. For example, a conversational AI asks the student questions such as, "Which school do you want to attend?" and "How much time do you study per day?" and records the student's responses. The information collection unit also collects information about the student's home study space and school's class style. For example, information about the student's home study space is collected and reflected in the study plan. The study plan generation unit generates a personalized study plan based on the information collected by the information collection unit. For example, the study plan generation unit suggests specific daily study content and time based on the desired school's entrance exam schedule, required subjects, and target score. The data aggregation unit aggregates study history data from wearable devices and study apps. For example, the data collection unit collects data on study time and progress in the study app to confirm whether the student is progressing according to the plan. The gap analysis unit analyzes the gap between the study history data aggregated by the data aggregation unit and the plan. For example, if progress is slower than planned based on learning history data, the system suggests additional study time. The plan correction unit appropriately corrects the plan based on the gaps analyzed by the gap analysis unit. For example, if progress is slow, the system adjusts the learning content. The motivation support unit provides encouragement in a manner suited to the student's personality when motivation drops. For example, it provides words of encouragement and specific advice. As a result, the learning support system according to the embodiment can provide optimal learning support to each student and maintain their motivation to achieve their goals.

[0030] The information collection unit can collect information about the student's home study space and school lesson style and reflect this in the study plan. The information collection unit, for example, collects information about the student's home study space and reflects this in the study plan. For example, it collects information such as whether the student can study in a quiet place and whether there is appropriate lighting, and suggests the optimal study environment. The information collection unit also collects information about the student's school lesson style and reflects this in the study plan. For example, it collects information such as whether the class is online or face-to-face, and the pace of the class, and provides a study plan accordingly. This makes it possible to provide the optimal study plan according to the student's learning environment.

[0031] The information collection unit can collect students' past learning history and grade data and analyze long-term learning trends. The information collection unit, for example, collects students' past test results and grade data and analyzes long-term learning trends. For example, it analyzes fluctuations in grades in specific subjects and identifies areas that need strengthening. The information collection unit also collects students' past learning history and analyzes long-term learning trends. For example, it analyzes past study time and study content and identifies learning progress patterns. This makes it possible to analyze long-term learning trends based on students' past learning history and grade data.

[0032] The information collecting unit can collect information about students' hobbies and interests and provide learning content based thereon. For example, the information collecting unit collects information about students' hobbies and interests and provides learning content based thereon. For example, a student who likes sports is provided with sports-related math problems. The information collecting unit also collects information about students' interests and provides learning content based thereon. For example, a student who likes music is provided with music-related history problems. In this way, learning content based on students' hobbies and interests can be provided.

[0033] The information gathering unit can analyze the friendships and social networks of students and build cooperative relationships for learning. The information gathering unit, for example, analyzes the friendships of students and builds cooperative relationships for learning. For example, it creates study groups with friends who have the same goals. The information gathering unit also analyzes the social networks of students and builds cooperative relationships for learning. For example, it provides opportunities for online collaborative learning. This makes it possible to build cooperative relationships for learning based on the friendships and social networks of students.

[0034] The learning plan generation unit can analyze a student's learning style (visual, auditory, experiential) and provide a learning plan that suits it. The learning plan generation unit, for example, analyzes a student's learning style and provides a learning plan that suits it. For example, a visual student is provided with teaching materials that make extensive use of diagrams and graphs. The learning plan generation unit also provides audio teaching materials to an auditory student. For example, recordings of lectures are provided. The learning plan generation unit also provides a learning plan that includes experiments and practical training to an experiential student. For example, fieldwork is suggested. This makes it possible to provide the optimal learning plan that suits the student's learning style.

[0035] The study plan generation unit can propose optimal study times by taking into account the student's lifestyle rhythm and sleep pattern. The study plan generation unit, for example, analyzes the student's lifestyle rhythm and proposes optimal study times. For example, it proposes early morning study times for a morning person. The study plan generation unit also considers the student's sleep patterns and proposes optimal study times. For example, it proposes nighttime study times for a night person. In this way, it is possible to propose optimal study times that suit the student's lifestyle rhythm and sleep pattern.

[0036] The learning plan generation unit can monitor the student's health condition and provide a learning plan that takes health into consideration. The learning plan generation unit, for example, monitors the student's stress level and provides a learning plan that takes health into consideration. For example, when stress is high, it suggests learning content that will help students relax. The learning plan generation unit also monitors the student's physical condition and provides a learning plan that takes health into consideration. For example, when the student is not feeling well, it suggests lighter learning content. In this way, it is possible to provide a learning plan that takes health into consideration.

[0037] The learning plan generation unit can propose learning support at home, taking into account the student's home environment and the parents' educational philosophy. The learning plan generation unit proposes learning support at home, taking into account the student's home environment, for example. For example, if the parents are proactive about education, it provides a learning plan in cooperation with the parents. The learning plan generation unit also proposes learning support at home, taking into account the parents' educational philosophy. For example, if the parents have strict educational philosophy, it adjusts the learning plan in cooperation with the parents. In this way, it is possible to provide learning support based on the student's home environment and the parents' educational philosophy.

[0038] The data aggregator can collect students' learning history data in real time and instantly provide feedback on the analysis results. The data aggregator, for example, collects data from learning apps in real time and instantly provides feedback on the analysis results. For example, it can check study time and progress in real time and provide appropriate advice. The data aggregator can also collect data from wearable devices in real time and instantly provide feedback on the analysis results. For example, it can evaluate learning efficiency based on heart rate and activity level. This makes it possible to collect students' learning history data in real time and instantly provide feedback on the analysis results.

[0039] The data aggregator can predict a student's learning patterns based on learning history data and provide proactive advice. The data aggregator, for example, predicts a student's learning patterns based on learning history data and provides proactive advice. For example, if a student tends to lose concentration during a certain time period, the data aggregator can suggest taking a break during that time period. The data aggregator can also analyze past learning data to predict learning patterns. For example, it can analyze fluctuations in grades in a specific subject and identify areas that need strengthening. This makes it possible to predict a student's learning patterns and provide proactive advice.

[0040] The data aggregator can compare the learning history data with other students and provide a benchmark. The data aggregator, for example, compares the learning history data with other students and provides a benchmark. For example, the student can compare their own study time with the average study time of students with the same goal and evaluate their own study time. The data aggregator can also provide a benchmark based on past grade data. For example, the student can compare their grades with those of students in the same grade and evaluate their own grades. This makes it possible to compare the learning history data with other students and provide a benchmark.

[0041] The data aggregator can visualize the learning history data, allowing students to intuitively understand their own progress. The data aggregator, for example, visualizes the learning history data, allowing students to intuitively understand their own progress. For example, it may use graphs or charts to display fluctuations in study time and grades. The data aggregator also provides an interactive dashboard, allowing students to check their own progress in real time. For example, it may visually display their learning progress and confirm the degree of goal achievement. In this way, it is possible to visualize the learning history data, allowing students to intuitively understand their own progress.

[0042] The plan correction unit can monitor the student's learning progress in real time and instantly correct the learning plan. The plan correction unit, for example, monitors data from a learning app in real time and instantly corrects the learning plan. For example, if progress is falling behind, it suggests additional study time. The plan correction unit also monitors data from a wearable device in real time and instantly corrects the learning plan. For example, it evaluates learning efficiency based on heart rate and activity level. This makes it possible to monitor the student's learning progress in real time and instantly correct the learning plan.

[0043] The plan correction unit can collect student feedback and flexibly adjust the study plan based on that. The plan correction unit, for example, collects student feedback and flexibly adjusts the study plan based on that. For example, it provides additional support for subjects that the student finds difficult. The plan correction unit also adjusts the study plan to reflect the student's opinion. For example, it incorporates the study content that the student desires. This makes it possible to flexibly adjust the study plan based on the student's feedback.

[0044] The plan correction unit saves the revision history of the learning plan and can make optimal revisions by referring to past revisions. The plan correction unit, for example, saves the revision history of the learning plan and makes optimal revisions by referring to past revisions. For example, it reapplies revisions that were effective in the past. The plan correction unit also adjusts the learning plan based on the revision history. For example, it analyzes past revisions and selects the optimal revision method. In this way, it is possible to save the revision history of the learning plan and make optimal revisions by referring to past revisions.

[0045] The plan correction unit can share the corrections to the learning plan with the student's parents and teachers, and work together to provide learning support. The plan correction unit, for example, shares the content of the corrections to the learning plan with the student's parents and teachers, and work together to provide learning support. For example, it allows the parents and teachers to provide additional support. The plan correction unit also shares the content of the corrections to the learning plan, and work together to provide learning support. For example, it works with the parents and teachers to adjust the learning plan. This allows the corrections to the learning plan to be shared with the student's parents and teachers, and work together to provide learning support.

[0046] The motivation support unit can provide a learning program that incorporates game elements to improve student motivation. The motivation support unit can, for example, provide a learning program that incorporates game elements to improve student motivation. For example, the learning content can be in the form of a quiz, allowing students to earn points. The motivation support unit can also introduce a level-up system into the learning program. For example, the level can be increased and rewards can be earned according to the progress of learning. In this way, a learning program that incorporates game elements can be provided to improve student motivation.

[0047] The motivation support department can work with students' friends and family to build a support system for improving motivation. The motivation support department can, for example, work with students' friends and family to build a support system for improving motivation. For example, by studying together with friends, students can encourage each other. The motivation support department can also work with families to support learning. For example, families can check the progress of students' studies and offer words of encouragement. In this way, a support system for improving motivation can be built in cooperation with students' friends and family.

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

[0049] The learning support system can also collect students' physical activity data and reflect it in their learning plans. For example, based on data from wearable devices, it can suggest learning plans that incorporate moderate exercise for students who are not getting enough exercise. It can also provide plans that include breaks during times when students are likely to lose concentration, based on physical activity data. This makes it possible to provide learning plans that take students' health into account.

[0050] The learning support system can also collect students' dietary data and reflect it in their study plans. For example, it can provide dietary advice to improve concentration by taking into account the nutritional balance of meals. It can also suggest optimal study times based on meal timings. This allows it to provide study plans that take students' dietary habits into account.

[0051] The learning support system can also collect students' sleep data and reflect it in their study plans. For example, it can monitor sleep quality and propose study plans that prioritize rest for students who are sleep-deprived. It can also suggest optimal study times based on sleep data. This makes it possible to provide study plans that take students' sleep status into account.

[0052] The learning support system can also provide learning tools that suit each student's learning style. For example, visually-oriented students can be provided with learning materials that make extensive use of diagrams and graphs. Audio-oriented students can also be provided with audio-oriented learning materials. Furthermore, experience-oriented students can be provided with learning tools that include experiments and practical training. This makes it possible to provide optimal learning tools that suit each student's learning style.

[0053] Learning support systems can also visualize students' learning progress and make it easier to understand. For example, they can display fluctuations in study time and grades in graphs and charts. They can also provide interactive dashboards that allow students to check their progress in real time. This helps students intuitively understand their learning progress and maintain motivation to achieve their goals.

[0054] The learning support system can also compare a student's learning history data with other students and provide a benchmark. For example, a student can evaluate their own study time by comparing it with the average study time of students with the same goals. It can also provide a benchmark based on past performance data. This allows students to objectively evaluate their own learning situation and identify areas for improvement.

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

[0056] Step 1: The information gathering unit collects information about the student's preferred school, target score, study habits, and personality. For example, the conversational AI asks the student questions such as, "Which school do you want to attend?" and "How much time do you study per day?" and records the student's answers. The information gathering unit also collects information about the student's home study space and school's teaching style. Step 2: The study plan generator generates a personalized study plan based on the information collected by the information collector. For example, it suggests specific daily study content and time based on the entrance exam dates of the desired school, required subjects, and target scores. Step 3: The data aggregation unit aggregates learning history data from wearable devices and learning apps. For example, it collects data on study time and progress in learning apps to confirm whether students are progressing as planned. Step 4: The gap analysis unit analyzes the gap between the learning history data aggregated by the data aggregation unit and the plan. For example, if progress is slower than planned based on the learning history data, the gap analysis unit suggests additional study time. Step 5: The plan modification unit modifies the plan as appropriate based on the gaps analyzed by the gap analysis unit. For example, if progress is slow, the learning content is adjusted. Step 6: When motivation drops, the Motivation Support Team provides encouragement in a way that suits the student's personality, such as providing words of encouragement or specific advice.

[0057] (Example 2) The learning support system according to an embodiment of the present invention is a system that collects information such as each student's preferred school, target score, study habits, personality, etc., and provides a personalized learning plan. This allows the learning support system to provide optimal learning support to each student, maintaining their motivation to achieve their goals.

[0058] A learning support system according to an embodiment includes an information collection unit, a study plan generation unit, a data aggregation unit, a gap analysis unit, a plan correction unit, and a motivation support unit. The information collection unit collects information about a student's preferred school, target score, study habits, and personality. For example, a conversational AI asks the student questions such as, "Which school do you want to attend?" and "How much time do you study per day?" and records the student's responses. The information collection unit also collects information about the student's home study space and school's class style. For example, information about the student's home study space is collected and reflected in the study plan. The study plan generation unit generates a personalized study plan based on the information collected by the information collection unit. For example, the study plan generation unit suggests specific daily study content and time based on the desired school's entrance exam schedule, required subjects, and target score. The data aggregation unit aggregates study history data from wearable devices and study apps. For example, the data collection unit collects data on study time and progress in the study app to confirm whether the student is progressing according to the plan. The gap analysis unit analyzes the gap between the study history data aggregated by the data aggregation unit and the plan. For example, if progress is slower than planned based on learning history data, the system suggests additional study time. The plan correction unit appropriately corrects the plan based on the gaps analyzed by the gap analysis unit. For example, if progress is slow, the system adjusts the learning content. The motivation support unit provides encouragement in a manner suited to the student's personality when motivation drops. For example, it provides words of encouragement and specific advice. As a result, the learning support system according to the embodiment can provide optimal learning support to each student and maintain their motivation to achieve their goals.

[0059] The information collection unit can collect information about the student's home study space and school lesson style and reflect this in the study plan. The information collection unit, for example, collects information about the student's home study space and reflects this in the study plan. For example, it collects information such as whether the student can study in a quiet place and whether there is appropriate lighting, and suggests the optimal study environment. The information collection unit also collects information about the student's school lesson style and reflects this in the study plan. For example, it collects information such as whether the class is online or face-to-face, and the pace of the class, and provides a study plan accordingly. This makes it possible to provide the optimal study plan according to the student's learning environment.

[0060] The information collection unit can collect students' past learning history and grade data and analyze long-term learning trends. The information collection unit, for example, collects students' past test results and grade data and analyzes long-term learning trends. For example, it analyzes fluctuations in grades in specific subjects and identifies areas that need strengthening. The information collection unit also collects students' past learning history and analyzes long-term learning trends. For example, it analyzes past study time and study content and identifies learning progress patterns. This makes it possible to analyze long-term learning trends based on students' past learning history and grade data.

[0061] The information collection unit can use the emotion estimation function to analyze the emotions of students during a dialogue in real time and generate questions that correspond to their emotions. For example, the information collection unit uses the emotion estimation function to analyze emotions in real time during a dialogue with a student. For example, if a student is feeling stressed, it generates questions that will help the student relax. The information collection unit also generates questions that correspond to the student's emotions. For example, if a student is excited, it asks questions that will further pique the student's interest. In this way, questions that correspond to the student's emotions can be generated in real time.

[0062] The information collecting unit can collect information about students' hobbies and interests and provide learning content based thereon. For example, the information collecting unit collects information about students' hobbies and interests and provides learning content based thereon. For example, a student who likes sports is provided with sports-related math problems. The information collecting unit also collects information about students' interests and provides learning content based thereon. For example, a student who likes music is provided with music-related history problems. In this way, learning content based on students' hobbies and interests can be provided.

[0063] The information gathering unit can analyze the friendships and social networks of students and build cooperative relationships for learning. The information gathering unit, for example, analyzes the friendships of students and builds cooperative relationships for learning. For example, it creates study groups with friends who have the same goals. The information gathering unit also analyzes the social networks of students and builds cooperative relationships for learning. For example, it provides opportunities for online collaborative learning. This makes it possible to build cooperative relationships for learning based on the friendships and social networks of students.

[0064] The information collection unit uses the emotion estimation function to design dialogues that will increase students' motivation and draw out their motivation to learn. The information collection unit, for example, uses the emotion estimation function to design dialogues that will increase students' motivation. For example, when a student is excited, the information collection unit asks questions that will further pique their interest. The information collection unit also designs dialogues that correspond to the student's emotional state. For example, when a student is feeling down, the information collection unit offers words of encouragement. This makes it possible to design dialogues that will increase students' motivation and draw out their motivation to learn.

[0065] The learning plan generation unit can analyze a student's learning style (visual, auditory, experiential) and provide a learning plan that suits it. The learning plan generation unit, for example, analyzes a student's learning style and provides a learning plan that suits it. For example, a visual student is provided with teaching materials that make extensive use of diagrams and graphs. The learning plan generation unit also provides audio teaching materials to an auditory student. For example, recordings of lectures are provided. The learning plan generation unit also provides a learning plan that includes experiments and practical training to an experiential student. For example, fieldwork is suggested. This makes it possible to provide the optimal learning plan that suits the student's learning style.

[0066] The study plan generation unit can propose optimal study times by taking into account the student's lifestyle rhythm and sleep pattern. The study plan generation unit, for example, analyzes the student's lifestyle rhythm and proposes optimal study times. For example, it proposes early morning study times for a morning person. The study plan generation unit also considers the student's sleep patterns and proposes optimal study times. For example, it proposes nighttime study times for a night person. In this way, it is possible to propose optimal study times that suit the student's lifestyle rhythm and sleep pattern.

[0067] The lesson plan generation unit can dynamically adjust the lesson plan according to the emotional state of the student using the emotion estimation function. The lesson plan generation unit, for example, dynamically adjusts the lesson plan according to the emotional state of the student using the emotion estimation function. For example, if the student is feeling stressed, the lesson plan generation unit suggests learning content that will help the student relax. The lesson plan generation unit also adjusts the lesson plan according to the emotional state of the student. For example, if the student is excited, the lesson plan generation unit suggests learning content that will help the student improve their concentration. In this way, the lesson plan can be dynamically adjusted according to the emotional state of the student.

[0068] The learning plan generation unit can monitor the student's health condition and provide a learning plan that takes health into consideration. The learning plan generation unit, for example, monitors the student's stress level and provides a learning plan that takes health into consideration. For example, when stress is high, it suggests learning content that will help students relax. The learning plan generation unit also monitors the student's physical condition and provides a learning plan that takes health into consideration. For example, when the student is not feeling well, it suggests lighter learning content. In this way, it is possible to provide a learning plan that takes health into consideration.

[0069] The learning plan generation unit can propose learning support at home, taking into account the student's home environment and the parents' educational philosophy. The learning plan generation unit proposes learning support at home, taking into account the student's home environment, for example. For example, if the parents are proactive about education, it provides a learning plan in cooperation with the parents. The learning plan generation unit also proposes learning support at home, taking into account the parents' educational philosophy. For example, if the parents have strict educational philosophy, it adjusts the learning plan in cooperation with the parents. In this way, it is possible to provide learning support based on the student's home environment and the parents' educational philosophy.

[0070] The learning plan generation unit can use the emotion estimation function to identify an environment in which the student can best concentrate and recommend studying in that environment. The learning plan generation unit, for example, uses the emotion estimation function to identify an environment in which the student can best concentrate. For example, if studying in a quiet place is effective, the learning plan generation unit recommends studying in that environment. The learning plan generation unit also identifies the optimal learning environment depending on the student's emotional state. For example, if studying in a bright place is effective, the learning plan generation unit recommends studying in that environment. This makes it possible to recommend studying in an environment in which the student can best concentrate.

[0071] The data aggregator can collect students' learning history data in real time and instantly provide feedback on the analysis results. The data aggregator, for example, collects data from learning apps in real time and instantly provides feedback on the analysis results. For example, it can check study time and progress in real time and provide appropriate advice. The data aggregator can also collect data from wearable devices in real time and instantly provide feedback on the analysis results. For example, it can evaluate learning efficiency based on heart rate and activity level. This makes it possible to collect students' learning history data in real time and instantly provide feedback on the analysis results.

[0072] The data aggregator can predict a student's learning patterns based on learning history data and provide proactive advice. The data aggregator, for example, predicts a student's learning patterns based on learning history data and provides proactive advice. For example, if a student tends to lose concentration during a certain time period, the data aggregator can suggest taking a break during that time period. The data aggregator can also analyze past learning data to predict learning patterns. For example, it can analyze fluctuations in grades in a specific subject and identify areas that need strengthening. This makes it possible to predict a student's learning patterns and provide proactive advice.

[0073] The data aggregation unit can use the emotion estimation function to analyze changes in students' emotions from learning history data and reflect the changes in their learning plans. The data aggregation unit, for example, uses the emotion estimation function to analyze changes in students' emotions from learning history data. For example, if stress tends to increase as learning time increases, the data aggregation unit can suggest taking a break. The data aggregation unit also adjusts the learning plan based on changes in students' emotions. For example, it adjusts the learning content according to changes in emotions. This makes it possible to analyze changes in students' emotions from learning history data and reflect the changes in their learning plans.

[0074] The data aggregator can compare the learning history data with other students and provide a benchmark. The data aggregator, for example, compares the learning history data with other students and provides a benchmark. For example, the student can compare their own study time with the average study time of students with the same goal and evaluate their own study time. The data aggregator can also provide a benchmark based on past grade data. For example, the student can compare their grades with those of students in the same grade and evaluate their own grades. This makes it possible to compare the learning history data with other students and provide a benchmark.

[0075] The data aggregator can visualize the learning history data, allowing students to intuitively understand their own progress. The data aggregator, for example, visualizes the learning history data, allowing students to intuitively understand their own progress. For example, it may use graphs or charts to display fluctuations in study time and grades. The data aggregator also provides an interactive dashboard, allowing students to check their own progress in real time. For example, it may visually display their learning progress and confirm the degree of goal achievement. In this way, it is possible to visualize the learning history data, allowing students to intuitively understand their own progress.

[0076] The data aggregator uses the emotion estimation function to analyze fluctuations in a student's motivation from learning history data, and can send encouraging messages at appropriate times. The data aggregator, for example, uses the emotion estimation function to analyze fluctuations in a student's motivation from learning history data. For example, it sends encouraging messages when motivation is low. The data aggregator also sends encouraging messages according to the student's emotional state. For example, it sends messages at appropriate times according to changes in emotion. This makes it possible to analyze fluctuations in a student's motivation from learning history data, and send encouraging messages at appropriate times.

[0077] The plan correction unit can monitor the student's learning progress in real time and instantly correct the learning plan. The plan correction unit, for example, monitors data from a learning app in real time and instantly corrects the learning plan. For example, if progress is falling behind, it suggests additional study time. The plan correction unit also monitors data from a wearable device in real time and instantly corrects the learning plan. For example, it evaluates learning efficiency based on heart rate and activity level. This makes it possible to monitor the student's learning progress in real time and instantly correct the learning plan.

[0078] The plan correction unit can collect student feedback and flexibly adjust the study plan based on that. The plan correction unit, for example, collects student feedback and flexibly adjusts the study plan based on that. For example, it provides additional support for subjects that the student finds difficult. The plan correction unit also adjusts the study plan to reflect the student's opinion. For example, it incorporates the study content that the student desires. This makes it possible to flexibly adjust the study plan based on the student's feedback.

[0079] The plan correction unit can use the emotion estimation function to revise the study plan according to the emotional state of the student. The plan correction unit, for example, uses the emotion estimation function to revise the study plan according to the emotional state of the student. For example, if the student is feeling stressed, the plan correction unit suggests study content that will help the student relax. The plan correction unit also adjusts the study plan according to the emotional state of the student. For example, if the student is excited, the plan correction unit suggests study content that will help the student improve their concentration. In this way, the study plan can be revised according to the emotional state of the student.

[0080] The plan correction unit saves the revision history of the learning plan and can make optimal revisions by referring to past revisions. The plan correction unit, for example, saves the revision history of the learning plan and makes optimal revisions by referring to past revisions. For example, it reapplies revisions that were effective in the past. The plan correction unit also adjusts the learning plan based on the revision history. For example, it analyzes past revisions and selects the optimal revision method. In this way, it is possible to save the revision history of the learning plan and make optimal revisions by referring to past revisions.

[0081] The plan correction unit can share the corrections to the learning plan with the student's parents and teachers, and work together to provide learning support. The plan correction unit, for example, shares the content of the corrections to the learning plan with the student's parents and teachers, and work together to provide learning support. For example, it allows the parents and teachers to provide additional support. The plan correction unit also shares the content of the corrections to the learning plan, and work together to provide learning support. For example, it works with the parents and teachers to adjust the learning plan. This allows the corrections to the learning plan to be shared with the student's parents and teachers, and work together to provide learning support.

[0082] The plan correction unit can use the emotion estimation function to propose a special study plan when a student's motivation drops. The plan correction unit, for example, uses the emotion estimation function to propose a special study plan when a student's motivation drops. For example, it provides special assignments to increase motivation. The plan correction unit also proposes a special study plan depending on the student's emotional state. For example, it introduces a reward system when motivation drops. This makes it possible to propose a special study plan when a student's motivation drops.

[0083] The motivation support unit can monitor fluctuations in a student's motivation in real time and respond immediately. The motivation support unit can, for example, monitor fluctuations in a student's motivation in real time and respond immediately. For example, it can send an encouraging message when motivation is low. The motivation support unit can also respond immediately according to the student's emotional state. For example, it can send a message at an appropriate time according to changes in emotion. This allows the unit to monitor fluctuations in a student's motivation in real time and respond immediately.

[0084] The motivation support unit records students' successful experiences and allows them to reflect on past successful experiences when their motivation drops. The motivation support unit, for example, records students' successful experiences and allows them to reflect on past successful experiences when their motivation drops. For example, it may remind students of high scores they received in past tests. The motivation support unit also increases students' motivation based on their successful experiences. For example, reflecting on past successful experiences can increase self-esteem. This allows students to record their successful experiences and allows them to reflect on past successful experiences when their motivation drops.

[0085] The motivation support unit can use the emotion estimation function to generate an encouraging message according to the emotional state of the student. The motivation support unit, for example, uses the emotion estimation function to generate an encouraging message according to the emotional state of the student. For example, when a student is feeling down, the motivation support unit sends encouraging words. The motivation support unit also generates an encouraging message according to the emotional state of the student. For example, the message is sent at an appropriate time according to changes in emotion. This makes it possible to generate an encouraging message according to the emotional state of the student.

[0086] The motivation support unit can provide a learning program that incorporates game elements to improve student motivation. The motivation support unit can, for example, provide a learning program that incorporates game elements to improve student motivation. For example, the learning content can be in the form of a quiz, allowing students to earn points. The motivation support unit can also introduce a level-up system into the learning program. For example, the level can be increased and rewards can be earned according to the progress of learning. In this way, a learning program that incorporates game elements can be provided to improve student motivation.

[0087] The motivation support department can work with students' friends and family to build a support system for improving motivation. The motivation support department can, for example, work with students' friends and family to build a support system for improving motivation. For example, by studying together with friends, students can encourage each other. The motivation support department can also work with families to support learning. For example, families can check the progress of students' studies and offer words of encouragement. In this way, a support system for improving motivation can be built in cooperation with students' friends and family.

[0088] The motivation support unit can use the emotion estimation function to introduce a reward system according to the student's emotional state, thereby increasing motivation to learn. The motivation support unit, for example, uses the emotion estimation function to introduce a reward system according to the student's emotional state. For example, when motivation to learn is high, additional rewards are provided. The motivation support unit also adjusts the reward system according to the student's emotional state. For example, the content of the reward is changed according to changes in emotion. In this way, a reward system according to the student's emotional state can be introduced, thereby increasing motivation to learn.

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

[0090] The learning support system can also collect students' physical activity data and reflect it in their learning plans. For example, based on data from wearable devices, it can suggest learning plans that incorporate moderate exercise for students who are not getting enough exercise. It can also provide plans that include breaks during times when students are likely to lose concentration, based on physical activity data. This makes it possible to provide learning plans that take students' health into account.

[0091] The learning support system can also collect students' dietary data and reflect it in their study plans. For example, it can provide dietary advice to improve concentration by taking into account the nutritional balance of meals. It can also suggest optimal study times based on meal timings. This allows it to provide study plans that take students' dietary habits into account.

[0092] The learning support system can also collect students' sleep data and reflect it in their study plans. For example, it can monitor sleep quality and propose study plans that prioritize rest for students who are sleep-deprived. It can also suggest optimal study times based on sleep data. This makes it possible to provide study plans that take students' sleep status into account.

[0093] The learning support system can also use a student's emotion estimation function to monitor stress levels in real time while studying and suggest relaxation methods at appropriate times. For example, when stress levels are high, it can suggest deep breathing or light stretching. The emotion estimation function can also be used to provide music that helps students relax. This can reduce students' stress and improve their learning efficiency.

[0094] The learning support system also uses a student's emotion estimation function to monitor fluctuations in motivation during learning in real time and send encouraging messages at appropriate times. For example, when a student's motivation is declining, it can send a message reminding them of past successes. Also, when a student is excited, it can use the emotion estimation function to send a message encouraging them to try harder. This helps maintain student motivation and increase their willingness to learn.

[0095] The learning support system can also use a student's emotion estimation function to monitor fluctuations in concentration during learning in real time and suggest breaks at appropriate times. For example, when concentration is declining, it can suggest a short break. The emotion estimation function can also be used to provide advice on how to maintain concentration when a student is concentrating. This helps maintain students' concentration and improves learning efficiency.

[0096] The learning support system can also use the student's emotion estimation function to provide learning content that matches the student's emotional state during the study. For example, when emotions are high, it can provide more difficult questions. On the other hand, when emotions are calm, it can provide learning content that helps students relax. This allows it to provide optimal learning content according to the student's emotional state.

[0097] The learning support system can also provide learning tools that suit each student's learning style. For example, visually-oriented students can be provided with learning materials that make extensive use of diagrams and graphs. Audio-oriented students can also be provided with audio-oriented learning materials. Furthermore, experience-oriented students can be provided with learning tools that include experiments and practical training. This makes it possible to provide optimal learning tools that suit each student's learning style.

[0098] Learning support systems can also visualize students' learning progress and make it easier to understand. For example, they can display fluctuations in study time and grades in graphs and charts. They can also provide interactive dashboards that allow students to check their progress in real time. This helps students intuitively understand their learning progress and maintain motivation to achieve their goals.

[0099] The learning support system can also compare a student's learning history data with other students and provide a benchmark. For example, a student can evaluate their own study time by comparing it with the average study time of students with the same goals. It can also provide a benchmark based on past performance data. This allows students to objectively evaluate their own learning situation and identify areas for improvement.

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

[0101] Step 1: The information gathering unit collects information about the student's preferred school, target score, study habits, and personality. For example, the conversational AI asks the student questions such as, "Which school do you want to attend?" and "How much time do you study per day?" and records the student's answers. The information gathering unit also collects information about the student's home study space and school's teaching style. Step 2: The study plan generator generates a personalized study plan based on the information collected by the information collector. For example, it suggests specific daily study content and time based on the entrance exam dates of the desired school, required subjects, and target scores. Step 3: The data aggregation unit aggregates learning history data from wearable devices and learning apps. For example, it collects data on study time and progress in learning apps to confirm whether students are progressing as planned. Step 4: The gap analysis unit analyzes the gap between the learning history data aggregated by the data aggregation unit and the plan. For example, if progress is slower than planned based on the learning history data, the gap analysis unit suggests additional study time. Step 5: The plan modification unit modifies the plan as appropriate based on the gaps analyzed by the gap analysis unit. For example, if progress is slow, the learning content is adjusted. Step 6: When motivation drops, the Motivation Support Team provides encouragement in a way that suits the student's personality, such as providing words of encouragement or specific advice.

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

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

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

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

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. The information gathering department collects information on students' preferred schools, target scores, study habits, and personalities. a learning plan generation unit that generates a personalized learning plan based on the information collected by the information collection unit; a data aggregation unit that aggregates learning history data from wearable devices and learning apps; a gap analysis unit that analyzes a gap between the learning history data aggregated by the data aggregation unit and a plan; a plan modification unit that appropriately modifies the plan based on the gap analyzed by the gap analysis unit; A motivation support unit that provides encouragement in a manner suited to the student's personality when motivation drops. A system characterized by:

2. The information collecting unit Collect information about the student's home study space and school teaching style and reflect it in the study plan.

2. The system of claim 1.

3. The learning plan generation unit Analyze the student's learning style (visual, auditory, experiential) and provide a learning plan accordingly 2. The system of claim 1.

4. The data aggregation unit Collecting the student's learning history data in real time and providing immediate feedback on the analysis results 2. The system of claim 1.

5. The plan correction unit Monitor the student's learning progress in real time and instantly modify the learning plan.

2. The system of claim 1.

6. The motivation support unit Monitor fluctuations in student motivation in real time and respond immediately 2. The system of claim 1.

7. The information collecting unit Analyzing the student's emotions during the conversation in real time and generating questions according to the emotions 2. The system of claim 1.

8. The learning plan generation unit Dynamically adjusting the learning plan according to the student's emotional state.

2. The system of claim 1.

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