System

Abstract

An object of a system according to an embodiment is to propose an optimal study plan based on an individual's behavior pattern and record.SOLUTION: A system according to an embodiment includes a data collection unit, a data analysis unit, and a study plan creation unit. The data collection unit collects data on a behavior pattern and a record of an individual. The data analysis unit analyzes the data collected by the data collection unit. The study plan generation unit generates a study plan based on a result of the analysis by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to create optimal study plans based on an individual's behavioral patterns and grades.

[0005] The system according to the embodiment aims to propose an optimal study plan based on an individual's behavioral patterns and grades. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a study plan generation unit. The data collection unit collects data related to an individual's behavioral patterns and grades. The data analysis unit analyzes the data collected by the data collection unit. The study plan generation unit generates a study plan based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal study plan based on an individual's behavioral patterns and grades. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The study plan proposal system according to an embodiment of the present invention is a system that learns and analyzes an individual's behavioral patterns and grades, and uses a generation AI to propose an optimal study plan. As a result, the study plan proposal system can provide an optimal study plan for an individual and support efficient learning.

[0029] A study plan proposal system according to an embodiment includes a data collection unit, a data analysis unit, and a study plan generation unit. The data collection unit collects data related to an individual's behavioral patterns and grades. For example, the data collection unit collects log data on the use of a learning app using a smartphone or a computer. The data collection unit can also collect school report cards. The data collection unit can also record daily study time and study content. The data analysis unit analyzes the collected data. For example, the data analysis unit uses a generation AI to understand an individual's study patterns and grade trends. The data analysis unit can also identify strengths and weaknesses in certain subjects or areas. The data analysis unit can also analyze the data using statistical analysis or machine learning algorithms. The study plan generation unit generates a study plan based on the analysis results. For example, the study plan generation unit uses the generation AI to generate an optimal study plan for an individual. The study plan generation unit can also suggest specific study content, study time, and study order. The study plan generation unit receives prompts, including instructions on what the user wants the generation AI to do, as input to the generation AI, and generates a study plan based on the prompts. As a result, the study plan proposal system according to the embodiment can provide an optimal study plan for an individual and support efficient learning.

[0030] The data collection unit can use a wearable device to collect biometric data such as heart rate and stress level and analyze the correlation with learning efficiency. For example, the data collection unit uses a wearable device to monitor heart rate and stress level in real time during learning and collects the data. For example, it analyzes heart rate fluctuations to measure concentration level and fatigue level. The data collection unit also uses a wearable device linked to a learning app to collect the user's biometric data. For example, it suggests taking a break to relax when stress levels are high. The data collection unit also analyzes the biometric data obtained from the wearable device and clarifies the correlation with learning efficiency. For example, it shows that studying during times when heart rate is stable improves efficiency. This makes it possible to collect biometric data and analyze the data to improve learning efficiency.

[0031] The data collection unit can collect not only school report cards but also data on extracurricular activities and hobbies to propose a comprehensive study plan. For example, the data collection unit collects data on the user's extracurricular activities and hobbies in addition to school report cards to propose a comprehensive study plan. For example, a balanced study plan is created taking into account data on sports and music activities. The data collection unit also adds a data input function for extracurricular activities and hobbies to the learning app to understand the user's overall lifestyle. For example, a study plan that takes into account weekend activities and time spent on hobbies is proposed. The data collection unit also integrates the user's grade data and extracurricular activity data to customize the study plan. For example, for a user who participates in a lot of sports, a study schedule that takes physical strength into account is proposed. This makes it possible to provide a study plan that takes into account the user's overall lifestyle.

[0032] The data collection unit can collect feedback from family and friends and perform data analysis that takes social factors into account. For example, the data collection unit adds a feedback function from family and friends to a learning app to collect opinions on the user's learning situation. For example, encouragement and advice from parents and friends is incorporated as data. The data collection unit also collects feedback from family and friends and performs data analysis that takes social factors into account. For example, it analyzes the impact of home environment and friendships on learning. The data collection unit also adds a function to the learning app that reflects feedback from family and friends and incorporates social factors into the user's learning plan. For example, it adjusts the learning schedule based on family opinions. This makes it possible to perform data analysis that takes social factors into account.

[0033] The data analysis unit can have the generation AI automatically generate quizzes and tests and analyze the results to measure the user's level of understanding of the learning content. For example, the data analysis unit has the generation AI automatically generate quizzes and tests to measure the user's level of understanding of the learning content. For example, the data analysis unit generates questions based on the learning content and analyzes the results. The data analysis unit also has the generation AI provide individually customized quizzes and tests to measure the user's level of understanding of the learning content. For example, the data analysis unit generates questions to strengthen the user's weak points. The data analysis unit also analyzes the results of the quizzes and tests automatically generated by the generation AI to evaluate the user's level of understanding. For example, the data analysis unit measures the user's level of understanding based on the percentage of correct answers and the time it took to answer. This makes it possible to automatically measure the user's level of understanding of the learning content and reflect the analysis results in the learning plan.

[0034] The data analysis unit can perform data analysis based on different learning styles and suggest the optimal learning method. For example, the data analysis unit performs data analysis based on different learning styles and suggests the optimal learning method for the user. For example, visual learning materials are suggested for a user for whom visual learning is effective. The data analysis unit also performs data analysis according to learning style and suggests a method for improving the user's learning efficiency. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The data analysis unit also performs data analysis based on different learning styles and customizes the user's learning method. For example, practical exercises are suggested for a user for whom tactile learning is effective. In this way, the optimal learning method can be provided to the user.

[0035] The data analysis unit can compare the analysis of learning patterns with data from other users and set benchmarks. For example, the data analysis unit compares the analysis of learning patterns with data from other users and sets benchmarks. For example, the data analysis unit evaluates learning progress by comparing with users of the same age group or grade. The data analysis unit also sets benchmarks for learning patterns based on data from other users and evaluates the user's learning efficiency. For example, it uses the learning patterns of top performers as a reference. The data analysis unit also compares the analysis of learning patterns with data from other users to identify the user's strengths and weaknesses. For example, it compares grades in the same subject and sets benchmarks. This makes it possible to evaluate learning patterns based on comparisons with other users.

[0036] The study plan generation unit can incorporate gamification elements into the study plan to increase the user's motivation. The study plan generation unit, for example, incorporates gamification elements into the study plan to increase the user's motivation. For example, a system can be introduced that allows the user to earn points and badges according to their learning progress. The study plan generation unit can also use gamification elements to make the study plan fun. For example, the study content can be put into a game format, and the user can progress through quizzes and challenges. The study plan generation unit can also incorporate gamification elements into the study plan to maintain the user's motivation. For example, rewards and benefits can be provided each time a learning goal is achieved. In this way, incorporating gamification elements can increase the user's motivation.

[0037] The study plan generation unit can incorporate refreshment time and break times into the study plan to support efficient learning. The study plan generation unit, for example, incorporates refreshment time and break times into the study plan to support efficient learning. For example, it suggests a five-minute break every hour. The study plan generation unit also incorporates refreshment time and break times into the study plan to maintain the user's concentration. For example, short breaks can improve learning efficiency. The study plan generation unit also incorporates break times into the study plan to reduce user fatigue. For example, it suggests activities to refresh the user after a long period of study. In this way, efficient learning can be supported by incorporating refreshment time and break times.

[0038] The study plan generation unit can incorporate time for collaborative learning and discussion with other users into the study plan, thereby enhancing the social learning effect. The study plan generation unit, for example, incorporates time for collaborative learning and discussion with other users into the study plan, thereby enhancing the social learning effect. For example, it suggests online group discussions. The study plan generation unit also incorporates time for collaborative learning and discussion into the study plan to deepen the user's understanding. For example, it includes pair work and group projects in the plan. The study plan generation unit also incorporates time for interaction with other users into the study plan, thereby promoting the social learning effect. For example, it suggests a discussion forum related to the learning content. This makes it possible to enhance the social learning effect through collaborative learning and discussion with other users.

[0039] The feedback unit can introduce success stories of other users when providing feedback to increase motivation. The feedback unit, for example, can introduce success stories of other users when providing feedback to increase motivation. For example, it can share the experiences of users who have overcome the same challenges. The feedback unit can also incorporate success stories of other users into the feedback to increase user motivation. For example, it can provide specific advice based on the success stories. The feedback unit can also introduce success stories of other users when providing feedback to provide inspiration for users to achieve their goals. For example, it can introduce success stories in the form of videos or articles. In this way, it is possible to increase user motivation by introducing success stories of other users.

[0040] The feedback unit can convert the content of the feedback into visual notes or infographics to make it easier to understand visually. For example, the feedback unit can convert the content of the feedback into visual notes or infographics to make it easier to understand visually. For example, the feedback unit can display learning progress and results in graphs or charts. The feedback unit can also visually communicate the content of the feedback using visual notes or infographics. For example, the progress of the learning plan can be shown using icons or illustrations. The feedback unit can also create visual notes or infographics to make the content of the feedback easier to understand visually. For example, the feedback unit can make it easier to understand learning results at a glance. This can make the content of the feedback easier to understand visually.

[0041] The motivation maintenance unit allows the generation AI to automatically visualize the learning progress, allowing the user to feel a sense of accomplishment. For example, the motivation maintenance unit allows the generation AI to automatically visualize the learning progress, allowing the user to feel a sense of accomplishment. For example, the learning progress is displayed in a graph or chart. The motivation maintenance unit also allows the generation AI to visualize the learning progress, increasing the user's motivation. For example, the degree of achievement of learning goals is shown with icons or illustrations. The motivation maintenance unit also allows the user to feel a sense of accomplishment by visualizing the learning progress. For example, the learning results can be grasped at a glance. In this way, visualizing the learning progress can allow the user to feel a sense of accomplishment.

[0042] The motivation maintenance unit can incorporate a competitive element with other users and provide rankings and badges. For example, the motivation maintenance unit can incorporate a competitive element with other users to maintain motivation and provide rankings and badges. For example, a ranking can be displayed according to learning progress, and badges can be earned by placing high. The motivation maintenance unit can also incorporate a competitive element to increase user motivation. For example, a system can be introduced in which points can be earned according to learning results and users can compete for rankings. The motivation maintenance unit can also incorporate a competitive element with other users into the learning plan to maintain motivation. For example, a system can be provided in which badges can be earned each time a learning goal is achieved. In this way, the motivation of the user can be increased by incorporating an element of competition with other users.

[0043] The motivation maintenance unit allows a user to share their learning progress with family and friends and receive social support. The motivation maintenance unit, for example, allows a user to share their learning progress with family and friends and receive social support. For example, they can share their learning results on social media and receive encouragement from family and friends. The motivation maintenance unit also increases the user's motivation by sharing their learning progress. For example, they can share their learning goals with family and friends and receive encouragement for achieving them. The motivation maintenance unit also allows a user to share their learning progress with family and friends and receive social support. For example, they can report their learning results and receive feedback from family and friends. In this way, they can receive social support by sharing their learning progress.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The data collection unit can also collect data about the user's learning environment. For example, it collects environmental data such as the lighting, temperature, and noise level of the learning location and analyzes the impact on learning efficiency. The data collection unit can also suggest an optimal learning environment based on the data about the user's learning environment. For example, if a quiet environment is required, it can suggest the use of noise-canceling headphones. The data collection unit can also provide real-time feedback according to changes in the learning environment. For example, if the lighting is dim, it can advise the user to brighten it. This can optimize the user's learning environment and improve learning efficiency.

[0046] The data collection unit can also collect data on the user's diet and sleep patterns. For example, it can record the type of food the user eats and the amount and quality of sleep they get, and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal study schedule based on the user's diet and sleep patterns. For example, it can schedule important study content for the time period after a sufficient amount of sleep. The data collection unit can also provide real-time feedback based on changes in the user's diet and sleep patterns. For example, it can suggest taking a break if the user is not getting enough sleep. This makes it possible to provide a study plan that takes the user's lifestyle habits into account.

[0047] The data collection unit can also collect data on the user's exercise habits. For example, it can record how much exercise the user does, the type and frequency of exercise, and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal study schedule based on the user's exercise habits. For example, it can adjust the plan so that learning occurs in a refreshed state after exercise. The data collection unit can also provide real-time feedback according to changes in the user's exercise habits. For example, it can suggest light exercise if the user is not getting enough exercise. This makes it possible to provide a study plan that takes the user's exercise habits into consideration.

[0048] The data collection unit can also collect data on the user's learning style. For example, it can record whether the user prefers visual, auditory, or tactile learning styles and analyze the impact on learning efficiency. The data collection unit can also suggest optimal learning methods based on the user's learning style. For example, it can suggest visual learning materials to a user who is an effective visual learner. The data collection unit can also provide real-time feedback according to changes in the user's learning style. For example, it can suggest audio learning materials if auditory learning is effective. This makes it possible to provide a learning plan that takes the user's learning style into consideration.

[0049] The data collection unit can also collect data on the user's learning goals. For example, it can record the short-term and long-term goals set by the user and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal learning plan based on the learning goals. For example, it can suggest specific steps to achieve short-term goals. The data collection unit can also provide real-time feedback as the user's learning goals change. For example, it can advise the user to reset their goals if they are having difficulty achieving them. This makes it possible to provide a learning plan that takes the user's learning goals into consideration.

[0050] The data collection unit can also collect data on the user's learning interests. For example, it can record the subjects and topics in which the user is interested and analyze the impact this has on learning efficiency. The data collection unit can also suggest optimal study plans based on the user's interests. For example, it can prioritize subjects that the user is interested in and incorporate them into the study plan. The data collection unit can also provide real-time feedback according to changes in the user's interests. For example, it can suggest new topics if interest is waning. This makes it possible to provide a study plan that takes the user's interests into consideration.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The data collection unit collects data on individual behavioral patterns and grades. For example, the data collection unit may collect log data when using a learning app on a smartphone or computer. It may also record school report cards, daily study time, and study content. Step 2: The data analysis unit analyzes the collected data. For example, generative AI can be used to understand an individual's learning patterns and academic performance trends, and identify strengths and weaknesses in specific subjects and areas. Statistical analysis and machine learning algorithms can also be used to analyze the data. Step 3: The study plan generator generates a study plan based on the analysis results. For example, it uses the generation AI to generate an optimal study plan for each individual, proposing specific study content, study time, and study order. It also receives prompts from the user that include instructions on what the user wants the generation AI to do, and generates a study plan based on those prompts.

[0053] (Example 2) The study plan proposal system according to an embodiment of the present invention is a system that learns and analyzes an individual's behavioral patterns and grades, and uses a generation AI to propose an optimal study plan. As a result, the study plan proposal system can provide an optimal study plan for an individual and support efficient learning.

[0054] A study plan proposal system according to an embodiment includes a data collection unit, a data analysis unit, and a study plan generation unit. The data collection unit collects data related to an individual's behavioral patterns and grades. For example, the data collection unit collects log data on the use of a learning app using a smartphone or a computer. The data collection unit can also collect school report cards. The data collection unit can also record daily study time and study content. The data analysis unit analyzes the collected data. For example, the data analysis unit uses a generation AI to understand an individual's study patterns and grade trends. The data analysis unit can also identify strengths and weaknesses in certain subjects or areas. The data analysis unit can also analyze the data using statistical analysis or machine learning algorithms. The study plan generation unit generates a study plan based on the analysis results. For example, the study plan generation unit uses the generation AI to generate an optimal study plan for an individual. The study plan generation unit can also suggest specific study content, study time, and study order. The study plan generation unit receives prompts, including instructions on what the user wants the generation AI to do, as input to the generation AI, and generates a study plan based on the prompts. As a result, the study plan proposal system according to the embodiment can provide an optimal study plan for an individual and support efficient learning.

[0055] The data collection unit can analyze the user's facial expressions and voice while using the learning app to collect emotional data. For example, the data collection unit uses a camera and microphone to analyze the user's facial expressions and voice in real time while using the learning app to collect emotional data. For example, the data collection unit analyzes the user's facial expressions and tone of voice while the user is solving a problem to measure stress and concentration levels. The data collection unit also incorporates an emotion analysis function into the learning app to record emotional changes when the user tackles a specific task. For example, the data collection unit analyzes reactions to difficult problems to collect emotional data. The data collection unit also collects emotional data from the user while using the learning app and provides real-time feedback based on the user's learning progress and level of understanding. For example, the data collection unit displays an encouraging message when the user is experiencing difficulty. In this way, the user's emotional data can be collected and reflected in a study plan.

[0056] The data collection unit can use a wearable device to collect biometric data such as heart rate and stress level and analyze the correlation with learning efficiency. For example, the data collection unit uses a wearable device to monitor heart rate and stress level in real time during learning and collects the data. For example, it analyzes heart rate fluctuations to measure concentration level and fatigue level. The data collection unit also uses a wearable device linked to a learning app to collect the user's biometric data. For example, it suggests taking a break to relax when stress levels are high. The data collection unit also analyzes the biometric data obtained from the wearable device and clarifies the correlation with learning efficiency. For example, it shows that studying during times when heart rate is stable improves efficiency. This makes it possible to collect biometric data and analyze the data to improve learning efficiency.

[0057] The data collection unit can use the emotion estimation function to monitor the user's emotions in real time while studying and collect data according to changes in emotions. For example, the data collection unit incorporates the emotion estimation function into a learning app and analyzes the user's facial expressions and voice to monitor emotions in real time. For example, it records emotional changes when the user solves problems. The data collection unit also uses the emotion estimation function to collect emotional data of the user while studying and provides feedback according to the user's progress and level of understanding. For example, it displays an encouraging message when the user experiences difficulty. The data collection unit also utilizes the emotion estimation function to collect emotional data of the user in real time while using the learning app. For example, it records emotional changes when the user tackles a specific task and uses the information to help adjust the study plan. This allows the user's emotional data to be collected in real time and reflected in the study plan.

[0058] The data collection unit can collect not only school report cards but also data on extracurricular activities and hobbies to propose a comprehensive study plan. For example, the data collection unit collects data on the user's extracurricular activities and hobbies in addition to school report cards to propose a comprehensive study plan. For example, a balanced study plan is created taking into account data on sports and music activities. The data collection unit also adds a data input function for extracurricular activities and hobbies to the learning app to understand the user's overall lifestyle. For example, a study plan that takes into account weekend activities and time spent on hobbies is proposed. The data collection unit also integrates the user's grade data and extracurricular activity data to customize the study plan. For example, for a user who participates in a lot of sports, a study schedule that takes physical strength into account is proposed. This makes it possible to provide a study plan that takes into account the user's overall lifestyle.

[0059] The data collection unit can collect feedback from family and friends and perform data analysis that takes social factors into account. For example, the data collection unit adds a feedback function from family and friends to a learning app to collect opinions on the user's learning situation. For example, encouragement and advice from parents and friends is incorporated as data. The data collection unit also collects feedback from family and friends and performs data analysis that takes social factors into account. For example, it analyzes the impact of home environment and friendships on learning. The data collection unit also adds a function to the learning app that reflects feedback from family and friends and incorporates social factors into the user's learning plan. For example, it adjusts the learning schedule based on family opinions. This makes it possible to perform data analysis that takes social factors into account.

[0060] The data collection unit can use the emotion estimation function to collect the user's emotional reactions to the learning environment and suggest an optimal learning environment. The data collection unit, for example, incorporates the emotion estimation function into a learning app and collects the user's emotional reactions to room brightness and volume. For example, the data collection unit analyzes the emotional data in response to changes in brightness and volume and suggests an optimal learning environment. The data collection unit also uses the emotion estimation function to collect the user's emotional data about the learning environment and adjusts the environment to improve learning efficiency. For example, if a quiet environment is preferred, the data collection unit suggests noise cancellation. The data collection unit also adds the emotion estimation function to the learning app and monitors the user's emotional reactions to the learning environment in real time. For example, the data collection unit suggests ways to improve concentration by adjusting the room brightness and volume. This makes it possible to provide an optimal learning environment based on the user's emotional reactions.

[0061] The data analysis unit can take into account the user's emotional data when analyzing learning patterns and analyze the impact of emotional changes on learning outcomes. For example, the data analysis unit takes into account the user's emotional data when analyzing learning patterns and analyzes the impact of emotional changes on learning outcomes. For example, it analyzes how positive emotions affect learning outcomes. The data analysis unit also analyzes changes in learning patterns based on the emotional data and clarifies the impact of emotional changes on learning efficiency. For example, it analyzes learning outcomes during periods of high stress. The data analysis unit also incorporates emotional data into the analysis of learning patterns and quantitatively evaluates the impact of emotional changes on learning outcomes. For example, it analyzes the correlation between emotional scores and learning outcomes. This makes it possible to analyze the impact of emotional changes on learning outcomes and reflect it in learning plans.

[0062] The data analysis unit can have the generation AI automatically generate quizzes and tests and analyze the results to measure the user's level of understanding of the learning content. For example, the data analysis unit has the generation AI automatically generate quizzes and tests to measure the user's level of understanding of the learning content. For example, the data analysis unit generates questions based on the learning content and analyzes the results. The data analysis unit also has the generation AI provide individually customized quizzes and tests to measure the user's level of understanding of the learning content. For example, the data analysis unit generates questions to strengthen the user's weak points. The data analysis unit also analyzes the results of the quizzes and tests automatically generated by the generation AI to evaluate the user's level of understanding. For example, the data analysis unit measures the user's level of understanding based on the percentage of correct answers and the time it took to answer. This makes it possible to automatically measure the user's level of understanding of the learning content and reflect the analysis results in the learning plan.

[0063] The data analysis unit can perform data analysis based on different learning styles and suggest the optimal learning method. For example, the data analysis unit performs data analysis based on different learning styles and suggests the optimal learning method for the user. For example, visual learning materials are suggested for a user for whom visual learning is effective. The data analysis unit also performs data analysis according to learning style and suggests a method for improving the user's learning efficiency. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The data analysis unit also performs data analysis based on different learning styles and customizes the user's learning method. For example, practical exercises are suggested for a user for whom tactile learning is effective. In this way, the optimal learning method can be provided to the user.

[0064] The data analysis unit can compare the analysis of learning patterns with data from other users and set benchmarks. For example, the data analysis unit compares the analysis of learning patterns with data from other users and sets benchmarks. For example, the data analysis unit evaluates learning progress by comparing with users of the same age group or grade. The data analysis unit also sets benchmarks for learning patterns based on data from other users and evaluates the user's learning efficiency. For example, it uses the learning patterns of top performers as a reference. The data analysis unit also compares the analysis of learning patterns with data from other users to identify the user's strengths and weaknesses. For example, it compares grades in the same subject and sets benchmarks. This makes it possible to evaluate learning patterns based on comparisons with other users.

[0065] The data analysis unit can use the emotion estimation function to analyze the stress level felt by the user while studying and propose a study plan to reduce stress. The data analysis unit, for example, uses the emotion estimation function to analyze the stress level felt by the user while studying. For example, it calculates a stress score by analyzing facial expressions and voice while studying. The data analysis unit also proposes a study plan to reduce the user's stress based on the stress level analysis results. For example, it suggests a relaxing study environment and break times. The data analysis unit also uses the emotion estimation function to monitor the user's stress level in real time and adjust the study plan to reduce stress. For example, it suggests lighter study content during times of high stress. In this way, the user's stress level can be analyzed and a study plan to reduce stress can be provided.

[0066] The study plan generation unit can take into account the user's emotional data and suggest study content that will elicit positive emotions. The study plan generation unit, for example, takes into account the user's emotional data in a study plan and suggests study content that will elicit positive emotions. For example, it prioritizes incorporating subjects and topics that the user enjoys learning. The study plan generation unit also suggests study content that will make the user feel positive emotions based on the emotional data. For example, it creates a study plan centered around themes that the user is interested in or subjects that the user is good at. The study plan generation unit also reflects the emotional data in the study plan and suggests study content that will elicit positive emotions. For example, it sets short-term goals that are likely to give the user a sense of accomplishment. This makes it possible to provide study content that will elicit positive emotions in the user.

[0067] The study plan generation unit can incorporate gamification elements into the study plan to increase the user's motivation. The study plan generation unit, for example, incorporates gamification elements into the study plan to increase the user's motivation. For example, a system can be introduced that allows the user to earn points and badges according to their learning progress. The study plan generation unit can also use gamification elements to make the study plan fun. For example, the study content can be put into a game format, and the user can progress through quizzes and challenges. The study plan generation unit can also incorporate gamification elements into the study plan to maintain the user's motivation. For example, rewards and benefits can be provided each time a learning goal is achieved. In this way, incorporating gamification elements can increase the user's motivation.

[0068] The study plan generation unit can use the emotion estimation function to identify the time periods when the user can concentrate best and propose a study plan tailored to that time period. The study plan generation unit, for example, uses the emotion estimation function to identify the time periods when the user can concentrate best and propose a study plan tailored to that time period. For example, important study content is placed during times when concentration is strongest. The study plan generation unit also analyzes the user's emotion data to identify the time periods when the user can concentrate best. For example, the plan is adjusted so that study takes place during times when the emotion score is high. The study plan generation unit also uses the emotion estimation function to identify the time periods when the user can concentrate best and create a study plan tailored to that time period. For example, lighter study content is proposed during times when concentration is weaker. This makes it possible to provide a study plan tailored to the time periods when the user can concentrate best.

[0069] The study plan generation unit can incorporate refreshment time and break times into the study plan to support efficient learning. The study plan generation unit, for example, incorporates refreshment time and break times into the study plan to support efficient learning. For example, it suggests a five-minute break every hour. The study plan generation unit also incorporates refreshment time and break times into the study plan to maintain the user's concentration. For example, short breaks can improve learning efficiency. The study plan generation unit also incorporates break times into the study plan to reduce user fatigue. For example, it suggests activities to refresh the user after a long period of study. In this way, efficient learning can be supported by incorporating refreshment time and break times.

[0070] The study plan generation unit can incorporate time for collaborative learning and discussion with other users into the study plan, thereby enhancing the social learning effect. The study plan generation unit, for example, incorporates time for collaborative learning and discussion with other users into the study plan, thereby enhancing the social learning effect. For example, it suggests online group discussions. The study plan generation unit also incorporates time for collaborative learning and discussion into the study plan to deepen the user's understanding. For example, it includes pair work and group projects in the plan. The study plan generation unit also incorporates time for interaction with other users into the study plan, thereby promoting the social learning effect. For example, it suggests a discussion forum related to the learning content. This makes it possible to enhance the social learning effect through collaborative learning and discussion with other users.

[0071] The study plan generation unit can use the emotion estimation function to monitor how the user feels about the study plan and adjust the plan. The study plan generation unit, for example, uses the emotion estimation function to monitor how the user feels about the study plan and adjust the plan. For example, if the user is feeling stressed, the plan is relaxed. The study plan generation unit also monitors the user's emotions about the study plan based on the user's emotion data and adjusts the plan as necessary. For example, if there are few positive emotions, the plan is revised. The study plan generation unit also uses the emotion estimation function to monitor the user's emotion data in real time and adjust the study plan. For example, content that the user can enjoy learning is added. This makes it possible to adjust the study plan based on the user's emotions.

[0072] The feedback unit can take the user's emotional data into consideration when providing feedback and provide positive feedback preferentially. For example, the feedback unit can use words of praise that make the user feel a sense of accomplishment. The feedback unit can also provide positive feedback to the user based on the emotional data. For example, the feedback can emphasize the points where the user has made efforts. The feedback unit can also take the user's emotional data into consideration when providing feedback and provide feedback that makes the user feel positive. For example, the feedback unit can specifically show the user's progress and encourage them. In this way, the feedback can be provided taking the user's emotional data into consideration.

[0073] The feedback unit can use the emotion estimation function to analyze what emotions the user has in response to the feedback and make adjustments based on the emotions. The feedback unit, for example, uses the emotion estimation function to analyze what emotions the user has in response to the feedback and make adjustments based on the emotions. For example, if the user has strong negative emotions, the feedback unit reconsiders the content of the feedback. The feedback unit also analyzes the user's emotions in response to the feedback based on the user's emotion data and makes adjustments to elicit positive emotions. For example, the feedback unit provides feedback that encourages the user. The feedback unit also uses the emotion estimation function to analyze the user's emotion data and adjusts the content of the feedback. For example, the feedback unit provides feedback that motivates the user. This makes it possible to adjust the feedback based on the user's emotions.

[0074] The feedback unit can introduce success stories of other users when providing feedback to increase motivation. The feedback unit, for example, can introduce success stories of other users when providing feedback to increase motivation. For example, it can share the experiences of users who have overcome the same challenges. The feedback unit can also incorporate success stories of other users into the feedback to increase user motivation. For example, it can provide specific advice based on the success stories. The feedback unit can also introduce success stories of other users when providing feedback to provide inspiration for users to achieve their goals. For example, it can introduce success stories in the form of videos or articles. In this way, it is possible to increase user motivation by introducing success stories of other users.

[0075] The feedback unit can convert the content of the feedback into visual notes or infographics to make it easier to understand visually. For example, the feedback unit can convert the content of the feedback into visual notes or infographics to make it easier to understand visually. For example, the feedback unit can display learning progress and results in graphs or charts. The feedback unit can also visually communicate the content of the feedback using visual notes or infographics. For example, the progress of the learning plan can be shown using icons or illustrations. The feedback unit can also create visual notes or infographics to make the content of the feedback easier to understand visually. For example, the feedback unit can make it easier to understand learning results at a glance. This can make the content of the feedback easier to understand visually.

[0076] The feedback unit can use the emotion estimation function to monitor the emotion of the user when receiving feedback and provide feedback at optimal timing. The feedback unit, for example, uses the emotion estimation function to monitor the emotion of the user when receiving feedback and provide feedback at optimal timing. For example, feedback is provided when the user is relaxed. The feedback unit also monitors the emotion of the user when receiving feedback based on the user's emotion data and provides feedback at appropriate timing. For example, feedback is provided when the user is concentrating. The feedback unit also uses the emotion estimation function to monitor the user's emotion data in real time and provide feedback at optimal timing. For example, feedback is provided when the user is feeling positive emotions. This makes it possible to provide feedback at optimal timing based on the user's emotion.

[0077] The motivation maintenance unit can take the user's emotional data into consideration and set goals that will elicit positive emotions. For example, in order to maintain motivation, the motivation maintenance unit can take the user's emotional data into consideration and set goals that will elicit positive emotions. For example, it can set short-term goals that will make the user feel a sense of accomplishment. The motivation maintenance unit can also set goals that will make the user feel positive emotions based on the emotional data. For example, it can set goals related to topics that interest the user. The motivation maintenance unit can also set goals that take the emotional data into consideration and make the user feel positive emotions in order to maintain motivation. For example, it can set goals based on the user's successful experiences. This makes it possible to set goals that will elicit positive emotions from the user.

[0078] The motivation maintenance unit allows the generation AI to automatically visualize the learning progress, allowing the user to feel a sense of accomplishment. For example, the motivation maintenance unit allows the generation AI to automatically visualize the learning progress, allowing the user to feel a sense of accomplishment. For example, the learning progress is displayed in a graph or chart. The motivation maintenance unit also allows the generation AI to visualize the learning progress, increasing the user's motivation. For example, the degree of achievement of learning goals is shown with icons or illustrations. The motivation maintenance unit also allows the user to feel a sense of accomplishment by visualizing the learning progress. For example, the learning results can be grasped at a glance. In this way, visualizing the learning progress can allow the user to feel a sense of accomplishment.

[0079] The motivation maintenance unit can use the emotion estimation function to identify the factor that most motivates the user and provide support based on that factor. The motivation maintenance unit, for example, uses the emotion estimation function to identify the factor that most motivates the user and provide support based on that factor. For example, it identifies the factor that makes the user feel a sense of accomplishment and provides feedback based on that. The motivation maintenance unit also analyzes the user's emotion data to identify the factor that most motivates the user. For example, it provides support centered on themes that the user is interested in or subjects that the user is good at. The motivation maintenance unit also uses the emotion estimation function to identify the factor that increases the user's motivation and provides support based on that factor. For example, it suggests content that the user can enjoy learning. This makes it possible to provide support based on the factor that most motivates the user.

[0080] The motivation maintenance unit can incorporate a competitive element with other users and provide rankings and badges. For example, the motivation maintenance unit can incorporate a competitive element with other users to maintain motivation and provide rankings and badges. For example, a ranking can be displayed according to learning progress, and badges can be earned by placing high. The motivation maintenance unit can also incorporate a competitive element to increase user motivation. For example, a system can be introduced in which points can be earned according to learning results and users can compete for rankings. The motivation maintenance unit can also incorporate a competitive element with other users into the learning plan to maintain motivation. For example, a system can be provided in which badges can be earned each time a learning goal is achieved. In this way, the motivation of the user can be increased by incorporating an element of competition with other users.

[0081] The motivation maintenance unit allows a user to share their learning progress with family and friends and receive social support. The motivation maintenance unit, for example, allows a user to share their learning progress with family and friends and receive social support. For example, they can share their learning results on social media and receive encouragement from family and friends. The motivation maintenance unit also increases the user's motivation by sharing their learning progress. For example, they can share their learning goals with family and friends and receive encouragement for achieving them. The motivation maintenance unit also allows a user to share their learning progress with family and friends and receive social support. For example, they can report their learning results and receive feedback from family and friends. In this way, they can receive social support by sharing their learning progress.

[0082] The motivation maintenance unit can use the emotion estimation function to monitor how the user feels about learning and propose motivation maintenance measures based on the emotions. The motivation maintenance unit, for example, can use the emotion estimation function to monitor how the user feels about learning and propose motivation maintenance measures based on the emotions. For example, it can propose learning content that will evoke positive emotions in the user. The motivation maintenance unit can also monitor the user's emotions about learning based on the user's emotion data and propose motivation maintenance measures. For example, it can prioritize suggesting content that the user can enjoy while learning. The motivation maintenance unit can also use the emotion estimation function to monitor the user's emotion data in real time and propose motivation maintenance measures based on the emotions. For example, it can set goals that will make the user feel a sense of accomplishment. This makes it possible to propose motivation maintenance measures based on the user's emotions.

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

[0084] The data collection unit can also collect data about the user's learning environment. For example, it collects environmental data such as the lighting, temperature, and noise level of the learning location and analyzes the impact on learning efficiency. The data collection unit can also suggest an optimal learning environment based on the data about the user's learning environment. For example, if a quiet environment is required, it can suggest the use of noise-canceling headphones. The data collection unit can also provide real-time feedback according to changes in the learning environment. For example, if the lighting is dim, it can advise the user to brighten it. This can optimize the user's learning environment and improve learning efficiency.

[0085] The data collection unit can also collect data on the user's diet and sleep patterns. For example, it can record the type of food the user eats and the amount and quality of sleep they get, and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal study schedule based on the user's diet and sleep patterns. For example, it can schedule important study content for the time period after a sufficient amount of sleep. The data collection unit can also provide real-time feedback based on changes in the user's diet and sleep patterns. For example, it can suggest taking a break if the user is not getting enough sleep. This makes it possible to provide a study plan that takes the user's lifestyle habits into account.

[0086] The data collection unit can also collect data on the user's exercise habits. For example, it can record how much exercise the user does, the type and frequency of exercise, and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal study schedule based on the user's exercise habits. For example, it can adjust the plan so that learning occurs in a refreshed state after exercise. The data collection unit can also provide real-time feedback according to changes in the user's exercise habits. For example, it can suggest light exercise if the user is not getting enough exercise. This makes it possible to provide a study plan that takes the user's exercise habits into consideration.

[0087] The data collection unit can also collect data on the user's learning style. For example, it can record whether the user prefers visual, auditory, or tactile learning styles and analyze the impact on learning efficiency. The data collection unit can also suggest optimal learning methods based on the user's learning style. For example, it can suggest visual learning materials to a user who is an effective visual learner. The data collection unit can also provide real-time feedback according to changes in the user's learning style. For example, it can suggest audio learning materials if auditory learning is effective. This makes it possible to provide a learning plan that takes the user's learning style into consideration.

[0088] The data collection unit can also collect data on the user's learning goals. For example, it can record the short-term and long-term goals set by the user and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal learning plan based on the learning goals. For example, it can suggest specific steps to achieve short-term goals. The data collection unit can also provide real-time feedback as the user's learning goals change. For example, it can advise the user to reset their goals if they are having difficulty achieving them. This makes it possible to provide a learning plan that takes the user's learning goals into consideration.

[0089] The data collection unit can also collect data on the user's motivation to learn. For example, it can record the situations in which the user's motivation increases and analyze the impact on learning efficiency. The data collection unit can also propose an optimal study plan based on motivation. For example, it can arrange important study content during times when motivation is high. The data collection unit can also provide real-time feedback according to changes in the user's motivation. For example, it can display an encouraging message if motivation is declining. This makes it possible to provide a study plan that takes the user's motivation into consideration.

[0090] The data collection unit can also collect data on the user's stress level regarding learning. For example, it can record the situations in which the user feels stressed and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal learning plan based on the stress level. For example, it can arrange important learning content during times when stress is low. The data collection unit can also provide real-time feedback according to changes in the user's stress level. For example, if stress is increasing, it can suggest taking a break to relax. This makes it possible to provide a learning plan that takes the user's stress level into consideration.

[0091] The data collection unit can also collect data on the user's feelings about learning. For example, it can record the situations in which the user feels positive emotions and analyze the impact on learning efficiency. The data collection unit can also suggest an optimal study plan based on emotions. For example, it can arrange important study content during times when positive emotions are high. The data collection unit can also provide real-time feedback according to changes in the user's emotions. For example, it can display an encouraging message if positive emotions are declining. This makes it possible to provide a study plan that takes the user's emotions into consideration.

[0092] The data collection unit can also collect data on the user's level of concentration when studying. For example, it can record the situations in which the user's concentration improves and analyze the impact on study efficiency. The data collection unit can also suggest an optimal study plan based on the user's level of concentration. For example, it can arrange important study content during times when the user is most likely to concentrate. The data collection unit can also provide real-time feedback according to changes in the user's level of concentration. For example, it can suggest taking a break if the user's concentration is declining. This makes it possible to provide a study plan that takes the user's level of concentration into account.

[0093] The data collection unit can also collect data on the user's learning interests. For example, it can record the subjects and topics in which the user is interested and analyze the impact this has on learning efficiency. The data collection unit can also suggest optimal study plans based on the user's interests. For example, it can prioritize subjects that the user is interested in and incorporate them into the study plan. The data collection unit can also provide real-time feedback according to changes in the user's interests. For example, it can suggest new topics if interest is waning. This makes it possible to provide a study plan that takes the user's interests into consideration.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The data collection unit collects data on individual behavioral patterns and grades. For example, the data collection unit may collect log data when using a learning app on a smartphone or computer. It may also record school report cards, daily study time, and study content. Step 2: The data analysis unit analyzes the collected data. For example, generative AI can be used to understand an individual's learning patterns and academic performance trends, and identify strengths and weaknesses in specific subjects and areas. Statistical analysis and machine learning algorithms can also be used to analyze the data. Step 3: The study plan generator generates a study plan based on the analysis results. For example, it uses the generation AI to generate an optimal study plan for each individual, proposing specific study content, study time, and study order. It also receives prompts from the user that include instructions on what the user wants the generation AI to do, and generates a study plan based on those prompts.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] 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]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection department that collects data on individual behavior patterns and performance; a data analysis unit that analyzes the data collected by the data collection unit; a study plan generation unit that generates a study plan based on the results of the analysis by the data analysis unit. A system characterized by:

2. The data collection unit Analyzing users' facial expressions and voices while using learning apps to collect emotional data 2. The system of claim 1.

3. The data collection unit Wearable devices will be used to collect biometric data such as heart rate and stress levels, and the correlation with learning efficiency will be analyzed.

2. The system of claim 1.

4. The data collection unit The emotions of the user during learning are monitored in real time, and the data is collected in response to changes in the emotions.

2. The system of claim 1.

5. The data collection unit Collect not only school report cards but also the above data on extracurricular activities and hobbies to propose a comprehensive study plan 2. The system of claim 1.

6. The data collection unit Collect feedback from family and friends and analyze data that takes social factors into account 2. The system of claim 1.

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