system

Abstract

The system according to the embodiment aims to predict early signs of mental stress in students and notify them appropriately. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a notification unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. The notification unit notifies the user of the signs of stress predicted by the prediction unit.

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 had the problem of making it difficult to detect signs of mental stress in students early and respond appropriately.

[0005] The system according to the embodiment aims to predict early signs of mental stress in students and notify them appropriately. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a notification unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. The notification unit notifies the user of the signs of stress predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict signs of mental stress in students at an early stage and notify them appropriately. [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) A stress detection system according to an embodiment of the present invention utilizes a generative AI to detect subtle changes in students' behavior and predict their mental stress. In this stress detection system, the generative AI detects subtle changes that can be observed in students' everyday school life, such as sloppy handwriting, increased forgetting, the content of essays and impressions, the speed and appetite of eating lunch, and how students spend their recess. Next, the system predicts students' mental stress based on the subtle changes detected by the generative AI. Finally, the generative AI notifies teachers and counselors of the predicted stress signs. This allows teachers and counselors to respond promptly and reduce students' mental stress. For example, with regard to data collection methods, teachers scan students' notebooks and homework for sloppy handwriting and collect the data digitally. Teachers record the increase in forgetting items using a checklist and collect the data digitally. The content of essays and impressions submitted by students is scanned and collected digitally. Teachers observe and record the speed and appetite of eating lunch and collect the data digitally. Teachers observe and record how students spend their recess time, collecting the data digitally. Regarding the analysis methods used by the AI, for example, to analyze disordered handwriting, the AI ​​analyzes changes in character shape and pen pressure. Regarding an increase in forgetting things, the AI ​​analyzes checklist data to determine the frequency and patterns of forgetting things. Regarding the content of impressions and essays, the AI ​​performs text analysis to analyze changes in emotions and themes. Regarding lunch eating speed and appetite, the AI ​​analyzes observational data to analyze changes in appetite and eating speed. Regarding how students spend their recess time, the AI ​​analyzes observational data to analyze changes in behavioral patterns. Finally, regarding notification methods, for example, if the AI ​​detects signs of stress, it will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the underlying data, allowing teachers and counselors to respond quickly. This enables the stress detection system to detect even the slightest changes in students' behavior, predict mental stress, and notify teachers and counselors, enabling early response.

[0029] A stress detection system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a notification unit. The collection unit collects data from students' everyday school lives. For example, the collection unit collects data such as poor handwriting, an increase in forgetting things, the content of impressions and essays, the speed and appetite of students eating lunch, and how students spend recess. The collection unit may, for example, be scanned by a teacher to collect digital data from students' notebooks and homework. The collection unit may also be configured to record an increase in students forgetting things using a checklist and collect the digital data from the record. The collection unit may also be configured to scan impressions and essays submitted by students to collect the digital data from the record. The collection unit may also collect data on the speed and appetite of students eating lunch, as observed and recorded by teachers. The collection unit may also collect data on how students spend recess, as observed and recorded by teachers. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may include a character analysis unit that analyzes changes in character shape and writing pressure. The analysis unit may also include a lost item analysis unit that analyzes the frequency and pattern of students forgetting things. The analysis unit may further include a text analysis unit that analyzes changes in emotions and themes. The analysis unit may also include an appetite analysis unit that analyzes changes in appetite and eating speed. The analysis unit may also include a behavior analysis unit that analyzes changes in behavioral patterns. The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. For example, the prediction unit predicts mental stress in students based on data such as poor handwriting and an increase in forgetfulness, the content of student impressions and essays, the speed and appetite of students eating lunch, and how they spend recess. The prediction unit can predict signs of stress based on the results of the data analysis using the generation AI. The notification unit notifies teachers and counselors of the signs of stress predicted by the prediction unit. For example, if the generation AI detects signs of stress, the notification unit notifies teachers and counselors in real time via email or app notification. The notification includes specific signs of stress and the underlying data, allowing teachers and counselors to respond quickly.As a result, the stress detection system according to the embodiment can detect even the slightest changes in students, predict mental stress, and notify teachers and counselors, enabling early response.

[0030] The collection unit can collect data including sloppy handwriting, an increase in forgetting things, the content of impressions and essays, the speed and appetite of eating lunch, and how students spend recess. For example, to collect data on sloppy handwriting, a teacher can scan students' notebooks and homework and collect the data as digital data. To collect data on an increase in forgetting things, a teacher can record the data using a checklist and collect the data as digital data. To collect data on the content of impressions and essays, a teacher can observe and record the data on how students eat lunch and their appetite and collect the data as digital data. To collect data on how students spend recess, a teacher can observe and record the data and collect the data as digital data. In this way, the collection unit can provide data for detecting signs of stress by collecting subtle changes that can be detected from students' everyday school life.

[0031] The analysis unit may include a character analysis unit that analyzes changes in character shape and writing pressure. For example, the character analysis unit analyzes changes in character size, shape, and writing pressure to analyze character shape. The character analysis unit can also analyze the strength and fluctuation patterns of writing pressure to analyze changes in writing pressure. For example, the character analysis unit analyzes the shape of characters in a student's notebook or homework to detect disorder patterns. The character analysis unit can also analyze changes in a student's writing pressure to detect signs of stress. The character analysis unit can also analyze changes in the size and spacing of a student's characters to detect changes in emotions. In this way, the character analysis unit can detect signs of stress in a student by analyzing changes in character shape and writing pressure.

[0032] The analysis unit may include a lost item analysis unit that analyzes the frequency and pattern of lost items. For example, the lost item analysis unit analyzes the number of times lost items occur and the frequency per period to analyze the frequency of lost items. The lost item analysis unit can also analyze whether lost items occur more frequently on specific days of the week or during specific time periods to analyze the pattern of lost items. For example, the lost item analysis unit analyzes how often students lose items to detect signs of stress. The lost item analysis unit can also analyze patterns of students losing items to detect stress associated with specific days of the week or specific time periods. The lost item analysis unit can also analyze the types of items students lose to detect stress associated with specific subjects or activities. In this way, the lost item analysis unit can detect signs of stress in students by analyzing the frequency and pattern of lost items.

[0033] The analysis unit may include a text analysis unit that analyzes changes in emotion or theme. For example, the text analysis unit analyzes changes from positive to negative emotion to analyze changes in emotion. The text analysis unit can also analyze changes in topics or shifts in interest to analyze changes in theme. For example, the text analysis unit analyzes the content of students' impressions or essays to detect changes in emotion. The text analysis unit can also analyze changes in theme in students' writing to detect changes in their interests. The text analysis unit can also analyze how students express themselves in their writing to detect signs of stress. In this way, the text analysis unit can detect signs of stress in students by analyzing changes in emotion or theme.

[0034] The analysis unit may include an appetite analysis unit that analyzes changes in appetite and changes in eating speed. For example, the appetite analysis unit analyzes increases or decreases in the amount of food eaten and the amount of food left over to analyze changes in appetite. The appetite analysis unit can also analyze changes in the time it takes to eat and changes in the pace at which the student eats to analyze changes in eating speed. For example, the appetite analysis unit analyzes the speed at which students eat their school lunch to detect signs of stress. The appetite analysis unit can also analyze changes in students' appetites to detect changes in their health condition and emotions. The appetite analysis unit can also analyze the amount and type of food the students eat to detect changes in their nutritional balance and eating habits. In this way, the appetite analysis unit can detect signs of stress in students by analyzing changes in appetite and changes in eating speed.

[0035] The analysis unit may include a behavior analysis unit that analyzes changes in behavioral patterns. For example, the behavior analysis unit analyzes changes in the type, frequency, and time of activity to analyze changes in behavioral patterns. The behavior analysis unit can also analyze changes in interactions with friends and behavior to analyze changes in behavioral patterns. For example, the behavior analysis unit analyzes how children and students spend their recess to detect signs of stress. The behavior analysis unit can also analyze changes in children and students' behavioral patterns to detect stress related to specific activities. The behavior analysis unit can also analyze children and students' friendships and social behavior to detect changes in emotions. In this way, the behavior analysis unit can detect signs of stress in children and students by analyzing changes in behavioral patterns.

[0036] The notification unit can notify teachers and counselors of signs of stress in real time. For example, if the generation AI detects signs of stress, the notification unit will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the data that supports them. For example, the notification unit may notify signs of stress based on data such as a student's sloppy handwriting or increased forgetfulness, the content of their impressions and essays, the speed at which they eat lunch, their appetite, and how they spend their recess. The notification unit can notify teachers and counselors of signs of stress in real time so that they can respond promptly. This allows teachers and counselors to respond promptly by notifying them of signs of stress in real time.

[0037] The collection unit can analyze the student's past data collection history and select the optimal collection method. For example, if the student has previously resisted data collection, the collection unit can collect data in a more natural way. If the student has previously preferred data collection at a specific time of day, the collection unit can also collect data at that time of day. If the student has previously accepted data collection using a specific method, the collection unit can also preferentially use that method. This allows the collection unit to select the optimal collection method by analyzing the student's past data collection history and improve the efficiency of data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0038] When collecting data, the collection unit can filter the data based on the student's current learning situation and areas of interest. For example, when collecting data during a student's math class, the collection unit prioritizes collecting data related to math. The collection unit can also collect data related to topics that the student is interested in to obtain information that is likely to attract their attention. The collection unit can also collect data during classes of subjects that the student is weak at to detect signs of stress early. This allows the collection unit to collect more relevant data by filtering the data based on the student's current learning situation and areas of interest. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the students. For example, when the students are in a classroom, the collection unit prioritizes collecting data related to lessons. When the students are in the schoolyard, the collection unit can also prioritize collecting data related to how they spend their recess. When the students are in the library, the collection unit can also prioritize collecting data related to reading. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0040] The collection unit can analyze the social media activities of students and collect related data when collecting data. For example, if a student is active on social media, the collection unit collects data related to that activity. The collection unit can also analyze the content posted by students on social media to detect changes in their emotions. The collection unit can also analyze the student's friendships on social media to detect signs of stress early. In this way, the collection unit can collect related data and detect signs of stress early by analyzing social media activities. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0041] The character analysis unit can analyze in detail changes in character shape and writing pressure. For example, the character analysis unit analyzes in detail changes in character size, shape, and writing pressure to analyze character shape in detail. The character analysis unit can also analyze in detail the strength and fluctuation patterns of writing pressure to analyze changes in writing pressure in detail. For example, the character analysis unit analyzes in detail the shape of characters in a student's notebook or homework to detect patterns of disorder. The character analysis unit can also detect signs of stress by analyzing in detail changes in the student's writing pressure. The character analysis unit can also detect changes in emotions by analyzing in detail changes in the size and spacing of the student's characters. In this way, the character analysis unit can detect signs of stress by analyzing in detail changes in character shape and writing pressure.

[0042] The lost item analysis unit can analyze in detail the frequency and patterns of lost items. For example, the lost item analysis unit analyzes in detail the number of times lost items occur and the frequency per period to analyze in detail the frequency of lost items. The lost item analysis unit can also analyze in detail whether lost items occur more frequently on specific days of the week or during specific time periods to analyze in detail the pattern of lost items. For example, the lost item analysis unit analyzes in detail the frequency of lost items by students to detect signs of stress. The lost item analysis unit can also analyze in detail the patterns of lost items by students to detect stress associated with specific days of the week or time periods. The lost item analysis unit can also analyze in detail the types of items left behind by students to detect stress associated with specific subjects or activities. In this way, the lost item analysis unit can detect signs of stress by analyzing in detail the frequency and patterns of lost items.

[0043] The text analysis unit can analyze changes in emotions and themes in detail. For example, the text analysis unit analyzes changes from positive to negative emotions in detail to analyze changes in emotions in detail. The text analysis unit can also analyze changes in topics and shifts in interest in detail to analyze changes in themes in detail. For example, the text analysis unit analyzes the content of students' impressions and essays in detail to detect changes in emotions. The text analysis unit can also analyze changes in themes in students' writing in detail to detect changes in interests and concerns. The text analysis unit can also analyze the way students express themselves in their writing in detail to detect signs of stress. In this way, the text analysis unit can detect signs of stress by analyzing changes in emotions and themes in detail.

[0044] The appetite analysis unit can analyze in detail changes in appetite and changes in eating speed. For example, to analyze changes in appetite in detail, the appetite analysis unit analyzes in detail increases or decreases in the amount of food eaten and the amount of food left over. To analyze changes in eating speed in detail, the appetite analysis unit can also analyze in detail changes in the time it takes to eat and the pace at which the student eats. For example, the appetite analysis unit analyzes in detail the speed at which students eat their school lunch to detect signs of stress. The appetite analysis unit can also analyze in detail changes in students' appetites to detect changes in their health condition and emotions. The appetite analysis unit can also analyze in detail the amount and type of food the students eat to detect changes in their nutritional balance and eating habits. In this way, the appetite analysis unit can detect signs of stress by analyzing changes in appetite and changes in eating speed in detail.

[0045] The behavior analysis unit can analyze changes in behavior patterns in detail. For example, the behavior analysis unit analyzes changes in the type, frequency, and time of activity in detail to analyze changes in behavior patterns in detail. The behavior analysis unit can also analyze changes in interactions with friends and behavior in detail to analyze changes in behavior patterns in detail. For example, the behavior analysis unit can analyze in detail how children and students spend their recess to detect signs of stress. The behavior analysis unit can also analyze changes in children and students' behavior patterns in detail to detect stress related to specific activities. The behavior analysis unit can also analyze children and students' friendships and social behavior in detail to detect changes in emotions. In this way, the behavior analysis unit can detect signs of stress by analyzing changes in behavior patterns in detail.

[0046] When making predictions, the prediction unit can optimize the prediction algorithm by referring to past stress data. For example, the prediction unit references the student's past stress data, detects similar patterns, and optimizes the prediction algorithm. The prediction unit can also predict signs of stress in specific situations based on the student's past stress data. The prediction unit can also analyze the student's past stress data and improve the accuracy of the prediction algorithm. In this way, the prediction unit can improve the accuracy of the prediction algorithm by referring to the past stress data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0047] At the time of prediction, the prediction unit can customize the stress signs based on the student's current living situation. The prediction unit customizes the stress signs, for example, by taking into account the student's home environment and lifestyle. The prediction unit can also customize the stress signs by taking into account the student's current health condition. The prediction unit can also customize the stress signs by taking into account the student's current learning situation. This allows the prediction unit to customize the stress signs based on the student's current living situation, enabling more accurate prediction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0048] When making predictions, the prediction unit can predict signs of stress by taking into account the geographical location information of the student. For example, if the student is in a classroom, the prediction unit predicts signs of stress related to classes. If the student is in the schoolyard, the prediction unit can also predict signs of stress related to how the student spends recess. If the student is in the library, the prediction unit can also predict signs of stress related to reading. In this way, the prediction unit can predict signs of stress more accurately by taking into account the geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0049] During prediction, the prediction unit can analyze the social media activity of the student to predict signs of stress. For example, if the student is actively active on social media, the prediction unit predicts signs of stress related to that activity. The prediction unit can also analyze the content posted by the student on social media and predict signs of stress based on changes in emotions. The prediction unit can also analyze the student's friendships on social media to predict signs of stress. In this way, the prediction unit can predict signs of stress early by analyzing social media activity. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0050] The notification unit can adjust the level of detail of the notification based on the importance of the stress sign when notifying. For example, if the stress sign is of high importance, the notification unit provides a notification including detailed information. For example, if the stress sign is of low importance, the notification unit can also provide a brief notification. The notification unit can also adjust the frequency of notifications according to the importance. In this way, the notification unit can provide appropriate information by adjusting the level of detail of the notification based on the importance of the stress sign. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0051] The notification unit can apply different notification methods depending on the category of the stress sign when notifying. For example, if the stress sign is emotion-related, the notification unit can provide a visual notification. If the stress sign is related to a learning situation, the notification unit can also provide a text-based notification. If the stress sign is related to a behavioral pattern, the notification unit can also provide an audio notification. This allows the notification unit to apply different notification methods depending on the category of the stress sign, thereby enabling more effective notification. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0052] When notifying, the notification unit can adjust the timing of the notification based on the timing of the occurrence of the stress sign. For example, the notification unit issues a notification immediately after the stress sign occurs. The notification unit can also issue notifications periodically if the stress sign continues. The notification unit can also stop the notification if the stress sign is resolved. In this way, the notification unit can adjust the timing of the notification based on the timing of the stress sign, thereby enabling notification at an appropriate time. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0053] The notification unit can adjust the order of notifications based on the relevance of the stress signs when making notifications. For example, the notification unit prioritizes notifications of stress signs with high importance. The notification unit can also postpone notifications of stress signs with low importance. The notification unit can also optimize the order of notifications based on the relevance of the stress signs. In this way, the notification unit can prioritize notification of important information by adjusting the order of notifications based on the relevance of the stress signs. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can also collect physiological data of students. For example, the collection unit measures the students' heart rates and electrodermal activity and collects them as digital data. The collection unit can also record the students' sleep patterns and collect them as digital data. The collection unit can also record the students' exercise levels and collect them as digital data. In this way, the collection unit can more accurately detect signs of stress by collecting the students' physiological data.

[0056] The prediction unit can predict signs of stress by taking into account the child's / student's home environment. For example, the prediction unit predicts signs of stress by taking into account stress factors within the child's / student's home. The prediction unit can also predict signs of stress by taking into account the child's / student's support system within the child's / student's home. The prediction unit can also predict signs of stress by taking into account the child's / student's home lifestyle habits. In this way, the prediction unit can predict signs of stress more accurately by taking into account the home environment.

[0057] The collection unit can also collect learning outcomes of students. For example, the collection unit collects test scores and grades of students as digital data. The collection unit can also record the status of homework submission by students and collect this as digital data. The collection unit can also record comments and participation by students during class and collect this as digital data. In this way, the collection unit can more accurately detect signs of stress by collecting learning outcomes.

[0058] The prediction unit can optimize the prediction algorithm by referring to the student's past stress data. For example, the prediction unit can refer to the student's past stress data, detect similar patterns, and optimize the prediction algorithm. The prediction unit can also predict signs of stress in specific situations based on the student's past stress data. The prediction unit can also analyze the student's past stress data to improve the accuracy of the prediction algorithm. In this way, the prediction unit can improve the accuracy of the prediction algorithm by referring to the past stress data.

[0059] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the students. For example, when the students are in the classroom, the collection unit prioritizes collecting data related to lessons. When the students are in the schoolyard, the collection unit can also prioritize collecting data related to how they spend their recess. When the students are in the library, the collection unit can also prioritize collecting data related to reading. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection department collects data from students' everyday school life. For example, data on poor handwriting, increased forgetting, the content of essays and essays, the speed and appetite of students eating lunch, and how they spend their recesses is collected. Collection methods include teachers scanning students' notebooks and homework to collect digital data, using checklists to record the increase in forgetting, scanning essays and essays, and observing and recording the speed and appetite of students eating lunch, and how they spend their recesses. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it includes a character analysis unit that analyzes changes in character shape and writing pressure, a lost item analysis unit that analyzes the frequency and patterns of lost items, a text analysis unit that analyzes changes in emotions and themes, an appetite analysis unit that analyzes changes in appetite and eating speed, and a behavior analysis unit that analyzes changes in behavior patterns. Step 3: The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. For example, it predicts mental stress in students based on data such as poor handwriting or an increase in forgetfulness, the content of essays and impressions, the speed at which students eat lunch, their appetite, and how they spend their recess. Using generative AI, the prediction unit can predict signs of stress based on the results of data analysis. Step 4: The notification unit notifies teachers and counselors of the stress signs predicted by the prediction unit. For example, if the generation AI detects signs of stress, it will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the data that supports them.

[0062] (Example 2) A stress detection system according to an embodiment of the present invention utilizes a generative AI to detect subtle changes in students' behavior and predict their mental stress. In this stress detection system, the generative AI detects subtle changes that can be observed in students' everyday school life, such as sloppy handwriting, increased forgetting, the content of essays and impressions, the speed and appetite of eating lunch, and how students spend their recess. Next, the system predicts students' mental stress based on the subtle changes detected by the generative AI. Finally, the generative AI notifies teachers and counselors of the predicted stress signs. This allows teachers and counselors to respond promptly and reduce students' mental stress. For example, with regard to data collection methods, teachers scan students' notebooks and homework for sloppy handwriting and collect the data digitally. Teachers record the increase in forgetting items using a checklist and collect the data digitally. The content of essays and impressions submitted by students is scanned and collected digitally. Teachers observe and record the speed and appetite of eating lunch and collect the data digitally. Teachers observe and record how students spend their recess time, collecting the data digitally. Regarding the analysis methods used by the AI, for example, to analyze disordered handwriting, the AI ​​analyzes changes in character shape and pen pressure. Regarding an increase in forgetting things, the AI ​​analyzes checklist data to determine the frequency and patterns of forgetting things. Regarding the content of impressions and essays, the AI ​​performs text analysis to analyze changes in emotions and themes. Regarding lunch eating speed and appetite, the AI ​​analyzes observational data to analyze changes in appetite and eating speed. Regarding how students spend their recess time, the AI ​​analyzes observational data to analyze changes in behavioral patterns. Finally, regarding notification methods, for example, if the AI ​​detects signs of stress, it will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the underlying data, allowing teachers and counselors to respond quickly. This enables the stress detection system to detect even the slightest changes in students' behavior, predict mental stress, and notify teachers and counselors, enabling early response.

[0063] A stress detection system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a notification unit. The collection unit collects data from students' everyday school lives. For example, the collection unit collects data such as poor handwriting, an increase in forgetting things, the content of impressions and essays, the speed and appetite of students eating lunch, and how students spend recess. The collection unit may, for example, be scanned by a teacher to collect digital data from students' notebooks and homework. The collection unit may also be configured to record an increase in students forgetting things using a checklist and collect the digital data from the record. The collection unit may also be configured to scan impressions and essays submitted by students to collect the digital data from the record. The collection unit may also collect data on the speed and appetite of students eating lunch, as observed and recorded by teachers. The collection unit may also collect data on how students spend recess, as observed and recorded by teachers. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may include a character analysis unit that analyzes changes in character shape and writing pressure. The analysis unit may also include a lost item analysis unit that analyzes the frequency and pattern of students forgetting things. The analysis unit may further include a text analysis unit that analyzes changes in emotions and themes. The analysis unit may also include an appetite analysis unit that analyzes changes in appetite and eating speed. The analysis unit may also include a behavior analysis unit that analyzes changes in behavioral patterns. The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. For example, the prediction unit predicts mental stress in students based on data such as poor handwriting and an increase in forgetfulness, the content of student impressions and essays, the speed and appetite of students eating lunch, and how they spend recess. The prediction unit can predict signs of stress based on the results of the data analysis using the generation AI. The notification unit notifies teachers and counselors of the signs of stress predicted by the prediction unit. For example, if the generation AI detects signs of stress, the notification unit notifies teachers and counselors in real time via email or app notification. The notification includes specific signs of stress and the underlying data, allowing teachers and counselors to respond quickly.As a result, the stress detection system according to the embodiment can detect even the slightest changes in students, predict mental stress, and notify teachers and counselors, enabling early response.

[0064] The collection unit can collect data including sloppy handwriting, an increase in forgetting things, the content of impressions and essays, the speed and appetite of eating lunch, and how students spend recess. For example, to collect data on sloppy handwriting, a teacher can scan students' notebooks and homework and collect the data as digital data. To collect data on an increase in forgetting things, a teacher can record the data using a checklist and collect the data as digital data. To collect data on the content of impressions and essays, a teacher can observe and record the data on how students eat lunch and their appetite and collect the data as digital data. To collect data on how students spend recess, a teacher can observe and record the data and collect the data as digital data. In this way, the collection unit can provide data for detecting signs of stress by collecting subtle changes that can be detected from students' everyday school life.

[0065] The analysis unit may include a character analysis unit that analyzes changes in character shape and writing pressure. For example, the character analysis unit analyzes changes in character size, shape, and writing pressure to analyze character shape. The character analysis unit can also analyze the strength and fluctuation patterns of writing pressure to analyze changes in writing pressure. For example, the character analysis unit analyzes the shape of characters in a student's notebook or homework to detect disorder patterns. The character analysis unit can also analyze changes in a student's writing pressure to detect signs of stress. The character analysis unit can also analyze changes in the size and spacing of a student's characters to detect changes in emotions. In this way, the character analysis unit can detect signs of stress in a student by analyzing changes in character shape and writing pressure.

[0066] The analysis unit may include a lost item analysis unit that analyzes the frequency and pattern of lost items. For example, the lost item analysis unit analyzes the number of times lost items occur and the frequency per period to analyze the frequency of lost items. The lost item analysis unit can also analyze whether lost items occur more frequently on specific days of the week or during specific time periods to analyze the pattern of lost items. For example, the lost item analysis unit analyzes how often students lose items to detect signs of stress. The lost item analysis unit can also analyze patterns of students losing items to detect stress associated with specific days of the week or specific time periods. The lost item analysis unit can also analyze the types of items students lose to detect stress associated with specific subjects or activities. In this way, the lost item analysis unit can detect signs of stress in students by analyzing the frequency and pattern of lost items.

[0067] The analysis unit may include a text analysis unit that analyzes changes in emotion or theme. For example, the text analysis unit analyzes changes from positive to negative emotion to analyze changes in emotion. The text analysis unit can also analyze changes in topics or shifts in interest to analyze changes in theme. For example, the text analysis unit analyzes the content of students' impressions or essays to detect changes in emotion. The text analysis unit can also analyze changes in theme in students' writing to detect changes in their interests. The text analysis unit can also analyze how students express themselves in their writing to detect signs of stress. In this way, the text analysis unit can detect signs of stress in students by analyzing changes in emotion or theme.

[0068] The analysis unit may include an appetite analysis unit that analyzes changes in appetite and changes in eating speed. For example, the appetite analysis unit analyzes increases or decreases in the amount of food eaten and the amount of food left over to analyze changes in appetite. The appetite analysis unit can also analyze changes in the time it takes to eat and changes in the pace at which the student eats to analyze changes in eating speed. For example, the appetite analysis unit analyzes the speed at which students eat their school lunch to detect signs of stress. The appetite analysis unit can also analyze changes in students' appetites to detect changes in their health condition and emotions. The appetite analysis unit can also analyze the amount and type of food the students eat to detect changes in their nutritional balance and eating habits. In this way, the appetite analysis unit can detect signs of stress in students by analyzing changes in appetite and changes in eating speed.

[0069] The analysis unit may include a behavior analysis unit that analyzes changes in behavioral patterns. For example, the behavior analysis unit analyzes changes in the type, frequency, and time of activity to analyze changes in behavioral patterns. The behavior analysis unit can also analyze changes in interactions with friends and behavior to analyze changes in behavioral patterns. For example, the behavior analysis unit analyzes how children and students spend their recess to detect signs of stress. The behavior analysis unit can also analyze changes in children and students' behavioral patterns to detect stress related to specific activities. The behavior analysis unit can also analyze children and students' friendships and social behavior to detect changes in emotions. In this way, the behavior analysis unit can detect signs of stress in children and students by analyzing changes in behavioral patterns.

[0070] The notification unit can notify teachers and counselors of signs of stress in real time. For example, if the generation AI detects signs of stress, the notification unit will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the data that supports them. For example, the notification unit may notify signs of stress based on data such as a student's sloppy handwriting or increased forgetfulness, the content of their impressions and essays, the speed at which they eat lunch, their appetite, and how they spend their recess. The notification unit can notify teachers and counselors of signs of stress in real time so that they can respond promptly. This allows teachers and counselors to respond promptly by notifying them of signs of stress in real time.

[0071] The collection unit can estimate the emotions of students and adjust the timing of data collection based on the estimated emotions. For example, if a student is feeling stressed, the collection unit avoids collecting data during class and collects data during breaks or after school. If a student is relaxed, the collection unit can collect data during class to obtain data in a more natural state. If a student is concentrating, the collection unit can collect data at specific times during class to accurately grasp their learning situation. This allows the collection unit to collect more accurate data by adjusting the timing of data collection according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The collection unit can analyze the student's past data collection history and select the optimal collection method. For example, if the student has previously resisted data collection, the collection unit can collect data in a more natural way. If the student has previously preferred data collection at a specific time of day, the collection unit can also collect data at that time of day. If the student has previously accepted data collection using a specific method, the collection unit can also preferentially use that method. This allows the collection unit to select the optimal collection method by analyzing the student's past data collection history and improve the efficiency of data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] When collecting data, the collection unit can filter the data based on the student's current learning situation and areas of interest. For example, when collecting data during a student's math class, the collection unit prioritizes collecting data related to math. The collection unit can also collect data related to topics that the student is interested in to obtain information that is likely to attract their attention. The collection unit can also collect data during classes of subjects that the student is weak at to detect signs of stress early. This allows the collection unit to collect more relevant data by filtering the data based on the student's current learning situation and areas of interest. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The collection unit can estimate the emotions of students and determine the priority of data to be collected based on the estimated emotions. For example, if a student is feeling stressed, the collection unit can prioritize collecting data related to emotions. If a student is relaxed, the collection unit can also prioritize collecting data related to their learning situation. If a student is concentrating, the collection unit can also prioritize collecting data related to their learning outcomes. In this way, the collection unit can prioritize collecting important data by determining the priority of data according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the students. For example, when the students are in a classroom, the collection unit prioritizes collecting data related to lessons. When the students are in the schoolyard, the collection unit can also prioritize collecting data related to how they spend their recess. When the students are in the library, the collection unit can also prioritize collecting data related to reading. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] The collection unit can analyze the social media activities of students and collect related data when collecting data. For example, if a student is active on social media, the collection unit collects data related to that activity. The collection unit can also analyze the content posted by students on social media to detect changes in their emotions. The collection unit can also analyze the student's friendships on social media to detect signs of stress early. In this way, the collection unit can collect related data and detect signs of stress early by analyzing social media activities. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The analysis unit can estimate the emotions of students and adjust the way the analysis is presented based on the estimated emotions. For example, if a student is feeling stressed, the analysis unit can display the analysis results simply to make them easier to understand. If a student is relaxed, the analysis unit can display detailed analysis results to encourage deeper understanding. If a student is concentrating, the analysis unit can visually emphasize the analysis results to attract their attention. This allows the analysis unit to adjust the way the analysis is presented according to the student's emotions, thereby deepening their understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The character analysis unit can analyze in detail changes in character shape and writing pressure. For example, the character analysis unit analyzes in detail changes in character size, shape, and writing pressure to analyze character shape in detail. The character analysis unit can also analyze in detail the strength and fluctuation patterns of writing pressure to analyze changes in writing pressure in detail. For example, the character analysis unit analyzes in detail the shape of characters in a student's notebook or homework to detect patterns of disorder. The character analysis unit can also detect signs of stress by analyzing in detail changes in the student's writing pressure. The character analysis unit can also detect changes in emotions by analyzing in detail changes in the size and spacing of the student's characters. In this way, the character analysis unit can detect signs of stress by analyzing in detail changes in character shape and writing pressure.

[0079] The lost item analysis unit can analyze in detail the frequency and patterns of lost items. For example, the lost item analysis unit analyzes in detail the number of times lost items occur and the frequency per period to analyze in detail the frequency of lost items. The lost item analysis unit can also analyze in detail whether lost items occur more frequently on specific days of the week or during specific time periods to analyze in detail the pattern of lost items. For example, the lost item analysis unit analyzes in detail the frequency of lost items by students to detect signs of stress. The lost item analysis unit can also analyze in detail the patterns of lost items by students to detect stress associated with specific days of the week or time periods. The lost item analysis unit can also analyze in detail the types of items left behind by students to detect stress associated with specific subjects or activities. In this way, the lost item analysis unit can detect signs of stress by analyzing in detail the frequency and patterns of lost items.

[0080] The text analysis unit can analyze changes in emotions and themes in detail. For example, the text analysis unit analyzes changes from positive to negative emotions in detail to analyze changes in emotions in detail. The text analysis unit can also analyze changes in topics and shifts in interest in detail to analyze changes in themes in detail. For example, the text analysis unit analyzes the content of students' impressions and essays in detail to detect changes in emotions. The text analysis unit can also analyze changes in themes in students' writing in detail to detect changes in interests and concerns. The text analysis unit can also analyze the way students express themselves in their writing in detail to detect signs of stress. In this way, the text analysis unit can detect signs of stress by analyzing changes in emotions and themes in detail.

[0081] The appetite analysis unit can analyze in detail changes in appetite and changes in eating speed. For example, to analyze changes in appetite in detail, the appetite analysis unit analyzes in detail increases or decreases in the amount of food eaten and the amount of food left over. To analyze changes in eating speed in detail, the appetite analysis unit can also analyze in detail changes in the time it takes to eat and the pace at which the student eats. For example, the appetite analysis unit analyzes in detail the speed at which students eat their school lunch to detect signs of stress. The appetite analysis unit can also analyze in detail changes in students' appetites to detect changes in their health condition and emotions. The appetite analysis unit can also analyze in detail the amount and type of food the students eat to detect changes in their nutritional balance and eating habits. In this way, the appetite analysis unit can detect signs of stress by analyzing changes in appetite and changes in eating speed in detail.

[0082] The behavior analysis unit can analyze changes in behavior patterns in detail. For example, the behavior analysis unit analyzes changes in the type, frequency, and time of activity in detail to analyze changes in behavior patterns in detail. The behavior analysis unit can also analyze changes in interactions with friends and behavior in detail to analyze changes in behavior patterns in detail. For example, the behavior analysis unit can analyze in detail how children and students spend their recess to detect signs of stress. The behavior analysis unit can also analyze changes in children and students' behavior patterns in detail to detect stress related to specific activities. The behavior analysis unit can also analyze children and students' friendships and social behavior in detail to detect changes in emotions. In this way, the behavior analysis unit can detect signs of stress by analyzing changes in behavior patterns in detail.

[0083] The prediction unit can estimate the emotions of students and adjust the method for predicting signs of stress based on the estimated emotions. For example, if a student is feeling stressed, the prediction unit predicts signs of stress by focusing on data related to emotions. If a student is relaxed, the prediction unit can also predict signs of stress by focusing on data related to the learning situation. If a student is concentrating, the prediction unit can also predict signs of stress by focusing on data related to learning outcomes. This allows the prediction unit to adjust the method for predicting signs of stress according to the student's emotions, enabling more accurate predictions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] When making predictions, the prediction unit can optimize the prediction algorithm by referring to past stress data. For example, the prediction unit references the student's past stress data, detects similar patterns, and optimizes the prediction algorithm. The prediction unit can also predict signs of stress in specific situations based on the student's past stress data. The prediction unit can also analyze the student's past stress data and improve the accuracy of the prediction algorithm. In this way, the prediction unit can improve the accuracy of the prediction algorithm by referring to the past stress data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] At the time of prediction, the prediction unit can customize the stress signs based on the student's current living situation. The prediction unit customizes the stress signs, for example, by taking into account the student's home environment and lifestyle. The prediction unit can also customize the stress signs by taking into account the student's current health condition. The prediction unit can also customize the stress signs by taking into account the student's current learning situation. This allows the prediction unit to customize the stress signs based on the student's current living situation, enabling more accurate prediction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The prediction unit can estimate the emotions of students and prioritize signs of stress based on the estimated emotions. For example, if a student is feeling stressed, the prediction unit can prioritize predicting signs of stress related to the emotion. If a student is relaxed, the prediction unit can also prioritize predicting signs of stress related to the learning situation. If a student is concentrating, the prediction unit can also prioritize predicting signs of stress related to learning outcomes. In this way, the prediction unit can prioritize signs of stress according to the student's emotions, thereby predicting important signs preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] When making predictions, the prediction unit can predict signs of stress by taking into account the geographical location information of the student. For example, if the student is in a classroom, the prediction unit predicts signs of stress related to classes. If the student is in the schoolyard, the prediction unit can also predict signs of stress related to how the student spends recess. If the student is in the library, the prediction unit can also predict signs of stress related to reading. In this way, the prediction unit can predict signs of stress more accurately by taking into account the geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During prediction, the prediction unit can analyze the social media activity of the student to predict signs of stress. For example, if the student is actively active on social media, the prediction unit predicts signs of stress related to that activity. The prediction unit can also analyze the content posted by the student on social media and predict signs of stress based on changes in emotions. The prediction unit can also analyze the student's friendships on social media to predict signs of stress. In this way, the prediction unit can predict signs of stress early by analyzing social media activity. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The notification unit can estimate the emotions of students and adjust the way notifications are expressed based on the estimated emotions. For example, if a student is feeling stressed, the notification unit can provide a simple, easy-to-understand notification. If a student is relaxed, the notification unit can also provide a notification that includes detailed information. If a student is concentrating, the notification unit can also provide a notification that focuses on the main points. This allows the notification unit to adjust the way notifications are expressed according to the student's emotions, enabling more effective notifications. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The notification unit can adjust the level of detail of the notification based on the importance of the stress sign when notifying. For example, if the stress sign is of high importance, the notification unit provides a notification including detailed information. For example, if the stress sign is of low importance, the notification unit can also provide a brief notification. The notification unit can also adjust the frequency of notifications according to the importance. In this way, the notification unit can provide appropriate information by adjusting the level of detail of the notification based on the importance of the stress sign. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The notification unit can apply different notification methods depending on the category of the stress sign when notifying. For example, if the stress sign is emotion-related, the notification unit can provide a visual notification. If the stress sign is related to a learning situation, the notification unit can also provide a text-based notification. If the stress sign is related to a behavioral pattern, the notification unit can also provide an audio notification. This allows the notification unit to apply different notification methods depending on the category of the stress sign, thereby enabling more effective notification. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The notification unit can estimate the emotions of students and determine the priority of notifications based on the estimated emotions. For example, if a student is feeling stressed, the notification unit can prioritize notifications related to emotions. If a student is relaxed, the notification unit can also prioritize notifications related to learning status. If a student is concentrating, the notification unit can also prioritize notifications related to learning outcomes. In this way, the notification unit can prioritize important notifications by determining the priority of notifications according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] When notifying, the notification unit can adjust the timing of the notification based on the timing of the occurrence of the stress sign. For example, the notification unit issues a notification immediately after the stress sign occurs. The notification unit can also issue notifications periodically if the stress sign continues. The notification unit can also stop the notification if the stress sign is resolved. In this way, the notification unit can adjust the timing of the notification based on the timing of the stress sign, thereby enabling notification at an appropriate time. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The notification unit can adjust the order of notifications based on the relevance of the stress signs when making notifications. For example, the notification unit prioritizes notifications of stress signs with high importance. The notification unit can also postpone notifications of stress signs with low importance. The notification unit can also optimize the order of notifications based on the relevance of the stress signs. In this way, the notification unit can prioritize notification of important information by adjusting the order of notifications based on the relevance of the stress signs. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on students using the camera 42 and microphone 38B of the smart device 14 and digitizes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts signs of stress based on the analysis results. The notification unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and notifies teachers or counselors. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on students using the camera 42 and microphone 238 of the smart glasses 214 and digitizes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts signs of stress based on the analysis results. The notification unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and notifies teachers or counselors. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on students using the camera 42 and microphone 238 of the headset-type terminal 314, and digitizes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts signs of stress based on the analysis results. The notification unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and notifies teachers or counselors. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on students using the camera 42 and microphone 238 of the robot 414, and digitizes the data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts signs of stress based on the analysis results. The notification unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and notifies teachers or counselors.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The collection unit can also collect physiological data of students. For example, the collection unit measures the students' heart rates and electrodermal activity and collects them as digital data. The collection unit can also record the students' sleep patterns and collect them as digital data. The collection unit can also record the students' exercise levels and collect them as digital data. In this way, the collection unit can more accurately detect signs of stress by collecting the students' physiological data.

[0097] The analysis unit can also analyze the social media activities of students. For example, the analysis unit can analyze the content of students' social media posts to detect changes in their emotions. The analysis unit can also analyze the students' friendships on social media to detect signs of stress. The analysis unit can also analyze the frequency of the students' social media activities to detect signs of stress. In this way, the analysis unit can detect signs of stress in students by analyzing their social media activities.

[0098] The prediction unit can predict signs of stress by taking into account the child's / student's home environment. For example, the prediction unit predicts signs of stress by taking into account stress factors within the child's / student's home. The prediction unit can also predict signs of stress by taking into account the child's / student's support system within the child's / student's home. The prediction unit can also predict signs of stress by taking into account the child's / student's home lifestyle habits. In this way, the prediction unit can predict signs of stress more accurately by taking into account the home environment.

[0099] The notification unit can estimate the emotions of the students and customize the content of the notification based on the estimated emotions. For example, if the students are feeling stressed, the notification unit can send a notification that includes an encouraging message. If the students are relaxed, the notification unit can also send a notification that includes detailed information. If the students are concentrating, the notification unit can also send a notification that focuses on the main points. This allows the notification unit to customize the content of the notification according to the students' emotions, enabling more effective notifications.

[0100] The collection unit can also collect learning outcomes of students. For example, the collection unit collects test scores and grades of students as digital data. The collection unit can also record the status of homework submission by students and collect this as digital data. The collection unit can also record comments and participation by students during class and collect this as digital data. In this way, the collection unit can more accurately detect signs of stress by collecting learning outcomes.

[0101] The analysis unit can estimate the emotions of students and adjust the accuracy of the analysis based on the estimated emotions. For example, if a student is feeling stressed, the analysis unit can focus on emotion-related data in its analysis. If a student is relaxed, the analysis unit can also focus on learning status-related data in its analysis. If a student is concentrating, the analysis unit can also focus on learning outcomes-related data in its analysis. This allows the analysis unit to adjust the accuracy of its analysis according to the student's emotions, enabling more accurate analysis.

[0102] The prediction unit can optimize the prediction algorithm by referring to the student's past stress data. For example, the prediction unit can refer to the student's past stress data, detect similar patterns, and optimize the prediction algorithm. The prediction unit can also predict signs of stress in specific situations based on the student's past stress data. The prediction unit can also analyze the student's past stress data to improve the accuracy of the prediction algorithm. In this way, the prediction unit can improve the accuracy of the prediction algorithm by referring to the past stress data.

[0103] The notification unit can estimate the emotions of the student and adjust the timing of the notification based on the estimated emotions. For example, if the student is feeling stressed, the notification unit can immediately issue a notification. If the student is relaxed, the notification unit can also issue a notification at an appropriate time. If the student is concentrating, the notification unit can also adjust the timing of the notification so as not to interfere with their studies. This allows the notification unit to adjust the timing of the notification according to the student's emotions, enabling more effective notifications.

[0104] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the students. For example, when the students are in the classroom, the collection unit prioritizes collecting data related to lessons. When the students are in the schoolyard, the collection unit can also prioritize collecting data related to how they spend their recess. When the students are in the library, the collection unit can also prioritize collecting data related to reading. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0105] The analysis unit can estimate the emotions of students and adjust the way the analysis is presented based on the estimated emotions. For example, if a student is feeling stressed, the analysis unit can display the analysis results simply to make them easier to understand. If a student is relaxed, the analysis unit can display detailed analysis results to encourage deeper understanding. If a student is concentrating, the analysis unit can visually emphasize the analysis results to draw their attention. In this way, the analysis unit can adjust the way the analysis is presented according to the student's emotions, thereby deepening their understanding of the analysis results.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The data collection department collects data from students' everyday school life. For example, data on poor handwriting, increased forgetting, the content of essays and essays, the speed and appetite of students eating lunch, and how they spend their recesses is collected. Collection methods include teachers scanning students' notebooks and homework to collect digital data, using checklists to record the increase in forgetting, scanning essays and essays, and observing and recording the speed and appetite of students eating lunch, and how they spend their recesses. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it includes a character analysis unit that analyzes changes in character shape and writing pressure, a lost item analysis unit that analyzes the frequency and patterns of lost items, a text analysis unit that analyzes changes in emotions and themes, an appetite analysis unit that analyzes changes in appetite and eating speed, and a behavior analysis unit that analyzes changes in behavior patterns. Step 3: The prediction unit predicts signs of stress based on the data analyzed by the analysis unit. For example, it predicts mental stress in students based on data such as poor handwriting or an increase in forgetfulness, the content of essays and impressions, the speed at which students eat lunch, their appetite, and how they spend their recess. Using generative AI, the prediction unit can predict signs of stress based on the results of data analysis. Step 4: The notification unit notifies teachers and counselors of the stress signs predicted by the prediction unit. For example, if the generation AI detects signs of stress, it will notify teachers and counselors in real time via email or app notification. The notification will include specific signs of stress and the data that supports them.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts signs of stress based on the data analyzed by the analysis unit; a notification unit that notifies the user of the stress sign predicted by the prediction unit. A system characterized by:

2. The collecting unit Data will be collected including poor handwriting, increased forgetfulness, the content of essays and reviews, the speed at which students eat lunch, their appetite, and how they spend their recess.

2. The system of claim 1.

3. The analysis unit Equipped with a character analysis unit that analyzes changes in character shape and writing pressure 2. The system of claim 1.

4. The analysis unit Equipped with a lost item analysis unit that analyzes the frequency and patterns of lost items 2. The system of claim 1.

5. The analysis unit Includes a text analysis section that analyzes changes in sentiment or theme 2. The system of claim 1.

6. The analysis unit Equipped with an appetite analysis unit that analyzes changes in appetite and eating speed 2. The system of claim 1.

7. The analysis unit Equipped with a behavior analysis unit that analyzes changes in behavior patterns 2. The system of claim 1.

8. The notification unit Notifying teachers and counselors of signs of stress in real time 2. The system of claim 1.

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