system

Abstract

The system according to this embodiment aims to enable individuals with autism to live independently and enrich their leisure time. [Solution] The system according to this embodiment comprises a personalized assistant unit, a social skills training unit, an emotion recognition unit, an independent living support robot unit, and a communication support tool unit. The personalized assistant unit assists with daily schedule management and reminder setting. The social skills training unit provides training to learn dialogue simulations and appropriate responses according to the situation, as provided by the personalized assistant unit. The emotion recognition unit recognizes emotions provided by the social skills training unit and provides appropriate feedback. The independent living support robot unit assists with simple tasks within the home, as provided by the emotion recognition unit. The communication support tool unit provides communication support using speech recognition and generation technology provided by the independent living support robot unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that autistic people cannot receive sufficient support after graduating from school, and it is difficult for them to live independently and enjoy their leisure time.

[0005] The system according to the embodiment aims to enable autistic people to live independently and enjoy their leisure time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a personalized assistant unit, a social skills training unit, an emotion recognition unit, an independent living support robot unit, and a communication support tool unit. The personalized assistant unit assists with daily schedule management and reminder setting. The social skills training unit provides training to learn dialogue simulations and appropriate responses in different situations, as provided by the personalized assistant unit. The emotion recognition unit recognizes emotions provided by the social skills training unit and provides appropriate feedback. The independent living support robot unit assists with simple tasks within the home, as provided by the emotion recognition unit. The communication support tool unit provides communication support using speech recognition and generation technologies provided by the independent living support robot unit. [Effects of the Invention]

[0007] The system according to this embodiment enables individuals with autism to live independently and enrich their leisure time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The autism support system according to an embodiment of the present invention is an AI and robot-based system aimed at supporting the independence of individuals with autism. This autism support system provides a personalized assistant to help manage daily schedules and set reminders. For example, it has functions to notify users of wake-up times, medication times, and meal times. It also provides social skills training to learn conversational simulations and appropriate responses in different situations. For example, it provides scenarios for learning conversational simulations and appropriate responses in different situations. Furthermore, it includes a system that recognizes emotions and provides appropriate feedback. This helps individuals with autism understand and control their emotions. Independent living support robots are also introduced to assist with simple tasks in the home (cooking, cleaning, laundry, etc.). This improves the quality of independent living. Furthermore, communication support tools using speech recognition and generation technology are also provided. For example, applications with functions such as answering simple questions and providing necessary information are included. A health monitoring system is also provided. It collects and analyzes health data such as heart rate and sleep patterns in real time and sounds an alert if an abnormality is detected. Furthermore, a platform is provided to form a support community for parents, where they can exchange information and seek advice. A stress management app is also provided, offering relaxation methods when feeling stressed or anxious. For example, it provides guidance on meditation and deep breathing, and plays relaxing music. An online education platform is also provided, which individually manages learning progress and provides appropriate learning materials. Customized learning plans tailored to individuals with autism are offered. Furthermore, a space is provided where real-world situations can be simulated using VR technology, allowing individuals to practice social and vocational skills. This makes it possible to improve adaptability in real-world environments. Finally, a function is provided that aggregates and analyzes the cases of other individuals with autism to suggest effective methods for solutions and growth plans tailored to the individual's personality. In this way, the autism support system can support the daily lives of individuals with autism and improve their emotional recognition and communication.

[0029] The autism support system according to this embodiment comprises a personalized assistant unit, a social skills training unit, an emotion recognition unit, an independent living support robot unit, and a communication support tool unit. The personalized assistant unit assists with daily schedule management and reminder setting. For example, the personalized assistant unit notifies the user of wake-up time, medication time, and meal times. The personalized assistant unit can also automatically generate a schedule based on the user's daily rhythm, for example. The personalized assistant unit can also estimate the user's emotions and adjust the method of reminder notifications. The social skills training unit provides scenarios for learning dialogue simulations and appropriate responses in different situations. For example, the social skills training unit allows the user to improve their dialogue skills through dialogue simulations. The social skills training unit can also estimate the user's emotions and adjust the training scenarios. The emotion recognition unit recognizes emotions and provides appropriate feedback. For example, the emotion recognition unit recognizes the user's emotions using facial recognition or voice analysis. The emotion recognition unit can also adjust the content and timing of feedback based on the user's emotions. The independent living support robot unit assists with simple tasks within the home. For example, it assists with tasks such as cooking, cleaning, and laundry. The independent living support robot unit can also estimate the user's emotions and adjust the priority and content of tasks. The communication support tool unit answers simple questions and provides necessary information. For example, the communication support tool unit engages in dialogue with the user using speech recognition and generation technology. The communication support tool unit can also estimate the user's emotions and adjust the method and content of communication. As a result, the autism support system according to this embodiment can support the daily life of autistic individuals and improve their emotion recognition and communication.

[0030] The personalized assistant unit assists with daily schedule management and reminder setting. Specifically, it has functions to notify users of their wake-up time, medication times, meal times, and so on. These notifications are made according to a schedule automatically generated based on the user's daily rhythm. For example, if a user has a habit of waking up at 7 AM every morning, the personalized assistant unit will set an alarm for 7 AM to encourage them to get up. Also, if there is a set time for taking medication, it will display a reminder at that time to encourage the user to take their medication. Furthermore, the personalized assistant unit can estimate the user's emotions and adjust the way reminder notifications are delivered. For example, if the user is feeling stressed, it will notify them in a soft voice or with vibration, depending on the user's state. AI technology is used to estimate emotions by analyzing the user's facial expressions, voice tone, and behavioral patterns. As a result, the personalized assistant unit can make the user's life more comfortable and reduce stress. In addition, the personalized assistant unit can learn from the user's past behavioral data and optimize future schedules. For example, if a user has a habit of performing a specific task on a particular day of the week, that task can be automatically incorporated into the schedule. This allows users to manage their daily lives more efficiently.

[0031] The Social Skills Training Department provides scenarios for learning dialogue simulations and appropriate responses in different situations. Specifically, it conducts simulations to improve users' dialogue skills. For example, users can converse with a virtual conversation partner and learn appropriate responses and expressions. The simulations reproduce common situations in daily life, enabling users to respond appropriately in real-life situations. The Social Skills Training Department can also estimate the user's emotions and adjust the training scenario accordingly. For example, if the user is nervous, the scenario can be simplified or changed to something more relaxing. Emotion estimation uses facial recognition and voice analysis. This allows users to improve their dialogue skills at their own pace. Furthermore, the Social Skills Training Department has a function to record the user's progress and evaluate the effectiveness of the training. For example, it collects data such as how often the user gave appropriate responses and which scenarios they found difficult, and incorporates this into the next training session. This allows users to continuously improve their skills.

[0032] The emotion recognition unit recognizes emotions and provides appropriate feedback. Specifically, it recognizes the user's emotions using facial recognition and voice analysis. For example, if the user is smiling, it will provide feedback such as "You seem happy," and conversely, if the user looks sad, it will ask, "Is there something bothering you?" The emotion recognition unit can also adjust the content and timing of feedback based on the user's emotions. For example, if the user is feeling stressed, it will provide advice on how to relax. AI technology is used for emotion recognition, analyzing the user's facial expressions, voice tone, and behavioral patterns. This allows the emotion recognition unit to accurately grasp the user's emotions and provide appropriate feedback. Furthermore, the emotion recognition unit can accumulate user emotion data and analyze long-term emotional fluctuations. For example, it can analyze what emotions a user is likely to experience at specific times or in specific situations, and take preventative measures. This allows users to better understand their own emotions and deal with them appropriately.

[0033] The Independent Living Support Robot Division assists with simple tasks within the home. Specifically, it assists with tasks such as cooking, cleaning, and laundry. For example, the robot can help with cooking by chopping ingredients or stirring pots in the kitchen. A cleaning robot cleans the floor, and a laundry robot washes and dries clothes. This reduces the burden of daily life for the user. Furthermore, the Independent Living Support Robot Division can estimate the user's emotions and adjust the priority and content of tasks accordingly. For example, if the user is tired, the robot will prioritize tasks to create a relaxing environment. Emotion estimation uses AI technology that analyzes the user's facial expressions, tone of voice, and behavioral patterns. This allows the Independent Living Support Robot Division to provide optimal support tailored to the user's condition. Moreover, the robot can not only operate according to user instructions but also autonomously judge and execute tasks. For example, if it detects an accumulation of garbage, it will automatically collect and dispose of it. This allows the user to maintain a more comfortable living environment.

[0034] The communication support tool unit answers simple questions and provides necessary information. Specifically, it engages in dialogue with the user using speech recognition and generation technology. For example, if the user asks, "What's the weather like today?", the tool provides weather information. If the user asks, "What's my next appointment?", it checks the schedule and informs the user of their next appointment. Furthermore, the communication support tool unit can estimate the user's emotions and adjust the method and content of communication accordingly. For example, if the user is agitated, it will speak in a calm tone or offer advice to help them relax. Emotion estimation uses facial recognition and speech analysis. This allows the communication support tool unit to provide optimal dialogue tailored to the user's state. In addition, the tool can learn from the user's past dialogue history and provide more personalized information. For example, if the user has specific hobbies or interests, it will provide topics based on that information. This allows the user to enjoy more fulfilling communication.

[0035] The personalized assistant unit can notify users of wake-up times, medication times, and meal times. For example, the personalized assistant unit can set wake-up times based on the user's daily rhythm. It can also set medication times based on a doctor's instructions. Furthermore, it can set meal times based on nutritional balance. This helps the user manage their daily schedule. Some or all of the above processes in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's daily rhythm data into AI to generate an optimal schedule.

[0036] The Social Skills Training Unit can provide scenarios for learning dialogue simulations and appropriate responses in different situations. For example, the Social Skills Training Unit can help users improve their dialogue skills through dialogue simulations. It can also estimate the user's emotions and adjust the training scenario accordingly. For instance, if the user is nervous, it can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows the user to improve their dialogue skills. Some or all of the above processes in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input user emotion data into AI to generate an optimal training scenario.

[0037] The emotion recognition unit can recognize emotions and provide appropriate feedback. For example, the emotion recognition unit recognizes the user's emotions using facial recognition or voice analysis. For example, the emotion recognition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the emotion recognition unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the emotion recognition unit can adjust the content and timing of feedback based on the user's emotions. For example, if the user is tense, it can provide relaxing feedback. If the user is relaxed, it can provide challenging feedback. If the user is excited, it can provide feedback to help them calm down. This helps the user understand and control their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation.

[0038] The Independent Living Support Robot can assist with simple tasks within the home. For example, it can assist with tasks such as cooking, cleaning, and laundry. The Independent Living Support Robot can also estimate the user's emotions and adjust the priority and content of tasks accordingly. For example, if the user is stressed, it can prioritize relaxing tasks. If the user is relaxed, it can prioritize challenging tasks. If the user is in a hurry, it can prioritize tasks that can be completed quickly. This can improve the user's quality of independent living. Some or all of the above processing in the Independent Living Support Robot may be performed using AI or not. For example, the Independent Living Support Robot can input the user's emotional data into AI to generate optimal tasks.

[0039] The communication support tool unit can answer simple questions and provide necessary information. For example, it can engage in dialogue with the user using speech recognition and generation technologies. It can also provide appropriate answers to user questions. Furthermore, the communication support tool unit can estimate the user's emotions and adjust the method and content of communication accordingly. For example, if the user is nervous, it can communicate in a calm tone. If the user is relaxed, it can communicate in a cheerful tone. If the user is in a hurry, it can communicate quickly and concisely. This allows the user to quickly obtain the necessary information. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input user question data into AI and generate appropriate answers.

[0040] The health monitoring unit can collect and analyze health data such as heart rate and sleep patterns in real time, and can sound an alert if an abnormality is detected. For example, the health monitoring unit can measure heart rate with a sensor and collect data in real time. The health monitoring unit can also monitor sleep patterns and issue an alert if an abnormality is detected. For example, it can issue an alert if the heart rate is abnormally high. It can also issue an alert if there is a disturbance in the sleep pattern. This allows for real-time monitoring of the user's health status and early detection of abnormalities. Some or all of the above processing in the health monitoring unit may be performed using AI, or it may be performed without AI. For example, the health monitoring unit can input heart rate data into AI to detect abnormalities.

[0041] The Support Community Department can provide a platform for parents of autistic individuals to exchange information and seek advice from each other. For example, it can offer forums and chat functions to facilitate information exchange and consultation among parents. It can also provide video call functionality, allowing parents to communicate directly with one another. For instance, parents can post questions in the forum and receive answers from other parents. They can also exchange information in real time using the chat function. Furthermore, they can consult face-to-face via video calls. This facilitates information exchange and consultation among parents. Some or all of the above processes within the Support Community Department may be performed using AI or not. For example, the Support Community Department can input conversation data between parents into AI to provide appropriate advice.

[0042] The stress management app can suggest relaxation methods when users feel stressed or anxious. For example, it can provide guidance on meditation or deep breathing. It can also play relaxing music. For instance, if the user is feeling stressed, it can provide guidance on meditation. If the user is relaxed, it can play relaxing music. If the user is in a hurry, it can provide methods for relaxing in a short amount of time. This allows the app to provide appropriate relaxation methods when users feel stressed or anxious. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's emotional data into AI and generate the optimal relaxation method.

[0043] The education and training platform can individually manage learning progress and provide appropriate learning materials. For example, the education and training platform can analyze a user's learning history and provide optimal learning materials. It can also estimate a user's emotions and adjust the content and order of learning materials accordingly. For example, if a user is nervous, it can provide relaxing materials. If a user is relaxed, it can provide challenging materials. If a user is excited, it can provide materials to help them calm down. This allows for the provision of a customized learning plan tailored to the user. Some or all of the above processes in the education and training platform may be performed using AI or not. For example, the education and training platform can input user learning data into AI to generate optimal learning materials.

[0044] The VR training unit can simulate real-world situations and provide a space for practicing social and professional skills. For example, the VR training unit can simulate workplace scenarios and social situations. Furthermore, the VR training unit can estimate the user's emotions and adjust the training scenario accordingly. For example, if the user is tense, it can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows users to improve their adaptability in real-world environments. Some or all of the above processes in the VR training unit may be performed using AI or not. For example, the VR training unit can input user emotion data into AI to generate an optimal training scenario.

[0045] The Problem-Solving Plan Proposal Department can propose effective solutions and growth plans tailored to the individual characteristics of people with autism by aggregating and analyzing case studies of other individuals with autism. For example, the Problem-Solving Plan Proposal Department can build a database and collect case studies of other individuals with autism. It can also estimate the user's emotions and adjust the content and order of solutions accordingly. For example, if the user is anxious, it can provide a relaxing solution. If the user is relaxed, it can provide a challenging solution. If the user is excited, it can provide a solution to help them calm down. This allows the department to provide the user with the most suitable solutions and growth plans. Some or all of the above processes in the Problem-Solving Plan Proposal Department may be performed using AI or not. For example, the Problem-Solving Plan Proposal Department can input the user's emotional data into AI to generate the optimal solution.

[0046] The personalized assistant unit can analyze the user's past behavioral patterns and automatically generate an optimal schedule. For example, if the user has previously taken medication at a specific time, the personalized assistant unit can set a reminder for that time. It can also set an exercise reminder for a specific day of the week if the user has previously exercised on that day. Furthermore, if the user has previously eaten at a specific time, it can set a meal reminder for that time. This allows the personalized assistant unit to provide an optimal schedule based on the user's past behavioral patterns. Some or all of the above processes in the personalized assistant unit may be performed using AI, or not. For example, the personalized assistant unit can input user behavioral data into AI to generate an optimal schedule.

[0047] The personalized assistant unit can dynamically change the timing of reminders according to the user's lifestyle. For example, if the user is a night owl, the personalized assistant unit can set reminder notifications to be sent at night. If the user is an early riser, it can set reminder notifications to be sent in the morning. Furthermore, if the user has an irregular lifestyle, it can set reminder notifications to be sent according to the user's daily rhythm. This allows the personalized assistant unit to provide reminder timing that matches the user's lifestyle. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's lifestyle data into AI and generate reminder timing.

[0048] The personalized assistant unit can customize the content of reminders by taking into account the user's geographical location. For example, if the user is at home, the personalized assistant unit can display reminders for tasks to be done at home. If the user is at work, it can display reminders for tasks to be done at work. If the user is out, it can display reminders for tasks to be done while out. This allows the system to provide reminder content based on the user's location. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's location data into AI and generate reminder content.

[0049] The personalized assistant unit can analyze a user's social media activity and provide relevant reminders. For example, if a user posts on social media about attending an event, the personalized assistant unit can set a reminder for that event. It can also set a reminder if a user posts on social media about meeting a friend. Furthermore, if a user posts on social media about performing a specific task, it can set a reminder for that task. This allows the personalized assistant unit to provide reminders based on the user's social media activity. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's social media data into AI and generate relevant reminders.

[0050] The Social Skills Training Unit can analyze a user's past training history and automatically generate optimal scenarios. For example, the Social Skills Training Unit can provide similar scenarios based on scenarios in which the user has previously succeeded. It can also provide scenarios that include improvements based on scenarios in which the user has previously failed. Furthermore, it can provide scenarios of the optimal difficulty level based on the user's past training history. This allows the unit to provide optimal scenarios based on the user's past training history. Some or all of the above processes in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input user training data into AI to generate optimal scenarios.

[0051] The social skills training unit can dynamically change the difficulty level of training according to the user's reaction speed. For example, if the user's reaction is slow, the social skills training unit can lower the difficulty level of training. Conversely, if the user's reaction is fast, it can also increase the difficulty level of training. Furthermore, it can adjust the pace of training according to the user's reaction speed. This allows the unit to provide training difficulty levels that are appropriate to the user's reaction speed. Some or all of the above processes in the social skills training unit may be performed using AI or not. For example, the social skills training unit can input user reaction data into AI and generate training difficulty levels.

[0052] The Social Skills Training Unit can customize training scenarios by taking into account the user's geographical location. For example, if the user is at home, the Social Skills Training Unit can provide training scenarios that can be done at home. If the user is at work, it can provide training scenarios that can be done at work. If the user is out, it can provide training scenarios that can be done while out. This allows for the provision of training scenarios based on the user's location information. Some or all of the above processing in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input the user's location data into AI and generate training scenarios.

[0053] The Social Skills Training Department can analyze a user's social media activity and provide relevant training scenarios. For example, if a user posts about a conversation on social media, the Social Skills Training Department can provide a training scenario related to that conversation. It can also provide a training scenario related to a specific situation if the user posts about that situation. Furthermore, if the user posts about a specific skill on social media, it can provide a training scenario related to that skill. This allows the department to provide training scenarios based on the user's social media activity. Some or all of the above processing in the Social Skills Training Department may be performed using AI or not. For example, the Social Skills Training Department can input the user's social media data into AI and generate relevant training scenarios.

[0054] The emotion recognition unit can analyze the user's past emotional data and automatically generate optimal feedback. For example, the emotion recognition unit can provide similar feedback based on feedback that helped the user relax in the past. It can also provide feedback that includes areas for improvement based on feedback that the user found challenging in the past. Furthermore, it can provide optimal feedback based on the user's past emotional data. This allows for the provision of optimal feedback based on the user's past emotional data. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's emotional data into AI to generate optimal feedback.

[0055] The emotion recognition unit can dynamically improve the accuracy of emotion recognition in response to the user's facial expressions and tone of voice. For example, if the user's facial expression changes, the emotion recognition unit adjusts its emotion recognition algorithm. It can also adjust its emotion recognition algorithm if the user's tone of voice changes. Furthermore, it can dynamically improve the accuracy of emotion recognition in response to the user's facial expressions and tone of voice. This makes it possible to provide emotion recognition accuracy based on the user's facial expressions and tone of voice. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input user facial expression data and tone of voice data into AI to improve the accuracy of emotion recognition.

[0056] The emotion recognition unit can customize the content of the feedback by taking into account the user's geographical location. For example, if the user is at home, the emotion recognition unit can provide feedback that can be done at home. If the user is at work, it can provide feedback that can be done at work. If the user is out, it can provide feedback that can be done while out. This allows the unit to provide feedback based on the user's location. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's location data into AI and generate the content of the feedback.

[0057] The emotion recognition unit can analyze a user's social media activity and provide relevant feedback. For example, if a user posts about an emotion on social media, the emotion recognition unit can provide feedback related to that emotion. It can also provide feedback related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide feedback related to that skill. This allows for the provision of feedback based on the user's social media activity. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's social media data into AI and generate relevant feedback.

[0058] The Independent Living Support Robot Unit can analyze a user's past task history and automatically generate an optimal task schedule. For example, the Independent Living Support Robot Unit can set tasks for specific times based on tasks the user has performed at those times in the past. It can also set tasks for specific days of the week based on tasks the user has performed on those days in the past. Furthermore, it can provide an optimal task schedule based on the user's past task history. This allows the Independent Living Support Robot Unit to provide an optimal task schedule based on the user's past task history. Some or all of the above processes in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input user task data into AI and generate an optimal task schedule.

[0059] The independent living support robot unit can dynamically change the timing of task execution according to the user's lifestyle. For example, if the user has a nocturnal lifestyle, the robot unit can set task execution to the evening. If the user has an early-rising lifestyle, it can set task execution to the morning. Furthermore, if the user has an irregular lifestyle, it can set task execution to match the user's daily rhythm. This allows the robot unit to provide task execution timing that matches the user's lifestyle. Some or all of the above processing in the independent living support robot unit may be performed using AI or not. For example, the independent living support robot unit can input the user's lifestyle data into AI and generate task execution timing.

[0060] The Independent Living Support Robot Unit can customize task content by taking into account the user's geographical location. For example, if the user is at home, the Independent Living Support Robot Unit can provide tasks that can be performed at home. If the user is at work, it can provide tasks that can be performed at work. If the user is out, it can provide tasks that can be performed while out. This allows the robot to provide task content based on the user's location. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input the user's location data into AI and generate task content.

[0061] The Independent Living Support Robot Unit can analyze a user's social media activity and provide relevant tasks. For example, if a user posts about household chores on social media, the Independent Living Support Robot Unit can provide tasks related to those chores. It can also provide tasks related to specific events if a user posts about a particular event, or tasks related to specific skills if a user posts about those skills. This allows the robot to provide tasks based on the user's social media activity. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input the user's social media data into AI and generate relevant tasks.

[0062] The communication support tool unit can analyze a user's past communication history and automatically generate the optimal communication method. For example, the communication support tool unit can provide similar methods based on the user's past successful communication methods. It can also provide methods that include improvements based on the user's past unsuccessful communication methods. Furthermore, it can provide the optimal method based on the user's past communication history. In this way, it can provide the optimal communication method based on the user's past communication history. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's communication data into AI and generate the optimal method.

[0063] The communication support tool can dynamically change the content of communication according to the user's response speed. For example, if the user's response is slow, the communication support tool can make the content of communication concise. Conversely, if the user's response is fast, it can make the content of communication more detailed. It can also adjust the pace of communication according to the user's response speed. This allows the communication to be tailored to the user's response speed. Some or all of the above processing in the communication support tool may be performed using AI or not. For example, the communication support tool can input user response data into AI and generate communication content.

[0064] The communication support tool unit can customize the content of communication by taking into account the user's geographical location information. For example, if the user is at home, the communication support tool unit can provide communication that can be done at home. If the user is at work, it can provide communication that can be done at work. If the user is out, it can provide communication that can be done while out. This allows the communication content to be provided based on the user's location information. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's location information data into AI and generate the content of communication.

[0065] The communication support tool unit can analyze a user's social media activity and provide relevant communication content. For example, if a user posts about a conversation on social media, the communication support tool unit can provide communication content related to that conversation. It can also provide communication content related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide communication content related to that skill. In this way, it can provide communication content based on the user's social media activity. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's social media data into AI and generate relevant communication content.

[0066] The health monitoring unit can analyze the user's past health data and automatically generate the optimal monitoring method. For example, if the user has recorded health data at a specific time in the past, the health monitoring unit can set monitoring for that time. It can also set monitoring for a specific day of the week if the user has recorded health data on that day in the past. Furthermore, it can provide the optimal monitoring method based on the user's past health data. This allows the health monitoring unit to provide the optimal monitoring method based on the user's past health data. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's health data into AI to generate the optimal monitoring method.

[0067] The health monitoring unit can dynamically change the timing of monitoring according to the user's lifestyle. For example, if the user has a nocturnal lifestyle, the health monitoring unit can set the monitoring timing to the evening. If the user has an early-rising lifestyle, it can set the monitoring timing to the morning. Furthermore, if the user has an irregular lifestyle, it can set the monitoring timing to match the user's daily rhythm. This allows the unit to provide monitoring timing that matches the user's lifestyle. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's lifestyle data into AI and generate monitoring timing.

[0068] The health monitoring unit can customize the monitoring content by taking into account the user's geographical location information. For example, if the user is at home, the health monitoring unit can provide monitoring that can be performed at home. If the user is at work, it can provide monitoring that can be performed at work. If the user is out, it can provide monitoring that can be performed while out. This allows the system to provide monitoring content based on the user's location information. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's location data into AI and generate monitoring content.

[0069] The health monitoring unit can analyze a user's social media activity and provide relevant monitoring content. For example, if a user makes a health-related post on social media, the health monitoring unit can provide health-related monitoring content. It can also provide monitoring content related to a specific situation if the user makes a post about that situation on social media. Furthermore, if the user makes a post about a specific skill on social media, it can provide monitoring content related to that skill. This allows the system to provide monitoring content based on the user's social media activity. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's social media data into AI and generate relevant monitoring content.

[0070] The Support Community Department can analyze a user's past community participation history and automatically generate the optimal participation method. For example, the Support Community Department can provide similar methods based on the user's past successful community participation methods. It can also provide methods that include improvements based on the user's past unsuccessful community participation methods. Furthermore, it can provide the optimal method based on the user's past community participation history. This allows the Support Community Department to provide the optimal participation method based on the user's past community participation history. Some or all of the above processing in the Support Community Department may be performed using AI or not. For example, the Support Community Department can input the user's community participation data into AI and generate the optimal method.

[0071] The support community department can customize community content by taking into account the user's geographical location. For example, if the user is at home, the support community department can provide community content that can be done at home. If the user is at work, it can provide community content that can be done at work. If the user is out, it can provide community content that can be done while out. This allows for the provision of community content based on the user's location. Some or all of the above processing in the support community department may be performed using AI or not. For example, the support community department can input the user's location data into AI and generate community content.

[0072] The stress management app can analyze the user's past stress data and automatically generate the optimal relaxation method. For example, the stress management app can provide similar methods based on methods that have helped the user relax in the past. It can also provide methods that include improvements based on methods that have caused the user stress in the past. Furthermore, it can provide the optimal relaxation method based on the user's past stress data. This allows the app to provide the optimal relaxation method based on the user's past stress data. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's stress data into AI and generate the optimal relaxation method.

[0073] The stress management app can customize relaxation methods by taking into account the user's geographical location. For example, if the user is at home, the stress management app can provide relaxation methods that can be done at home. If the user is at work, it can provide relaxation methods that can be done at work. If the user is out, it can provide relaxation methods that can be done while out. This allows the app to provide relaxation methods based on the user's location. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's location data into AI and generate relaxation methods.

[0074] The education and training platform can analyze a user's past learning history and automatically generate optimal learning materials. For example, the education and training platform can provide similar materials based on materials the user has successfully completed in the past. It can also provide materials that include improvements based on materials the user has failed at in the past. Furthermore, it can provide optimal learning materials based on the user's past learning history. This allows for the provision of optimal learning materials based on the user's past learning history. Some or all of the above processes in the education and training platform may be performed using AI or not. For example, the education and training platform can input the user's learning data into AI to generate optimal learning materials.

[0075] The education and training platform can customize the content of educational materials by taking into account the user's geographical location. For example, if the user is at home, the education and training platform can provide educational materials that can be used at home. If the user is at work, it can provide educational materials that can be used at work. If the user is out, it can provide educational materials that can be used while out. This allows for the provision of educational material content based on the user's location. Some or all of the above processing in the education and training platform may be performed using AI or not. For example, the education and training platform can input the user's location data into AI and generate educational material content.

[0076] The VR training unit can analyze a user's past training history and automatically generate the optimal scenario. For example, the VR training unit can provide similar scenarios based on scenarios the user has succeeded in in the past. It can also provide scenarios that include improvements based on scenarios the user has failed in in the past. Furthermore, it can provide scenarios of the optimal difficulty level based on the user's past training history. In this way, it can provide the optimal scenario based on the user's past training history. Some or all of the above processes in the VR training unit may be performed using AI or not. For example, the VR training unit can input the user's training data into AI and generate the optimal scenario.

[0077] The VR training unit can customize training scenarios by taking into account the user's geographical location. For example, if the user is at home, the VR training unit can provide a training scenario that can be done at home. If the user is at work, it can provide a training scenario that can be done at work. If the user is out, it can provide a training scenario that can be done while out. This allows the VR training unit to provide training scenarios based on the user's location information. Some or all of the above processing in the VR training unit may be performed using AI or not. For example, the VR training unit can input the user's location data into AI and generate a training scenario.

[0078] The problem-solving plan proposal unit can analyze the user's past problem-solving history and automatically generate the optimal solution. For example, the problem-solving plan proposal unit can provide similar solutions based on solutions the user has successfully solved in the past. It can also provide solutions that include improvements based on solutions the user has failed to solve in the past. Furthermore, it can provide the optimal solution based on the user's past problem-solving history. In this way, it can provide the optimal solution based on the user's past problem-solving history. Some or all of the above processes in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input the user's problem-solving data into AI and generate the optimal solution.

[0079] The problem-solving plan proposal unit can customize the content of solutions by taking into account the user's geographical location information. For example, if the user is at home, the problem-solving plan proposal unit can provide solutions that can be implemented at home. If the user is at work, it can provide solutions that can be implemented at work. If the user is out, it can provide solutions that can be implemented while out. This allows the system to provide solutions based on the user's location information. Some or all of the above processing in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input the user's location data into AI and generate the content of the solutions.

[0080] The Problem-Solving Plan Proposal Unit can analyze a user's social media activity and provide relevant solutions. For example, if a user posts about a problem on social media, the Problem-Solving Plan Proposal Unit can provide a solution related to that problem. It can also provide a solution related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide a solution related to that skill. In this way, solutions can be provided based on the user's social media activity. Some or all of the above processing in the Problem-Solving Plan Proposal Unit may be performed using AI or not. For example, the Problem-Solving Plan Proposal Unit can input the user's social media data into AI and generate relevant solutions.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The autism support system can further include a behavior prediction unit that analyzes the user's behavioral patterns and predicts their next actions. For example, the behavior prediction unit can predict the user's most likely next actions based on their past behavioral data. It can also predict actions based on the user's schedule and daily rhythm. Furthermore, the behavior prediction unit can provide appropriate reminders and support based on the predicted actions. This enhances support for the user's daily life.

[0083] The autism support system can also include a health prediction unit that analyzes the user's health data and predicts their health status. For example, the health prediction unit predicts changes in the user's health status based on data such as their heart rate and sleep patterns. Furthermore, the health prediction unit can assess health risks based on the user's diet and exercise data. In addition, the health prediction unit can propose appropriate health management measures based on the predicted health status. This allows the system to support the user's health management.

[0084] The autism support system can further include a learning prediction unit that analyzes the user's learning data and predicts learning progress. For example, the learning prediction unit predicts what the user should learn next based on their past learning data. It can also predict learning progress based on the user's learning pace and level of understanding. Furthermore, the learning prediction unit can provide appropriate learning materials and plans based on the predicted learning progress. This allows for effective support of the user's learning.

[0085] The autism support system can also include a lifestyle improvement unit that analyzes the user's daily rhythm and improves their quality of life. For example, the lifestyle improvement unit can suggest lifestyle improvements based on the user's sleep patterns and dietary data. It can also provide appropriate exercise plans based on the user's exercise data. Furthermore, the lifestyle improvement unit can provide reminders and support according to the user's daily rhythm. This can improve the user's quality of life.

[0086] The autism support system can also include a hobby support section that analyzes the user's hobbies and interests and supports their hobby activities. For example, the hobby support section can suggest new hobbies based on the user's past hobby activity data. It can also introduce relevant events and activities based on the user's interests. Furthermore, the hobby support section can provide resources and tools to support the user's hobby activities, thereby enriching the user's hobby experiences.

[0087] The following briefly describes the processing flow for example form 1.

[0088] Step 1: The personalized assistant unit helps with daily schedule management and setting reminders. For example, it can remind you of wake-up times, medication times, and meal times. It can also automatically generate schedules based on the user's daily rhythm and adjust how reminders are notified by estimating the user's emotions. Step 2: The social skills training section provides scenarios for learning dialogue simulations and appropriate responses in different situations. For example, users can improve their dialogue skills through dialogue simulations. It can also estimate the user's emotions and adjust the training scenario accordingly. Step 3: The emotion recognition unit recognizes emotions and provides appropriate feedback. For example, it can recognize the user's emotions using facial recognition or voice analysis and adjust the content and timing of the feedback based on the user's emotions. Step 4: The Independent Living Support Robot section assists with simple tasks within the home. For example, it can assist with tasks such as cooking, cleaning, and laundry, and can also estimate the user's emotions to adjust the priority and content of tasks. Step 5: The communication support tool unit uses speech recognition and generation technology to interact with the user, answering simple questions and providing necessary information. It can also estimate the user's emotions and adjust the method and content of communication accordingly.

[0089] (Example of form 2) The autism support system according to an embodiment of the present invention is an AI and robot-based system aimed at supporting the independence of individuals with autism. This autism support system provides a personalized assistant to help manage daily schedules and set reminders. For example, it has functions to notify users of wake-up times, medication times, and meal times. It also provides social skills training to learn conversational simulations and appropriate responses in different situations. For example, it provides scenarios for learning conversational simulations and appropriate responses in different situations. Furthermore, it includes a system that recognizes emotions and provides appropriate feedback. This helps individuals with autism understand and control their emotions. Independent living support robots are also introduced to assist with simple tasks in the home (cooking, cleaning, laundry, etc.). This improves the quality of independent living. Furthermore, communication support tools using speech recognition and generation technology are also provided. For example, applications with functions such as answering simple questions and providing necessary information are included. A health monitoring system is also provided. It collects and analyzes health data such as heart rate and sleep patterns in real time and sounds an alert if an abnormality is detected. Furthermore, a platform is provided to form a support community for parents, where they can exchange information and seek advice. A stress management app is also provided, offering relaxation methods when feeling stressed or anxious. For example, it provides guidance on meditation and deep breathing, and plays relaxing music. An online education platform is also provided, which individually manages learning progress and provides appropriate learning materials. Customized learning plans tailored to individuals with autism are offered. Furthermore, a space is provided where real-world situations can be simulated using VR technology, allowing individuals to practice social and vocational skills. This makes it possible to improve adaptability in real-world environments. Finally, a function is provided that aggregates and analyzes the cases of other individuals with autism to suggest effective methods for solutions and growth plans tailored to the individual's personality. In this way, the autism support system can support the daily lives of individuals with autism and improve their emotional recognition and communication.

[0090] The autism support system according to this embodiment comprises a personalized assistant unit, a social skills training unit, an emotion recognition unit, an independent living support robot unit, and a communication support tool unit. The personalized assistant unit assists with daily schedule management and reminder setting. For example, the personalized assistant unit notifies the user of wake-up time, medication time, and meal times. The personalized assistant unit can also automatically generate a schedule based on the user's daily rhythm, for example. The personalized assistant unit can also estimate the user's emotions and adjust the method of reminder notifications. The social skills training unit provides scenarios for learning dialogue simulations and appropriate responses in different situations. For example, the social skills training unit allows the user to improve their dialogue skills through dialogue simulations. The social skills training unit can also estimate the user's emotions and adjust the training scenarios. The emotion recognition unit recognizes emotions and provides appropriate feedback. For example, the emotion recognition unit recognizes the user's emotions using facial recognition or voice analysis. The emotion recognition unit can also adjust the content and timing of feedback based on the user's emotions. The independent living support robot unit assists with simple tasks within the home. For example, it assists with tasks such as cooking, cleaning, and laundry. The independent living support robot unit can also estimate the user's emotions and adjust the priority and content of tasks. The communication support tool unit answers simple questions and provides necessary information. For example, the communication support tool unit engages in dialogue with the user using speech recognition and generation technology. The communication support tool unit can also estimate the user's emotions and adjust the method and content of communication. As a result, the autism support system according to this embodiment can support the daily life of autistic individuals and improve their emotion recognition and communication.

[0091] The personalized assistant unit assists with daily schedule management and reminder setting. Specifically, it has functions to notify users of their wake-up time, medication times, meal times, and so on. These notifications are made according to a schedule automatically generated based on the user's daily rhythm. For example, if a user has a habit of waking up at 7 AM every morning, the personalized assistant unit will set an alarm for 7 AM to encourage them to get up. Also, if there is a set time for taking medication, it will display a reminder at that time to encourage the user to take their medication. Furthermore, the personalized assistant unit can estimate the user's emotions and adjust the way reminder notifications are delivered. For example, if the user is feeling stressed, it will notify them in a soft voice or with vibration, depending on the user's state. AI technology is used to estimate emotions by analyzing the user's facial expressions, voice tone, and behavioral patterns. As a result, the personalized assistant unit can make the user's life more comfortable and reduce stress. In addition, the personalized assistant unit can learn from the user's past behavioral data and optimize future schedules. For example, if a user has a habit of performing a specific task on a particular day of the week, that task can be automatically incorporated into the schedule. This allows users to manage their daily lives more efficiently.

[0092] The Social Skills Training Department provides scenarios for learning dialogue simulations and appropriate responses in different situations. Specifically, it conducts simulations to improve users' dialogue skills. For example, users can converse with a virtual conversation partner and learn appropriate responses and expressions. The simulations reproduce common situations in daily life, enabling users to respond appropriately in real-life situations. The Social Skills Training Department can also estimate the user's emotions and adjust the training scenario accordingly. For example, if the user is nervous, the scenario can be simplified or changed to something more relaxing. Emotion estimation uses facial recognition and voice analysis. This allows users to improve their dialogue skills at their own pace. Furthermore, the Social Skills Training Department has a function to record the user's progress and evaluate the effectiveness of the training. For example, it collects data such as how often the user gave appropriate responses and which scenarios they found difficult, and incorporates this into the next training session. This allows users to continuously improve their skills.

[0093] The emotion recognition unit recognizes emotions and provides appropriate feedback. Specifically, it recognizes the user's emotions using facial recognition and voice analysis. For example, if the user is smiling, it will provide feedback such as "You seem happy," and conversely, if the user looks sad, it will ask, "Is there something bothering you?" The emotion recognition unit can also adjust the content and timing of feedback based on the user's emotions. For example, if the user is feeling stressed, it will provide advice on how to relax. AI technology is used for emotion recognition, analyzing the user's facial expressions, voice tone, and behavioral patterns. This allows the emotion recognition unit to accurately grasp the user's emotions and provide appropriate feedback. Furthermore, the emotion recognition unit can accumulate user emotion data and analyze long-term emotional fluctuations. For example, it can analyze what emotions a user is likely to experience at specific times or in specific situations, and take preventative measures. This allows users to better understand their own emotions and deal with them appropriately.

[0094] The Independent Living Support Robot Division assists with simple tasks within the home. Specifically, it assists with tasks such as cooking, cleaning, and laundry. For example, the robot can help with cooking by chopping ingredients or stirring pots in the kitchen. A cleaning robot cleans the floor, and a laundry robot washes and dries clothes. This reduces the burden of daily life for the user. Furthermore, the Independent Living Support Robot Division can estimate the user's emotions and adjust the priority and content of tasks accordingly. For example, if the user is tired, the robot will prioritize tasks to create a relaxing environment. Emotion estimation uses AI technology that analyzes the user's facial expressions, tone of voice, and behavioral patterns. This allows the Independent Living Support Robot Division to provide optimal support tailored to the user's condition. Moreover, the robot can not only operate according to user instructions but also autonomously judge and execute tasks. For example, if it detects an accumulation of garbage, it will automatically collect and dispose of it. This allows the user to maintain a more comfortable living environment.

[0095] The communication support tool unit answers simple questions and provides necessary information. Specifically, it engages in dialogue with the user using speech recognition and generation technology. For example, if the user asks, "What's the weather like today?", the tool provides weather information. If the user asks, "What's my next appointment?", it checks the schedule and informs the user of their next appointment. Furthermore, the communication support tool unit can estimate the user's emotions and adjust the method and content of communication accordingly. For example, if the user is agitated, it will speak in a calm tone or offer advice to help them relax. Emotion estimation uses facial recognition and speech analysis. This allows the communication support tool unit to provide optimal dialogue tailored to the user's state. In addition, the tool can learn from the user's past dialogue history and provide more personalized information. For example, if the user has specific hobbies or interests, it will provide topics based on that information. This allows the user to enjoy more fulfilling communication.

[0096] The personalized assistant unit can notify users of wake-up times, medication times, and meal times. For example, the personalized assistant unit can set wake-up times based on the user's daily rhythm. It can also set medication times based on a doctor's instructions. Furthermore, it can set meal times based on nutritional balance. This helps the user manage their daily schedule. Some or all of the above processes in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's daily rhythm data into AI to generate an optimal schedule.

[0097] The Social Skills Training Unit can provide scenarios for learning dialogue simulations and appropriate responses in different situations. For example, the Social Skills Training Unit can help users improve their dialogue skills through dialogue simulations. It can also estimate the user's emotions and adjust the training scenario accordingly. For instance, if the user is nervous, it can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows the user to improve their dialogue skills. Some or all of the above processes in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input user emotion data into AI to generate an optimal training scenario.

[0098] The emotion recognition unit can recognize emotions and provide appropriate feedback. For example, the emotion recognition unit recognizes the user's emotions using facial recognition or voice analysis. For example, the emotion recognition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the emotion recognition unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the emotion recognition unit can adjust the content and timing of feedback based on the user's emotions. For example, if the user is tense, it can provide relaxing feedback. If the user is relaxed, it can provide challenging feedback. If the user is excited, it can provide feedback to help them calm down. This helps the user understand and control their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation.

[0099] The Independent Living Support Robot can assist with simple tasks within the home. For example, it can assist with tasks such as cooking, cleaning, and laundry. The Independent Living Support Robot can also estimate the user's emotions and adjust the priority and content of tasks accordingly. For example, if the user is stressed, it can prioritize relaxing tasks. If the user is relaxed, it can prioritize challenging tasks. If the user is in a hurry, it can prioritize tasks that can be completed quickly. This can improve the user's quality of independent living. Some or all of the above processing in the Independent Living Support Robot may be performed using AI or not. For example, the Independent Living Support Robot can input the user's emotional data into AI to generate optimal tasks.

[0100] The communication support tool unit can answer simple questions and provide necessary information. For example, it can engage in dialogue with the user using speech recognition and generation technologies. It can also provide appropriate answers to user questions. Furthermore, the communication support tool unit can estimate the user's emotions and adjust the method and content of communication accordingly. For example, if the user is nervous, it can communicate in a calm tone. If the user is relaxed, it can communicate in a cheerful tone. If the user is in a hurry, it can communicate quickly and concisely. This allows the user to quickly obtain the necessary information. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input user question data into AI and generate appropriate answers.

[0101] The health monitoring unit can collect and analyze health data such as heart rate and sleep patterns in real time, and can sound an alert if an abnormality is detected. For example, the health monitoring unit can measure heart rate with a sensor and collect data in real time. The health monitoring unit can also monitor sleep patterns and issue an alert if an abnormality is detected. For example, it can issue an alert if the heart rate is abnormally high. It can also issue an alert if there is a disturbance in the sleep pattern. This allows for real-time monitoring of the user's health status and early detection of abnormalities. Some or all of the above processing in the health monitoring unit may be performed using AI, or it may be performed without AI. For example, the health monitoring unit can input heart rate data into AI to detect abnormalities.

[0102] The Support Community Department can provide a platform for parents of autistic individuals to exchange information and seek advice from each other. For example, it can offer forums and chat functions to facilitate information exchange and consultation among parents. It can also provide video call functionality, allowing parents to communicate directly with one another. For instance, parents can post questions in the forum and receive answers from other parents. They can also exchange information in real time using the chat function. Furthermore, they can consult face-to-face via video calls. This facilitates information exchange and consultation among parents. Some or all of the above processes within the Support Community Department may be performed using AI or not. For example, the Support Community Department can input conversation data between parents into AI to provide appropriate advice.

[0103] The stress management app can suggest relaxation methods when users feel stressed or anxious. For example, it can provide guidance on meditation or deep breathing. It can also play relaxing music. For instance, if the user is feeling stressed, it can provide guidance on meditation. If the user is relaxed, it can play relaxing music. If the user is in a hurry, it can provide methods for relaxing in a short amount of time. This allows the app to provide appropriate relaxation methods when users feel stressed or anxious. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's emotional data into AI and generate the optimal relaxation method.

[0104] The education and training platform can individually manage learning progress and provide appropriate learning materials. For example, the education and training platform can analyze a user's learning history and provide optimal learning materials. It can also estimate a user's emotions and adjust the content and order of learning materials accordingly. For example, if a user is nervous, it can provide relaxing materials. If a user is relaxed, it can provide challenging materials. If a user is excited, it can provide materials to help them calm down. This allows for the provision of a customized learning plan tailored to the user. Some or all of the above processes in the education and training platform may be performed using AI or not. For example, the education and training platform can input user learning data into AI to generate optimal learning materials.

[0105] The VR training unit can simulate real-world situations and provide a space for practicing social and professional skills. For example, the VR training unit can simulate workplace scenarios and social situations. Furthermore, the VR training unit can estimate the user's emotions and adjust the training scenario accordingly. For example, if the user is tense, it can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows users to improve their adaptability in real-world environments. Some or all of the above processes in the VR training unit may be performed using AI or not. For example, the VR training unit can input user emotion data into AI to generate an optimal training scenario.

[0106] The Problem-Solving Plan Proposal Department can propose effective solutions and growth plans tailored to the individual characteristics of people with autism by aggregating and analyzing case studies of other individuals with autism. For example, the Problem-Solving Plan Proposal Department can build a database and collect case studies of other individuals with autism. It can also estimate the user's emotions and adjust the content and order of solutions accordingly. For example, if the user is anxious, it can provide a relaxing solution. If the user is relaxed, it can provide a challenging solution. If the user is excited, it can provide a solution to help them calm down. This allows the department to provide the user with the most suitable solutions and growth plans. Some or all of the above processes in the Problem-Solving Plan Proposal Department may be performed using AI or not. For example, the Problem-Solving Plan Proposal Department can input the user's emotional data into AI to generate the optimal solution.

[0107] The personalized assistant unit can estimate the user's emotions and adjust the way reminders are notified based on those emotions. For example, if the user is stressed, the personalized assistant unit can notify reminders with a gentle sound. If the user is relaxed, it can notify reminders with a bright sound. If the user is in a hurry, it can notify reminders with a short, concise sound. This allows the personalized assistant unit to provide reminder notifications that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input user emotion data into a generative AI and have the generative AI execute the reminder notification method.

[0108] The personalized assistant unit can analyze the user's past behavioral patterns and automatically generate an optimal schedule. For example, if the user has previously taken medication at a specific time, the personalized assistant unit can set a reminder for that time. It can also set an exercise reminder for a specific day of the week if the user has previously exercised on that day. Furthermore, if the user has previously eaten at a specific time, it can set a meal reminder for that time. This allows the personalized assistant unit to provide an optimal schedule based on the user's past behavioral patterns. Some or all of the above processes in the personalized assistant unit may be performed using AI, or not. For example, the personalized assistant unit can input user behavioral data into AI to generate an optimal schedule.

[0109] The personalized assistant unit can dynamically change the timing of reminders according to the user's lifestyle. For example, if the user is a night owl, the personalized assistant unit can set reminder notifications to be sent at night. If the user is an early riser, it can set reminder notifications to be sent in the morning. Furthermore, if the user has an irregular lifestyle, it can set reminder notifications to be sent according to the user's daily rhythm. This allows the personalized assistant unit to provide reminder timing that matches the user's lifestyle. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's lifestyle data into AI and generate reminder timing.

[0110] The personalized assistant unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the personalized assistant unit can prioritize and display only important notifications. If the user is relaxed, it can display all notifications. If the user is in a hurry, it can prioritize and display only urgent notifications. This allows for notification prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input user emotion data into a generative AI and have the generative AI prioritize notifications.

[0111] The personalized assistant unit can customize the content of reminders by taking into account the user's geographical location. For example, if the user is at home, the personalized assistant unit can display reminders for tasks to be done at home. If the user is at work, it can display reminders for tasks to be done at work. If the user is out, it can display reminders for tasks to be done while out. This allows the system to provide reminder content based on the user's location. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's location data into AI and generate reminder content.

[0112] The personalized assistant unit can analyze a user's social media activity and provide relevant reminders. For example, if a user posts on social media about attending an event, the personalized assistant unit can set a reminder for that event. It can also set a reminder if a user posts on social media about meeting a friend. Furthermore, if a user posts on social media about performing a specific task, it can set a reminder for that task. This allows the personalized assistant unit to provide reminders based on the user's social media activity. Some or all of the above processing in the personalized assistant unit may be performed using AI or not. For example, the personalized assistant unit can input the user's social media data into AI and generate relevant reminders.

[0113] The social skills training unit can estimate the user's emotions and adjust the training scenario based on the estimated emotions. For example, if the user is nervous, the social skills training unit can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows the system to provide training scenarios tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the social skills training unit may be performed using AI or not. For example, the social skills training unit can input user emotion data into a generative AI and generate a training scenario.

[0114] The Social Skills Training Unit can analyze a user's past training history and automatically generate optimal scenarios. For example, the Social Skills Training Unit can provide similar scenarios based on scenarios in which the user has previously succeeded. It can also provide scenarios that include improvements based on scenarios in which the user has previously failed. Furthermore, it can provide scenarios of the optimal difficulty level based on the user's past training history. This allows the unit to provide optimal scenarios based on the user's past training history. Some or all of the above processes in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input user training data into AI to generate optimal scenarios.

[0115] The social skills training unit can dynamically change the difficulty level of training according to the user's reaction speed. For example, if the user's reaction is slow, the social skills training unit can lower the difficulty level of training. Conversely, if the user's reaction is fast, it can also increase the difficulty level of training. Furthermore, it can adjust the pace of training according to the user's reaction speed. This allows the unit to provide training difficulty levels that are appropriate to the user's reaction speed. Some or all of the above processes in the social skills training unit may be performed using AI or not. For example, the social skills training unit can input user reaction data into AI and generate training difficulty levels.

[0116] The social skills training unit can estimate the user's emotions and adjust the training sequence based on the estimated emotions. For example, if the user is nervous, the social skills training unit can provide relaxing training first. If the user is relaxed, it can provide challenging training first. If the user is excited, it can provide training to calm down first. This allows the unit to provide a training sequence that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the social skills training unit may be performed using AI or not. For example, the social skills training unit can input user emotion data into a generative AI and generate a training sequence.

[0117] The Social Skills Training Unit can customize training scenarios by taking into account the user's geographical location. For example, if the user is at home, the Social Skills Training Unit can provide training scenarios that can be done at home. If the user is at work, it can provide training scenarios that can be done at work. If the user is out, it can provide training scenarios that can be done while out. This allows for the provision of training scenarios based on the user's location information. Some or all of the above processing in the Social Skills Training Unit may be performed using AI or not. For example, the Social Skills Training Unit can input the user's location data into AI and generate training scenarios.

[0118] The Social Skills Training Department can analyze a user's social media activity and provide relevant training scenarios. For example, if a user posts about a conversation on social media, the Social Skills Training Department can provide a training scenario related to that conversation. It can also provide a training scenario related to a specific situation if the user posts about that situation. Furthermore, if the user posts about a specific skill on social media, it can provide a training scenario related to that skill. This allows the department to provide training scenarios based on the user's social media activity. Some or all of the above processing in the Social Skills Training Department may be performed using AI or not. For example, the Social Skills Training Department can input the user's social media data into AI and generate relevant training scenarios.

[0119] The emotion recognition unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is tense, the emotion recognition unit can provide relaxing feedback. If the user is relaxed, it can also provide challenging feedback. If the user is excited, it can provide feedback to help them calm down. This allows the unit to provide feedback that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input user emotion data into a generative AI and generate feedback content.

[0120] The emotion recognition unit can analyze the user's past emotional data and automatically generate optimal feedback. For example, the emotion recognition unit can provide similar feedback based on feedback that helped the user relax in the past. It can also provide feedback that includes areas for improvement based on feedback that the user found challenging in the past. Furthermore, it can provide optimal feedback based on the user's past emotional data. This allows for the provision of optimal feedback based on the user's past emotional data. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's emotional data into AI to generate optimal feedback.

[0121] The emotion recognition unit can dynamically improve the accuracy of emotion recognition in response to the user's facial expressions and tone of voice. For example, if the user's facial expression changes, the emotion recognition unit adjusts its emotion recognition algorithm. It can also adjust its emotion recognition algorithm if the user's tone of voice changes. Furthermore, it can dynamically improve the accuracy of emotion recognition in response to the user's facial expressions and tone of voice. This makes it possible to provide emotion recognition accuracy based on the user's facial expressions and tone of voice. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input user facial expression data and tone of voice data into AI to improve the accuracy of emotion recognition.

[0122] The emotion recognition unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is tense, the emotion recognition unit can provide feedback at a time that helps them relax. If the user is relaxed, it can provide feedback at a time that is challenging. If the user is excited, it can provide feedback at a time that helps them calm down. This allows the unit to provide feedback timing that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input user emotion data into a generative AI and generate feedback timing.

[0123] The emotion recognition unit can customize the content of the feedback by taking into account the user's geographical location. For example, if the user is at home, the emotion recognition unit can provide feedback that can be done at home. If the user is at work, it can provide feedback that can be done at work. If the user is out, it can provide feedback that can be done while out. This allows the unit to provide feedback based on the user's location. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's location data into AI and generate the content of the feedback.

[0124] The emotion recognition unit can analyze a user's social media activity and provide relevant feedback. For example, if a user posts about an emotion on social media, the emotion recognition unit can provide feedback related to that emotion. It can also provide feedback related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide feedback related to that skill. This allows for the provision of feedback based on the user's social media activity. Some or all of the above processing in the emotion recognition unit may be performed using AI or not. For example, the emotion recognition unit can input the user's social media data into AI and generate relevant feedback.

[0125] The Independent Living Support Robot Unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the Independent Living Support Robot Unit will prioritize relaxing tasks. If the user is relaxed, it can also prioritize challenging tasks. If the user is in a hurry, it can prioritize tasks that can be completed quickly. This allows the robot to provide task priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input user emotion data into a generative AI and generate task priorities.

[0126] The Independent Living Support Robot Unit can analyze a user's past task history and automatically generate an optimal task schedule. For example, the Independent Living Support Robot Unit can set tasks for specific times based on tasks the user has performed at those times in the past. It can also set tasks for specific days of the week based on tasks the user has performed on those days in the past. Furthermore, it can provide an optimal task schedule based on the user's past task history. This allows the Independent Living Support Robot Unit to provide an optimal task schedule based on the user's past task history. Some or all of the above processes in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input user task data into AI and generate an optimal task schedule.

[0127] The independent living support robot unit can dynamically change the timing of task execution according to the user's lifestyle. For example, if the user has a nocturnal lifestyle, the robot unit can set task execution to the evening. If the user has an early-rising lifestyle, it can set task execution to the morning. Furthermore, if the user has an irregular lifestyle, it can set task execution to match the user's daily rhythm. This allows the robot unit to provide task execution timing that matches the user's lifestyle. Some or all of the above processing in the independent living support robot unit may be performed using AI or not. For example, the independent living support robot unit can input the user's lifestyle data into AI and generate task execution timing.

[0128] The Independent Living Support Robot Unit can estimate the user's emotions and adjust the task content based on the estimated emotions. For example, if the user is stressed, the Independent Living Support Robot Unit can provide a relaxing task. If the user is relaxed, it can also provide a challenging task. If the user is in a hurry, it can provide a task that can be completed quickly. This allows the robot to provide task content that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input user emotion data into a generative AI and generate task content.

[0129] The Independent Living Support Robot Unit can customize task content by taking into account the user's geographical location. For example, if the user is at home, the Independent Living Support Robot Unit can provide tasks that can be performed at home. If the user is at work, it can provide tasks that can be performed at work. If the user is out, it can provide tasks that can be performed while out. This allows the robot to provide task content based on the user's location. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input the user's location data into AI and generate task content.

[0130] The Independent Living Support Robot Unit can analyze a user's social media activity and provide relevant tasks. For example, if a user posts about household chores on social media, the Independent Living Support Robot Unit can provide tasks related to those chores. It can also provide tasks related to specific events if a user posts about a particular event, or tasks related to specific skills if a user posts about those skills. This allows the robot to provide tasks based on the user's social media activity. Some or all of the above processing in the Independent Living Support Robot Unit may be performed using AI or not. For example, the Independent Living Support Robot Unit can input the user's social media data into AI and generate relevant tasks.

[0131] The communication support tool unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is nervous, the communication support tool unit can communicate in a calm tone. If the user is relaxed, it can communicate in a cheerful tone. If the user is in a hurry, it can communicate quickly and concisely. This allows the system to provide a communication method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input user emotion data into a generative AI and generate a communication method.

[0132] The communication support tool unit can analyze a user's past communication history and automatically generate the optimal communication method. For example, the communication support tool unit can provide similar methods based on the user's past successful communication methods. It can also provide methods that include improvements based on the user's past unsuccessful communication methods. Furthermore, it can provide the optimal method based on the user's past communication history. In this way, it can provide the optimal communication method based on the user's past communication history. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's communication data into AI and generate the optimal method.

[0133] The communication support tool can dynamically change the content of communication according to the user's response speed. For example, if the user's response is slow, the communication support tool can make the content of communication concise. Conversely, if the user's response is fast, it can make the content of communication more detailed. It can also adjust the pace of communication according to the user's response speed. This allows the communication to be tailored to the user's response speed. Some or all of the above processing in the communication support tool may be performed using AI or not. For example, the communication support tool can input user response data into AI and generate communication content.

[0134] The communication support tool unit can estimate the user's emotions and adjust the order of communication based on the estimated emotions. For example, if the user is nervous, the communication support tool unit can first provide relaxing communication. If the user is relaxed, it can also first provide challenging communication. If the user is excited, it can also first provide calming communication. This allows the communication order to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input user emotion data into a generative AI and generate a communication order.

[0135] The communication support tool unit can customize the content of communication by taking into account the user's geographical location information. For example, if the user is at home, the communication support tool unit can provide communication that can be done at home. If the user is at work, it can provide communication that can be done at work. If the user is out, it can provide communication that can be done while out. This allows the communication content to be provided based on the user's location information. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's location information data into AI and generate the content of communication.

[0136] The communication support tool unit can analyze a user's social media activity and provide relevant communication content. For example, if a user posts about a conversation on social media, the communication support tool unit can provide communication content related to that conversation. It can also provide communication content related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide communication content related to that skill. In this way, it can provide communication content based on the user's social media activity. Some or all of the above processing in the communication support tool unit may be performed using AI or not. For example, the communication support tool unit can input the user's social media data into AI and generate relevant communication content.

[0137] The health monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the health monitoring unit can increase the monitoring frequency. If the user is relaxed, it can decrease the monitoring frequency. If the user is in a hurry, it can shorten the monitoring frequency. This allows the system to provide a monitoring frequency that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input user emotion data into a generative AI and generate a monitoring frequency.

[0138] The health monitoring unit can analyze the user's past health data and automatically generate the optimal monitoring method. For example, if the user has recorded health data at a specific time in the past, the health monitoring unit can set monitoring for that time. It can also set monitoring for a specific day of the week if the user has recorded health data on that day in the past. Furthermore, it can provide the optimal monitoring method based on the user's past health data. This allows the health monitoring unit to provide the optimal monitoring method based on the user's past health data. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's health data into AI to generate the optimal monitoring method.

[0139] The health monitoring unit can dynamically change the timing of monitoring according to the user's lifestyle. For example, if the user has a nocturnal lifestyle, the health monitoring unit can set the monitoring timing to the evening. If the user has an early-rising lifestyle, it can set the monitoring timing to the morning. Furthermore, if the user has an irregular lifestyle, it can set the monitoring timing to match the user's daily rhythm. This allows the unit to provide monitoring timing that matches the user's lifestyle. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's lifestyle data into AI and generate monitoring timing.

[0140] The health monitoring unit can estimate the user's emotions and adjust the monitoring content based on the estimated emotions. For example, if the user is stressed, the health monitoring unit will prioritize monitoring stress-related health data. If the user is relaxed, it can also prioritize monitoring relaxation-related health data. If the user is in a hurry, it can also prioritize monitoring health data that can be recorded quickly. This allows the system to provide monitoring content that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input user emotion data into a generative AI and generate monitoring content.

[0141] The health monitoring unit can customize the monitoring content by taking into account the user's geographical location information. For example, if the user is at home, the health monitoring unit can provide monitoring that can be performed at home. If the user is at work, it can provide monitoring that can be performed at work. If the user is out, it can provide monitoring that can be performed while out. This allows the system to provide monitoring content based on the user's location information. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's location data into AI and generate monitoring content.

[0142] The health monitoring unit can analyze a user's social media activity and provide relevant monitoring content. For example, if a user makes a health-related post on social media, the health monitoring unit can provide health-related monitoring content. It can also provide monitoring content related to a specific situation if the user makes a post about that situation on social media. Furthermore, if the user makes a post about a specific skill on social media, it can provide monitoring content related to that skill. This allows the system to provide monitoring content based on the user's social media activity. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input the user's social media data into AI and generate relevant monitoring content.

[0143] The support community unit can estimate a user's emotions and adjust how they participate in the community based on those emotions. For example, if a user is feeling anxious, the support community unit can provide a relaxing way to participate in the community. If a user is relaxed, it can provide a more active way to participate. If a user is excited, it can provide a way to calm down. This allows the support community unit to provide community participation methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support community unit may be performed using AI or not. For example, the support community unit can input user emotion data into a generative AI and generate ways to participate in the community.

[0144] The Support Community Department can analyze a user's past community participation history and automatically generate the optimal participation method. For example, the Support Community Department can provide similar methods based on the user's past successful community participation methods. It can also provide methods that include improvements based on the user's past unsuccessful community participation methods. Furthermore, it can provide the optimal method based on the user's past community participation history. This allows the Support Community Department to provide the optimal participation method based on the user's past community participation history. Some or all of the above processing in the Support Community Department may be performed using AI or not. For example, the Support Community Department can input the user's community participation data into AI and generate the optimal method.

[0145] The support community section can estimate the user's emotions and adjust the community content based on those emotions. For example, if the user is feeling anxious, the support community section can provide relaxing community content. If the user is relaxed, it can provide challenging community content. If the user is excited, it can provide community content to help them calm down. This allows the community content to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support community section may be performed using AI or not. For example, the support community section can input user emotion data into a generative AI and generate community content.

[0146] The support community department can customize community content by taking into account the user's geographical location. For example, if the user is at home, the support community department can provide community content that can be done at home. If the user is at work, it can provide community content that can be done at work. If the user is out, it can provide community content that can be done while out. This allows for the provision of community content based on the user's location. Some or all of the above processing in the support community department may be performed using AI or not. For example, the support community department can input the user's location data into AI and generate community content.

[0147] The stress management app can estimate the user's emotions and adjust relaxation methods based on those emotions. For example, if the user is feeling stressed, the stress management app can provide guidance on meditation or deep breathing. If the user is relaxed, it can also provide relaxing music. If the user is in a hurry, it can provide methods for relaxing in a short amount of time. This allows the app to provide relaxation methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input user emotion data into a generative AI and generate relaxation methods.

[0148] The stress management app can analyze the user's past stress data and automatically generate the optimal relaxation method. For example, the stress management app can provide similar methods based on methods that have helped the user relax in the past. It can also provide methods that include improvements based on methods that have caused the user stress in the past. Furthermore, it can provide the optimal relaxation method based on the user's past stress data. This allows the app to provide the optimal relaxation method based on the user's past stress data. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's stress data into AI and generate the optimal relaxation method.

[0149] The stress management app can estimate the user's emotions and adjust the order of relaxation methods based on the estimated emotions. For example, if the user is tense, the stress management app can first provide relaxation methods. If the user is relaxed, it can first provide challenging methods. If the user is excited, it can first provide methods to calm down. This allows the app to provide a sequence of relaxation methods that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input user emotion data into a generative AI and generate a sequence of relaxation methods.

[0150] The stress management app can customize relaxation methods by taking into account the user's geographical location. For example, if the user is at home, the stress management app can provide relaxation methods that can be done at home. If the user is at work, it can provide relaxation methods that can be done at work. If the user is out, it can provide relaxation methods that can be done while out. This allows the app to provide relaxation methods based on the user's location. Some or all of the above processing in the stress management app may be performed using AI or not. For example, the stress management app can input the user's location data into AI and generate relaxation methods.

[0151] The education and training platform can estimate the user's emotions and adjust the content of the learning materials based on the estimated emotions. For example, if the user is nervous, the education and training platform can provide relaxing materials. If the user is relaxed, it can provide challenging materials. If the user is excited, it can provide materials to help them calm down. This allows the platform to provide learning materials that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the education and training platform may be performed using AI or not. For example, the education and training platform can input user emotion data into a generative AI and generate learning material content.

[0152] The education and training platform can analyze a user's past learning history and automatically generate optimal learning materials. For example, the education and training platform can provide similar materials based on materials the user has successfully completed in the past. It can also provide materials that include improvements based on materials the user has failed at in the past. Furthermore, it can provide optimal learning materials based on the user's past learning history. This allows for the provision of optimal learning materials based on the user's past learning history. Some or all of the above processes in the education and training platform may be performed using AI or not. For example, the education and training platform can input the user's learning data into AI to generate optimal learning materials.

[0153] The education and training platform can estimate the user's emotions and adjust the order of learning materials based on those emotions. For example, if the user is nervous, the education and training platform can provide relaxing materials first. If the user is relaxed, it can provide challenging materials first. If the user is excited, it can provide materials to help them calm down first. This allows the platform to provide a learning material order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the education and training platform may be performed using AI or not. For example, the education and training platform can input user emotion data into a generative AI and generate a learning material order.

[0154] The education and training platform can customize the content of educational materials by taking into account the user's geographical location. For example, if the user is at home, the education and training platform can provide educational materials that can be used at home. If the user is at work, it can provide educational materials that can be used at work. If the user is out, it can provide educational materials that can be used while out. This allows for the provision of educational material content based on the user's location. Some or all of the above processing in the education and training platform may be performed using AI or not. For example, the education and training platform can input the user's location data into AI and generate educational material content.

[0155] The VR training unit can estimate the user's emotions and adjust the training scenario based on those emotions. For example, if the user is nervous, the VR training unit can provide a relaxing scenario. If the user is relaxed, it can provide a challenging scenario. If the user is excited, it can provide a scenario to help them calm down. This allows the system to provide training scenarios tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the VR training unit may be performed using AI or not. For example, the VR training unit can input user emotion data into a generative AI and generate a training scenario.

[0156] The VR training unit can analyze a user's past training history and automatically generate the optimal scenario. For example, the VR training unit can provide similar scenarios based on scenarios the user has succeeded in in the past. It can also provide scenarios that include improvements based on scenarios the user has failed in in the past. Furthermore, it can provide scenarios of the optimal difficulty level based on the user's past training history. In this way, it can provide the optimal scenario based on the user's past training history. Some or all of the above processes in the VR training unit may be performed using AI or not. For example, the VR training unit can input the user's training data into AI and generate the optimal scenario.

[0157] The VR training unit can estimate the user's emotions and adjust the training sequence based on the estimated emotions. For example, if the user is nervous, the VR training unit can provide relaxing training first. If the user is relaxed, it can provide challenging training first. If the user is excited, it can provide training to calm down first. This allows the training sequence to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the VR training unit may be performed using AI or not. For example, the VR training unit can input user emotion data into a generative AI and generate a training sequence.

[0158] The VR training unit can customize training scenarios by taking into account the user's geographical location. For example, if the user is at home, the VR training unit can provide a training scenario that can be done at home. If the user is at work, it can provide a training scenario that can be done at work. If the user is out, it can provide a training scenario that can be done while out. This allows the VR training unit to provide training scenarios based on the user's location information. Some or all of the above processing in the VR training unit may be performed using AI or not. For example, the VR training unit can input the user's location data into AI and generate a training scenario.

[0159] The problem-solving plan proposal unit can estimate the user's emotions and adjust the content of the solution based on the estimated emotions. For example, if the user is tense, the problem-solving plan proposal unit can provide a relaxing solution. If the user is relaxed, it can also provide a challenging solution. If the user is excited, it can provide a solution to help them calm down. In this way, the solution content can be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input user emotion data into a generative AI and generate the content of the solution.

[0160] The problem-solving plan proposal unit can analyze the user's past problem-solving history and automatically generate the optimal solution. For example, the problem-solving plan proposal unit can provide similar solutions based on solutions the user has successfully solved in the past. It can also provide solutions that include improvements based on solutions the user has failed to solve in the past. Furthermore, it can provide the optimal solution based on the user's past problem-solving history. In this way, it can provide the optimal solution based on the user's past problem-solving history. Some or all of the above processes in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input the user's problem-solving data into AI and generate the optimal solution.

[0161] The problem-solving plan proposal unit can estimate the user's emotions and adjust the order of solutions based on the estimated emotions. For example, if the user is tense, the problem-solving plan proposal unit can first provide a relaxing solution. If the user is relaxed, it can first provide a challenging solution. If the user is excited, it can first provide a solution to calm down. This allows the system to provide a solution order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input user emotion data into a generative AI and generate a solution order.

[0162] The problem-solving plan proposal unit can customize the content of solutions by taking into account the user's geographical location information. For example, if the user is at home, the problem-solving plan proposal unit can provide solutions that can be implemented at home. If the user is at work, it can provide solutions that can be implemented at work. If the user is out, it can provide solutions that can be implemented while out. This allows the system to provide solutions based on the user's location information. Some or all of the above processing in the problem-solving plan proposal unit may be performed using AI or not. For example, the problem-solving plan proposal unit can input the user's location data into AI and generate the content of the solutions.

[0163] The Problem-Solving Plan Proposal Unit can analyze a user's social media activity and provide relevant solutions. For example, if a user posts about a problem on social media, the Problem-Solving Plan Proposal Unit can provide a solution related to that problem. It can also provide a solution related to a specific situation if a user posts about a particular situation on social media. Furthermore, if a user posts about a specific skill on social media, it can provide a solution related to that skill. In this way, solutions can be provided based on the user's social media activity. Some or all of the above processing in the Problem-Solving Plan Proposal Unit may be performed using AI or not. For example, the Problem-Solving Plan Proposal Unit can input the user's social media data into AI and generate relevant solutions.

[0164] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0165] The autism support system may also include a stress assessment unit that estimates the user's emotions and evaluates the user's stress level based on those emotions. The stress assessment unit can, for example, analyze the user's facial expressions and voice data to quantify the stress level. It can also collect physiological data such as the user's heart rate and skin electrical activity to assess the stress level. Furthermore, based on the user's stress level, the stress assessment unit can suggest relaxation methods and stress reduction measures. This can support the user's stress management.

[0166] The autism support system can further include a behavior prediction unit that analyzes the user's behavioral patterns and predicts their next actions. For example, the behavior prediction unit can predict the user's most likely next actions based on their past behavioral data. It can also predict actions based on the user's schedule and daily rhythm. Furthermore, the behavior prediction unit can provide appropriate reminders and support based on the predicted actions. This enhances support for the user's daily life.

[0167] The autism support system can also include a motivation enhancement unit that estimates the user's emotions and improves the user's motivation based on those emotions. For example, if the user is feeling down, the motivation enhancement unit can provide encouraging messages. It can also suggest steps to achieve goals if the user is losing motivation. Furthermore, the motivation enhancement unit can provide appropriate rewards and incentives according to the user's emotions. This helps maintain the user's motivation and supports goal achievement.

[0168] The autism support system can also include a health prediction unit that analyzes the user's health data and predicts their health status. For example, the health prediction unit predicts changes in the user's health status based on data such as their heart rate and sleep patterns. Furthermore, the health prediction unit can assess health risks based on the user's diet and exercise data. In addition, the health prediction unit can propose appropriate health management measures based on the predicted health status. This allows the system to support the user's health management.

[0169] The autism support system can further include a communication enhancement unit that estimates the user's emotions and improves the user's communication skills based on those estimated emotions. For example, if the user is feeling anxious, the communication enhancement unit can provide a relaxing dialogue scenario. It can also provide simple dialogue practice if the user lacks confidence in dialogue. Furthermore, the communication enhancement unit can provide appropriate feedback and advice according to the user's emotions, thereby improving the user's communication skills.

[0170] The autism support system can further include a learning prediction unit that analyzes the user's learning data and predicts learning progress. For example, the learning prediction unit predicts what the user should learn next based on their past learning data. It can also predict learning progress based on the user's learning pace and level of understanding. Furthermore, the learning prediction unit can provide appropriate learning materials and plans based on the predicted learning progress. This allows for effective support of the user's learning.

[0171] The autism support system can further include a stress reduction unit that estimates the user's emotions and reduces the user's stress based on those estimated emotions. For example, if the user is feeling stressed, the stress reduction unit can provide relaxing music or meditation guidance. It can also provide reassuring messages if the user is feeling anxious. Furthermore, the stress reduction unit can suggest appropriate stress reduction measures based on the user's emotions. This can help the user manage their stress.

[0172] The autism support system can also include a lifestyle improvement unit that analyzes the user's daily rhythm and improves their quality of life. For example, the lifestyle improvement unit can suggest lifestyle improvements based on the user's sleep patterns and dietary data. It can also provide appropriate exercise plans based on the user's exercise data. Furthermore, the lifestyle improvement unit can provide reminders and support according to the user's daily rhythm. This can improve the user's quality of life.

[0173] The autism support system may also include a social skills enhancement unit that estimates the user's emotions and improves the user's social skills based on those estimated emotions. For example, if the user is feeling anxious, the social skills enhancement unit can provide relaxing social scenarios. It can also suggest appropriate coping mechanisms if the user is experiencing difficulties in social situations. Furthermore, the social skills enhancement unit can provide appropriate feedback and advice based on the user's emotions, thereby improving the user's social skills.

[0174] The autism support system can also include a hobby support section that analyzes the user's hobbies and interests and supports their hobby activities. For example, the hobby support section can suggest new hobbies based on the user's past hobby activity data. It can also introduce relevant events and activities based on the user's interests. Furthermore, the hobby support section can provide resources and tools to support the user's hobby activities, thereby enriching the user's hobby experiences.

[0175] The following briefly describes the processing flow for example form 2.

[0176] Step 1: The personalized assistant unit helps with daily schedule management and setting reminders. For example, it can remind you of wake-up times, medication times, and meal times. It can also automatically generate schedules based on the user's daily rhythm and adjust how reminders are notified by estimating the user's emotions. Step 2: The social skills training section provides scenarios for learning dialogue simulations and appropriate responses in different situations. For example, users can improve their dialogue skills through dialogue simulations. It can also estimate the user's emotions and adjust the training scenario accordingly. Step 3: The emotion recognition unit recognizes emotions and provides appropriate feedback. For example, it can recognize the user's emotions using facial recognition or voice analysis and adjust the content and timing of the feedback based on the user's emotions. Step 4: The Independent Living Support Robot section assists with simple tasks within the home. For example, it can assist with tasks such as cooking, cleaning, and laundry, and can also estimate the user's emotions to adjust the priority and content of tasks. Step 5: The communication support tool unit uses speech recognition and generation technology to interact with the user, answering simple questions and providing necessary information. It can also estimate the user's emotions and adjust the method and content of communication accordingly.

[0177] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0178] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0179] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0180] Each of the multiple elements described above, including the personalized assistant unit, social skills training unit, emotion recognition unit, independent living support robot unit, and communication support tool unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the personalized assistant unit is implemented by the control unit 46A of the smart device 14 and automatically generates a schedule based on the user's daily rhythm. The social skills training unit is implemented by the specific processing unit 290 of the data processing unit 12 and helps the user improve their conversational skills through conversational simulation. The emotion recognition unit recognizes the user's emotions using the camera 42 and microphone 38B of the smart device 14 and provides appropriate feedback via the control unit 46A. The independent living support robot unit is implemented by the specific processing unit 290 of the data processing unit 12 and assists with simple tasks within the home. The communication support tool unit is implemented by the control unit 46A of the smart device 14 and engages in conversation with the user using speech recognition and generation technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0181] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0182] As shown in Figure 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.

[0183] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0184] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0185] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0187] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0188] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0189] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0190] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0191] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0192] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0193] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0195] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0196] Each of the multiple elements described above, including the personalized assistant unit, social skills training unit, emotion recognition unit, independent living support robot unit, and communication support tool unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the personalized assistant unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates a schedule based on the user's daily rhythm. The social skills training unit is implemented by the specific processing unit 290 of the data processing unit 12 and helps the user improve their conversational skills through conversational simulation. The emotion recognition unit recognizes the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and provides appropriate feedback via the control unit 46A. The independent living support robot unit is implemented by the specific processing unit 290 of the data processing unit 12 and assists with simple tasks within the home. The communication support tool unit is implemented by the control unit 46A of the smart glasses 214 and engages in conversation with the user using speech recognition and generation technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0197] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0198] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0199] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0200] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0201] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0203] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0204] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0205] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0206] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0207] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0208] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0209] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0210] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0211] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0212] Each of the multiple elements described above, including the personalized assistant unit, social skills training unit, emotion recognition unit, independent living support robot unit, and communication support tool unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the personalized assistant unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates a schedule based on the user's daily rhythm. The social skills training unit is implemented by the specific processing unit 290 of the data processing unit 12 and helps the user improve their conversational skills through conversational simulation. The emotion recognition unit recognizes the user's emotions using the camera 42 and microphone 238 of the headset terminal 314 and provides appropriate feedback via the control unit 46A. The independent living support robot unit is implemented by the specific processing unit 290 of the data processing unit 12 and assists with simple tasks within the home. The communication support tool unit is implemented by the control unit 46A of the headset terminal 314 and engages in conversation with the user using speech recognition and generation technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0213] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0214] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0216] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0221] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0223] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0224] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0225] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0226] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0227] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0228] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0229] Each of the multiple elements described above, including the personalized assistant unit, social skills training unit, emotion recognition unit, independent living support robot unit, and communication support tool unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the personalized assistant unit is implemented by the control unit 46A of the robot 414 and automatically generates a schedule based on the user's daily rhythm. The social skills training unit is implemented by the specific processing unit 290 of the data processing unit 12 and helps the user improve their conversational skills through conversational simulation. The emotion recognition unit recognizes the user's emotions using the camera 42 and microphone 238 of the robot 414 and provides appropriate feedback via the control unit 46A. The independent living support robot unit is implemented by the specific processing unit 290 of the data processing unit 12 and assists with simple tasks within the home. The communication support tool unit is implemented by the control unit 46A of the robot 414 and engages in conversation with the user using speech recognition and generation technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0230] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0231] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0232] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0233] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0234] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0235] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0236] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0237] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0238] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0239] 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.

[0240] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0241] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0242] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0243] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0244] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0245] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0246] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0247] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0248] (Note 1) The Personalized Assistant section assists with daily schedule management and reminder setting, The Personalized Assistant Unit provides dialogue simulations and a Social Skills Training Unit for learning appropriate responses in different situations, The emotion recognition unit recognizes emotions provided by the aforementioned social skills training unit and provides appropriate feedback, The robot unit assists with simple tasks within the home, provided by the emotion recognition unit, and The robot comprises a communication support tool unit that uses speech recognition and generation technology provided by the aforementioned independent living support robot unit. A system characterized by the following features. (Note 2) The aforementioned personalized assistant unit is It notifies you of your wake-up time, medication time, and meal times. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Social Skills Training Department It provides scenarios for learning dialogue simulations and appropriate responses in different situations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The emotion recognition unit, Recognize emotions and provide appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Independent Living Support Robot Division Supporting simple household tasks The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned communication support tool unit is: Answer simple questions and provide the necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 7) It collects and analyzes health data such as heart rate and sleep patterns in real time. It is equipped with a health monitoring unit that sounds an alert if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes a support community department that provides a platform for parents of children with autism to exchange information and seek advice from each other. The system described in Appendix 1, characterized by the features described herein. (Note 9) It includes a stress management app section that suggests relaxation methods when you feel stressed or anxious. The system described in Appendix 1, characterized by the features described herein. (Note 10) It includes an educational and training platform that individually manages learning progress and provides appropriate learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 11) It features a VR training department that simulates real-world situations and provides a space to practice social and professional skills. The system described in Appendix 1, characterized by the features described herein. (Note 12) By compiling and analyzing case studies of other individuals with autism, we can develop solutions and growth plans tailored to the individual needs of each person with autism. It has a problem-solving plan proposal department that proposes effective methods. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned personalized assistant unit is It estimates the user's emotions and adjusts how reminders are notified based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned personalized assistant unit is Analyzes the user's past behavior patterns and automatically generates the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned personalized assistant unit is The timing of reminders is dynamically changed according to the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned personalized assistant unit is It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned personalized assistant unit is Customize reminder content based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned personalized assistant unit is Analyzes users' social media activity and provides relevant reminders. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Social Skills Training Department It estimates the user's emotions and adjusts the training scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Social Skills Training Department Analyze the user's past training history and automatically generate the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Social Skills Training Department The difficulty level of the training is dynamically changed according to the user's reaction speed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Social Skills Training Department It estimates the user's emotions and adjusts the training sequence based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The social skill training unit customizes the training scenario in consideration of the user's geographical location information. The system according to appended note 1, characterized in that. (Appended note 24) The social skill training unit analyzes the user's social media activities and provides relevant training scenarios. The system according to appended note 1, characterized in that. (Appended note 25) The emotion recognition unit estimates the user's emotion and adjusts the content of the feedback based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 26) The emotion recognition unit analyzes the user's past emotion data and automatically generates optimal feedback. The system according to appended note 1, characterized in that. (Appended note 27) The emotion recognition unit dynamically improves the accuracy of emotion recognition according to the user's expression and voice tone. The system according to appended note 1, characterized in that. (Appended note 28) The emotion recognition unit estimates the user's emotion and adjusts the timing of the feedback based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 29) The emotion recognition unit customizes the content of the feedback in consideration of the user's geographical location information. The system according to appended note 1, characterized in that. (Appended note 30) The emotion recognition unit analyzes the user's social media activities and provides relevant feedback. The system according to appended note 1, characterized in that. (Note 31) The aforementioned Independent Living Support Robot Division It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Independent Living Support Robot Division Analyzes the user's past task history and automatically generates the optimal task schedule. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Independent Living Support Robot Division Dynamically change the timing of task execution according to the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Independent Living Support Robot Division It estimates the user's emotions and adjusts the task content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Independent Living Support Robot Division Customize task content by taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Independent Living Support Robot Division Analyze users' social media activity and provide relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned communication support tool unit is: It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned communication support tool unit is: Analyze the user's past communication history and automatically generate an optimal communication method The system according to appended note 1, characterized in that it does this (Appended note 39) The communication support tool section Dynamically change the content of the communication according to the user's reaction speed The system according to appended note 1, characterized in that it does this (Appended note 40) The communication support tool section Estimate the user's emotion and adjust the order of communication based on the estimated user emotion The system according to appended note 1, characterized in that it does this (Appended note 41) The communication support tool section Customize the content of the communication considering the user's geographical location information The system according to appended note 1, characterized in that it does this (Appended note 42) The communication support tool section Analyze the user's social media activities and provide relevant communication content The system according to appended note 1, characterized in that it does this (Appended note 43) The health monitoring section Estimate the user's emotion and adjust the monitoring frequency based on the estimated user emotion The system according to appended note 1, characterized in that it does this (Appended note 44) The health monitoring section Analyze the user's past health data and automatically generate an optimal monitoring method The system according to appended note 1, characterized in that it does this (Appended note 45) The health monitoring section Dynamically change the timing of monitoring according to the user's life rhythm The system described in Appendix 1, characterized by the features described herein. (Note 46) The SM monitoring unit is, The system estimates the user's emotions and adjusts the monitoring content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The SM monitoring unit is, Customize monitoring content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 48) The SM monitoring unit is, Analyze users' social media activity and provide relevant monitoring information. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned Support Community Department It estimates user sentiment and adjusts how users participate in the community based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned Support Community Department It analyzes the user's past community participation history and automatically generates the optimal way to participate. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned Support Community Department It estimates user sentiment and adjusts community content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned Support Community Department Customize community content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned stress management application unit is It estimates the user's emotions and adjusts relaxation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned stress management application unit is It analyzes the user's past stress data and automatically generates the optimal relaxation method. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned stress management application unit is It estimates the user's emotions and adjusts the order of relaxation methods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned stress management application unit is Customize relaxation methods by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned education and training platform section is: The system estimates the user's emotions and adjusts the content of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned education and training platform section is: Analyzes the user's past learning history and automatically generates the most suitable learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned education and training platform section is: It estimates the user's emotions and adjusts the order of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned education and training platform section is: Customize the content of the learning materials by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned VR training unit, It estimates the user's emotions and adjusts the training scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned VR training unit, Analyze the user's past training history and automatically generate the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned VR training unit, It estimates the user's emotions and adjusts the training sequence based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned VR training unit, Customize the training scenario by taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned problem-solving plan proposal department, The system estimates the user's emotions and adjusts the solution based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 66) The aforementioned problem-solving plan proposal department, It analyzes the user's past problem-solving history and automatically generates the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 67) The aforementioned problem-solving plan proposal department, It estimates the user's emotions and adjusts the order of solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 68) The aforementioned problem-solving plan proposal department, Customize the solution based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 69) The aforementioned problem-solving plan proposal department, We analyze users' social media activity and provide relevant solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0249] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Personalized Assistant Department assists with daily schedule management and reminder setting, The Personalized Assistant Unit provides dialogue simulations and a Social Skills Training Unit for learning appropriate responses in different situations, The emotion recognition unit recognizes emotions provided by the aforementioned social skills training unit and provides appropriate feedback, The robot unit assists with simple tasks within the home, provided by the emotion recognition unit, and The robot comprises a communication support tool unit that uses speech recognition and generation technology provided by the aforementioned independent living support robot unit. A system characterized by the following features.

2. The aforementioned personalized assistant unit is It notifies you of your wake-up time, medication time, and meal times. The system according to feature 1.

3. The aforementioned Social Skills Training Department It provides scenarios for learning dialogue simulations and appropriate responses in different situations. The system according to feature 1.

4. The emotion recognition unit, Recognize emotions and provide appropriate feedback. The system according to feature 1.

5. The aforementioned Independent Living Support Robot Division Supporting simple household tasks The system according to feature 1.

6. The aforementioned communication support tool unit is: Answer simple questions and provide the necessary information. The system according to feature 1.

7. It collects and analyzes health data such as heart rate and sleep patterns in real time. It is equipped with a health monitoring unit that sounds an alert if an abnormality is detected. The system according to feature 1.

8. It includes a support community department that provides a platform for parents of children with autism to exchange information and seek advice from each other. The system according to feature 1.

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