system

Abstract

The system according to this embodiment aims to enable caregivers to receive personalized care advice. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a progress management unit, and a stress detection unit. The reception unit receives input from the caregiver. The generation unit analyzes the information received by the reception unit and generates personalized care advice. The provision unit provides the advice generated by the generation unit to the caregiver. The progress management unit manages the caregiver's progress based on the advice provided by the provision unit. The stress detection unit detects the caregiver's stress level.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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 in 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 prior art, there is a problem that it is difficult for caregivers to receive individualized care advice and the mental burden is large.

[0005] The system according to the embodiment aims to enable caregivers to receive individualized care advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress management unit, and a stress detection unit. The reception unit receives input from the caregiver. The generation unit analyzes the information received by the reception unit and generates personalized care advice. The provision unit provides the advice generated by the generation unit to the caregiver. The progress management unit manages the caregiver's progress based on the advice provided by the provision unit. The stress detection unit detects the caregiver's stress level. [Effects of the Invention]

[0007] The system according to this embodiment allows caregivers to receive personalized care advice. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 such as 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the 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 AI ​​personal care coach system according to an embodiment of the present invention is a system for supporting family caregivers of dementia patients. This system reduces the mental burden on caregivers and supports the acquisition of appropriate care methods by providing personalized care advice and listening training 24 hours a day, 365 days a year through a smartphone app. For example, an AI agent provides a 24-hour chat interface and enables hands-free operation using voice recognition. Furthermore, it also provides visual guidance using AR technology and progress management through a personalized dashboard. The business model includes a basic plan (monthly fee), a premium plan (with online support from experts), and license sales for medical institutions and nursing care facilities. Depending on the user's needs, it provides functions such as 24 / 7 support, personalized advice, listening training, stress reduction, and learning support. Technically, it includes an advanced dialogue system using natural language processing, a predictive model using machine learning, health monitoring using sensor technology, care simulation and training using AR / VR, and stress level detection using emotion analysis. The target audience is the working generation in their 40s to 70s, including families who are caring for parents or spouses with dementia at home, and middle managers who are struggling to balance caregiving and work. To address the challenges faced by these target groups, the system provides 24 / 7 AI-powered personalized care advice, real-time stress detection and suggestion of mitigation techniques, proactive support through BPSD prediction algorithms, flexible work arrangement suggestions, and care service coordination functions. The use of generative AI includes natural language dialogue systems, image recognition AI for analyzing patient expressions and behavior, voice emotion analysis AI for detecting caregiver stress, and reinforcement learning for optimizing personalized care plans. This enables the AI ​​personal care coach system to improve caregivers' quality of life, significantly reduce caregiver turnover, extend the period of home-based living for dementia patients and improve their quality of life, and build a sustainable care system and reduce social costs through AI technology. Ultimately, the AI ​​personal care coach system can reduce the mental burden on caregivers and support them in acquiring appropriate care methods.

[0029] The AI ​​personal care coach system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress management unit, and a stress detection unit. The reception unit receives input from the caregiver. Caregiver input includes, but is not limited to, text input, voice input, and image input. The reception unit receives input from the caregiver, for example, through a smartphone application. The reception unit can also receive voice input using speech recognition technology. The generation unit analyzes the information received by the reception unit and generates personalized care advice. The generation unit generates advice based on the caregiver's past data, current situation, and specific needs, for example, using a generation AI. The generation unit generates care advice, for example, using a text generation AI (e.g., LLM). The generation unit can also optimize the personalized care plan using reinforcement learning. The provision unit provides the advice generated by the generation unit to the caregiver. The provision unit provides advice, for example, through a smartphone application. The provision unit can also provide advice in voice using speech synthesis technology. The progress management unit manages the caregiver's progress based on the advice provided by the provision unit. The progress management unit manages the caregiver's progress, for example, using a personalized dashboard. The progress management unit can also analyze the caregiver's progress data and provide appropriate feedback. The stress detection unit detects the caregiver's stress level. The stress detection unit detects the caregiver's stress level using, for example, emotion analysis technology. The stress detection unit detects the caregiver's stress using, for example, voice emotion analysis AI. The stress detection unit can also monitor the caregiver's biometric data using sensor technology and detect their stress level. As a result, the AI ​​personal care coach system according to this embodiment can reduce the caregiver's mental burden and support them in learning appropriate care methods.

[0030] The reception desk receives input from caregivers. Caregiver input includes, but is not limited to, text input, voice input, and image input. The reception desk accepts caregiver input, for example, through a smartphone app. The smartphone app features a user-friendly interface designed to allow caregivers to easily input information. For example, with text input, caregivers can enter questions and situations into the app's chat box. With voice input, speech recognition technology can convert the caregiver's voice into text and input it into the system. Speech recognition technology utilizes natural language processing (NLP) to accurately understand the caregiver's utterances and process them as appropriate information. With image input, caregivers can use their smartphone camera to take photos of the care recipient's condition and environment and send them through the app. This allows the reception desk to support diverse input methods, enabling caregivers to provide information in the most convenient way. Furthermore, the reception desk centrally manages the input information and transmits it quickly and accurately to the generation department and other departments. This improves the overall efficiency and reliability of the system.

[0031] The generation unit analyzes the information received by the reception unit and generates personalized care advice. For example, the generation unit uses a generation AI to generate advice based on the caregiver's past data, current situation, and specific needs. The generation AI utilizes a large-scale language model (LLM) to analyze the caregiver's input in detail and provide optimal advice. For example, if a caregiver asks about a specific symptom or situation, the generation AI refers to past databases and medical knowledge to suggest specific coping methods and precautions. The generation unit can also optimize the personalized care plan using reinforcement learning. Reinforcement learning evaluates the effectiveness of the advice based on the caregiver's feedback and reflects this in the next advice, providing a continuously optimized care plan. For example, if a caregiver reports the results of acting according to the provided advice, the generation unit learns from the results and reflects them in the next advice. This allows the generation unit to provide highly personalized advice that meets the caregiver's needs. Furthermore, the generation unit can generate advice that takes into account the caregiver's stress level and emotional state, providing support to reduce the caregiver's mental burden.

[0032] The delivery unit provides caregivers with advice generated by the generation unit. The delivery unit provides advice, for example, through a smartphone app. The smartphone app features an intuitive user interface, designed to allow caregivers to easily review and implement the advice. For example, advice may be displayed not only in text format but also in a visually easy-to-understand format using diagrams and videos. The delivery unit can also provide advice via voice using speech synthesis technology. Speech synthesis technology generates natural speech, allowing caregivers to receive advice hands-free. This enables caregivers to access information smoothly even while performing caregiving tasks, as they can review the advice without using their hands. Furthermore, the delivery unit can reduce the burden on caregivers and provide necessary information at the optimal time by adjusting the frequency and timing of advice delivery. For example, it can adjust the timing of advice notifications according to the caregiver's schedule and situation, providing information at the most convenient time for the caregiver. This allows the delivery unit to provide advice effectively and efficiently to caregivers, improving the quality of care.

[0033] The Progress Management Department manages caregivers' progress based on advice provided by the Service Provider Department. For example, the Progress Management Department manages caregivers' progress using personalized dashboards. The dashboards visually display the actions taken and goals achieved by caregivers, allowing them to grasp their progress at a glance. For example, it displays a history of actions taken by caregivers and a list of goals achieved, showing progress in graphs and charts. The Progress Management Department can also analyze caregiver progress data and provide appropriate feedback. For example, if a caregiver achieves a specific goal, it can send a message praising their achievement to boost their motivation. If progress is behind schedule or problems arise, it can provide specific advice and support for improvement. Furthermore, by accumulating caregiver progress data over the long term and conducting trend analysis, the Progress Management Department can understand caregivers' growth and areas for improvement. This allows the Progress Management Department to effectively manage caregivers' progress and provide continuous support.

[0034] The stress detection unit detects the caregiver's stress level. For example, it uses emotion analysis technology to detect the caregiver's stress level. Emotion analysis technology analyzes emotions from the caregiver's voice and text to detect signs of stress. For example, voice emotion analysis AI can be used to detect signs of stress and fatigue from the caregiver's voice. Voice emotion analysis AI analyzes features such as tone, pitch, and speaking speed to evaluate the caregiver's emotional state. The stress detection unit can also monitor the caregiver's biometric data using sensor technology to detect stress levels. For example, it can monitor biometric data such as heart rate and electrocutaneous activity (EDA) in real time to detect signs of stress. This allows the stress detection unit to accurately understand the caregiver's stress level and provide support at the appropriate time. Furthermore, based on the detected stress level, the stress detection unit can provide the caregiver with advice on relaxation methods and stress reduction. For example, it can send messages recommending deep breathing, stretching, or taking short breaks. This allows the stress detection unit to reduce the caregiver's mental burden and support the maintenance of a healthy care environment.

[0035] The voice recognition unit can perform hands-free operation using voice recognition. For example, the voice recognition unit recognizes the caregiver's voice input using voice recognition technology and performs the operation. For example, the voice recognition unit achieves high-precision voice recognition using a voice recognition algorithm. The voice recognition unit can also support multiple languages. For example, the voice recognition unit recognizes voice input in multiple languages ​​such as English, Japanese, and French. The voice recognition unit has a function to learn the voice characteristics of specific speakers in order to improve the accuracy of voice recognition. For example, the voice recognition unit improves the accuracy of voice recognition by accumulating voice data of caregivers and learning the voice characteristics of each caregiver. This allows the voice recognition unit to be operated by caregivers without using their hands. Some or all of the above processing in the voice recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice recognition unit can input the caregiver's voice data into a generative AI and have the generative AI perform voice recognition.

[0036] The visual guidance unit can provide visual guidance using AR technology. For example, the visual guidance unit can provide visual guidance to caregivers using an AR device. For example, the visual guidance unit can overlay information onto the caregiver's field of view using AR technology. The visual guidance unit can also provide guidance in real time according to the caregiver's movements. For example, the visual guidance unit can display appropriate guidance when the caregiver performs a specific action. The visual guidance unit can also provide interactive guidance to help the caregiver's visual understanding. For example, the visual guidance unit can display step-by-step guidance when the caregiver performs a specific operation. This allows the visual guidance unit to provide guidance that is easy for the caregiver to understand visually. Some or all of the above processing in the visual guidance unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visual guidance unit can input information to be displayed on an AR device into a generative AI and have the generative AI execute the visual guidance.

[0037] The health monitoring unit can monitor the health status of caregivers using sensor technology. For example, the health monitoring unit can collect biometric data of caregivers using sensors. For example, the health monitoring unit can collect data such as heart rate, blood pressure, and body temperature. The health monitoring unit can also analyze the collected data and evaluate the health status of caregivers. For example, the health monitoring unit can evaluate the health risks of caregivers based on the collected data. The health monitoring unit can also monitor the health status of caregivers in real time and issue alerts if abnormalities are detected. For example, the health monitoring unit will issue an alert if the heart rate is abnormally high. In this way, the health monitoring unit can support the health management of caregivers. Some or all of the above processing in the health monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health monitoring unit can input the collected biometric data into a generative AI and have the generative AI perform a health status evaluation.

[0038] The emotion analysis unit can detect the caregiver's stress level through emotion analysis. For example, the emotion analysis unit detects the caregiver's stress using voice emotion analysis AI. For example, the emotion analysis unit analyzes the caregiver's voice data and evaluates the stress level. The emotion analysis unit can also detect the caregiver's stress using facial recognition technology. For example, the emotion analysis unit analyzes the caregiver's facial expression data and evaluates the stress level. The emotion analysis unit can also monitor the caregiver's stress level in real time and issue an alert if the stress level is high. For example, the emotion analysis unit issues an alert if the caregiver's stress level exceeds a certain threshold. This allows the emotion analysis unit to reduce the caregiver's mental burden. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input the caregiver's voice data into a generative AI and have the generative AI perform a stress level evaluation.

[0039] The reception desk can analyze the caregiver's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the caregiver has frequently used in the past. For example, the reception desk may suggest the optimal reception method for a specific time period based on the caregiver's past input history. For example, the reception desk may analyze the caregiver's past input history and select the most efficient reception method. In this way, the reception desk can select the optimal reception method by analyzing the caregiver's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the caregiver's past input data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0040] The reception unit can filter information based on the caregiver's current situation and areas of interest at the time of reception. For example, the reception unit may receive only relevant information based on the caregiver's current situation. For example, the reception unit may prioritize receiving relevant information based on the caregiver's areas of interest. For example, the reception unit may filter the information to be most relevant, taking into account the caregiver's current situation and areas of interest. In this way, the reception unit can receive only relevant information by filtering based on the caregiver's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the caregiver's current situation data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving highly relevant information by considering the caregiver's geographical location information at the time of reception. For example, the reception unit prioritizes receiving relevant information based on the caregiver's current location. For example, the reception unit filters the most relevant information by considering the caregiver's geographical location information. For example, the reception unit prioritizes receiving information of high importance based on the caregiver's current location. In this way, the reception unit can provide appropriate information by prioritizing the receipt of highly relevant information by considering the caregiver's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform information filtering.

[0042] The reception desk can analyze the caregiver's social media activity and receive relevant information at the time of reception. For example, the reception desk analyzes the caregiver's social media activity and prioritizes receiving relevant information. For example, the reception desk filters out highly important information from the caregiver's social media activity. For example, the reception desk receives the most relevant information considering the caregiver's social media activity. In this way, the reception desk can prioritize receiving relevant information by analyzing the caregiver's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the caregiver's social media data into a generating AI and have the generating AI perform information filtering.

[0043] The generation unit can adjust the level of detail of advice based on the importance of the caregiver when generating advice. For example, if the caregiver needs highly important advice, the generation unit will generate detailed advice. For example, if the caregiver needs less important advice, the generation unit will generate simple advice. The generation unit adjusts the level of detail of the advice to the optimal level based on the importance of the caregiver. In this way, the generation unit can provide appropriate advice by adjusting the level of detail of the advice based on the importance of the caregiver. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0044] The generation unit can apply different advice algorithms depending on the caregiver's category when generating advice. For example, if the caregiver is a novice, the generation unit applies a basic advice algorithm. If the caregiver is experienced, the generation unit applies an advanced advice algorithm. The generation unit applies the optimal advice algorithm depending on the caregiver's category. In this way, the generation unit can provide the caregiver with the most suitable advice by applying different advice algorithms depending on the caregiver's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver category data into a generation AI and have the generation AI execute the application of the advice algorithm.

[0045] The generation unit can determine the priority of advice based on the caregiver's submission timing when generating advice. For example, the generation unit determines the priority based on the timing of the advice submitted by the caregiver. For example, the generation unit generates the optimal advice according to the caregiver's submission timing. For example, the generation unit adjusts the priority of advice taking into account the caregiver's submission timing. In this way, the generation unit can provide advice at the appropriate time by determining the priority of advice based on the caregiver's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver submission timing data into a generation AI and have the generation AI perform the determination of advice priority.

[0046] The generation unit can adjust the order of advice based on the caregiver's relevance when generating advice. For example, the generation unit will prioritize generating advice when the caregiver needs highly relevant advice. The generation unit adjusts the order of advice based on the caregiver's relevance, for example. The generation unit determines the optimal order of advice by considering the caregiver's relevance, for example. This allows the generation unit to prioritize important advice by adjusting the order of advice based on the caregiver's relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver relevance data into a generation AI and have the generation AI perform the adjustment of the advice order.

[0047] The service provider can analyze the caregiver's past responses when providing advice and select the optimal method of providing it. For example, the service provider analyzes the caregiver's past responses and selects the optimal method of providing advice. For example, the service provider proposes an effective method of providing advice based on the caregiver's past responses. For example, the service provider selects the optimal method of providing advice considering the caregiver's past responses. In this way, the service provider can select the optimal method of providing advice by analyzing the caregiver's past responses. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the caregiver's past responses into a generating AI and have the generating AI select the optimal method of providing advice.

[0048] The service provider can customize the means of providing advice based on the caregiver's current situation. For example, the service provider can select the optimal means of providing advice based on the caregiver's current situation. For example, the service provider can customize the means of providing advice considering the caregiver's current situation. For example, the service provider can propose the optimal means of providing advice according to the caregiver's current situation. In this way, the service provider can select the optimal means of providing advice by customizing the means of providing advice based on the caregiver's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the caregiver's current situation into a generating AI and have the generating AI perform the customization of the means of providing advice.

[0049] The service provider can select the optimal method of providing advice by considering the caregiver's geographical location information. For example, the service provider can select the optimal method of providing advice based on the caregiver's current location. For example, the service provider can customize the means of providing advice by considering the caregiver's geographical location information. For example, the service provider can propose the optimal method of providing advice according to the caregiver's current location. In this way, the service provider can provide appropriate advice by selecting the optimal method of providing advice by considering the caregiver's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the method of providing advice.

[0050] The service provider can analyze the caregiver's social media activity and propose a means of providing advice. For example, the service provider can analyze the caregiver's social media activity and propose the most suitable means of providing advice. For example, the service provider can select an effective means of providing advice from the caregiver's social media activity. For example, the service provider can propose the most suitable means of providing advice, taking into account the caregiver's social media activity. In this way, the service provider can propose the most suitable means of providing advice by analyzing the caregiver's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of means of providing advice.

[0051] The progress management unit can select the optimal management method by referring to the caregiver's past progress data during progress management. For example, the progress management unit can refer to the caregiver's past progress data and select the optimal management method. For example, the progress management unit can propose an effective management method based on the caregiver's past progress data. For example, the progress management unit can select the optimal management method by considering the caregiver's past progress data. In this way, the progress management unit can select the optimal management method by referring to the caregiver's past progress data. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the caregiver's past progress data into a generating AI and have the generating AI perform the selection of a management method.

[0052] The progress management unit can customize management methods based on the caregiver's current situation during progress management. For example, the progress management unit selects the optimal management method based on the caregiver's current situation. For example, the progress management unit customizes the management method considering the caregiver's current situation. For example, the progress management unit proposes the optimal management method according to the caregiver's current situation. In this way, the progress management unit can select the optimal management method by customizing the management method based on the caregiver's current situation. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input data on the caregiver's current situation into a generating AI and have the generating AI perform the customization of the management method.

[0053] The progress management unit can select the optimal management method when managing progress, taking into account the caregiver's geographical location information. For example, the progress management unit selects the optimal management method based on the caregiver's current location. For example, the progress management unit customizes the management means by taking into account the caregiver's geographical location information. For example, the progress management unit proposes the optimal management method according to the caregiver's current location. In this way, the progress management unit can perform appropriate management by selecting the optimal management method by taking into account the caregiver's geographical location information. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the management method.

[0054] The progress management department can analyze the caregiver's social media activity and propose management methods during progress management. For example, the progress management department can analyze the caregiver's social media activity and propose the optimal management method. For example, the progress management department can select an effective management method from the caregiver's social media activity. For example, the progress management department can propose the optimal management method considering the caregiver's social media activity. In this way, the progress management department can propose the optimal management method by analyzing the caregiver's social media activity. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of management methods.

[0055] The stress detection unit can select the optimal detection method by referring to the caregiver's past stress data when stress is detected. For example, the stress detection unit selects the optimal detection method by referring to the caregiver's past stress data. For example, the stress detection unit proposes an effective detection method from the caregiver's past stress data. For example, the stress detection unit selects the optimal detection method by considering the caregiver's past stress data. In this way, the stress detection unit can select the optimal detection method by referring to the caregiver's past stress data. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without using AI. For example, the stress detection unit can input the caregiver's past stress data into a generating AI and have the generating AI perform the selection of a detection method.

[0056] The stress detection unit can customize the detection means based on the caregiver's current situation when stress is detected. For example, the stress detection unit selects the optimal detection means based on the caregiver's current situation. For example, the stress detection unit customizes the detection means considering the caregiver's current situation. For example, the stress detection unit proposes the optimal detection means according to the caregiver's current situation. In this way, the stress detection unit can select the optimal detection means by customizing the detection means based on the caregiver's current situation. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without AI. For example, the stress detection unit can input the caregiver's current situation data into a generating AI and have the generating AI perform the customization of the detection means.

[0057] The stress detection unit can select the optimal detection method when stress is detected, taking into account the caregiver's geographical location information. For example, the stress detection unit selects the optimal detection method based on the caregiver's current location. For example, the stress detection unit customizes the detection means by taking into account the caregiver's geographical location information. For example, the stress detection unit proposes the optimal detection method according to the caregiver's current location. As a result, the stress detection unit can perform appropriate detection by selecting the optimal detection method by taking into account the caregiver's geographical location information. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without using AI. For example, the stress detection unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the detection method.

[0058] The stress detection unit can analyze the caregiver's social media activity and propose detection methods when stress is detected. For example, the stress detection unit analyzes the caregiver's social media activity and proposes the optimal detection method. For example, the stress detection unit selects an effective detection method from the caregiver's social media activity. For example, the stress detection unit proposes the optimal detection method considering the caregiver's social media activity. Thus, the stress detection unit can propose the optimal detection method by analyzing the caregiver's social media activity. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without AI. For example, the stress detection unit can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of detection methods.

[0059] The speech recognition unit can select the optimal recognition method by referring to the caregiver's past voice data during speech recognition. For example, the speech recognition unit refers to the caregiver's past voice data and selects the optimal recognition method. For example, the speech recognition unit proposes an effective recognition method from the caregiver's past voice data. For example, the speech recognition unit selects the optimal recognition method by considering the caregiver's past voice data. In this way, the speech recognition unit can select the optimal recognition method by referring to the caregiver's past voice data. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input the caregiver's past voice data into a generating AI and have the generating AI perform the selection of a recognition method.

[0060] The speech recognition unit can select the optimal recognition method while considering the caregiver's geographical location information during speech recognition. For example, the speech recognition unit selects the optimal recognition method based on the caregiver's current location. For example, the speech recognition unit customizes the recognition means while considering the caregiver's geographical location information. For example, the speech recognition unit proposes the optimal recognition method according to the caregiver's current location. As a result, the speech recognition unit can perform appropriate recognition by selecting the optimal recognition method while considering the caregiver's geographical location information. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the recognition method.

[0061] The visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data during visual guidance. For example, the visual guidance unit selects the optimal guidance method by referring to the caregiver's past visual data. For example, the visual guidance unit proposes an effective guidance method from the caregiver's past visual data. For example, the visual guidance unit selects the optimal guidance method by considering the caregiver's past visual data. In this way, the visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data. Some or all of the above processing in the visual guidance unit may be performed using AI, for example, or without using AI. For example, the visual guidance unit can input the caregiver's past visual data into a generating AI and have the generating AI perform the selection of a guidance method.

[0062] The visual guidance unit can select the optimal guidance method during visual guidance by considering the caregiver's geographical location information. For example, the visual guidance unit selects the optimal guidance method based on the caregiver's current location. For example, the visual guidance unit customizes the guidance means by considering the caregiver's geographical location information. For example, the visual guidance unit proposes the optimal guidance method according to the caregiver's current location. As a result, the visual guidance unit can provide appropriate guidance by selecting the optimal guidance method by considering the caregiver's geographical location information. Some or all of the above processing in the visual guidance unit may be performed using AI, for example, or without AI. For example, the visual guidance unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the guidance method.

[0063] The health monitoring unit can select the optimal monitoring method by referring to the caregiver's past health data during health monitoring. For example, the health monitoring unit can refer to the caregiver's past health data and select the optimal monitoring method. For example, the health monitoring unit can propose an effective monitoring method based on the caregiver's past health data. For example, the health monitoring unit can select the optimal monitoring method by considering the caregiver's past health data. In this way, the health monitoring unit can select the optimal monitoring method by referring to the caregiver's past health data. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the caregiver's past health data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0064] The health monitoring unit can select the optimal monitoring method when monitoring health, taking into account the caregiver's geographical location information. For example, the health monitoring unit selects the optimal monitoring method based on the caregiver's current location. For example, the health monitoring unit customizes the monitoring means by taking into account the caregiver's geographical location information. For example, the health monitoring unit proposes the optimal monitoring method according to the caregiver's current location. As a result, the health monitoring unit can perform appropriate monitoring by selecting the optimal monitoring method by taking into account the caregiver's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the monitoring method.

[0065] The emotion analysis unit can select the optimal analysis method by referring to the caregiver's past emotional data during emotion analysis. For example, the emotion analysis unit refers to the caregiver's past emotional data and selects the optimal analysis method. For example, the emotion analysis unit proposes an effective analysis method from the caregiver's past emotional data. For example, the emotion analysis unit selects the optimal analysis method by considering the caregiver's past emotional data. In this way, the emotion analysis unit can select the optimal analysis method by referring to the caregiver's past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the caregiver's past emotional data into a generating AI and have the generating AI perform the selection of the analysis method.

[0066] The emotion analysis unit can select the optimal analysis method while considering the caregiver's geographical location information during emotion analysis. For example, the emotion analysis unit selects the optimal analysis method based on the caregiver's current location. For example, the emotion analysis unit customizes the analysis means while considering the caregiver's geographical location information. For example, the emotion analysis unit proposes the optimal analysis method according to the caregiver's current location. In this way, the emotion analysis unit can perform appropriate analysis by selecting the optimal analysis method while considering the caregiver's geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the analysis method.

[0067] The sentiment analysis unit can analyze the caregiver's social media activity and propose analysis methods during sentiment analysis. For example, the sentiment analysis unit analyzes the caregiver's social media activity and proposes the optimal analysis method. For example, the sentiment analysis unit selects an effective analysis method from the caregiver's social media activity. For example, the sentiment analysis unit proposes the optimal analysis method considering the caregiver's social media activity. In this way, the sentiment analysis unit can propose the optimal analysis method by analyzing the caregiver's social media activity. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of analysis methods.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The reception desk can suggest the most suitable input method when receiving input from caregivers, by referring to the caregiver's past input history. For example, it can prioritize suggesting input methods that the caregiver has frequently used in the past. It can also suggest the most suitable input method for a specific time of day based on the caregiver's past input history. Furthermore, it can analyze the caregiver's past input history and select the most efficient input method. In this way, the reception desk can suggest the most suitable input method by analyzing the caregiver's past input history.

[0070] The reception desk can prioritize receiving highly relevant information by considering the caregiver's geographical location. For example, it can prioritize receiving relevant information based on the caregiver's current location. It can also filter the information to be most relevant by considering the caregiver's geographical location. Furthermore, it can prioritize receiving information of high importance based on the caregiver's current location. As a result, the reception desk can provide appropriate information by prioritizing the receipt of highly relevant information by considering the caregiver's geographical location.

[0071] The service provider can analyze the caregiver's past reactions when providing advice and select the most appropriate method of delivery. For example, it can analyze the caregiver's past reactions and select the most appropriate method of advice delivery. It can also propose effective advice delivery methods based on the caregiver's past reactions. Furthermore, it can select the most appropriate method of advice delivery by considering the caregiver's past reactions. In this way, the service provider can select the most appropriate method of advice delivery by analyzing the caregiver's past reactions.

[0072] The progress management department can select the optimal management method by referring to the caregiver's past progress data during progress management. For example, it can select the optimal management method by referring to the caregiver's past progress data. It can also propose effective management methods based on the caregiver's past progress data. Furthermore, it can select the optimal management method by considering the caregiver's past progress data. In this way, the progress management department can select the optimal management method by referring to the caregiver's past progress data.

[0073] The stress detection unit can select the optimal detection method by referring to the caregiver's past stress data when stress is detected. For example, it can select the optimal detection method by referring to the caregiver's past stress data. It can also suggest an effective detection method based on the caregiver's past stress data. Furthermore, it can select the optimal detection method by considering the caregiver's past stress data. In this way, the stress detection unit can select the optimal detection method by referring to the caregiver's past stress data.

[0074] The visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data during visual guidance. For example, it can select the optimal guidance method by referring to the caregiver's past visual data. It can also suggest an effective guidance method based on the caregiver's past visual data. Furthermore, it can select the optimal guidance method by considering the caregiver's past visual data. In this way, the visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The reception desk receives input from caregivers. Caregiver input includes text input, voice input, and image input. The reception desk can receive caregiver input via a smartphone app and can also accept voice input using speech recognition technology. Step 2: The generation unit analyzes the information received by the reception unit and generates personalized care advice. The generation unit uses generation AI to generate advice based on the caregiver's past data, current situation, and specific needs. The generation unit can also generate care advice using text generation AI (e.g., LLM) and optimize the personalized care plan using reinforcement learning. Step 3: The delivery unit provides the caregiver with the advice generated by the generation unit. The delivery unit can provide advice via a smartphone app, and can also provide advice in voice using speech synthesis technology. Step 4: The progress management department manages the caregiver's progress based on the advice provided by the service provider. The progress management department can also manage the caregiver's progress using a personalized dashboard and analyze the caregiver's progress data to provide appropriate feedback. Step 5: The stress detection unit detects the caregiver's stress level. The stress detection unit can detect the caregiver's stress level using emotion analysis technology, and can also detect stress levels by monitoring the caregiver's biometric data using voice emotion analysis AI or sensor technology.

[0077] (Example of form 2) The AI ​​personal care coach system according to an embodiment of the present invention is a system for supporting family caregivers of dementia patients. This system reduces the mental burden on caregivers and supports the acquisition of appropriate care methods by providing personalized care advice and listening training 24 hours a day, 365 days a year through a smartphone app. For example, an AI agent provides a 24-hour chat interface and enables hands-free operation using voice recognition. Furthermore, it also provides visual guidance using AR technology and progress management through a personalized dashboard. The business model includes a basic plan (monthly fee), a premium plan (with online support from experts), and license sales for medical institutions and nursing care facilities. Depending on the user's needs, it provides functions such as 24 / 7 support, personalized advice, listening training, stress reduction, and learning support. Technically, it includes an advanced dialogue system using natural language processing, a predictive model using machine learning, health monitoring using sensor technology, care simulation and training using AR / VR, and stress level detection using emotion analysis. The target audience is the working generation in their 40s to 70s, including families who are caring for parents or spouses with dementia at home, and middle managers who are struggling to balance caregiving and work. To address the challenges faced by these target groups, the system provides 24 / 7 AI-powered personalized care advice, real-time stress detection and suggestion of mitigation techniques, proactive support through BPSD prediction algorithms, flexible work arrangement suggestions, and care service coordination functions. The use of generative AI includes natural language dialogue systems, image recognition AI for analyzing patient expressions and behavior, voice emotion analysis AI for detecting caregiver stress, and reinforcement learning for optimizing personalized care plans. This enables the AI ​​personal care coach system to improve caregivers' quality of life, significantly reduce caregiver turnover, extend the period of home-based living for dementia patients and improve their quality of life, and build a sustainable care system and reduce social costs through AI technology. Ultimately, the AI ​​personal care coach system can reduce the mental burden on caregivers and support them in acquiring appropriate care methods.

[0078] The AI ​​personal care coach system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress management unit, and a stress detection unit. The reception unit receives input from the caregiver. Caregiver input includes, but is not limited to, text input, voice input, and image input. The reception unit receives input from the caregiver, for example, through a smartphone application. The reception unit can also receive voice input using speech recognition technology. The generation unit analyzes the information received by the reception unit and generates personalized care advice. The generation unit generates advice based on the caregiver's past data, current situation, and specific needs, for example, using a generation AI. The generation unit generates care advice, for example, using a text generation AI (e.g., LLM). The generation unit can also optimize the personalized care plan using reinforcement learning. The provision unit provides the advice generated by the generation unit to the caregiver. The provision unit provides advice, for example, through a smartphone application. The provision unit can also provide advice in voice using speech synthesis technology. The progress management unit manages the caregiver's progress based on the advice provided by the provision unit. The progress management unit manages the caregiver's progress, for example, using a personalized dashboard. The progress management unit can also analyze the caregiver's progress data and provide appropriate feedback. The stress detection unit detects the caregiver's stress level. The stress detection unit detects the caregiver's stress level using, for example, emotion analysis technology. The stress detection unit detects the caregiver's stress using, for example, voice emotion analysis AI. The stress detection unit can also monitor the caregiver's biometric data using sensor technology and detect their stress level. As a result, the AI ​​personal care coach system according to this embodiment can reduce the caregiver's mental burden and support them in learning appropriate care methods.

[0079] The reception desk receives input from caregivers. Caregiver input includes, but is not limited to, text input, voice input, and image input. The reception desk accepts caregiver input, for example, through a smartphone app. The smartphone app features a user-friendly interface designed to allow caregivers to easily input information. For example, with text input, caregivers can enter questions and situations into the app's chat box. With voice input, speech recognition technology can convert the caregiver's voice into text and input it into the system. Speech recognition technology utilizes natural language processing (NLP) to accurately understand the caregiver's utterances and process them as appropriate information. With image input, caregivers can use their smartphone camera to take photos of the care recipient's condition and environment and send them through the app. This allows the reception desk to support diverse input methods, enabling caregivers to provide information in the most convenient way. Furthermore, the reception desk centrally manages the input information and transmits it quickly and accurately to the generation department and other departments. This improves the overall efficiency and reliability of the system.

[0080] The generation unit analyzes the information received by the reception unit and generates personalized care advice. For example, the generation unit uses a generation AI to generate advice based on the caregiver's past data, current situation, and specific needs. The generation AI utilizes a large-scale language model (LLM) to analyze the caregiver's input in detail and provide optimal advice. For example, if a caregiver asks about a specific symptom or situation, the generation AI refers to past databases and medical knowledge to suggest specific coping methods and precautions. The generation unit can also optimize the personalized care plan using reinforcement learning. Reinforcement learning evaluates the effectiveness of the advice based on the caregiver's feedback and reflects this in the next advice, providing a continuously optimized care plan. For example, if a caregiver reports the results of acting according to the provided advice, the generation unit learns from the results and reflects them in the next advice. This allows the generation unit to provide highly personalized advice that meets the caregiver's needs. Furthermore, the generation unit can generate advice that takes into account the caregiver's stress level and emotional state, providing support to reduce the caregiver's mental burden.

[0081] The delivery unit provides caregivers with advice generated by the generation unit. The delivery unit provides advice, for example, through a smartphone app. The smartphone app features an intuitive user interface, designed to allow caregivers to easily review and implement the advice. For example, advice may be displayed not only in text format but also in a visually easy-to-understand format using diagrams and videos. The delivery unit can also provide advice via voice using speech synthesis technology. Speech synthesis technology generates natural speech, allowing caregivers to receive advice hands-free. This enables caregivers to access information smoothly even while performing caregiving tasks, as they can review the advice without using their hands. Furthermore, the delivery unit can reduce the burden on caregivers and provide necessary information at the optimal time by adjusting the frequency and timing of advice delivery. For example, it can adjust the timing of advice notifications according to the caregiver's schedule and situation, providing information at the most convenient time for the caregiver. This allows the delivery unit to provide advice effectively and efficiently to caregivers, improving the quality of care.

[0082] The Progress Management Department manages caregivers' progress based on advice provided by the Service Provider Department. For example, the Progress Management Department manages caregivers' progress using personalized dashboards. The dashboards visually display the actions taken and goals achieved by caregivers, allowing them to grasp their progress at a glance. For example, it displays a history of actions taken by caregivers and a list of goals achieved, showing progress in graphs and charts. The Progress Management Department can also analyze caregiver progress data and provide appropriate feedback. For example, if a caregiver achieves a specific goal, it can send a message praising their achievement to boost their motivation. If progress is behind schedule or problems arise, it can provide specific advice and support for improvement. Furthermore, by accumulating caregiver progress data over the long term and conducting trend analysis, the Progress Management Department can understand caregivers' growth and areas for improvement. This allows the Progress Management Department to effectively manage caregivers' progress and provide continuous support.

[0083] The stress detection unit detects the caregiver's stress level. For example, it uses emotion analysis technology to detect the caregiver's stress level. Emotion analysis technology analyzes emotions from the caregiver's voice and text to detect signs of stress. For example, voice emotion analysis AI can be used to detect signs of stress and fatigue from the caregiver's voice. Voice emotion analysis AI analyzes features such as tone, pitch, and speaking speed to evaluate the caregiver's emotional state. The stress detection unit can also monitor the caregiver's biometric data using sensor technology to detect stress levels. For example, it can monitor biometric data such as heart rate and electrocutaneous activity (EDA) in real time to detect signs of stress. This allows the stress detection unit to accurately understand the caregiver's stress level and provide support at the appropriate time. Furthermore, based on the detected stress level, the stress detection unit can provide the caregiver with advice on relaxation methods and stress reduction. For example, it can send messages recommending deep breathing, stretching, or taking short breaks. This allows the stress detection unit to reduce the caregiver's mental burden and support the maintenance of a healthy care environment.

[0084] The voice recognition unit can perform hands-free operation using voice recognition. For example, the voice recognition unit recognizes the caregiver's voice input using voice recognition technology and performs the operation. For example, the voice recognition unit achieves high-precision voice recognition using a voice recognition algorithm. The voice recognition unit can also support multiple languages. For example, the voice recognition unit recognizes voice input in multiple languages ​​such as English, Japanese, and French. The voice recognition unit has a function to learn the voice characteristics of specific speakers in order to improve the accuracy of voice recognition. For example, the voice recognition unit improves the accuracy of voice recognition by accumulating voice data of caregivers and learning the voice characteristics of each caregiver. This allows the voice recognition unit to be operated by caregivers without using their hands. Some or all of the above processing in the voice recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice recognition unit can input the caregiver's voice data into a generative AI and have the generative AI perform voice recognition.

[0085] The visual guidance unit can provide visual guidance using AR technology. For example, the visual guidance unit can provide visual guidance to caregivers using an AR device. For example, the visual guidance unit can overlay information onto the caregiver's field of view using AR technology. The visual guidance unit can also provide guidance in real time according to the caregiver's movements. For example, the visual guidance unit can display appropriate guidance when the caregiver performs a specific action. The visual guidance unit can also provide interactive guidance to help the caregiver's visual understanding. For example, the visual guidance unit can display step-by-step guidance when the caregiver performs a specific operation. This allows the visual guidance unit to provide guidance that is easy for the caregiver to understand visually. Some or all of the above processing in the visual guidance unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visual guidance unit can input information to be displayed on an AR device into a generative AI and have the generative AI execute the visual guidance.

[0086] The health monitoring unit can monitor the health status of caregivers using sensor technology. For example, the health monitoring unit can collect biometric data of caregivers using sensors. For example, the health monitoring unit can collect data such as heart rate, blood pressure, and body temperature. The health monitoring unit can also analyze the collected data and evaluate the health status of caregivers. For example, the health monitoring unit can evaluate the health risks of caregivers based on the collected data. The health monitoring unit can also monitor the health status of caregivers in real time and issue alerts if abnormalities are detected. For example, the health monitoring unit will issue an alert if the heart rate is abnormally high. In this way, the health monitoring unit can support the health management of caregivers. Some or all of the above processing in the health monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health monitoring unit can input the collected biometric data into a generative AI and have the generative AI perform a health status evaluation.

[0087] The emotion analysis unit can detect the caregiver's stress level through emotion analysis. For example, the emotion analysis unit detects the caregiver's stress using voice emotion analysis AI. For example, the emotion analysis unit analyzes the caregiver's voice data and evaluates the stress level. The emotion analysis unit can also detect the caregiver's stress using facial recognition technology. For example, the emotion analysis unit analyzes the caregiver's facial expression data and evaluates the stress level. The emotion analysis unit can also monitor the caregiver's stress level in real time and issue an alert if the stress level is high. For example, the emotion analysis unit issues an alert if the caregiver's stress level exceeds a certain threshold. This allows the emotion analysis unit to reduce the caregiver's mental burden. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input the caregiver's voice data into a generative AI and have the generative AI perform a stress level evaluation.

[0088] The reception unit can estimate the caregiver's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the caregiver is stressed, the reception unit can delay the timing of input acceptance to give the caregiver time to relax. For example, if the caregiver is relaxed, the reception unit can accept input immediately and provide a quick response. For example, if the caregiver is in a hurry, the reception unit can advance the timing of input acceptance and provide a quick response. This allows the reception unit to reduce the burden on the caregiver. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the caregiver's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reception desk can analyze the caregiver's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the caregiver has frequently used in the past. For example, the reception desk may suggest the optimal reception method for a specific time period based on the caregiver's past input history. For example, the reception desk may analyze the caregiver's past input history and select the most efficient reception method. In this way, the reception desk can select the optimal reception method by analyzing the caregiver's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the caregiver's past input data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0090] The reception unit can filter information based on the caregiver's current situation and areas of interest at the time of reception. For example, the reception unit may receive only relevant information based on the caregiver's current situation. For example, the reception unit may prioritize receiving relevant information based on the caregiver's areas of interest. For example, the reception unit may filter the information to be most relevant, taking into account the caregiver's current situation and areas of interest. In this way, the reception unit can receive only relevant information by filtering based on the caregiver's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the caregiver's current situation data into a generating AI and have the generating AI perform the filtering.

[0091] The reception desk can estimate the caregiver's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the caregiver is stressed, the reception desk will postpone receiving less important information. For example, if the caregiver is relaxed, the reception desk will prioritize receiving more important information. For example, if the caregiver is in a hurry, the reception desk will prioritize receiving information that requires a quick response. In this way, the reception desk can prioritize receiving important information by determining the priority of information to receive based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input caregiver emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0092] The reception unit can prioritize receiving highly relevant information by considering the caregiver's geographical location information at the time of reception. For example, the reception unit prioritizes receiving relevant information based on the caregiver's current location. For example, the reception unit filters the most relevant information by considering the caregiver's geographical location information. For example, the reception unit prioritizes receiving information of high importance based on the caregiver's current location. In this way, the reception unit can provide appropriate information by prioritizing the receipt of highly relevant information by considering the caregiver's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform information filtering.

[0093] The reception desk can analyze the caregiver's social media activity and receive relevant information at the time of reception. For example, the reception desk analyzes the caregiver's social media activity and prioritizes receiving relevant information. For example, the reception desk filters out highly important information from the caregiver's social media activity. For example, the reception desk receives the most relevant information considering the caregiver's social media activity. In this way, the reception desk can prioritize receiving relevant information by analyzing the caregiver's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the caregiver's social media data into a generating AI and have the generating AI perform information filtering.

[0094] The generation unit can estimate the caregiver's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the caregiver is stressed, the generation unit generates simple and easy-to-understand advice. For example, if the caregiver is relaxed, the generation unit generates detailed advice. For example, if the caregiver is in a hurry, the generation unit generates advice that can be acted on quickly. In this way, the generation unit can provide advice that is easy for the caregiver to understand by adjusting the way advice is expressed based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not using AI. For example, the generation unit can input caregiver emotion data into the generation AI and have the generation AI adjust the way advice is expressed.

[0095] The generation unit can adjust the level of detail of advice based on the importance of the caregiver when generating advice. For example, if the caregiver needs highly important advice, the generation unit will generate detailed advice. For example, if the caregiver needs less important advice, the generation unit will generate simple advice. The generation unit adjusts the level of detail of the advice to the optimal level based on the importance of the caregiver. In this way, the generation unit can provide appropriate advice by adjusting the level of detail of the advice based on the importance of the caregiver. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0096] The generation unit can apply different advice algorithms depending on the caregiver's category when generating advice. For example, if the caregiver is a novice, the generation unit applies a basic advice algorithm. If the caregiver is experienced, the generation unit applies an advanced advice algorithm. The generation unit applies the optimal advice algorithm depending on the caregiver's category. In this way, the generation unit can provide the caregiver with the most suitable advice by applying different advice algorithms depending on the caregiver's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver category data into a generation AI and have the generation AI execute the application of the advice algorithm.

[0097] The generation unit can estimate the caregiver's emotions and adjust the length of the advice based on the estimated emotions. For example, if the caregiver is stressed, the generation unit generates short, concise advice. For example, if the caregiver is relaxed, the generation unit generates detailed advice. For example, if the caregiver is in a hurry, the generation unit generates short, actionable advice. In this way, the generation unit can provide advice of an appropriate length for the caregiver by adjusting the length based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not using AI. For example, the generation unit can input caregiver emotion data into the generation AI and have the generation AI adjust the length of the advice.

[0098] The generation unit can determine the priority of advice based on the caregiver's submission timing when generating advice. For example, the generation unit determines the priority based on the timing of the advice submitted by the caregiver. For example, the generation unit generates the optimal advice according to the caregiver's submission timing. For example, the generation unit adjusts the priority of advice taking into account the caregiver's submission timing. In this way, the generation unit can provide advice at the appropriate time by determining the priority of advice based on the caregiver's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver submission timing data into a generation AI and have the generation AI perform the determination of advice priority.

[0099] The generation unit can adjust the order of advice based on the caregiver's relevance when generating advice. For example, the generation unit will prioritize generating advice when the caregiver needs highly relevant advice. The generation unit adjusts the order of advice based on the caregiver's relevance, for example. The generation unit determines the optimal order of advice by considering the caregiver's relevance, for example. This allows the generation unit to prioritize important advice by adjusting the order of advice based on the caregiver's relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input caregiver relevance data into a generation AI and have the generation AI perform the adjustment of the advice order.

[0100] The service provider can estimate the caregiver's emotions and adjust the way advice is delivered based on the estimated emotions. For example, if the caregiver is stressed, the service provider will provide simple and easy-to-understand advice. For example, if the caregiver is relaxed, the service provider will provide detailed advice. For example, if the caregiver is in a hurry, the service provider will provide advice that can be acted on quickly. In this way, by adjusting the way advice is delivered based on the caregiver's emotions, the service provider can provide advice that is easy for the caregiver to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 service provider may be performed using AI or not using AI. For example, the service provider can input caregiver emotion data into a generative AI and have the generative AI adjust the way advice is delivered.

[0101] The service provider can analyze the caregiver's past responses when providing advice and select the optimal method of providing it. For example, the service provider analyzes the caregiver's past responses and selects the optimal method of providing advice. For example, the service provider proposes an effective method of providing advice based on the caregiver's past responses. For example, the service provider selects the optimal method of providing advice considering the caregiver's past responses. In this way, the service provider can select the optimal method of providing advice by analyzing the caregiver's past responses. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the caregiver's past responses into a generating AI and have the generating AI select the optimal method of providing advice.

[0102] The service provider can customize the means of providing advice based on the caregiver's current situation. For example, the service provider can select the optimal means of providing advice based on the caregiver's current situation. For example, the service provider can customize the means of providing advice considering the caregiver's current situation. For example, the service provider can propose the optimal means of providing advice according to the caregiver's current situation. In this way, the service provider can select the optimal means of providing advice by customizing the means of providing advice based on the caregiver's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the caregiver's current situation into a generating AI and have the generating AI perform the customization of the means of providing advice.

[0103] The service provider can estimate the caregiver's emotions and determine the order in which to provide advice based on the estimated emotions. For example, if the caregiver is stressed, the service provider will postpone less important advice. For example, if the caregiver is relaxed, the service provider will prioritize providing more important advice. For example, if the caregiver is in a hurry, the service provider will prioritize providing advice that requires immediate attention. In this way, the service provider can prioritize important advice by determining the order in which to provide advice based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 service provider may be performed using AI or not using AI. For example, the service provider can input caregiver emotion data into a generative AI and have the generative AI determine the order in which to provide advice.

[0104] The service provider can select the optimal method of providing advice by considering the caregiver's geographical location information. For example, the service provider can select the optimal method of providing advice based on the caregiver's current location. For example, the service provider can customize the means of providing advice by considering the caregiver's geographical location information. For example, the service provider can propose the optimal method of providing advice according to the caregiver's current location. In this way, the service provider can provide appropriate advice by selecting the optimal method of providing advice by considering the caregiver's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the method of providing advice.

[0105] The service provider can analyze the caregiver's social media activity and propose a means of providing advice. For example, the service provider can analyze the caregiver's social media activity and propose the most suitable means of providing advice. For example, the service provider can select an effective means of providing advice from the caregiver's social media activity. For example, the service provider can propose the most suitable means of providing advice, taking into account the caregiver's social media activity. In this way, the service provider can propose the most suitable means of providing advice by analyzing the caregiver's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of means of providing advice.

[0106] The progress management unit can estimate the caregiver's emotions and adjust the progress management method based on the estimated emotions. For example, if the caregiver is stressed, the progress management unit can reduce the frequency of progress management and provide time for relaxation. For example, if the caregiver is relaxed, the progress management unit can perform detailed progress management. For example, if the caregiver is in a hurry, the progress management unit can perform rapid progress management. In this way, the progress management unit can reduce the burden on the caregiver by adjusting the progress management method based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input caregiver emotion data into the generative AI and have the generative AI perform the adjustment of the progress management method.

[0107] The progress management unit can select the optimal management method by referring to the caregiver's past progress data during progress management. For example, the progress management unit can refer to the caregiver's past progress data and select the optimal management method. For example, the progress management unit can propose an effective management method based on the caregiver's past progress data. For example, the progress management unit can select the optimal management method by considering the caregiver's past progress data. In this way, the progress management unit can select the optimal management method by referring to the caregiver's past progress data. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the caregiver's past progress data into a generating AI and have the generating AI perform the selection of a management method.

[0108] The progress management unit can customize management methods based on the caregiver's current situation during progress management. For example, the progress management unit selects the optimal management method based on the caregiver's current situation. For example, the progress management unit customizes the management method considering the caregiver's current situation. For example, the progress management unit proposes the optimal management method according to the caregiver's current situation. In this way, the progress management unit can select the optimal management method by customizing the management method based on the caregiver's current situation. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input data on the caregiver's current situation into a generating AI and have the generating AI perform the customization of the management method.

[0109] The progress management unit can estimate the caregiver's emotions and determine the priority of progress management based on the estimated emotions. For example, if the caregiver is stressed, the progress management unit will postpone less important progress management tasks. For example, if the caregiver is relaxed, the progress management unit will prioritize more important progress management tasks. For example, if the caregiver is in a hurry, the progress management unit will prioritize progress management tasks that require immediate attention. In this way, the progress management unit can prioritize important progress management tasks by determining the priority of progress management based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input caregiver emotion data into a generative AI and have the generative AI perform the determination of progress management priorities.

[0110] The progress management unit can select the optimal management method when managing progress, taking into account the caregiver's geographical location information. For example, the progress management unit selects the optimal management method based on the caregiver's current location. For example, the progress management unit customizes the management means by taking into account the caregiver's geographical location information. For example, the progress management unit proposes the optimal management method according to the caregiver's current location. In this way, the progress management unit can perform appropriate management by selecting the optimal management method by taking into account the caregiver's geographical location information. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the management method.

[0111] The progress management department can analyze the caregiver's social media activity and propose management methods during progress management. For example, the progress management department can analyze the caregiver's social media activity and propose the optimal management method. For example, the progress management department can select an effective management method from the caregiver's social media activity. For example, the progress management department can propose the optimal management method considering the caregiver's social media activity. In this way, the progress management department can propose the optimal management method by analyzing the caregiver's social media activity. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of management methods.

[0112] The stress detection unit can estimate the caregiver's emotions and adjust the stress detection method based on the estimated emotions. For example, if the caregiver is feeling stressed, the stress detection unit applies a simple stress detection method. For example, if the caregiver is relaxed, the stress detection unit applies a detailed stress detection method. For example, if the caregiver is in a hurry, the stress detection unit applies a method for rapid stress detection. In this way, the stress detection unit can reduce the caregiver's burden by adjusting the stress detection method based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 detection unit may be performed using AI or not using AI. For example, the stress detection unit can input the caregiver's emotion data into the generative AI and have the generative AI adjust the stress detection method.

[0113] The stress detection unit can select the optimal detection method by referring to the caregiver's past stress data when stress is detected. For example, the stress detection unit selects the optimal detection method by referring to the caregiver's past stress data. For example, the stress detection unit proposes an effective detection method from the caregiver's past stress data. For example, the stress detection unit selects the optimal detection method by considering the caregiver's past stress data. In this way, the stress detection unit can select the optimal detection method by referring to the caregiver's past stress data. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without using AI. For example, the stress detection unit can input the caregiver's past stress data into a generating AI and have the generating AI perform the selection of a detection method.

[0114] The stress detection unit can customize the detection means based on the caregiver's current situation when stress is detected. For example, the stress detection unit selects the optimal detection means based on the caregiver's current situation. For example, the stress detection unit customizes the detection means considering the caregiver's current situation. For example, the stress detection unit proposes the optimal detection means according to the caregiver's current situation. In this way, the stress detection unit can select the optimal detection means by customizing the detection means based on the caregiver's current situation. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without AI. For example, the stress detection unit can input the caregiver's current situation data into a generating AI and have the generating AI perform the customization of the detection means.

[0115] The stress detection unit can estimate the caregiver's emotions and determine the priority of stress detection based on the estimated emotions. For example, if the caregiver is stressed, the stress detection unit will postpone low-priority stress detection. For example, if the caregiver is relaxed, the stress detection unit will prioritize high-priority stress detection. For example, if the caregiver is in a hurry, the stress detection unit will prioritize stress detection that requires immediate attention. In this way, the stress detection unit can prioritize important stress detection by determining the priority of stress detection based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 detection unit may be performed using AI or not using AI. For example, the stress detection unit can input caregiver emotion data into the generative AI and have the generative AI perform the determination of stress detection priorities.

[0116] The stress detection unit can select the optimal detection method when stress is detected, taking into account the caregiver's geographical location information. For example, the stress detection unit selects the optimal detection method based on the caregiver's current location. For example, the stress detection unit customizes the detection means by taking into account the caregiver's geographical location information. For example, the stress detection unit proposes the optimal detection method according to the caregiver's current location. As a result, the stress detection unit can perform appropriate detection by selecting the optimal detection method by taking into account the caregiver's geographical location information. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without using AI. For example, the stress detection unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the detection method.

[0117] The stress detection unit can analyze the caregiver's social media activity and propose detection methods when stress is detected. For example, the stress detection unit analyzes the caregiver's social media activity and proposes the optimal detection method. For example, the stress detection unit selects an effective detection method from the caregiver's social media activity. For example, the stress detection unit proposes the optimal detection method considering the caregiver's social media activity. Thus, the stress detection unit can propose the optimal detection method by analyzing the caregiver's social media activity. Some or all of the above processing in the stress detection unit may be performed using AI, for example, or without AI. For example, the stress detection unit can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of detection methods.

[0118] The speech recognition unit can estimate the caregiver's emotions and adjust the accuracy of speech recognition based on the estimated emotions. For example, if the caregiver is stressed, the speech recognition unit increases the accuracy of speech recognition and reduces misrecognition. For example, if the caregiver is relaxed, the speech recognition unit applies normal speech recognition accuracy. For example, if the caregiver is in a hurry, the speech recognition unit adjusts the accuracy to recognize speech quickly. In this way, the speech recognition unit can reduce misrecognition by adjusting the accuracy of speech recognition based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input caregiver emotion data into the generative AI and have the generative AI perform the adjustment of speech recognition accuracy.

[0119] The speech recognition unit can select the optimal recognition method by referring to the caregiver's past voice data during speech recognition. For example, the speech recognition unit refers to the caregiver's past voice data and selects the optimal recognition method. For example, the speech recognition unit proposes an effective recognition method from the caregiver's past voice data. For example, the speech recognition unit selects the optimal recognition method by considering the caregiver's past voice data. In this way, the speech recognition unit can select the optimal recognition method by referring to the caregiver's past voice data. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input the caregiver's past voice data into a generating AI and have the generating AI perform the selection of a recognition method.

[0120] The speech recognition unit can estimate the caregiver's emotions and determine the priority of speech recognition based on the estimated emotions. For example, if the caregiver is stressed, the speech recognition unit will postpone less important speech recognition. For example, if the caregiver is relaxed, the speech recognition unit will prioritize more important speech recognition. For example, if the caregiver is in a hurry, the speech recognition unit will prioritize speech recognition that requires a quick response. In this way, the speech recognition unit can prioritize important speech recognition by determining the priority of speech recognition based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 speech recognition unit may be performed using AI or not using AI. For example, the speech recognition unit can input caregiver emotion data into the generative AI and have the generative AI perform the determination of speech recognition priorities.

[0121] The speech recognition unit can select the optimal recognition method while considering the caregiver's geographical location information during speech recognition. For example, the speech recognition unit selects the optimal recognition method based on the caregiver's current location. For example, the speech recognition unit customizes the recognition means while considering the caregiver's geographical location information. For example, the speech recognition unit proposes the optimal recognition method according to the caregiver's current location. As a result, the speech recognition unit can perform appropriate recognition by selecting the optimal recognition method while considering the caregiver's geographical location information. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the recognition method.

[0122] The visual guidance unit can estimate the caregiver's emotions and adjust the display method of the visual guidance based on the estimated emotions. For example, if the caregiver is stressed, the visual guidance unit provides a simple and highly visible display method. For example, if the caregiver is relaxed, the visual guidance unit provides a display method that includes detailed information. For example, if the caregiver is in a hurry, the visual guidance unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the visual guidance based on the caregiver's emotions, the visual guidance unit can provide a display method that is easy for the caregiver to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 visual guidance unit may be performed using AI, for example, or not using AI. For example, the visual guidance unit can input caregiver emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0123] The visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data during visual guidance. For example, the visual guidance unit selects the optimal guidance method by referring to the caregiver's past visual data. For example, the visual guidance unit proposes an effective guidance method from the caregiver's past visual data. For example, the visual guidance unit selects the optimal guidance method by considering the caregiver's past visual data. In this way, the visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data. Some or all of the above processing in the visual guidance unit may be performed using AI, for example, or without using AI. For example, the visual guidance unit can input the caregiver's past visual data into a generating AI and have the generating AI perform the selection of a guidance method.

[0124] The visual guidance unit can estimate the caregiver's emotions and prioritize visual guidance based on the estimated emotions. For example, if the caregiver is stressed, the visual guidance unit will postpone less important visual guidance. For example, if the caregiver is relaxed, the visual guidance unit will prioritize more important visual guidance. For example, if the caregiver is in a hurry, the visual guidance unit will prioritize visual guidance that requires immediate attention. In this way, the visual guidance unit can prioritize important guidance by determining the priority of visual guidance based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 visual guidance unit may be performed using AI or not using AI. For example, the visual guidance unit can input caregiver emotion data into a generative AI and have the generative AI perform the determination of visual guidance priorities.

[0125] The visual guidance unit can select the optimal guidance method during visual guidance by considering the caregiver's geographical location information. For example, the visual guidance unit selects the optimal guidance method based on the caregiver's current location. For example, the visual guidance unit customizes the guidance means by considering the caregiver's geographical location information. For example, the visual guidance unit proposes the optimal guidance method according to the caregiver's current location. As a result, the visual guidance unit can provide appropriate guidance by selecting the optimal guidance method by considering the caregiver's geographical location information. Some or all of the above processing in the visual guidance unit may be performed using AI, for example, or without AI. For example, the visual guidance unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the guidance method.

[0126] The health monitoring unit can estimate the caregiver's emotions and adjust the health monitoring method based on the estimated emotions. For example, if the caregiver is stressed, the health monitoring unit applies a simple health monitoring method. For example, if the caregiver is relaxed, the health monitoring unit applies a detailed health monitoring method. For example, if the caregiver is in a hurry, the health monitoring unit applies a method for quickly monitoring the health status. In this way, the health monitoring unit can reduce the burden on the caregiver by adjusting the health monitoring method based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 health monitoring unit may be performed using AI, for example, or not using AI. For example, the health monitoring unit can input caregiver emotion data into the generative AI and have the generative AI perform the adjustment of the health monitoring method.

[0127] The health monitoring unit can select the optimal monitoring method by referring to the caregiver's past health data during health monitoring. For example, the health monitoring unit can refer to the caregiver's past health data and select the optimal monitoring method. For example, the health monitoring unit can propose an effective monitoring method based on the caregiver's past health data. For example, the health monitoring unit can select the optimal monitoring method by considering the caregiver's past health data. In this way, the health monitoring unit can select the optimal monitoring method by referring to the caregiver's past health data. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the caregiver's past health data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0128] The health monitoring unit can estimate the caregiver's emotions and determine the priority of health monitoring based on the estimated emotions. For example, if the caregiver is stressed, the health monitoring unit will postpone less important health monitoring. For example, if the caregiver is relaxed, the health monitoring unit will prioritize high-priority health monitoring. For example, if the caregiver is in a hurry, the health monitoring unit will prioritize health monitoring that requires immediate attention. In this way, the health monitoring unit can prioritize important monitoring by determining the priority of health monitoring based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 health monitoring unit may be performed using AI or not using AI. For example, the health monitoring unit can input caregiver emotion data into a generative AI and have the generative AI determine the priority of health monitoring.

[0129] The health monitoring unit can select the optimal monitoring method when monitoring health, taking into account the caregiver's geographical location information. For example, the health monitoring unit selects the optimal monitoring method based on the caregiver's current location. For example, the health monitoring unit customizes the monitoring means by taking into account the caregiver's geographical location information. For example, the health monitoring unit proposes the optimal monitoring method according to the caregiver's current location. As a result, the health monitoring unit can perform appropriate monitoring by selecting the optimal monitoring method by taking into account the caregiver's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the monitoring method.

[0130] The emotion analysis unit can estimate the caregiver's emotions and adjust the emotion analysis method based on the estimated emotions. For example, if the caregiver is stressed, the emotion analysis unit applies a simple emotion analysis method. For example, if the caregiver is relaxed, the emotion analysis unit applies a detailed emotion analysis method. For example, if the caregiver is in a hurry, the emotion analysis unit applies a rapid emotion analysis method. In this way, the emotion analysis unit can reduce the caregiver's burden by adjusting the emotion analysis method based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or not using AI. For example, the emotion analysis unit can input the caregiver's emotion data into the generative AI and have the generative AI perform the adjustment of the emotion analysis method.

[0131] The emotion analysis unit can select the optimal analysis method by referring to the caregiver's past emotional data during emotion analysis. For example, the emotion analysis unit refers to the caregiver's past emotional data and selects the optimal analysis method. For example, the emotion analysis unit proposes an effective analysis method from the caregiver's past emotional data. For example, the emotion analysis unit selects the optimal analysis method by considering the caregiver's past emotional data. In this way, the emotion analysis unit can select the optimal analysis method by referring to the caregiver's past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the caregiver's past emotional data into a generating AI and have the generating AI perform the selection of the analysis method.

[0132] The emotion analysis unit can estimate the caregiver's emotions and determine the priority of emotion analysis based on the estimated emotions. For example, if the caregiver is stressed, the emotion analysis unit will postpone less important emotion analyses. For example, if the caregiver is relaxed, the emotion analysis unit will prioritize more important emotion analyses. For example, if the caregiver is in a hurry, the emotion analysis unit will prioritize emotion analyses that require immediate attention. In this way, the emotion analysis unit can prioritize important analyses by determining the priority of emotion analysis based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 emotion analysis unit may be performed using AI or not using AI. For example, the emotion analysis unit can input caregiver emotion data into a generative AI and have the generative AI determine the priority of emotion analysis.

[0133] The emotion analysis unit can select the optimal analysis method while considering the caregiver's geographical location information during emotion analysis. For example, the emotion analysis unit selects the optimal analysis method based on the caregiver's current location. For example, the emotion analysis unit customizes the analysis means while considering the caregiver's geographical location information. For example, the emotion analysis unit proposes the optimal analysis method according to the caregiver's current location. In this way, the emotion analysis unit can perform appropriate analysis by selecting the optimal analysis method while considering the caregiver's geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the caregiver's geographical location data into a generating AI and have the generating AI perform the selection of the analysis method.

[0134] The sentiment analysis unit can analyze the caregiver's social media activity and propose analysis methods during sentiment analysis. For example, the sentiment analysis unit analyzes the caregiver's social media activity and proposes the optimal analysis method. For example, the sentiment analysis unit selects an effective analysis method from the caregiver's social media activity. For example, the sentiment analysis unit proposes the optimal analysis method considering the caregiver's social media activity. In this way, the sentiment analysis unit can propose the optimal analysis method by analyzing the caregiver's social media activity. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the caregiver's social media data into a generating AI and have the generating AI execute the proposal of analysis methods.

[0135] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0136] The reception desk can suggest the most suitable input method when receiving input from caregivers, by referring to the caregiver's past input history. For example, it can prioritize suggesting input methods that the caregiver has frequently used in the past. It can also suggest the most suitable input method for a specific time of day based on the caregiver's past input history. Furthermore, it can analyze the caregiver's past input history and select the most efficient input method. In this way, the reception desk can suggest the most suitable input method by analyzing the caregiver's past input history.

[0137] The speech recognition unit can estimate the caregiver's emotions and adjust the accuracy of speech recognition based on those emotions. For example, if the caregiver is stressed, the accuracy of speech recognition can be increased to reduce misrecognition. If the caregiver is relaxed, the normal speech recognition accuracy can be applied. Furthermore, if the caregiver is in a hurry, the accuracy can be adjusted to recognize speech quickly. In this way, the speech recognition unit can reduce misrecognition by adjusting the accuracy of speech recognition based on the caregiver's emotions.

[0138] The visual guidance unit can estimate the caregiver's emotions and adjust the display method of the visual guidance based on those emotions. For example, if the caregiver is stressed, it can provide a simple and highly visible display method. If the caregiver is relaxed, it can provide a display method that includes detailed information. Furthermore, if the caregiver is in a hurry, it can provide a display method that gets straight to the point. In this way, the visual guidance unit can provide a display method that is easy for the caregiver to understand by adjusting the display method of the visual guidance based on the caregiver's emotions.

[0139] The health monitoring unit can estimate the caregiver's emotions and adjust the health monitoring method based on those emotions. For example, if the caregiver is stressed, a simple health monitoring method can be applied. If the caregiver is relaxed, a more detailed health monitoring method can be applied. Furthermore, if the caregiver is in a hurry, a method for quickly monitoring their health can be applied. In this way, the health monitoring unit can reduce the burden on caregivers by adjusting the health monitoring method based on their emotions.

[0140] The emotion analysis unit can estimate the caregiver's emotions and adjust the emotion analysis method based on the estimated emotions. For example, if the caregiver is stressed, a simple emotion analysis method can be applied. If the caregiver is relaxed, a more detailed emotion analysis method can be applied. Furthermore, if the caregiver is in a hurry, a rapid emotion analysis method can be applied. In this way, the emotion analysis unit can reduce the caregiver's burden by adjusting the emotion analysis method based on the caregiver's emotions.

[0141] The reception desk can prioritize receiving highly relevant information by considering the caregiver's geographical location. For example, it can prioritize receiving relevant information based on the caregiver's current location. It can also filter the information to be most relevant by considering the caregiver's geographical location. Furthermore, it can prioritize receiving information of high importance based on the caregiver's current location. As a result, the reception desk can provide appropriate information by prioritizing the receipt of highly relevant information by considering the caregiver's geographical location.

[0142] The service provider can analyze the caregiver's past reactions when providing advice and select the most appropriate method of delivery. For example, it can analyze the caregiver's past reactions and select the most appropriate method of advice delivery. It can also propose effective advice delivery methods based on the caregiver's past reactions. Furthermore, it can select the most appropriate method of advice delivery by considering the caregiver's past reactions. In this way, the service provider can select the most appropriate method of advice delivery by analyzing the caregiver's past reactions.

[0143] The progress management department can select the optimal management method by referring to the caregiver's past progress data during progress management. For example, it can select the optimal management method by referring to the caregiver's past progress data. It can also propose effective management methods based on the caregiver's past progress data. Furthermore, it can select the optimal management method by considering the caregiver's past progress data. In this way, the progress management department can select the optimal management method by referring to the caregiver's past progress data.

[0144] The stress detection unit can select the optimal detection method by referring to the caregiver's past stress data when stress is detected. For example, it can select the optimal detection method by referring to the caregiver's past stress data. It can also suggest an effective detection method based on the caregiver's past stress data. Furthermore, it can select the optimal detection method by considering the caregiver's past stress data. In this way, the stress detection unit can select the optimal detection method by referring to the caregiver's past stress data.

[0145] The visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data during visual guidance. For example, it can select the optimal guidance method by referring to the caregiver's past visual data. It can also suggest an effective guidance method based on the caregiver's past visual data. Furthermore, it can select the optimal guidance method by considering the caregiver's past visual data. In this way, the visual guidance unit can select the optimal guidance method by referring to the caregiver's past visual data.

[0146] The following briefly describes the processing flow for example form 2.

[0147] Step 1: The reception desk receives input from caregivers. Caregiver input includes text input, voice input, and image input. The reception desk can receive caregiver input via a smartphone app and can also accept voice input using speech recognition technology. Step 2: The generation unit analyzes the information received by the reception unit and generates personalized care advice. The generation unit uses generation AI to generate advice based on the caregiver's past data, current situation, and specific needs. The generation unit can also generate care advice using text generation AI (e.g., LLM) and optimize the personalized care plan using reinforcement learning. Step 3: The delivery unit provides the caregiver with the advice generated by the generation unit. The delivery unit can provide advice via a smartphone app, and can also provide advice in voice using speech synthesis technology. Step 4: The progress management department manages the caregiver's progress based on the advice provided by the service provider. The progress management department can also manage the caregiver's progress using a personalized dashboard and analyze the caregiver's progress data to provide appropriate feedback. Step 5: The stress detection unit detects the caregiver's stress level. The stress detection unit can detect the caregiver's stress level using emotion analysis technology, and can also detect stress levels by monitoring the caregiver's biometric data using voice emotion analysis AI or sensor technology.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress management unit, stress detection unit, voice recognition unit, visual guidance unit, health monitoring unit, and emotion analysis unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the caregiver. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates personalized care advice. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generated advice to the caregiver. The progress management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the caregiver's progress. The stress detection unit is implemented, for example, by the control unit 46A of the smart device 14 and detects the caregiver's stress level. The voice recognition unit is implemented, for example, by the control unit 46A of the smart device 14 and performs hands-free operation by voice recognition. The visual guidance unit is implemented, for example, by the control unit 46A of the smart device 14, and provides visual guidance using AR technology. The health monitoring unit is implemented, for example, by the control unit 46A of the smart device 14, and monitors the health status of the caregiver. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects the stress level of the caregiver. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress management unit, stress detection unit, voice recognition unit, visual guidance unit, health monitoring unit, and emotion analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the caregiver. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates personalized care advice. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated advice to the caregiver. The progress management unit is implemented by the identification processing unit 290 of the data processing unit 12 and manages the caregiver's progress. The stress detection unit is implemented by the control unit 46A of the smart glasses 214 and detects the caregiver's stress level. The voice recognition unit is implemented by the control unit 46A of the smart glasses 214 and performs hands-free operation by voice recognition. The visual guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides visual guidance using AR technology. The health monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, and monitors the health status of the caregiver. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects the stress level of the caregiver. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress management unit, stress detection unit, voice recognition unit, visual guidance unit, health monitoring unit, and emotion analysis unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the caregiver. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates personalized care advice. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the generated advice to the caregiver. The progress management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the caregiver's progress. The stress detection unit is implemented, for example, by the control unit 46A of the headset terminal 314 and detects the caregiver's stress level. The voice recognition unit is implemented, for example, by the control unit 46A of the headset terminal 314 and performs hands-free operation by voice recognition. The visual guidance unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides visual guidance using AR technology. The health monitoring unit is implemented, for example, by the control unit 46A of the headset terminal 314, and monitors the health status of the caregiver. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects the stress level of the caregiver. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress management unit, stress detection unit, voice recognition unit, visual guidance unit, health monitoring unit, and emotion analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the caregiver. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized care advice. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated advice to the caregiver. The progress management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages the caregiver's progress. The stress detection unit is implemented, for example, by the control unit 46A of the robot 414 and detects the caregiver's stress level. The voice recognition unit is implemented, for example, by the control unit 46A of the robot 414 and performs hands-free operation by voice recognition. The visual guidance unit is implemented, for example, by the control unit 46A of the robot 414, and provides visual guidance using AR technology. The health monitoring unit is implemented, for example, by the control unit 46A of the robot 414, and monitors the health status of the caregiver. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects the stress level of the caregiver. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] (Note 1) The reception area accepts input from caregivers, A generation unit analyzes the information received by the reception unit and generates personalized care advice, A providing unit that provides advice generated by the generation unit to the caregiver, A progress management unit manages the caregiver's progress based on the advice provided by the aforementioned provision unit, It includes a stress detection unit that detects the caregiver's stress level. A system characterized by the following features. (Note 2) It features a voice recognition unit that enables hands-free operation via voice recognition. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a visual guidance unit that provides visual guidance using AR technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a health monitoring unit that uses sensor technology to monitor the health status of caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with an emotion analysis unit that detects the caregiver's stress level through emotion analysis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the caregiver's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the caregiver's past input history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During registration, filtering is performed based on the caregiver's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the caregiver's emotions and prioritizes the information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is At the time of registration, the system prioritizes accepting information that is highly relevant, taking into account the caregiver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is At the time of registration, the caregiver's social media activity is analyzed and relevant information is collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the caregiver's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating advice, adjust the level of detail based on the importance of the caregiver. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating advice, different advice algorithms are applied depending on the caregiver's category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the caregiver's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating advice, the priority of the advice is determined based on when the caregiver submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating advice, the order of advice is adjusted based on the caregiver's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the caregiver's emotions and adjusts the way advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, we analyze the caregiver's past reactions to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, customize the delivery method based on the caregiver's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the caregiver's emotions and determines the order in which advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, the most suitable method of delivery will be selected, taking into account the caregiver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we analyze the caregiver's social media activity and propose methods for providing it. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress management unit, The system estimates the caregiver's emotions and adjusts the progress management method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned progress management unit, When managing progress, refer to the caregiver's past progress data to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress management unit, When managing progress, customize the management methods based on the caregiver's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned progress management unit, The system estimates the caregiver's emotions and prioritizes progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned progress management unit, When managing progress, the optimal management method will be selected considering the geographical location information of caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned progress management unit, During progress management, we analyze caregivers' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The stress detection unit, The system estimates the caregiver's emotions and adjusts the stress detection method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The stress detection unit, When detecting stress, the optimal detection method is selected by referring to the caregiver's past stress data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The stress detection unit, When stress is detected, the detection method is customized based on the caregiver's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The stress detection unit, The system estimates the caregiver's emotions and prioritizes stress detection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The stress detection unit, When detecting stress, the optimal detection method is selected considering the caregiver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The stress detection unit, When stress is detected, we analyze the caregiver's social media activity and propose detection methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned speech recognition unit, The system estimates the caregiver's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned speech recognition unit, During speech recognition, the system selects the optimal recognition method by referring to the caregiver's past voice data. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned speech recognition unit, The system estimates the caregiver's emotions and determines the priority of speech recognition based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned speech recognition unit, During speech recognition, the system selects the optimal recognition method by considering the caregiver's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned visual guidance unit is The system estimates the caregiver's emotions and adjusts the display of visual guidance based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The visual guidance unit selects an optimal guidance method by referring to the past visual data of the caregiver during visual guidance The system according to supplementary note 3, characterized by this. (Supplementary note 42) The visual guidance unit estimates the emotion of the caregiver and determines the priority of visual guidance based on the estimated emotion of the caregiver The system according to supplementary note 3, characterized by this. (Supplementary note 43) The visual guidance unit selects an optimal guidance method by considering the geographical location information of the caregiver during visual guidance The system according to supplementary note 3, characterized by this. (Supplementary note 44) The health monitoring unit estimates the emotion of the caregiver and adjusts the health monitoring method based on the estimated emotion of the caregiver The system according to supplementary note 4, characterized by this. (Supplementary note 45) The health monitoring unit selects an optimal monitoring method by referring to the past health data of the caregiver during health monitoring The system according to supplementary note 4, characterized by this. (Supplementary note 46) The health monitoring unit estimates the emotion of the caregiver and determines the priority of health monitoring based on the estimated emotion of the caregiver The system according to supplementary note 4, characterized by this. (Supplementary note 47) The health monitoring unit selects an optimal monitoring method by considering the geographical location information of the caregiver during health monitoring The system according to supplementary note 4, characterized by this. (Supplementary note 48) The emotion analysis unit estimates the emotion of the caregiver and adjusts the emotion analysis method based on the estimated emotion of the caregiver The system according to appended note 5, characterized in that... (Appended note 49) The emotion analysis unit selects an optimal analysis method by referring to the caregiver's past emotion data during emotion analysis The system according to appended note 5, characterized in that... (Appended note 50) The emotion analysis unit estimates the caregiver's emotion and determines the priority order of emotion analysis based on the estimated caregiver's emotion The system according to appended note 5, characterized in that... (Appended note 51) The emotion analysis unit selects an optimal analysis method by considering the caregiver's geographical location information during emotion analysis The system according to appended note 5, characterized in that... (Appended note 52) The emotion analysis unit analyzes the caregiver's social media activities and proposes analysis means during emotion analysis The system according to appended note 5, characterized in that...

Explanation of reference numerals

[0220] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. The reception area accepts input from caregivers, A generation unit analyzes the information received by the reception unit and generates personalized care advice, A providing unit that provides advice generated by the generation unit to the caregiver, A progress management unit manages the caregiver's progress based on the advice provided by the aforementioned provision unit, It includes a stress detection unit that detects the caregiver's stress level. A system characterized by the following features.

2. It features a voice recognition unit that enables hands-free operation via voice recognition. The system according to feature 1.

3. It is equipped with a visual guidance unit that provides visual guidance using AR technology. The system according to feature 1.

4. It includes a health monitoring unit that uses sensor technology to monitor the health status of caregivers. The system according to feature 1.

5. It is equipped with an emotion analysis unit that detects the caregiver's stress level through emotion analysis. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the caregiver's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the caregiver's past input history to select the most suitable registration method. The system according to feature 1.

8. The aforementioned reception unit is During registration, filtering is performed based on the caregiver's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the caregiver's emotions and prioritizes the information to be received based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is At the time of registration, the system prioritizes accepting information that is highly relevant, taking into account the caregiver's geographical location. The system according to feature 1.

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