system

Abstract

The system according to this embodiment aims to provide emotional support to the user, predict changes in health status, and facilitate collaboration with medical staff. [Solution] The system according to the embodiment comprises an emotion analysis unit, a care provision unit, a collection unit, a prediction unit, a coordination unit, and a customization unit. The emotion analysis unit analyzes the user's emotions. The care provision unit provides emotional support based on the emotions analyzed by the emotion analysis unit. The collection unit collects the user's health data. The prediction unit analyzes the health data collected by the collection unit and predicts changes in the user's health status. The coordination unit notifies medical staff of any abnormalities predicted by the prediction unit. The customization unit performs customization to adapt to the user's home environment.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, emotional support for users who need care, prediction of changes in health conditions, and cooperation with medical staff are not sufficiently carried out, and there is room for improvement.

[0005] (There are no specific instructions for these tags, so they are left unchanged) The system according to the embodiment aims to provide emotional support for users, predict changes in health conditions, and realize cooperation with medical staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an emotion analysis unit, a care provision unit, a data collection unit, a prediction unit, a coordination unit, and a customization unit. The emotion analysis unit analyzes the user's emotions. The care provision unit provides emotional support based on the emotions analyzed by the emotion analysis unit. The data collection unit collects the user's health data. The prediction unit analyzes the health data collected by the data collection unit and predicts changes in the user's health status. The coordination unit notifies medical staff of any abnormalities predicted by the prediction unit. The customization unit performs customization to adapt to the user's home environment. [Effects of the Invention]

[0007] The system according to this embodiment can provide emotional support to the user, predict changes in health status, and facilitate collaboration with medical staff. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 receiving 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 receiving 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 care robot system according to an embodiment of the present invention is a system that provides emotional support to people who need care and realizes personalized care tailored to their specific needs. This system supports the user's emotions, provides care tailored to specific needs, incorporates an AI-based prediction and early detection system, and enables real-time collaboration with medical staff. It also adds customization functions to adapt the care robot to the home environment. For example, the care robot system analyzes the user's facial expressions and tone of voice to understand their emotions. If the user is sad, the care robot can offer words of comfort. This allows the user to receive emotional support. Next, the care robot system learns the user's health condition and daily life patterns and provides optimal care based on that. For example, if the user needs to take medication at a specific time, the care robot can provide the medication at that time. This allows the user to receive care that is tailored to them. Furthermore, the care robot system collects the user's health data, and by having AI analyze it, it can predict changes in health condition and detect abnormalities early. For example, it can monitor changes in the user's body temperature and heart rate, and notify medical staff if an abnormality is detected. This allows for constant monitoring of the user's health condition and early response. Furthermore, the care robot system transmits the user's health data to medical staff in real time, allowing them to take appropriate action based on that data. For example, if the user's blood pressure suddenly rises, medical staff can immediately take countermeasures. This ensures the user receives prompt and appropriate medical care. Finally, the care robot system can adjust its operation to suit the user's home environment. For example, it can set a movement route that matches the layout of the user's home and the arrangement of furniture. This allows the care robot to adapt to the home environment and provide user-friendly care.The goal of this care robot system is to create a society where people who need care can live with peace of mind by providing emotional support, personalized care, AI-powered prediction and early detection, real-time collaboration with medical staff, and adaptation to the home environment.

[0029] The care robot system according to this embodiment comprises an emotion analysis unit, a care provision unit, a data collection unit, a prediction unit, a coordination unit, and a customization unit. The emotion analysis unit analyzes the user's emotions. The emotion analysis unit, for example, analyzes the user's facial expressions and tone of voice to understand their emotions. The emotion analysis unit, for example, uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The emotion analysis unit can also use voice analysis technology to analyze the user's tone of voice and estimate their emotions. The emotion analysis unit, for example, monitors changes in the user's facial expressions in real time and detects changes in their emotions. The care provision unit provides emotional support based on the emotions analyzed by the emotion analysis unit. For example, if the user is sad, the care provision unit offers words of comfort. The care provision unit can also provide reassuring information if the user is feeling anxious. For example, if the user is happy, the care provision unit shares that emotion and provides positive feedback. The data collection unit collects the user's health data. The data collection unit collects health data such as heart rate, blood pressure, and body temperature. The data collection unit collects the user's health data in real time, for example, using a wearable device. The data collection unit can also periodically collect the user's health data and store it in a database. The prediction unit analyzes the health data collected by the data collection unit and predicts changes in health status. The prediction unit analyzes health data using AI, for example, and detects abnormalities. The prediction unit analyzes health data using machine learning algorithms, for example, and predicts signs of abnormalities. The prediction unit can also analyze health data using deep learning technology to detect abnormalities early. The communication unit notifies medical staff of abnormalities predicted by the prediction unit. The communication unit notifies medical staff in real time when an abnormality is detected. The communication unit transmits the user's health data to medical staff to prompt appropriate action. The communication unit can also support communication with medical staff, enabling a rapid response. The customization unit performs customization to adapt to the user's home environment. The customization section adjusts the operation of the care robot to match, for example, the layout of the user's home and the arrangement of furniture.The customization unit, for example, sets a travel route based on the user's home environment. The customization unit can also customize the functions of the care robot to suit the user's home environment. As a result, the care robot system according to this embodiment can perform user emotion analysis, emotional support, health data collection, health status prediction, notification to medical staff, and adapt to the home environment.

[0030] The emotion analysis unit analyzes the user's emotions. For example, it analyzes the user's facial expressions and voice tone to understand their emotions. Specifically, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. Facial recognition technology can detect feature points on the user's face and capture subtle changes in facial expression. For example, it analyzes eyebrow movements and the degree to which the corners of the mouth are turned up to determine whether the user is happy or sad. The emotion analysis unit can also use voice analysis technology to analyze the user's voice tone and estimate their emotions. Voice analysis technology analyzes voice features such as pitch, strength, and rhythm to estimate the user's emotional state. For example, if the voice is trembling, it is determined that the user is feeling anxious, and if the voice is bright and exhilarating, it is determined that the user is feeling happy. The emotion analysis unit combines these technologies to analyze the user's emotions from multiple angles and perform more accurate emotion estimation. Furthermore, the emotion analysis unit monitors changes in the user's facial expressions in real time and detects changes in emotions. For example, if the user suddenly changes their facial expression during a conversation, it immediately captures that change and analyzes the change in emotion. This allows the emotion analysis unit to constantly understand the user's emotional state and provide the basic information necessary to take appropriate action.

[0031] The caregiving department provides emotional support based on the emotions analyzed by the emotion analysis department. Specifically, if a user is sad, they offer words of comfort. For example, they might say, "It's okay, I'm here, so don't worry," to soothe the user's feelings. The caregiving department can also provide reassuring information if a user is feeling anxious. For example, they might say, "There's no problem with the current situation. It will be resolved soon, so please don't worry," to reduce the user's anxiety. Furthermore, if a user is happy, the caregiving department shares that emotion and provides positive feedback. For example, they might say, "That's wonderful! Let's celebrate together," to amplify the user's joy. Through this emotional support, the caregiving department can promote the user's mental stability and provide a better caregiving experience. In addition, based on data from the emotion analysis department, the caregiving department can automatically select and execute appropriate responses according to the user's emotional state. This allows the caregiving department to provide flexible responses that are attentive to the user's emotions and improve user satisfaction.

[0032] The data collection unit collects user health data. Specifically, it collects health data such as heart rate, blood pressure, and body temperature. The data collection unit collects user health data in real time, for example, using wearable devices. Wearable devices are attached to the user's wrist or chest and can continuously monitor data such as heart rate, blood pressure, and body temperature. This allows the data collection unit to constantly understand the user's health status and respond immediately if an abnormality occurs. The data collection unit can also collect user health data periodically and store it in a database. For example, by collecting daily health data at regular intervals and storing it in the database, it is possible to track changes in health status over the long term. Furthermore, the data collection unit can link user health data with other systems and departments and share data as needed. For example, the collected health data can be made accessible to the prediction unit and the linkage unit to support early detection of abnormalities and rapid response. This allows the data collection unit to collect health data efficiently and effectively, improving the overall performance of the system.

[0033] The prediction unit analyzes health data collected by the collection unit and predicts changes in health status. Specifically, it uses AI to analyze health data and detect anomalies. The AI ​​uses machine learning algorithms to analyze health data and predict signs of anomalies. For example, by analyzing heart rate and blood pressure data and detecting unusual patterns, signs of anomalies can be detected early. The prediction unit can also use deep learning technology to analyze health data and detect anomalies early. Deep learning technology performs advanced analysis based on large amounts of data and can detect subtle signs of anomalies. As a result, the prediction unit can quickly and accurately analyze collected health data and predict changes in health status. Furthermore, the prediction unit can utilize historical data and statistical information to evaluate long-term health risks and perform trend analysis. For example, it can predict fluctuations in specific health risks based on historical health data and formulate future countermeasures. In addition, the prediction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the prediction unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0034] The collaboration unit notifies medical staff of any abnormalities predicted by the prediction unit. Specifically, if an abnormality is detected, it notifies medical staff in real time. For example, if the user's heart rate suddenly increases, the collaboration unit immediately notifies medical staff to prompt appropriate action. The collaboration unit can also transmit the user's health data to medical staff, providing detailed information. This allows medical staff to accurately understand the user's health status and take prompt action. Furthermore, the collaboration unit supports communication with medical staff, enabling a rapid response. For example, if medical staff have questions about the user's health status, the collaboration unit provides answers to those questions, facilitating smooth communication. The collaboration unit can also quickly provide medical staff with the information they need, supporting appropriate action. In this way, the collaboration unit can strengthen collaboration with medical staff and effectively support the user's health management.

[0035] The customization unit performs customizations to adapt to the user's home environment. Specifically, it adjusts the operation of the care robot to match the layout of the user's home and the arrangement of furniture. For example, by scanning the user's home layout in advance and registering the furniture arrangement in a database, it sets a route that allows the care robot to move efficiently. The customization unit also sets movement routes based on the user's home environment. For example, it sets a route that avoids narrow passages and areas with steps, ensuring that the care robot can move safely. Furthermore, the customization unit can also customize the functions of the care robot to match the user's home environment. For example, if the user needs to perform a specific action in a specific location, that action can be programmed in advance, and the care robot can be set to perform that action automatically. In this way, the customization unit can realize the operation of the care robot optimized for the user's home environment and support the user's life. In addition, the customization unit can continuously improve the operation and functions of the care robot based on user feedback. For example, if the user is dissatisfied with a particular action, the action can be reviewed and improved based on that feedback. In this way, the customization unit can respond flexibly to the user's needs and improve the performance of the care robot.

[0036] The care delivery unit can learn the user's health condition and daily routines and provide personalized care based on that information. For example, if the user needs to take medication at a specific time, the care delivery unit can provide the medication at that time. The care delivery unit can also provide meals according to the user's meal times. The care delivery unit can also provide appropriate care based on the user's sleep patterns. This allows for the provision of optimal care based on the user's health condition and daily routines. Health condition is evaluated using factors such as heart rate, blood pressure, and body temperature. Daily routines are analyzed using factors such as meal times and sleep rhythms. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's health data into a generating AI and have the generating AI execute personalized care plans.

[0037] The prediction unit collects the user's health data, uses AI to predict changes in health status, and can detect abnormalities early. For example, the prediction unit collects health data such as heart rate, blood pressure, and body temperature, and analyzes it using AI. For example, the prediction unit uses machine learning algorithms to analyze health data and predict signs of abnormalities. For example, the prediction unit can also use deep learning technology to analyze health data and detect abnormalities early. In this way, by collecting health data and predicting changes in health status using AI, abnormalities can be detected early. Health data includes, for example, types such as heart rate, blood pressure, and body temperature. AI is implemented using technologies such as machine learning and deep learning. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the collected health data into a generating AI and have the generating AI perform predictions of changes in health status.

[0038] The collaboration unit can transmit information from the prediction unit to medical staff in real time, providing them with information to take appropriate action. For example, if an abnormality is detected, the collaboration unit will notify medical staff in real time. For example, the collaboration unit can transmit the user's health data to medical staff to prompt appropriate action. The collaboration unit can also support communication with medical staff, enabling a rapid response. This allows for a rapid and appropriate response by transmitting information from the prediction unit to medical staff in real time. Real time is defined by criteria such as the frequency of data updates and delay time. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input information from the prediction unit into a generating AI and have the generating AI execute the notification content for medical staff.

[0039] The customization unit can adjust the operation of the care robot to suit the user's home environment. For example, the customization unit can set the movement route of the care robot according to the layout of the user's house and the arrangement of furniture. The customization unit can also customize the functions of the care robot based on the user's home environment. This allows for user-friendly care by adjusting the operation of the care robot to suit the user's home environment. The home environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's home environment data into a generating AI and have the generating AI perform the adjustment of the care robot's operation.

[0040] The care provision department can analyze the user's past care history and select the optimal care method. For example, the care provision department can select the optimal care method based on the care method the user preferred in the past. For example, the care provision department can also select a care method suitable for a specific time of day based on the user's past care history. For example, the care provision department can analyze the user's past care history and optimize the frequency and content of care. In this way, the optimal care method can be selected by analyzing the past care history. The care history is analyzed based on criteria such as the content and frequency of past care. Some or all of the above processes in the care provision department may be performed using AI, for example, or without AI. For example, the care provision department can input the user's past care data into a generating AI and have the generating AI select the optimal care method.

[0041] The care delivery unit can adjust the care provided to take into account the user's physical condition. For example, if the user is tired, the care delivery unit can provide relaxing care. For example, if the user is active, the care delivery unit can also provide care that includes moderate exercise. For example, the care delivery unit can monitor the user's physical condition and adjust the care in real time. This allows for the provision of more appropriate care by taking the physical condition into consideration. The physical condition is evaluated by factors such as heart rate, blood pressure, and body temperature. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's physical condition data into a generating AI and have the generating AI perform the adjustment of the care.

[0042] The care provision department can select the optimal care method when providing care, taking into account the user's geographical location information. For example, if the user is at home, the care provision department will provide a care method suitable for the home environment. For example, if the user is out, the care provision department can also provide a care method for when the user is out. For example, the care provision department will select the optimal care method in real time based on the user's geographical location information. This allows for the selection of the optimal care method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the care provision department may be performed using AI, for example, or without AI. For example, the care provision department can input the user's geographical location data into a generating AI and have the generating AI select the optimal care method.

[0043] The care provider department can analyze the user's social media activity when providing care and provide relevant care content. For example, if the user is active on social media, the care provider department can provide care content that emphasizes communication. For example, if the user is inactive on social media, the care provider department can also provide care content that alleviates feelings of loneliness. For example, the care provider department can analyze the user's social media activity and provide care content based on their interests and concerns. In this way, by analyzing social media activity, it is possible to provide care content that is appropriate for the user. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the care provider department may be performed using AI, for example, or without AI. For example, the care provider department can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant care content.

[0044] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can select the optimal collection timing based on the user's past health data. For example, the data collection unit can analyze the user's past health data and select a collection method suitable for a specific time period. For example, the data collection unit can optimize the collection frequency and method based on the user's past health data. This allows the optimal collection method to be selected by analyzing past health data. Health data includes, for example, heart rate, blood pressure, and body temperature. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal collection method.

[0045] The data collection unit can collect health data while considering the user's lifestyle patterns. For example, the data collection unit can collect health data in accordance with the user's meal times. The data collection unit can also collect health data at an appropriate time, considering the user's sleep patterns. For example, the data collection unit can select the optimal collection timing based on the user's lifestyle patterns. This allows for the collection of more accurate health data by considering lifestyle patterns. Lifestyle patterns are analyzed based on elements such as meal times and sleep rhythms. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the collection of health data.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit will prioritize the collection of health data related to the home environment. For example, if the user is out, the data collection unit can also prioritize the collection of health data from the user's location. For example, the data collection unit will collect optimal health data in real time based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0047] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user is active on social media, the data collection unit can collect health data related to communication. For example, if the user is inactive on social media, the data collection unit can also collect health data related to feelings of loneliness. For example, the data collection unit can analyze the user's social media activity and collect health data based on their interests. This allows for the collection of relevant health data by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant health data.

[0048] The prediction unit can improve prediction accuracy by analyzing the user's past health data and learning patterns of health changes. For example, the prediction unit can learn patterns of health changes at specific times or situations based on the user's past health data. The prediction unit can also learn health responses to specific triggers (e.g., specific meals or exercises) by analyzing the user's past health data. For example, the prediction unit can identify situations in which changes in health are predicted based on the user's past health data and prepare countermeasures in advance. This allows the prediction unit to learn patterns of health changes by analyzing past health data, thereby improving prediction accuracy. Health data includes, for example, heart rate, blood pressure, and body temperature. Patterns of health changes are analyzed based on criteria such as temporal changes and frequency. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can input the user's past health data into a generating AI and have the generating AI perform the learning of health change patterns.

[0049] The prediction unit can use the user's physical responses in conjunction with the prediction of their health status. For example, the prediction unit can monitor the user's heart rate and correct the prediction results according to changes in their health status. The prediction unit can also measure the user's skin electrical response and quantitatively evaluate changes in their health status. For example, the prediction unit can analyze the user's breathing pattern and predict changes in their health status in real time. This allows for more accurate predictions of health status by using physical responses in conjunction with the prediction unit. Physical responses include, for example, heart rate and skin electrical response. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's physical response data into a generating AI and have the generating AI perform the health status prediction.

[0050] The prediction unit can make predictions about a user's health condition by taking into account the user's living environment. For example, the prediction unit can make predictions considering that changes in health condition are predicted when the room temperature is high. The prediction unit can also make predictions considering that changes in health condition are predicted when the lighting is dim. For example, the prediction unit can reflect sounds in the living environment (e.g., noise or silence) in its predictions to more accurately grasp changes in health condition. This makes it possible to make more accurate predictions about health condition by taking the living environment into account. The living environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's living environment data into a generating AI and have the generating AI perform the health condition prediction.

[0051] The prediction unit can analyze the user's social media activity when predicting health status to enhance the accuracy of the prediction. For example, the prediction unit can analyze the content of the user's social media posts to enhance changes in health status. The prediction unit can also analyze the frequency of the user's social media interactions to enhance changes in health status. For example, the prediction unit can analyze the user's social media reactions (e.g., likes and comments) to enhance changes in health status. In this way, the accuracy of the prediction can be enhanced by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the enhancement of the prediction accuracy.

[0052] The integration unit can analyze the user's past health data and select the optimal notification method. For example, the integration unit can select the optimal notification timing based on the user's past health data. For example, the integration unit can analyze the user's past health data and select a notification method suitable for a specific time period. For example, the integration unit can optimize the notification frequency and method based on the user's past health data. This allows the optimal notification method to be selected by analyzing past health data. Health data includes, for example, heart rate, blood pressure, and body temperature. Some or all of the above processing in the integration unit may be performed using, for example, AI, or without AI. For example, the integration unit can input the user's past health data into a generating AI and have the generating AI select the optimal notification method.

[0053] The liaison unit can adjust the content of notifications to medical staff, taking into account the user's physical condition. For example, if the user is tired, the liaison unit can notify medical staff of detailed health data. For example, if the user is active, the liaison unit can notify medical staff of simple health data. The liaison unit can monitor the user's physical condition and adjust the notification content in real time. This allows for the provision of more appropriate notifications by taking the physical condition into consideration. The physical condition is evaluated by factors such as heart rate, blood pressure, and body temperature. Some or all of the above processing in the liaison unit may be performed using AI, for example, or without AI. For example, the liaison unit can input the user's physical condition data into a generating AI and have the generating AI adjust the notification content.

[0054] The collaboration unit can select the optimal notification method when notifying medical staff, taking into account the user's geographical location information. For example, if the user is at home, the collaboration unit will prioritize notifications related to the home environment. For example, if the user is out, the collaboration unit can also prioritize notifications related to the user's location. The collaboration unit selects the optimal notification method in real time, for example, based on the user's geographical location information. This allows the optimal notification method to be selected by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0055] The collaboration unit can analyze the user's social media activity and provide relevant notification content when notifying medical staff. For example, if the user is active on social media, the collaboration unit can provide notification content related to communication. For example, if the user is inactive on social media, the collaboration unit can also provide notification content related to feelings of loneliness. For example, the collaboration unit can analyze the user's social media activity and provide notification content based on their interests. In this way, relevant notification content can be provided by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's social media data into a generating AI and have the generating AI provide relevant notification content.

[0056] The customization unit can analyze the user's past home environment data and select the optimal customization method. For example, the customization unit can select the optimal customization method based on the user's past home environment data. The customization unit can also analyze the user's past home environment data and select a customization method suitable for a specific time period. For example, the customization unit can optimize the frequency and method of customization based on the user's past home environment data. This allows for the selection of the optimal customization method by analyzing past home environment data. Home environment data includes, for example, types such as the structure of the residence and family composition. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can input the user's past home environment data into a generating AI and have the generating AI select the optimal customization method.

[0057] The customization unit can adjust the customization content while considering the user's lifestyle patterns. For example, the customization unit can provide customization content that matches the user's meal times. For example, the customization unit can also provide appropriate customization content by considering the user's sleep patterns. For example, the customization unit can select the optimal customization content based on the user's lifestyle patterns. This allows for the provision of more appropriate customization content by considering lifestyle patterns. Lifestyle patterns are analyzed using elements such as meal times and sleep rhythms. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the adjustment of the customization content.

[0058] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is at home, the customization unit can provide a customization method suitable for the home environment. For example, if the user is out, the customization unit can also provide a customization method for when the user is out. The customization unit selects the optimal customization method in real time based on the user's geographical location information. This allows for the selection of the optimal customization method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal customization method.

[0059] The customization unit can analyze the user's social media activity during the customization process and provide relevant customization content. For example, if the user is very active on social media, the customization unit can provide customization content that emphasizes communication. For example, if the user is inactive on social media, the customization unit can also provide customization content that reduces feelings of loneliness. For example, the customization unit can analyze the user's social media activity and provide customization content based on their interests. This allows the unit to provide relevant customization content by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI provide relevant customization content.

[0060] The customization unit can adjust the operation of the care robot to suit the user's home environment during customization. For example, the customization unit can set the movement route of the care robot to match the layout of the user's house. The customization unit can also adjust the operation of the care robot to match the arrangement of the user's furniture. For example, the customization unit can optimize the operation of the care robot based on the user's home environment. By adjusting the operation of the care robot to suit the home environment, it is possible to provide more user-friendly care. The home environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's home environment data into a generating AI and have the generating AI perform the adjustment of the care robot's operation.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The care robot system can also be equipped with an environmental sensor unit. This unit collects environmental data from the user's surroundings and optimizes the robot's operation. For example, if the room temperature is high, the environmental sensor unit can automatically adjust the air conditioner to maintain a comfortable temperature. It can also adjust the brightness of the lighting to ensure the user's visual comfort. Furthermore, the environmental sensor unit can monitor noise levels and take necessary measures to provide a quiet environment. This optimizes the user's living environment and provides comfortable care.

[0063] The care robot system can also be equipped with a nutrition management unit. This unit manages the user's diet and optimizes nutritional balance. For example, if the user needs to consume a specific nutrient, the nutrition management unit can suggest a meal containing that nutrient. Furthermore, the nutrition management unit can adjust the meal content based on the user's health data. For instance, if blood sugar levels are high, it can suggest a low-carbohydrate meal. This enables appropriate nutritional management tailored to the user's health condition.

[0064] The care robot system can also be equipped with an exercise support unit. This unit supports the user's exercise habits and promotes health maintenance. For example, if the user is not getting enough exercise, the exercise support unit can suggest an appropriate exercise program. Furthermore, the exercise support unit can adjust the intensity and frequency of exercise based on the user's health data. For instance, if the user has a high heart rate, it can suggest light exercise. This enables appropriate exercise support tailored to the user's health condition.

[0065] The care robot system can also be equipped with a sleep management unit. This unit monitors the user's sleep patterns and supports high-quality sleep. For example, if the user suffers from insomnia, the sleep management unit can provide a relaxing environment. Furthermore, the sleep management unit can evaluate sleep quality based on the user's health data and suggest improvements. For instance, it can monitor heart rate and breathing patterns during sleep and notify medical staff if abnormalities are detected. This can improve the user's sleep quality.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The emotion analysis unit analyzes the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to understand their emotions. Using facial recognition technology and voice analysis technology, it monitors the user's facial expressions and tone of voice in real time and detects changes in their emotions. Step 2: The caregiving department provides emotional support based on the emotions analyzed by the emotion analysis department. For example, if the user is sad, they offer words of comfort; if they are anxious, they provide reassuring information; and if they are happy, they share that emotion and provide positive feedback. Step 3: The data collection unit collects the user's health data. For example, health data such as heart rate, blood pressure, and body temperature are collected in real time using a wearable device and periodically stored in a database. Step 4: The prediction unit analyzes the health data collected by the collection unit and predicts changes in health status. For example, it uses AI, machine learning algorithms, and deep learning technology to analyze the health data, predict signs of abnormalities, and detect them early. Step 5: The collaboration unit notifies medical staff of any anomalies predicted by the prediction unit. For example, if an anomaly is detected, it notifies medical staff in real time and sends the user's health data to prompt appropriate action. Step 6: The customization section performs customizations to adapt to the user's home environment. For example, it adjusts the operation of the care robot to match the layout of the user's house and the arrangement of furniture, sets the movement route, and customizes the functions of the care robot to suit the home environment.

[0068] (Example of form 2) The care robot system according to an embodiment of the present invention is a system that provides emotional support to people who need care and realizes personalized care tailored to their specific needs. This system supports the user's emotions, provides care tailored to specific needs, incorporates an AI-based prediction and early detection system, and enables real-time collaboration with medical staff. It also adds customization functions to adapt the care robot to the home environment. For example, the care robot system analyzes the user's facial expressions and tone of voice to understand their emotions. If the user is sad, the care robot can offer words of comfort. This allows the user to receive emotional support. Next, the care robot system learns the user's health condition and daily life patterns and provides optimal care based on that. For example, if the user needs to take medication at a specific time, the care robot can provide the medication at that time. This allows the user to receive care that is tailored to them. Furthermore, the care robot system collects the user's health data, and by having AI analyze it, it can predict changes in health condition and detect abnormalities early. For example, it can monitor changes in the user's body temperature and heart rate, and notify medical staff if an abnormality is detected. This allows for constant monitoring of the user's health condition and early response. Furthermore, the care robot system transmits the user's health data to medical staff in real time, allowing them to take appropriate action based on that data. For example, if the user's blood pressure suddenly rises, medical staff can immediately take countermeasures. This ensures the user receives prompt and appropriate medical care. Finally, the care robot system can adjust its operation to suit the user's home environment. For example, it can set a movement route that matches the layout of the user's home and the arrangement of furniture. This allows the care robot to adapt to the home environment and provide user-friendly care.The goal of this care robot system is to create a society where people who need care can live with peace of mind by providing emotional support, personalized care, AI-powered prediction and early detection, real-time collaboration with medical staff, and adaptation to the home environment.

[0069] The care robot system according to this embodiment comprises an emotion analysis unit, a care provision unit, a data collection unit, a prediction unit, a coordination unit, and a customization unit. The emotion analysis unit analyzes the user's emotions. The emotion analysis unit, for example, analyzes the user's facial expressions and tone of voice to understand their emotions. The emotion analysis unit, for example, uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The emotion analysis unit can also use voice analysis technology to analyze the user's tone of voice and estimate their emotions. The emotion analysis unit, for example, monitors changes in the user's facial expressions in real time and detects changes in their emotions. The care provision unit provides emotional support based on the emotions analyzed by the emotion analysis unit. For example, if the user is sad, the care provision unit offers words of comfort. The care provision unit can also provide reassuring information if the user is feeling anxious. For example, if the user is happy, the care provision unit shares that emotion and provides positive feedback. The data collection unit collects the user's health data. The data collection unit collects health data such as heart rate, blood pressure, and body temperature. The data collection unit collects the user's health data in real time, for example, using a wearable device. The data collection unit can also periodically collect the user's health data and store it in a database. The prediction unit analyzes the health data collected by the data collection unit and predicts changes in health status. The prediction unit analyzes health data using AI, for example, and detects abnormalities. The prediction unit analyzes health data using machine learning algorithms, for example, and predicts signs of abnormalities. The prediction unit can also analyze health data using deep learning technology to detect abnormalities early. The communication unit notifies medical staff of abnormalities predicted by the prediction unit. The communication unit notifies medical staff in real time when an abnormality is detected. The communication unit transmits the user's health data to medical staff to prompt appropriate action. The communication unit can also support communication with medical staff, enabling a rapid response. The customization unit performs customization to adapt to the user's home environment. The customization section adjusts the operation of the care robot to match, for example, the layout of the user's home and the arrangement of furniture.The customization unit, for example, sets a travel route based on the user's home environment. The customization unit can also customize the functions of the care robot to suit the user's home environment. As a result, the care robot system according to this embodiment can perform user emotion analysis, emotional support, health data collection, health status prediction, notification to medical staff, and adapt to the home environment.

[0070] The emotion analysis unit analyzes the user's emotions. For example, it analyzes the user's facial expressions and voice tone to understand their emotions. Specifically, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. Facial recognition technology can detect feature points on the user's face and capture subtle changes in facial expression. For example, it analyzes eyebrow movements and the degree to which the corners of the mouth are turned up to determine whether the user is happy or sad. The emotion analysis unit can also use voice analysis technology to analyze the user's voice tone and estimate their emotions. Voice analysis technology analyzes voice features such as pitch, strength, and rhythm to estimate the user's emotional state. For example, if the voice is trembling, it is determined that the user is feeling anxious, and if the voice is bright and exhilarating, it is determined that the user is feeling happy. The emotion analysis unit combines these technologies to analyze the user's emotions from multiple angles and perform more accurate emotion estimation. Furthermore, the emotion analysis unit monitors changes in the user's facial expressions in real time and detects changes in emotions. For example, if the user suddenly changes their facial expression during a conversation, it immediately captures that change and analyzes the change in emotion. This allows the emotion analysis unit to constantly understand the user's emotional state and provide the basic information necessary to take appropriate action.

[0071] The caregiving department provides emotional support based on the emotions analyzed by the emotion analysis department. Specifically, if a user is sad, they offer words of comfort. For example, they might say, "It's okay, I'm here, so don't worry," to soothe the user's feelings. The caregiving department can also provide reassuring information if a user is feeling anxious. For example, they might say, "There's no problem with the current situation. It will be resolved soon, so please don't worry," to reduce the user's anxiety. Furthermore, if a user is happy, the caregiving department shares that emotion and provides positive feedback. For example, they might say, "That's wonderful! Let's celebrate together," to amplify the user's joy. Through this emotional support, the caregiving department can promote the user's mental stability and provide a better caregiving experience. In addition, based on data from the emotion analysis department, the caregiving department can automatically select and execute appropriate responses according to the user's emotional state. This allows the caregiving department to provide flexible responses that are attentive to the user's emotions and improve user satisfaction.

[0072] The data collection unit collects user health data. Specifically, it collects health data such as heart rate, blood pressure, and body temperature. The data collection unit collects user health data in real time, for example, using wearable devices. Wearable devices are attached to the user's wrist or chest and can continuously monitor data such as heart rate, blood pressure, and body temperature. This allows the data collection unit to constantly understand the user's health status and respond immediately if an abnormality occurs. The data collection unit can also collect user health data periodically and store it in a database. For example, by collecting daily health data at regular intervals and storing it in the database, it is possible to track changes in health status over the long term. Furthermore, the data collection unit can link user health data with other systems and departments and share data as needed. For example, the collected health data can be made accessible to the prediction unit and the linkage unit to support early detection of abnormalities and rapid response. This allows the data collection unit to collect health data efficiently and effectively, improving the overall performance of the system.

[0073] The prediction unit analyzes health data collected by the collection unit and predicts changes in health status. Specifically, it uses AI to analyze health data and detect anomalies. The AI ​​uses machine learning algorithms to analyze health data and predict signs of anomalies. For example, by analyzing heart rate and blood pressure data and detecting unusual patterns, signs of anomalies can be detected early. The prediction unit can also use deep learning technology to analyze health data and detect anomalies early. Deep learning technology performs advanced analysis based on large amounts of data and can detect subtle signs of anomalies. As a result, the prediction unit can quickly and accurately analyze collected health data and predict changes in health status. Furthermore, the prediction unit can utilize historical data and statistical information to evaluate long-term health risks and perform trend analysis. For example, it can predict fluctuations in specific health risks based on historical health data and formulate future countermeasures. In addition, the prediction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the prediction unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The collaboration unit notifies medical staff of any abnormalities predicted by the prediction unit. Specifically, if an abnormality is detected, it notifies medical staff in real time. For example, if the user's heart rate suddenly increases, the collaboration unit immediately notifies medical staff to prompt appropriate action. The collaboration unit can also transmit the user's health data to medical staff, providing detailed information. This allows medical staff to accurately understand the user's health status and take prompt action. Furthermore, the collaboration unit supports communication with medical staff, enabling a rapid response. For example, if medical staff have questions about the user's health status, the collaboration unit provides answers to those questions, facilitating smooth communication. The collaboration unit can also quickly provide medical staff with the information they need, supporting appropriate action. In this way, the collaboration unit can strengthen collaboration with medical staff and effectively support the user's health management.

[0075] The customization unit performs customizations to adapt to the user's home environment. Specifically, it adjusts the operation of the care robot to match the layout of the user's home and the arrangement of furniture. For example, by scanning the user's home layout in advance and registering the furniture arrangement in a database, it sets a route that allows the care robot to move efficiently. The customization unit also sets movement routes based on the user's home environment. For example, it sets a route that avoids narrow passages and areas with steps, ensuring that the care robot can move safely. Furthermore, the customization unit can also customize the functions of the care robot to match the user's home environment. For example, if the user needs to perform a specific action in a specific location, that action can be programmed in advance, and the care robot can be set to perform that action automatically. In this way, the customization unit can realize the operation of the care robot optimized for the user's home environment and support the user's life. In addition, the customization unit can continuously improve the operation and functions of the care robot based on user feedback. For example, if the user is dissatisfied with a particular action, the action can be reviewed and improved based on that feedback. In this way, the customization unit can respond flexibly to the user's needs and improve the performance of the care robot.

[0076] The emotion analysis unit can analyze the user's facial expressions and tone of voice to understand their emotions. For example, the emotion analysis unit can use facial recognition technology to analyze the user's facial expressions and estimate their emotions. The emotion analysis unit can also use voice analysis technology to analyze the user's tone of voice and estimate their emotions. For example, the emotion analysis unit can monitor changes in the user's facial expressions in real time and detect changes in their emotions. This improves the accuracy of understanding emotions by analyzing the user's facial expressions and tone of voice. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, or not using AI. For example, the emotion analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The caregiving department can provide emotional support based on information from the emotion analysis department. For example, if the user is sad, the caregiving department can offer words of comfort. For example, if the user is feeling anxious, the caregiving department can also provide reassuring information. For example, if the user is happy, the caregiving department can share that emotion and provide positive feedback. This allows the caregiving department to provide appropriate emotional support based on information from the emotion analysis department. Emotional support can be implemented through methods such as psychological counseling and relaxation techniques. Some or all of the above processes in the caregiving department may be performed using AI, for example, or without AI. For example, the caregiving department can input information from the emotion analysis department into a generating AI and have the generating AI execute the content of the emotional support.

[0078] The care delivery unit can learn the user's health condition and daily routines and provide personalized care based on that information. For example, if the user needs to take medication at a specific time, the care delivery unit can provide the medication at that time. The care delivery unit can also provide meals according to the user's meal times. The care delivery unit can also provide appropriate care based on the user's sleep patterns. This allows for the provision of optimal care based on the user's health condition and daily routines. Health condition is evaluated using factors such as heart rate, blood pressure, and body temperature. Daily routines are analyzed using factors such as meal times and sleep rhythms. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's health data into a generating AI and have the generating AI execute personalized care plans.

[0079] The prediction unit collects the user's health data, uses AI to predict changes in health status, and can detect abnormalities early. For example, the prediction unit collects health data such as heart rate, blood pressure, and body temperature, and analyzes it using AI. For example, the prediction unit uses machine learning algorithms to analyze health data and predict signs of abnormalities. For example, the prediction unit can also use deep learning technology to analyze health data and detect abnormalities early. In this way, by collecting health data and predicting changes in health status using AI, abnormalities can be detected early. Health data includes, for example, types such as heart rate, blood pressure, and body temperature. AI is implemented using technologies such as machine learning and deep learning. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the collected health data into a generating AI and have the generating AI perform predictions of changes in health status.

[0080] The collaboration unit can transmit information from the prediction unit to medical staff in real time, providing them with information to take appropriate action. For example, if an abnormality is detected, the collaboration unit will notify medical staff in real time. For example, the collaboration unit can transmit the user's health data to medical staff to prompt appropriate action. The collaboration unit can also support communication with medical staff, enabling a rapid response. This allows for a rapid and appropriate response by transmitting information from the prediction unit to medical staff in real time. Real time is defined by criteria such as the frequency of data updates and delay time. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input information from the prediction unit into a generating AI and have the generating AI execute the notification content for medical staff.

[0081] The customization unit can adjust the operation of the care robot to suit the user's home environment. For example, the customization unit can set the movement route of the care robot according to the layout of the user's house and the arrangement of furniture. The customization unit can also customize the functions of the care robot based on the user's home environment. This allows for user-friendly care by adjusting the operation of the care robot to suit the user's home environment. The home environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's home environment data into a generating AI and have the generating AI perform the adjustment of the care robot's operation.

[0082] The emotion analysis unit can estimate the user's emotions and dynamically adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit can increase the accuracy of the analysis and obtain more detailed emotional data. For example, if the user is relaxed, the emotion analysis unit can also decrease the accuracy of the analysis and obtain simpler emotional data. For example, if the user's emotions change rapidly, the emotion analysis unit can increase the frequency of the analysis and track the emotional changes in real time. This allows for more accurate emotion analysis by dynamically adjusting the accuracy of the analysis based on the user'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-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotional data into the generative AI and have the generative AI adjust the accuracy of the analysis.

[0083] The emotion analysis unit can improve its analysis accuracy by analyzing the user's past emotional data and learning patterns of emotional change. For example, the emotion analysis unit can learn emotional patterns in specific time periods or situations based on the user's past emotional data. The emotion analysis unit can also learn emotional responses to specific triggers (e.g., specific music or videos) by analyzing the user's past emotional data. For example, the emotion analysis unit can identify situations in which emotional changes are predicted based on the user's past emotional data and prepare countermeasures in advance. This allows the unit to learn patterns of emotional change by analyzing past emotional data, thereby improving analysis accuracy. Patterns of emotional change are analyzed based on criteria such as temporal change and frequency. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform the learning of emotional change patterns.

[0084] The emotion analysis unit can analyze a user's emotions more accurately by using the user's physical responses in conjunction with the emotional analysis. For example, the emotion analysis unit can monitor the user's heart rate and correct the analysis results according to changes in emotion. The emotion analysis unit can also quantitatively evaluate the intensity of emotions by measuring the user's skin electrical responses. For example, the emotion analysis unit can analyze the user's breathing patterns and track changes in emotion in real time. This allows for a more accurate analysis of emotions by using physical responses in conjunction with the emotional analysis unit. Physical responses include, for example, heart rate and skin electrical responses. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's physical response data into a generating AI and have the generating AI perform the emotional analysis.

[0085] The emotion analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions of the user. For example, if the user is feeling stressed, the emotion analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the emotion analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the emotion analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. 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 without AI. For example, the emotion analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0086] The emotion analysis unit can analyze changes in emotions while considering the user's living environment. For example, the emotion analysis unit will analyze the user's emotions considering that they are more likely to become irritable when the room temperature is high. The emotion analysis unit can also analyze the user's emotions considering that they are more likely to become depressed when the lighting is dim. For example, the emotion analysis unit will reflect sounds in the living environment (e.g., noise or silence) in its analysis to grasp changes in emotions more accurately. In this way, changes in emotions can be analyzed more accurately by considering the living environment. The living environment is considered by factors such as the structure of the residence and the family structure. 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 user's living environment data into a generating AI and have the generating AI perform the analysis of changes in emotions.

[0087] The emotion analysis unit can analyze the user's social media activity during emotion analysis and supplement changes in emotion. For example, the emotion analysis unit can analyze the content of the user's social media posts and supplement changes in emotion. The emotion analysis unit can also analyze the frequency of the user's social media interactions and supplement changes in emotion. For example, the emotion analysis unit can analyze the user's social media reactions (e.g., likes and comments) and supplement changes in emotion. In this way, changes in emotion can be supplemented by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. 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 user's social media data into a generating AI and have the generating AI perform the supplementation of changes in emotion.

[0088] The care delivery unit can estimate the user's emotions and dynamically adjust the content of care based on the estimated emotions. For example, if the user is sad, the care delivery unit can offer words of comfort. For example, if the user is feeling anxious, the care delivery unit can also provide reassuring information. For example, if the user is happy, the care delivery unit can share that emotion and provide positive feedback. This allows for the provision of more appropriate care by dynamically adjusting the content of care based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 care delivery unit may be performed using AI or not using AI. For example, the care delivery unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the content of care.

[0089] The care provision department can analyze the user's past care history and select the optimal care method. For example, the care provision department can select the optimal care method based on the care method the user preferred in the past. For example, the care provision department can also select a care method suitable for a specific time of day based on the user's past care history. For example, the care provision department can analyze the user's past care history and optimize the frequency and content of care. In this way, the optimal care method can be selected by analyzing the past care history. The care history is analyzed based on criteria such as the content and frequency of past care. Some or all of the above processes in the care provision department may be performed using AI, for example, or without AI. For example, the care provision department can input the user's past care data into a generating AI and have the generating AI select the optimal care method.

[0090] The care delivery unit can adjust the care provided to take into account the user's physical condition. For example, if the user is tired, the care delivery unit can provide relaxing care. For example, if the user is active, the care delivery unit can also provide care that includes moderate exercise. For example, the care delivery unit can monitor the user's physical condition and adjust the care in real time. This allows for the provision of more appropriate care by taking the physical condition into consideration. The physical condition is evaluated by factors such as heart rate, blood pressure, and body temperature. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's physical condition data into a generating AI and have the generating AI perform the adjustment of the care.

[0091] The care delivery unit can estimate the user's emotions and determine the priority of care based on the estimated emotions. For example, if the user is stressed, the care delivery unit will provide care that prioritizes stress reduction. For example, if the user is relaxed, the care delivery unit may prioritize routine care. For example, if the user's emotions change rapidly, the care delivery unit will provide care that prioritizes emotional stability. This allows for more effective care by determining the priority of care based on 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 care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of care.

[0092] The care provision department can select the optimal care method when providing care, taking into account the user's geographical location information. For example, if the user is at home, the care provision department will provide a care method suitable for the home environment. For example, if the user is out, the care provision department can also provide a care method for when the user is out. For example, the care provision department will select the optimal care method in real time based on the user's geographical location information. This allows for the selection of the optimal care method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the care provision department may be performed using AI, for example, or without AI. For example, the care provision department can input the user's geographical location data into a generating AI and have the generating AI select the optimal care method.

[0093] The care provider department can analyze the user's social media activity when providing care and provide relevant care content. For example, if the user is active on social media, the care provider department can provide care content that emphasizes communication. For example, if the user is inactive on social media, the care provider department can also provide care content that alleviates feelings of loneliness. For example, the care provider department can analyze the user's social media activity and provide care content based on their interests and concerns. In this way, by analyzing social media activity, it is possible to provide care content that is appropriate for the user. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the care provider department may be performed using AI, for example, or without AI. For example, the care provider department can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant care content.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, the data collection unit collects health data more frequently when the user is stressed. For example, the data collection unit can also reduce the collection frequency when the user is relaxed. For example, the data collection unit collects health data in real time when the user's emotions change rapidly. This allows for the collection of more appropriate health data by adjusting the collection timing based on emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of health data collection.

[0095] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can select the optimal collection timing based on the user's past health data. For example, the data collection unit can analyze the user's past health data and select a collection method suitable for a specific time period. For example, the data collection unit can optimize the collection frequency and method based on the user's past health data. This allows the optimal collection method to be selected by analyzing past health data. Health data includes, for example, heart rate, blood pressure, and body temperature. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal collection method.

[0096] The data collection unit can collect health data while considering the user's lifestyle patterns. For example, the data collection unit can collect health data in accordance with the user's meal times. The data collection unit can also collect health data at an appropriate time, considering the user's sleep patterns. For example, the data collection unit can select the optimal collection timing based on the user's lifestyle patterns. This allows for the collection of more accurate health data by considering lifestyle patterns. Lifestyle patterns are analyzed based on elements such as meal times and sleep rhythms. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the collection of health data.

[0097] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit may prioritize collecting general health data. For example, if the user's emotions change rapidly, the data collection unit will prioritize collecting emotion-related data. This allows for the priority collection of important data by prioritizing data based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI determine the data priority.

[0098] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit will prioritize the collection of health data related to the home environment. For example, if the user is out, the data collection unit can also prioritize the collection of health data from the user's location. For example, the data collection unit will collect optimal health data in real time based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0099] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user is active on social media, the data collection unit can collect health data related to communication. For example, if the user is inactive on social media, the data collection unit can also collect health data related to feelings of loneliness. For example, the data collection unit can analyze the user's social media activity and collect health data based on their interests. This allows for the collection of relevant health data by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant health data.

[0100] The prediction unit can estimate the user's emotions and dynamically adjust the accuracy of the prediction based on the estimated emotions. For example, if the user is stressed, the prediction unit can increase the accuracy of the prediction and make a more detailed prediction. For example, if the user is relaxed, the prediction unit can also decrease the accuracy of the prediction and make a simpler prediction. For example, if the user's emotions change rapidly, the prediction unit can increase the frequency of predictions and make predictions in real time. This allows for more accurate predictions by dynamically adjusting the accuracy of the prediction based on 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's emotion data into the generative AI and have the generative AI adjust the accuracy of the prediction.

[0101] The prediction unit can improve prediction accuracy by analyzing the user's past health data and learning patterns of health changes. For example, the prediction unit can learn patterns of health changes at specific times or situations based on the user's past health data. The prediction unit can also learn health responses to specific triggers (e.g., specific meals or exercises) by analyzing the user's past health data. For example, the prediction unit can identify situations in which changes in health are predicted based on the user's past health data and prepare countermeasures in advance. This allows the prediction unit to learn patterns of health changes by analyzing past health data, thereby improving prediction accuracy. Health data includes, for example, heart rate, blood pressure, and body temperature. Patterns of health changes are analyzed based on criteria such as temporal changes and frequency. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can input the user's past health data into a generating AI and have the generating AI perform the learning of health change patterns.

[0102] The prediction unit can use the user's physical responses in conjunction with the prediction of their health status. For example, the prediction unit can monitor the user's heart rate and correct the prediction results according to changes in their health status. The prediction unit can also measure the user's skin electrical response and quantitatively evaluate changes in their health status. For example, the prediction unit can analyze the user's breathing pattern and predict changes in their health status in real time. This allows for more accurate predictions of health status by using physical responses in conjunction with the prediction unit. Physical responses include, for example, heart rate and skin electrical response. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's physical response data into a generating AI and have the generating AI perform the health status prediction.

[0103] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions of the user. For example, if the user is stressed, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can provide a concise display method. This allows for more appropriate display by adjusting the display method based on 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the prediction results.

[0104] The prediction unit can make predictions about a user's health condition by taking into account the user's living environment. For example, the prediction unit can make predictions considering that changes in health condition are predicted when the room temperature is high. The prediction unit can also make predictions considering that changes in health condition are predicted when the lighting is dim. For example, the prediction unit can reflect sounds in the living environment (e.g., noise or silence) in its predictions to more accurately grasp changes in health condition. This makes it possible to make more accurate predictions about health condition by taking the living environment into account. The living environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's living environment data into a generating AI and have the generating AI perform the health condition prediction.

[0105] The prediction unit can analyze the user's social media activity when predicting health status to enhance the accuracy of the prediction. For example, the prediction unit can analyze the content of the user's social media posts to enhance changes in health status. The prediction unit can also analyze the frequency of the user's social media interactions to enhance changes in health status. For example, the prediction unit can analyze the user's social media reactions (e.g., likes and comments) to enhance changes in health status. In this way, the accuracy of the prediction can be enhanced by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the enhancement of the prediction accuracy.

[0106] The communication unit can estimate the user's emotions and adjust the content of notifications sent to medical staff based on the estimated emotions. For example, if the user is stressed, the communication unit can notify medical staff of detailed health data. For example, if the user is relaxed, the communication unit can notify medical staff of simple health data. For example, if the user's emotions change rapidly, the communication unit can notify medical staff of detailed health data in real time. This allows for more appropriate notifications by adjusting the content of notifications based on 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 communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input the user's emotion data into the generative AI and have the generative AI adjust the content of the notifications.

[0107] The integration unit can analyze the user's past health data and select the optimal notification method. For example, the integration unit can select the optimal notification timing based on the user's past health data. For example, the integration unit can analyze the user's past health data and select a notification method suitable for a specific time period. For example, the integration unit can optimize the notification frequency and method based on the user's past health data. This allows the optimal notification method to be selected by analyzing past health data. Health data includes, for example, heart rate, blood pressure, and body temperature. Some or all of the above processing in the integration unit may be performed using, for example, AI, or without AI. For example, the integration unit can input the user's past health data into a generating AI and have the generating AI select the optimal notification method.

[0108] The liaison unit can adjust the content of notifications to medical staff, taking into account the user's physical condition. For example, if the user is tired, the liaison unit can notify medical staff of detailed health data. For example, if the user is active, the liaison unit can notify medical staff of simple health data. The liaison unit can monitor the user's physical condition and adjust the notification content in real time. This allows for the provision of more appropriate notifications by taking the physical condition into consideration. The physical condition is evaluated by factors such as heart rate, blood pressure, and body temperature. Some or all of the above processing in the liaison unit may be performed using AI, for example, or without AI. For example, the liaison unit can input the user's physical condition data into a generating AI and have the generating AI adjust the notification content.

[0109] The integration unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is feeling stressed, the integration unit will prioritize stress-related notifications. For example, if the user is relaxed, the integration unit may also prioritize general health data notifications. For example, if the user's emotions change rapidly, the integration unit will prioritize emotion-related notifications. This allows important notifications to be prioritized by determining notification priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 integration unit may be performed using AI or not. For example, the integration unit can input the user's emotion data into a generative AI and have the generative AI determine the notification priorities.

[0110] The collaboration unit can select the optimal notification method when notifying medical staff, taking into account the user's geographical location information. For example, if the user is at home, the collaboration unit will prioritize notifications related to the home environment. For example, if the user is out, the collaboration unit can also prioritize notifications related to the user's location. The collaboration unit selects the optimal notification method in real time, for example, based on the user's geographical location information. This allows the optimal notification method to be selected by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0111] The collaboration unit can analyze the user's social media activity and provide relevant notification content when notifying medical staff. For example, if the user is active on social media, the collaboration unit can provide notification content related to communication. For example, if the user is inactive on social media, the collaboration unit can also provide notification content related to feelings of loneliness. For example, the collaboration unit can analyze the user's social media activity and provide notification content based on their interests. In this way, relevant notification content can be provided by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's social media data into a generating AI and have the generating AI provide relevant notification content.

[0112] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is feeling stressed, the customization unit can provide customization content aimed at stress reduction. For example, if the user is relaxed, the customization unit can also provide customization content to maintain relaxation. For example, if the user's emotions change rapidly, the customization unit can provide customization content aimed at stabilizing those emotions. This allows for more appropriate customization by adjusting the customization content based on 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-described processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the customization content.

[0113] The customization unit can analyze the user's past home environment data and select the optimal customization method. For example, the customization unit can select the optimal customization method based on the user's past home environment data. The customization unit can also analyze the user's past home environment data and select a customization method suitable for a specific time period. For example, the customization unit can optimize the frequency and method of customization based on the user's past home environment data. This allows for the selection of the optimal customization method by analyzing past home environment data. Home environment data includes, for example, types such as the structure of the residence and family composition. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can input the user's past home environment data into a generating AI and have the generating AI select the optimal customization method.

[0114] The customization unit can adjust the customization content while considering the user's lifestyle patterns. For example, the customization unit can provide customization content that matches the user's meal times. For example, the customization unit can also provide appropriate customization content by considering the user's sleep patterns. For example, the customization unit can select the optimal customization content based on the user's lifestyle patterns. This allows for the provision of more appropriate customization content by considering lifestyle patterns. Lifestyle patterns are analyzed using elements such as meal times and sleep rhythms. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the adjustment of the customization content.

[0115] The customization unit can estimate the user's emotions and determine the priority of customizations based on the estimated emotions. For example, if the user is feeling stressed, the customization unit can provide customizations that prioritize stress reduction. For example, if the user is relaxed, the customization unit can also prioritize everyday customizations. For example, if the user's emotions change rapidly, the customization unit can provide customizations that prioritize emotional stability. This allows important customizations to be prioritized by determining the priority of customizations based on 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 customization unit may be performed using AI, or not using AI. For example, the customization unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of customizations.

[0116] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is at home, the customization unit can provide a customization method suitable for the home environment. For example, if the user is out, the customization unit can also provide a customization method for when the user is out. The customization unit selects the optimal customization method in real time based on the user's geographical location information. This allows for the selection of the optimal customization method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or address information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal customization method.

[0117] The customization unit can analyze the user's social media activity during the customization process and provide relevant customization content. For example, if the user is very active on social media, the customization unit can provide customization content that emphasizes communication. For example, if the user is inactive on social media, the customization unit can also provide customization content that reduces feelings of loneliness. For example, the customization unit can analyze the user's social media activity and provide customization content based on their interests. This allows the unit to provide relevant customization content by analyzing social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI provide relevant customization content.

[0118] The customization unit can adjust the operation of the care robot to suit the user's home environment during customization. For example, the customization unit can set the movement route of the care robot to match the layout of the user's house. The customization unit can also adjust the operation of the care robot to match the arrangement of the user's furniture. For example, the customization unit can optimize the operation of the care robot based on the user's home environment. By adjusting the operation of the care robot to suit the home environment, it is possible to provide more user-friendly care. The home environment is considered by factors such as the structure of the residence and the family structure. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's home environment data into a generating AI and have the generating AI perform the adjustment of the care robot's operation.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The care robot system can also be equipped with a voice recognition unit. The voice recognition unit analyzes the user's voice commands and controls the operation of the care robot. For example, if the user says, "Bring me some water," the voice recognition unit can analyze the command and instruct the care robot to bring the water. The voice recognition unit can also analyze the tone and speed of the user's voice and estimate the user's emotional state. This makes it possible to operate the care robot through voice commands, improving convenience for the user. Furthermore, the voice recognition unit can monitor changes in the user's voice and detect changes in their health condition early. For example, if the voice is hoarse, it can detect a sign of a cold and notify medical staff.

[0121] The care robot system can also be equipped with an environmental sensor unit. This unit collects environmental data from the user's surroundings and optimizes the robot's operation. For example, if the room temperature is high, the environmental sensor unit can automatically adjust the air conditioner to maintain a comfortable temperature. It can also adjust the brightness of the lighting to ensure the user's visual comfort. Furthermore, the environmental sensor unit can monitor noise levels and take necessary measures to provide a quiet environment. This optimizes the user's living environment and provides comfortable care.

[0122] The care robot system can also be equipped with a reminder unit. This unit manages the user's schedule and reminds them of important tasks. For example, it can notify the user via voice or visual means to ensure they don't forget when to take their medication. Furthermore, the reminder unit can adjust its notification method based on the user's emotional state. For instance, if the user is feeling stressed, it can use a gentle tone to remind them. This helps the user remember and complete important tasks.

[0123] The care robot system can also be equipped with an entertainment provision unit. This unit provides appropriate entertainment based on the user's emotional state. For example, if the user is sad, it can play relaxing music. If the user is bored, it can offer engaging videos or games. Furthermore, the entertainment provision unit can analyze the user's past entertainment history and provide personalized content. This allows for entertainment tailored to the user's emotional state, improving their quality of life.

[0124] The care robot system can also be equipped with a feedback collection unit. This unit collects feedback from the user and uses it to improve the robot's operation. For example, if a user is dissatisfied with the robot's performance, this feedback can be collected and used to improve the system. The feedback collection unit can also adjust its feedback collection method based on the user's emotional state. For instance, if the user is relaxed, it can request more detailed feedback. This allows for improvements to the care robot that reflect the user's opinions.

[0125] The care robot system can also be equipped with a nutrition management unit. This unit manages the user's diet and optimizes nutritional balance. For example, if the user needs to consume a specific nutrient, the nutrition management unit can suggest a meal containing that nutrient. Furthermore, the nutrition management unit can adjust the meal content based on the user's health data. For instance, if blood sugar levels are high, it can suggest a low-carbohydrate meal. This enables appropriate nutritional management tailored to the user's health condition.

[0126] The care robot system can also be equipped with an exercise support unit. This unit supports the user's exercise habits and promotes health maintenance. For example, if the user is not getting enough exercise, the exercise support unit can suggest an appropriate exercise program. Furthermore, the exercise support unit can adjust the intensity and frequency of exercise based on the user's health data. For instance, if the user has a high heart rate, it can suggest light exercise. This enables appropriate exercise support tailored to the user's health condition.

[0127] The care robot system can also be equipped with a sleep management unit. This unit monitors the user's sleep patterns and supports high-quality sleep. For example, if the user suffers from insomnia, the sleep management unit can provide a relaxing environment. Furthermore, the sleep management unit can evaluate sleep quality based on the user's health data and suggest improvements. For instance, it can monitor heart rate and breathing patterns during sleep and notify medical staff if abnormalities are detected. This can improve the user's sleep quality.

[0128] The care robot system can also be equipped with an emergency response unit. This unit responds quickly to any abnormalities in the user's health. For example, if the user falls, the emergency response unit can immediately notify medical staff and prompt necessary action. Furthermore, the emergency response unit can adjust its response based on the user's emotional state. For instance, if the user is panicking, it can offer calming words. This enables a swift and appropriate response in emergencies.

[0129] The care robot system can also be equipped with a communication support unit. This unit assists with communication between the user and their family and friends. For example, if the user wants to have a video call with family members who live far away, the unit can help with the setup. Furthermore, the unit can adjust the communication method based on the user's emotional state. For instance, if the user is feeling lonely, it can encourage them to contact family members more frequently. This helps maintain the user's social connections and supports their mental health.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The emotion analysis unit analyzes the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to understand their emotions. Using facial recognition technology and voice analysis technology, it monitors the user's facial expressions and tone of voice in real time and detects changes in their emotions. Step 2: The caregiving department provides emotional support based on the emotions analyzed by the emotion analysis department. For example, if the user is sad, they offer words of comfort; if they are anxious, they provide reassuring information; and if they are happy, they share that emotion and provide positive feedback. Step 3: The data collection unit collects the user's health data. For example, health data such as heart rate, blood pressure, and body temperature are collected in real time using a wearable device and periodically stored in a database. Step 4: The prediction unit analyzes the health data collected by the collection unit and predicts changes in health status. For example, it uses AI, machine learning algorithms, and deep learning technology to analyze the health data, predict signs of abnormalities, and detect them early. Step 5: The collaboration unit notifies medical staff of any anomalies predicted by the prediction unit. For example, if an anomaly is detected, it notifies medical staff in real time and sends the user's health data to prompt appropriate action. Step 6: The customization section performs customizations to adapt to the user's home environment. For example, it adjusts the operation of the care robot to match the layout of the user's house and the arrangement of furniture, sets the movement route, and customizes the functions of the care robot to suit the home environment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the emotion analysis unit, care provision unit, collection unit, prediction unit, collaboration unit, and customization unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the emotion analysis unit analyzes the user's facial expressions and tone of voice using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A understands the emotions. The care provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides emotional support based on the results of the emotion analysis unit. The collection unit collects health data using the sensors of the smart device 14 and transmits it to the data processing unit 12. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts changes in health status by analyzing the collected health data. The collaboration unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and notifies medical staff of the predicted abnormalities. The customization unit is implemented, for example, in the control unit 46A of the smart device 14, and adjusts the operation of the care robot to suit the user's home environment. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the emotion analysis unit, care provision unit, collection unit, prediction unit, coordination unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the emotion analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to analyze the user's facial expressions and tone of voice, and the control unit 46A understands the emotions. The care provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides emotional support based on the results of the emotion analysis unit. The collection unit collects health data using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected health data to predict changes in health status. The coordination unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and notifies medical staff of the predicted abnormalities. The customization unit is implemented, for example, by the control unit 46A of the smart glasses 214, and adjusts the operation of the care robot to suit the user's home environment. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0167] Each of the multiple elements described above, including the emotion analysis unit, care provision unit, collection unit, prediction unit, coordination unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the emotion analysis unit analyzes the user's facial expressions and tone of voice using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A understands the emotions. The care provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides emotional support based on the results of the emotion analysis unit. The collection unit collects health data using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected health data to predict changes in health status. The coordination unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies medical staff of the predicted abnormalities. The customization unit is implemented in the control unit 46A of the headset terminal 314, for example, and adjusts the operation of the care robot to suit the user's home environment. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[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 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.

[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 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).

[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] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the emotion analysis unit, care provision unit, collection unit, prediction unit, coordination unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the emotion analysis unit uses the camera 42 and microphone 238 of the robot 414 to analyze the user's facial expressions and tone of voice, and the control unit 46A understands the emotions. The care provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides emotional support based on the results of the emotion analysis unit. The collection unit collects health data using the sensors of the robot 414 and transmits it to the data processing unit 12. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts changes in health status by analyzing the collected health data. The coordination unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and notifies medical staff of the predicted abnormalities. The customization unit is implemented, for example, by the control unit 46A of the robot 414, and adjusts the operation of the care robot to suit the user's home environment. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) An emotion analysis unit that analyzes the user's emotions, A caregiving unit that provides emotional support based on the emotions analyzed by the aforementioned emotion analysis unit, A data collection unit that collects user health data, A prediction unit analyzes the health data collected by the aforementioned collection unit and predicts changes in health status, A communication unit that notifies medical staff of the abnormality predicted by the prediction unit, It includes a customization section to adapt to the user's home environment. A system characterized by the following features. (Note 2) The aforementioned emotion analysis unit, It analyzes the user's facial expressions and tone of voice to understand their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned care provision department, We provide emotional support based on information from the emotion analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned care provision department, It learns the user's health condition and daily routines, and provides personalized care based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, The system collects users' health data, uses AI to predict changes in their health status, and detects abnormalities early. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, The prediction unit transmits information to medical staff in real time, providing them with the information to take appropriate action. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned customization unit is The operation of the care robot is adjusted to suit the user's home environment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned emotion analysis unit, It estimates the user's emotions and dynamically adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned emotion analysis unit, By analyzing the user's past emotional data and learning patterns of emotional change, the accuracy of the analysis is improved. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned emotion analysis unit, When analyzing a user's emotions, the system uses the user's physical reactions in conjunction with the analysis to provide a more accurate emotional analysis. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned emotion analysis unit, The system estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned emotion analysis unit, During emotion analysis, changes in emotions are analyzed while taking into account the user's living environment. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned emotion analysis unit, During sentiment analysis, the user's social media activity is analyzed to complement the understanding of emotional changes. The system described in Appendix 2, characterized by the features described herein. (Note 14) The aforementioned care provision department, It estimates the user's emotions and dynamically adjusts the content of care based on the estimated emotions of the user. The system described in Appendix 3, characterized by the features described herein. (Note 15) The aforementioned care provision department, The system analyzes the user's past care history and selects the most suitable care method. The system described in Appendix 3, characterized by the features described herein. (Note 16) The aforementioned care provision department, When providing care, the content of care will be adjusted taking into consideration the user's physical condition. The system described in Appendix 3, characterized by the features described herein. (Note 17) The aforementioned care provision department, The system estimates the user's emotions and determines the priority of care based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 18) The aforementioned care provision department, When providing care, the optimal care method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 19) The aforementioned care provision department, When providing care, we analyze the user's social media activity and provide care tailored to their needs. The system described in Appendix 3, characterized by the features described herein. (Note 20) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is When collecting health data, the data is collected while taking into account the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is It estimates the user's emotions and determines the priority of data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is When collecting health data, we analyze the user's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, It estimates the user's emotions and dynamically adjusts the accuracy of the prediction based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, By analyzing users' past health data and learning patterns of changes in their health status, we can improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, When predicting health status, the prediction is made in conjunction with the user's physical responses. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When predicting health status, the prediction is made taking into account the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, When predicting health status, the system analyzes the user's social media activity to enhance the accuracy of the prediction. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the content of notifications to medical staff based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, Analyze the user's past health data to select the optimal notification method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, When notifying medical staff, adjust the content of the notification to take into account the user's physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, The system estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, When notifying medical staff, the most suitable notification method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, When notifying medical staff, the system analyzes the user's social media activity and provides relevant notification content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned customization unit is We analyze the user's past home environment data to select the optimal customization method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned customization unit is During customization, we adjust the customization settings to take into account the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned customization unit is During customization, the system analyzes the user's social media activity and provides relevant customization options. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned customization unit is During customization, the operation of the care robot is adjusted to suit the user's home environment. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An emotion analysis unit that analyzes the user's emotions, A caregiving unit that provides emotional support based on the emotions analyzed by the aforementioned emotion analysis unit, A data collection unit that collects user health data, A prediction unit analyzes the health data collected by the aforementioned collection unit and predicts changes in health status, A communication unit that notifies medical staff of the abnormality predicted by the prediction unit, It includes a customization section to adapt to the user's home environment. A system characterized by the following features.

2. The aforementioned emotion analysis unit, It analyzes the user's facial expressions and tone of voice to understand their emotions. The system according to feature 1.

3. The aforementioned care provision department, Emotional support is provided based on information from the aforementioned emotion analysis unit. The system according to feature 1.

4. The aforementioned care provision department, It learns the user's health condition and daily routines, and provides personalized care based on that information. The system according to feature 1.

5. The prediction unit, The system collects users' health data, uses AI to predict changes in their health status, and detects abnormalities early. The system according to feature 1.

6. The aforementioned linkage unit is, The prediction unit transmits information to medical staff in real time, providing them with the information to take appropriate action. The system according to feature 1.

7. The aforementioned customization unit is The operation of the care robot is adjusted to suit the user's home environment. The system according to feature 1.

8. The aforementioned emotion analysis unit, It estimates the user's emotions and dynamically adjusts the accuracy of the analysis based on the estimated emotions. The system according to feature 2.

9. The aforementioned emotion analysis unit, By analyzing the user's past emotional data and learning patterns of emotional change, the accuracy of the analysis is improved. The system according to feature 2.

10. The aforementioned emotion analysis unit, When analyzing a user's emotions, the system uses the user's physical reactions in conjunction with the analysis to provide a more accurate emotional analysis. The system according to feature 2.

Images