System

Abstract

To provide a system capable of swiftly detecting abnormalities in biological information during home care and nursing and taking appropriate action.SOLUTION: A system in an embodiment includes an acquisition unit, an upload unit, an analysis unit, a detection unit, and a notification unit. The acquisition unit periodically obtains biometric information such as pulse rate, oxygen concentration, and blood pressure. The upload unit uploads the biometric information acquired by the acquisition unit to the Cloud. The analysis unit analyzes the biometric information uploaded by the upload unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit sends notifications to doctors when an abnormality is detected by the detection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that it was difficult to quickly detect abnormalities in biological information and take appropriate measures during home care or nursing care.

[0005] The system according to the embodiment aims to quickly detect abnormalities in biological information during home care or nursing care and to take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an upload unit, an analysis unit, a detection unit, and a notification unit. The acquisition unit periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The upload unit uploads the biological information acquired by the acquisition unit to the cloud. The analysis unit analyzes the biological information uploaded by the upload unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit sends a notification to a doctor when an abnormality is detected by the detection unit. Effect of the Invention

[0007] The system according to the embodiment can quickly detect abnormalities in biological information during home care or nursing care, and can take appropriate measures. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A nursing and care support system according to an embodiment of the present invention is a system that supports people who need nursing and care at home. This nursing and care support system uses a beacon device that allows a nurse or caregiver to periodically acquire biological information such as pulse, oxygen concentration, and blood pressure, and upload it to the cloud. This beacon device is attached to the body of the person being cared for and periodically acquires biological information. The acquired data is uploaded to the cloud, and the cloud analyzes daily data value trends and past medical data. If the analysis shows that there is a possibility of an abnormality, a response such as an emergency doctor is made. This system allows the nurse or caregiver to constantly monitor the health condition of the person being cared for and quickly take necessary measures, even when the nurse or caregiver does not have enough time. For example, a beacon device is attached to the body of the person being cared for and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. For example, this data is acquired every hour and uploaded to the cloud. At this time, the data is encrypted before being transmitted, so security is also ensured. Next, the acquired data is analyzed on the cloud side. Past medical data and daily data value trends are stored in the cloud, and whether or not an abnormality has occurred is determined based on these data. For example, if an abnormality is detected, such as a pulse rate that is significantly higher than normal or a drop in oxygen concentration, an alert is issued. When an alert is issued, a notification is sent to a doctor, and an emergency dispatch is made as necessary. For example, a doctor visits the home of the care recipient and performs the necessary medical treatment. In this way, the health condition of the care recipient can be constantly monitored, and if an abnormality occurs, it can be dealt with quickly. With this system, even if a nurse or caregiver does not have enough time, the health condition of the care recipient can be constantly monitored and the necessary response can be made quickly. For example, even if there is no family member or a third-party caregiver does not have enough time, the health condition of the care recipient can be constantly monitored and if an abnormality occurs, it can be dealt with quickly by using this system. As a result, the nursing and care support system can constantly monitor the health condition of the care recipient and can quickly deal with an abnormality.

[0029] The nursing / care support system according to the embodiment includes an acquisition unit, an upload unit, an analysis unit, a detection unit, and a notification unit. The acquisition unit periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The acquisition unit acquires the biological information using, for example, a beacon device attached to the body of the care recipient. The beacon device acquires the biological information every hour, for example, and uploads it to the cloud. The upload unit uploads the biological information acquired by the acquisition unit to the cloud. The upload unit, for example, encrypts the acquired data and transmits it to the cloud. For encryption, an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman) can be used. The analysis unit analyzes the biological information uploaded by the upload unit. For example, the analysis unit analyzes data based on past medical data and daily trends of data values. For analysis, a statistical analysis method such as moving average and regression analysis can be used. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit detects an abnormality, for example, when the pulse rate is significantly higher than normal or when the oxygen concentration is low. Criteria for the abnormality can be set, for example, as an upper limit or lower limit of the pulse rate, or a threshold value of the oxygen concentration. The notification unit transmits a notification to a doctor when an abnormality is detected by the detection unit. The notification unit transmits a notification to a doctor by, for example, email, SMS, or app notification. As a result, the nursing / care support system according to the embodiment can periodically obtain biological information, upload it to the cloud, and analyze it to detect an abnormality and promptly notify a doctor.

[0030] The acquisition unit periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The acquisition unit acquires the biological information using, for example, a beacon device attached to the body of the care recipient. The beacon device acquires the biological information every hour, for example, and uploads it to the cloud. Specifically, the beacon device is a small and lightweight device that is attached to the arm or chest of the care recipient. This allows the biological information to be continuously monitored without interfering with the care recipient's daily life. The beacon device measures the pulse, oxygen concentration, and blood pressure using a built-in sensor, and collects these data in real time. Furthermore, the beacon device transmits the acquired data to a relay device such as a smartphone or tablet using wireless communication technology such as Bluetooth (registered trademark) or Wi-Fi. The relay device plays a role of uploading the data received from the beacon device to a cloud server. This allows the acquisition unit to efficiently and accurately collect the biological information of the care recipient and transmit it to the cloud. In addition, the beacon device is battery-powered and can be used continuously for a long time. When the remaining battery power is low, a notification is sent to the user to inform him or her that charging is required. This allows the acquisition unit to constantly collect data stably.

[0031] The upload unit uploads the biometric information acquired by the acquisition unit to the cloud. The upload unit, for example, encrypts the acquired data and transmits it to the cloud. For encryption, for example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. Specifically, the upload unit temporarily stores the acquired biometric information in a relay device, checks the integrity of the data, and then performs encryption processing. AES is a symmetric key encryption method that realizes high-speed and secure data encryption. On the other hand, RSA is a public key encryption method that is used to increase the confidentiality of data. The upload unit ensures the security of data by using these encryption algorithms in combination. The encrypted data is transmitted to a cloud server via the Internet. The cloud server has a high security level and stores and manages the data. The upload unit monitors the data transmission status in real time, and attempts to retransmit the data if a transmission error occurs. This makes it possible to minimize data loss and delays. In addition, the upload unit can flexibly set the frequency and timing of data transmission. For example, by adjusting the data acquisition interval and transmission interval, the network load is reduced and efficient data management is realized. This enables the upload unit to upload the acquired biometric information to the cloud safely and quickly, thereby improving the reliability of the entire system.

[0032] The analysis unit analyzes the biometric information uploaded by the upload unit. The analysis unit analyzes the data based on, for example, past medical data and daily trends of data values. For the analysis, for example, a statistical analysis method such as moving average or regression analysis can be used. Specifically, the analysis unit periodically acquires the biometric information stored in the cloud server and performs preprocessing of the data. The preprocessing includes removing noise from the data and complementing missing values. Next, the moving average is used to grasp the trend of the data and detect outliers. The moving average is a method of calculating the average value of data over a certain period of time and smoothing the fluctuations in the data. This makes it possible to remove short-term fluctuations and clarify long-term trends. Furthermore, regression analysis is used to model the relationship between the data and predict future data. Regression analysis is a method of expressing the relationship between multiple variables in a mathematical formula and predicting the trend of the data. The analysis unit performs detailed analysis of the biometric information by combining and using these statistical analysis methods, thereby realizing early detection and prediction of abnormalities. In addition, the analysis unit uses a machine learning algorithm to learn the pattern of the data and improve the accuracy of abnormality detection. For example, a classification model for abnormality detection is constructed and it is determined whether or not new data is abnormal when it is input. This enables the analysis unit to perform advanced analysis of the acquired biological information and monitor the health condition of the care recipient in real time.

[0033] The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit detects abnormalities, for example, when the pulse rate is significantly higher than normal or when the oxygen concentration is low. For example, the upper and lower limits of the pulse rate and the threshold value of the oxygen concentration can be set as the criteria for abnormalities. Specifically, the detection unit monitors the data provided by the analysis unit in real time and compares it with the set reference value. The reference value is set by a doctor or an expert and is adjusted based on the individual health condition and past data of the care recipient. When the detection unit detects an abnormal value that exceeds the reference value, it immediately issues an alert. The alert is not only sent as a notification within the system, but also to a doctor or a caregiver. This allows for a quick response when an abnormality occurs. Furthermore, the detection unit can issue different levels of alerts depending on the type and degree of the abnormality. For example, in the case of a mild abnormality, an alert is issued to call attention, and in the case of a severe abnormality, an emergency alert is issued. This allows for an appropriate response depending on the importance of the abnormality. In addition, the detection unit can record the frequency and pattern of occurrence of abnormalities, which can be used for long-term health management. This enables the detection unit to continuously monitor the health condition of the care recipient, and realizes early detection of abnormalities and rapid response.

[0034] The notification unit transmits a notification to the doctor when an abnormality is detected by the detection unit. The notification unit transmits the notification to the doctor by, for example, email, SMS, app notification, or other methods. Specifically, the notification unit receives an alert from the detection unit and transmits a notification to a pre-set contact. In the case of email notification, an email containing detailed information about the abnormality and recommended countermeasures is transmitted. In the case of SMS notification, a concise message is transmitted to inform the doctor of the occurrence of the abnormality. In the case of app notification, a notification is transmitted in real time through a dedicated application, and the doctor can check the detailed information through the app. Furthermore, the notification unit records the transmission history of the notification so that it can be checked later. This makes it possible to track the occurrence of the abnormality in the past and the response history. Furthermore, the notification unit can transmit notifications to multiple contacts at the same time. For example, notifications can be transmitted not only to doctors but also to caregivers and family members to encourage a prompt response. This makes it possible for the notification unit to quickly and reliably convey information to all parties involved when an abnormality occurs, and to support appropriate responses. Furthermore, the notification unit can set the priority of the notification. For example, in the case of an abnormality with a high degree of urgency, a high-priority notification is transmitted to encourage a prompt response. This enables the notification unit to provide an appropriate notification according to the severity of the abnormality, thereby ensuring the safety of the care recipient.

[0035] The acquisition unit includes a beacon device attached to the body of the care recipient. For example, a Bluetooth beacon or an RFID (Radio Frequency Identification) tag can be used as the beacon device. A Bluetooth beacon can operate for a long time with low power consumption, and places little burden on the body of the care recipient when attached to the body of the care recipient. Since an RFID tag transmits data using wireless communication, data can be easily acquired even when attached to the body of the care recipient. This allows the beacon device attached to the body of the care recipient to periodically acquire biological information.

[0036] The upload unit can encrypt the acquired data and transmit it to the cloud. For the encryption, for example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. AES is a symmetric key encryption method that realizes high-speed and secure encryption. RSA is a public key encryption method that is used to ensure the confidentiality of data. For example, the upload unit encrypts the acquired data with AES and transmits it to the cloud. The upload unit can also encrypt and decrypt data using RSA. This allows the data to be transmitted to the cloud while ensuring security by encrypting the data.

[0037] The analysis unit can analyze data based on past medical data or daily trends in numerical data. Past medical data includes, for example, medical records from the past year and data related to a specific disease. Daily trends in numerical data can be analyzed using statistical analysis methods such as moving average and regression analysis. For example, the analysis unit detects abnormalities based on past medical data by comparing it with current data. The analysis unit can also analyze daily trends in numerical data to detect signs of abnormalities at an early stage. This allows for accurate detection of abnormalities by analyzing based on past medical data and daily trends in numerical data.

[0038] The detection unit can detect an abnormality when the pulse rate is significantly higher than normal or when the oxygen concentration is low. Criteria for abnormality can be set, for example, as upper and lower limits of the pulse rate, a threshold value of the oxygen concentration, etc. For example, the detection unit detects an abnormality when the pulse rate is higher than the normal value by 20% or more. The detection unit can also detect an abnormality when the oxygen concentration is lowered to less than 90%. This allows for a rapid response by detecting an abnormality in the pulse rate or oxygen concentration.

[0039] The notification unit can transmit a notification to request an emergency dispatch to a doctor when an abnormality is detected. The notification unit transmits the notification to the doctor by, for example, email, SMS, app notification, or other method. The content of the notification includes, for example, the type of abnormality, the urgency, the current condition of the person being cared for, and the like. For example, the notification unit transmits a notification to the doctor by email when an abnormality in the pulse is detected. The notification unit can also transmit a notification to the doctor by SMS when an abnormality in the oxygen concentration is detected. This makes it possible to promptly notify a doctor when an abnormality is detected and request an emergency dispatch.

[0040] The acquisition unit can analyze the past health data of the care recipient and determine the optimal acquisition timing. For example, the acquisition unit acquires data during the most stable time period based on the past health data of the care recipient. The past health data includes, for example, health records from the past year and data on specific illnesses. For example, the acquisition unit acquires data during a time period when abnormalities are likely to occur based on the past abnormal data of the care recipient. The acquisition unit can also determine the optimal acquisition timing by taking into account the life rhythm of the care recipient. In this way, more accurate data can be collected by determining the optimal acquisition timing based on the past health data.

[0041] The acquisition unit can filter the data based on the activity level and environmental conditions of the care recipient when acquiring the biological information. For example, when the care recipient is exercising, the acquisition unit acquires data when the care recipient is resting after exercising. The activity level can be measured using, for example, a pedometer or a heart rate monitor. In addition, when the care recipient is out, the acquisition unit can also acquire data after the care recipient returns home. The environmental conditions can take into account, for example, temperature, humidity, noise level, and the like. For example, when the care recipient is in a noisy environment, the acquisition unit acquires data in a quiet environment. This allows more accurate data to be acquired by filtering the data based on the activity level and environmental conditions.

[0042] When acquiring the biological information, the acquisition unit can preferentially acquire highly relevant data in consideration of the geographical location information of the care recipient. For example, when the care recipient is at high altitude, the acquisition unit preferentially acquires oxygen concentration data. The geographical location information can be acquired using, for example, GPS data or a location information service. In addition, when the care recipient is in an urban area, the acquisition unit can also preferentially acquire pulse data. Furthermore, when the care recipient is at home, the acquisition unit can also preferentially acquire blood pressure data. In this way, by preferentially acquiring highly relevant data in consideration of the geographical location information, more appropriate data can be collected.

[0043] The acquisition unit can analyze the diet and medicine intake status of the care recipient when acquiring the biological information, and acquire related data. For example, the acquisition unit acquires blood glucose level data immediately after the care recipient eats a meal. The diet and medicine intake status can be recorded using, for example, a food diary or a medicine taking record. The acquisition unit can also acquire pulse data immediately after the care recipient takes medicine. Furthermore, the acquisition unit can also acquire oxygen concentration data after the care recipient eats a specific meal. This allows more accurate data to be collected by analyzing the diet and medicine intake status and acquiring related data.

[0044] The upload unit can monitor the network state when uploading data and perform uploading at the optimal timing. For example, when the network is congested, the upload unit uploads data during a time period when the network is not congested. The network state can be monitored, for example, in terms of bandwidth, delay, packet loss, etc. Furthermore, when the network speed is slow, the upload unit can upload data after the speed has recovered. Furthermore, when the network is stable, the upload unit can upload data immediately. In this way, by monitoring the network state and performing uploading at the optimal timing, it is possible to improve the efficiency of data transmission.

[0045] The upload unit can adjust the encryption strength based on the importance of the data when uploading the data. For example, the upload unit applies high-strength encryption to important data and uploads it. The encryption strength can be adjusted, for example, by adjusting the key length, the selection of the encryption algorithm, and the like. The upload unit can also apply medium-strength encryption to general data and upload it. Furthermore, the upload unit can also apply low-strength encryption to data of low importance and upload it. In this way, by adjusting the encryption strength based on the importance of the data, it is possible to transmit data efficiently while ensuring security.

[0046] When uploading data, the uploading unit can prioritize uploading highly relevant data in consideration of the geographical location information of the care recipient. For example, when the care recipient is at high altitude, the uploading unit prioritizes uploading oxygen concentration data. The geographical location information can be acquired, for example, using GPS data or a location information service. In addition, when the care recipient is in an urban area, the uploading unit can also prioritize uploading pulse data. Furthermore, when the care recipient is at home, the uploading unit can also prioritize uploading blood pressure data. In this way, by prioritized uploading of highly relevant data in consideration of the geographical location information, more appropriate data can be transmitted.

[0047] The uploading unit can analyze the social media activity of the care recipient when uploading data, and upload related data. For example, if the care recipient posts about health on social media, the uploading unit prioritizes uploading the data. For example, the social media activity can be analyzed such as the content of the post, the number of likes, and the content of comments. In addition, if the care recipient reports an abnormality on social media, the uploading unit can also prioritize uploading the data. Furthermore, if the care recipient reports a specific symptom on social media, the uploading unit can also prioritize uploading the data. In this way, by analyzing social media activity and uploading related data, more appropriate data can be transmitted.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past medical data. For example, the analysis unit detects abnormalities by comparing current data based on past medical data. Past medical data includes, for example, medical records from the past year and data related to specific diseases. The analysis unit can also refer to past medical data to find specific patterns and reflect them in the analysis. Furthermore, the analysis unit can also use past medical data to optimize the analysis algorithm. As a result, the accuracy of the analysis can be improved by referring to past medical data.

[0049] The analysis unit can analyze the data taking into consideration the lifestyle habits and environmental conditions of the care recipient during the analysis. The analysis unit analyzes the data taking into consideration, for example, the dietary habits of the care recipient. Lifestyle habits include, for example, diet, exercise, sleep, etc. The analysis unit can also analyze the data taking into consideration the exercise habits of the care recipient. Furthermore, the analysis unit can analyze the data taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. As a result, by analyzing the data taking into consideration the lifestyle habits and environmental conditions, more accurate analysis results can be obtained.

[0050] The analysis unit can analyze the data taking into consideration the geographical distribution of the care recipient. For example, if the care recipient lives in a high altitude, the analysis unit focuses on the oxygen concentration data during the analysis. The geographical distribution can be acquired using, for example, GPS data or a location information service. Furthermore, if the care recipient lives in an urban area, the analysis unit can also focus on the pulse data during the analysis. Furthermore, if the care recipient lives in a rural area, the analysis unit can also focus on the blood pressure data during the analysis. In this way, by analyzing the data taking into consideration the geographical distribution, more accurate analysis results can be obtained.

[0051] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the care recipient. The analysis unit, for example, refers to literature related to the symptoms of the care recipient and reflects the literature in the analysis. Examples of related literature include academic papers and medical guidelines. The analysis unit can also refer to the past medical records of the care recipient and reflect the literature in the analysis. Furthermore, the analysis unit can also refer to literature related to the lifestyle habits of the care recipient and reflect the literature in the analysis. In this way, the accuracy of the analysis can be improved by referring to related literature.

[0052] The detection unit can improve the accuracy of detection when detecting an anomaly by referring to past anomaly data. For example, the detection unit detects an anomaly by comparing the past anomaly data with current data. The past anomaly data includes, for example, anomaly records from the past year and data related to specific anomalies. The detection unit can also refer to the past anomaly data to find specific patterns and reflect them in the detection. Furthermore, the detection unit can also use the past anomaly data to optimize the detection algorithm. Thereby, the detection accuracy can be improved by referring to the past anomaly data.

[0053] When detecting an abnormality, the detection unit can detect the abnormality taking into consideration the lifestyle habits and environmental conditions of the care recipient. The detection unit detects the abnormality, for example, taking into consideration the eating habits of the care recipient. Lifestyle habits include, for example, eating, exercise, sleep, etc. The detection unit can also detect the abnormality taking into consideration the exercise habits of the care recipient. Furthermore, the detection unit can also detect the abnormality taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. As a result, by detecting the abnormality taking into consideration the lifestyle habits and environmental conditions, more accurate abnormality detection can be performed.

[0054] The detection unit can detect the abnormality by considering the geographical distribution of the care recipient. For example, if the care recipient lives at high altitude, the detection unit detects the abnormality by focusing on the abnormality in oxygen concentration. The geographical distribution can be acquired by using, for example, GPS data or a location information service. Furthermore, if the care recipient lives in an urban area, the detection unit can also detect the abnormality by focusing on the abnormality in pulse rate. Furthermore, if the care recipient lives in a rural area, the detection unit can also detect the abnormality by focusing on the abnormality in blood pressure. In this way, by detecting the abnormality by considering the geographical distribution, more accurate abnormality detection can be performed.

[0055] When detecting an abnormality, the detection unit can improve the accuracy of detection by referring to literature related to the care recipient. The detection unit, for example, refers to literature related to the symptoms of the care recipient and reflects it in the abnormality detection. The related literature includes, for example, academic papers and medical guidelines. The detection unit can also refer to the past medical records of the care recipient and reflect it in the abnormality detection. Furthermore, the detection unit can also refer to literature related to the lifestyle habits of the care recipient and reflect it in the abnormality detection. In this way, the detection accuracy can be improved by referring to the related literature.

[0056] The notification unit can select the optimal notification method by referring to past notification history at the time of notification. The notification unit selects the most effective notification method based on, for example, the past notification history. The notification method includes, for example, email, SMS, and app notification. The notification unit can also refer to the past notification history, find a specific pattern, and reflect it in the notification. Furthermore, the notification unit can also optimize the notification algorithm by using the past notification history. As a result, the optimal notification method can be selected by referring to the past notification history.

[0057] The notification unit can provide notification while taking into consideration the lifestyle habits and environmental conditions of the care recipient. The notification unit provides notification while taking into consideration, for example, the eating habits of the care recipient. Lifestyle habits include, for example, eating, exercise, sleep, etc. The notification unit can also provide notification while taking into consideration the exercise habits of the care recipient. Furthermore, the notification unit can also provide notification while taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. In this way, by providing notification while taking into consideration the lifestyle habits and environmental conditions, more appropriate notification can be provided.

[0058] The notification unit can select the optimal notification method in consideration of the geographical location information of the care recipient at the time of notification. For example, when the care recipient is at high altitude, the notification unit preferentially transmits a notification regarding an abnormality in oxygen concentration. The geographical location information can be acquired using, for example, GPS data or a location information service. In addition, when the care recipient is in an urban area, the notification unit can also preferentially transmit a notification regarding an abnormality in pulse rate. Furthermore, when the care recipient is at home, the notification unit can also preferentially transmit a notification regarding an abnormality in blood pressure. In this way, more appropriate notification can be provided by selecting the optimal notification method in consideration of the geographical location information.

[0059] At the time of notification, the notification unit can analyze the social media activity of the care recipient and provide a related notification. For example, when the care recipient posts on social media about health, the notification unit sends a notification based on the content of the post. For example, the social media activity can be analyzed such as the content of the post, the number of likes, and the content of the comments. In addition, when the care recipient reports an abnormality on social media, the notification unit can also send a notification based on the content. Furthermore, when the care recipient reports a specific symptom on social media, the notification unit can also send a notification based on the content. In this way, more appropriate notifications can be provided by analyzing social media activity and providing related notifications.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The nursing and care support system can further include a sleep analysis unit that monitors the sleep patterns of the care recipient. The sleep analysis unit can analyze the quality and duration of the care recipient's sleep, and send a notification to the notification unit if an abnormality is detected. For example, an alert can be generated if the care recipient is not getting enough sleep or if abnormal movements are detected during sleep. The sleep analysis unit can also upload the sleep data of the care recipient to the cloud and compare it with past data for analysis. This allows the care recipient's sleep state to be constantly monitored, and necessary measures can be taken promptly.

[0062] The nursing and care support system can further include a meal recorder that records the meal contents of the care recipient. The meal recorder can record the contents and calories of the meals consumed by the care recipient and analyze the nutritional balance. For example, an alert can be generated if the care recipient is consuming an excess or deficiency of a specific nutrient. The meal recorder can also upload the care recipient's meal data to the cloud and compare it with past data for analysis. This allows the care recipient's meal contents to be constantly monitored and necessary measures to be taken promptly.

[0063] The nursing and care support system can further include a motion analysis unit that monitors the amount of exercise of the care recipient. The motion analysis unit can analyze the number of steps and exercise time of the care recipient, and send a notification to the notification unit if an abnormality is detected. For example, an alert can be generated if the care recipient is not exercising enough or is exercising too much. The motion analysis unit can also upload the exercise data of the care recipient to the cloud and compare it with past data for analysis. This allows the exercise status of the care recipient to be constantly monitored and necessary measures to be taken promptly.

[0064] The nursing and care support system can further include a medication recorder that records the medication status of the care recipient. The medication recorder can record the type and time of medication taken by the care recipient and analyze the medication compliance status. For example, an alert can be generated if the care recipient forgets to take medication or takes too much. The medication recorder can also upload the care recipient's medication data to the cloud and compare it with past data for analysis. This allows the care recipient's medication status to be constantly monitored and necessary measures to be taken promptly.

[0065] The nursing and care support system can further include a social activity recording unit that records the social activities of the care recipient. The social activity recording unit can record the social activities and events in which the care recipient participates and analyze the social connections. For example, an alert can be generated if the care recipient is isolated or lacks social activities. The social activity recording unit can also upload the social activity data of the care recipient to the cloud and compare it with past data for analysis. This allows the social connections of the care recipient to be constantly monitored and necessary measures to be taken promptly.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The acquisition unit periodically acquires biological information such as pulse rate, oxygen concentration, blood pressure, etc. For example, the biological information is acquired using a beacon device attached to the body of the care recipient, and the biological information is acquired every hour. Step 2: The upload unit uploads the biometric information acquired by the acquisition unit to the cloud. For example, the acquired data is encrypted and transmitted to the cloud. An encryption algorithm such as AES or RSA can be used for the encryption. Step 3: The analysis unit analyzes the biometric information uploaded by the upload unit. For example, the analysis unit analyzes the data based on past medical data and daily trends in data values. Statistical analysis methods such as moving average and regression analysis can be used for the analysis. Step 4: The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, it detects abnormalities such as when the pulse rate is significantly higher than normal or when the oxygen concentration is low. The criteria for abnormalities can be set to the upper and lower limits of the pulse rate, the threshold of the oxygen concentration, etc. Step 5: The notification unit sends a notification to the doctor when an abnormality is detected by the detection unit. For example, the notification is sent to the doctor by email, SMS, app notification, or other method.

[0068] (Example 2) A nursing and care support system according to an embodiment of the present invention is a system that supports people who need nursing and care at home. This nursing and care support system uses a beacon device that allows a nurse or caregiver to periodically acquire biological information such as pulse, oxygen concentration, and blood pressure, and upload it to the cloud. This beacon device is attached to the body of the person being cared for and periodically acquires biological information. The acquired data is uploaded to the cloud, and the cloud analyzes daily data value trends and past medical data. If the analysis shows that there is a possibility of an abnormality, a response such as an emergency doctor is made. This system allows the nurse or caregiver to constantly monitor the health condition of the person being cared for and quickly take necessary measures, even when the nurse or caregiver does not have enough time. For example, a beacon device is attached to the body of the person being cared for and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. For example, this data is acquired every hour and uploaded to the cloud. At this time, the data is encrypted before being transmitted, so security is also ensured. Next, the acquired data is analyzed on the cloud side. Past medical data and daily data value trends are stored in the cloud, and whether or not an abnormality has occurred is determined based on these data. For example, if an abnormality is detected, such as a pulse rate that is significantly higher than normal or a drop in oxygen concentration, an alert is issued. When an alert is issued, a notification is sent to a doctor, and an emergency dispatch is made as necessary. For example, a doctor visits the home of the care recipient and performs the necessary medical treatment. In this way, the health condition of the care recipient can be constantly monitored, and if an abnormality occurs, it can be dealt with quickly. With this system, even if a nurse or caregiver does not have enough time, the health condition of the care recipient can be constantly monitored and the necessary response can be made quickly. For example, even if there is no family member or a third-party caregiver does not have enough time, the health condition of the care recipient can be constantly monitored and if an abnormality occurs, it can be dealt with quickly by using this system. As a result, the nursing and care support system can constantly monitor the health condition of the care recipient and can quickly deal with an abnormality.

[0069] The nursing / care support system according to the embodiment includes an acquisition unit, an upload unit, an analysis unit, a detection unit, and a notification unit. The acquisition unit periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The acquisition unit acquires the biological information using, for example, a beacon device attached to the body of the care recipient. The beacon device acquires the biological information every hour, for example, and uploads it to the cloud. The upload unit uploads the biological information acquired by the acquisition unit to the cloud. The upload unit, for example, encrypts the acquired data and transmits it to the cloud. For encryption, an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman) can be used. The analysis unit analyzes the biological information uploaded by the upload unit. For example, the analysis unit analyzes data based on past medical data and daily trends of data values. For analysis, a statistical analysis method such as moving average and regression analysis can be used. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit detects an abnormality, for example, when the pulse rate is significantly higher than normal or when the oxygen concentration is low. Criteria for the abnormality can be set, for example, as an upper limit or lower limit of the pulse rate, or a threshold value of the oxygen concentration. The notification unit transmits a notification to a doctor when an abnormality is detected by the detection unit. The notification unit transmits a notification to a doctor by, for example, email, SMS, or app notification. As a result, the nursing / care support system according to the embodiment can periodically obtain biological information, upload it to the cloud, and analyze it to detect an abnormality and promptly notify a doctor.

[0070] The acquisition unit periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The acquisition unit acquires the biological information using, for example, a beacon device attached to the body of the care recipient. The beacon device acquires the biological information every hour, for example, and uploads it to the cloud. Specifically, the beacon device is a small and lightweight device that is attached to the arm or chest of the care recipient. This allows the biological information to be continuously monitored without interfering with the care recipient's daily life. The beacon device measures the pulse, oxygen concentration, and blood pressure using a built-in sensor, and collects these data in real time. Furthermore, the beacon device transmits the acquired data to a relay device such as a smartphone or tablet using wireless communication technology such as Bluetooth or Wi-Fi. The relay device plays a role in uploading the data received from the beacon device to a cloud server. This allows the acquisition unit to efficiently and accurately collect the biological information of the care recipient and transmit it to the cloud. In addition, the beacon device is battery-powered and can be used continuously for a long time. When the remaining battery power is low, a notification is sent to the user to inform him or her that charging is required. This allows the acquisition unit to constantly collect data stably.

[0071] The upload unit uploads the biometric information acquired by the acquisition unit to the cloud. The upload unit, for example, encrypts the acquired data and transmits it to the cloud. For encryption, for example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. Specifically, the upload unit temporarily stores the acquired biometric information in a relay device, checks the integrity of the data, and then performs encryption processing. AES is a symmetric key encryption method that realizes high-speed and secure data encryption. On the other hand, RSA is a public key encryption method that is used to increase the confidentiality of data. The upload unit ensures the security of data by using these encryption algorithms in combination. The encrypted data is transmitted to a cloud server via the Internet. The cloud server has a high security level and stores and manages the data. The upload unit monitors the data transmission status in real time, and attempts to retransmit the data if a transmission error occurs. This makes it possible to minimize data loss and delays. In addition, the upload unit can flexibly set the frequency and timing of data transmission. For example, by adjusting the data acquisition interval and transmission interval, the network load is reduced and efficient data management is realized. This enables the upload unit to upload the acquired biometric information to the cloud safely and quickly, thereby improving the reliability of the entire system.

[0072] The analysis unit analyzes the biometric information uploaded by the upload unit. The analysis unit analyzes the data based on, for example, past medical data and daily trends of data values. For the analysis, for example, a statistical analysis method such as moving average or regression analysis can be used. Specifically, the analysis unit periodically acquires the biometric information stored in the cloud server and performs preprocessing of the data. The preprocessing includes removing noise from the data and complementing missing values. Next, the moving average is used to grasp the trend of the data and detect outliers. The moving average is a method of calculating the average value of data over a certain period of time and smoothing the fluctuations in the data. This makes it possible to remove short-term fluctuations and clarify long-term trends. Furthermore, regression analysis is used to model the relationship between the data and predict future data. Regression analysis is a method of expressing the relationship between multiple variables in a mathematical formula and predicting the trend of the data. The analysis unit performs detailed analysis of the biometric information by combining and using these statistical analysis methods, thereby realizing early detection and prediction of abnormalities. In addition, the analysis unit uses a machine learning algorithm to learn the pattern of the data and improve the accuracy of abnormality detection. For example, a classification model for abnormality detection is constructed and it is determined whether or not new data is abnormal when it is input. This enables the analysis unit to perform advanced analysis of the acquired biological information and monitor the health condition of the care recipient in real time.

[0073] The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit detects abnormalities, for example, when the pulse rate is significantly higher than normal or when the oxygen concentration is low. For example, the upper and lower limits of the pulse rate and the threshold value of the oxygen concentration can be set as the criteria for abnormalities. Specifically, the detection unit monitors the data provided by the analysis unit in real time and compares it with the set reference value. The reference value is set by a doctor or an expert and is adjusted based on the individual health condition and past data of the care recipient. When the detection unit detects an abnormal value that exceeds the reference value, it immediately issues an alert. The alert is not only sent as a notification within the system, but also to a doctor or a caregiver. This allows for a quick response when an abnormality occurs. Furthermore, the detection unit can issue different levels of alerts depending on the type and degree of the abnormality. For example, in the case of a mild abnormality, an alert is issued to call attention, and in the case of a severe abnormality, an emergency alert is issued. This allows for an appropriate response depending on the importance of the abnormality. In addition, the detection unit can record the frequency and pattern of occurrence of abnormalities, which can be used for long-term health management. This enables the detection unit to continuously monitor the health condition of the care recipient, and realizes early detection of abnormalities and rapid response.

[0074] The notification unit transmits a notification to the doctor when an abnormality is detected by the detection unit. The notification unit transmits the notification to the doctor by, for example, email, SMS, app notification, or other methods. Specifically, the notification unit receives an alert from the detection unit and transmits a notification to a pre-set contact. In the case of email notification, an email containing detailed information about the abnormality and recommended countermeasures is transmitted. In the case of SMS notification, a concise message is transmitted to inform the doctor of the occurrence of the abnormality. In the case of app notification, a notification is transmitted in real time through a dedicated application, and the doctor can check the detailed information through the app. Furthermore, the notification unit records the transmission history of the notification so that it can be checked later. This makes it possible to track the occurrence of the abnormality in the past and the response history. Furthermore, the notification unit can transmit notifications to multiple contacts at the same time. For example, notifications can be transmitted not only to doctors but also to caregivers and family members to encourage a prompt response. This makes it possible for the notification unit to quickly and reliably convey information to all parties involved when an abnormality occurs, and to support appropriate responses. Furthermore, the notification unit can set the priority of the notification. For example, in the case of an abnormality with a high degree of urgency, a high-priority notification is transmitted to encourage a prompt response. This enables the notification unit to provide an appropriate notification according to the severity of the abnormality, thereby ensuring the safety of the care recipient.

[0075] The acquisition unit includes a beacon device attached to the body of the care recipient. For example, a Bluetooth beacon or an RFID (Radio Frequency Identification) tag can be used as the beacon device. A Bluetooth beacon can operate for a long time with low power consumption, and places little burden on the body of the care recipient when attached to the body of the care recipient. Since an RFID tag transmits data using wireless communication, data can be easily acquired even when attached to the body of the care recipient. This allows the beacon device attached to the body of the care recipient to periodically acquire biological information.

[0076] The upload unit can encrypt the acquired data and transmit it to the cloud. For the encryption, for example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. AES is a symmetric key encryption method that realizes high-speed and secure encryption. RSA is a public key encryption method that is used to ensure the confidentiality of data. For example, the upload unit encrypts the acquired data with AES and transmits it to the cloud. The upload unit can also encrypt and decrypt data using RSA. This allows the data to be transmitted to the cloud while ensuring security by encrypting the data.

[0077] The analysis unit can analyze data based on past medical data or daily trends in numerical data. Past medical data includes, for example, medical records from the past year and data related to a specific disease. Daily trends in numerical data can be analyzed using statistical analysis methods such as moving average and regression analysis. For example, the analysis unit detects abnormalities based on past medical data by comparing it with current data. The analysis unit can also analyze daily trends in numerical data to detect signs of abnormalities at an early stage. This allows for accurate detection of abnormalities by analyzing based on past medical data and daily trends in numerical data.

[0078] The detection unit can detect an abnormality when the pulse rate is significantly higher than normal or when the oxygen concentration is low. Criteria for abnormality can be set, for example, as upper and lower limits of the pulse rate, a threshold value of the oxygen concentration, etc. For example, the detection unit detects an abnormality when the pulse rate is higher than the normal value by 20% or more. The detection unit can also detect an abnormality when the oxygen concentration is lowered to less than 90%. This allows for a rapid response by detecting an abnormality in the pulse rate or oxygen concentration.

[0079] The notification unit can transmit a notification to request an emergency dispatch to a doctor when an abnormality is detected. The notification unit transmits the notification to the doctor by, for example, email, SMS, app notification, or other method. The content of the notification includes, for example, the type of abnormality, the urgency, the current condition of the person being cared for, and the like. For example, the notification unit transmits a notification to the doctor by email when an abnormality in the pulse is detected. The notification unit can also transmit a notification to the doctor by SMS when an abnormality in the oxygen concentration is detected. This makes it possible to promptly notify a doctor when an abnormality is detected and request an emergency dispatch.

[0080] The acquisition unit can estimate the user's emotion and adjust the acquisition frequency of the bioinformation based on the estimated user's emotion. The acquisition unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the acquisition unit increases the acquisition frequency to collect detailed data. In addition, when the user is relaxed, the acquisition unit can also reduce the acquisition frequency to reduce the burden. Furthermore, when the user is in a hurry, the acquisition unit can temporarily reduce the acquisition frequency and acquire the data all at once later. This allows more appropriate data to be collected by adjusting the acquisition frequency of the bioinformation according to the user's emotion.

[0081] The acquisition unit can analyze the past health data of the care recipient and determine the optimal acquisition timing. For example, the acquisition unit acquires data during the most stable time period based on the past health data of the care recipient. The past health data includes, for example, health records from the past year and data on specific illnesses. For example, the acquisition unit acquires data during a time period when abnormalities are likely to occur based on the past abnormal data of the care recipient. The acquisition unit can also determine the optimal acquisition timing by taking into account the life rhythm of the care recipient. In this way, more accurate data can be collected by determining the optimal acquisition timing based on the past health data.

[0082] The acquisition unit can filter the data based on the activity level and environmental conditions of the care recipient when acquiring the biological information. For example, when the care recipient is exercising, the acquisition unit acquires data when the care recipient is resting after exercising. The activity level can be measured using, for example, a pedometer or a heart rate monitor. In addition, when the care recipient is out, the acquisition unit can also acquire data after the care recipient returns home. The environmental conditions can take into account, for example, temperature, humidity, noise level, and the like. For example, when the care recipient is in a noisy environment, the acquisition unit acquires data in a quiet environment. This allows more accurate data to be acquired by filtering the data based on the activity level and environmental conditions.

[0083] The acquisition unit can estimate the user's emotion and determine the priority of the biometric information to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion using, for example, a technique such as facial expression recognition or voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the acquisition unit preferentially acquires pulse and blood pressure data. In addition, when the user is relaxed, the acquisition unit can also preferentially acquire oxygen concentration data. Furthermore, when the user is in a hurry, the acquisition unit can also preferentially acquire only the most important data. As a result, important data can be preferentially acquired by determining the priority of the biometric information based on the user's emotion.

[0084] When acquiring the biological information, the acquisition unit can preferentially acquire highly relevant data in consideration of the geographical location information of the care recipient. For example, when the care recipient is at high altitude, the acquisition unit preferentially acquires oxygen concentration data. The geographical location information can be acquired using, for example, GPS data or a location information service. In addition, when the care recipient is in an urban area, the acquisition unit can also preferentially acquire pulse data. Furthermore, when the care recipient is at home, the acquisition unit can also preferentially acquire blood pressure data. In this way, by preferentially acquiring highly relevant data in consideration of the geographical location information, more appropriate data can be collected.

[0085] The acquisition unit can analyze the diet and medicine intake status of the care recipient when acquiring the biological information, and acquire related data. For example, the acquisition unit acquires blood glucose level data immediately after the care recipient eats a meal. The diet and medicine intake status can be recorded using, for example, a food diary or a medicine taking record. The acquisition unit can also acquire pulse data immediately after the care recipient takes medicine. Furthermore, the acquisition unit can also acquire oxygen concentration data after the care recipient eats a specific meal. This allows more accurate data to be collected by analyzing the diet and medicine intake status and acquiring related data.

[0086] The upload unit can estimate the user's emotion and adjust the data upload frequency based on the estimated user's emotion. The upload unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the upload unit increases the upload frequency to transmit detailed data. In addition, when the user is relaxed, the upload unit can also reduce the upload frequency to reduce the burden. Furthermore, when the user is in a hurry, the upload unit can temporarily reduce the upload frequency and transmit the data all at once later. In this way, more appropriate data can be transmitted by adjusting the data upload frequency according to the user's emotion.

[0087] The upload unit can monitor the network state when uploading data and perform uploading at the optimal timing. For example, when the network is congested, the upload unit uploads data during a time period when the network is not congested. The network state can be monitored, for example, in terms of bandwidth, delay, packet loss, etc. Furthermore, when the network speed is slow, the upload unit can upload data after the speed has recovered. Furthermore, when the network is stable, the upload unit can upload data immediately. In this way, by monitoring the network state and performing uploading at the optimal timing, it is possible to improve the efficiency of data transmission.

[0088] The upload unit can adjust the encryption strength based on the importance of the data when uploading the data. For example, the upload unit applies high-strength encryption to important data and uploads it. The encryption strength can be adjusted, for example, by adjusting the key length, the selection of the encryption algorithm, and the like. The upload unit can also apply medium-strength encryption to general data and upload it. Furthermore, the upload unit can also apply low-strength encryption to data of low importance and upload it. In this way, by adjusting the encryption strength based on the importance of the data, it is possible to transmit data efficiently while ensuring security.

[0089] The upload unit can estimate the user's emotion and determine the priority of data to be uploaded based on the estimated user's emotion. The upload unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the upload unit preferentially uploads important data. In addition, when the user is relaxed, the upload unit can also upload all data evenly. Furthermore, when the user is in a hurry, the upload unit can also preferentially upload only the most important data. In this way, important data can be preferentially transmitted by determining the priority of data based on the user's emotion.

[0090] When uploading data, the uploading unit can prioritize uploading highly relevant data in consideration of the geographical location information of the care recipient. For example, when the care recipient is at high altitude, the uploading unit prioritizes uploading oxygen concentration data. The geographical location information can be acquired, for example, using GPS data or a location information service. In addition, when the care recipient is in an urban area, the uploading unit can also prioritize uploading pulse data. Furthermore, when the care recipient is at home, the uploading unit can also prioritize uploading blood pressure data. In this way, by prioritized uploading of highly relevant data in consideration of the geographical location information, more appropriate data can be transmitted.

[0091] The uploading unit can analyze the social media activity of the care recipient when uploading data, and upload related data. For example, if the care recipient posts about health on social media, the uploading unit prioritizes uploading the data. For example, the social media activity can be analyzed such as the content of the post, the number of likes, and the content of comments. In addition, if the care recipient reports an abnormality on social media, the uploading unit can also prioritize uploading the data. Furthermore, if the care recipient reports a specific symptom on social media, the uploading unit can also prioritize uploading the data. In this way, by analyzing social media activity and uploading related data, more appropriate data can be transmitted.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. The analysis unit estimates the user's emotions using technologies such as facial expression recognition and voice analysis. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the analysis unit performs a detailed analysis to aim for early detection of abnormalities. In addition, when the user is relaxed, the analysis unit can also perform a normal analysis and check the trend of the data. Furthermore, when the user is in a hurry, the analysis unit can also perform a simplified analysis and detect only important abnormalities. As a result, a more appropriate analysis result can be obtained by adjusting the analysis algorithm based on the user's emotions.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past medical data. For example, the analysis unit detects abnormalities by comparing current data based on past medical data. Past medical data includes, for example, medical records from the past year and data related to specific diseases. The analysis unit can also refer to past medical data to find specific patterns and reflect them in the analysis. Furthermore, the analysis unit can also use past medical data to optimize the analysis algorithm. As a result, the accuracy of the analysis can be improved by referring to past medical data.

[0094] The analysis unit can analyze the data taking into consideration the lifestyle habits and environmental conditions of the care recipient during the analysis. The analysis unit analyzes the data taking into consideration, for example, the dietary habits of the care recipient. Lifestyle habits include, for example, diet, exercise, sleep, etc. The analysis unit can also analyze the data taking into consideration the exercise habits of the care recipient. Furthermore, the analysis unit can analyze the data taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. As a result, by analyzing the data taking into consideration the lifestyle habits and environmental conditions, more accurate analysis results can be obtained.

[0095] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, technologies such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit provides a simple and highly visible display method when the user is feeling stressed. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the display method of the analysis result based on the user's emotion.

[0096] The analysis unit can analyze the data taking into consideration the geographical distribution of the care recipient. For example, if the care recipient lives in a high altitude, the analysis unit focuses on the oxygen concentration data during the analysis. The geographical distribution can be acquired using, for example, GPS data or a location information service. Furthermore, if the care recipient lives in an urban area, the analysis unit can also focus on the pulse data during the analysis. Furthermore, if the care recipient lives in a rural area, the analysis unit can also focus on the blood pressure data during the analysis. In this way, by analyzing the data taking into consideration the geographical distribution, more accurate analysis results can be obtained.

[0097] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the care recipient. The analysis unit, for example, refers to literature related to the symptoms of the care recipient and reflects the literature in the analysis. Examples of related literature include academic papers and medical guidelines. The analysis unit can also refer to the past medical records of the care recipient and reflect the literature in the analysis. Furthermore, the analysis unit can also refer to literature related to the lifestyle habits of the care recipient and reflect the literature in the analysis. In this way, the accuracy of the analysis can be improved by referring to related literature.

[0098] The detection unit can estimate the user's emotion and adjust the anomaly detection criteria based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the detection unit tightens the anomaly detection criteria when the user is stressed. In addition, the detection unit can also normalize the anomaly detection criteria when the user is relaxed. Furthermore, the detection unit can loosen the anomaly detection criteria when the user is in a hurry. This allows more appropriate anomaly detection to be performed by adjusting the anomaly detection criteria based on the user's emotion.

[0099] The detection unit can improve the accuracy of detection when detecting an anomaly by referring to past anomaly data. For example, the detection unit detects an anomaly by comparing the past anomaly data with current data. The past anomaly data includes, for example, anomaly records from the past year and data related to specific anomalies. The detection unit can also refer to the past anomaly data to find specific patterns and reflect them in the detection. Furthermore, the detection unit can also use the past anomaly data to optimize the detection algorithm. Thereby, the detection accuracy can be improved by referring to the past anomaly data.

[0100] When detecting an abnormality, the detection unit can detect the abnormality taking into consideration the lifestyle habits and environmental conditions of the care recipient. The detection unit detects the abnormality, for example, taking into consideration the eating habits of the care recipient. Lifestyle habits include, for example, eating, exercise, sleep, etc. The detection unit can also detect the abnormality taking into consideration the exercise habits of the care recipient. Furthermore, the detection unit can also detect the abnormality taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. As a result, by detecting the abnormality taking into consideration the lifestyle habits and environmental conditions, more accurate abnormality detection can be performed.

[0101] The detection unit can estimate the user's emotion and determine the priority of anomaly detection based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the detection unit preferentially detects important anomalies when the user is stressed. The detection unit can also detect all anomalies equally when the user is relaxed. Furthermore, the detection unit can also preferentially detect only the most important anomalies when the user is in a hurry. As a result, important anomalies can be preferentially detected by determining the priority of anomaly detection based on the user's emotion.

[0102] The detection unit can detect the abnormality by considering the geographical distribution of the care recipient. For example, if the care recipient lives at high altitude, the detection unit detects the abnormality by focusing on the abnormality in oxygen concentration. The geographical distribution can be acquired by using, for example, GPS data or a location information service. Furthermore, if the care recipient lives in an urban area, the detection unit can also detect the abnormality by focusing on the abnormality in pulse rate. Furthermore, if the care recipient lives in a rural area, the detection unit can also detect the abnormality by focusing on the abnormality in blood pressure. In this way, by detecting the abnormality by considering the geographical distribution, more accurate abnormality detection can be performed.

[0103] When detecting an abnormality, the detection unit can improve the accuracy of detection by referring to literature related to the care recipient. The detection unit, for example, refers to literature related to the symptoms of the care recipient and reflects it in the abnormality detection. The related literature includes, for example, academic papers and medical guidelines. The detection unit can also refer to the past medical records of the care recipient and reflect it in the abnormality detection. Furthermore, the detection unit can also refer to literature related to the lifestyle habits of the care recipient and reflect it in the abnormality detection. In this way, the detection accuracy can be improved by referring to the related literature.

[0104] The notification unit can estimate the user's emotion and adjust the content of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the notification unit sends a concise and to the point notification when the user is feeling stressed. The notification unit can also send a notification including detailed information when the user is relaxed. Furthermore, the notification unit can also send a quick and concise notification when the user is in a hurry. This allows for more appropriate notification by adjusting the content of the notification based on the user's emotion.

[0105] The notification unit can select the optimal notification method by referring to past notification history at the time of notification. The notification unit selects the most effective notification method based on, for example, the past notification history. The notification method includes, for example, email, SMS, and app notification. The notification unit can also refer to the past notification history, find a specific pattern, and reflect it in the notification. Furthermore, the notification unit can also optimize the notification algorithm by using the past notification history. As a result, the optimal notification method can be selected by referring to the past notification history.

[0106] The notification unit can provide notification while taking into consideration the lifestyle habits and environmental conditions of the care recipient. The notification unit provides notification while taking into consideration, for example, the eating habits of the care recipient. Lifestyle habits include, for example, eating, exercise, sleep, etc. The notification unit can also provide notification while taking into consideration the exercise habits of the care recipient. Furthermore, the notification unit can also provide notification while taking into consideration the living environment of the care recipient. Environmental conditions include, for example, temperature, humidity, noise level, etc. In this way, by providing notification while taking into consideration the lifestyle habits and environmental conditions, more appropriate notification can be provided.

[0107] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the notification unit prioritizes sending important notifications when the user is feeling stressed. In addition, the notification unit can also send all notifications equally when the user is relaxed. Furthermore, the notification unit can also prioritize sending only the most important notifications when the user is in a hurry. In this way, important notifications can be prioritized by determining the priority of notifications based on the user's emotion.

[0108] The notification unit can select the optimal notification method in consideration of the geographical location information of the care recipient at the time of notification. For example, when the care recipient is at high altitude, the notification unit preferentially transmits a notification regarding an abnormality in oxygen concentration. The geographical location information can be acquired using, for example, GPS data or a location information service. In addition, when the care recipient is in an urban area, the notification unit can also preferentially transmit a notification regarding an abnormality in pulse rate. Furthermore, when the care recipient is at home, the notification unit can also preferentially transmit a notification regarding an abnormality in blood pressure. In this way, more appropriate notification can be provided by selecting the optimal notification method in consideration of the geographical location information.

[0109] At the time of notification, the notification unit can analyze the social media activity of the care recipient and provide a related notification. For example, when the care recipient posts on social media about health, the notification unit sends a notification based on the content of the post. For example, the social media activity can be analyzed such as the content of the post, the number of likes, and the content of the comments. In addition, when the care recipient reports an abnormality on social media, the notification unit can also send a notification based on the content. Furthermore, when the care recipient reports a specific symptom on social media, the notification unit can also send a notification based on the content. In this way, more appropriate notifications can be provided by analyzing social media activity and providing related notifications.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The nursing and care support system can further include a sleep analysis unit that monitors the sleep patterns of the care recipient. The sleep analysis unit can analyze the quality and duration of the care recipient's sleep, and send a notification to the notification unit if an abnormality is detected. For example, an alert can be generated if the care recipient is not getting enough sleep or if abnormal movements are detected during sleep. The sleep analysis unit can also upload the sleep data of the care recipient to the cloud and compare it with past data for analysis. This allows the care recipient's sleep state to be constantly monitored, and necessary measures can be taken promptly.

[0112] The nursing and care support system can further include a meal recorder that records the meal contents of the care recipient. The meal recorder can record the contents and calories of the meals consumed by the care recipient and analyze the nutritional balance. For example, an alert can be generated if the care recipient is consuming an excess or deficiency of a specific nutrient. The meal recorder can also upload the care recipient's meal data to the cloud and compare it with past data for analysis. This allows the care recipient's meal contents to be constantly monitored and necessary measures to be taken promptly.

[0113] The nursing and care support system can further include a motion analysis unit that monitors the amount of exercise of the care recipient. The motion analysis unit can analyze the number of steps and exercise time of the care recipient, and send a notification to the notification unit if an abnormality is detected. For example, an alert can be generated if the care recipient is not exercising enough or is exercising too much. The motion analysis unit can also upload the exercise data of the care recipient to the cloud and compare it with past data for analysis. This allows the exercise status of the care recipient to be constantly monitored and necessary measures to be taken promptly.

[0114] The nursing and care support system can further include a medication recorder that records the medication status of the care recipient. The medication recorder can record the type and time of medication taken by the care recipient and analyze the medication compliance status. For example, an alert can be generated if the care recipient forgets to take medication or takes too much. The medication recorder can also upload the care recipient's medication data to the cloud and compare it with past data for analysis. This allows the care recipient's medication status to be constantly monitored and necessary measures to be taken promptly.

[0115] The nursing / care support system may further include an emotion analysis unit that estimates the emotion of the care recipient and adjusts the response based on the estimated emotion. The emotion analysis unit analyzes the facial expression and voice of the care recipient to estimate the emotional state. For example, if the care recipient is feeling stressed, a notification is sent to provide a relaxing environment. If the care recipient is relaxed, a normal response can be made. This makes it possible to respond appropriately according to the emotional state of the care recipient.

[0116] The nursing and care support system can further include a social activity recording unit that records the social activities of the care recipient. The social activity recording unit can record the social activities and events in which the care recipient participates and analyze the social connections. For example, an alert can be generated if the care recipient is isolated or lacks social activities. The social activity recording unit can also upload the social activity data of the care recipient to the cloud and compare it with past data for analysis. This allows the social connections of the care recipient to be constantly monitored and necessary measures to be taken promptly.

[0117] The nursing / care support system may further include a notification adjustment unit that estimates the emotion of the care recipient and adjusts the content of the notification based on the estimated emotion. The notification adjustment unit analyzes the facial expression and voice of the care recipient to estimate the emotional state. For example, if the care recipient is feeling stressed, a simple notification that focuses on the main points can be sent. Also, if the care recipient is relaxed, a notification including detailed information can be sent. This makes it possible to provide an appropriate notification according to the emotional state of the care recipient.

[0118] The nursing / care support system may further include an abnormality detection adjustment unit that estimates the emotion of the care recipient and adjusts the abnormality detection standard based on the estimated emotion. The abnormality detection adjustment unit analyzes the facial expression and voice of the care recipient to estimate the emotional state. For example, if the care recipient is feeling stressed, the abnormality detection standard can be set to be strict. On the other hand, if the care recipient is relaxed, the abnormality detection standard can be set to be normal. This allows for appropriate abnormality detection according to the emotional state of the care recipient.

[0119] The nursing / care support system may further include an upload adjustment unit that estimates the emotion of the care recipient and adjusts the data upload frequency based on the estimated emotion. The upload adjustment unit analyzes the facial expression and voice of the care recipient to estimate the emotional state. For example, if the care recipient is feeling stressed, the upload frequency can be increased to transmit detailed data. Also, if the care recipient is relaxed, the upload frequency can be reduced to reduce the burden on the care recipient. This allows appropriate data uploading according to the emotional state of the care recipient.

[0120] The nursing and care support system can further include an analysis adjustment unit that estimates the emotions of the care recipient and adjusts the analysis algorithm based on the estimated emotions. The analysis adjustment unit analyzes the facial expressions and voice of the care recipient to estimate the emotional state. For example, if the care recipient is feeling stressed, a detailed analysis is performed to aim for early detection of abnormalities. Also, if the care recipient is relaxed, a normal analysis is performed to check the data trend. This allows appropriate analysis to be performed according to the emotional state of the care recipient.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The acquisition unit periodically acquires biological information such as pulse rate, oxygen concentration, blood pressure, etc. For example, the biological information is acquired using a beacon device attached to the body of the care recipient, and the biological information is acquired every hour. Step 2: The upload unit uploads the biometric information acquired by the acquisition unit to the cloud. For example, the acquired data is encrypted and transmitted to the cloud. An encryption algorithm such as AES or RSA can be used for the encryption. Step 3: The analysis unit analyzes the biometric information uploaded by the upload unit. For example, the analysis unit analyzes the data based on past medical data or daily trends in data values. Statistical analysis methods such as moving average and regression analysis can be used for the analysis. Step 4: The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, it detects abnormalities such as when the pulse rate is significantly higher than normal or when the oxygen concentration is low. The criteria for abnormalities can be set to the upper and lower pulse limits, the oxygen concentration threshold, etc. Step 5: The notification unit sends a notification to the doctor when an abnormality is detected by the detection unit. For example, the notification is sent to the doctor by email, SMS, app notification, or other method.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned acquisition unit, upload unit, analysis unit, detection unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is attached to the body of the care recipient using a beacon device of the smart device 14, and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The upload unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that encrypts the acquired data and transmits it to the cloud. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that analyzes the data uploaded to the cloud. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects an abnormality based on the analyzed data. The notification unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits a notification to a doctor when an abnormality is detected. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned acquisition unit, upload unit, analysis unit, detection unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is attached to the body of the care recipient using a beacon device of the smart glasses 214, and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The upload unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that encrypts the acquired data and transmits it to the cloud. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that analyzes the data uploaded to the cloud. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects an abnormality based on the analyzed data. The notification unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits a notification to a doctor when an abnormality is detected. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned acquisition unit, upload unit, analysis unit, detection unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is attached to the body of the care recipient using a beacon device of the headset type terminal 314, and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The upload unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that encrypts the acquired data and transmits it to the cloud. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that analyzes the data uploaded to the cloud. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects an abnormality based on the analyzed data. The notification unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits a notification to a doctor when an abnormality is detected. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] Each of the multiple elements including the above-mentioned acquisition unit, upload unit, analysis unit, detection unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is attached to the body of the care recipient using a beacon device of the robot 414, and periodically acquires biological information such as pulse, oxygen concentration, and blood pressure. The upload unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that encrypts the acquired data and transmits it to the cloud. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that analyzes the data uploaded to the cloud. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects an abnormality based on the analyzed data. The notification unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits a notification to a doctor when an abnormality is detected. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) An acquisition unit that periodically acquires biological information such as pulse, oxygen concentration, and blood pressure; an upload unit that uploads the biometric information acquired by the acquisition unit to a cloud; an analysis unit that analyzes the biometric information uploaded by the upload unit; a detection unit that detects an abnormality based on the data analyzed by the analysis unit; A notification unit that transmits a notification to a doctor when an abnormality is detected by the detection unit. A system characterized by: (Appendix 2) The acquisition unit is Equipped with a beacon device that can be attached to the body of the person receiving care 2. The system of claim 1, (Appendix 3) The upload unit is The captured data is encrypted and sent to the cloud 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze data based on historical clinical data or daily trends 2. The system of claim 1, (Appendix 5) The detection unit is Detects abnormalities such as a significantly higher pulse rate than normal or a decrease in oxygen concentration 2. The system of claim 1, (Appendix 6) The notification unit is If an abnormality is detected, a notification will be sent to request an emergency call from a doctor. 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a user's emotion and adjust the frequency of acquiring biometric information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze the care recipient's past health data and determine the optimal timing for obtaining the data 2. The system of claim 1, (Appendix 9) The acquisition unit is When capturing vital signs, the data is filtered based on the care recipient's activity level and environmental conditions. 2. The system of claim 1, (Appendix 10) The acquisition unit is A user's emotion is estimated, and a priority order of biometric information to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring biometric information, the system prioritizes acquiring data that is highly relevant by taking into account the geographic location information of the care recipient. 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring vital signs, the diet and medication intake of the care recipient are analyzed to acquire relevant data. 2. The system of claim 1, (Appendix 13) The upload unit is Inferring user sentiment and adjusting the frequency of data uploads based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The upload unit is When uploading data, monitor the network status and upload at the optimal time. 2. The system of claim 1, (Appendix 15) The upload unit is Adjust encryption strength when uploading data based on the importance of the data 2. The system of claim 1, (Appendix 16) The upload unit is Infer user sentiment and prioritize data to be uploaded based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The upload unit is When uploading data, the geographic location of the care recipient is taken into consideration to prioritize uploading the most relevant data. 2. The system of claim 1, (Appendix 18) The upload unit is When uploading data, analyze the social media activity of the care recipient and upload relevant data 2. The system of claim 1, (Appendix 19) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is When analyzing, past medical data is referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing data, the lifestyle and environmental conditions of the care recipient are taken into consideration. 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The analysis unit is When analyzing data, the geographic distribution of care recipients is taken into account. 2. The system of claim 1, (Appendix 24) The analysis unit is When analyzing, refer to related literature on the care recipient to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 25) The detection unit is Infer user sentiment and adjust anomaly detection criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The detection unit is When detecting anomalies, improve the accuracy of detection by referring to past anomaly data. 2. The system of claim 1, (Appendix 27) The detection unit is When detecting an abnormality, the care recipient's lifestyle and environmental conditions are taken into consideration. 2. The system of claim 1, (Appendix 28) The detection unit is Estimate user emotions and prioritize anomaly detection based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The detection unit is When detecting an abnormality, the geographical distribution of care recipients is taken into account. 2. The system of claim 1, (Appendix 30) The detection unit is When detecting an abnormality, improve the accuracy of the detection by referring to related literature on the care recipient. 2. The system of claim 1, (Appendix 31) The notification unit is Infer user emotions and adjust notification content based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The notification unit is When notifying, select the optimal notification method by referring to past notification history 2. The system of claim 1, (Appendix 33) The notification unit is When notifying users, the care recipient's lifestyle and environmental conditions are taken into consideration. 2. The system of claim 1, (Appendix 34) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 35) The notification unit is When notifying, the optimal notification method is selected taking into account the geographic location information of the care recipient. 2. The system of claim 1, (Appendix 36) The notification unit is Upon notification, analyze the social media activity of the care recipient and provide relevant notifications 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that periodically acquires biological information such as pulse rate, oxygen concentration, and blood pressure; an upload unit that uploads the biometric information acquired by the acquisition unit to a cloud; an analysis unit that analyzes the biometric information uploaded by the upload unit; a detection unit that detects an abnormality based on the data analyzed by the analysis unit; A notification unit that transmits a notification to a doctor when an abnormality is detected by the detection unit. A system characterized by:

2. The acquisition unit is Equipped with a beacon device that can be attached to the body of the person receiving care 2. The system of claim 1 .

3. The analysis unit is Analyze data based on historical clinical data or daily trends 2. The system of claim 1 .

4. The detection unit is Detects abnormalities such as a significantly higher pulse rate than normal or a decrease in oxygen concentration 2. The system of claim 1 .

5. The notification unit is If an abnormality is detected, a notification will be sent to request an emergency call from a doctor.

2. The system of claim 1 .

6. The acquisition unit is To estimate a user's emotion and adjust the frequency of acquiring biometric information based on the estimated user's emotion.

2. The system of claim 1 .

7. The acquisition unit is Analyze the care recipient's past health data and determine the optimal timing for obtaining the data 2. The system of claim 1 .

8. The acquisition unit is When capturing vital signs, the data is filtered based on the care recipient's activity level and environmental conditions.

2. The system of claim 1 .

Images