system

Abstract

We provide the system. [Solution] A data processing method for collecting and analyzing medical history, living conditions, and family relationship data of elderly people, A state determination means that detects an emergency and generates response options according to the situation, A user interface means for presenting the user with response options and obtaining their approval, Communication methods for sharing information and coordinating with medical institutions and families, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern aging societies, there is a need to create an environment in which the elderly can receive appropriate and prompt medical care and nursing care even in emergencies without being isolated. However, it is difficult to make accurate judgments while respecting the will and values of users, and there may be cases where smooth cooperation with family members and medical institutions living in remote areas cannot be achieved. It is important to solve this problem and create a method for ensuring the dignity and safety of the elderly.

Means for Solving the Problems

[0005] This invention includes data processing means for collecting and analyzing data on the medical history, living situation, and family relationships of elderly individuals. Furthermore, it includes state determination means for detecting emergencies and generating optimal response options according to the situation. The generated response options are presented to the user and provided through a user interface means for obtaining approval. In addition, the system includes communication means for quickly sharing information and coordinating with medical institutions and family members, enabling appropriate responses that reflect the user's wishes to the greatest extent possible, even in emergency situations.

[0006] "Elderly" refers to adults who may require physical and psychological support as they age.

[0007] "Medical history" refers to a detailed record of an individual patient's past medical treatments, therapies, and health status.

[0008] "Living conditions" refers to information about an individual's daily life, habits, and living environment.

[0009] "Family relationship data" refers to information about an individual's family structure and relationships with relatives.

[0010] "Data processing means" refers to technologies and devices used to analyze collected information and derive meaningful conclusions.

[0011] An "emergency situation" refers to a situation in which an unexpected danger or a sudden change in health occurs.

[0012] "State determination means" refers to technologies and devices used to make appropriate responses and decisions based on the current situation and data.

[0013] "Response options" refer to specific choices or methods of action for a particular situation or problem.

[0014] "User interface means" refers to the hardware and software that users use to interact with a system.

[0015] "Communication means" refers to technologies and devices for transmitting and receiving information.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. <9000000>It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered 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), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement this invention, a network system is configured that includes a device (terminal) used daily by elderly people and a server for central data processing. The system operates based on the interaction between the terminal, the server, and the user.

[0038] server

[0039] The server is the central hub for collecting and analyzing information across the entire system. It manages medical history, lifestyle data, and family relationship data collected from healthcare institutions and user terminals. The server utilizes AI models to analyze the data and build individual user profiles. These profiles are used to predict emergency response tendencies based on the user's past health history and values.

[0040] terminal

[0041] The terminal is a device used daily by elderly individuals, serving as both a data collection device and a user interface. The terminal monitors the user's biometric and activity information in real time via wearable sensors. When an anomaly is detected, the terminal issues voice and visual alerts to the user and transmits the details to a server.

[0042] User

[0043] The user is the subject of their own decision-making based on the information obtained through the system. Based on the information provided by the terminal, the user makes choices regarding health management and responses to emergencies in their daily life. The system also presents response options generated during emergencies and seeks the user's opinion and approval.

[0044] Specific example

[0045] Consider a scenario where an elderly person is living their daily life at home. The device constantly monitors the user's walking data and heart rate, and sends the data to a server. The server analyzes the data by comparing it with past medical history to check for any abnormalities. For example, if an abnormality such as a fall is detected, the device immediately alerts the user and simultaneously reports the details to the server. Based on this data, the server generates the most appropriate response options and contacts medical institutions or notifies family members. The user can review the options provided via the device and choose the response they believe is best.

[0046] This system aims to provide multifaceted support to enable elderly people to live more secure lives according to their own will.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The device monitors the user's biometric information (e.g., heart rate, steps taken, body temperature, etc.) in real time. This data is acquired via sensor devices and transmitted to the device.

[0050] Step 2:

[0051] The device sends collected biometric information to the server at regular intervals. The data is transferred using secure communication protocols and with security in mind.

[0052] Step 3:

[0053] The server stores the received biometric information in a database. This data is integrated with the user's past medical history and previously collected lifestyle data.

[0054] Step 4:

[0055] The server uses AI algorithms to analyze the collected data and evaluate the user's state. This allows for the identification of normal and abnormal states.

[0056] Step 5:

[0057] If the device detects an anomaly (for example, a fall or a sudden change in heart rate), it will immediately notify the user with an alert. This notification will be made via an audio alert or on-screen display.

[0058] Step 6:

[0059] Upon detecting an anomaly, the terminal immediately sends the information, along with details, to the server. The server then prepares a rapid response based on this information.

[0060] Step 7:

[0061] The server generates emergency response options. This process includes selecting recommended actions based on the user profile.

[0062] Step 8:

[0063] The server sends the generated response options to the terminal, which then presents them to the user. The user reviews the presented options and selects or approves the desired response.

[0064] Step 9:

[0065] If the user does not select a response option or does not respond, a pre-configured automated response protocol will be executed.

[0066] Step 10:

[0067] Based on the selected response, the server will contact medical institutions and family members as needed to provide appropriate follow-up. This process enables a rapid medical response.

[0068] (Example 1)

[0069] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0070] When elderly people live their daily lives, they need to be able to respond quickly to physical abnormalities and medical emergencies. However, with existing technology, it is difficult to grasp the health status of elderly people in real time and to provide efficient and appropriate countermeasures. Furthermore, there is a lack of capability to respond in situations where rapid information sharing with family and medical institutions is required. A system is needed to solve these problems and enable elderly people to live more independently and with greater peace of mind.

[0071] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0072] In this invention, the server includes information processing means, data collection means for collecting biometric and activity information in real time, and warning means for issuing alerts when an anomaly is detected. This makes it possible to constantly monitor the health status of elderly people, promptly propose countermeasures when an anomaly occurs, and share information with relevant parties.

[0073] "Information processing means" refers to a device or system that has the function of collecting information on the medical history, living situation, and family relationships of elderly people, and analyzing the data.

[0074] "Data collection means" refers to a device or system that has the function of collecting biometric and activity information of elderly people in real time using sensors.

[0075] A "warning device" is a device or system that alerts the user through sound or visual means when an anomaly is detected.

[0076] A "decision support tool" is a device or system that uses an AI model to analyze collected data and generate the optimal response option in an emergency situation.

[0077] "User interface means" refers to a device or system that has the function of presenting the user with response options and supporting decision-making.

[0078] "Communication means" refers to a device or system equipped with network functions for sharing information and coordinating with external organizations and relatives.

[0079] To implement this invention, a system is constructed in which a device (terminal) used daily by the elderly and a central device (server) for processing data work together. Specifically, the terminal uses devices such as wearable sensors to monitor the user's biometric and activity information in real time. This makes it possible to accurately collect data such as heart rate, steps taken, and body temperature.

[0080] The server receives this data and analyzes it using an AI model. The AI ​​model compares it with past medical history and lifestyle data to determine if there are any abnormalities. This process can be carried out using data analysis software such as Python or R. If an abnormality is detected, the server immediately generates appropriate countermeasures and sends notifications.

[0081] Users manage their daily health and respond to emergencies based on information provided through the device. The device has the ability to emit voice and visual alerts in case of an emergency, notifying the user of the situation. It also presents response options generated by the server, helping the user select the most appropriate one.

[0082] As a concrete example, when an elderly person is living at home, the device monitors the user's walking data and heart rate and sends the data to a server. The server inputs this data into an AI model and analyzes it by comparing it with past medical history. If an abnormality is detected, for example, if there is a possibility of a fall, the device will issue a warning to the user, and the server will generate options to enable contact with medical institutions or relatives.

[0083] This system aims to support elderly people in living independent lives with peace of mind.

[0084] Example prompt: "Explain how to compare real-time biometric information of elderly users with historical medical data to suggest the most appropriate response options when an anomaly is detected."

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The device collects the user's biometric information (e.g., heart rate, body temperature) and activity information (e.g., steps taken, distance traveled) in real time through wearable sensors. This data is temporarily stored within the device. The input data is the user's sensor information, and the output is the temporarily stored data.

[0088] Step 2:

[0089] The device analyzes the collected data to determine if there are any abnormalities. If an abnormality is detected, for example, if the heart rate exceeds a certain threshold, the device will emit an audio or visual alert. This process involves data calculations comparing the data to a threshold, and the output indicates whether or not an abnormality exists.

[0090] Step 3:

[0091] The terminal sends data to the server indicating that an anomaly has been detected. The data sent includes details of the anomaly (e.g., type of anomaly, detection time, user location). The input is information about the anomaly, and the output is the data sent to the server.

[0092] Step 4:

[0093] The server inputs data received from the terminal into an AI model, which then compares it with the user's medical history and lifestyle for a detailed analysis. This analysis involves trend analysis based on past data, and the output includes the cause and urgency of the anomaly.

[0094] Step 5:

[0095] The server generates possible response options based on the results of the anomaly analysis. For example, it might consider contacting medical institutions, notifying relatives, or arranging for emergency response. An AI generative model is used for this, and the output is a list of response options.

[0096] Step 6:

[0097] The server sends the generated response options to the terminal and presents them to the user. The user reviews the presented options and chooses an action based on their own judgment. The input is the response options, and the output is the user's choice.

[0098] This series of processes allows elderly people to maintain an independent lifestyle while enabling them to respond quickly and appropriately in emergencies.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] For elderly people to live their daily lives with peace of mind, it is necessary to monitor their health status in real time and to respond quickly and appropriately when abnormalities are detected. However, existing systems have limitations in terms of automated response functions for immediate response to emergencies involving the elderly, as well as in real-time data collection and analysis. This invention aims to solve these problems and improve the safety and health management of the elderly.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes data collection means for monitoring the health status of elderly people in real time, state determination means for detecting abnormalities and issuing warnings via smart devices, as well as generating response options, and user interface means for presenting response options to the user according to the situation and obtaining approval. This enables elderly people to respond quickly in emergencies.

[0104] "Data collection methods" refer to systems for recording the health status of elderly people in real time, and involve acquiring biometric and activity information using wearable devices and smart devices.

[0105] The "status determination means" is a function that analyzes collected data to detect changes or abnormalities in health status, sends alerts to smart devices, and generates appropriate response options.

[0106] A "user interface means" is an interface that presents the generated response options to the user and obtains their approval, enabling interaction with the user via a smart device.

[0107] "Communication means" refers to network means that enable smooth collaboration by sharing the results of anomaly detection and the generation of response options with healthcare providers and family members.

[0108] The system for implementing this invention mainly consists of the interaction between a server, a terminal, and a user. The server manages the health information of the elderly and analyzes the data in real time as needed. Specifically, the server receives real-time biometric information collected from wearable devices via connected terminals and uses an AI model to determine whether there are any abnormalities. Data processing is performed using cloud services such as AWS®, and databases such as DynamoDB are used.

[0109] A smartphone is used as the terminal, acquiring data from wearable sensors via Bluetooth. The terminal receives real-time data through an application developed in Java® or Kotlin, and provides the user with audio warnings and visual alerts when an anomaly is detected. It also sends the anomaly detection information to a server and presents the user with corresponding response options. For example, if a sudden increase in heart rate is detected, a warning is immediately sent to the user, and details of the healthcare provider to contact as the optimal course of action are provided.

[0110] Based on information provided by the device, users select actions for their daily lives and, if necessary, approve response options generated by an AI model. This allows elderly individuals to respond quickly and independently, even in emergencies. The generated data enables rapid contact with healthcare providers and family members, allowing them to maintain a sense of security.

[0111] As a concrete example, consider a scenario where an elderly person suspects they are unwell while taking a walk. If a wearable device detects an abnormal heart rate, an alert is immediately sent from the smartphone, and a server analyzes the information to determine if contact with a medical institution is necessary. The result is then displayed on the smartphone, allowing the elderly person to quickly decide on a course of action.

[0112] Examples of prompt statements include:

[0113] "An elderly person experienced a sudden increase in heart rate. Based on this information, please list possible countermeasures."

[0114] This is one possible explanation. In this way, this system provides an environment in which elderly people can manage their own health and respond appropriately in emergencies.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The device receives biometric information in real time from wearable devices via Bluetooth. This biometric data includes heart rate, steps taken, and body temperature. This information is temporarily stored in an application on the device and compared to alert settings. If an anomaly is detected, the device issues an alert.

[0118] Step 2:

[0119] The terminal transmits biometric information to the server when an anomaly is detected. The biometric data from the detected anomaly is used as input and transferred to the server. The server receives this information, analyzes the data using an AI model, and evaluates the cause and trend of the anomaly. The output generates detailed information about the anomaly and predicted response options.

[0120] Step 3:

[0121] The server sends the abnormal condition along with the generated response options to the terminal as a result. Based on the received data, the terminal prompts the user for confirmation through the user interface. The user selects a response option and approves the action to be taken in the next step.

[0122] Step 4:

[0123] After the user selects and approves a response option, the device sends that information back to the server. This input information is used by the server as a guide to determine the next action to take. The server activates communication means to contact healthcare providers or family members as needed. The output generates notification content and emergency contact information.

[0124] Step 5:

[0125] The server stores records of implemented countermeasures in a database and uses them as a learning tool for future improvements. Input includes user response history and feedback. Output is an updated, optimized response option to be used the next time anomaly is detected.

[0126] In this way, the entire system aims to monitor the health status of elderly people and to respond quickly and accurately when abnormalities occur.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] The system of this invention aims to improve the quality of life for the elderly by comprehensively managing their health and emotions and providing appropriate responses. To this end, a terminal, server, emotion engine, and communication means for comprehensively managing and utilizing these are constructed.

[0129] server

[0130] The server plays a central role in integrating and analyzing medical history, living conditions, and family relationship data. In addition, the server incorporates an emotion engine, and user emotional information is also stored in the database. Through AI analysis, the server assesses the user's overall state, including changes in their emotions. This information plays a crucial role in making decisions in emergencies and everyday situations.

[0131] terminal

[0132] The device acquires the user's biometric and emotional data through sensors and cameras. Emotional data is collected based on analysis of voice and facial expressions. The device transmits the collected emotional data to a server in real time. This data allows for a rapid assessment of the user's current state and supports necessary interactions.

[0133] User

[0134] By using these systems on a daily basis, users can receive support tailored to their health and emotional changes. Users receive notifications and suggested support options from their devices and can provide feedback on their emotions and state. This feedback contributes to the learning of the emotion engine, enabling more personalized support.

[0135] Emotional Engine

[0136] The emotion engine is a system that analyzes a user's emotions and proposes appropriate responses to the server. Using machine learning algorithms, the engine learns the user's general mood tendencies based on past emotional data and performs predictive analysis.

[0137] Specific example

[0138] For example, imagine an elderly person is going about their normal activities at home when the device analyzes their facial expressions and tone of voice to detect emotions suggesting stress. This information is immediately sent to a server and analyzed by an emotion engine. If the situation is determined not to be urgent, the device will notify the user with suggestions for relaxation or to contact family members. By utilizing the emotion engine, the system can understand the user's emotional state and provide appropriate life support and medical assistance.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The device utilizes emotion recognition sensors to analyze the user's voice and facial expressions in real time, collecting emotional data. This includes voice tone and facial expressions.

[0142] Step 2:

[0143] The device sends the collected emotional data to the server. A secure protocol is used for communication, and data transfer is performed quickly.

[0144] Step 3:

[0145] The server activates the emotion engine and comprehensively analyzes the received emotion data. It compares it with past emotion history and evaluates the current emotional state.

[0146] Step 4:

[0147] The server generates response options to offer the user based on their emotional state. This step also takes into account the user's values ​​and past preferences.

[0148] Step 5:

[0149] The server sends the generated response options to the device. These options may include, for example, suggestions for relaxation exercises or notifications to family members.

[0150] Step 6:

[0151] The device presents the user with response options. It prompts the user to take the necessary action through on-screen displays and voice instructions.

[0152] Step 7:

[0153] The user reviews the presentation on their device and selects or customizes the appropriate options as needed. The user's selection is then fed back into the system.

[0154] Step 8:

[0155] The server records user feedback in its emotion engine and uses it to generate future responses. This process allows the system to continuously provide improved responses to users.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0158] There is a need to effectively manage the health and emotional states of the elderly and to provide rapid and appropriate responses to emergencies and unstable emotional states. However, conventional systems have the challenge of not being able to adequately consider individual health and emotional states in data integration and analysis. Therefore, there is a need to develop a system that enables individualized responses based on more sophisticated analysis.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes information gathering means, integrated analysis means, and emotion analysis means. This enables the detailed collection and analysis of each user's health information and the predictive evaluation of their emotional state, thereby allowing for the presentation of personalized countermeasures.

[0161] "Information gathering methods" refer to systems that acquire health and emotional information of elderly people using sensors and cameras.

[0162] An "integrated analysis tool" is a function that integrates collected health and emotional information and comprehensively analyzes it using machine learning algorithms.

[0163] "Emotional analysis tools" are functions that analyze and predictively evaluate the user's emotional state.

[0164] A "notification method" is a function that presents the user with appropriate countermeasures based on the user's health and emotional state.

[0165] "Communication means" refers to a network-based system for sharing information with users and external parties.

[0166] A "generative AI model" is an artificial intelligence model used to optimize future countermeasures based on collected feedback.

[0167] A "protocol" is a set of rules that define the procedures for automatically responding in an emergency.

[0168] This invention is a system that comprehensively manages the health and emotional state of elderly individuals and provides appropriate responses. Therefore, it comprises a terminal, a server, an emotion engine, and communication means for managing these components.

[0169] The device uses sensors and cameras to acquire the user's biometric and emotional data. Biometric data includes heart rate and body temperature, while emotional data is collected by analyzing voice and facial expressions. The device transmits the collected data to a server in real time. For example, the device captures the user's facial expressions and analyzes them to identify emotions indicating stress.

[0170] The server stores received data in a database and integrates it with information such as medical history, living situation, and family relationships. This information is analyzed using an emotion engine, and machine learning algorithms are used to evaluate the user's state. Specifically, an AI model is used to predict emotional trends and generate necessary countermeasures. This system allows the server to gain a deep understanding of the user's health and emotional state and provide personalized countermeasures.

[0171] Through this system, users can receive support tailored to their health status and emotional changes. Based on notifications from their device, they can choose whether or not to accept relaxation suggestions and can also consider notifying their family. This feedback is then sent back to the server and used as learning material for the emotion engine.

[0172] Specific example

[0173] For example, suppose an elderly person is going about their normal activities at home, and the device analyzes their facial expressions and tone of voice, detecting emotions that suggest stress. This information is immediately sent to a server and analyzed by an emotion engine. If the system determines that the situation is not urgent, the device will send a notification to the user suggesting relaxation or prompting them to contact family members.

[0174] Example of a prompt

[0175] "Describe in detail the algorithmic steps for proposing appropriate health support measures based on emotional data received from users."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The device collects the user's biometric and emotional data using sensors and cameras. Inputs for this collection include heart rate, body temperature, voice, and facial expressions. Specifically, the camera captures the user's face, and the microphone records voice data. The obtained data is recognized as emotional information indicating stress levels and happiness.

[0179] Step 2:

[0180] The device transmits collected biometric and emotional data to the server in real time. The input is the data collected in step 1, and the output is the integrated data transferred to the server. Specifically, the data is packaged and sent to the server using a secure communication protocol. Accurate data transmission is ensured by waiting for acknowledgment of receipt from the server.

[0181] Step 3:

[0182] The server integrates data received from the terminal with medical history, lifestyle information, and family relationship information, and stores it in a database. The input is various integrated health information, and the output is the stored comprehensive data. On the server, an emotion engine uses machine learning algorithms to evaluate the user's health and emotional state.

[0183] Step 4:

[0184] The server evaluates the user's health and emotional state based on the analysis results of the emotion engine and generates specific countermeasures. The input is the analysis results of the emotion engine, and the output is the specific countermeasures presented to the user. The generated countermeasures are put into concrete forms such as suggestions for relaxation or recommendations to contact family members.

[0185] Step 5:

[0186] The terminal notifies the user of countermeasures and collects feedback from the user. The input is the countermeasures from the server, and the output is the user's feedback. Specifically, the terminal notifies the user using voice and visual interfaces and presents feedback options. This feedback is sent back to the server and used as training material for the generative AI model.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] To detect health problems and emotional fluctuations faced by the elderly early and improve their quality of life, comprehensive, real-time data analysis is necessary. However, conventional systems have struggled to accurately grasp the emotional state of the elderly and provide appropriate responses. Furthermore, there have been problems with insufficient smooth information sharing and collaboration with medical institutions and related parties. Solving these challenges and realizing better care for the elderly is essential.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes information processing means for analyzing medical history and social environment, situation evaluation means for evaluating emergencies and suggesting countermeasures, and emotion analysis means for suggesting support through emotion analysis. This makes it possible to quickly and accurately grasp the health status and emotional fluctuations of elderly people and provide appropriate support and medical responses.

[0192] An "information processing means" is a system component that has the function of collecting and analyzing the medical history, social environment, and related data of elderly users.

[0193] A "situation evaluation tool" is a part of a system that monitors the user's situation, detects emergencies, and generates appropriate countermeasures.

[0194] An "information presentation means" is a function that displays generated countermeasures to the user and provides an interface to help them understand that information.

[0195] "Communication methods" refer to data exchange systems aimed at sharing information between medical facilities, related parties, and users, and facilitating smooth collaboration.

[0196] An "emotional analysis tool" is a system component that has an analytical function to analyze the emotional state of a user and suggest appropriate support measures.

[0197] Embodiments of this invention include an advanced system for managing the health status and emotions of elderly individuals. The server performs comprehensive data analysis through information processing means that analyze the user's medical history, social environment, and related party data, and generates appropriate countermeasures through situation evaluation means that detect emergencies.

[0198] The device uses sensors and cameras to acquire biometric and emotional data from elderly individuals. The collected data is transmitted to a server in real time and analyzed by an emotion analysis system. This analysis allows for an understanding of the user's emotional state, enabling timely provision of necessary support.

[0199] Users receive solutions generated via their devices and act according to the instructions. For example, if a user is feeling stressed, the device can suggest relaxation methods and automatically send notifications when it's necessary to contact medical institutions or relevant parties.

[0200] The system operates by integrating image processing technology using OpenCV, a custom algorithm (virtual library) for analyzing specific emotions, and a server communication module for data transmission.

[0201] As a concrete example, a device captures video footage of a specific elderly person relaxing at home and sends the video data to a server. The server uses a generative AI model to analyze the emotional state and returns appropriate responses to the device based on the generated prompt messages.

[0202] An example of a prompt for the generating AI model is, "Please tell me what relaxation methods should be suggested when an elderly person is feeling stressed." By providing this information to the device, users can receive more comprehensive care.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device uses sensors and cameras to capture biometric and emotional data from elderly individuals. The input is real-time audio and video data, and the output is analyzable digital data. The device then formats this data and prepares it for transmission to a server.

[0206] Step 2:

[0207] The server receives biometric and emotional data transmitted from the terminal. The input is digital data from the terminal, and the output is initial analysis results. The server prepares this data for analysis, integrates it with medical history and social data through information processing tools, and creates a comprehensive user profile.

[0208] Step 3:

[0209] The server uses sentiment analysis tools to analyze the emotional components of the received data. The input is integrated user profile data, and the output is a detailed analysis of the user's current emotional state. The server uses a generative AI model to generate appropriate countermeasures based on this emotional state.

[0210] Step 4:

[0211] The server understands the countermeasures generated using a generative AI model and forms prompt sentences as needed. The input is the result of sentiment analysis, and the output is a set of prompt sentences and specific countermeasures. The server transmits this information to the terminal via an information presentation device.

[0212] Step 5:

[0213] The terminal displays prompt messages received from the server to the user. Input is countermeasure information from the server, and output provides the user with visual and auditory notifications. The terminal displays information to the user in real time and prompts them to take necessary actions.

[0214] Step 6:

[0215] Based on the device's presentation, users engage in daily activities or try recommended relaxation methods. Any feedback from users is entered into the device and used to improve future responses. This feedback is analyzed by the system's learning mechanisms and reflected in future responses.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] To implement this invention, a network system is configured that includes a device (terminal) used daily by elderly people and a server for central data processing. The system operates based on the interaction between the terminal, the server, and the user.

[0233] server

[0234] The server is the central hub for collecting and analyzing information across the entire system. It manages medical history, lifestyle data, and family relationship data collected from healthcare institutions and user terminals. The server utilizes AI models to analyze the data and build individual user profiles. These profiles are used to predict emergency response tendencies based on the user's past health history and values.

[0235] terminal

[0236] The terminal is a device used daily by elderly individuals, serving as both a data collection device and a user interface. The terminal monitors the user's biometric and activity information in real time via wearable sensors. When an anomaly is detected, the terminal issues voice and visual alerts to the user and transmits the details to a server.

[0237] User

[0238] The user is the subject of their own decision-making based on the information obtained through the system. Based on the information provided by the terminal, the user makes choices regarding health management and responses to emergencies in their daily life. The system also presents response options generated during emergencies and seeks the user's opinion and approval.

[0239] Specific example

[0240] Consider a scenario where an elderly person is living their daily life at home. The device constantly monitors the user's walking data and heart rate, and sends the data to a server. The server analyzes the data by comparing it with past medical history to check for any abnormalities. For example, if an abnormality such as a fall is detected, the device immediately alerts the user and simultaneously reports the details to the server. Based on this data, the server generates the most appropriate response options and contacts medical institutions or notifies family members. The user can review the options provided via the device and choose the response they believe is best.

[0241] This system aims to provide multifaceted support to enable elderly people to live more secure lives according to their own will.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device monitors the user's biometric information (e.g., heart rate, steps taken, body temperature, etc.) in real time. This data is acquired via sensor devices and transmitted to the device.

[0245] Step 2:

[0246] The device sends collected biometric information to the server at regular intervals. The data is transferred using secure communication protocols and with security in mind.

[0247] Step 3:

[0248] The server stores the received biometric information in a database. This data is integrated with the user's past medical history and previously collected lifestyle data.

[0249] Step 4:

[0250] The server uses AI algorithms to analyze the collected data and evaluate the user's state. This allows for the identification of normal and abnormal states.

[0251] Step 5:

[0252] If the device detects an anomaly (for example, a fall or a sudden change in heart rate), it will immediately notify the user with an alert. This notification will be made via an audio alert or on-screen display.

[0253] Step 6:

[0254] Upon detecting an anomaly, the terminal immediately sends the information, along with details, to the server. The server then prepares a rapid response based on this information.

[0255] Step 7:

[0256] The server generates emergency response options. This process includes selecting recommended actions based on the user profile.

[0257] Step 8:

[0258] The server sends the generated response options to the terminal, which then presents them to the user. The user reviews the presented options and selects or approves the desired response.

[0259] Step 9:

[0260] If the user does not select a response option or does not respond, a pre-configured automated response protocol will be executed.

[0261] Step 10:

[0262] Based on the selected response, the server will contact medical institutions and family members as needed to provide appropriate follow-up. This process enables a rapid medical response.

[0263] (Example 1)

[0264] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] When elderly people live their daily lives, they need to be able to respond quickly to physical abnormalities and medical emergencies. However, with existing technology, it is difficult to grasp the health status of elderly people in real time and to provide efficient and appropriate countermeasures. Furthermore, there is a lack of capability to respond in situations where rapid information sharing with family and medical institutions is required. A system is needed to solve these problems and enable elderly people to live more independently and with greater peace of mind.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes information processing means, data collection means for collecting biometric and activity information in real time, and warning means for issuing alerts when an anomaly is detected. This makes it possible to constantly monitor the health status of elderly people, promptly propose countermeasures when an anomaly occurs, and share information with relevant parties.

[0268] "Information processing means" refers to a device or system that has the function of collecting information on the medical history, living situation, and family relationships of elderly people, and analyzing the data.

[0269] "Data collection means" refers to a device or system that has the function of collecting biometric and activity information of elderly people in real time using sensors.

[0270] A "warning device" is a device or system that alerts the user through sound or visual means when an anomaly is detected.

[0271] A "decision support tool" is a device or system that uses an AI model to analyze collected data and generate the optimal response option in an emergency situation.

[0272] "User interface means" refers to a device or system that has the function of presenting the user with response options and supporting decision-making.

[0273] "Communication means" refers to a device or system equipped with network functions for sharing information and coordinating with external organizations and relatives.

[0274] To implement this invention, a system is constructed in which a device (terminal) used daily by the elderly and a central device (server) for processing data work together. Specifically, the terminal uses devices such as wearable sensors to monitor the user's biometric and activity information in real time. This makes it possible to accurately collect data such as heart rate, steps taken, and body temperature.

[0275] The server receives this data and analyzes it using an AI model. The AI ​​model compares it with past medical history and lifestyle data to determine if there are any abnormalities. This process can be carried out using data analysis software such as Python or R. If an abnormality is detected, the server immediately generates appropriate countermeasures and sends notifications.

[0276] Users manage their daily health and respond to emergencies based on information provided through the device. The device has the ability to emit voice and visual alerts in case of an emergency, notifying the user of the situation. It also presents response options generated by the server, helping the user select the most appropriate one.

[0277] As a concrete example, when an elderly person is living at home, the device monitors the user's walking data and heart rate and sends the data to a server. The server inputs this data into an AI model and analyzes it by comparing it with past medical history. If an abnormality is detected, for example, if there is a possibility of a fall, the device will issue a warning to the user, and the server will generate options to enable contact with medical institutions or relatives.

[0278] This system aims to support elderly people in living independent lives with peace of mind.

[0279] Example prompt sentence: "Please explain the method of presenting the optimal response options when detecting abnormalities by comparing the real-time biometric information of elderly users with past medical data."

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The terminal collects the user's biometric information (e.g., heart rate, body temperature) and activity information (e.g., number of steps, moving distance) in real time through wearable sensors. This data is temporarily stored inside the terminal. The input data is the user's sensor information, and the output is the temporarily stored data.

[0283] Step 2:

[0284] The terminal analyzes the collected data to determine whether there are any abnormalities. If an abnormality is detected, for example, if the heart rate exceeds the reference value, the terminal sends a voice or visual alert. In this process, data calculations are performed to compare with the reference value, and the output is the presence or absence of an abnormality.

[0285] Step 3:

[0286] The terminal sends the data with detected abnormalities to the server. The data to be sent includes details of the abnormality (e.g., type of abnormality, detection time, user's location). The input is information related to the abnormality, and the output is the data sent to the server.

[0287] Step 4:

[0288] The server inputs the data received from the terminal into the AI model and performs a detailed analysis by comparing it with the user's medical history and lifestyle habits. In this analysis, trend analysis based on past data is performed, and the output is the cause and urgency of the abnormality.

[0289] Step 5:

[0290] The server generates possible response options based on the results of the anomaly analysis. For example, it might consider contacting medical institutions, notifying relatives, or arranging for emergency response. An AI generative model is used for this, and the output is a list of response options.

[0291] Step 6:

[0292] The server sends the generated response options to the terminal and presents them to the user. The user reviews the presented options and chooses an action based on their own judgment. The input is the response options, and the output is the user's choice.

[0293] This series of processes allows elderly people to maintain an independent lifestyle while enabling them to respond quickly and appropriately in emergencies.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] For elderly people to live their daily lives with peace of mind, it is necessary to monitor their health status in real time and to respond quickly and appropriately when abnormalities are detected. However, existing systems have limitations in terms of automated response functions for immediate response to emergencies involving the elderly, as well as in real-time data collection and analysis. This invention aims to solve these problems and improve the safety and health management of the elderly.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes data collection means for monitoring the health status of elderly people in real time, state determination means for detecting abnormalities and issuing warnings via smart devices, as well as generating response options, and user interface means for presenting response options to the user according to the situation and obtaining approval. This enables elderly people to respond quickly in emergencies.

[0299] "Data collection methods" refer to systems for recording the health status of elderly people in real time, and involve acquiring biometric and activity information using wearable devices and smart devices.

[0300] The "status determination means" is a function that analyzes collected data to detect changes or abnormalities in health status, sends alerts to smart devices, and generates appropriate response options.

[0301] A "user interface means" is an interface that presents the generated response options to the user and obtains their approval, enabling interaction with the user via a smart device.

[0302] "Communication means" refers to network means that enable smooth collaboration by sharing the results of anomaly detection and the generation of response options with healthcare providers and family members.

[0303] The system for implementing this invention mainly consists of the interaction between a server, a terminal, and a user. The server manages the health information of the elderly and analyzes the data in real time as needed. Specifically, the server receives real-time biometric information collected from wearable devices via connected terminals and uses an AI model to determine whether there are any abnormalities. Data processing is performed using cloud services such as AWS, and databases such as DynamoDB are used.

[0304] As the terminal, a smartphone is used to acquire data from the wearable sensor via Bluetooth. The terminal receives real-time data through an app developed in Java or Kotlin, and provides voice warnings and visual alerts to the user when an abnormality is detected. In addition, the abnormality detection information is sent to the server, and corresponding options based on it are presented to the user. For example, when a sudden increase in heart rate is detected, a warning is immediately sent to the user, and details of the medical provider to be contacted as the optimal response are provided.

[0305] Based on the information provided by the terminal, the user selects their actions in daily life and approves the response options by the generated AI model as needed. As a result, the elderly can quickly respond according to their own will even in an emergency. Based on the generated data, quick contact can be made with medical providers and family members, so a reassuring life can be maintained.

[0306] As a specific example, consider a scenario where an elderly person suspects poor physical condition while walking. When the wearable device detects an abnormal heart rate, a warning is immediately sent from the smartphone, and the server analyzes the information and determines that contact with a medical institution is necessary. Then, by displaying the result on the smartphone, the elderly person can quickly decide on a response.

[0307] Examples of prompt sentences include

[0308] "The heart rate of the elderly person has suddenly increased. Based on this information, list possible response measures."

[0309] can be considered. In this way, this system provides an environment in which the elderly can manage their own health and respond accurately even in an emergency.

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The device receives biometric information in real time from wearable devices via Bluetooth. This biometric data includes heart rate, steps taken, and body temperature. This information is temporarily stored in an application on the device and compared to alert settings. If an anomaly is detected, the device issues an alert.

[0313] Step 2:

[0314] The terminal transmits biometric information to the server when an anomaly is detected. The biometric data from the detected anomaly is used as input and transferred to the server. The server receives this information, analyzes the data using an AI model, and evaluates the cause and trend of the anomaly. The output generates detailed information about the anomaly and predicted response options.

[0315] Step 3:

[0316] The server sends the abnormal condition along with the generated response options to the terminal as a result. Based on the received data, the terminal prompts the user for confirmation through the user interface. The user selects a response option and approves the action to be taken in the next step.

[0317] Step 4:

[0318] After the user selects and approves a response option, the device sends that information back to the server. This input information is used by the server as a guide to determine the next action to take. The server activates communication means to contact healthcare providers or family members as needed. The output generates notification content and emergency contact information.

[0319] Step 5:

[0320] The server stores records of implemented countermeasures in a database and uses them as a learning tool for future improvements. Input includes user response history and feedback. Output is an updated, optimized response option to be used the next time anomaly is detected.

[0321] In this way, the entire system aims to monitor the health status of elderly people and to respond quickly and accurately when abnormalities occur.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] The system of this invention aims to improve the quality of life for the elderly by comprehensively managing their health and emotions and providing appropriate responses. To this end, a terminal, server, emotion engine, and communication means for comprehensively managing and utilizing these are constructed.

[0324] server

[0325] The server plays a central role in integrating and analyzing medical history, living conditions, and family relationship data. In addition, the server incorporates an emotion engine, and user emotional information is also stored in the database. Through AI analysis, the server assesses the user's overall state, including changes in their emotions. This information plays a crucial role in making decisions in emergencies and everyday situations.

[0326] terminal

[0327] The device acquires the user's biometric and emotional data through sensors and cameras. Emotional data is collected based on analysis of voice and facial expressions. The device transmits the collected emotional data to a server in real time. This data allows for a rapid assessment of the user's current state and supports necessary interactions.

[0328] User

[0329] By using these systems on a daily basis, users can receive support tailored to their health and emotional changes. Users receive notifications and suggested support options from their devices and can provide feedback on their emotions and state. This feedback contributes to the learning of the emotion engine, enabling more personalized support.

[0330] Emotional Engine

[0331] The emotion engine is a system that analyzes a user's emotions and proposes appropriate responses to the server. Using machine learning algorithms, the engine learns the user's general mood tendencies based on past emotional data and performs predictive analysis.

[0332] Specific example

[0333] For example, imagine an elderly person is going about their normal activities at home when the device analyzes their facial expressions and tone of voice to detect emotions suggesting stress. This information is immediately sent to a server and analyzed by an emotion engine. If the situation is determined not to be urgent, the device will notify the user with suggestions for relaxation or to contact family members. By utilizing the emotion engine, the system can understand the user's emotional state and provide appropriate life support and medical assistance.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The device utilizes emotion recognition sensors to analyze the user's voice and facial expressions in real time, collecting emotional data. This includes voice tone and facial expressions.

[0337] Step 2:

[0338] The device sends the collected emotional data to the server. A secure protocol is used for communication, and data transfer is performed quickly.

[0339] Step 3:

[0340] The server activates the emotion engine and comprehensively analyzes the received emotion data. It compares it with past emotion history and evaluates the current emotional state.

[0341] Step 4:

[0342] The server generates response options to offer the user based on their emotional state. This step also takes into account the user's values ​​and past preferences.

[0343] Step 5:

[0344] The server sends the generated response options to the device. These options may include, for example, suggestions for relaxation exercises or notifications to family members.

[0345] Step 6:

[0346] The device presents the user with response options. It prompts the user to take the necessary action through on-screen displays and voice instructions.

[0347] Step 7:

[0348] The user reviews the presentation on their device and selects or customizes the appropriate options as needed. The user's selection is then fed back into the system.

[0349] Step 8:

[0350] The server records user feedback in its emotion engine and uses it to generate future responses. This process allows the system to continuously provide improved responses to users.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] There is a need to effectively manage the health and emotional states of the elderly and to provide rapid and appropriate responses to emergencies and unstable emotional states. However, conventional systems have the challenge of not being able to adequately consider individual health and emotional states in data integration and analysis. Therefore, there is a need to develop a system that enables individualized responses based on more sophisticated analysis.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes information gathering means, integrated analysis means, and emotion analysis means. This enables the detailed collection and analysis of each user's health information and the predictive evaluation of their emotional state, thereby allowing for the presentation of personalized countermeasures.

[0356] "Information gathering methods" refer to systems that acquire health and emotional information of elderly people using sensors and cameras.

[0357] An "integrated analysis tool" is a function that integrates collected health and emotional information and comprehensively analyzes it using machine learning algorithms.

[0358] "Emotional analysis tools" are functions that analyze and predictively evaluate the user's emotional state.

[0359] A "notification method" is a function that presents the user with appropriate countermeasures based on the user's health and emotional state.

[0360] "Communication means" refers to a network-based system for sharing information with users and external parties.

[0361] A "generative AI model" is an artificial intelligence model used to optimize future countermeasures based on collected feedback.

[0362] A "protocol" is a set of rules that define the procedures for automatically responding in an emergency.

[0363] This invention is a system that comprehensively manages the health and emotional state of elderly individuals and provides appropriate responses. Therefore, it comprises a terminal, a server, an emotion engine, and communication means for managing these components.

[0364] The device uses sensors and cameras to acquire the user's biometric and emotional data. Biometric data includes heart rate and body temperature, while emotional data is collected by analyzing voice and facial expressions. The device transmits the collected data to a server in real time. For example, the device captures the user's facial expressions and analyzes them to identify emotions indicating stress.

[0365] The server stores received data in a database and integrates it with information such as medical history, living situation, and family relationships. This information is analyzed using an emotion engine, and machine learning algorithms are used to evaluate the user's state. Specifically, an AI model is used to predict emotional trends and generate necessary countermeasures. This system allows the server to gain a deep understanding of the user's health and emotional state and provide personalized countermeasures.

[0366] Through this system, users can receive support tailored to their health status and emotional changes. Based on notifications from their device, they can choose whether or not to accept relaxation suggestions and can also consider notifying their family. This feedback is then sent back to the server and used as learning material for the emotion engine.

[0367] Specific example

[0368] For example, suppose an elderly person is going about their normal activities at home, and the device analyzes their facial expressions and tone of voice, detecting emotions that suggest stress. This information is immediately sent to a server and analyzed by an emotion engine. If the system determines that the situation is not urgent, the device will send a notification to the user suggesting relaxation or prompting them to contact family members.

[0369] Example of a prompt

[0370] "Describe in detail the algorithmic steps for proposing appropriate health support measures based on emotional data received from users."

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device collects the user's biometric and emotional data using sensors and cameras. Inputs for this collection include heart rate, body temperature, voice, and facial expressions. Specifically, the camera captures the user's face, and the microphone records voice data. The obtained data is recognized as emotional information indicating stress levels and happiness.

[0374] Step 2:

[0375] The device transmits collected biometric and emotional data to the server in real time. The input is the data collected in step 1, and the output is the integrated data transferred to the server. Specifically, the data is packaged and sent to the server using a secure communication protocol. Accurate data transmission is ensured by waiting for acknowledgment of receipt from the server.

[0376] Step 3:

[0377] The server integrates data received from the terminal with medical history, lifestyle information, and family relationship information, and stores it in a database. The input is various integrated health information, and the output is the stored comprehensive data. On the server, an emotion engine uses machine learning algorithms to evaluate the user's health and emotional state.

[0378] Step 4:

[0379] The server evaluates the user's health and emotional state based on the analysis results of the emotion engine and generates specific countermeasures. The input is the analysis results of the emotion engine, and the output is the specific countermeasures presented to the user. The generated countermeasures are put into concrete forms such as suggestions for relaxation or recommendations to contact family members.

[0380] Step 5:

[0381] The terminal notifies the user of countermeasures and collects feedback from the user. The input is the countermeasures from the server, and the output is the user's feedback. Specifically, the terminal notifies the user using voice and visual interfaces and presents feedback options. This feedback is sent back to the server and used as training material for the generative AI model.

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] To detect health problems and emotional fluctuations faced by the elderly early and improve their quality of life, comprehensive, real-time data analysis is necessary. However, conventional systems have struggled to accurately grasp the emotional state of the elderly and provide appropriate responses. Furthermore, there have been problems with insufficient smooth information sharing and collaboration with medical institutions and related parties. Solving these challenges and realizing better care for the elderly is essential.

[0385] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0386] In this invention, the server includes information processing means for analyzing medical history and social environment, situation evaluation means for evaluating emergencies and suggesting countermeasures, and emotion analysis means for suggesting support through emotion analysis. This makes it possible to quickly and accurately grasp the health status and emotional fluctuations of elderly people and provide appropriate support and medical responses.

[0387] An "information processing means" is a system component that has the function of collecting and analyzing the medical history, social environment, and related data of elderly users.

[0388] A "situation evaluation tool" is a part of a system that monitors the user's situation, detects emergencies, and generates appropriate countermeasures.

[0389] An "information presentation means" is a function that displays generated countermeasures to the user and provides an interface to help them understand that information.

[0390] "Communication methods" refer to data exchange systems aimed at sharing information between medical facilities, related parties, and users, and facilitating smooth collaboration.

[0391] An "emotional analysis tool" is a system component that has an analytical function to analyze the emotional state of a user and suggest appropriate support measures.

[0392] Embodiments of this invention include an advanced system for managing the health status and emotions of elderly individuals. The server performs comprehensive data analysis through information processing means that analyze the user's medical history, social environment, and related party data, and generates appropriate countermeasures through situation evaluation means that detect emergencies.

[0393] The device uses sensors and cameras to acquire biometric and emotional data from elderly individuals. The collected data is transmitted to a server in real time and analyzed by an emotion analysis system. This analysis allows for an understanding of the user's emotional state, enabling timely provision of necessary support.

[0394] Users receive solutions generated via their devices and act according to the instructions. For example, if a user is feeling stressed, the device can suggest relaxation methods and automatically send notifications when it's necessary to contact medical institutions or relevant parties.

[0395] The system operates by integrating image processing technology using OpenCV, a custom algorithm (virtual library) for analyzing specific emotions, and a server communication module for data transmission.

[0396] As a concrete example, a device captures video footage of a specific elderly person relaxing at home and sends the video data to a server. The server uses a generative AI model to analyze the emotional state and returns appropriate responses to the device based on the generated prompt messages.

[0397] An example of a prompt for the generating AI model is, "Please tell me what relaxation methods should be suggested when an elderly person is feeling stressed." By providing this information to the device, users can receive more comprehensive care.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The device uses sensors and cameras to capture biometric and emotional data from elderly individuals. The input is real-time audio and video data, and the output is analyzable digital data. The device then formats this data and prepares it for transmission to a server.

[0401] Step 2:

[0402] The server receives biometric and emotional data transmitted from the terminal. The input is digital data from the terminal, and the output is initial analysis results. The server prepares this data for analysis, integrates it with medical history and social data through information processing tools, and creates a comprehensive user profile.

[0403] Step 3:

[0404] The server uses sentiment analysis tools to analyze the emotional components of the received data. The input is integrated user profile data, and the output is a detailed analysis of the user's current emotional state. The server uses a generative AI model to generate appropriate countermeasures based on this emotional state.

[0405] Step 4:

[0406] The server understands the countermeasures generated using a generative AI model and forms prompt sentences as needed. The input is the result of sentiment analysis, and the output is a set of prompt sentences and specific countermeasures. The server transmits this information to the terminal via an information presentation device.

[0407] Step 5:

[0408] The terminal displays prompt messages received from the server to the user. Input is countermeasure information from the server, and output provides the user with visual and auditory notifications. The terminal displays information to the user in real time and prompts them to take necessary actions.

[0409] Step 6:

[0410] Based on the device's presentation, users engage in daily activities or try recommended relaxation methods. Any feedback from users is entered into the device and used to improve future responses. This feedback is analyzed by the system's learning mechanisms and reflected in future responses.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] To implement this invention, a network system is configured that includes a device (terminal) used daily by elderly people and a server for central data processing. The system operates based on the interaction between the terminal, the server, and the user.

[0428] server

[0429] The server is the central hub for collecting and analyzing information across the entire system. It manages medical history, lifestyle data, and family relationship data collected from healthcare institutions and user terminals. The server utilizes AI models to analyze the data and build individual user profiles. These profiles are used to predict emergency response tendencies based on the user's past health history and values.

[0430] terminal

[0431] The terminal is a device used daily by elderly individuals, serving as both a data collection device and a user interface. The terminal monitors the user's biometric and activity information in real time via wearable sensors. When an anomaly is detected, the terminal issues voice and visual alerts to the user and transmits the details to a server.

[0432] User

[0433] The user is the subject of their own decision-making based on the information obtained through the system. Based on the information provided by the terminal, the user makes choices regarding health management and responses to emergencies in their daily life. The system also presents response options generated during emergencies and seeks the user's opinion and approval.

[0434] Specific example

[0435] Consider a scenario where an elderly person is living their daily life at home. The device constantly monitors the user's walking data and heart rate, and sends the data to a server. The server analyzes the data by comparing it with past medical history to check for any abnormalities. For example, if an abnormality such as a fall is detected, the device immediately alerts the user and simultaneously reports the details to the server. Based on this data, the server generates the most appropriate response options and contacts medical institutions or notifies family members. The user can review the options provided via the device and choose the response they believe is best.

[0436] This system aims to provide multifaceted support to enable elderly people to live more secure lives according to their own will.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The device monitors the user's biometric information (e.g., heart rate, steps taken, body temperature, etc.) in real time. This data is acquired via sensor devices and transmitted to the device.

[0440] Step 2:

[0441] The device sends collected biometric information to the server at regular intervals. The data is transferred using secure communication protocols and with security in mind.

[0442] Step 3:

[0443] The server stores the received biometric information in a database. This data is integrated with the user's past medical history and previously collected lifestyle data.

[0444] Step 4:

[0445] The server uses AI algorithms to analyze the collected data and evaluate the user's state. This allows for the identification of normal and abnormal states.

[0446] Step 5:

[0447] If the device detects an anomaly (for example, a fall or a sudden change in heart rate), it will immediately notify the user with an alert. This notification will be made via an audio alert or on-screen display.

[0448] Step 6:

[0449] Upon detecting an anomaly, the terminal immediately sends the information, along with details, to the server. The server then prepares a rapid response based on this information.

[0450] Step 7:

[0451] The server generates emergency response options. This process includes selecting recommended actions based on the user profile.

[0452] Step 8:

[0453] The server sends the generated response options to the terminal, which then presents them to the user. The user reviews the presented options and selects or approves the desired response.

[0454] Step 9:

[0455] If the user does not select a response option or does not respond, a pre-configured automated response protocol will be executed.

[0456] Step 10:

[0457] Based on the selected response, the server will contact medical institutions and family members as needed to provide appropriate follow-up. This process enables a rapid medical response.

[0458] (Example 1)

[0459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] When elderly people live their daily lives, they need to be able to respond quickly to physical abnormalities and medical emergencies. However, with existing technology, it is difficult to grasp the health status of elderly people in real time and to provide efficient and appropriate countermeasures. Furthermore, there is a lack of capability to respond in situations where rapid information sharing with family and medical institutions is required. A system is needed to solve these problems and enable elderly people to live more independently and with greater peace of mind.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes information processing means, data collection means for collecting biometric and activity information in real time, and warning means for issuing alerts when an anomaly is detected. This makes it possible to constantly monitor the health status of elderly people, promptly propose countermeasures when an anomaly occurs, and share information with relevant parties.

[0463] "Information processing means" refers to a device or system that has the function of collecting information on the medical history, living situation, and family relationships of elderly people, and analyzing the data.

[0464] "Data collection means" refers to a device or system that has the function of collecting biometric and activity information of elderly people in real time using sensors.

[0465] A "warning device" is a device or system that alerts the user through sound or visual means when an anomaly is detected.

[0466] A "decision support tool" is a device or system that uses an AI model to analyze collected data and generate the optimal response option in an emergency situation.

[0467] "User interface means" refers to a device or system that has the function of presenting the user with response options and supporting decision-making.

[0468] "Communication means" refers to a device or system equipped with network functions for sharing information and coordinating with external organizations and relatives.

[0469] To implement this invention, a system is constructed in which a device (terminal) used daily by the elderly and a central device (server) for processing data work together. Specifically, the terminal uses devices such as wearable sensors to monitor the user's biometric and activity information in real time. This makes it possible to accurately collect data such as heart rate, steps taken, and body temperature.

[0470] The server receives this data and analyzes it using an AI model. The AI ​​model compares it with past medical history and lifestyle data to determine if there are any abnormalities. This process can be carried out using data analysis software such as Python or R. If an abnormality is detected, the server immediately generates appropriate countermeasures and sends notifications.

[0471] Users manage their daily health and respond to emergencies based on information provided through the device. The device has the ability to emit voice and visual alerts in case of an emergency, notifying the user of the situation. It also presents response options generated by the server, helping the user select the most appropriate one.

[0472] As a concrete example, when an elderly person is living at home, the device monitors the user's walking data and heart rate and sends the data to a server. The server inputs this data into an AI model and analyzes it by comparing it with past medical history. If an abnormality is detected, for example, if there is a possibility of a fall, the device will issue a warning to the user, and the server will generate options to enable contact with medical institutions or relatives.

[0473] This system aims to support elderly people in living independent lives with peace of mind.

[0474] Example prompt: "Explain how to compare real-time biometric information of elderly users with historical medical data to suggest the most appropriate response options when an anomaly is detected."

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The device collects the user's biometric information (e.g., heart rate, body temperature) and activity information (e.g., steps taken, distance traveled) in real time through wearable sensors. This data is temporarily stored within the device. The input data is the user's sensor information, and the output is the temporarily stored data.

[0478] Step 2:

[0479] The device analyzes the collected data to determine if there are any abnormalities. If an abnormality is detected, for example, if the heart rate exceeds a certain threshold, the device will emit an audio or visual alert. This process involves data calculations comparing the data to a threshold, and the output indicates whether or not an abnormality exists.

[0480] Step 3:

[0481] The terminal sends data to the server indicating that an anomaly has been detected. The data sent includes details of the anomaly (e.g., type of anomaly, detection time, user location). The input is information about the anomaly, and the output is the data sent to the server.

[0482] Step 4:

[0483] The server inputs data received from the terminal into an AI model, which then compares it with the user's medical history and lifestyle for a detailed analysis. This analysis involves trend analysis based on past data, and the output includes the cause and urgency of the anomaly.

[0484] Step 5:

[0485] The server generates possible response options based on the results of the anomaly analysis. For example, it might consider contacting medical institutions, notifying relatives, or arranging for emergency response. An AI generative model is used for this, and the output is a list of response options.

[0486] Step 6:

[0487] The server sends the generated response options to the terminal and presents them to the user. The user reviews the presented options and chooses an action based on their own judgment. The input is the response options, and the output is the user's choice.

[0488] This series of processes allows elderly people to maintain an independent lifestyle while enabling them to respond quickly and appropriately in emergencies.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] For elderly people to live their daily lives with peace of mind, it is necessary to monitor their health status in real time and to respond quickly and appropriately when abnormalities are detected. However, existing systems have limitations in terms of automated response functions for immediate response to emergencies involving the elderly, as well as in real-time data collection and analysis. This invention aims to solve these problems and improve the safety and health management of the elderly.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes data collection means for monitoring the health status of elderly people in real time, state determination means for detecting abnormalities and issuing warnings via smart devices, as well as generating response options, and user interface means for presenting response options to the user according to the situation and obtaining approval. This enables elderly people to respond quickly in emergencies.

[0494] "Data collection methods" refer to systems for recording the health status of elderly people in real time, and involve acquiring biometric and activity information using wearable devices and smart devices.

[0495] The "status determination means" is a function that analyzes collected data to detect changes or abnormalities in health status, sends alerts to smart devices, and generates appropriate response options.

[0496] A "user interface means" is an interface that presents the generated response options to the user and obtains their approval, enabling interaction with the user via a smart device.

[0497] "Communication means" refers to network means that enable smooth collaboration by sharing the results of anomaly detection and the generation of response options with healthcare providers and family members.

[0498] The system for implementing this invention mainly consists of the interaction between a server, a terminal, and a user. The server manages the health information of the elderly and analyzes the data in real time as needed. Specifically, the server receives real-time biometric information collected from wearable devices via connected terminals and uses an AI model to determine whether there are any abnormalities. Data processing is performed using cloud services such as AWS, and databases such as DynamoDB are used.

[0499] A smartphone is used as the terminal, acquiring data from wearable sensors via Bluetooth. The terminal receives real-time data through an application developed in Java or Kotlin, and provides the user with audio warnings and visual alerts when an anomaly is detected. It also sends the anomaly detection information to a server, which then presents the user with response options. For example, if a sudden increase in heart rate is detected, a warning is immediately sent to the user, and details of the healthcare provider to contact as the optimal course of action are provided.

[0500] Based on information provided by the device, users select actions for their daily lives and, if necessary, approve response options generated by an AI model. This allows elderly individuals to respond quickly and independently, even in emergencies. The generated data enables rapid contact with healthcare providers and family members, allowing them to maintain a sense of security.

[0501] As a concrete example, consider a scenario where an elderly person suspects they are unwell while taking a walk. If a wearable device detects an abnormal heart rate, an alert is immediately sent from the smartphone, and a server analyzes the information to determine if contact with a medical institution is necessary. The result is then displayed on the smartphone, allowing the elderly person to quickly decide on a course of action.

[0502] Examples of prompt statements include:

[0503] "An elderly person experienced a sudden increase in heart rate. Based on this information, please list possible countermeasures."

[0504] This is one possible explanation. In this way, this system provides an environment in which elderly people can manage their own health and respond appropriately in emergencies.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The device receives biometric information in real time from wearable devices via Bluetooth. This biometric data includes heart rate, steps taken, and body temperature. This information is temporarily stored in an application on the device and compared to alert settings. If an anomaly is detected, the device issues an alert.

[0508] Step 2:

[0509] The terminal transmits biometric information to the server when an anomaly is detected. The biometric data from the detected anomaly is used as input and transferred to the server. The server receives this information, analyzes the data using an AI model, and evaluates the cause and trend of the anomaly. The output generates detailed information about the anomaly and predicted response options.

[0510] Step 3:

[0511] The server sends the abnormal condition along with the generated response options to the terminal as a result. Based on the received data, the terminal prompts the user for confirmation through the user interface. The user selects a response option and approves the action to be taken in the next step.

[0512] Step 4:

[0513] After the user selects and approves a response option, the device sends that information back to the server. This input information is used by the server as a guide to determine the next action to take. The server activates communication means to contact healthcare providers or family members as needed. The output generates notification content and emergency contact information.

[0514] Step 5:

[0515] The server stores records of implemented countermeasures in a database and uses them as a learning tool for future improvements. Input includes user response history and feedback. Output is an updated, optimized response option to be used the next time anomaly is detected.

[0516] In this way, the entire system aims to monitor the health status of elderly people and to respond quickly and accurately when abnormalities occur.

[0517] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0518] The system of this invention aims to improve the quality of life for the elderly by comprehensively managing their health and emotions and providing appropriate responses. To this end, a terminal, server, emotion engine, and communication means for comprehensively managing and utilizing these are constructed.

[0519] server

[0520] The server plays a central role in integrating and analyzing medical history, living conditions, and family relationship data. In addition, the server incorporates an emotion engine, and user emotional information is also stored in the database. Through AI analysis, the server assesses the user's overall state, including changes in their emotions. This information plays a crucial role in making decisions in emergencies and everyday situations.

[0521] terminal

[0522] The device acquires the user's biometric and emotional data through sensors and cameras. Emotional data is collected based on analysis of voice and facial expressions. The device transmits the collected emotional data to a server in real time. This data allows for a rapid assessment of the user's current state and supports necessary interactions.

[0523] User

[0524] By using these systems on a daily basis, users can receive support tailored to their health and emotional changes. Users receive notifications and suggested support options from their devices and can provide feedback on their emotions and state. This feedback contributes to the learning of the emotion engine, enabling more personalized support.

[0525] Emotional Engine

[0526] The emotion engine is a system that analyzes a user's emotions and proposes appropriate responses to the server. Using machine learning algorithms, the engine learns the user's general mood tendencies based on past emotional data and performs predictive analysis.

[0527] Specific example

[0528] For example, imagine an elderly person is going about their normal activities at home when the device analyzes their facial expressions and tone of voice to detect emotions suggesting stress. This information is immediately sent to a server and analyzed by an emotion engine. If the situation is determined not to be urgent, the device will notify the user with suggestions for relaxation or to contact family members. By utilizing the emotion engine, the system can understand the user's emotional state and provide appropriate life support and medical assistance.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The device utilizes emotion recognition sensors to analyze the user's voice and facial expressions in real time, collecting emotional data. This includes voice tone and facial expressions.

[0532] Step 2:

[0533] The device sends the collected emotional data to the server. A secure protocol is used for communication, and data transfer is performed quickly.

[0534] Step 3:

[0535] The server activates the emotion engine and comprehensively analyzes the received emotion data. It compares it with past emotion history and evaluates the current emotional state.

[0536] Step 4:

[0537] The server generates response options to offer the user based on their emotional state. This step also takes into account the user's values ​​and past preferences.

[0538] Step 5:

[0539] The server sends the generated response options to the device. These options may include, for example, suggestions for relaxation exercises or notifications to family members.

[0540] Step 6:

[0541] The device presents the user with response options. It prompts the user to take the necessary action through on-screen displays and voice instructions.

[0542] Step 7:

[0543] The user reviews the presentation on their device and selects or customizes the appropriate options as needed. The user's selection is then fed back into the system.

[0544] Step 8:

[0545] The server records user feedback in its emotion engine and uses it to generate future responses. This process allows the system to continuously provide improved responses to users.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] There is a need to effectively manage the health and emotional states of the elderly and to provide rapid and appropriate responses to emergencies and unstable emotional states. However, conventional systems have the challenge of not being able to adequately consider individual health and emotional states in data integration and analysis. Therefore, there is a need to develop a system that enables individualized responses based on more sophisticated analysis.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes information gathering means, integrated analysis means, and emotion analysis means. This enables the detailed collection and analysis of each user's health information and the predictive evaluation of their emotional state, thereby allowing for the presentation of personalized countermeasures.

[0551] "Information gathering methods" refer to systems that acquire health and emotional information of elderly people using sensors and cameras.

[0552] An "integrated analysis tool" is a function that integrates collected health and emotional information and comprehensively analyzes it using machine learning algorithms.

[0553] "Emotional analysis tools" are functions that analyze and predictively evaluate the user's emotional state.

[0554] A "notification method" is a function that presents the user with appropriate countermeasures based on the user's health and emotional state.

[0555] "Communication means" refers to a network-based system for sharing information with users and external parties.

[0556] A "generative AI model" is an artificial intelligence model used to optimize future countermeasures based on collected feedback.

[0557] A "protocol" is a set of rules that define the procedures for automatically responding in an emergency.

[0558] This invention is a system that comprehensively manages the health and emotional state of elderly individuals and provides appropriate responses. Therefore, it comprises a terminal, a server, an emotion engine, and communication means for managing these components.

[0559] The device uses sensors and cameras to acquire the user's biometric and emotional data. Biometric data includes heart rate and body temperature, while emotional data is collected by analyzing voice and facial expressions. The device transmits the collected data to a server in real time. For example, the device captures the user's facial expressions and analyzes them to identify emotions indicating stress.

[0560] The server stores received data in a database and integrates it with information such as medical history, living situation, and family relationships. This information is analyzed using an emotion engine, and machine learning algorithms are used to evaluate the user's state. Specifically, an AI model is used to predict emotional trends and generate necessary countermeasures. This system allows the server to gain a deep understanding of the user's health and emotional state and provide personalized countermeasures.

[0561] Through this system, users can receive support tailored to their health status and emotional changes. Based on notifications from their device, they can choose whether or not to accept relaxation suggestions and can also consider notifying their family. This feedback is then sent back to the server and used as learning material for the emotion engine.

[0562] Specific example

[0563] For example, suppose an elderly person is going about their normal activities at home, and the device analyzes their facial expressions and tone of voice, detecting emotions that suggest stress. This information is immediately sent to a server and analyzed by an emotion engine. If the system determines that the situation is not urgent, the device will send a notification to the user suggesting relaxation or prompting them to contact family members.

[0564] Example of a prompt

[0565] "Describe in detail the algorithmic steps for proposing appropriate health support measures based on emotional data received from users."

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The device collects the user's biometric and emotional data using sensors and cameras. Inputs for this collection include heart rate, body temperature, voice, and facial expressions. Specifically, the camera captures the user's face, and the microphone records voice data. The obtained data is recognized as emotional information indicating stress levels and happiness.

[0569] Step 2:

[0570] The device transmits collected biometric and emotional data to the server in real time. The input is the data collected in step 1, and the output is the integrated data transferred to the server. Specifically, the data is packaged and sent to the server using a secure communication protocol. Accurate data transmission is ensured by waiting for acknowledgment of receipt from the server.

[0571] Step 3:

[0572] The server integrates data received from the terminal with medical history, lifestyle information, and family relationship information, and stores it in a database. The input is various integrated health information, and the output is the stored comprehensive data. On the server, an emotion engine uses machine learning algorithms to evaluate the user's health and emotional state.

[0573] Step 4:

[0574] The server evaluates the user's health and emotional state based on the analysis results of the emotion engine and generates specific countermeasures. The input is the analysis results of the emotion engine, and the output is the specific countermeasures presented to the user. The generated countermeasures are put into concrete forms such as suggestions for relaxation or recommendations to contact family members.

[0575] Step 5:

[0576] The terminal notifies the user of countermeasures and collects feedback from the user. The input is the countermeasures from the server, and the output is the user's feedback. Specifically, the terminal notifies the user using voice and visual interfaces and presents feedback options. This feedback is sent back to the server and used as training material for the generative AI model.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] To detect health problems and emotional fluctuations faced by the elderly early and improve their quality of life, comprehensive, real-time data analysis is necessary. However, conventional systems have struggled to accurately grasp the emotional state of the elderly and provide appropriate responses. Furthermore, there have been problems with insufficient smooth information sharing and collaboration with medical institutions and related parties. Solving these challenges and realizing better care for the elderly is essential.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes information processing means for analyzing medical history and social environment, situation evaluation means for evaluating emergencies and suggesting countermeasures, and emotion analysis means for suggesting support through emotion analysis. This makes it possible to quickly and accurately grasp the health status and emotional fluctuations of elderly people and provide appropriate support and medical responses.

[0582] An "information processing means" is a system component that has the function of collecting and analyzing the medical history, social environment, and related data of elderly users.

[0583] A "situation evaluation tool" is a part of a system that monitors the user's situation, detects emergencies, and generates appropriate countermeasures.

[0584] An "information presentation means" is a function that displays generated countermeasures to the user and provides an interface to help them understand that information.

[0585] "Communication methods" refer to data exchange systems aimed at sharing information between medical facilities, related parties, and users, and facilitating smooth collaboration.

[0586] An "emotional analysis tool" is a system component that has an analytical function to analyze the emotional state of a user and suggest appropriate support measures.

[0587] Embodiments of this invention include an advanced system for managing the health status and emotions of elderly individuals. The server performs comprehensive data analysis through information processing means that analyze the user's medical history, social environment, and related party data, and generates appropriate countermeasures through situation evaluation means that detect emergencies.

[0588] The device uses sensors and cameras to acquire biometric and emotional data from elderly individuals. The collected data is transmitted to a server in real time and analyzed by an emotion analysis system. This analysis allows for an understanding of the user's emotional state, enabling timely provision of necessary support.

[0589] Users receive solutions generated via their devices and act according to the instructions. For example, if a user is feeling stressed, the device can suggest relaxation methods and automatically send notifications when it's necessary to contact medical institutions or relevant parties.

[0590] The system operates by integrating image processing technology using OpenCV, a custom algorithm (virtual library) for analyzing specific emotions, and a server communication module for data transmission.

[0591] As a concrete example, a device captures video footage of a specific elderly person relaxing at home and sends the video data to a server. The server uses a generative AI model to analyze the emotional state and returns appropriate responses to the device based on the generated prompt messages.

[0592] An example of a prompt for the generating AI model is, "Please tell me what relaxation methods should be suggested when an elderly person is feeling stressed." By providing this information to the device, users can receive more comprehensive care.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The device uses sensors and cameras to capture biometric and emotional data from elderly individuals. The input is real-time audio and video data, and the output is analyzable digital data. The device then formats this data and prepares it for transmission to a server.

[0596] Step 2:

[0597] The server receives biometric and emotional data transmitted from the terminal. The input is digital data from the terminal, and the output is initial analysis results. The server prepares this data for analysis, integrates it with medical history and social data through information processing tools, and creates a comprehensive user profile.

[0598] Step 3:

[0599] The server uses sentiment analysis tools to analyze the emotional components of the received data. The input is integrated user profile data, and the output is a detailed analysis of the user's current emotional state. The server uses a generative AI model to generate appropriate countermeasures based on this emotional state.

[0600] Step 4:

[0601] The server understands the countermeasures generated using a generative AI model and forms prompt sentences as needed. The input is the result of sentiment analysis, and the output is a set of prompt sentences and specific countermeasures. The server transmits this information to the terminal via an information presentation device.

[0602] Step 5:

[0603] The terminal displays prompt messages received from the server to the user. Input is countermeasure information from the server, and output provides the user with visual and auditory notifications. The terminal displays information to the user in real time and prompts them to take necessary actions.

[0604] Step 6:

[0605] Based on the device's presentation, users engage in daily activities or try recommended relaxation methods. Any feedback from users is entered into the device and used to improve future responses. This feedback is analyzed by the system's learning mechanisms and reflected in future responses.

[0606] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0612] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0613] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0614] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0615] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0616] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0617] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0618] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] To implement this invention, a network system is configured that includes a device (terminal) used daily by elderly people and a server for central data processing. The system operates based on the interaction between the terminal, the server, and the user.

[0624] server

[0625] The server is the central hub for collecting and analyzing information across the entire system. It manages medical history, lifestyle data, and family relationship data collected from healthcare institutions and user terminals. The server utilizes AI models to analyze the data and build individual user profiles. These profiles are used to predict emergency response tendencies based on the user's past health history and values.

[0626] terminal

[0627] The terminal is a device used daily by elderly individuals, serving as both a data collection device and a user interface. The terminal monitors the user's biometric and activity information in real time via wearable sensors. When an anomaly is detected, the terminal issues voice and visual alerts to the user and transmits the details to a server.

[0628] User

[0629] The user is the subject of their own decision-making based on the information obtained through the system. Based on the information provided by the terminal, the user makes choices regarding health management and responses to emergencies in their daily life. The system also presents response options generated during emergencies and seeks the user's opinion and approval.

[0630] Specific example

[0631] Consider a scenario where an elderly person is living their daily life at home. The device constantly monitors the user's walking data and heart rate, and sends the data to a server. The server analyzes the data by comparing it with past medical history to check for any abnormalities. For example, if an abnormality such as a fall is detected, the device immediately alerts the user and simultaneously reports the details to the server. Based on this data, the server generates the most appropriate response options and contacts medical institutions or notifies family members. The user can review the options provided via the device and choose the response they believe is best.

[0632] This system aims to provide multifaceted support to enable elderly people to live more secure lives according to their own will.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The device monitors the user's biometric information (e.g., heart rate, steps taken, body temperature, etc.) in real time. This data is acquired via sensor devices and transmitted to the device.

[0636] Step 2:

[0637] The device sends collected biometric information to the server at regular intervals. The data is transferred using secure communication protocols and with security in mind.

[0638] Step 3:

[0639] The server stores the received biometric information in a database. This data is integrated with the user's past medical history and previously collected lifestyle data.

[0640] Step 4:

[0641] The server uses AI algorithms to analyze the collected data and evaluate the user's state. This allows for the identification of normal and abnormal states.

[0642] Step 5:

[0643] If the device detects an anomaly (for example, a fall or a sudden change in heart rate), it will immediately notify the user with an alert. This notification will be made via an audio alert or on-screen display.

[0644] Step 6:

[0645] Upon detecting an anomaly, the terminal immediately sends the information, along with details, to the server. The server then prepares a rapid response based on this information.

[0646] Step 7:

[0647] The server generates emergency response options. This process includes selecting recommended actions based on the user profile.

[0648] Step 8:

[0649] The server sends the generated response options to the terminal, which then presents them to the user. The user reviews the presented options and selects or approves the desired response.

[0650] Step 9:

[0651] If the user does not select a response option or does not respond, a pre-configured automated response protocol will be executed.

[0652] Step 10:

[0653] Based on the selected response, the server will contact medical institutions and family members as needed to provide appropriate follow-up. This process enables a rapid medical response.

[0654] (Example 1)

[0655] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] When elderly people live their daily lives, they need to be able to respond quickly to physical abnormalities and medical emergencies. However, with existing technology, it is difficult to grasp the health status of elderly people in real time and to provide efficient and appropriate countermeasures. Furthermore, there is a lack of capability to respond in situations where rapid information sharing with family and medical institutions is required. A system is needed to solve these problems and enable elderly people to live more independently and with greater peace of mind.

[0657] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0658] In this invention, the server includes information processing means, data collection means for collecting biometric and activity information in real time, and warning means for issuing alerts when an anomaly is detected. This makes it possible to constantly monitor the health status of elderly people, promptly propose countermeasures when an anomaly occurs, and share information with relevant parties.

[0659] "Information processing means" refers to a device or system that has the function of collecting information on the medical history, living situation, and family relationships of elderly people, and analyzing the data.

[0660] "Data collection means" refers to a device or system that has the function of collecting biometric and activity information of elderly people in real time using sensors.

[0661] A "warning device" is a device or system that alerts the user through sound or visual means when an anomaly is detected.

[0662] A "decision support tool" is a device or system that uses an AI model to analyze collected data and generate the optimal response option in an emergency situation.

[0663] "User interface means" refers to a device or system that has the function of presenting the user with response options and supporting decision-making.

[0664] "Communication means" refers to a device or system equipped with network functions for sharing information and coordinating with external organizations and relatives.

[0665] To implement this invention, a system is constructed in which a device (terminal) used daily by the elderly and a central device (server) for processing data work together. Specifically, the terminal uses devices such as wearable sensors to monitor the user's biometric and activity information in real time. This makes it possible to accurately collect data such as heart rate, steps taken, and body temperature.

[0666] The server receives this data and analyzes it using an AI model. The AI ​​model compares it with past medical history and lifestyle data to determine if there are any abnormalities. This process can be carried out using data analysis software such as Python or R. If an abnormality is detected, the server immediately generates appropriate countermeasures and sends notifications.

[0667] Users manage their daily health and respond to emergencies based on information provided through the device. The device has the ability to emit voice and visual alerts in case of an emergency, notifying the user of the situation. It also presents response options generated by the server, helping the user select the most appropriate one.

[0668] As a concrete example, when an elderly person is living at home, the device monitors the user's walking data and heart rate and sends the data to a server. The server inputs this data into an AI model and analyzes it by comparing it with past medical history. If an abnormality is detected, for example, if there is a possibility of a fall, the device will issue a warning to the user, and the server will generate options to enable contact with medical institutions or relatives.

[0669] This system aims to support elderly people in living independent lives with peace of mind.

[0670] Example prompt: "Explain how to compare real-time biometric information of elderly users with historical medical data to suggest the most appropriate response options when an anomaly is detected."

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The device collects the user's biometric information (e.g., heart rate, body temperature) and activity information (e.g., steps taken, distance traveled) in real time through wearable sensors. This data is temporarily stored within the device. The input data is the user's sensor information, and the output is the temporarily stored data.

[0674] Step 2:

[0675] The device analyzes the collected data to determine if there are any abnormalities. If an abnormality is detected, for example, if the heart rate exceeds a certain threshold, the device will emit an audio or visual alert. This process involves data calculations comparing the data to a threshold, and the output indicates whether or not an abnormality exists.

[0676] Step 3:

[0677] The terminal sends data to the server indicating that an anomaly has been detected. The data sent includes details of the anomaly (e.g., type of anomaly, detection time, user location). The input is information about the anomaly, and the output is the data sent to the server.

[0678] Step 4:

[0679] The server inputs data received from the terminal into an AI model, which then compares it with the user's medical history and lifestyle for a detailed analysis. This analysis involves trend analysis based on past data, and the output includes the cause and urgency of the anomaly.

[0680] Step 5:

[0681] The server generates possible response options based on the results of the anomaly analysis. For example, it might consider contacting medical institutions, notifying relatives, or arranging for emergency response. An AI generative model is used for this, and the output is a list of response options.

[0682] Step 6:

[0683] The server sends the generated response options to the terminal and presents them to the user. The user reviews the presented options and chooses an action based on their own judgment. The input is the response options, and the output is the user's choice.

[0684] This series of processes allows elderly people to maintain an independent lifestyle while enabling them to respond quickly and appropriately in emergencies.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] For elderly people to live their daily lives with peace of mind, it is necessary to monitor their health status in real time and to respond quickly and appropriately when abnormalities are detected. However, existing systems have limitations in terms of automated response functions for immediate response to emergencies involving the elderly, as well as in real-time data collection and analysis. This invention aims to solve these problems and improve the safety and health management of the elderly.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes data collection means for monitoring the health status of elderly people in real time, state determination means for detecting abnormalities and issuing warnings via smart devices, as well as generating response options, and user interface means for presenting response options to the user according to the situation and obtaining approval. This enables elderly people to respond quickly in emergencies.

[0690] "Data collection methods" refer to systems for recording the health status of elderly people in real time, and involve acquiring biometric and activity information using wearable devices and smart devices.

[0691] The "status determination means" is a function that analyzes collected data to detect changes or abnormalities in health status, sends alerts to smart devices, and generates appropriate response options.

[0692] A "user interface means" is an interface that presents the generated response options to the user and obtains their approval, enabling interaction with the user via a smart device.

[0693] "Communication means" refers to network means that enable smooth collaboration by sharing the results of anomaly detection and the generation of response options with healthcare providers and family members.

[0694] The system for implementing this invention mainly consists of the interaction between a server, a terminal, and a user. The server manages the health information of the elderly and analyzes the data in real time as needed. Specifically, the server receives real-time biometric information collected from wearable devices via connected terminals and uses an AI model to determine whether there are any abnormalities. Data processing is performed using cloud services such as AWS, and databases such as DynamoDB are used.

[0695] A smartphone is used as the terminal, acquiring data from wearable sensors via Bluetooth. The terminal receives real-time data through an application developed in Java or Kotlin, and provides the user with audio warnings and visual alerts when an anomaly is detected. It also sends the anomaly detection information to a server, which then presents the user with response options. For example, if a sudden increase in heart rate is detected, a warning is immediately sent to the user, and details of the healthcare provider to contact as the optimal course of action are provided.

[0696] Based on information provided by the device, users select actions for their daily lives and, if necessary, approve response options generated by an AI model. This allows elderly individuals to respond quickly and independently, even in emergencies. The generated data enables rapid contact with healthcare providers and family members, allowing them to maintain a sense of security.

[0697] As a concrete example, consider a scenario where an elderly person suspects they are unwell while taking a walk. If a wearable device detects an abnormal heart rate, an alert is immediately sent from the smartphone, and a server analyzes the information to determine if contact with a medical institution is necessary. The result is then displayed on the smartphone, allowing the elderly person to quickly decide on a course of action.

[0698] Examples of prompt statements include:

[0699] "An elderly person experienced a sudden increase in heart rate. Based on this information, please list possible countermeasures."

[0700] This is one possible explanation. In this way, this system provides an environment in which elderly people can manage their own health and respond appropriately in emergencies.

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] The device receives biometric information in real time from wearable devices via Bluetooth. This biometric data includes heart rate, steps taken, and body temperature. This information is temporarily stored in an application on the device and compared to alert settings. If an anomaly is detected, the device issues an alert.

[0704] Step 2:

[0705] The terminal transmits biometric information to the server when an anomaly is detected. The biometric data from the detected anomaly is used as input and transferred to the server. The server receives this information, analyzes the data using an AI model, and evaluates the cause and trend of the anomaly. The output generates detailed information about the anomaly and predicted response options.

[0706] Step 3:

[0707] The server sends the abnormal condition along with the generated response options to the terminal as a result. Based on the received data, the terminal prompts the user for confirmation through the user interface. The user selects a response option and approves the action to be taken in the next step.

[0708] Step 4:

[0709] After the user selects and approves a response option, the device sends that information back to the server. This input information is used by the server as a guide to determine the next action to take. The server activates communication means to contact healthcare providers or family members as needed. The output generates notification content and emergency contact information.

[0710] Step 5:

[0711] The server stores records of implemented countermeasures in a database and uses them as a learning tool for future improvements. Input includes user response history and feedback. Output is an updated, optimized response option to be used the next time anomaly is detected.

[0712] In this way, the entire system aims to monitor the health status of elderly people and to respond quickly and accurately when abnormalities occur.

[0713] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0714] The system of this invention aims to improve the quality of life for the elderly by comprehensively managing their health and emotions and providing appropriate responses. To this end, a terminal, server, emotion engine, and communication means for comprehensively managing and utilizing these are constructed.

[0715] server

[0716] The server plays a central role in integrating and analyzing medical history, living conditions, and family relationship data. In addition, the server incorporates an emotion engine, and user emotional information is also stored in the database. Through AI analysis, the server assesses the user's overall state, including changes in their emotions. This information plays a crucial role in making decisions in emergencies and everyday situations.

[0717] terminal

[0718] The device acquires the user's biometric and emotional data through sensors and cameras. Emotional data is collected based on analysis of voice and facial expressions. The device transmits the collected emotional data to a server in real time. This data allows for a rapid assessment of the user's current state and supports necessary interactions.

[0719] User

[0720] By using these systems on a daily basis, users can receive support tailored to their health and emotional changes. Users receive notifications and suggested support options from their devices and can provide feedback on their emotions and state. This feedback contributes to the learning of the emotion engine, enabling more personalized support.

[0721] Emotional Engine

[0722] The emotion engine is a system that analyzes a user's emotions and proposes appropriate responses to the server. Using machine learning algorithms, the engine learns the user's general mood tendencies based on past emotional data and performs predictive analysis.

[0723] Specific example

[0724] For example, imagine an elderly person is going about their normal activities at home when the device analyzes their facial expressions and tone of voice to detect emotions suggesting stress. This information is immediately sent to a server and analyzed by an emotion engine. If the situation is determined not to be urgent, the device will notify the user with suggestions for relaxation or to contact family members. By utilizing the emotion engine, the system can understand the user's emotional state and provide appropriate life support and medical assistance.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The device utilizes emotion recognition sensors to analyze the user's voice and facial expressions in real time, collecting emotional data. This includes voice tone and facial expressions.

[0728] Step 2:

[0729] The device sends the collected emotional data to the server. A secure protocol is used for communication, and data transfer is performed quickly.

[0730] Step 3:

[0731] The server activates the emotion engine and comprehensively analyzes the received emotion data. It compares it with past emotion history and evaluates the current emotional state.

[0732] Step 4:

[0733] The server generates response options to offer the user based on their emotional state. This step also takes into account the user's values ​​and past preferences.

[0734] Step 5:

[0735] The server sends the generated response options to the device. These options may include, for example, suggestions for relaxation exercises or notifications to family members.

[0736] Step 6:

[0737] The device presents the user with response options. It prompts the user to take the necessary action through on-screen displays and voice instructions.

[0738] Step 7:

[0739] The user reviews the presentation on their device and selects or customizes the appropriate options as needed. The user's selection is then fed back into the system.

[0740] Step 8:

[0741] The server records user feedback in its emotion engine and uses it to generate future responses. This process allows the system to continuously provide improved responses to users.

[0742] (Example 2)

[0743] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] There is a need to effectively manage the health and emotional states of the elderly and to provide rapid and appropriate responses to emergencies and unstable emotional states. However, conventional systems have the challenge of not being able to adequately consider individual health and emotional states in data integration and analysis. Therefore, there is a need to develop a system that enables individualized responses based on more sophisticated analysis.

[0745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0746] In this invention, the server includes information gathering means, integrated analysis means, and emotion analysis means. This enables the detailed collection and analysis of each user's health information and the predictive evaluation of their emotional state, thereby allowing for the presentation of personalized countermeasures.

[0747] "Information gathering methods" refer to systems that acquire health and emotional information of elderly people using sensors and cameras.

[0748] An "integrated analysis tool" is a function that integrates collected health and emotional information and comprehensively analyzes it using machine learning algorithms.

[0749] "Emotional analysis tools" are functions that analyze and predictively evaluate the user's emotional state.

[0750] A "notification method" is a function that presents the user with appropriate countermeasures based on the user's health and emotional state.

[0751] "Communication means" refers to a network-based system for sharing information with users and external parties.

[0752] A "generative AI model" is an artificial intelligence model used to optimize future countermeasures based on collected feedback.

[0753] A "protocol" is a set of rules that define the procedures for automatically responding in an emergency.

[0754] This invention is a system that comprehensively manages the health and emotional state of elderly individuals and provides appropriate responses. Therefore, it comprises a terminal, a server, an emotion engine, and communication means for managing these components.

[0755] The device uses sensors and cameras to acquire the user's biometric and emotional data. Biometric data includes heart rate and body temperature, while emotional data is collected by analyzing voice and facial expressions. The device transmits the collected data to a server in real time. For example, the device captures the user's facial expressions and analyzes them to identify emotions indicating stress.

[0756] The server stores received data in a database and integrates it with information such as medical history, living situation, and family relationships. This information is analyzed using an emotion engine, and machine learning algorithms are used to evaluate the user's state. Specifically, an AI model is used to predict emotional trends and generate necessary countermeasures. This system allows the server to gain a deep understanding of the user's health and emotional state and provide personalized countermeasures.

[0757] Through this system, users can receive support tailored to their health status and emotional changes. Based on notifications from their device, they can choose whether or not to accept relaxation suggestions and can also consider notifying their family. This feedback is then sent back to the server and used as learning material for the emotion engine.

[0758] Specific example

[0759] For example, suppose an elderly person is going about their normal activities at home, and the device analyzes their facial expressions and tone of voice, detecting emotions that suggest stress. This information is immediately sent to a server and analyzed by an emotion engine. If the system determines that the situation is not urgent, the device will send a notification to the user suggesting relaxation or prompting them to contact family members.

[0760] Example of a prompt

[0761] "Describe in detail the algorithmic steps for proposing appropriate health support measures based on emotional data received from users."

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The device collects the user's biometric and emotional data using sensors and cameras. Inputs for this collection include heart rate, body temperature, voice, and facial expressions. Specifically, the camera captures the user's face, and the microphone records voice data. The obtained data is recognized as emotional information indicating stress levels and happiness.

[0765] Step 2:

[0766] The device transmits collected biometric and emotional data to the server in real time. The input is the data collected in step 1, and the output is the integrated data transferred to the server. Specifically, the data is packaged and sent to the server using a secure communication protocol. Accurate data transmission is ensured by waiting for acknowledgment of receipt from the server.

[0767] Step 3:

[0768] The server integrates data received from the terminal with medical history, lifestyle information, and family relationship information, and stores it in a database. The input is various integrated health information, and the output is the stored comprehensive data. On the server, an emotion engine uses machine learning algorithms to evaluate the user's health and emotional state.

[0769] Step 4:

[0770] The server evaluates the user's health and emotional state based on the analysis results of the emotion engine and generates specific countermeasures. The input is the analysis results of the emotion engine, and the output is the specific countermeasures presented to the user. The generated countermeasures are put into concrete forms such as suggestions for relaxation or recommendations to contact family members.

[0771] Step 5:

[0772] The terminal notifies the user of countermeasures and collects feedback from the user. The input is the countermeasures from the server, and the output is the user's feedback. Specifically, the terminal notifies the user using voice and visual interfaces and presents feedback options. This feedback is sent back to the server and used as training material for the generative AI model.

[0773] (Application Example 2)

[0774] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] To detect health problems and emotional fluctuations faced by the elderly early and improve their quality of life, comprehensive, real-time data analysis is necessary. However, conventional systems have struggled to accurately grasp the emotional state of the elderly and provide appropriate responses. Furthermore, there have been problems with insufficient smooth information sharing and collaboration with medical institutions and related parties. Solving these challenges and realizing better care for the elderly is essential.

[0776] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0777] In this invention, the server includes information processing means for analyzing medical history and social environment, situation evaluation means for evaluating emergencies and suggesting countermeasures, and emotion analysis means for suggesting support through emotion analysis. This makes it possible to quickly and accurately grasp the health status and emotional fluctuations of elderly people and provide appropriate support and medical responses.

[0778] An "information processing means" is a system component that has the function of collecting and analyzing the medical history, social environment, and related data of elderly users.

[0779] A "situation evaluation tool" is a part of a system that monitors the user's situation, detects emergencies, and generates appropriate countermeasures.

[0780] An "information presentation means" is a function that displays generated countermeasures to the user and provides an interface to help them understand that information.

[0781] "Communication methods" refer to data exchange systems aimed at sharing information between medical facilities, related parties, and users, and facilitating smooth collaboration.

[0782] An "emotional analysis tool" is a system component that has an analytical function to analyze the emotional state of a user and suggest appropriate support measures.

[0783] Embodiments of this invention include an advanced system for managing the health status and emotions of elderly individuals. The server performs comprehensive data analysis through information processing means that analyze the user's medical history, social environment, and related party data, and generates appropriate countermeasures through situation evaluation means that detect emergencies.

[0784] The device uses sensors and cameras to acquire biometric and emotional data from elderly individuals. The collected data is transmitted to a server in real time and analyzed by an emotion analysis system. This analysis allows for an understanding of the user's emotional state, enabling timely provision of necessary support.

[0785] Users receive solutions generated via their devices and act according to the instructions. For example, if a user is feeling stressed, the device can suggest relaxation methods and automatically send notifications when it's necessary to contact medical institutions or relevant parties.

[0786] The system operates by integrating image processing technology using OpenCV, a custom algorithm (virtual library) for analyzing specific emotions, and a server communication module for data transmission.

[0787] As a concrete example, a device captures video footage of a specific elderly person relaxing at home and sends the video data to a server. The server uses a generative AI model to analyze the emotional state and returns appropriate responses to the device based on the generated prompt messages.

[0788] An example of a prompt for the generating AI model is, "Please tell me what relaxation methods should be suggested when an elderly person is feeling stressed." By providing this information to the device, users can receive more comprehensive care.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The device uses sensors and cameras to capture biometric and emotional data from elderly individuals. The input is real-time audio and video data, and the output is analyzable digital data. The device then formats this data and prepares it for transmission to a server.

[0792] Step 2:

[0793] The server receives biometric and emotional data transmitted from the terminal. The input is digital data from the terminal, and the output is initial analysis results. The server prepares this data for analysis, integrates it with medical history and social data through information processing tools, and creates a comprehensive user profile.

[0794] Step 3:

[0795] The server uses sentiment analysis tools to analyze the emotional components of the received data. The input is integrated user profile data, and the output is a detailed analysis of the user's current emotional state. The server uses a generative AI model to generate appropriate countermeasures based on this emotional state.

[0796] Step 4:

[0797] The server understands the countermeasures generated using a generative AI model and forms prompt sentences as needed. The input is the result of sentiment analysis, and the output is a set of prompt sentences and specific countermeasures. The server transmits this information to the terminal via an information presentation device.

[0798] Step 5:

[0799] The terminal displays prompt messages received from the server to the user. Input is countermeasure information from the server, and output provides the user with visual and auditory notifications. The terminal displays information to the user in real time and prompts them to take necessary actions.

[0800] Step 6:

[0801] Based on the device's presentation, users engage in daily activities or try recommended relaxation methods. Any feedback from users is entered into the device and used to improve future responses. This feedback is analyzed by the system's learning mechanisms and reflected in future responses.

[0802] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0803] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0805] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0806] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0807] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0808] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0809] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0810] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0811] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0812] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0813] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0814] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0815] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0816] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0817] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0818] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0819] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0820] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0821] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0822] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A data processing method for collecting and analyzing medical history, living conditions, and family relationship data of elderly people,

[0826] A state determination means that detects an emergency and generates response options according to the situation,

[0827] A user interface means for presenting the user with response options and obtaining their approval,

[0828] Communication methods for sharing information and coordinating with medical institutions and families,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, which performs an automated response based on a pre-configured protocol in the event of an emergency.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising means for collecting feedback from users and their families and learning to improve future responses.

[0834] "Example 1"

[0835] (Claim 1)

[0836] Information processing tools for collecting and analyzing information on the medical history, living conditions, and family relationships of elderly people,

[0837] A data collection means that uses sensors to collect biological and activity information in real time and detect anomalies,

[0838] A warning system that issues an alert to the user via voice or visual means when an anomaly is detected,

[0839] A decision-making support tool that uses an AI model to analyze data and generate optimal response options in emergency situations,

[0840] A user interface means for presenting users with response options and supporting their decision-making,

[0841] Communication methods for sharing and collaborating with external organizations and relatives,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, which automatically takes action based on pre-set guidelines when an anomaly is detected.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising a learning function to collect feedback from users and their relatives and to improve future responses.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] A data collection method for monitoring the health status of the elderly in real time,

[0850] A state determination means that detects an anomaly, issues a warning via a smart device, and generates response options,

[0851] A user interface means for presenting users with situation-appropriate response options and obtaining their approval,

[0852] Communication methods for sharing information and collaborating with healthcare providers and families,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, which automatically takes action based on pre-configured settings in the event of an emergency.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising a learning means for analyzing feedback from users and their families and optimizing future responses.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] Information gathering methods for collecting and analyzing health information of the elderly,

[0861] An integrated analysis method including machine learning algorithms for integrating and analyzing information,

[0862] A sentiment analysis method that analyzes and predictively evaluates the emotional state of a user,

[0863] A notification system that presents countermeasures based on emotional state and health status,

[0864] Communication means for sharing information with users and external parties,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, which has the ability to acquire user feedback and use a generated AI model to optimize the next response.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising a function to automatically respond in accordance with a protocol for responding to an emergency.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] Information processing means for collecting and analyzing the medical history, social environment, and related data of elderly users,

[0873] A situation evaluation means that detects an emergency and generates countermeasures appropriate to the situation,

[0874] A means of presenting information to users to show them countermeasures and gain their understanding,

[0875] Communication methods for sharing information and coordinating with medical facilities and related parties,

[0876] A means of emotional analysis that analyzes the emotional state of users and suggests appropriate support,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which performs an automated response based on pre-set procedures when an emergency occurs.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising means for collecting feedback from users and stakeholders and learning to improve future responses. [Explanation of symbols]

[0882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing method for collecting and analyzing medical history, living conditions, and family relationship data of elderly people, A state determination means that detects an emergency and generates response options according to the situation, A user interface means for presenting the user with response options and obtaining their approval, Communication methods for sharing information and coordinating with medical institutions and families, A system that includes this.

2. The system according to claim 1, which performs an automated response based on a pre-configured protocol in the event of an emergency.

3. The system according to claim 1, comprising means for collecting feedback from users and their families and learning to improve future responses.

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