system

Abstract

We provide the system. [Solution] Information gathering means for acquiring biometric information, An analysis means for analyzing the aforementioned biological information and detecting abnormalities, A notification means for informing of the aforementioned abnormality, A communication means that reports an abnormality using the aforementioned notification means, A dialogue method that uses conversational artificial intelligence to communicate with users, A plan generation means for generating a care plan suitable for the user, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, the need to effectively monitor the safety and health of the elderly is increasing, but there is a problem that it is difficult to detect abnormalities in real time and respond quickly with the current methods. In addition, there is a need for an effective means to reduce the burden on caregivers and alleviate the loneliness of the elderly.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system comprising: an information gathering means for acquiring biological information; an analysis means for analyzing and detecting abnormalities; a notification means for reporting abnormalities; a dialogue means for communicating via interactive artificial intelligence; and a plan generation means for generating individually tailored care plans. This enables real-time health management for the elderly and supports a safe and secure life.

[0006] "Biometric information" refers to data that indicates the physical state of an individual, such as heart rate, body temperature, and activity level, measured from that individual.

[0007] "Information gathering means" refers to devices and sensors that acquire biometric information and supply it to a system as data.

[0008] "Analysis means" refers to an algorithm or process used to analyze collected biological information and detect abnormalities.

[0009] An "abnormality" refers to a state that deviates from the normal pattern or standard, and includes changes in health status or deviations in behavior.

[0010] "Notification means" refers to communication methods or devices used to inform relevant parties of anomalies when they are detected.

[0011] "Communication methods" refer to methods that use email, messaging applications, dedicated apps, etc., to transmit notifications of abnormalities to external terminals.

[0012] "Dialogue tools" refer to functions or systems that use artificial intelligence to facilitate communication with users.

[0013] "Plan generation means" refers to a function that creates an individually tailored care plan based on the user's health status and behavioral data.

[0014] A "system" is a collection of devices or programs that function integrally by combining the means described above.

Brief Description of the Drawings

[0015] [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 to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a monitoring and communication system for supporting the independent living of elderly people. This system has the function of collecting the user's biometric information, detecting and notifying of abnormalities, and a communication function that utilizes interactive artificial intelligence.

[0037] System Overview

[0038] Data collection

[0039] The device uses wearable sensors and cameras to acquire biometric information such as the user's heart rate, body temperature, and activity level in real time, and transmits this data to a server. For example, when a user wears a wristwatch-type device while going about their daily life, their heart rate and steps are continuously recorded.

[0040] Data analysis and anomaly detection

[0041] The server uses AI algorithms to analyze the collected biometric information for abnormalities in health status and behavior. This process makes it possible to quickly detect abnormalities when a user exhibits unusual activity or physiological changes. For example, if a user's heart rate suddenly rises significantly above normal levels, it will be detected as a health abnormality.

[0042] Notifications and alerts

[0043] When an anomaly is detected, the server immediately notifies family members or caregivers of the information. This notification is sent via email, SMS, or a dedicated application. This allows those involved to quickly understand the situation and take the necessary actions.

[0044] Communication support

[0045] Users can communicate daily through an AI chatbot. The chatbot analyzes voice commands and messages from users and provides appropriate responses. This feature helps reduce feelings of loneliness and provides reminders to help with daily health management. For example, it will prompt users with "It's time to take your medication" when it's almost time.

[0046] Individual care plan

[0047] The server generates an individually tailored care plan based on the user's health status and behavioral patterns. This plan includes recommended exercise, diet, and lifestyle changes. The terminal directly displays these suggestions to the user, helping them incorporate them into their daily life. For example, if a lack of exercise is detected, a suggestion such as "Let's take a 15-minute walk today" will be made.

[0048] As described above, this system supports a safe and independent life for the elderly by constantly monitoring their health status and promptly providing countermeasures in the event of an abnormality.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The device periodically acquires biometric information such as the user's heart rate, body temperature, and activity level using sensors. This involves the use of wearable devices, which continuously monitor data and transmit the information to a server at regular intervals.

[0052] Step 2:

[0053] The server receives biometric information transmitted from the terminal and records it in a database. Time information is added to this record, and as the data accumulates, it becomes possible to analyze health status over the long term.

[0054] Step 3:

[0055] The server uses AI algorithms to detect anomalies from the accumulated data in the database. Specifically, it analyzes abnormal fluctuations by comparing them with the user's normal behavior patterns and health status. For example, it detects an anomaly if the heart rate exceeds a certain threshold.

[0056] Step 4:

[0057] When an anomaly is detected, the server generates an alert and immediately notifies designated family members or caregivers via email or SMS. This notification includes details of the anomaly and recommended actions to take.

[0058] Step 5:

[0059] Users can communicate directly with the system using an AI chatbot. The chatbot generates responses in natural language to user questions and commands, providing necessary information and reminders.

[0060] Step 6:

[0061] The server generates personalized care plans based on the user's health data. This includes exercise programs and dietary suggestions, which are then communicated to the user's device. For example, it might notify the user with a message like, "We recommend light stretching every morning."

[0062] Through this series of processes, the system can comprehensively support the health status of elderly individuals and respond quickly and appropriately in the event of an abnormality.

[0063] (Example 1)

[0064] 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."

[0065] To support independent living for the elderly, a system is needed that monitors biometric data in real time, quickly and accurately detects abnormalities in health conditions, and provides appropriate care as needed. However, current technology has challenges such as delays in data transmission and inaccuracies in analysis because these processes operate individually. Furthermore, there is a need for an integrated approach that supports daily health management while reducing feelings of isolation.

[0066] 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.

[0067] In this invention, the server includes terminal means for collecting biometric data, computation means for analyzing the biometric data and identifying anomalies, and notification means for notifying the anomalies. This enables real-time data collection and analysis, and provides rapid notification and appropriate interactive support in the event of an anomaly.

[0068] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and activity level, and is acquired through various sensors.

[0069] "Terminal means" refers to devices or equipment for acquiring biometric data, including wearable sensors and cameras.

[0070] "Computational means" refers to computing devices and algorithms used to analyze acquired biometric data and identify the user's health status and any abnormalities.

[0071] "Notification means" refers to methods for informing users or their related parties of abnormal health conditions, and includes notification devices and software.

[0072] "Information transmission means" refers to communication methods for quickly sharing abnormal information with external parties, particularly those using email, SMS, or dedicated application notifications.

[0073] "Interactive means" refers to systems that enable interaction with users, utilizing conversational machine learning and artificial intelligence technologies.

[0074] "Plan generation means" refers to a process or device that formulates an individually optimized health plan based on the user's health data.

[0075] This invention is a system for monitoring the health status of elderly people in real time and supporting their independent living. An embodiment of this system is shown below.

[0076] The device uses wearable sensors and cameras to acquire the user's biometric data. This allows for the real-time collection of important health information such as heart rate, body temperature, and activity level. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0077] The server uses machine learning frameworks (e.g., TENSORFLOW® and PyTorch) to analyze the received biometric data. This makes it possible to accurately identify abnormalities in the user's health status and behavioral patterns. If an abnormality is detected as a result of the analysis, the server immediately activates a notification system to inform relevant parties.

[0078] This notification is promptly communicated to the user's family or caregivers via email, SMS, or a dedicated application notification. For example, if the heart rate exceeds the normal range, it is immediately detected as an abnormality, and the family is notified.

[0079] Users can communicate through an AI-powered dialogue system. It utilizes OpenAI® generative AI models and other technologies to appropriately respond to voice commands and messages from users. This dialogue allows users to live their daily lives with a sense of security. It also includes a reminder function to support daily health management; for example, users will be notified when it's time to take their medication.

[0080] Furthermore, the server generates a care plan optimized for each user based on the acquired data and past history. The proposed plan includes recommended exercise levels and dietary content, and this information is presented to the user through the terminal. For example, if a lack of exercise is detected, the terminal will make a specific suggestion to the user, such as, "Let's take a 15-minute walk today."

[0081] This allows users to live a safer and more independent life while constantly being aware of their own health status.

[0082] Examples of prompts to input into a generative AI model:

[0083] "Please explain a health monitoring system for the elderly. Please describe in detail the process for detecting abnormal heart rates."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The device acquires the user's biometric data using wearable sensors. Specifically, the sensors measure the user's heart rate, body temperature, activity level, etc., and convert this data into a digital format. The input is an analog signal, which is then output as digital data.

[0087] Step 2:

[0088] The device transmits acquired digital data to a server using wireless communication. It establishes a stable connection via Bluetooth or Wi-Fi to quickly send data to the server. The input is processed biometric digital data, which is then output by being transmitted to the server.

[0089] Step 3:

[0090] The server analyzes the received biometric data using AI algorithms. Specifically, it uses machine learning frameworks such as TensorFlow to identify anomalies based on pre-trained models. In this process, biometric data is input, and analysis results indicating anomalies are output.

[0091] Step 4:

[0092] The server, upon analysis, will notify relevant parties using notification mechanisms if an anomaly is detected. Notifications will be sent via email, SMS, or a dedicated application. The input is the anomaly detection information, which is then output as notification information sent to the relevant devices and addresses.

[0093] Step 5:

[0094] Users can communicate through the AI ​​dialogue system. The system analyzes user voice commands and text input, and generates responses using natural language processing technology. Here, user input is received, and an AI-generated response message is output.

[0095] Step 6:

[0096] The server generates individualized care plans based on the user's health data. Using the results of AI-driven data analysis, it creates plans that suggest optimal exercise levels and dietary content. The analyzed health data is used as input to generate the individualized plan.

[0097] Step 7:

[0098] The terminal presents the generated care plan to the user. It displays the proposal on the terminal's screen and prompts the user to take action through voice and notifications. The input is the care plan sent from the server, which is then output as information presented to the user.

[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] Currently, there are insufficient means to support the independent living of the elderly. In particular, there is a lack of continuous monitoring of health status, prompt response in case of abnormalities, and support for daily communication aimed at reducing feelings of isolation. Accordingly, there is a need for the provision of prompt and appropriate health management plans and the realization of behavioral recommendations through integration with external devices.

[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 information gathering means for acquiring biometric data, analysis means for analyzing biometric data and detecting anomalies, dialogue means for responding to the user using interactive artificial intelligence, and action suggestion means for coordinating with external devices to encourage recommended actions to the user. This enables real-time monitoring of the health status of the elderly and rapid response, as well as reducing feelings of loneliness through AI-assisted communication and enabling more accurate health management.

[0104] "Biometric data" refers to information about the user's health, such as heart rate, body temperature, and activity level, which are acquired in real time.

[0105] "Information gathering means" refers to devices and methods for continuously acquiring a user's biometric data, and wearable devices fall into this category.

[0106] "Analysis means" refers to processes and technologies used to determine normal and abnormal states using collected biological data.

[0107] "Notification means" refers to a system for informing users and relevant parties of anomalies detected through analysis, and includes notifications and alarms.

[0108] "Communication methods" refer to devices or functions used to transmit abnormal information to external devices or individuals, and include telephone calls, emails, and short message transmissions.

[0109] "Dialogue means" refers to an AI-powered interface for exchanging information with users, and includes conversations and message exchanges.

[0110] "Plan generation means" refers to methods and techniques for formulating an appropriate health management plan based on the user's health condition.

[0111] "Action suggestion means" refers to a mechanism for recommending specific actions to users, including making suggestions and giving instructions that may involve collaboration with external devices.

[0112] Modes for carrying out the invention:

[0113] The system for implementing this invention utilizes a wearable device and a smartphone application to continuously monitor the health status of elderly individuals and provide notifications when necessary. The server implements an AI model using Python to analyze various biometric data in real time and quickly detect abnormal patterns. As a result, if an abnormality occurs in the user's health status, family members, caregivers, and other relevant parties are promptly notified.

[0114] The server acquires biometric data from wearable devices via Bluetooth. The collected data is sent to a server in the cloud, where an AI model analyzes it using libraries such as TensorFlow and scikit-learn. If an anomaly is identified through this analysis, a notification is sent via email, SMS, or a dedicated smartphone application.

[0115] The device is equipped with an AI chatbot to facilitate smooth communication with users. The chatbot uses natural language processing technology to understand voice input from users and provides health advice and everyday conversation as needed. This feature can help reduce feelings of loneliness and promote health management.

[0116] For example, if a user forgets to take their regular medication, the system will detect this and notify them via voice message through their device saying, "It's time to take your medication." Also, if a user's heart rate suddenly increases, the server will send an alarm to their caregiver.

[0117] An example of a prompt message would be: "Write Python code to send a notification when an elderly person's heart rate exceeds the normal range. The sensor used should be capable of measuring heart rate in real time, and SMS notification should be implemented as the method for detecting an abnormality."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives biometric data collected by wearable devices via Bluetooth from the terminal. Inputs include biometric data such as heart rate, body temperature, and activity level, while output is a dataset that has undergone normalization and transformation. The data is acquired in real time and stored in a database to prepare for subsequent analysis.

[0121] Step 2:

[0122] The server analyzes biometric data stored in the database using an AI model. The input is the dataset stored in step 1, and the output is the anomaly detection result. Specifically, it loads a machine learning model using libraries such as TensorFlow and feeds the data into the model to detect anomalies.

[0123] Step 3:

[0124] The server notifies external stakeholders based on the anomalies detected as a result of the analysis. The input is the anomaly detection result obtained in step 2, and the output is the notification message. When an anomaly is detected, the server performs specific processing to generate and send email, SMS, or dedicated app notifications to the relevant parties.

[0125] Step 4:

[0126] The device initiates interaction with the user through an AI chatbot. Input consists of voice commands and text messages from the user, and output is a response message. Using natural language processing technology, the chatbot analyzes the user's input, such as medical consultations or everyday conversations, and generates and returns an appropriate response to the user.

[0127] Step 5:

[0128] The server generates a health management plan based on the user's health data and sends it to the terminal. The input is the user's health data that is continuously collected, and the output is a customized health management plan. If the user is not getting enough exercise, the server generates a specific goal, such as "Today's exercise goal is 10,000 steps," and displays it on the terminal's screen.

[0129] This series of processes allows users to continue living independently with peace of mind.

[0130] 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.

[0131] This invention is a system for comprehensively managing the health and emotional care of the elderly, providing personalized care to users through monitoring biometric information and recognizing emotions. The system centers around an emotion engine, which grasps the user's physical and emotional state in real time and provides appropriate responses.

[0132] System Overview

[0133] Data collection and emotion recognition

[0134] The device collects the user's biometric information through wearable sensors and acquires emotional data using a microphone and text interface. When the user speaks to the system, the emotion engine analyzes the emotion from the voice and text. For example, if the user says, "I'm a little tired today," the emotion engine will detect "fatigue."

[0135] Data Analysis

[0136] The server comprehensively analyzes this biometric and emotional data to assess the user's current physical and mental state. By identifying health abnormalities from biometric information and emotional changes from emotional data, it enables more effective interventions. For example, if a high heart rate and the emotion "stress" are detected, the server determines that relaxation is necessary.

[0137] Notifications and communications

[0138] If an anomaly is detected, the server will notify family members or caregivers via email or a dedicated app. Emotional information will also be reported, allowing families to use it to support the user's mental well-being. For example, a detailed notification might state, "Heart rate is high; stress detected."

[0139] Promoting communication

[0140] Users can interact with an AI chatbot and receive emotion-based feedback. The chatbot generates appropriate responses based on the user's emotions, helping to alleviate feelings of loneliness. For example, the chatbot might suggest, "You seem a little tired today, how about a nap?"

[0141] Care plan generation and proposal

[0142] The server creates an optimal care plan based on the user's biometric information and emotional state. This plan includes exercise, dietary improvements, and mental care necessary for maintaining health. The device notifies the user of these plans and encourages them to follow them. Specifically, when stress is detected, it recommends a plan such as, "Try yoga in the evening to relax."

[0143] This invention provides an environment in which elderly people can live with peace of mind, and supports a high quality of independent living by providing support not only physically but also emotionally.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The device acquires biometric information such as the user's heart rate, body temperature, and activity level through wearable sensors. It also records the user's speech using a voice input function and sends it to the emotion engine for analysis.

[0147] Step 2:

[0148] The server receives biometric information and voice data transmitted from the terminal. The biometric information is recorded in a database, and the voice data is processed by an emotion engine for emotion analysis.

[0149] Step 3:

[0150] The server evaluates the analyzed biometric information using an AI algorithm to detect any abnormalities. Simultaneously, it determines the user's emotional state based on the analysis results from the emotion engine. For example, if "anger" is detected, that emotional state is recorded.

[0151] Step 4:

[0152] When an anomaly or a specific emotional state is detected, the server immediately sends an alert to family members or caregivers. This notification includes details of the anomaly and the user's emotional state, and is sent via email or app notification.

[0153] Step 5:

[0154] Users can interact with the system through an AI chatbot and receive emotion-based responses. The chatbot will offer comfort, encouragement, or relaxation suggestions tailored to the user's emotions. For example, it might send a message like, "Try to relax a little and take a deep breath."

[0155] Step 6:

[0156] The server integrates biometric and emotional data to generate a personalized care plan. This plan includes exercise and dietary recommendations tailored to the user's current health and emotional state. The device notifies the user of this plan and prompts them to take necessary actions. For example, it might suggest, "To alleviate anxiety, try listening to relaxing music at night."

[0157] Through this series of processes, the system comprehensively supports the health and emotional well-being of older adults, helping them to lead stable lives.

[0158] (Example 2)

[0159] 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."

[0160] In the daily lives of the elderly, it is difficult to grasp their health and emotional states in real time and provide appropriate interventions and care plans. In particular, there is a need for comprehensive support that takes into account not only biometric information but also emotional information, but there are few systems that can realize this. Furthermore, there is a need for a method to quickly notify family members and caregivers when abnormalities occur and to promote two-way communication.

[0161] 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.

[0162] In this invention, the server includes means for aggregating and analyzing biometric and emotional information, detection means for detecting anomalies, and communication means for reporting anomalies. This enables a comprehensive understanding of the user's health and emotional state, real-time care plan proposals, and rapid notification in the event of an anomaly.

[0163] "Biometric information" refers to physical data such as pulse rate, blood pressure, and body temperature that is acquired to indicate the user's health status.

[0164] "Device" refers to all equipment used to collect biometric information from users.

[0165] "Processing" refers to calculations and operations performed to aggregate and analyze biological and emotional information.

[0166] "Detection" is the process of identifying and recognizing anomalies from analyzed information.

[0167] "Communication" refers to a means of transmitting information to external devices in the event of an abnormality, and includes email and messaging systems.

[0168] "Recognition" is the process of estimating and understanding the user's emotional state from voice and text data.

[0169] "Dialogue" is the process of generating appropriate responses based on the user's emotional state and engaging in two-way communication with the user.

[0170] "Generation" refers to a series of tasks performed to create a care plan tailored to the user and notify the user's device.

[0171] A "plan" is a set of guidelines designed to propose specific activities aimed at maintaining the user's health and providing emotional support.

[0172] This invention provides a system to support health management and emotional care for the elderly. The system collects and analyzes the user's biometric and emotional information to understand their health and emotional state and propose an appropriate care plan.

[0173] Specifically, the device acquires biometric information using wearable sensors attached to the user. This includes data such as pulse rate, blood pressure, and body temperature. It also uses voice input to directly collect emotional information from the user as text data. This process utilizes a common speech-to-text API for speech recognition software.

[0174] Next, the server can use database management software and data analysis libraries to analyze the aggregated data. This makes it possible to assess the user's health status and detect anomalies. For example, statistical analysis can be performed using the Python Pandas library. If an anomaly is detected, the server will send an emergency alert to family members or caregivers using electronic communication. This communication will utilize email or push notification services.

[0175] Furthermore, users can receive emotion-based feedback through an interactive AI on their device. This AI dialogue uses a generative AI model to generate appropriate responses tailored to the user's emotions. For example, if a user inputs "I've been feeling lonely lately," the AI ​​might respond with "How about relaxing by watching your favorite movie?"

[0176] Ultimately, the server generates a care plan based on these analysis results to support the user's health maintenance. This plan includes daily exercise, dietary improvements, and mental health care, and is communicated to the user via their device. The proposed plan enables the user to pursue a better quality of life.

[0177] A concrete example of a prompt message would be, "Generate calming advice for when the user feels anxious." In this way, the invention provides an environment that comprehensively and individually supports the elderly.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The device acquires biometric information from wearable sensors attached to the user. The input consists of data such as pulse rate, blood pressure, and body temperature obtained from the wearable sensors. This data is transmitted to the device via wireless communication such as Bluetooth. Specifically, the device collects data from the sensors at regular intervals and stores it in a buffer. The output is the accumulated biometric data set.

[0181] Step 2:

[0182] The device records the user's voice using a microphone and converts it into text data using speech recognition software. The input is the user's voice (e.g., "I'm a little tired today"). The voice data is converted to text using a common speech recognition API. Specifically, the device processes the recorded voice in real time and converts it to text. The output is sentiment information in text format.

[0183] Step 3:

[0184] The server aggregates biometric and emotional information transmitted from terminals and stores it in a database. Input consists of a biometric data set and emotional information in text format. A database management system, such as SQL, can be used. Specifically, the server classifies the received data by identifier and stores it as a new entry in the database. The output is integrated data prepared for analysis.

[0185] Step 4:

[0186] The server analyzes integrated data to assess the user's health and emotional state. The input is an integrated dataset. Statistical analysis is performed using the Python Pandas library. Specifically, the server detects anomalies based on certain thresholds and identifies potential problems. The output is a report of the assessed health and emotional state.

[0187] Step 5:

[0188] If the server detects an anomaly based on the analysis results, it sends a notification to family members or caregivers via communication. The input is the anomaly detection information from the analysis. Transmission methods include email using the SMTP protocol and push notifications via a dedicated app. Specifically, the server generates a notification message using a template and sends it to the designated recipient. The output is the notified anomaly report.

[0189] Step 6:

[0190] The user receives emotion-based feedback through an interactive AI on the device. The input is text entered by the user (e.g., "I've been feeling lonely lately"). A generative AI model is used for the interactive AI to generate appropriate responses. Specifically, the device receives user input, sends prompts to the generative AI model, and displays the generated response on the screen. The output is the displayed AI response.

[0191] Step 7:

[0192] The server generates a care plan based on the user's health and emotional state and notifies the terminal. Inputs are analysis results and evaluated state information. The generated care plan includes exercise, diet, and mental health care. Specifically, the server creates a plan based on a pre-configured algorithm and sends it to the terminal. The output is the care plan notified to the user.

[0193] (Application Example 2)

[0194] 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".

[0195] In modern elderly care, emotional care is becoming increasingly important in addition to physical health management. However, many existing systems focus on detecting health abnormalities based on biometric information, and have limitations in providing individualized care that responds immediately to changes in the user's emotions. In particular, managing feelings of loneliness and stress is a challenge for the elderly. This invention aims to provide a system that integrates these emotional aspects of care.

[0196] 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.

[0197] In this invention, the server includes a sensor means for acquiring biometric data, a computation means for analyzing the biometric data and detecting abnormalities, and a communication means for engaging in dialogue with the user using an interactive algorithm. This enables simultaneous monitoring of the biometric information and emotional state of elderly individuals, providing a real-time response plan when an abnormality is detected, and reducing feelings of loneliness.

[0198] "Biometric data" refers to information that indicates a user's physical condition, and is usually acquired through wearable sensors or similar devices.

[0199] "Sensing means" refers to devices and technologies used to acquire a user's biometric data.

[0200] "Computational means" refers to devices or technologies that analyze health status based on acquired biological data and perform processing to determine whether or not there are abnormalities.

[0201] "Information provision means" refers to a method or device for notifying users or caregivers of important information, such as abnormality detection.

[0202] "Communication means" refers to a method or technique for transmitting a notification via an information provision means to an external device.

[0203] A "conversational algorithm" is an artificial intelligence-based technology used to facilitate smooth communication with users.

[0204] A "communication tool" is a method or device that uses an interactive algorithm to exchange information with a user.

[0205] A "care plan generation tool" is a technology or device that creates an optimal care plan based on the individual health and emotional state of the user.

[0206] "Suggested means" refers to a technology or method for presenting recommended activities or actions to users.

[0207] "Support measures" refer to methods and techniques that promote a sense of security and relaxation in accordance with the emotional state of elderly people.

[0208] The system for realizing this invention is for comprehensively managing the biological information and emotional state of elderly individuals. This system mainly includes sensor means, computing means, information provision means, communication means, interactive algorithms, and support means.

[0209] The server acquires biometric data in real time through wearable sensors and monitors the user's health status. The acquired data is analyzed by computational means, and if an abnormality is detected, a notification is sent to family members or caregivers through an information provision system. This enables a rapid response.

[0210] The device plays a role in two-way communication with the user using an interactive algorithm. Specifically, it utilizes AI technology to analyze the user's emotional state and generate appropriate feedback and encouraging messages. This supports the user's mental health and reduces feelings of loneliness.

[0211] Users are expected to live their daily lives according to a care plan generated by the server. This care plan includes recommended exercises, diets, and relaxation methods, which are appropriately suggested through support systems. For example, if stress is detected, relaxation activities will be suggested.

[0212] For example, when a user's heart rate increases and stress is detected, a suggestion such as "Why not relax by listening to some soothing music?" is made. This suggestion is generated based on a generative AI model, and an example of a prompt statement is as follows:

[0213] "Please suggest relaxing activities. The user's heart rate is elevated, indicating they are experiencing stress, so please offer advice in a gentle and calming tone."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server collects the user's biometric data from input signals received from wearable sensors. This data includes information such as heart rate, blood pressure, and body temperature. This input data is stored as time-series data and used as the basis for subsequent analysis.

[0217] Step 2:

[0218] The server analyzes the collected biometric data using computational methods. During this process, an anomaly detection algorithm is applied to monitor fluctuations in heart rate and blood pressure. As a result of the analysis, anomalies such as a higher-than-normal heart rate are output. This allows for real-time assessment of the user's health status.

[0219] Step 3:

[0220] If an anomaly is detected, the server will notify family members or caregivers through information provision channels. Specific numerical data and status information will be transmitted via email or a dedicated app. The input for this notification is the previously analyzed results, and the output is the applied notification message.

[0221] Step 4:

[0222] The device communicates with the user using an interactive algorithm. It analyzes emotions based on voice and text input from the user and runs a generative AI model. Based on this input data, it generates and outputs a feedback message tailored to the user. In this process, the AI ​​responds based on the generated prompt text.

[0223] Step 5:

[0224] Users receive feedback on a care plan generated by the server. This plan includes specific recommended actions, which they are encouraged to incorporate into their daily lives. Biometric data and the user's emotional state are used as input to create the plan, and personalized recommended activities are presented as output. This enables users to maintain their health and live a mentally stable life.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention relates to a monitoring and communication system for supporting the independent living of elderly people. This system has the function of collecting the user's biometric information, detecting and notifying of abnormalities, and a communication function that utilizes interactive artificial intelligence.

[0242] System Overview

[0243] Data collection

[0244] The device uses wearable sensors and cameras to acquire biometric information such as the user's heart rate, body temperature, and activity level in real time, and transmits this data to a server. For example, when a user wears a wristwatch-type device while going about their daily life, their heart rate and steps are continuously recorded.

[0245] Data analysis and anomaly detection

[0246] The server uses AI algorithms to analyze the collected biometric information for abnormalities in health status and behavior. This process makes it possible to quickly detect abnormalities when a user exhibits unusual activity or physiological changes. For example, if a user's heart rate suddenly rises significantly above normal levels, it will be detected as a health abnormality.

[0247] Notifications and alerts

[0248] When an anomaly is detected, the server immediately notifies family members or caregivers of the information. This notification is sent via email, SMS, or a dedicated application. This allows those involved to quickly understand the situation and take the necessary actions.

[0249] Communication support

[0250] Users can communicate daily through an AI chatbot. The chatbot analyzes voice commands and messages from users and provides appropriate responses. This feature helps reduce feelings of loneliness and provides reminders to help with daily health management. For example, it will prompt users with "It's time to take your medication" when it's almost time.

[0251] Individual care plan

[0252] The server generates an individually tailored care plan based on the user's health status and behavioral patterns. This plan includes recommended exercise, diet, and lifestyle changes. The terminal directly displays these suggestions to the user, helping them incorporate them into their daily life. For example, if a lack of exercise is detected, a suggestion such as "Let's take a 15-minute walk today" will be made.

[0253] As described above, this system supports a safe and independent life for the elderly by constantly monitoring their health status and promptly providing countermeasures in the event of an abnormality.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The device periodically acquires biometric information such as the user's heart rate, body temperature, and activity level using sensors. This involves the use of wearable devices, which continuously monitor data and transmit the information to a server at regular intervals.

[0257] Step 2:

[0258] The server receives biometric information transmitted from the terminal and records it in a database. Time information is added to this record, and as the data accumulates, it becomes possible to analyze health status over the long term.

[0259] Step 3:

[0260] The server uses AI algorithms to detect anomalies from the accumulated data in the database. Specifically, it analyzes abnormal fluctuations by comparing them with the user's normal behavior patterns and health status. For example, it detects an anomaly if the heart rate exceeds a certain threshold.

[0261] Step 4:

[0262] When an anomaly is detected, the server generates an alert and immediately notifies designated family members or caregivers via email or SMS. This notification includes details of the anomaly and recommended actions to take.

[0263] Step 5:

[0264] Users can communicate directly with the system using an AI chatbot. The chatbot generates responses in natural language to user questions and commands, providing necessary information and reminders.

[0265] Step 6:

[0266] The server generates personalized care plans based on the user's health data. This includes exercise programs and dietary suggestions, which are then communicated to the user's device. For example, it might notify the user with a message like, "We recommend light stretching every morning."

[0267] Through this series of processes, the system can comprehensively support the health status of elderly individuals and respond quickly and appropriately in the event of an abnormality.

[0268] (Example 1)

[0269] 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."

[0270] To support independent living for the elderly, a system is needed that monitors biometric data in real time, quickly and accurately detects abnormalities in health conditions, and provides appropriate care as needed. However, current technology has challenges such as delays in data transmission and inaccuracies in analysis because these processes operate individually. Furthermore, there is a need for an integrated approach that supports daily health management while reducing feelings of isolation.

[0271] 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.

[0272] In this invention, the server includes terminal means for collecting biometric data, computation means for analyzing the biometric data and identifying anomalies, and notification means for notifying the anomalies. This enables real-time data collection and analysis, and provides rapid notification and appropriate interactive support in the event of an anomaly.

[0273] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and activity level, and is acquired through various sensors.

[0274] "Terminal means" refers to devices or equipment for acquiring biometric data, including wearable sensors and cameras.

[0275] "Computational means" refers to computing devices and algorithms used to analyze acquired biometric data and identify the user's health status and any abnormalities.

[0276] "Notification means" refers to methods for informing users or their related parties of abnormal health conditions, and includes notification devices and software.

[0277] "Information transmission means" refers to communication methods for quickly sharing abnormal information with external parties, particularly those using email, SMS, or dedicated application notifications.

[0278] "Interactive means" refers to systems that enable interaction with users, utilizing conversational machine learning and artificial intelligence technologies.

[0279] "Plan generation means" refers to a process or device that formulates an individually optimized health plan based on the user's health data.

[0280] This invention is a system for monitoring the health status of the elderly in real time and supporting independent living. Its embodiments are shown below.

[0281] The terminal uses wearable sensors, cameras, etc. to acquire the user's biological data. As a result, important health information such as heart rate, body temperature, and activity level is collected in real time. These data are transmitted to the server via Bluetooth or Wi-Fi.

[0282] The server uses a machine learning framework (e.g., TensorFlow or PyTorch) to analyze the received biological data. This makes it possible to accurately identify abnormalities in the user's health status and behavior patterns. If an abnormality is detected as a result of the analysis, the server immediately operates notification means to notify the relevant parties of this information.

[0283] This notification is promptly transmitted to the user's family members and caregivers via email, SMS, dedicated application notifications, etc. For example, if the heart rate exceeds the normal range, it is immediately detected as an abnormality and a notification is sent to the family.

[0284] The user can communicate through an AI-powered dialogue system. Utilizing a generative AI model such as OpenAI's, it appropriately responds to voice commands and messages from the user. Through this dialogue, it becomes possible for the user to live their daily life with a sense of security. It also includes a reminder function to support daily health management. For example, when the time to take medicine approaches, the user is notified.

[0285] Furthermore, the server generates a care plan optimized for each user based on the acquired data and past history. The proposed plan includes recommended exercise amounts and dietary content, and this information is presented to the user through the terminal. For example, if a lack of exercise is detected, the terminal makes a specific proposal to the user such as "Let's take a 15-minute walk today."

[0286] This enables users to lead a safer and more independent life while constantly being aware of their health status.

[0287] Example of a prompt sentence to input into the generative AI model:

[0288] "Please explain the health monitoring system for the elderly. Please teach me in detail the process of detecting abnormal heart rates."

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] The terminal acquires the user's biological data using a wearable sensor. Specifically, the sensor measures the user's heart rate, body temperature, activity level, etc., and converts these data into digital form. The input obtained is an analog signal, which is output as digital data.

[0292] Step 2:

[0293] The terminal transmits the acquired digital data to the server using wireless communication. A stable connection is established via Bluetooth or Wi-Fi, and the data is quickly sent to the server. The input is the processed biological digital data, which is output in the form of transmitting it to the server.

[0294] Step 3:

[0295] The server analyzes the received biological data using an AI algorithm. Specifically, using a machine learning framework such as TensorFlow, based on a pre-trained model, abnormal values are identified. In this process, biological data is input, and an analysis result indicating abnormal values is output.

[0296] Step 4:

[0297] The server, upon analysis, will notify relevant parties using notification mechanisms if an anomaly is detected. Notifications will be sent via email, SMS, or a dedicated application. The input is the anomaly detection information, which is then output as notification information sent to the relevant devices and addresses.

[0298] Step 5:

[0299] Users can communicate through the AI ​​dialogue system. The system analyzes user voice commands and text input, and generates responses using natural language processing technology. Here, user input is received, and an AI-generated response message is output.

[0300] Step 6:

[0301] The server generates individualized care plans based on the user's health data. Using the results of AI-driven data analysis, it creates plans that suggest optimal exercise levels and dietary content. The analyzed health data is used as input to generate the individualized plan.

[0302] Step 7:

[0303] The terminal presents the generated care plan to the user. It displays the proposal on the terminal's screen and prompts the user to take action through voice and notifications. The input is the care plan sent from the server, which is then output as information presented to the user.

[0304] (Application Example 1)

[0305] 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."

[0306] Currently, there are not enough means to support the independent living of the elderly. In particular, there is a lack of continuous monitoring of the health status, prompt response in case of abnormalities, and even support for daily communication aimed at reducing feelings of isolation. Along with this, there is a demand for the provision of a rapid and appropriate health management plan and the realization of action recommendations through cooperation with external devices.

[0307] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0308] In this invention, the server includes information collection means for acquiring biological data, analysis means for analyzing the biological data to detect abnormalities, dialogue means for interacting with the user using an interactive artificial intelligence, and action prompting means for promoting recommended actions to the user in cooperation with an external device. As a result, real-time monitoring and prompt response to the health status of the elderly become possible, and loneliness can be reduced through communication support by AI, enabling more accurate health management.

[0309] "Biological data" is information related to the health of the user, such as heart rate, body temperature, activity level, etc., which is acquired in real time.

[0310] "Information collection means" refers to a device or method for continuously acquiring the biological data of the user, such as a wearable terminal.

[0311] "Analysis means" refers to a process or technology for determining normal and abnormal states using the collected biological data.

[0312] "Notification means" refers to a mechanism for notifying the user or relevant persons of the abnormalities detected by the analysis, such as notifications and alarms.

[0313] "Communication means" refers to a device or function for transmitting abnormal information to an external device or person, such as a phone call, email, or short message information transmission.

[0314] "Dialogue means" refers to an AI-powered interface for exchanging information with users, and includes conversations and message exchanges.

[0315] "Plan generation means" refers to methods and techniques for formulating an appropriate health management plan based on the user's health condition.

[0316] "Action suggestion means" refers to a mechanism for recommending specific actions to users, including making suggestions and giving instructions that may involve collaboration with external devices.

[0317] Modes for carrying out the invention:

[0318] The system for implementing this invention utilizes a wearable device and a smartphone application to continuously monitor the health status of elderly individuals and provide notifications when necessary. The server implements an AI model using Python to analyze various biometric data in real time and quickly detect abnormal patterns. As a result, if an abnormality occurs in the user's health status, family members, caregivers, and other relevant parties are promptly notified.

[0319] The server acquires biometric data from wearable devices via Bluetooth. The collected data is sent to a server in the cloud, where an AI model analyzes it using libraries such as TensorFlow and scikit-learn. If an anomaly is identified through this analysis, a notification is sent via email, SMS, or a dedicated smartphone application.

[0320] The device is equipped with an AI chatbot to facilitate smooth communication with users. The chatbot uses natural language processing technology to understand voice input from users and provides health advice and everyday conversation as needed. This feature can help reduce feelings of loneliness and promote health management.

[0321] For example, if a user forgets to take their regular medication, the system will detect this and notify them via voice message through their device saying, "It's time to take your medication." Also, if a user's heart rate suddenly increases, the server will send an alarm to their caregiver.

[0322] An example of a prompt message would be: "Write Python code to send a notification when an elderly person's heart rate exceeds the normal range. The sensor used should be capable of measuring heart rate in real time, and SMS notification should be implemented as the method for detecting an abnormality."

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server receives biometric data collected by wearable devices via Bluetooth from the terminal. Inputs include biometric data such as heart rate, body temperature, and activity level, while output is a dataset that has undergone normalization and transformation. The data is acquired in real time and stored in a database to prepare for subsequent analysis.

[0326] Step 2:

[0327] The server analyzes biometric data stored in the database using an AI model. The input is the dataset stored in step 1, and the output is the anomaly detection result. Specifically, it loads a machine learning model using libraries such as TensorFlow and feeds the data into the model to detect anomalies.

[0328] Step 3:

[0329] The server notifies external stakeholders based on the anomalies detected as a result of the analysis. The input is the anomaly detection result obtained in step 2, and the output is the notification message. When an anomaly is detected, the server performs specific processing to generate and send email, SMS, or dedicated app notifications to the relevant parties.

[0330] Step 4:

[0331] The device initiates interaction with the user through an AI chatbot. Input consists of voice commands and text messages from the user, and output is a response message. Using natural language processing technology, the chatbot analyzes the user's input, such as medical consultations or everyday conversations, and generates and returns an appropriate response to the user.

[0332] Step 5:

[0333] The server generates a health management plan based on the user's health data and sends it to the terminal. The input is the user's health data that is continuously collected, and the output is a customized health management plan. If the user is not getting enough exercise, the server generates a specific goal, such as "Today's exercise goal is 10,000 steps," and displays it on the terminal's screen.

[0334] This series of processes allows users to continue living independently with peace of mind.

[0335] 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.

[0336] This invention is a system for comprehensively managing the health and emotional care of the elderly, providing personalized care to users through monitoring biometric information and recognizing emotions. The system centers around an emotion engine, which grasps the user's physical and emotional state in real time and provides appropriate responses.

[0337] System Overview

[0338] Data collection and emotion recognition

[0339] The device collects the user's biometric information through wearable sensors and acquires emotional data using a microphone and text interface. When the user speaks to the system, the emotion engine analyzes the emotion from the voice and text. For example, if the user says, "I'm a little tired today," the emotion engine will detect "fatigue."

[0340] Data Analysis

[0341] The server comprehensively analyzes this biometric and emotional data to assess the user's current physical and mental state. By identifying health abnormalities from biometric information and emotional changes from emotional data, it enables more effective interventions. For example, if a high heart rate and the emotion "stress" are detected, the server determines that relaxation is necessary.

[0342] Notifications and communications

[0343] If an anomaly is detected, the server will notify family members or caregivers via email or a dedicated app. Emotional information will also be reported, allowing families to use it to support the user's mental well-being. For example, a detailed notification might state, "Heart rate is high; stress detected."

[0344] Promoting communication

[0345] Users can interact with an AI chatbot and receive emotion-based feedback. The chatbot generates appropriate responses based on the user's emotions, helping to alleviate feelings of loneliness. For example, the chatbot might suggest, "You seem a little tired today, how about a nap?"

[0346] Care plan generation and proposal

[0347] The server creates an optimal care plan based on the user's biometric information and emotional state. This plan includes exercise, dietary improvements, and mental care necessary for maintaining health. The device notifies the user of these plans and encourages them to follow them. Specifically, when stress is detected, it recommends a plan such as, "Try yoga in the evening to relax."

[0348] This invention provides an environment in which elderly people can live with peace of mind, and supports a high quality of independent living by providing support not only physically but also emotionally.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] The device acquires biometric information such as the user's heart rate, body temperature, and activity level through wearable sensors. It also records the user's speech using a voice input function and sends it to the emotion engine for analysis.

[0352] Step 2:

[0353] The server receives biometric information and voice data transmitted from the terminal. The biometric information is recorded in a database, and the voice data is processed by an emotion engine for emotion analysis.

[0354] Step 3:

[0355] The server evaluates the analyzed biometric information using an AI algorithm to detect any abnormalities. Simultaneously, it determines the user's emotional state based on the analysis results from the emotion engine. For example, if "anger" is detected, that emotional state is recorded.

[0356] Step 4:

[0357] When an anomaly or a specific emotional state is detected, the server immediately sends an alert to family members or caregivers. This notification includes details of the anomaly and the user's emotional state, and is sent via email or app notification.

[0358] Step 5:

[0359] Users can interact with the system through an AI chatbot and receive emotion-based responses. The chatbot will offer comfort, encouragement, or relaxation suggestions tailored to the user's emotions. For example, it might send a message like, "Try to relax a little and take a deep breath."

[0360] Step 6:

[0361] The server integrates biometric and emotional data to generate a personalized care plan. This plan includes exercise and dietary recommendations tailored to the user's current health and emotional state. The device notifies the user of this plan and prompts them to take necessary actions. For example, it might suggest, "To alleviate anxiety, try listening to relaxing music at night."

[0362] Through this series of processes, the system comprehensively supports the health and emotional well-being of older adults, helping them to lead stable lives.

[0363] (Example 2)

[0364] 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".

[0365] In the daily lives of the elderly, it is difficult to grasp their health and emotional states in real time and provide appropriate interventions and care plans. In particular, there is a need for comprehensive support that takes into account not only biometric information but also emotional information, but there are few systems that can realize this. Furthermore, there is a need for a method to quickly notify family members and caregivers when abnormalities occur and to promote two-way communication.

[0366] 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.

[0367] In this invention, the server includes means for aggregating and analyzing biometric and emotional information, detection means for detecting anomalies, and communication means for reporting anomalies. This enables a comprehensive understanding of the user's health and emotional state, real-time care plan proposals, and rapid notification in the event of an anomaly.

[0368] "Biometric information" refers to physical data such as pulse rate, blood pressure, and body temperature that is acquired to indicate the user's health status.

[0369] "Device" refers to all equipment used to collect biometric information from users.

[0370] "Processing" refers to calculations and operations performed to aggregate and analyze biological and emotional information.

[0371] "Detection" is the process of identifying and recognizing anomalies from analyzed information.

[0372] "Communication" refers to a means of transmitting information to external devices in the event of an abnormality, and includes email and messaging systems.

[0373] "Recognition" is the process of estimating and understanding the user's emotional state from voice and text data.

[0374] "Dialogue" is the process of generating appropriate responses based on the user's emotional state and engaging in two-way communication with the user.

[0375] "Generation" refers to a series of tasks performed to create a care plan tailored to the user and notify the user's device.

[0376] A "plan" is a set of guidelines designed to propose specific activities aimed at maintaining the user's health and providing emotional support.

[0377] This invention provides a system to support health management and emotional care for the elderly. The system collects and analyzes the user's biometric and emotional information to understand their health and emotional state and propose an appropriate care plan.

[0378] Specifically, the device acquires biometric information using wearable sensors attached to the user. This includes data such as pulse rate, blood pressure, and body temperature. It also uses voice input to directly collect emotional information from the user as text data. This process utilizes a common speech-to-text API for speech recognition software.

[0379] Next, the server can use database management software and data analysis libraries to analyze the aggregated data. This makes it possible to assess the user's health status and detect anomalies. For example, statistical analysis can be performed using the Python Pandas library. If an anomaly is detected, the server will send an emergency alert to family members or caregivers using electronic communication. This communication will utilize email or push notification services.

[0380] Furthermore, users can receive emotion-based feedback through an interactive AI on their device. This AI dialogue uses a generative AI model to generate appropriate responses tailored to the user's emotions. For example, if a user inputs "I've been feeling lonely lately," the AI ​​might respond with "How about relaxing by watching your favorite movie?"

[0381] Ultimately, the server generates a care plan based on these analysis results to support the user's health maintenance. This plan includes daily exercise, dietary improvements, and mental health care, and is communicated to the user via their device. The proposed plan enables the user to pursue a better quality of life.

[0382] A concrete example of a prompt message would be, "Generate calming advice for when the user feels anxious." In this way, the invention provides an environment that comprehensively and individually supports the elderly.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The device acquires biometric information from wearable sensors attached to the user. The input consists of data such as pulse rate, blood pressure, and body temperature obtained from the wearable sensors. This data is transmitted to the device via wireless communication such as Bluetooth. Specifically, the device collects data from the sensors at regular intervals and stores it in a buffer. The output is the accumulated biometric data set.

[0386] Step 2:

[0387] The device records the user's voice using a microphone and converts it into text data using speech recognition software. The input is the user's voice (e.g., "I'm a little tired today"). The voice data is converted to text using a common speech recognition API. Specifically, the device processes the recorded voice in real time and converts it to text. The output is sentiment information in text format.

[0388] Step 3:

[0389] The server aggregates biometric and emotional information transmitted from terminals and stores it in a database. Input consists of a biometric data set and emotional information in text format. A database management system, such as SQL, can be used. Specifically, the server classifies the received data by identifier and stores it as a new entry in the database. The output is integrated data prepared for analysis.

[0390] Step 4:

[0391] The server analyzes integrated data to assess the user's health and emotional state. The input is an integrated dataset. Statistical analysis is performed using the Python Pandas library. Specifically, the server detects anomalies based on certain thresholds and identifies potential problems. The output is a report of the assessed health and emotional state.

[0392] Step 5:

[0393] If the server detects an anomaly based on the analysis results, it sends a notification to family members or caregivers via communication. The input is the anomaly detection information from the analysis. Transmission methods include email using the SMTP protocol and push notifications via a dedicated app. Specifically, the server generates a notification message using a template and sends it to the designated recipient. The output is the notified anomaly report.

[0394] Step 6:

[0395] The user receives emotion-based feedback through an interactive AI on the device. The input is text entered by the user (e.g., "I've been feeling lonely lately"). A generative AI model is used for the interactive AI to generate appropriate responses. Specifically, the device receives user input, sends prompts to the generative AI model, and displays the generated response on the screen. The output is the displayed AI response.

[0396] Step 7:

[0397] The server generates a care plan based on the user's health and emotional state and notifies the terminal. Inputs are analysis results and evaluated state information. The generated care plan includes exercise, diet, and mental health care. Specifically, the server creates a plan based on a pre-configured algorithm and sends it to the terminal. The output is the care plan notified to the user.

[0398] (Application Example 2)

[0399] 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."

[0400] In modern elderly care, emotional care is becoming increasingly important in addition to physical health management. However, many existing systems focus on detecting health abnormalities based on biometric information, and have limitations in providing individualized care that responds immediately to changes in the user's emotions. In particular, managing feelings of loneliness and stress is a challenge for the elderly. This invention aims to provide a system that integrates these emotional aspects of care.

[0401] 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.

[0402] In this invention, the server includes a sensor means for acquiring biometric data, a computation means for analyzing the biometric data and detecting abnormalities, and a communication means for engaging in dialogue with the user using an interactive algorithm. This enables simultaneous monitoring of the biometric information and emotional state of elderly individuals, providing a real-time response plan when an abnormality is detected, and reducing feelings of loneliness.

[0403] "Biometric data" refers to information that indicates a user's physical condition, and is usually acquired through wearable sensors or similar devices.

[0404] "Sensing means" refers to devices and technologies used to acquire a user's biometric data.

[0405] "Computational means" refers to devices or technologies that analyze health status based on acquired biological data and perform processing to determine whether or not there are abnormalities.

[0406] "Information provision means" refers to a method or device for notifying users or caregivers of important information, such as abnormality detection.

[0407] "Communication means" refers to a method or technique for transmitting a notification via an information provision means to an external device.

[0408] A "conversational algorithm" is an artificial intelligence-based technology used to facilitate smooth communication with users.

[0409] A "communication tool" is a method or device that uses an interactive algorithm to exchange information with a user.

[0410] A "care plan generation tool" is a technology or device that creates an optimal care plan based on the individual health and emotional state of the user.

[0411] "Suggested means" refers to a technology or method for presenting recommended activities or actions to users.

[0412] "Support measures" refer to methods and techniques that promote a sense of security and relaxation in accordance with the emotional state of elderly people.

[0413] The system for realizing this invention is for comprehensively managing the biological information and emotional state of elderly individuals. This system mainly includes sensor means, computing means, information provision means, communication means, interactive algorithms, and support means.

[0414] The server acquires biometric data in real time through wearable sensors and monitors the user's health status. The acquired data is analyzed by computational means, and if an abnormality is detected, a notification is sent to family members or caregivers through an information provision system. This enables a rapid response.

[0415] The device plays a role in two-way communication with the user using an interactive algorithm. Specifically, it utilizes AI technology to analyze the user's emotional state and generate appropriate feedback and encouraging messages. This supports the user's mental health and reduces feelings of loneliness.

[0416] Users are expected to live their daily lives according to a care plan generated by the server. This care plan includes recommended exercises, diets, and relaxation methods, which are appropriately suggested through support systems. For example, if stress is detected, relaxation activities will be suggested.

[0417] For example, when a user's heart rate increases and stress is detected, a suggestion such as "Why not relax by listening to some soothing music?" is made. This suggestion is generated based on a generative AI model, and an example of a prompt statement is as follows:

[0418] "Please suggest relaxing activities. The user's heart rate is elevated, indicating they are experiencing stress, so please offer advice in a gentle and calming tone."

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The server collects the user's biometric data from input signals received from wearable sensors. This data includes information such as heart rate, blood pressure, and body temperature. This input data is stored as time-series data and used as the basis for subsequent analysis.

[0422] Step 2:

[0423] The server analyzes the collected biometric data using computational methods. During this process, an anomaly detection algorithm is applied to monitor fluctuations in heart rate and blood pressure. As a result of the analysis, anomalies such as a higher-than-normal heart rate are output. This allows for real-time assessment of the user's health status.

[0424] Step 3:

[0425] If an anomaly is detected, the server will notify family members or caregivers through information provision channels. Specific numerical data and status information will be transmitted via email or a dedicated app. The input for this notification is the previously analyzed results, and the output is the applied notification message.

[0426] Step 4:

[0427] The device communicates with the user using an interactive algorithm. It analyzes emotions based on voice and text input from the user and runs a generative AI model. Based on this input data, it generates and outputs a feedback message tailored to the user. In this process, the AI ​​responds based on the generated prompt text.

[0428] Step 5:

[0429] Users receive feedback on a care plan generated by the server. This plan includes specific recommended actions, which they are encouraged to incorporate into their daily lives. Biometric data and the user's emotional state are used as input to create the plan, and personalized recommended activities are presented as output. This enables users to maintain their health and live a mentally stable life.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] This invention relates to a monitoring and communication system for supporting the independent living of elderly people. This system has the function of collecting the user's biometric information, detecting and notifying of abnormalities, and a communication function that utilizes interactive artificial intelligence.

[0447] System Overview

[0448] Data collection

[0449] The device uses wearable sensors and cameras to acquire biometric information such as the user's heart rate, body temperature, and activity level in real time, and transmits this data to a server. For example, when a user wears a wristwatch-type device while going about their daily life, their heart rate and steps are continuously recorded.

[0450] Data analysis and anomaly detection

[0451] The server uses AI algorithms to analyze the collected biometric information for abnormalities in health status and behavior. This process makes it possible to quickly detect abnormalities when a user exhibits unusual activity or physiological changes. For example, if a user's heart rate suddenly rises significantly above normal levels, it will be detected as a health abnormality.

[0452] Notifications and alerts

[0453] When an anomaly is detected, the server immediately notifies family members or caregivers of the information. This notification is sent via email, SMS, or a dedicated application. This allows those involved to quickly understand the situation and take the necessary actions.

[0454] Communication support

[0455] Users can communicate daily through an AI chatbot. The chatbot analyzes voice commands and messages from users and provides appropriate responses. This feature helps reduce feelings of loneliness and provides reminders to help with daily health management. For example, it will prompt users with "It's time to take your medication" when it's almost time.

[0456] Individual care plan

[0457] The server generates an individually tailored care plan based on the user's health status and behavioral patterns. This plan includes recommended exercise, diet, and lifestyle changes. The terminal directly displays these suggestions to the user, helping them incorporate them into their daily life. For example, if a lack of exercise is detected, a suggestion such as "Let's take a 15-minute walk today" will be made.

[0458] As described above, this system supports a safe and independent life for the elderly by constantly monitoring their health status and promptly providing countermeasures in the event of an abnormality.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The device periodically acquires biometric information such as the user's heart rate, body temperature, and activity level using sensors. This involves the use of wearable devices, which continuously monitor data and transmit the information to a server at regular intervals.

[0462] Step 2:

[0463] The server receives biometric information transmitted from the terminal and records it in a database. Time information is added to this record, and as the data accumulates, it becomes possible to analyze health status over the long term.

[0464] Step 3:

[0465] The server uses AI algorithms to detect anomalies from the accumulated data in the database. Specifically, it analyzes abnormal fluctuations by comparing them with the user's normal behavior patterns and health status. For example, it detects an anomaly if the heart rate exceeds a certain threshold.

[0466] Step 4:

[0467] When an anomaly is detected, the server generates an alert and immediately notifies designated family members or caregivers via email or SMS. This notification includes details of the anomaly and recommended actions to take.

[0468] Step 5:

[0469] Users can communicate directly with the system using an AI chatbot. The chatbot generates responses in natural language to user questions and commands, providing necessary information and reminders.

[0470] Step 6:

[0471] The server generates personalized care plans based on the user's health data. This includes exercise programs and dietary suggestions, which are then communicated to the user's device. For example, it might notify the user with a message like, "We recommend light stretching every morning."

[0472] Through this series of processes, the system can comprehensively support the health status of elderly individuals and respond quickly and appropriately in the event of an abnormality.

[0473] (Example 1)

[0474] 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."

[0475] To support independent living for the elderly, a system is needed that monitors biometric data in real time, quickly and accurately detects abnormalities in health conditions, and provides appropriate care as needed. However, current technology has challenges such as delays in data transmission and inaccuracies in analysis because these processes operate individually. Furthermore, there is a need for an integrated approach that supports daily health management while reducing feelings of isolation.

[0476] 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.

[0477] In this invention, the server includes terminal means for collecting biometric data, computation means for analyzing the biometric data and identifying anomalies, and notification means for notifying the anomalies. This enables real-time data collection and analysis, and provides rapid notification and appropriate interactive support in the event of an anomaly.

[0478] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and activity level, and is acquired through various sensors.

[0479] "Terminal means" refers to devices or equipment for acquiring biometric data, including wearable sensors and cameras.

[0480] "Computational means" refers to computing devices and algorithms used to analyze acquired biometric data and identify the user's health status and any abnormalities.

[0481] "Notification means" refers to methods for informing users or their related parties of abnormal health conditions, and includes notification devices and software.

[0482] "Information transmission means" refers to communication methods for quickly sharing abnormal information with external parties, particularly those using email, SMS, or dedicated application notifications.

[0483] "Interactive means" refers to systems that enable interaction with users, utilizing conversational machine learning and artificial intelligence technologies.

[0484] "Plan generation means" refers to a process or device that formulates an individually optimized health plan based on the user's health data.

[0485] This invention is a system for monitoring the health status of elderly people in real time and supporting their independent living. An embodiment of this system is shown below.

[0486] The device uses wearable sensors and cameras to acquire the user's biometric data. This allows for the real-time collection of important health information such as heart rate, body temperature, and activity level. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0487] The server uses machine learning frameworks (e.g., TensorFlow and PyTorch) to analyze the received biometric data. This makes it possible to accurately identify anomalies in the user's health status and behavioral patterns. If an anomaly is detected as a result of the analysis, the server immediately activates a notification system to inform relevant parties.

[0488] This notification is promptly communicated to the user's family or caregivers via email, SMS, or a dedicated application notification. For example, if the heart rate exceeds the normal range, it is immediately detected as an abnormality, and the family is notified.

[0489] Users can communicate through an AI-powered dialogue system. It utilizes OpenAI's generative AI models and other tools to appropriately respond to user voice commands and messages. This dialogue allows users to live their daily lives with a sense of security. It also includes a reminder function to support daily health management; for example, it notifies the user when it's time to take medication.

[0490] Furthermore, the server generates a care plan optimized for each user based on the acquired data and past history. The proposed plan includes recommended exercise levels and dietary content, and this information is presented to the user through the terminal. For example, if a lack of exercise is detected, the terminal will make a specific suggestion to the user, such as, "Let's take a 15-minute walk today."

[0491] This allows users to live a safer and more independent life while constantly being aware of their own health status.

[0492] Examples of prompts to input into a generative AI model:

[0493] "Please explain a health monitoring system for the elderly. Please describe in detail the process for detecting abnormal heart rates."

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The device acquires the user's biometric data using wearable sensors. Specifically, the sensors measure the user's heart rate, body temperature, activity level, etc., and convert this data into a digital format. The input is an analog signal, which is then output as digital data.

[0497] Step 2:

[0498] The device transmits acquired digital data to a server using wireless communication. It establishes a stable connection via Bluetooth or Wi-Fi to quickly send data to the server. The input is processed biometric digital data, which is then output by being transmitted to the server.

[0499] Step 3:

[0500] The server analyzes the received biometric data using AI algorithms. Specifically, it uses machine learning frameworks such as TensorFlow to identify anomalies based on pre-trained models. In this process, biometric data is input, and analysis results indicating anomalies are output.

[0501] Step 4:

[0502] The server, upon analysis, will notify relevant parties using notification mechanisms if an anomaly is detected. Notifications will be sent via email, SMS, or a dedicated application. The input is the anomaly detection information, which is then output as notification information sent to the relevant devices and addresses.

[0503] Step 5:

[0504] Users can communicate through the AI ​​dialogue system. The system analyzes user voice commands and text input, and generates responses using natural language processing technology. Here, user input is received, and an AI-generated response message is output.

[0505] Step 6:

[0506] The server generates individualized care plans based on the user's health data. Using the results of AI-driven data analysis, it creates plans that suggest optimal exercise levels and dietary content. The analyzed health data is used as input to generate the individualized plan.

[0507] Step 7:

[0508] The terminal presents the generated care plan to the user. It displays the proposal on the terminal's screen and prompts the user to take action through voice and notifications. The input is the care plan sent from the server, which is then output as information presented to the user.

[0509] (Application Example 1)

[0510] 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."

[0511] Currently, there are insufficient means to support the independent living of the elderly. In particular, there is a lack of continuous monitoring of health status, prompt response in case of abnormalities, and support for daily communication aimed at reducing feelings of isolation. Accordingly, there is a need for the provision of prompt and appropriate health management plans and the realization of behavioral recommendations through integration with external devices.

[0512] 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.

[0513] In this invention, the server includes information gathering means for acquiring biometric data, analysis means for analyzing biometric data and detecting anomalies, dialogue means for responding to the user using interactive artificial intelligence, and action suggestion means for coordinating with external devices to encourage recommended actions to the user. This enables real-time monitoring of the health status of the elderly and rapid response, as well as reducing feelings of loneliness through AI-assisted communication and enabling more accurate health management.

[0514] "Biometric data" refers to information about the user's health, such as heart rate, body temperature, and activity level, which are acquired in real time.

[0515] "Information gathering means" refers to devices and methods for continuously acquiring a user's biometric data, and wearable devices fall into this category.

[0516] "Analysis means" refers to processes and technologies used to determine normal and abnormal states using collected biological data.

[0517] "Notification means" refers to a system for informing users and relevant parties of anomalies detected through analysis, and includes notifications and alarms.

[0518] "Communication methods" refer to devices or functions used to transmit abnormal information to external devices or individuals, and include telephone calls, emails, and short message transmissions.

[0519] "Dialogue means" refers to an AI-powered interface for exchanging information with users, and includes conversations and message exchanges.

[0520] "Plan generation means" refers to methods and techniques for formulating an appropriate health management plan based on the user's health condition.

[0521] "Action suggestion means" refers to a mechanism for recommending specific actions to users, including making suggestions and giving instructions that may involve collaboration with external devices.

[0522] Modes for carrying out the invention:

[0523] The system for implementing this invention utilizes a wearable device and a smartphone application to continuously monitor the health status of elderly individuals and provide notifications when necessary. The server implements an AI model using Python to analyze various biometric data in real time and quickly detect abnormal patterns. As a result, if an abnormality occurs in the user's health status, family members, caregivers, and other relevant parties are promptly notified.

[0524] The server acquires biometric data from wearable devices via Bluetooth. The collected data is sent to a server in the cloud, where an AI model analyzes it using libraries such as TensorFlow and scikit-learn. If an anomaly is identified through this analysis, a notification is sent via email, SMS, or a dedicated smartphone application.

[0525] The device is equipped with an AI chatbot to facilitate smooth communication with users. The chatbot uses natural language processing technology to understand voice input from users and provides health advice and everyday conversation as needed. This feature can help reduce feelings of loneliness and promote health management.

[0526] For example, if a user forgets to take their regular medication, the system will detect this and notify them via voice message through their device saying, "It's time to take your medication." Also, if a user's heart rate suddenly increases, the server will send an alarm to their caregiver.

[0527] An example of a prompt message would be: "Write Python code to send a notification when an elderly person's heart rate exceeds the normal range. The sensor used should be capable of measuring heart rate in real time, and SMS notification should be implemented as the method for detecting an abnormality."

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The server receives biometric data collected by wearable devices via Bluetooth from the terminal. Inputs include biometric data such as heart rate, body temperature, and activity level, while output is a dataset that has undergone normalization and transformation. The data is acquired in real time and stored in a database to prepare for subsequent analysis.

[0531] Step 2:

[0532] The server analyzes biometric data stored in the database using an AI model. The input is the dataset stored in step 1, and the output is the anomaly detection result. Specifically, it loads a machine learning model using libraries such as TensorFlow and feeds the data into the model to detect anomalies.

[0533] Step 3:

[0534] The server notifies external stakeholders based on the anomalies detected as a result of the analysis. The input is the anomaly detection result obtained in step 2, and the output is the notification message. When an anomaly is detected, the server performs specific processing to generate and send email, SMS, or dedicated app notifications to the relevant parties.

[0535] Step 4:

[0536] The device initiates interaction with the user through an AI chatbot. Input consists of voice commands and text messages from the user, and output is a response message. Using natural language processing technology, the chatbot analyzes the user's input, such as medical consultations or everyday conversations, and generates and returns an appropriate response to the user.

[0537] Step 5:

[0538] The server generates a health management plan based on the user's health data and sends it to the terminal. The input is the user's health data that is continuously collected, and the output is a customized health management plan. If the user is not getting enough exercise, the server generates a specific goal, such as "Today's exercise goal is 10,000 steps," and displays it on the terminal's screen.

[0539] This series of processes allows users to continue living independently with peace of mind.

[0540] 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.

[0541] This invention is a system for comprehensively managing the health and emotional care of the elderly, providing personalized care to users through monitoring biometric information and recognizing emotions. The system centers around an emotion engine, which grasps the user's physical and emotional state in real time and provides appropriate responses.

[0542] System Overview

[0543] Data collection and emotion recognition

[0544] The device collects the user's biometric information through wearable sensors and acquires emotional data using a microphone and text interface. When the user speaks to the system, the emotion engine analyzes the emotion from the voice and text. For example, if the user says, "I'm a little tired today," the emotion engine will detect "fatigue."

[0545] Data Analysis

[0546] The server comprehensively analyzes this biometric and emotional data to assess the user's current physical and mental state. By identifying health abnormalities from biometric information and emotional changes from emotional data, it enables more effective interventions. For example, if a high heart rate and the emotion "stress" are detected, the server determines that relaxation is necessary.

[0547] Notifications and communications

[0548] If an anomaly is detected, the server will notify family members or caregivers via email or a dedicated app. Emotional information will also be reported, allowing families to use it to support the user's mental well-being. For example, a detailed notification might state, "Heart rate is high; stress detected."

[0549] Promoting communication

[0550] Users can interact with an AI chatbot and receive emotion-based feedback. The chatbot generates appropriate responses based on the user's emotions, helping to alleviate feelings of loneliness. For example, the chatbot might suggest, "You seem a little tired today, how about a nap?"

[0551] Care plan generation and proposal

[0552] The server creates an optimal care plan based on the user's biometric information and emotional state. This plan includes exercise, dietary improvements, and mental care necessary for maintaining health. The device notifies the user of these plans and encourages them to follow them. Specifically, when stress is detected, it recommends a plan such as, "Try yoga in the evening to relax."

[0553] This invention provides an environment in which elderly people can live with peace of mind, and supports a high quality of independent living by providing support not only physically but also emotionally.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The device acquires biometric information such as the user's heart rate, body temperature, and activity level through wearable sensors. It also records the user's speech using a voice input function and sends it to the emotion engine for analysis.

[0557] Step 2:

[0558] The server receives biometric information and voice data transmitted from the terminal. The biometric information is recorded in a database, and the voice data is processed by an emotion engine for emotion analysis.

[0559] Step 3:

[0560] The server evaluates the analyzed biometric information using an AI algorithm to detect any abnormalities. Simultaneously, it determines the user's emotional state based on the analysis results from the emotion engine. For example, if "anger" is detected, that emotional state is recorded.

[0561] Step 4:

[0562] When an anomaly or a specific emotional state is detected, the server immediately sends an alert to family members or caregivers. This notification includes details of the anomaly and the user's emotional state, and is sent via email or app notification.

[0563] Step 5:

[0564] Users can interact with the system through an AI chatbot and receive emotion-based responses. The chatbot will offer comfort, encouragement, or relaxation suggestions tailored to the user's emotions. For example, it might send a message like, "Try to relax a little and take a deep breath."

[0565] Step 6:

[0566] The server integrates biometric and emotional data to generate a personalized care plan. This plan includes exercise and dietary recommendations tailored to the user's current health and emotional state. The device notifies the user of this plan and prompts them to take necessary actions. For example, it might suggest, "To alleviate anxiety, try listening to relaxing music at night."

[0567] Through this series of processes, the system comprehensively supports the health and emotional well-being of older adults, helping them to lead stable lives.

[0568] (Example 2)

[0569] 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."

[0570] In the daily lives of the elderly, it is difficult to grasp their health and emotional states in real time and provide appropriate interventions and care plans. In particular, there is a need for comprehensive support that takes into account not only biometric information but also emotional information, but there are few systems that can realize this. Furthermore, there is a need for a method to quickly notify family members and caregivers when abnormalities occur and to promote two-way communication.

[0571] 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.

[0572] In this invention, the server includes means for aggregating and analyzing biometric and emotional information, detection means for detecting anomalies, and communication means for reporting anomalies. This enables a comprehensive understanding of the user's health and emotional state, real-time care plan proposals, and rapid notification in the event of an anomaly.

[0573] "Biometric information" refers to physical data such as pulse rate, blood pressure, and body temperature that is acquired to indicate the user's health status.

[0574] "Device" refers to all equipment used to collect biometric information from users.

[0575] "Processing" refers to calculations and operations performed to aggregate and analyze biological and emotional information.

[0576] "Detection" is the process of identifying and recognizing anomalies from analyzed information.

[0577] "Communication" refers to a means of transmitting information to external devices in the event of an abnormality, and includes email and messaging systems.

[0578] "Recognition" is the process of estimating and understanding the user's emotional state from voice and text data.

[0579] "Dialogue" is the process of generating appropriate responses based on the user's emotional state and engaging in two-way communication with the user.

[0580] "Generation" refers to a series of tasks performed to create a care plan tailored to the user and notify the user's device.

[0581] A "plan" is a set of guidelines designed to propose specific activities aimed at maintaining the user's health and providing emotional support.

[0582] This invention provides a system to support health management and emotional care for the elderly. The system collects and analyzes the user's biometric and emotional information to understand their health and emotional state and propose an appropriate care plan.

[0583] Specifically, the device acquires biometric information using wearable sensors attached to the user. This includes data such as pulse rate, blood pressure, and body temperature. It also uses voice input to directly collect emotional information from the user as text data. This process utilizes a common speech-to-text API for speech recognition software.

[0584] Next, the server can use database management software and data analysis libraries to analyze the aggregated data. This makes it possible to assess the user's health status and detect anomalies. For example, statistical analysis can be performed using the Python Pandas library. If an anomaly is detected, the server will send an emergency alert to family members or caregivers using electronic communication. This communication will utilize email or push notification services.

[0585] Furthermore, users can receive emotion-based feedback through an interactive AI on their device. This AI dialogue uses a generative AI model to generate appropriate responses tailored to the user's emotions. For example, if a user inputs "I've been feeling lonely lately," the AI ​​might respond with "How about relaxing by watching your favorite movie?"

[0586] Ultimately, the server generates a care plan based on these analysis results to support the user's health maintenance. This plan includes daily exercise, dietary improvements, and mental health care, and is communicated to the user via their device. The proposed plan enables the user to pursue a better quality of life.

[0587] A concrete example of a prompt message would be, "Generate calming advice for when the user feels anxious." In this way, the invention provides an environment that comprehensively and individually supports the elderly.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The device acquires biometric information from wearable sensors attached to the user. The input consists of data such as pulse rate, blood pressure, and body temperature obtained from the wearable sensors. This data is transmitted to the device via wireless communication such as Bluetooth. Specifically, the device collects data from the sensors at regular intervals and stores it in a buffer. The output is the accumulated biometric data set.

[0591] Step 2:

[0592] The device records the user's voice using a microphone and converts it into text data using speech recognition software. The input is the user's voice (e.g., "I'm a little tired today"). The voice data is converted to text using a common speech recognition API. Specifically, the device processes the recorded voice in real time and converts it to text. The output is sentiment information in text format.

[0593] Step 3:

[0594] The server aggregates biometric and emotional information transmitted from terminals and stores it in a database. Input consists of a biometric data set and emotional information in text format. A database management system, such as SQL, can be used. Specifically, the server classifies the received data by identifier and stores it as a new entry in the database. The output is integrated data prepared for analysis.

[0595] Step 4:

[0596] The server analyzes integrated data to assess the user's health and emotional state. The input is an integrated dataset. Statistical analysis is performed using the Python Pandas library. Specifically, the server detects anomalies based on certain thresholds and identifies potential problems. The output is a report of the assessed health and emotional state.

[0597] Step 5:

[0598] If the server detects an anomaly based on the analysis results, it sends a notification to family members or caregivers via communication. The input is the anomaly detection information from the analysis. Transmission methods include email using the SMTP protocol and push notifications via a dedicated app. Specifically, the server generates a notification message using a template and sends it to the designated recipient. The output is the notified anomaly report.

[0599] Step 6:

[0600] The user receives emotion-based feedback through an interactive AI on the device. The input is text entered by the user (e.g., "I've been feeling lonely lately"). A generative AI model is used for the interactive AI to generate appropriate responses. Specifically, the device receives user input, sends prompts to the generative AI model, and displays the generated response on the screen. The output is the displayed AI response.

[0601] Step 7:

[0602] The server generates a care plan based on the user's health and emotional state and notifies the terminal. Inputs are analysis results and evaluated state information. The generated care plan includes exercise, diet, and mental health care. Specifically, the server creates a plan based on a pre-configured algorithm and sends it to the terminal. The output is the care plan notified to the user.

[0603] (Application Example 2)

[0604] 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."

[0605] In modern elderly care, emotional care is becoming increasingly important in addition to physical health management. However, many existing systems focus on detecting health abnormalities based on biometric information, and have limitations in providing individualized care that responds immediately to changes in the user's emotions. In particular, managing feelings of loneliness and stress is a challenge for the elderly. This invention aims to provide a system that integrates these emotional aspects of care.

[0606] 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.

[0607] In this invention, the server includes a sensor means for acquiring biometric data, a computation means for analyzing the biometric data and detecting abnormalities, and a communication means for engaging in dialogue with the user using an interactive algorithm. This enables simultaneous monitoring of the biometric information and emotional state of elderly individuals, providing a real-time response plan when an abnormality is detected, and reducing feelings of loneliness.

[0608] "Biometric data" refers to information that indicates a user's physical condition, and is usually acquired through wearable sensors or similar devices.

[0609] "Sensing means" refers to devices and technologies used to acquire a user's biometric data.

[0610] "Computational means" refers to devices or technologies that analyze health status based on acquired biological data and perform processing to determine whether or not there are abnormalities.

[0611] "Information provision means" refers to a method or device for notifying users or caregivers of important information, such as abnormality detection.

[0612] "Communication means" refers to a method or technique for transmitting a notification via an information provision means to an external device.

[0613] A "conversational algorithm" is an artificial intelligence-based technology used to facilitate smooth communication with users.

[0614] A "communication tool" is a method or device that uses an interactive algorithm to exchange information with a user.

[0615] A "care plan generation tool" is a technology or device that creates an optimal care plan based on the individual health and emotional state of the user.

[0616] "Suggested means" refers to a technology or method for presenting recommended activities or actions to users.

[0617] "Support measures" refer to methods and techniques that promote a sense of security and relaxation in accordance with the emotional state of elderly people.

[0618] The system for realizing this invention is for comprehensively managing the biological information and emotional state of elderly individuals. This system mainly includes sensor means, computing means, information provision means, communication means, interactive algorithms, and support means.

[0619] The server acquires biometric data in real time through wearable sensors and monitors the user's health status. The acquired data is analyzed by computational means, and if an abnormality is detected, a notification is sent to family members or caregivers through an information provision system. This enables a rapid response.

[0620] The device plays a role in two-way communication with the user using an interactive algorithm. Specifically, it utilizes AI technology to analyze the user's emotional state and generate appropriate feedback and encouraging messages. This supports the user's mental health and reduces feelings of loneliness.

[0621] Users are expected to live their daily lives according to a care plan generated by the server. This care plan includes recommended exercises, diets, and relaxation methods, which are appropriately suggested through support systems. For example, if stress is detected, relaxation activities will be suggested.

[0622] For example, when a user's heart rate increases and stress is detected, a suggestion such as "Why not relax by listening to some soothing music?" is made. This suggestion is generated based on a generative AI model, and an example of a prompt statement is as follows:

[0623] "Please suggest relaxing activities. The user's heart rate is elevated, indicating they are experiencing stress, so please offer advice in a gentle and calming tone."

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The server collects the user's biometric data from input signals received from wearable sensors. This data includes information such as heart rate, blood pressure, and body temperature. This input data is stored as time-series data and used as the basis for subsequent analysis.

[0627] Step 2:

[0628] The server analyzes the collected biometric data using computational methods. During this process, an anomaly detection algorithm is applied to monitor fluctuations in heart rate and blood pressure. As a result of the analysis, anomalies such as a higher-than-normal heart rate are output. This allows for real-time assessment of the user's health status.

[0629] Step 3:

[0630] If an anomaly is detected, the server will notify family members or caregivers through information provision channels. Specific numerical data and status information will be transmitted via email or a dedicated app. The input for this notification is the previously analyzed results, and the output is the applied notification message.

[0631] Step 4:

[0632] The device communicates with the user using an interactive algorithm. It analyzes emotions based on voice and text input from the user and runs a generative AI model. Based on this input data, it generates and outputs a feedback message tailored to the user. In this process, the AI ​​responds based on the generated prompt text.

[0633] Step 5:

[0634] Users receive feedback on a care plan generated by the server. This plan includes specific recommended actions, which they are encouraged to incorporate into their daily lives. Biometric data and the user's emotional state are used as input to create the plan, and personalized recommended activities are presented as output. This enables users to maintain their health and live a mentally stable life.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Fourth Embodiment]

[0639] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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".

[0652] This invention relates to a monitoring and communication system for supporting the independent living of elderly people. This system has the function of collecting the user's biometric information, detecting and notifying of abnormalities, and a communication function that utilizes interactive artificial intelligence.

[0653] System Overview

[0654] Data collection

[0655] The device uses wearable sensors and cameras to acquire biometric information such as the user's heart rate, body temperature, and activity level in real time, and transmits this data to a server. For example, when a user wears a wristwatch-type device while going about their daily life, their heart rate and steps are continuously recorded.

[0656] Data analysis and anomaly detection

[0657] The server uses AI algorithms to analyze the collected biometric information for abnormalities in health status and behavior. This process makes it possible to quickly detect abnormalities when a user exhibits unusual activity or physiological changes. For example, if a user's heart rate suddenly rises significantly above normal levels, it will be detected as a health abnormality.

[0658] Notifications and alerts

[0659] When an anomaly is detected, the server immediately notifies family members or caregivers of the information. This notification is sent via email, SMS, or a dedicated application. This allows those involved to quickly understand the situation and take the necessary actions.

[0660] Communication support

[0661] Users can communicate daily through an AI chatbot. The chatbot analyzes voice commands and messages from users and provides appropriate responses. This feature helps reduce feelings of loneliness and provides reminders to help with daily health management. For example, it will prompt users with "It's time to take your medication" when it's almost time.

[0662] Individual care plan

[0663] The server generates an individually tailored care plan based on the user's health status and behavioral patterns. This plan includes recommended exercise, diet, and lifestyle changes. The terminal directly displays these suggestions to the user, helping them incorporate them into their daily life. For example, if a lack of exercise is detected, a suggestion such as "Let's take a 15-minute walk today" will be made.

[0664] As described above, this system supports a safe and independent life for the elderly by constantly monitoring their health status and promptly providing countermeasures in the event of an abnormality.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The device periodically acquires biometric information such as the user's heart rate, body temperature, and activity level using sensors. This involves the use of wearable devices, which continuously monitor data and transmit the information to a server at regular intervals.

[0668] Step 2:

[0669] The server receives biometric information transmitted from the terminal and records it in a database. Time information is added to this record, and as the data accumulates, it becomes possible to analyze health status over the long term.

[0670] Step 3:

[0671] The server uses AI algorithms to detect anomalies from the accumulated data in the database. Specifically, it analyzes abnormal fluctuations by comparing them with the user's normal behavior patterns and health status. For example, it detects an anomaly if the heart rate exceeds a certain threshold.

[0672] Step 4:

[0673] When an anomaly is detected, the server generates an alert and immediately notifies designated family members or caregivers via email or SMS. This notification includes details of the anomaly and recommended actions to take.

[0674] Step 5:

[0675] Users can communicate directly with the system using an AI chatbot. The chatbot generates responses in natural language to user questions and commands, providing necessary information and reminders.

[0676] Step 6:

[0677] The server generates personalized care plans based on the user's health data. This includes exercise programs and dietary suggestions, which are then communicated to the user's device. For example, it might notify the user with a message like, "We recommend light stretching every morning."

[0678] Through this series of processes, the system can comprehensively support the health status of elderly individuals and respond quickly and appropriately in the event of an abnormality.

[0679] (Example 1)

[0680] 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".

[0681] To support independent living for the elderly, a system is needed that monitors biometric data in real time, quickly and accurately detects abnormalities in health conditions, and provides appropriate care as needed. However, current technology has challenges such as delays in data transmission and inaccuracies in analysis because these processes operate individually. Furthermore, there is a need for an integrated approach that supports daily health management while reducing feelings of isolation.

[0682] 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.

[0683] In this invention, the server includes terminal means for collecting biometric data, computation means for analyzing the biometric data and identifying anomalies, and notification means for notifying the anomalies. This enables real-time data collection and analysis, and provides rapid notification and appropriate interactive support in the event of an anomaly.

[0684] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and activity level, and is acquired through various sensors.

[0685] "Terminal means" refers to devices or equipment for acquiring biometric data, including wearable sensors and cameras.

[0686] "Computational means" refers to computing devices and algorithms used to analyze acquired biometric data and identify the user's health status and any abnormalities.

[0687] "Notification means" refers to methods for informing users or their related parties of abnormal health conditions, and includes notification devices and software.

[0688] "Information transmission means" refers to communication methods for quickly sharing abnormal information with external parties, particularly those using email, SMS, or dedicated application notifications.

[0689] "Interactive means" refers to systems that enable interaction with users, utilizing conversational machine learning and artificial intelligence technologies.

[0690] "Plan generation means" refers to a process or device that formulates an individually optimized health plan based on the user's health data.

[0691] This invention is a system for monitoring the health status of elderly people in real time and supporting their independent living. An embodiment of this system is shown below.

[0692] The device uses wearable sensors and cameras to acquire the user's biometric data. This allows for the real-time collection of important health information such as heart rate, body temperature, and activity level. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0693] The server uses machine learning frameworks (e.g., TensorFlow and PyTorch) to analyze the received biometric data. This makes it possible to accurately identify anomalies in the user's health status and behavioral patterns. If an anomaly is detected as a result of the analysis, the server immediately activates a notification system to inform relevant parties.

[0694] This notification is promptly communicated to the user's family or caregivers via email, SMS, or a dedicated application notification. For example, if the heart rate exceeds the normal range, it is immediately detected as an abnormality, and the family is notified.

[0695] Users can communicate through an AI-powered dialogue system. It utilizes OpenAI's generative AI models and other tools to appropriately respond to user voice commands and messages. This dialogue allows users to live their daily lives with a sense of security. It also includes a reminder function to support daily health management; for example, it notifies the user when it's time to take medication.

[0696] Furthermore, the server generates a care plan optimized for each user based on the acquired data and past history. The proposed plan includes recommended exercise levels and dietary content, and this information is presented to the user through the terminal. For example, if a lack of exercise is detected, the terminal will make a specific suggestion to the user, such as, "Let's take a 15-minute walk today."

[0697] This allows users to live a safer and more independent life while constantly being aware of their own health status.

[0698] Examples of prompts to input into a generative AI model:

[0699] "Please explain a health monitoring system for the elderly. Please describe in detail the process for detecting abnormal heart rates."

[0700] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0701] Step 1:

[0702] The device acquires the user's biometric data using wearable sensors. Specifically, the sensors measure the user's heart rate, body temperature, activity level, etc., and convert this data into a digital format. The input is an analog signal, which is then output as digital data.

[0703] Step 2:

[0704] The device transmits acquired digital data to a server using wireless communication. It establishes a stable connection via Bluetooth or Wi-Fi to quickly send data to the server. The input is processed biometric digital data, which is then output by being transmitted to the server.

[0705] Step 3:

[0706] The server analyzes the received biometric data using AI algorithms. Specifically, it uses machine learning frameworks such as TensorFlow to identify anomalies based on pre-trained models. In this process, biometric data is input, and analysis results indicating anomalies are output.

[0707] Step 4:

[0708] The server, upon analysis, will notify relevant parties using notification mechanisms if an anomaly is detected. Notifications will be sent via email, SMS, or a dedicated application. The input is the anomaly detection information, which is then output as notification information sent to the relevant devices and addresses.

[0709] Step 5:

[0710] Users can communicate through the AI ​​dialogue system. The system analyzes user voice commands and text input, and generates responses using natural language processing technology. Here, user input is received, and an AI-generated response message is output.

[0711] Step 6:

[0712] The server generates individualized care plans based on the user's health data. Using the results of AI-driven data analysis, it creates plans that suggest optimal exercise levels and dietary content. The analyzed health data is used as input to generate the individualized plan.

[0713] Step 7:

[0714] The terminal presents the generated care plan to the user. It displays the proposal on the terminal's screen and prompts the user to take action through voice and notifications. The input is the care plan sent from the server, which is then output as information presented to the user.

[0715] (Application Example 1)

[0716] 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".

[0717] Currently, there are insufficient means to support the independent living of the elderly. In particular, there is a lack of continuous monitoring of health status, prompt response in case of abnormalities, and support for daily communication aimed at reducing feelings of isolation. Accordingly, there is a need for the provision of prompt and appropriate health management plans and the realization of behavioral recommendations through integration with external devices.

[0718] 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.

[0719] In this invention, the server includes information gathering means for acquiring biometric data, analysis means for analyzing biometric data and detecting anomalies, dialogue means for responding to the user using interactive artificial intelligence, and action suggestion means for coordinating with external devices to encourage recommended actions to the user. This enables real-time monitoring of the health status of the elderly and rapid response, as well as reducing feelings of loneliness through AI-assisted communication and enabling more accurate health management.

[0720] "Biometric data" refers to information about the user's health, such as heart rate, body temperature, and activity level, which are acquired in real time.

[0721] "Information gathering means" refers to devices and methods for continuously acquiring a user's biometric data, and wearable devices fall into this category.

[0722] "Analysis means" refers to processes and technologies used to determine normal and abnormal states using collected biological data.

[0723] "Notification means" refers to a system for informing users and relevant parties of anomalies detected through analysis, and includes notifications and alarms.

[0724] "Communication methods" refer to devices or functions used to transmit abnormal information to external devices or individuals, and include telephone calls, emails, and short message transmissions.

[0725] "Dialogue means" refers to an AI-powered interface for exchanging information with users, and includes conversations and message exchanges.

[0726] "Plan generation means" refers to methods and techniques for formulating an appropriate health management plan based on the user's health condition.

[0727] "Action suggestion means" refers to a mechanism for recommending specific actions to users, including making suggestions and giving instructions that may involve collaboration with external devices.

[0728] Modes for carrying out the invention:

[0729] The system for implementing this invention utilizes a wearable device and a smartphone application to continuously monitor the health status of elderly individuals and provide notifications when necessary. The server implements an AI model using Python to analyze various biometric data in real time and quickly detect abnormal patterns. As a result, if an abnormality occurs in the user's health status, family members, caregivers, and other relevant parties are promptly notified.

[0730] The server acquires biometric data from wearable devices via Bluetooth. The collected data is sent to a server in the cloud, where an AI model analyzes it using libraries such as TensorFlow and scikit-learn. If an anomaly is identified through this analysis, a notification is sent via email, SMS, or a dedicated smartphone application.

[0731] The device is equipped with an AI chatbot to facilitate smooth communication with users. The chatbot uses natural language processing technology to understand voice input from users and provides health advice and everyday conversation as needed. This feature can help reduce feelings of loneliness and promote health management.

[0732] For example, if a user forgets to take their regular medication, the system will detect this and notify them via voice message through their device saying, "It's time to take your medication." Also, if a user's heart rate suddenly increases, the server will send an alarm to their caregiver.

[0733] An example of a prompt message would be: "Write Python code to send a notification when an elderly person's heart rate exceeds the normal range. The sensor used should be capable of measuring heart rate in real time, and SMS notification should be implemented as the method for detecting an abnormality."

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server receives biometric data collected by wearable devices via Bluetooth from the terminal. Inputs include biometric data such as heart rate, body temperature, and activity level, while output is a dataset that has undergone normalization and transformation. The data is acquired in real time and stored in a database to prepare for subsequent analysis.

[0737] Step 2:

[0738] The server analyzes biometric data stored in the database using an AI model. The input is the dataset stored in step 1, and the output is the anomaly detection result. Specifically, it loads a machine learning model using libraries such as TensorFlow and feeds the data into the model to detect anomalies.

[0739] Step 3:

[0740] The server notifies external stakeholders based on the anomalies detected as a result of the analysis. The input is the anomaly detection result obtained in step 2, and the output is the notification message. When an anomaly is detected, the server performs specific processing to generate and send email, SMS, or dedicated app notifications to the relevant parties.

[0741] Step 4:

[0742] The device initiates interaction with the user through an AI chatbot. Input consists of voice commands and text messages from the user, and output is a response message. Using natural language processing technology, the chatbot analyzes the user's input, such as medical consultations or everyday conversations, and generates and returns an appropriate response to the user.

[0743] Step 5:

[0744] The server generates a health management plan based on the user's health data and sends it to the terminal. The input is the user's health data that is continuously collected, and the output is a customized health management plan. If the user is not getting enough exercise, the server generates a specific goal, such as "Today's exercise goal is 10,000 steps," and displays it on the terminal's screen.

[0745] This series of processes allows users to continue living independently with peace of mind.

[0746] 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.

[0747] This invention is a system for comprehensively managing the health and emotional care of the elderly, providing personalized care to users through monitoring biometric information and recognizing emotions. The system centers around an emotion engine, which grasps the user's physical and emotional state in real time and provides appropriate responses.

[0748] System Overview

[0749] Data collection and emotion recognition

[0750] The device collects the user's biometric information through wearable sensors and acquires emotional data using a microphone and text interface. When the user speaks to the system, the emotion engine analyzes the emotion from the voice and text. For example, if the user says, "I'm a little tired today," the emotion engine will detect "fatigue."

[0751] Data Analysis

[0752] The server comprehensively analyzes this biometric and emotional data to assess the user's current physical and mental state. By identifying health abnormalities from biometric information and emotional changes from emotional data, it enables more effective interventions. For example, if a high heart rate and the emotion "stress" are detected, the server determines that relaxation is necessary.

[0753] Notifications and communications

[0754] If an anomaly is detected, the server will notify family members or caregivers via email or a dedicated app. Emotional information will also be reported, allowing families to use it to support the user's mental well-being. For example, a detailed notification might state, "Heart rate is high; stress detected."

[0755] Promoting communication

[0756] Users can interact with an AI chatbot and receive emotion-based feedback. The chatbot generates appropriate responses based on the user's emotions, helping to alleviate feelings of loneliness. For example, the chatbot might suggest, "You seem a little tired today, how about a nap?"

[0757] Care plan generation and proposal

[0758] The server creates an optimal care plan based on the user's biometric information and emotional state. This plan includes exercise, dietary improvements, and mental care necessary for maintaining health. The device notifies the user of these plans and encourages them to follow them. Specifically, when stress is detected, it recommends a plan such as, "Try yoga in the evening to relax."

[0759] This invention provides an environment in which elderly people can live with peace of mind, and supports a high quality of independent living by providing support not only physically but also emotionally.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The device acquires biometric information such as the user's heart rate, body temperature, and activity level through wearable sensors. It also records the user's speech using a voice input function and sends it to the emotion engine for analysis.

[0763] Step 2:

[0764] The server receives biometric information and voice data transmitted from the terminal. The biometric information is recorded in a database, and the voice data is processed by an emotion engine for emotion analysis.

[0765] Step 3:

[0766] The server evaluates the analyzed biometric information using an AI algorithm to detect any abnormalities. Simultaneously, it determines the user's emotional state based on the analysis results from the emotion engine. For example, if "anger" is detected, that emotional state is recorded.

[0767] Step 4:

[0768] When an anomaly or a specific emotional state is detected, the server immediately sends an alert to family members or caregivers. This notification includes details of the anomaly and the user's emotional state, and is sent via email or app notification.

[0769] Step 5:

[0770] Users can interact with the system through an AI chatbot and receive emotion-based responses. The chatbot will offer comfort, encouragement, or relaxation suggestions tailored to the user's emotions. For example, it might send a message like, "Try to relax a little and take a deep breath."

[0771] Step 6:

[0772] The server integrates biometric and emotional data to generate a personalized care plan. This plan includes exercise and dietary recommendations tailored to the user's current health and emotional state. The device notifies the user of this plan and prompts them to take necessary actions. For example, it might suggest, "To alleviate anxiety, try listening to relaxing music at night."

[0773] Through this series of processes, the system comprehensively supports the health and emotional well-being of older adults, helping them to lead stable lives.

[0774] (Example 2)

[0775] 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".

[0776] In the daily lives of the elderly, it is difficult to grasp their health and emotional states in real time and provide appropriate interventions and care plans. In particular, there is a need for comprehensive support that takes into account not only biometric information but also emotional information, but there are few systems that can realize this. Furthermore, there is a need for a method to quickly notify family members and caregivers when abnormalities occur and to promote two-way communication.

[0777] 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.

[0778] In this invention, the server includes means for aggregating and analyzing biometric and emotional information, detection means for detecting anomalies, and communication means for reporting anomalies. This enables a comprehensive understanding of the user's health and emotional state, real-time care plan proposals, and rapid notification in the event of an anomaly.

[0779] "Biometric information" refers to physical data such as pulse rate, blood pressure, and body temperature that is acquired to indicate the user's health status.

[0780] "Device" refers to all equipment used to collect biometric information from users.

[0781] "Processing" refers to calculations and operations performed to aggregate and analyze biological and emotional information.

[0782] "Detection" is the process of identifying and recognizing anomalies from analyzed information.

[0783] "Communication" refers to a means of transmitting information to external devices in the event of an abnormality, and includes email and messaging systems.

[0784] "Recognition" is the process of estimating and understanding the user's emotional state from voice and text data.

[0785] "Dialogue" is the process of generating appropriate responses based on the user's emotional state and engaging in two-way communication with the user.

[0786] "Generation" refers to a series of tasks performed to create a care plan tailored to the user and notify the user's device.

[0787] A "plan" is a set of guidelines designed to propose specific activities aimed at maintaining the user's health and providing emotional support.

[0788] This invention provides a system to support health management and emotional care for the elderly. The system collects and analyzes the user's biometric and emotional information to understand their health and emotional state and propose an appropriate care plan.

[0789] Specifically, the device acquires biometric information using wearable sensors attached to the user. This includes data such as pulse rate, blood pressure, and body temperature. It also uses voice input to directly collect emotional information from the user as text data. This process utilizes a common speech-to-text API for speech recognition software.

[0790] Next, the server can use database management software and data analysis libraries to analyze the aggregated data. This makes it possible to assess the user's health status and detect anomalies. For example, statistical analysis can be performed using the Python Pandas library. If an anomaly is detected, the server will send an emergency alert to family members or caregivers using electronic communication. This communication will utilize email or push notification services.

[0791] Furthermore, users can receive emotion-based feedback through an interactive AI on their device. This AI dialogue uses a generative AI model to generate appropriate responses tailored to the user's emotions. For example, if a user inputs "I've been feeling lonely lately," the AI ​​might respond with "How about relaxing by watching your favorite movie?"

[0792] Ultimately, the server generates a care plan based on these analysis results to support the user's health maintenance. This plan includes daily exercise, dietary improvements, and mental health care, and is communicated to the user via their device. The proposed plan enables the user to pursue a better quality of life.

[0793] A concrete example of a prompt message would be, "Generate calming advice for when the user feels anxious." In this way, the invention provides an environment that comprehensively and individually supports the elderly.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The device acquires biometric information from wearable sensors attached to the user. The input consists of data such as pulse rate, blood pressure, and body temperature obtained from the wearable sensors. This data is transmitted to the device via wireless communication such as Bluetooth. Specifically, the device collects data from the sensors at regular intervals and stores it in a buffer. The output is the accumulated biometric data set.

[0797] Step 2:

[0798] The device records the user's voice using a microphone and converts it into text data using speech recognition software. The input is the user's voice (e.g., "I'm a little tired today"). The voice data is converted to text using a common speech recognition API. Specifically, the device processes the recorded voice in real time and converts it to text. The output is sentiment information in text format.

[0799] Step 3:

[0800] The server aggregates biometric and emotional information transmitted from terminals and stores it in a database. Input consists of a biometric data set and emotional information in text format. A database management system, such as SQL, can be used. Specifically, the server classifies the received data by identifier and stores it as a new entry in the database. The output is integrated data prepared for analysis.

[0801] Step 4:

[0802] The server analyzes integrated data to assess the user's health and emotional state. The input is an integrated dataset. Statistical analysis is performed using the Python Pandas library. Specifically, the server detects anomalies based on certain thresholds and identifies potential problems. The output is a report of the assessed health and emotional state.

[0803] Step 5:

[0804] If the server detects an anomaly based on the analysis results, it sends a notification to family members or caregivers via communication. The input is the anomaly detection information from the analysis. Transmission methods include email using the SMTP protocol and push notifications via a dedicated app. Specifically, the server generates a notification message using a template and sends it to the designated recipient. The output is the notified anomaly report.

[0805] Step 6:

[0806] The user receives emotion-based feedback through an interactive AI on the device. The input is text entered by the user (e.g., "I've been feeling lonely lately"). A generative AI model is used for the interactive AI to generate appropriate responses. Specifically, the device receives user input, sends prompts to the generative AI model, and displays the generated response on the screen. The output is the displayed AI response.

[0807] Step 7:

[0808] The server generates a care plan based on the user's health and emotional state and notifies the terminal. Inputs are analysis results and evaluated state information. The generated care plan includes exercise, diet, and mental health care. Specifically, the server creates a plan based on a pre-configured algorithm and sends it to the terminal. The output is the care plan notified to the user.

[0809] (Application Example 2)

[0810] 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".

[0811] In modern elderly care, emotional care is becoming increasingly important in addition to physical health management. However, many existing systems focus on detecting health abnormalities based on biometric information, and have limitations in providing individualized care that responds immediately to changes in the user's emotions. In particular, managing feelings of loneliness and stress is a challenge for the elderly. This invention aims to provide a system that integrates these emotional aspects of care.

[0812] 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.

[0813] In this invention, the server includes a sensor means for acquiring biometric data, a computation means for analyzing the biometric data and detecting abnormalities, and a communication means for engaging in dialogue with the user using an interactive algorithm. This enables simultaneous monitoring of the biometric information and emotional state of elderly individuals, providing a real-time response plan when an abnormality is detected, and reducing feelings of loneliness.

[0814] "Biometric data" refers to information that indicates a user's physical condition, and is usually acquired through wearable sensors or similar devices.

[0815] "Sensing means" refers to devices and technologies used to acquire a user's biometric data.

[0816] "Computational means" refers to devices or technologies that analyze health status based on acquired biological data and perform processing to determine whether or not there are abnormalities.

[0817] "Information provision means" refers to a method or device for notifying users or caregivers of important information, such as abnormality detection.

[0818] "Communication means" refers to a method or technique for transmitting a notification via an information provision means to an external device.

[0819] A "conversational algorithm" is an artificial intelligence-based technology used to facilitate smooth communication with users.

[0820] A "communication tool" is a method or device that uses an interactive algorithm to exchange information with a user.

[0821] A "care plan generation tool" is a technology or device that creates an optimal care plan based on the individual health and emotional state of the user.

[0822] "Suggested means" refers to a technology or method for presenting recommended activities or actions to users.

[0823] "Support measures" refer to methods and techniques that promote a sense of security and relaxation in accordance with the emotional state of elderly people.

[0824] The system for realizing this invention is for comprehensively managing the biological information and emotional state of elderly individuals. This system mainly includes sensor means, computing means, information provision means, communication means, interactive algorithms, and support means.

[0825] The server acquires biometric data in real time through wearable sensors and monitors the user's health status. The acquired data is analyzed by computational means, and if an abnormality is detected, a notification is sent to family members or caregivers through an information provision system. This enables a rapid response.

[0826] The device plays a role in two-way communication with the user using an interactive algorithm. Specifically, it utilizes AI technology to analyze the user's emotional state and generate appropriate feedback and encouraging messages. This supports the user's mental health and reduces feelings of loneliness.

[0827] Users are expected to live their daily lives according to a care plan generated by the server. This care plan includes recommended exercises, diets, and relaxation methods, which are appropriately suggested through support systems. For example, if stress is detected, relaxation activities will be suggested.

[0828] For example, when a user's heart rate increases and stress is detected, a suggestion such as "Why not relax by listening to some soothing music?" is made. This suggestion is generated based on a generative AI model, and an example of a prompt statement is as follows:

[0829] "Please suggest relaxing activities. The user's heart rate is elevated, indicating they are experiencing stress, so please offer advice in a gentle and calming tone."

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The server collects the user's biometric data from input signals received from wearable sensors. This data includes information such as heart rate, blood pressure, and body temperature. This input data is stored as time-series data and used as the basis for subsequent analysis.

[0833] Step 2:

[0834] The server analyzes the collected biometric data using computational methods. During this process, an anomaly detection algorithm is applied to monitor fluctuations in heart rate and blood pressure. As a result of the analysis, anomalies such as a higher-than-normal heart rate are output. This allows for real-time assessment of the user's health status.

[0835] Step 3:

[0836] If an anomaly is detected, the server will notify family members or caregivers through information provision channels. Specific numerical data and status information will be transmitted via email or a dedicated app. The input for this notification is the previously analyzed results, and the output is the applied notification message.

[0837] Step 4:

[0838] The device communicates with the user using an interactive algorithm. It analyzes emotions based on voice and text input from the user and runs a generative AI model. Based on this input data, it generates and outputs a feedback message tailored to the user. In this process, the AI ​​responds based on the generated prompt text.

[0839] Step 5:

[0840] Users receive feedback on a care plan generated by the server. This plan includes specific recommended actions, which they are encouraged to incorporate into their daily lives. Biometric data and the user's emotional state are used as input to create the plan, and personalized recommended activities are presented as output. This enables users to maintain their health and live a mentally stable life.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] The following is further disclosed regarding the embodiments described above.

[0863] (Claim 1)

[0864] Information gathering means for acquiring biometric information,

[0865] An analysis means for analyzing the aforementioned biological information and detecting abnormalities,

[0866] A notification means for informing of the aforementioned abnormality,

[0867] A communication means that reports an abnormality using the aforementioned notification means,

[0868] A dialogue method that uses conversational artificial intelligence to communicate with users,

[0869] A plan generation means for generating a care plan suitable for the user,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the analysis means continuously monitors the user's behavior patterns and detects behavior that is different from the norm.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the notification means is performed via email, short message, or dedicated app notification to inform an external terminal of an abnormality.

[0875] "Example 1"

[0876] (Claim 1)

[0877] A terminal device for collecting biometric data,

[0878] A computation means for analyzing the aforementioned biological data and identifying abnormalities,

[0879] A notification means for notifying the aforementioned abnormality,

[0880] Information transmission means for reporting an abnormality using the aforementioned notification means,

[0881] An interactive means of engaging with users through conversational machine learning,

[0882] A plan generation method for formulating a health plan suitable for the user,

[0883] Using specific devices, biometric data is acquired, analyzed, notified, and communicated via a communication environment, interacting with the user and generating an individualized health plan.

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the calculation means continuously monitors an individual's behavioral patterns and identifies atypical behavior.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the notification means uses a data message, a short message, or a dedicated application notification to notify an external device of an abnormality.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] Information collection means for acquiring biometric data,

[0892] An analysis means for analyzing the aforementioned biological data and detecting abnormalities,

[0893] A notification means for indicating the aforementioned abnormality,

[0894] A communication means that reports an abnormality using the aforementioned notification means,

[0895] A dialogue method that uses interactive artificial intelligence to respond to users,

[0896] A plan generation means for generating a health management plan suitable for the user,

[0897] A means of prompting users to take recommended actions in conjunction with an external device,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the analysis means continuously measures the user's behavior patterns and detects unusual behavior.

[0901] (Claim 3)

[0902] The notification means is provided by an external device via telecommunications means, short message, or dedicated program notification, according to claim 1.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A device for acquiring biological information,

[0906] A process for aggregating and analyzing the aforementioned biological information and emotional information,

[0907] Detection that detects an anomaly based on the above analysis,

[0908] Communication for reporting the aforementioned anomaly,

[0909] Recognition that recognizes emotions from voice and text data,

[0910] Dialogue for generating emotion-based responses,

[0911] The system generates a care plan tailored to the user and notifies the device,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the analysis process continuously evaluates the user's biological and emotional state and identifies abnormal patterns.

[0915] (Claim 3)

[0916] The system according to claim 1, wherein the aforementioned communication is performed using electronic communication to transmit an abnormality to an external device.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A sensor means for acquiring biometric data,

[0920] A computation means for analyzing the aforementioned biological data and detecting abnormalities,

[0921] A means for providing information to notify of the aforementioned abnormality,

[0922] A communication means that reports an anomaly using the aforementioned information provision means,

[0923] A communication method that uses interactive algorithms to engage in dialogue with users,

[0924] A plan generation means for generating a support plan suitable for the user,

[0925] A suggestion means that presents recommended activities based on the user's biometric information and emotional state,

[0926] Support measures that provide methods to promote relaxation based on the emotions of the elderly,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, wherein the calculation means continuously monitors the user's lifestyle patterns and detects unusual behavior.

[0930] (Claim 3)

[0931] The system according to claim 1, wherein the information provision means is performed by email, short message transmission, or dedicated application notification to notify an external device of an abnormality. [Explanation of symbols]

[0932] 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. Information gathering means for acquiring biometric information, An analysis means for analyzing the aforementioned biological information and detecting abnormalities, A notification means for informing of the aforementioned abnormality, A communication means that reports an abnormality using the aforementioned notification means, A dialogue method that uses conversational artificial intelligence to communicate with users, A plan generation means for generating a care plan suitable for the user, A system that includes this.

2. The system according to claim 1, wherein the analysis means continuously monitors the user's behavior patterns and detects behavior that is different from the norm.

3. The system according to claim 1, wherein the notification means is performed via email, short message, or dedicated app notification to inform an external terminal of an abnormality.

Images