System

Abstract

A system is provided.SOLUTION: A system includes means for acquiring life data of an elderly person, means for analyzing the life data and learning a health condition and a life habit of the elderly person, means for generating and providing a personalized health management plan based on the health condition and the life habit, means for acquiring a daily conversation with the elderly person and analyzing voice data, means for detecting a change in the health condition and the life habit based on an analysis result and providing appropriate advice, means for detecting an emergency situation and notifying an emergency contact destination, means for providing a course of action in an emergency, and means for collecting and providing information on hobbies and interests of the elderly person.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As our society ages, providing support for the elderly is becoming an increasingly important issue. Elderly people living alone face particularly serious issues, such as health management, emergency preparedness, and weakened social ties. These issues call for personalized support, but are often difficult to address using conventional methods. There is also a need to reduce the burden on the elderly themselves and provide an environment in which they can live with peace of mind. [Means for solving the problem]

[0005] The present invention provides a system that acquires and analyzes lifestyle data of elderly people and uses a generation AI to provide personalized health management plans and advice. Specifically, the system includes the following means:

[0006] Means of collecting data on the lifestyles of the elderly

[0007] A means of analyzing lifestyle data and learning about the health status and lifestyle habits of the elderly

[0008] A means of generating and delivering personalized health plans based on health status and lifestyle habits

[0009] A means of capturing everyday conversations with elderly people and analyzing the audio data

[0010] A means of detecting changes in health status and lifestyle habits based on analysis results and providing appropriate advice

[0011] A means of detecting emergencies and notifying emergency contacts

[0012] A means of providing guidelines for action in emergencies

[0013] A means of collecting and providing information about seniors' hobbies and interests

[0014] This will reduce the burden on the elderly and allow them to live independent lives with peace of mind, while also contributing to solving the aging problem of society as a whole.

[0015] "Lifestyle data" refers to information about various activities in the elderly person's daily life, including, for example, dietary content, amount of exercise, and sleep duration.

[0016] "Health data" refers to measurements and records related to the health status of an elderly person, including, for example, blood pressure, heart rate, and body temperature.

[0017] A "personalized health management plan" refers to specific suggestions and guidance for optimally maintaining and improving health, created based on each elderly person's individual lifestyle and health data.

[0018] "Generative AI" refers to a system that uses machine learning algorithms and artificial intelligence techniques to analyze data and learn patterns to generate various suggestions and decisions.

[0019] "Daily conversation" refers to the voice communication that takes place between the elderly person and the system, and is used as a means of understanding the elderly person's physical and mental condition and changes in their lifestyle habits.

[0020] "Emergency" refers to situations in which an elderly person requires immediate attention due to shock or health problems, including falls or sudden illness.

[0021] "Guidelines for action" refers to instructions and advice that outline specific actions that older people should take in emergencies or specific situations.

[0022] "Information about hobbies and interests" refers to suggestions for hobby activities, related news, event information, etc., that are collected and provided based on the individual interests and preferences of seniors. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] The present invention aims to provide a personalized health management system using generative AI to support the daily lives of the elderly. This system supports the daily lives of the elderly via a server and terminals, and is specifically implemented as follows.

[0045] System configuration

[0046] The system consists of the following main components:

[0047] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[0048] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[0049] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[0050] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[0051] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[0052] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[0053] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[0054] Program processing

[0055] Data collection and learning

[0056] Device: Elderly people enter data about their daily lives (such as diet, exercise, and sleep), or the data is automatically collected from a wearable device.

[0057] Terminal: Sends collected data to the server.

[0058] Server: Analyzes the received data and learns about the elderly person's health condition and lifestyle habits.

[0059] Server: Generative AI learns patterns from data and builds personalized health plans.

[0060] Analysis of everyday conversations and advice

[0061] User: An elderly person speaks everyday conversation into the device (e.g., "I can't sleep lately").

[0062] Terminal: Acquires voice data and sends it to the server.

[0063] Server: Analyzes voice data, converts speech into text, and detects changes in the elderly person's health and lifestyle habits.

[0064] Server: Based on the analysis results, it generates detailed advice and sends it to the device.

[0065] Device: Providing advice to the elderly (e.g., "Try listening to the following music to help you relax")

[0066] Emergency response

[0067] User: An elderly person speaks to the device saying "Help me."

[0068] Terminal: Acquires voice data and detects emergencies.

[0069] Device: Automatically notify emergency contacts and arrange for the nearest emergency services.

[0070] Device: Providing specific guidelines for behavior to the older adult (e.g., "Take a deep breath and sit down").

[0071] Reducing loneliness and providing information

[0072] User: An elderly person talks to the device, asking, "Has anything interesting happened recently?"

[0073] Terminal: Acquires voice data and sends it to the server.

[0074] Server: Generates the latest information based on the hobbies and interests of the elderly.

[0075] Terminals: Providing information to seniors (e.g., "There's a pottery class coming up nearby").

[0076] Device: AI chatbots can initiate everyday conversations with seniors, reducing their sense of loneliness.

[0077] Specific examples

[0078] Health management plan proposals

[0079] User: An elderly person says, "My knees have been hurting lately."

[0080] Terminal: Acquires voice data and sends it to the server.

[0081] Server: Analyzes the data and compares it with past lifestyle data.

[0082] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[0083] Terminal: Providing advice to seniors.

[0084] Emergency response

[0085] User: An elderly person talks to the device saying, "I feel like I'm going to fall."

[0086] Device: Detects emergencies and obtains location information.

[0087] Device: Call emergency contacts and emergency services.

[0088] Device: Communicate specific guidelines for action to the elderly.

[0089] conclusion

[0090] The system of this invention makes it possible to provide personalized support tailored to the individual needs of the elderly, reducing the burden on them and enabling them to live independently with peace of mind. It is also expected to contribute greatly to solving the aging problem of society as a whole.

[0091] The processing flow will be explained below.

[0092] Data collection and learning

[0093] Step 1:

[0094] User: Elderly people record data about their daily lives (e.g., diet, exercise, sleep) or wear a wearable device.

[0095] Step 2:

[0096] Terminal: Collects recorded life and health data.

[0097] Step 3:

[0098] Terminal: Periodically sends collected data to the server.

[0099] Step 4:

[0100] Server: Stores the received data in a database.

[0101] Step 5:

[0102] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly.

[0103] Step 6:

[0104] Server: Creates a personalized health management plan based on the data learned by the generative AI.

[0105] Analysis of everyday conversations and advice

[0106] Step 1:

[0107] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[0108] Step 2:

[0109] Terminal: Acquires voice data and sends it to the server.

[0110] Step 3:

[0111] Server: Analyzes the voice data and converts the spoken content into text.

[0112] Step 4:

[0113] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[0114] Step 5:

[0115] Server: Based on the analysis results, generates appropriate advice and sends it to the device.

[0116] Step 6:

[0117] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[0118] Emergency response

[0119] Step 1:

[0120] User: An elderly person speaks to the device saying "Help me."

[0121] Step 2:

[0122] Terminal: Acquires voice data and detects emergencies.

[0123] Step 3:

[0124] On your device: Send a notification to pre-defined emergency contacts.

[0125] Step 4:

[0126] Device: Automatically call the nearest emergency services.

[0127] Step 5:

[0128] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[0129] Reducing loneliness and providing information

[0130] Step 1:

[0131] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[0132] Step 2:

[0133] Terminal: Acquires voice data and sends it to the server.

[0134] Step 3:

[0135] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[0136] Step 4:

[0137] Server: Sends the generated information to the terminal.

[0138] Step 5:

[0139] Terminal: The terminal provides the user with information such as, "It seems there's a pottery class being held at the local community center next week. Would you like to participate?"

[0140] Step 6:

[0141] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[0142] conclusion

[0143] Through the above processing steps, the system of the present invention can provide personalized assistance according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[0144] Example 1

[0145] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] Health management for the elderly often takes a one-size-fits-all approach, without taking into account individual needs and differences in living environments. Furthermore, when elderly people live alone, it is difficult to respond appropriately to changes in their health status or emergencies in their daily lives. Furthermore, there is a lack of effective methods to reduce the sense of loneliness felt by elderly people. To address these issues, there is a need for personalized health management plans, emergency response functions, and everyday conversation support functions.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0148] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data and daily life data to the server, means for analyzing the received data in the server and learning the elderly person's health condition and lifestyle habits, means for generating and providing a personalized health management plan using a generative AI model, means for acquiring conversation data with the elderly person and transmitting it to the server, means for converting voice data to text and performing natural language processing to detect changes in the elderly person's health condition and lifestyle habits, means for generating and providing appropriate advice based on the analysis results, means for detecting voice input in an emergency and automatically notifying an emergency contact, means for providing specific guidelines for action by voice or text, means for collecting and providing information based on the elderly person's hobbies and interests, and means for reducing the elderly person's sense of loneliness through everyday conversations via a chatbot. This allows for providing elderly people with personalized support tailored to their individual needs, improving the accuracy of health management, accelerating emergency response, and reducing the elderly person's sense of loneliness.

[0149] "Biometric data" refers to information that indicates the physical condition of an elderly person, and includes physiological indicators such as heart rate, blood pressure, body temperature, and oxygen saturation.

[0150] "Daily life data" refers to information about the activities that elderly people engage in in their daily lives, including dietary content, amount of exercise, and sleep duration.

[0151] "Server" means a computer system that receives, stores, analyzes data, and processes and provides information using a generative AI model.

[0152] A "generative AI model" is an artificial intelligence algorithm or system that generates new information based on given input data by learning from large amounts of data.

[0153] A "personalized health management plan" is an individualized health promotion and management plan created based on the health status and lifestyle habits of each elderly person.

[0154] "Voice data" refers to data containing audio signals that are recordings of what the elderly person is saying to the terminal.

[0155] "Natural language processing" is a technology that converts voice data into text format, linguistically analyzes it as a string of characters, and understands and processes the content.

[0156] "Emergencies" refer to dangerous situations or health problems faced by older adults, such as falls, difficulty breathing, or sudden changes in blood pressure.

[0157] "Emergency Contacts" refers to people or organisations that should be contacted in the event of an emergency, such as family, friends, carers or emergency services.

[0158] "Action guidelines" are instructions that show specific actions that elderly people should take in specific situations, such as taking deep breaths, sitting down, and resting.

[0159] "Hobbies and interest-based information" refers to the latest information, events and activity opportunities related to areas and activities that interest seniors.

[0160] A "chatbot" is a program that uses artificial intelligence to engage in natural dialogue with users, and can converse through text or voice.

[0161] This invention is a system for supporting the daily lives of elderly people and personalizing their health management. The system utilizes terminals and servers to effectively utilize lifestyle and health data of elderly people using generative AI models.

[0162] System Overview

[0163] This system collects and analyzes biometric and daily life data from seniors to provide personalized health management plans and respond to emergencies. The hardware used includes wearable devices (e.g., Fitbit, Apple Watch) and internet-connected devices (e.g., smartphones, tablets). Analysis is performed using Python libraries (e.g., Pandas, Scikit-learn) and the Google® Cloud Speech-to-Text API, leveraging generative AI models (e.g., GPT-4®).

[0164] Explanation of program processing

[0165] Data collection

[0166] Elderly users manually enter data such as dietary habits, exercise, and sleep duration into the device, or use a wearable device to automatically collect this data, which is then sent to a server via the Internet.

[0167] Data analysis

[0168] The server analyzes the received data and learns about the elderly person's health status and lifestyle habits. It uses Python libraries Pandas and Scikit-learn to perform statistical analysis and outlier detection. It then uses a generative AI model to generate a personalized health management plan.

[0169] Analysis of everyday conversations

[0170] When an elderly user speaks into the device, the device captures this voice data and sends it to a server, which then converts the voice data into text using the Google Cloud Speech-to-Text API and performs natural language processing to detect changes in health status and lifestyle habits.

[0171] Generating Advice

[0172] The server generates appropriate advice based on the analysis results. Using the generative AI model, it creates specific health management advice (e.g., "Try listening to the following music to relax") and sends it to the device. The device then provides the advice to the elderly via voice or text.

[0173] Emergency response

[0174] When an elderly user speaks "help me" into the device, the device receives the voice data and detects an emergency. The device immediately transmits the data to the server, which then automatically notifies emergency contacts. Furthermore, the device provides specific instructions for action (e.g., "take a deep breath and sit down") via voice.

[0175] Providing information based on hobbies and interests

[0176] When an elderly user speaks to the device, asking, "Has anything interesting happened recently?", the device captures the voice data and sends it to the server. The server uses a generative AI model based on the elderly's hobbies and interests to generate the latest information. The device then provides this information to the elderly via voice or text. The chatbot function also allows for everyday conversations with the elderly, reducing their sense of loneliness.

[0177] Examples of concrete examples and prompts

[0178] Example 1: Proposing a health management plan

[0179] When an elderly user says, "My knees have been hurting lately," the device captures the voice data and sends it to the server. The server analyzes the data and compares it with past lifestyle data. Using a generative AI model, it generates the cause of the knee pain and recommends countermeasures (e.g., specific exercises). The device then provides advice to the elderly, such as, "Try the following exercises to relax."

[0180] Example 2: Emergency response

[0181] When an elderly user says to the device, "I feel like I'm going to fall," the device detects the emergency and obtains their location. The device then notifies emergency contacts and emergency services, and provides the elderly with advice such as, "Take a deep breath and sit down."

[0182] Prompt Sentence Examples

[0183] Healthcare plan: "I've been having knee pain lately. What's the best healthcare plan for this?"

[0184] Emergency response: "I feel like I'm going to collapse. What should I do?"

[0185] The system of this invention provides support tailored to the individual needs of the elderly, improves the accuracy of health management, and enables rapid response in emergencies, thereby improving the quality of life for the elderly and contributing to solving the aging problem in society as a whole.

[0186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0187] Step 1:

[0188] Data collection

[0189] Input: Manually entered data from seniors (dietary content, exercise, sleep duration) and automatically collected data from wearable devices.

[0190] How it works: The elderly user opens the application on the device and enters information about their diet, exercise, and sleep time. The wearable device also automatically collects data such as heart rate and number of steps taken.

[0191] Output: Collected life and biometric data.

[0192] Step 2:

[0193] Data transmission

[0194] Input: Collected life and biometric data.

[0195] How it works: Your device sends collected data to a server using Wi-Fi or a mobile network.

[0196] Output: The data sent to the server.

[0197] Step 3:

[0198] Data analysis

[0199] Input: Life and biometric data sent to the server.

[0200] How it works: The server uses Python libraries such as Pandas and Scikit-learn to perform statistical analysis of the data and detect outliers.

[0201] Output: Analysis results on the health status and lifestyle habits of elderly people.

[0202] Step 4:

[0203] Generate a health management plan

[0204] Input: Analysis results on health status and lifestyle habits.

[0205] How it works: The server uses a generative AI model (e.g., GPT-4) to generate a personalized health care plan based on the individual needs of the elderly person.

[0206] Output: A personalized health care plan.

[0207] Step 5:

[0208] Acquiring voice input

[0209] Input: Everyday conversations of elderly people (e.g., "I can't sleep these days").

[0210] Operation: The elderly user speaks everyday conversation into the device, and the device captures this voice data.

[0211] Output: The captured audio data.

[0212] Step 6:

[0213] Sending audio data

[0214] Input: Captured audio data.

[0215] Action: The device sends audio data to the server.

[0216] Output: The audio data sent to the server.

[0217] Step 7:

[0218] Analysis of audio data

[0219] Input: The audio data sent to the server.

[0220] How it works: The server uses the Google Cloud Speech-to-Text API to convert voice data into text, then uses natural language processing to detect changes in health and lifestyle habits.

[0221] Output: Text conversion results and analysis results of the audio data.

[0222] Step 8:

[0223] Generating Advice

[0224] Input: Text conversion results and analysis results of audio data.

[0225] How it works: The server uses a generative AI model (e.g., GPT-4) to generate appropriate advice based on the analysis results.

[0226] Output: Generated advice (e.g., "Try listening to the following music to relax")

[0227] Step 9:

[0228] Providing advice

[0229] Input: The generated advice.

[0230] How it works: The device provides the generated advice to the senior via voice or text.

[0231] Output: Advice given to the elderly.

[0232] Step 10:

[0233] Emergency voice input detection

[0234] Input: Urgent speech from an elderly person (e.g., "Help me").

[0235] Operation: When an elderly user speaks an emergency voice into the device, the device picks up the voice and detects an emergency.

[0236] Output: Emergency detection result.

[0237] Step 11:

[0238] Emergency notification

[0239] Input: Emergency detection results and location information of the elderly person.

[0240] What it does: Your device automatically calls your emergency contacts and dispatches the nearest emergency services.

[0241] Output: Notification of called emergency contacts and emergency services.

[0242] Step 12:

[0243] Providing guidelines for emergency situations

[0244] Input: Emergency detection result.

[0245] What it does: The device provides the senior with specific instructions for action via voice or text (e.g., "Take a deep breath and sit down").

[0246] Output: Provided course of action.

[0247] Step 13:

[0248] Information gathering based on hobbies and interests

[0249] Input: A request for information about a senior's hobbies and interests (e.g., "What's interesting going on these days?").

[0250] How it works: The elderly user asks questions about their hobbies and interests to the device. The device captures this voice data and sends it to the server.

[0251] Output: The audio data sent to the server.

[0252] Step 14:

[0253] Information generation

[0254] Input: A request for information about seniors' hobbies and interests.

[0255] How it works: The server uses a generative AI model (e.g., GPT-4) to generate information based on the elderly person's hobbies and interests.

[0256] Output: The information generated.

[0257] Step 15:

[0258] Providing information and conducting daily conversations

[0259] Input: Generated information.

[0260] How it works: The device provides the generated information to the elderly via voice or text, and also uses a chatbot function to engage in everyday conversations with the elderly, reducing their sense of loneliness.

[0261] Output: Information provided to the elderly and daily conversations carried out.

[0262] (Application example 1)

[0263] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0264] For seniors to maintain healthy eating habits, it is important to provide personalized meal plans based on their individual health conditions and lifestyle habits. However, manually creating such personalized plans and preparing and adjusting meals each time is extremely difficult and burdensome for seniors. Furthermore, without a system for responding quickly and appropriately in emergencies, seniors risk being put in dangerous situations. Furthermore, there is a lack of information and communication methods to reduce seniors' feelings of loneliness and enrich their daily lives.

[0265] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0266] In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; and means for generating a personalized meal plan based on the elderly's health condition and lifestyle habits and ordering food based on the meal plan. This allows the elderly to easily order and receive meals appropriate for their health condition, respond quickly to emergencies, and obtain information and communication to improve the quality of their daily lives.

[0267] "Elderly" refers to people who are older and require special support and consideration in their daily lives.

[0268] "Lifestyle data" refers to information about the subject's daily life, such as their diet, amount of exercise, and sleep time.

[0269] "Health status" refers to indicators that show the normality or abnormality of an individual's physical and mental state.

[0270] "Lifestyle habits" refers to the habits and patterns of behavior that an individual engages in on a daily basis.

[0271] A "personalized health care plan" refers to a health care policy that is customized to suit the characteristics and conditions of each individual.

[0272] "Daily conversation" refers to the normal conversational activities that the subject engages in on a daily basis.

[0273] "Audio data" refers to audio recorded by a subject and stored as digital information.

[0274] "Analysis results" refers to information obtained as a result of analysis based on collected data.

[0275] "Appropriate advice" refers to advice that is most beneficial to the subject based on the analysis results.

[0276] An "emergency" is a situation in which a subject is suddenly in danger and requires a rapid response.

[0277] "Emergency contacts" refers to the people or organizations that should be notified first in an emergency.

[0278] A "guideline" refers to specific guidance or advice on how to act in a particular situation.

[0279] "Information about hobbies and interests" refers to information related to the subject's personal preferences and interests.

[0280] A "personalized meal plan" refers to meal suggestions tailored to an individual's health and lifestyle.

[0281] "Means of ordering food" refers to the methods or mechanisms for ordering ingredients and meals based on the proposed meal plan, such as through a delivery service.

[0282] This invention provides a personalized health management system using generative AI to support the lives of the elderly. The system collects and analyzes lifestyle and health data of the elderly to provide individually tailored health management and meal plans. The system also has a wide range of functions, including emergency detection and response, analysis of the elderly's daily conversations, and provision of information related to hobbies and interests.

[0283] System configuration

[0284] The system consists of the following main components:

[0285] 1. Data collection method: Hardware such as smartphones and wearable devices are used to collect lifestyle and health data from elderly people. This automatically collects information such as dietary habits, exercise levels, and sleep duration.

[0286] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits. The analysis software used is a Python (registered trademark)-based generative AI library.

[0287] 3. Health management plan provision: A personalized health management plan is generated based on the analysis results and provided to the user. The proposed plan is notified to the user via a smartphone app.

[0288] 4. Voice data acquisition and analysis: Voice data is acquired through everyday conversations and analyzed. Voice recognition technology uses an API that converts voice into text (e.g., Google Speech-to-Text).

[0289] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly. The advice is displayed in text format on the smartphone or provided via a voice assistant.

[0290] 6. Emergency response: Detects an emergency and notifies emergency contacts. Emergency calls are made automatically via the smartphone's communication functions.

[0291] 7. Guidance: Providing specific guidance during emergencies (e.g., "Take a deep breath and sit down"), also provided in real time via the voice assistant.

[0292] 8. Information provision method: Collect and provide the latest information on the hobbies and interests of seniors. News and event information is automatically collected from the Internet.

[0293] 9. Meal plan generation and ordering: Generate a personalized meal plan based on the elderly person's health condition and lifestyle habits, and order food based on the meal plan. By linking with food delivery services, the suggested meals can be easily ordered.

[0294] Specific examples

[0295] Data collection and analysis:

[0296] Users input data using smartphones or wearable devices. The collected data is sent to a server and analyzed by a generative AI model. As a result of the analysis, a health management plan appropriate for the user's health condition is generated.

[0297] Analysis of everyday conversations and advice provided:

[0298] The user speaks to their smartphone saying, "My knees have been hurting lately." The voice data is captured and analyzed on the server. Based on the generated AI model, the results are compared with past data and the cause of the knee pain and countermeasures are provided.

[0299] Emergency Response:

[0300] When a user says "I feel like I'm going to fall," the system detects an emergency, captures their location, automatically notifies emergency contacts and emergency services, and instructs the user to "take a deep breath and sit down."

[0301] Information and loneliness relief:

[0302] When a user asks, "What's interesting lately?", the app provides the latest information based on their hobbies and interests. For example, information like "There's a pottery class opening nearby" will be displayed on the smartphone.

[0303] Meal plan suggestions and ordering:

[0304] When a user says, "I've been feeling unwell lately and have no appetite," the system analyzes the voice data and generates a meal plan tailored to their health condition. Based on the proposed plan, food is automatically ordered through a food delivery service.

[0305] Example prompt sentence:

[0306] "I've been feeling unwell lately and have no appetite."

[0307] "My knees hurt and I can't exercise"

[0308] "I think I'm having an allergic reaction, help me."

[0309] This will enable elderly people to easily order and receive meals that are appropriate for their health condition, respond quickly in emergencies, and receive information and communication that will improve the quality of their daily lives.

[0310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0311] Step 1:

[0312] Elderly people use smartphones or wearable devices to input lifestyle data, including dietary habits, exercise, and sleep duration. The input data is automatically collected by each device and centralized on the smartphone. This data is then used as input data to be sent to the server.

[0313] Step 2:

[0314] The device sends the collected life data to the server. The server receives the data and stores it in a database. This stored data is used for later analysis. The input data is the life data, and the output data is the data stored in the server.

[0315] Step 3:

[0316] The server performs analysis based on the stored data. A Python-based generative AI model analyzes the lifestyle data and learns about the elderly person's health status and lifestyle habits. This analysis identifies patterns in the data and estimates their health status. The input data is the stored lifestyle data, and the output data is the analysis results.

[0317] Step 4:

[0318] The server generates a personalized health management plan based on the generative AI model. Based on the analysis results, a plan suitable for each elderly person is automatically created. The generated health management plan is sent to the device. The input data is the analysis results, and the output data is the health management plan.

[0319] Step 5:

[0320] The device notifies the user of the generated health management plan. The health management plan is displayed to the user via a smartphone app. The user can then manage their daily health in accordance with the plan. The input data is the health management plan, and the output data is the notification to the user.

[0321] Step 6:

[0322] A user speaks to a smartphone, saying, "My knee has been hurting lately." Voice data is acquired and sent by the device to a server. The input data is the voice data, and the output data is the voice data sent to the server.

[0323] Step 7:

[0324] The server analyzes the voice data and converts it into text. To analyze the voice data, it uses speech recognition technology such as the Google Speech-to-Text API. This analysis results in data converted from voice data into text format. The input data is voice data, and the output data is text data.

[0325] Step 8:

[0326] The server uses the converted data to detect changes in the elderly person's health condition and lifestyle habits based on a generative AI model. Based on the analysis results, it generates appropriate advice and sends it to the device. The input data is text data, and the output data is the advice content.

[0327] Step 9:

[0328] The device notifies the user of the advice received from the server. The advice is displayed to the user through a smartphone app. For example, advice such as "Try the following exercise to relax" is provided. The input data is the advice content, and the output data is the notification content to the user.

[0329] Step 10:

[0330] When a user says "I'm about to fall down," the device acquires this voice data and sends it to the server. The input data is the voice data of the emergency, and the output data is the voice data sent to the server.

[0331] Step 11:

[0332] The server analyzes the received voice data and detects an emergency. By analyzing the voice data, the server confirms that an emergency has occurred. The input data is the voice data, and the output data is the emergency status determination result.

[0333] Step 12:

[0334] The server notifies the emergency contacts and takes emergency action, including notifying the nearest emergency service. The server obtains the user's location information and notifies the emergency contacts and emergency service. The input data is the emergency status determination result and location information, and the output data is the emergency call content.

[0335] Step 13:

[0336] The device provides the user with guidelines for what to do in an emergency. For example, specific guidelines such as "Take a deep breath and sit down" are provided in real time through the voice assistant. The input data is the emergency call content, and the output data is the guidelines for what to do.

[0337] Step 14:

[0338] When a user says, "Has anything interesting happened recently?", the device acquires this voice data and sends it to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[0339] Step 15:

[0340] The server analyzes the voice data, generates the latest information based on the elderly person's hobbies and interests, and sends it to the device. For example, information such as "A pottery class will be held nearby" is generated. The input data is the voice data, and the output data is the generated information.

[0341] Step 16:

[0342] The device notifies the user of the information received from the server. The latest events and news are displayed through the smartphone app. The input data is the generated information, and the output data is the notification content to the user.

[0343] Step 17:

[0344] The user says, "I've been feeling unwell lately and have no appetite," and the voice data is acquired and sent by the terminal to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[0345] Step 18:

[0346] The server analyzes the voice data and generates a meal plan based on the user's health status. Using a generative AI model, a meal plan appropriate for the user's health status and lifestyle is created and sent to the device. The input data is the voice data and analysis results, and the output data is the meal plan.

[0347] Step 19:

[0348] The terminal uses the generated meal plan to order food from a food delivery service. The order is easily completed through a smartphone app. The input data is the meal plan, and the output data is the order details.

[0349] Step 20:

[0350] The food delivery service delivers the food and the user can easily pick up the meal. The input data is the order details and the output data is the delivery status.

[0351] This allows users to easily order and receive meals that are suitable for their health condition, improving the quality of their daily lives.

[0352] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0353] This invention provides a personalized health management system that utilizes generative AI and an emotion engine to support the daily lives of the elderly. This system supports the daily lives of the elderly through a server and terminals, and is specifically implemented as follows:

[0354] System configuration

[0355] The system consists of the following main components:

[0356] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[0357] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[0358] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[0359] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[0360] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[0361] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[0362] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[0363] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[0364] Program processing

[0365] Data collection and learning

[0366] Device: Elderly people enter data on their daily lives (such as diet, exercise, and sleep), or the data is collected automatically through a wearable device.

[0367] Terminal: Periodically sends collected lifestyle and health data to the server.

[0368] Server: Stores the received data in a database and analyzes it.

[0369] Server: Learns about the health status and lifestyle habits of elderly people based on collected data.

[0370] Server: Generative AI learns patterns from data and builds personalized health plans.

[0371] Analysis of everyday conversations and emotion recognition

[0372] User: An elderly person speaks to the device (e.g., "I've been feeling depressed lately").

[0373] Terminal: Acquires voice data and sends it to the server.

[0374] Server: Analyzes the voice data and converts the spoken content into text.

[0375] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[0376] Server: The emotion engine analyzes the text and voice data to recognize the emotional state of the elderly.

[0377] Server: Based on the recognized emotional state, it generates appropriate advice and support and sends it to the device.

[0378] Device: Notifies the user of the advice received via text or voice (e.g., "You seem to be feeling down. Try some breathing exercises to relax.").

[0379] Emergency response

[0380] User: An elderly person speaks to the device saying "Help me."

[0381] Terminal: Acquires voice data and detects emergencies.

[0382] On your device: Send a notification to pre-defined emergency contacts.

[0383] Device: Automatically call the nearest emergency services.

[0384] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[0385] Reducing loneliness and providing information

[0386] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[0387] Terminal: Acquires voice data and sends it to the server.

[0388] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[0389] Device: Providing information to the device (e.g., "There's a pottery class coming up near me. Would you like to join?").

[0390] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[0391] Server: The emotion engine recognizes the user's emotions from everyday conversations and responds appropriately.

[0392] Specific examples

[0393] Health management plan proposals

[0394] User: An elderly person says, "My knees have been hurting lately."

[0395] Terminal: Acquires voice data and sends it to the server.

[0396] Server: Analyzes the data and compares it with past lifestyle data.

[0397] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[0398] Terminal: Providing advice to seniors.

[0399] Emotional awareness and mental care

[0400] User: An elderly person tells the device, "I've been feeling really lonely lately."

[0401] Terminal: Acquires voice data and sends it to the server.

[0402] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[0403] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[0404] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[0405] conclusion

[0406] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[0407] The processing flow will be explained below.

[0408] Data collection and learning

[0409] Step 1:

[0410] User: Elderly people manually enter their daily lifestyle data (e.g., diet, exercise, sleep duration) or wear a wearable device.

[0411] Step 2:

[0412] Device: Collects data entered by the user and automatically collected from wearable devices.

[0413] Step 3:

[0414] Terminal: Sends collected lifestyle and health data to the server.

[0415] Step 4:

[0416] Server: Stores the received data in a database.

[0417] Step 5:

[0418] Server: Analyzes data stored in the database and uses generative AI to learn about the health status and lifestyle habits of elderly people.

[0419] Step 6:

[0420] Server: Builds a personalized health management plan based on the learning results.

[0421] Analysis of everyday conversations and emotion recognition

[0422] Step 1:

[0423] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[0424] Step 2:

[0425] Terminal: Acquires voice data and sends it to the server.

[0426] Step 3:

[0427] Server: Analyzes the voice data and converts the spoken content into text.

[0428] Step 4:

[0429] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[0430] Step 5:

[0431] Server: The emotion engine analyzes the voice data and text to recognize the emotional state of the elderly.

[0432] Step 6:

[0433] Server: Generates appropriate advice based on the analysis results and emotional state and sends it to the device.

[0434] Step 7:

[0435] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[0436] Emergency response

[0437] Step 1:

[0438] User: An elderly person speaks to the device saying "Help me."

[0439] Step 2:

[0440] Terminal: Acquires voice data and detects emergencies.

[0441] Step 3:

[0442] On your device: Send a notification to pre-defined emergency contacts.

[0443] Step 4:

[0444] Device: Automatically call the nearest emergency services.

[0445] Step 5:

[0446] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[0447] Reducing loneliness and providing information

[0448] Step 1:

[0449] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[0450] Step 2:

[0451] Terminal: Acquires voice data and sends it to the server.

[0452] Step 3:

[0453] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[0454] Step 4:

[0455] Server: Sends the generated information to the terminal.

[0456] Step 5:

[0457] Terminal: The terminal provides information such as, "It seems that a pottery class will be held at the local community center next week. Would you like to participate?"

[0458] Step 6:

[0459] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[0460] Emotional awareness and mental care

[0461] Step 1:

[0462] User: An elderly person tells the device, "I've been feeling really lonely lately."

[0463] Step 2:

[0464] Terminal: Acquires voice data and sends it to the server.

[0465] Step 3:

[0466] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[0467] Step 4:

[0468] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[0469] Step 5:

[0470] Server: Sends the generated mental care plan to the terminal.

[0471] Step 6:

[0472] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[0473] conclusion

[0474] Through the above processing steps, the system of the present invention can provide personalized assistance and emotional support according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[0475] Example 2

[0476] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0477] Elderly people who live alone often find it difficult to manage their health and respond to emergencies in their daily lives. They are also prone to feelings of loneliness and mental anxiety, which can lead to a deterioration in their health. Current health care systems are unable to meet all of these needs. Therefore, there is a need for a system that can comprehensively support the daily lives of elderly people.

[0478] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting emergencies and notifying emergency contacts; means for providing action guidelines in emergencies; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing the elderly's emotional state from daily conversations using an emotion engine and providing appropriate support; and means for generating and providing a personalized health management plan using a generative AI model. This enables elderly people to monitor their health condition and respond quickly to emergencies, and by receiving psychological support, they can live more independently with peace of mind.

[0479] "Lifestyle data" refers to information about the activities and behaviors of elderly people in their daily lives, and specifically includes dietary content, amount of exercise, sleep duration, etc.

[0480] "Health data" refers to information relating to the health of the elderly person, and specifically includes heart rate, blood pressure, body temperature, weight, etc.

[0481] "Analysis methods" refer to methods and tools for processing collected data and analyzing the health status and lifestyle habits of older people.

[0482] A "personalized health management plan" refers to a health management plan that is customized based on the health condition and lifestyle of each elderly person.

[0483] "Voice data" refers to the voice information collected when a user speaks to a terminal, and by analyzing this information, the content of the speech can be understood.

[0484] An "emotion engine" refers to algorithms and software that recognize the emotional state of elderly people from their everyday conversations and provide appropriate support and advice.

[0485] A "generative AI model" is an artificial intelligence model that learns patterns from collected data and generates optimal health management plans and advice for seniors.

[0486] An "emergency" refers to a situation in which an elderly person is in danger or in need of help, and refers to the conditions and circumstances under which this situation can be detected quickly and appropriate action can be taken.

[0487] "Emergency Contact" refers to pre-defined contacts (such as family members or caregivers) who should be notified or contacted in the event of an emergency.

[0488] "Information on hobbies and interests" refers to information related to activities and areas of interest that older people can enjoy, and the purpose of providing this information is to improve the quality of life of older people.

[0489] The present invention provides a personalized health management system that utilizes a generative AI model and an emotion engine to support the daily lives of elderly people. This system supports the daily lives of elderly people through a server and a terminal, and is specifically implemented as follows.

[0490] System configuration

[0491] The system consists of the following main components:

[0492] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[0493] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[0494] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[0495] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[0496] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[0497] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[0498] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[0499] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[0500] Program processing

[0501] Data collection and learning

[0502] Device: Elderly people manually enter data about their daily lives (e.g., diet, exercise, and sleep), and also use wearable devices to automatically collect this data.

[0503] Terminal: Periodically sends collected lifestyle and health data to the server.

[0504] Server: The received data is stored in a central database and analyzed. Specific databases used include MySQL (registered trademark) and PostgreSQL.

[0505] Server: Analyzes the stored data and applies machine learning algorithms (e.g., random forests and neural networks) using programming languages ​​such as Python to learn about the health and lifestyle habits of the elderly.

[0506] Server: The generative AI model generates a health management plan from the collected data. For example, it suggests that the user's daily walking frequency has decreased and that they should take a short walk as a daily routine.

[0507] Analysis of everyday conversations and emotion recognition

[0508] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[0509] Device: Collects voice data and sends it to the server. Use a smart speaker or smartphone with a built-in microphone.

[0510] Server: Use a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe to convert the audio data into text.

[0511] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle based on the content of the speech.

[0512] Server: Analyzes text and voice data using an emotion engine to recognize the user's emotional state. Provides positive conversations and comforting words.

[0513] Terminal: Provides appropriate advice based on the recognized emotional state (e.g., "You seem to be feeling depressed. Try some breathing exercises to relax.").

[0514] Emergency response

[0515] User: An elderly person speaks to the device saying "Help me."

[0516] Terminal: Acquires voice data and detects emergencies.

[0517] Device: Sends text message and phone call notifications to pre-defined emergency contacts.

[0518] Device: Automatically contacts the nearest emergency services, using GPS data to communicate your location.

[0519] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[0520] Reducing loneliness and providing information

[0521] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[0522] Terminal: Acquires voice data and sends it to the server.

[0523] Server: Generates the latest relevant information based on the user's hobbies and interests. Collects relevant event information and news from the Internet.

[0524] Device: Providing collected information to the user (e.g., "There's a pottery class coming up near me. Would you like to join?").

[0525] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[0526] Server: The emotion engine recognizes the user's emotional state from everyday conversation and responds appropriately.

[0527] Specific examples

[0528] Health management plan proposals

[0529] User: An elderly person says, "My knees have been hurting lately."

[0530] Terminal: Acquires voice data and sends it to the server.

[0531] Server: Analyzes the data and compares it with past lifestyle data.

[0532] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[0533] Terminal: Providing advice to seniors.

[0534] Emotional awareness and mental care

[0535] User: An elderly person tells the device, "I've been feeling really lonely lately."

[0536] Terminal: Acquires voice data and sends it to the server.

[0537] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[0538] Server: Generates mental health plans (e.g., relaxation techniques to improve mood).

[0539] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[0540] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[0541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0542] Step 1: Data entry

[0543] User: Elderly people manually input data about their daily life (e.g., dietary details, amount of exercise, sleep time, etc.) into a dedicated application. The data input in this way includes the user's daily diet and exercise records.

[0544] Device: Data (e.g., heart rate, number of steps, sleep patterns, etc.) is automatically collected from a wearable device worn by the elderly. Biometric data from the wearable device is obtained as input.

[0545] Step 2: Send data

[0546] Device: The collected lifestyle and health data is periodically sent to the server. The data is securely transmitted using an encryption protocol. Specifically, the data is uploaded from the device to the server via the Internet.

[0547] Step 3: Save Data

[0548] Server: Stores the received data in a central database (e.g., MySQL or PostgreSQL). It takes the lifestyle and health data sent as input and adds new records to the database based on this.

[0549] Step 4: Data analysis

[0550] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly. Specifically, machine learning algorithms (e.g., random forests and neural networks) are applied using programming languages ​​such as Python. Data obtained from the database is used as input, and health status patterns are analyzed based on this.

[0551] Step 5: Generate a Health Management Plan

[0552] Server: Using the generative AI model, a personalized health management plan is generated from the learned data. Specifically, the generative AI model compares the plan with past data and outputs an appropriate health management plan. For example, instructions such as "The user's daily walking count has decreased, so suggest that they make short walks a daily routine" are included.

[0553] Step 6: Acquire audio data

[0554] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[0555] Terminal: The device uses a voice recognition function to acquire user speech. The input is voice data obtained through a microphone.

[0556] Step 7: Sending audio data

[0557] Terminal: Sends the acquired voice data to the server. The acquired voice data is input and is uploaded to the server.

[0558] Step 8: Transcribe speech to text

[0559] Server: To convert the audio data into text, a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe is used. The input is audio data and the output is text data.

[0560] Step 9: Analyzing the speech

[0561] Server: The generation AI analyzes the text data and detects changes in the user's health condition and lifestyle based on the content of their speech. The text data is input, and the analysis results are output based on this.

[0562] Step 10: Recognize your emotional state

[0563] Server: Using an emotion engine, the server recognizes the emotional state of the elderly person from text and voice data. The input is text and voice data, and the output is the recognized emotional state.

[0564] Step 11: Emotion-based advice generation

[0565] Server: Generates appropriate advice based on the recognized emotional state. For example, advice such as "You seem to be feeling depressed, so try some breathing exercises to relax" is generated.

[0566] Step 12: Advice Notification

[0567] Terminal: Notifies the user of the generated advice. The advice is sent from the server as input and is conveyed to the user in text or voice.

[0568] Step 13: Detecting an emergency

[0569] User: An elderly person speaks to the device saying "Help me."

[0570] Terminal: Emergency situations are detected using voice recognition functionality. Voice data acquired by a microphone is used as input.

[0571] Step 14: Emergency Notification

[0572] Device: Sends notifications to pre-defined emergency contacts and automatically calls the nearest emergency services. Emergency messages are sent based on the detected emergency information as input.

[0573] Step 15: Provide emergency guidelines

[0574] Terminal: Provides the user with emergency action guidelines. Examples include instructions such as "Sit down, take a deep breath, and wait until the ambulance arrives." Input is information about the detected emergency, and based on this, action guidelines are output.

[0575] Step 16: Provide information about your hobbies and interests

[0576] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[0577] Terminal: Acquires voice data and sends it to the server. Voice data obtained through a microphone is used as input.

[0578] Server: Generates relevant information based on the user's hobbies and interests. The input is text data and a hobby profile, and the output is relevant information. For example, the generated information is, "There's a pottery class being held nearby. Would you like to participate?"

[0579] Step 17: Start a conversation with your chatbot

[0580] Terminal: The AI ​​chatbot begins a conversation with the user by asking, "How was your day today?" It has a pre-defined prompt as input and starts a dialogue with the user as output.

[0581] Step 18: Emotion Recognition and Response

[0582] Server: The emotion engine recognizes the user's emotional state from everyday conversations and responds appropriately. The input is text data from the conversation, and the emotional state is output based on this. Conversations and comforting words designed to elicit positive emotions are provided.

[0583] In this way, the program processing of the system can be explained in detail with specific operations, inputs and outputs at each step.

[0584] (Application example 2)

[0585] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0586] There is a need for appropriate responses to the health management issues, loneliness, and physiological and psychological emergencies that seniors face in their daily lives. Furthermore, there is a lack of ways to provide more personalized, real-time, and effective support to seniors in physical stores. To address these issues, a system is needed that comprehensively analyzes the health status, lifestyle habits, and emotions of seniors and provides appropriate advice and support.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing emotions from the elderly's daily conversations and providing support appropriate to the situation; and means for providing advice in real time using a voice assistant when the elderly receives health and lifestyle support in a physical store. This enables more personalized health management and appropriate advice and support to be provided in real time in the elderly's daily life and in support at the physical store, thereby improving the quality of life of the elderly.

[0588] "Means for acquiring lifestyle data of elderly people" refers to methods and devices for collecting information on the diet, exercise, sleep, activity level, etc. of elderly people in their daily lives.

[0589] "Means for analyzing lifestyle data and learning about the health status and lifestyle habits of elderly people" refers to algorithms and software that process collected lifestyle data and understand the health status and daily behavior patterns of elderly people.

[0590] "Means for generating and providing personalized health management plans based on health status and lifestyle habits" refers to a method or system for creating individually optimized health management plans based on analyzed data and providing them to elderly people.

[0591] "Means for capturing everyday conversations with elderly people and analyzing the audio data" refers to technologies and tools for recording the voices spoken by elderly people, converting the voices into text, and analyzing the content.

[0592] "Means for detecting changes in health condition and lifestyle habits based on analysis results and providing appropriate advice" refers to methods and systems for identifying changes in health condition and lifestyle habits from the analysis results of voice data and providing advice to elderly people based on the results.

[0593] "Means for detecting emergencies and notifying emergency contacts" refers to a mechanism that automatically detects when something abnormal occurs with an elderly person and sends a notification to pre-registered contacts.

[0594] "Means for providing guidelines for action in an emergency" refers to a method or system for providing instructions to elderly people on how to act safely in the event of an emergency.

[0595] "Means for collecting and providing information on the hobbies and interests of older people" refers to methods and technologies for collecting information on areas and activities that interest older people and providing it to them.

[0596] "Means for recognizing emotions from everyday conversations with the elderly and providing support appropriate to the situation" refers to algorithms and systems that analyze the emotional state of elderly people from everyday conversations and provide support and advice accordingly.

[0597] "Means for providing real-time advice using a voice assistant when elderly people receive health and lifestyle support in physical stores" refers to methods and technologies for using a voice assistant to provide appropriate advice on the spot when elderly people receive health consultations or lifestyle support in physical stores.

[0598] This invention is a personalized health management system for the elderly that utilizes generative AI models and emotion engines, and specific embodiments for supporting the elderly in physical stores are described below.

[0599] System configuration

[0600] The system consists of a server, terminals, and related software. The server plays a central role in managing and analyzing the elderly's lifestyle and health data and generating appropriate support plans. The terminals (smartphones, tablets, in-store kiosk terminals, etc.) are used directly by the elderly as an interface.

[0601] Hardware and software used

[0602] Hardware: Smartphones, smart tablets, in-store KIOSK terminals

[0603] Software: Python, TENSORFLOW (registered trademark), OpenCV, Dialogflow, Firebase

[0604] Data Collection and Management

[0605] The device collects health and lifestyle data (such as diet, exercise, and sleep) entered by the elderly on a daily basis, and the collected data is stored in a cloud database using Firebase.

[0606] Health management plan generation and provision

[0607] The server analyzes the collected data using TensorFlow to learn about the health status and lifestyle habits of the elderly, and then creates an individually optimized health management plan from the generated data and provides it to the elderly.

[0608] Voice data processing and emotion recognition

[0609] When an elderly person speaks to the device in everyday conversation, the voice data is captured through the device and sent to the server. The server converts the voice data into text via Dialogflow and then performs natural language processing. An emotion engine is used to recognize the elderly person's emotional state from the text and voice data.

[0610] Emergency response

[0611] If the elderly person detects any abnormality, the device will recognize the emergency and immediately send a notification to emergency contacts and store staff, enabling a prompt response.

[0612] Information provision

[0613] The server collects and provides the latest relevant information and event information based on the elderly's hobbies and interests, for example, notifying them of nearby events and health articles.

[0614] Specific examples

[0615] For example, if an elderly person says, "My knees have been hurting lately," the system will analyze their voice and compare it with past data to suggest appropriate exercises and rest methods. If it detects a decline in emotion, it will offer relaxation techniques and even contact the customer promptly in the event of an emergency.

[0616] Prompt Sentence Examples

[0617] Here are some examples of prompts for generative AI models:

[0618] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[0619] In this way, the system of the present invention can support the lifestyles and health management of the elderly, allowing them to live more safely even in physical stores.

[0620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0621] Step 1:

[0622] Collecting data on the lifestyles of the elderly

[0623] Input: Users manually input data such as daily diet, exercise, and sleep duration using a smartphone or tablet, or data is automatically collected from a wearable device.

[0624] How it works: The device collects data entered by the elderly person and data sent from wearable devices, including data from a pedometer and information from a food tracking app.

[0625] Output: The acquired lifestyle data is temporarily saved and sent to the next step.

[0626] Step 2:

[0627] Data storage and management

[0628] Input: Elderly lifestyle data obtained in step 1.

[0629] Specific operation: The device sends the acquired data to the Firebase database in the cloud.

[0630] Data processing: The submitted data is stored in the Firebase database and organized chronologically.

[0631] Output: Organized lifestyle data is stored in a database and ready for analysis.

[0632] Step 3:

[0633] Analyzing data and generating a health management plan

[0634] Input: Life data stored in Firebase database.

[0635] Specific operation: The server uses TensorFlow to analyze the collected data and learn about the health status and lifestyle habits of the elderly.

[0636] Data processing: AI models generated through data analysis assess the health status of the elderly and generate personalized health management plans.

[0637] Output: Send the generated health management plan to the terminal.

[0638] Step 4:

[0639] Acquiring everyday conversations and analyzing audio data

[0640] Input: Voice data of everyday conversations spoken by the user to the device.

[0641] Specific operation: The device picks up the voice of the elderly person using a microphone and sends the voice data to the server.

[0642] Data processing: The server uses Dialogflow to convert voice data into text and perform natural language processing.

[0643] Output: Textual audio data and analysis results.

[0644] Step 5:

[0645] Emotion recognition and advice provision

[0646] Input: Text data output in Step 4 and analysis results.

[0647] Specific operation: The server uses an emotion engine to recognize the emotional state of the elderly person from text data and voice data.

[0648] Data processing: The emotion engine analyzes the emotional state and generates appropriate advice and mental care support depending on the situation.

[0649] Output: The generated advice is sent to the terminal and notifies the user.

[0650] Step 6:

[0651] Emergency detection and response

[0652] Input: Voice data of the user saying "help" to the device.

[0653] Specific operation: The device acquires voice data and analyzes it to detect emergencies.

[0654] Data processing: If an emergency situation is detected, the device will send a notification to pre-defined emergency contacts and automatically call the nearest emergency services.

[0655] Output: Notification to emergency contacts and emergency services.

[0656] Step 7:

[0657] Providing information based on the interests of the elderly

[0658] Input: Audio data of a user saying, "Has anything interesting happened recently?"

[0659] Specific operation: The terminal acquires the voice data and sends it to the server.

[0660] Data processing: The server collects the latest relevant information based on the user's hobbies and interests and generates suggestions.

[0661] Output: Information based on the elderly person's hobbies and interests is displayed on the device or provided via voice.

[0662] Example prompt sentence:

[0663] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[0664] The above is a specific processing flow for carrying out the present invention. This system effectively realizes health management and daily support for elderly people.

[0665] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0666] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0667] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0668] [Second embodiment]

[0669] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0670] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0671] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0672] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0673] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0674] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0675] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0676] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0677] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0678] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0679] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0680] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0681] The present invention aims to provide a personalized health management system using generative AI to support the daily lives of the elderly. This system supports the daily lives of the elderly via a server and terminals, and is specifically implemented as follows.

[0682] System configuration

[0683] The system consists of the following main components:

[0684] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[0685] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[0686] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[0687] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[0688] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[0689] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[0690] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[0691] Program processing

[0692] Data collection and learning

[0693] Device: Elderly people enter data about their daily lives (such as diet, exercise, and sleep), or the data is automatically collected from a wearable device.

[0694] Terminal: Sends collected data to the server.

[0695] Server: Analyzes the received data and learns about the elderly person's health condition and lifestyle habits.

[0696] Server: Generative AI learns patterns from data and builds personalized health plans.

[0697] Analysis of everyday conversations and advice

[0698] User: An elderly person speaks everyday conversation into the device (e.g., "I can't sleep lately").

[0699] Terminal: Acquires voice data and sends it to the server.

[0700] Server: Analyzes voice data, converts speech into text, and detects changes in the elderly person's health and lifestyle habits.

[0701] Server: Based on the analysis results, it generates detailed advice and sends it to the device.

[0702] Device: Providing advice to the elderly (e.g., "Try listening to the following music to help you relax")

[0703] Emergency response

[0704] User: An elderly person speaks to the device saying "Help me."

[0705] Terminal: Acquires voice data and detects emergencies.

[0706] Device: Automatically notify emergency contacts and arrange for the nearest emergency services.

[0707] Device: Providing specific guidelines for behavior to the older adult (e.g., "Take a deep breath and sit down").

[0708] Reducing loneliness and providing information

[0709] User: An elderly person talks to the device, asking, "Has anything interesting happened recently?"

[0710] Terminal: Acquires voice data and sends it to the server.

[0711] Server: Generates the latest information based on the hobbies and interests of the elderly.

[0712] Terminals: Providing information to seniors (e.g., "There's a pottery class coming up nearby").

[0713] Device: AI chatbots can initiate everyday conversations with seniors, reducing their sense of loneliness.

[0714] Specific examples

[0715] Health management plan proposals

[0716] User: An elderly person says, "My knees have been hurting lately."

[0717] Terminal: Acquires voice data and sends it to the server.

[0718] Server: Analyzes the data and compares it with past lifestyle data.

[0719] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[0720] Terminal: Providing advice to seniors.

[0721] Emergency response

[0722] User: An elderly person talks to the device saying, "I feel like I'm going to fall."

[0723] Device: Detects emergencies and obtains location information.

[0724] Device: Call emergency contacts and emergency services.

[0725] Device: Communicate specific guidelines for action to the elderly.

[0726] conclusion

[0727] The system of this invention makes it possible to provide personalized support tailored to the individual needs of the elderly, reducing the burden on them and enabling them to live independently with peace of mind. It is also expected to contribute greatly to solving the aging problem of society as a whole.

[0728] The processing flow will be explained below.

[0729] Data collection and learning

[0730] Step 1:

[0731] User: Elderly people record data about their daily lives (e.g., diet, exercise, sleep) or wear a wearable device.

[0732] Step 2:

[0733] Terminal: Collects recorded life and health data.

[0734] Step 3:

[0735] Terminal: Periodically sends collected data to the server.

[0736] Step 4:

[0737] Server: Stores the received data in a database.

[0738] Step 5:

[0739] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly.

[0740] Step 6:

[0741] Server: Creates a personalized health management plan based on the data learned by the generative AI.

[0742] Analysis of everyday conversations and advice

[0743] Step 1:

[0744] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[0745] Step 2:

[0746] Terminal: Acquires voice data and sends it to the server.

[0747] Step 3:

[0748] Server: Analyzes the voice data and converts the spoken content into text.

[0749] Step 4:

[0750] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[0751] Step 5:

[0752] Server: Based on the analysis results, generates appropriate advice and sends it to the device.

[0753] Step 6:

[0754] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[0755] Emergency response

[0756] Step 1:

[0757] User: An elderly person speaks to the device saying "Help me."

[0758] Step 2:

[0759] Terminal: Acquires voice data and detects emergencies.

[0760] Step 3:

[0761] On your device: Send a notification to pre-defined emergency contacts.

[0762] Step 4:

[0763] Device: Automatically call the nearest emergency services.

[0764] Step 5:

[0765] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[0766] Reducing loneliness and providing information

[0767] Step 1:

[0768] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[0769] Step 2:

[0770] Terminal: Acquires voice data and sends it to the server.

[0771] Step 3:

[0772] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[0773] Step 4:

[0774] Server: Sends the generated information to the terminal.

[0775] Step 5:

[0776] Terminal: The terminal provides the user with information such as, "It seems there's a pottery class being held at the local community center next week. Would you like to participate?"

[0777] Step 6:

[0778] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[0779] conclusion

[0780] Through the above processing steps, the system of the present invention can provide personalized assistance according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[0781] Example 1

[0782] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0783] Health management for the elderly often takes a one-size-fits-all approach, without taking into account individual needs and differences in living environments. Furthermore, when elderly people live alone, it is difficult to respond appropriately to changes in their health status or emergencies in their daily lives. Furthermore, there is a lack of effective methods to reduce the sense of loneliness felt by elderly people. To address these issues, there is a need for personalized health management plans, emergency response functions, and everyday conversation support functions.

[0784] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0785] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data and daily life data to the server, means for analyzing the received data in the server and learning the elderly person's health condition and lifestyle habits, means for generating and providing a personalized health management plan using a generative AI model, means for acquiring conversation data with the elderly person and transmitting it to the server, means for converting voice data to text and performing natural language processing to detect changes in the elderly person's health condition and lifestyle habits, means for generating and providing appropriate advice based on the analysis results, means for detecting voice input in an emergency and automatically notifying an emergency contact, means for providing specific guidelines for action by voice or text, means for collecting and providing information based on the elderly person's hobbies and interests, and means for reducing the elderly person's sense of loneliness through everyday conversations via a chatbot. This allows for providing elderly people with personalized support tailored to their individual needs, improving the accuracy of health management, accelerating emergency response, and reducing the elderly person's sense of loneliness.

[0786] "Biometric data" refers to information that indicates the physical condition of an elderly person, and includes physiological indicators such as heart rate, blood pressure, body temperature, and oxygen saturation.

[0787] "Daily life data" refers to information about the activities that elderly people engage in in their daily lives, including dietary content, amount of exercise, and sleep duration.

[0788] "Server" means a computer system that receives, stores, analyzes data, and processes and provides information using a generative AI model.

[0789] A "generative AI model" is an artificial intelligence algorithm or system that generates new information based on given input data by learning from large amounts of data.

[0790] A "personalized health management plan" is an individualized health promotion and management plan created based on the health status and lifestyle habits of each elderly person.

[0791] "Voice data" refers to data containing audio signals that are recordings of what the elderly person is saying to the terminal.

[0792] "Natural language processing" is a technology that converts voice data into text format, linguistically analyzes it as a string of characters, and understands and processes the content.

[0793] "Emergencies" refer to dangerous situations or health problems faced by older adults, such as falls, difficulty breathing, or sudden changes in blood pressure.

[0794] "Emergency Contacts" refers to people or organisations that should be contacted in the event of an emergency, such as family, friends, carers or emergency services.

[0795] "Action guidelines" are instructions that show specific actions that elderly people should take in specific situations, such as taking deep breaths, sitting down, and resting.

[0796] "Hobbies and interest-based information" refers to the latest information, events and activity opportunities related to areas and activities that interest seniors.

[0797] A "chatbot" is a program that uses artificial intelligence to engage in natural dialogue with users, and can converse through text or voice.

[0798] This invention is a system for supporting the daily lives of elderly people and personalizing their health management. The system utilizes terminals and servers to effectively utilize lifestyle and health data of elderly people using generative AI models.

[0799] System Overview

[0800] This system collects and analyzes biometric and daily life data from elderly people to provide personalized health management plans and respond to emergencies. The hardware used includes wearable devices (e.g., Fitbit, Apple Watch) and internet-connected devices (e.g., smartphones, tablets). Analysis is performed using Python libraries (e.g., Pandas, Scikit-learn) and the Google Cloud Speech-to-Text API, utilizing generative AI models (e.g., GPT-4).

[0801] Explanation of program processing

[0802] Data collection

[0803] Elderly users manually enter data such as dietary habits, exercise, and sleep duration into the device, or use a wearable device to automatically collect this data, which is then sent to a server via the Internet.

[0804] Data analysis

[0805] The server analyzes the received data and learns about the elderly person's health status and lifestyle habits. It uses Python libraries Pandas and Scikit-learn to perform statistical analysis and outlier detection. It then uses a generative AI model to generate a personalized health management plan.

[0806] Analysis of everyday conversations

[0807] When an elderly user speaks into the device, the device captures this voice data and sends it to a server, which then converts the voice data into text using the Google Cloud Speech-to-Text API and performs natural language processing to detect changes in health status and lifestyle habits.

[0808] Generating Advice

[0809] The server generates appropriate advice based on the analysis results. Using the generative AI model, it creates specific health management advice (e.g., "Try listening to the following music to relax") and sends it to the device. The device then provides the advice to the elderly via voice or text.

[0810] Emergency response

[0811] When an elderly user speaks "help me" into the device, the device receives the voice data and detects an emergency. The device immediately transmits the data to the server, which then automatically notifies emergency contacts. Furthermore, the device provides specific instructions for action (e.g., "take a deep breath and sit down") via voice.

[0812] Providing information based on hobbies and interests

[0813] When an elderly user speaks to the device, asking, "Has anything interesting happened recently?", the device captures the voice data and sends it to the server. The server uses a generative AI model based on the elderly's hobbies and interests to generate the latest information. The device then provides this information to the elderly via voice or text. The chatbot function also allows for everyday conversations with the elderly, reducing their sense of loneliness.

[0814] Examples of concrete examples and prompts

[0815] Example 1: Proposing a health management plan

[0816] When an elderly user says, "My knees have been hurting lately," the device captures the voice data and sends it to the server. The server analyzes the data and compares it with past lifestyle data. Using a generative AI model, it generates the cause of the knee pain and recommends countermeasures (e.g., specific exercises). The device then provides advice to the elderly, such as, "Try the following exercises to relax."

[0817] Example 2: Emergency response

[0818] When an elderly user says to the device, "I feel like I'm going to fall," the device detects the emergency and obtains their location. The device then notifies emergency contacts and emergency services, and provides the elderly with advice such as, "Take a deep breath and sit down."

[0819] Prompt Sentence Examples

[0820] Healthcare plan: "I've been having knee pain lately. What's the best healthcare plan for this?"

[0821] Emergency response: "I feel like I'm going to collapse. What should I do?"

[0822] The system of this invention provides support tailored to the individual needs of the elderly, improves the accuracy of health management, and enables rapid response in emergencies, thereby improving the quality of life for the elderly and contributing to solving the aging problem in society as a whole.

[0823] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] Data collection

[0826] Input: Manually entered data from seniors (dietary content, exercise, sleep duration) and automatically collected data from wearable devices.

[0827] How it works: The elderly user opens the application on the device and enters information about their diet, exercise, and sleep time. The wearable device also automatically collects data such as heart rate and number of steps taken.

[0828] Output: Collected life and biometric data.

[0829] Step 2:

[0830] Data transmission

[0831] Input: Collected life and biometric data.

[0832] How it works: Your device sends collected data to a server using Wi-Fi or a mobile network.

[0833] Output: The data sent to the server.

[0834] Step 3:

[0835] Data analysis

[0836] Input: Life and biometric data sent to the server.

[0837] How it works: The server uses Python libraries such as Pandas and Scikit-learn to perform statistical analysis of the data and detect outliers.

[0838] Output: Analysis results on the health status and lifestyle habits of elderly people.

[0839] Step 4:

[0840] Generate a health management plan

[0841] Input: Analysis results on health status and lifestyle habits.

[0842] How it works: The server uses a generative AI model (e.g., GPT-4) to generate a personalized health care plan based on the individual needs of the elderly person.

[0843] Output: A personalized health care plan.

[0844] Step 5:

[0845] Acquiring voice input

[0846] Input: Everyday conversations of elderly people (e.g., "I can't sleep these days").

[0847] Operation: The elderly user speaks everyday conversation into the device, and the device captures this voice data.

[0848] Output: The captured audio data.

[0849] Step 6:

[0850] Sending audio data

[0851] Input: Captured audio data.

[0852] Action: The device sends audio data to the server.

[0853] Output: The audio data sent to the server.

[0854] Step 7:

[0855] Analysis of audio data

[0856] Input: The audio data sent to the server.

[0857] How it works: The server uses the Google Cloud Speech-to-Text API to convert voice data into text, then uses natural language processing to detect changes in health and lifestyle habits.

[0858] Output: Text conversion results and analysis results of the audio data.

[0859] Step 8:

[0860] Generating Advice

[0861] Input: Text conversion results and analysis results of audio data.

[0862] How it works: The server uses a generative AI model (e.g., GPT-4) to generate appropriate advice based on the analysis results.

[0863] Output: Generated advice (e.g., "Try listening to the following music to relax")

[0864] Step 9:

[0865] Providing advice

[0866] Input: The generated advice.

[0867] How it works: The device provides the generated advice to the senior via voice or text.

[0868] Output: Advice given to the elderly.

[0869] Step 10:

[0870] Emergency voice input detection

[0871] Input: Urgent speech from an elderly person (e.g., "Help me").

[0872] Operation: When an elderly user speaks an emergency voice into the device, the device picks up the voice and detects an emergency.

[0873] Output: Emergency detection result.

[0874] Step 11:

[0875] Emergency notification

[0876] Input: Emergency detection results and location information of the elderly person.

[0877] What it does: Your device automatically calls your emergency contacts and dispatches the nearest emergency services.

[0878] Output: Notification of called emergency contacts and emergency services.

[0879] Step 12:

[0880] Providing guidelines for emergency situations

[0881] Input: Emergency detection result.

[0882] What it does: The device provides the senior with specific instructions for action via voice or text (e.g., "Take a deep breath and sit down").

[0883] Output: Provided course of action.

[0884] Step 13:

[0885] Information gathering based on hobbies and interests

[0886] Input: A request for information about a senior's hobbies and interests (e.g., "What's interesting going on these days?").

[0887] How it works: The elderly user asks questions about their hobbies and interests to the device. The device captures this voice data and sends it to the server.

[0888] Output: The audio data sent to the server.

[0889] Step 14:

[0890] Information generation

[0891] Input: A request for information about seniors' hobbies and interests.

[0892] How it works: The server uses a generative AI model (e.g., GPT-4) to generate information based on the elderly person's hobbies and interests.

[0893] Output: The information generated.

[0894] Step 15:

[0895] Providing information and conducting daily conversations

[0896] Input: Generated information.

[0897] How it works: The device provides the generated information to the elderly via voice or text, and also uses a chatbot function to engage in everyday conversations with the elderly, reducing their sense of loneliness.

[0898] Output: Information provided to the elderly and daily conversations carried out.

[0899] (Application example 1)

[0900] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0901] For seniors to maintain healthy eating habits, it is important to provide personalized meal plans based on their individual health conditions and lifestyle habits. However, manually creating such personalized plans and preparing and adjusting meals each time is extremely difficult and burdensome for seniors. Furthermore, without a system for responding quickly and appropriately in emergencies, seniors risk being put in dangerous situations. Furthermore, there is a lack of information and communication methods to reduce seniors' feelings of loneliness and enrich their daily lives.

[0902] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0903] In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; and means for generating a personalized meal plan based on the elderly's health condition and lifestyle habits and ordering food based on the meal plan. This allows the elderly to easily order and receive meals appropriate for their health condition, respond quickly to emergencies, and obtain information and communication to improve the quality of their daily lives.

[0904] "Elderly" refers to people who are older and require special support and consideration in their daily lives.

[0905] "Lifestyle data" refers to information about the subject's daily life, such as their diet, amount of exercise, and sleep time.

[0906] "Health status" refers to indicators that show the normality or abnormality of an individual's physical and mental state.

[0907] "Lifestyle habits" refers to the habits and patterns of behavior that an individual engages in on a daily basis.

[0908] A "personalized health care plan" refers to a health care policy that is customized to suit the characteristics and conditions of each individual.

[0909] "Daily conversation" refers to the normal conversational activities that the subject engages in on a daily basis.

[0910] "Audio data" refers to audio recorded by a subject and stored as digital information.

[0911] "Analysis results" refers to information obtained as a result of analysis based on collected data.

[0912] "Appropriate advice" refers to advice that is most beneficial to the subject based on the analysis results.

[0913] An "emergency" is a situation in which a subject is suddenly in danger and requires a rapid response.

[0914] "Emergency contacts" refers to the people or organizations that should be notified first in an emergency.

[0915] A "guideline" refers to specific guidance or advice on how to act in a particular situation.

[0916] "Information about hobbies and interests" refers to information related to the subject's personal preferences and interests.

[0917] A "personalized meal plan" refers to meal suggestions tailored to an individual's health and lifestyle.

[0918] "Means of ordering food" refers to the methods or mechanisms for ordering ingredients and meals based on the proposed meal plan, such as through a delivery service.

[0919] This invention provides a personalized health management system using generative AI to support the lives of the elderly. The system collects and analyzes lifestyle and health data of the elderly to provide individually tailored health management and meal plans. The system also has a wide range of functions, including emergency detection and response, analysis of the elderly's daily conversations, and provision of information related to hobbies and interests.

[0920] System configuration

[0921] The system consists of the following main components:

[0922] 1. Data collection method: Hardware such as smartphones and wearable devices are used to collect lifestyle and health data from elderly people. This automatically collects information such as dietary habits, exercise levels, and sleep duration.

[0923] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits. The analysis software used is a Python-based generative AI library.

[0924] 3. Health management plan provision: A personalized health management plan is generated based on the analysis results and provided to the user. The proposed plan is notified to the user via a smartphone app.

[0925] 4. Voice data acquisition and analysis: Voice data is acquired through everyday conversations and analyzed. Voice recognition technology uses an API that converts voice into text (e.g., Google Speech-to-Text).

[0926] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly. The advice is displayed in text format on the smartphone or provided via a voice assistant.

[0927] 6. Emergency response: Detects an emergency and notifies emergency contacts. Emergency calls are made automatically via the smartphone's communication functions.

[0928] 7. Guidance: Providing specific guidance during emergencies (e.g., "Take a deep breath and sit down"), also provided in real time via the voice assistant.

[0929] 8. Information provision method: Collect and provide the latest information on the hobbies and interests of seniors. News and event information is automatically collected from the Internet.

[0930] 9. Meal plan generation and ordering: Generate a personalized meal plan based on the elderly person's health condition and lifestyle habits, and order food based on the meal plan. By linking with food delivery services, the suggested meals can be easily ordered.

[0931] Specific examples

[0932] Data collection and analysis:

[0933] Users input data using smartphones or wearable devices. The collected data is sent to a server and analyzed by a generative AI model. As a result of the analysis, a health management plan appropriate for the user's health condition is generated.

[0934] Analysis of everyday conversations and advice provided:

[0935] The user speaks to their smartphone saying, "My knees have been hurting lately." The voice data is captured and analyzed on the server. Based on the generated AI model, the results are compared with past data and the cause of the knee pain and countermeasures are provided.

[0936] Emergency Response:

[0937] When a user says "I feel like I'm going to fall," the system detects an emergency, captures their location, automatically notifies emergency contacts and emergency services, and instructs the user to "take a deep breath and sit down."

[0938] Information and loneliness relief:

[0939] When a user asks, "What's interesting lately?", the app provides the latest information based on their hobbies and interests. For example, information like "There's a pottery class opening nearby" will be displayed on the smartphone.

[0940] Meal plan suggestions and ordering:

[0941] When a user says, "I've been feeling unwell lately and have no appetite," the system analyzes the voice data and generates a meal plan tailored to their health condition. Based on the proposed plan, food is automatically ordered through a food delivery service.

[0942] Example prompt sentence:

[0943] "I've been feeling unwell lately and have no appetite."

[0944] "My knees hurt and I can't exercise"

[0945] "I think I'm having an allergic reaction, help me."

[0946] This will enable elderly people to easily order and receive meals that are appropriate for their health condition, respond quickly in emergencies, and receive information and communication that will improve the quality of their daily lives.

[0947] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0948] Step 1:

[0949] Elderly people use smartphones or wearable devices to input lifestyle data, including dietary habits, exercise, and sleep duration. The input data is automatically collected by each device and centralized on the smartphone. This data is then used as input data to be sent to the server.

[0950] Step 2:

[0951] The device sends the collected life data to the server. The server receives the data and stores it in a database. This stored data is used for later analysis. The input data is the life data, and the output data is the data stored in the server.

[0952] Step 3:

[0953] The server performs analysis based on the stored data. A Python-based generative AI model analyzes the lifestyle data and learns about the elderly person's health status and lifestyle habits. This analysis identifies patterns in the data and estimates their health status. The input data is the stored lifestyle data, and the output data is the analysis results.

[0954] Step 4:

[0955] The server generates a personalized health management plan based on the generative AI model. Based on the analysis results, a plan suitable for each elderly person is automatically created. The generated health management plan is sent to the device. The input data is the analysis results, and the output data is the health management plan.

[0956] Step 5:

[0957] The device notifies the user of the generated health management plan. The health management plan is displayed to the user via a smartphone app. The user can then manage their daily health in accordance with the plan. The input data is the health management plan, and the output data is the notification to the user.

[0958] Step 6:

[0959] A user speaks to a smartphone, saying, "My knee has been hurting lately." Voice data is acquired and sent by the device to a server. The input data is the voice data, and the output data is the voice data sent to the server.

[0960] Step 7:

[0961] The server analyzes the voice data and converts it into text. To analyze the voice data, it uses speech recognition technology such as the Google Speech-to-Text API. This analysis results in data converted from voice data into text format. The input data is voice data, and the output data is text data.

[0962] Step 8:

[0963] The server uses the converted data to detect changes in the elderly person's health condition and lifestyle habits based on a generative AI model. Based on the analysis results, it generates appropriate advice and sends it to the device. The input data is text data, and the output data is the advice content.

[0964] Step 9:

[0965] The device notifies the user of the advice received from the server. The advice is displayed to the user through a smartphone app. For example, advice such as "Try the following exercise to relax" is provided. The input data is the advice content, and the output data is the notification content to the user.

[0966] Step 10:

[0967] When a user says "I'm about to fall down," the device acquires this voice data and sends it to the server. The input data is the voice data of the emergency, and the output data is the voice data sent to the server.

[0968] Step 11:

[0969] The server analyzes the received voice data and detects an emergency. By analyzing the voice data, the server confirms that an emergency has occurred. The input data is the voice data, and the output data is the emergency status determination result.

[0970] Step 12:

[0971] The server notifies the emergency contacts and takes emergency action, including notifying the nearest emergency service. The server obtains the user's location information and notifies the emergency contacts and emergency service. The input data is the emergency status determination result and location information, and the output data is the emergency call content.

[0972] Step 13:

[0973] The device provides the user with guidelines for what to do in an emergency. For example, specific guidelines such as "Take a deep breath and sit down" are provided in real time through the voice assistant. The input data is the emergency call content, and the output data is the guidelines for what to do.

[0974] Step 14:

[0975] When a user says, "Has anything interesting happened recently?", the device acquires this voice data and sends it to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[0976] Step 15:

[0977] The server analyzes the voice data, generates the latest information based on the elderly person's hobbies and interests, and sends it to the device. For example, information such as "A pottery class will be held nearby" is generated. The input data is the voice data, and the output data is the generated information.

[0978] Step 16:

[0979] The device notifies the user of the information received from the server. The latest events and news are displayed through the smartphone app. The input data is the generated information, and the output data is the notification content to the user.

[0980] Step 17:

[0981] The user says, "I've been feeling unwell lately and have no appetite," and the voice data is acquired and sent by the terminal to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[0982] Step 18:

[0983] The server analyzes the voice data and generates a meal plan based on the user's health status. Using a generative AI model, a meal plan appropriate for the user's health status and lifestyle is created and sent to the device. The input data is the voice data and analysis results, and the output data is the meal plan.

[0984] Step 19:

[0985] The terminal uses the generated meal plan to order food from a food delivery service. The order is easily completed through a smartphone app. The input data is the meal plan, and the output data is the order details.

[0986] Step 20:

[0987] The food delivery service delivers the food and the user can easily pick up the meal. The input data is the order details and the output data is the delivery status.

[0988] This allows users to easily order and receive meals that are suitable for their health condition, improving the quality of their daily lives.

[0989] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0990] This invention provides a personalized health management system that utilizes generative AI and an emotion engine to support the daily lives of the elderly. This system supports the daily lives of the elderly through a server and terminals, and is specifically implemented as follows:

[0991] System configuration

[0992] The system consists of the following main components:

[0993] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[0994] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[0995] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[0996] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[0997] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[0998] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[0999] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1000] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[1001] Program processing

[1002] Data collection and learning

[1003] Device: Elderly people enter data on their daily lives (such as diet, exercise, and sleep), or the data is collected automatically through a wearable device.

[1004] Terminal: Periodically sends collected lifestyle and health data to the server.

[1005] Server: Stores the received data in a database and analyzes it.

[1006] Server: Learns about the health status and lifestyle habits of elderly people based on collected data.

[1007] Server: Generative AI learns patterns from data and builds personalized health plans.

[1008] Analysis of everyday conversations and emotion recognition

[1009] User: An elderly person speaks to the device (e.g., "I've been feeling depressed lately").

[1010] Terminal: Acquires voice data and sends it to the server.

[1011] Server: Analyzes the voice data and converts the spoken content into text.

[1012] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[1013] Server: The emotion engine analyzes the text and voice data to recognize the emotional state of the elderly.

[1014] Server: Based on the recognized emotional state, it generates appropriate advice and support and sends it to the device.

[1015] Device: Notifies the user of the advice received via text or voice (e.g., "You seem to be feeling down. Try some breathing exercises to relax.").

[1016] Emergency response

[1017] User: An elderly person speaks to the device saying "Help me."

[1018] Terminal: Acquires voice data and detects emergencies.

[1019] On your device: Send a notification to pre-defined emergency contacts.

[1020] Device: Automatically call the nearest emergency services.

[1021] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1022] Reducing loneliness and providing information

[1023] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[1024] Terminal: Acquires voice data and sends it to the server.

[1025] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[1026] Device: Providing information to the device (e.g., "There's a pottery class coming up near me. Would you like to join?").

[1027] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1028] Server: The emotion engine recognizes the user's emotions from everyday conversations and responds appropriately.

[1029] Specific examples

[1030] Health management plan proposals

[1031] User: An elderly person says, "My knees have been hurting lately."

[1032] Terminal: Acquires voice data and sends it to the server.

[1033] Server: Analyzes the data and compares it with past lifestyle data.

[1034] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1035] Terminal: Providing advice to seniors.

[1036] Emotional awareness and mental care

[1037] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1038] Terminal: Acquires voice data and sends it to the server.

[1039] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1040] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[1041] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1042] conclusion

[1043] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[1044] The processing flow will be explained below.

[1045] Data collection and learning

[1046] Step 1:

[1047] User: Elderly people manually enter their daily lifestyle data (e.g., diet, exercise, sleep duration) or wear a wearable device.

[1048] Step 2:

[1049] Device: Collects data entered by the user and automatically collected from wearable devices.

[1050] Step 3:

[1051] Terminal: Sends collected lifestyle and health data to the server.

[1052] Step 4:

[1053] Server: Stores the received data in a database.

[1054] Step 5:

[1055] Server: Analyzes data stored in the database and uses generative AI to learn about the health status and lifestyle habits of elderly people.

[1056] Step 6:

[1057] Server: Builds a personalized health management plan based on the learning results.

[1058] Analysis of everyday conversations and emotion recognition

[1059] Step 1:

[1060] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[1061] Step 2:

[1062] Terminal: Acquires voice data and sends it to the server.

[1063] Step 3:

[1064] Server: Analyzes the voice data and converts the spoken content into text.

[1065] Step 4:

[1066] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[1067] Step 5:

[1068] Server: The emotion engine analyzes the voice data and text to recognize the emotional state of the elderly.

[1069] Step 6:

[1070] Server: Generates appropriate advice based on the analysis results and emotional state and sends it to the device.

[1071] Step 7:

[1072] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[1073] Emergency response

[1074] Step 1:

[1075] User: An elderly person speaks to the device saying "Help me."

[1076] Step 2:

[1077] Terminal: Acquires voice data and detects emergencies.

[1078] Step 3:

[1079] On your device: Send a notification to pre-defined emergency contacts.

[1080] Step 4:

[1081] Device: Automatically call the nearest emergency services.

[1082] Step 5:

[1083] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1084] Reducing loneliness and providing information

[1085] Step 1:

[1086] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[1087] Step 2:

[1088] Terminal: Acquires voice data and sends it to the server.

[1089] Step 3:

[1090] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[1091] Step 4:

[1092] Server: Sends the generated information to the terminal.

[1093] Step 5:

[1094] Terminal: The terminal provides information such as, "It seems that a pottery class will be held at the local community center next week. Would you like to participate?"

[1095] Step 6:

[1096] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1097] Emotional awareness and mental care

[1098] Step 1:

[1099] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1100] Step 2:

[1101] Terminal: Acquires voice data and sends it to the server.

[1102] Step 3:

[1103] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1104] Step 4:

[1105] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[1106] Step 5:

[1107] Server: Sends the generated mental care plan to the terminal.

[1108] Step 6:

[1109] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1110] conclusion

[1111] Through the above processing steps, the system of the present invention can provide personalized assistance and emotional support according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[1112] Example 2

[1113] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1114] Elderly people who live alone often find it difficult to manage their health and respond to emergencies in their daily lives. They are also prone to feelings of loneliness and mental anxiety, which can lead to a deterioration in their health. Current health care systems are unable to meet all of these needs. Therefore, there is a need for a system that can comprehensively support the daily lives of elderly people.

[1115] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting emergencies and notifying emergency contacts; means for providing action guidelines in emergencies; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing the elderly's emotional state from daily conversations using an emotion engine and providing appropriate support; and means for generating and providing a personalized health management plan using a generative AI model. This enables elderly people to monitor their health condition and respond quickly to emergencies, and by receiving psychological support, they can live more independently with peace of mind.

[1116] "Lifestyle data" refers to information about the activities and behaviors of elderly people in their daily lives, and specifically includes dietary content, amount of exercise, sleep duration, etc.

[1117] "Health data" refers to information relating to the health of the elderly person, and specifically includes heart rate, blood pressure, body temperature, weight, etc.

[1118] "Analysis methods" refer to methods and tools for processing collected data and analyzing the health status and lifestyle habits of older people.

[1119] A "personalized health management plan" refers to a health management plan that is customized based on the health condition and lifestyle of each elderly person.

[1120] "Voice data" refers to the voice information collected when a user speaks to a terminal, and by analyzing this information, the content of the speech can be understood.

[1121] An "emotion engine" refers to algorithms and software that recognize the emotional state of elderly people from their everyday conversations and provide appropriate support and advice.

[1122] A "generative AI model" is an artificial intelligence model that learns patterns from collected data and generates optimal health management plans and advice for seniors.

[1123] An "emergency" refers to a situation in which an elderly person is in danger or in need of help, and refers to the conditions and circumstances under which this situation can be detected quickly and appropriate action can be taken.

[1124] "Emergency Contact" refers to pre-defined contacts (such as family members or caregivers) who should be notified or contacted in the event of an emergency.

[1125] "Information on hobbies and interests" refers to information related to activities and areas of interest that older people can enjoy, and the purpose of providing this information is to improve the quality of life of older people.

[1126] The present invention provides a personalized health management system that utilizes a generative AI model and an emotion engine to support the daily lives of elderly people. This system supports the daily lives of elderly people through a server and a terminal, and is specifically implemented as follows.

[1127] System configuration

[1128] The system consists of the following main components:

[1129] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[1130] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[1131] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[1132] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[1133] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[1134] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[1135] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1136] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[1137] Program processing

[1138] Data collection and learning

[1139] Device: Elderly people manually enter data about their daily lives (e.g., diet, exercise, and sleep), and also use wearable devices to automatically collect this data.

[1140] Terminal: Periodically sends collected lifestyle and health data to the server.

[1141] Server: The received data is stored in a central database and analyzed. Specific databases used include MySQL and PostgreSQL.

[1142] Server: Analyzes the stored data and applies machine learning algorithms (e.g., random forests and neural networks) using programming languages ​​such as Python to learn about the health and lifestyle habits of the elderly.

[1143] Server: The generative AI model generates a health management plan from the collected data. For example, it suggests that the user's daily walking frequency has decreased and that they should take a short walk as a daily routine.

[1144] Analysis of everyday conversations and emotion recognition

[1145] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[1146] Device: Collects voice data and sends it to the server. Use a smart speaker or smartphone with a built-in microphone.

[1147] Server: Use a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe to convert the audio data into text.

[1148] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle based on the content of the speech.

[1149] Server: Analyzes text and voice data using an emotion engine to recognize the user's emotional state. Provides positive conversations and comforting words.

[1150] Terminal: Provides appropriate advice based on the recognized emotional state (e.g., "You seem to be feeling depressed. Try some breathing exercises to relax.").

[1151] Emergency response

[1152] User: An elderly person speaks to the device saying "Help me."

[1153] Terminal: Acquires voice data and detects emergencies.

[1154] Device: Sends text message and phone call notifications to pre-defined emergency contacts.

[1155] Device: Automatically contacts the nearest emergency services, using GPS data to communicate your location.

[1156] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1157] Reducing loneliness and providing information

[1158] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[1159] Terminal: Acquires voice data and sends it to the server.

[1160] Server: Generates the latest relevant information based on the user's hobbies and interests. Collects relevant event information and news from the Internet.

[1161] Device: Providing collected information to the user (e.g., "There's a pottery class coming up near me. Would you like to join?").

[1162] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1163] Server: The emotion engine recognizes the user's emotional state from everyday conversation and responds appropriately.

[1164] Specific examples

[1165] Health management plan proposals

[1166] User: An elderly person says, "My knees have been hurting lately."

[1167] Terminal: Acquires voice data and sends it to the server.

[1168] Server: Analyzes the data and compares it with past lifestyle data.

[1169] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1170] Terminal: Providing advice to seniors.

[1171] Emotional awareness and mental care

[1172] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1173] Terminal: Acquires voice data and sends it to the server.

[1174] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1175] Server: Generates mental health plans (e.g., relaxation techniques to improve mood).

[1176] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1177] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[1178] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1179] Step 1: Data entry

[1180] User: Elderly people manually input data about their daily life (e.g., dietary details, amount of exercise, sleep time, etc.) into a dedicated application. The data input in this way includes the user's daily diet and exercise records.

[1181] Device: Data (e.g., heart rate, number of steps, sleep patterns, etc.) is automatically collected from a wearable device worn by the elderly. Biometric data from the wearable device is obtained as input.

[1182] Step 2: Send data

[1183] Device: The collected lifestyle and health data is periodically sent to the server. The data is securely transmitted using an encryption protocol. Specifically, the data is uploaded from the device to the server via the Internet.

[1184] Step 3: Save Data

[1185] Server: Stores the received data in a central database (e.g., MySQL or PostgreSQL). It takes the lifestyle and health data sent as input and adds new records to the database based on this.

[1186] Step 4: Data analysis

[1187] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly. Specifically, machine learning algorithms (e.g., random forests and neural networks) are applied using programming languages ​​such as Python. Data obtained from the database is used as input, and health status patterns are analyzed based on this.

[1188] Step 5: Generate a Health Management Plan

[1189] Server: Using the generative AI model, a personalized health management plan is generated from the learned data. Specifically, the generative AI model compares the plan with past data and outputs an appropriate health management plan. For example, instructions such as "The user's daily walking count has decreased, so suggest that they make short walks a daily routine" are included.

[1190] Step 6: Acquire audio data

[1191] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[1192] Terminal: The device uses a voice recognition function to acquire user speech. The input is voice data obtained through a microphone.

[1193] Step 7: Sending audio data

[1194] Terminal: Sends the acquired voice data to the server. The acquired voice data is input and is uploaded to the server.

[1195] Step 8: Transcribe speech to text

[1196] Server: To convert the audio data into text, a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe is used. The input is audio data and the output is text data.

[1197] Step 9: Analyzing the speech

[1198] Server: The generation AI analyzes the text data and detects changes in the user's health condition and lifestyle based on the content of their speech. The text data is input, and the analysis results are output based on this.

[1199] Step 10: Recognize your emotional state

[1200] Server: Using an emotion engine, the server recognizes the emotional state of the elderly person from text and voice data. The input is text and voice data, and the output is the recognized emotional state.

[1201] Step 11: Emotion-based advice generation

[1202] Server: Generates appropriate advice based on the recognized emotional state. For example, advice such as "You seem to be feeling depressed, so try some breathing exercises to relax" is generated.

[1203] Step 12: Advice Notification

[1204] Terminal: Notifies the user of the generated advice. The advice is sent from the server as input and is conveyed to the user in text or voice.

[1205] Step 13: Detecting an emergency

[1206] User: An elderly person speaks to the device saying "Help me."

[1207] Terminal: Emergency situations are detected using voice recognition functionality. Voice data acquired by a microphone is used as input.

[1208] Step 14: Emergency Notification

[1209] Device: Sends notifications to pre-defined emergency contacts and automatically calls the nearest emergency services. Emergency messages are sent based on the detected emergency information as input.

[1210] Step 15: Provide emergency guidelines

[1211] Terminal: Provides the user with emergency action guidelines. Examples include instructions such as "Sit down, take a deep breath, and wait until the ambulance arrives." Input is information about the detected emergency, and based on this, action guidelines are output.

[1212] Step 16: Provide information about your hobbies and interests

[1213] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[1214] Terminal: Acquires voice data and sends it to the server. Voice data obtained through a microphone is used as input.

[1215] Server: Generates relevant information based on the user's hobbies and interests. The input is text data and a hobby profile, and the output is relevant information. For example, the generated information is, "There's a pottery class being held nearby. Would you like to participate?"

[1216] Step 17: Start a conversation with your chatbot

[1217] Terminal: The AI ​​chatbot begins a conversation with the user by asking, "How was your day today?" It has a pre-defined prompt as input and starts a dialogue with the user as output.

[1218] Step 18: Emotion Recognition and Response

[1219] Server: The emotion engine recognizes the user's emotional state from everyday conversations and responds appropriately. The input is text data from the conversation, and the emotional state is output based on this. Conversations and comforting words designed to elicit positive emotions are provided.

[1220] In this way, the program processing of the system can be explained in detail with specific operations, inputs and outputs at each step.

[1221] (Application example 2)

[1222] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1223] There is a need for appropriate responses to the health management issues, loneliness, and physiological and psychological emergencies that seniors face in their daily lives. Furthermore, there is a lack of ways to provide more personalized, real-time, and effective support to seniors in physical stores. To address these issues, a system is needed that comprehensively analyzes the health status, lifestyle habits, and emotions of seniors and provides appropriate advice and support.

[1224] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing emotions from the elderly's daily conversations and providing support appropriate to the situation; and means for providing advice in real time using a voice assistant when the elderly receives health and lifestyle support in a physical store. This enables more personalized health management and appropriate advice and support to be provided in real time in the elderly's daily life and in support at the physical store, thereby improving the quality of life of the elderly.

[1225] "Means for acquiring lifestyle data of elderly people" refers to methods and devices for collecting information on the diet, exercise, sleep, activity level, etc. of elderly people in their daily lives.

[1226] "Means for analyzing lifestyle data and learning about the health status and lifestyle habits of elderly people" refers to algorithms and software that process collected lifestyle data and understand the health status and daily behavior patterns of elderly people.

[1227] "Means for generating and providing personalized health management plans based on health status and lifestyle habits" refers to a method or system for creating individually optimized health management plans based on analyzed data and providing them to elderly people.

[1228] "Means for capturing everyday conversations with elderly people and analyzing the audio data" refers to technologies and tools for recording the voices spoken by elderly people, converting the voices into text, and analyzing the content.

[1229] "Means for detecting changes in health condition and lifestyle habits based on analysis results and providing appropriate advice" refers to methods and systems for identifying changes in health condition and lifestyle habits from the analysis results of voice data and providing advice to elderly people based on the results.

[1230] "Means for detecting emergencies and notifying emergency contacts" refers to a mechanism that automatically detects when something abnormal occurs with an elderly person and sends a notification to pre-registered contacts.

[1231] "Means for providing guidelines for action in an emergency" refers to a method or system for providing instructions to elderly people on how to act safely in the event of an emergency.

[1232] "Means for collecting and providing information on the hobbies and interests of older people" refers to methods and technologies for collecting information on areas and activities that interest older people and providing it to them.

[1233] "Means for recognizing emotions from everyday conversations with the elderly and providing support appropriate to the situation" refers to algorithms and systems that analyze the emotional state of elderly people from everyday conversations and provide support and advice accordingly.

[1234] "Means for providing real-time advice using a voice assistant when elderly people receive health and lifestyle support in physical stores" refers to methods and technologies for using a voice assistant to provide appropriate advice on the spot when elderly people receive health consultations or lifestyle support in physical stores.

[1235] This invention is a personalized health management system for the elderly that utilizes generative AI models and emotion engines, and specific embodiments for supporting the elderly in physical stores are described below.

[1236] System configuration

[1237] The system consists of a server, terminals, and related software. The server plays a central role in managing and analyzing the elderly's lifestyle and health data and generating appropriate support plans. The terminals (smartphones, tablets, in-store kiosk terminals, etc.) are used directly by the elderly as an interface.

[1238] Hardware and software used

[1239] Hardware: Smartphones, smart tablets, in-store KIOSK terminals

[1240] Software: Python, TensorFlow, OpenCV, Dialogflow, Firebase

[1241] Data Collection and Management

[1242] The device collects health and lifestyle data (such as diet, exercise, and sleep) entered by the elderly on a daily basis, and the collected data is stored in a cloud database using Firebase.

[1243] Health management plan generation and provision

[1244] The server analyzes the collected data using TensorFlow to learn about the health status and lifestyle habits of the elderly, and then creates an individually optimized health management plan from the generated data and provides it to the elderly.

[1245] Voice data processing and emotion recognition

[1246] When an elderly person speaks to the device in everyday conversation, the voice data is captured through the device and sent to the server. The server converts the voice data into text via Dialogflow and then performs natural language processing. An emotion engine is used to recognize the elderly person's emotional state from the text and voice data.

[1247] Emergency response

[1248] If the elderly person detects any abnormality, the device will recognize the emergency and immediately send a notification to emergency contacts and store staff, enabling a prompt response.

[1249] Information provision

[1250] The server collects and provides the latest relevant information and event information based on the elderly's hobbies and interests, for example, notifying them of nearby events and health articles.

[1251] Specific examples

[1252] For example, if an elderly person says, "My knees have been hurting lately," the system will analyze their voice and compare it with past data to suggest appropriate exercises and rest methods. If it detects a decline in emotion, it will offer relaxation techniques and even contact the customer promptly in the event of an emergency.

[1253] Prompt Sentence Examples

[1254] Here are some examples of prompts for generative AI models:

[1255] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[1256] In this way, the system of the present invention can support the lifestyles and health management of the elderly, allowing them to live more safely even in physical stores.

[1257] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1258] Step 1:

[1259] Collecting data on the lifestyles of the elderly

[1260] Input: Users manually input data such as daily diet, exercise, and sleep duration using a smartphone or tablet, or data is automatically collected from a wearable device.

[1261] How it works: The device collects data entered by the elderly person and data sent from wearable devices, including data from a pedometer and information from a food tracking app.

[1262] Output: The acquired lifestyle data is temporarily saved and sent to the next step.

[1263] Step 2:

[1264] Data storage and management

[1265] Input: Elderly lifestyle data obtained in step 1.

[1266] Specific operation: The device sends the acquired data to the Firebase database in the cloud.

[1267] Data processing: The submitted data is stored in the Firebase database and organized chronologically.

[1268] Output: Organized lifestyle data is stored in a database and ready for analysis.

[1269] Step 3:

[1270] Analyzing data and generating a health management plan

[1271] Input: Life data stored in Firebase database.

[1272] Specific operation: The server uses TensorFlow to analyze the collected data and learn about the health status and lifestyle habits of the elderly.

[1273] Data processing: AI models generated through data analysis assess the health status of the elderly and generate personalized health management plans.

[1274] Output: Send the generated health management plan to the terminal.

[1275] Step 4:

[1276] Acquiring everyday conversations and analyzing audio data

[1277] Input: Voice data of everyday conversations spoken by the user to the device.

[1278] Specific operation: The device picks up the voice of the elderly person using a microphone and sends the voice data to the server.

[1279] Data processing: The server uses Dialogflow to convert voice data into text and perform natural language processing.

[1280] Output: Textual audio data and analysis results.

[1281] Step 5:

[1282] Emotion recognition and advice provision

[1283] Input: Text data output in Step 4 and analysis results.

[1284] Specific operation: The server uses an emotion engine to recognize the emotional state of the elderly person from text data and voice data.

[1285] Data processing: The emotion engine analyzes the emotional state and generates appropriate advice and mental care support depending on the situation.

[1286] Output: The generated advice is sent to the terminal and notifies the user.

[1287] Step 6:

[1288] Emergency detection and response

[1289] Input: Voice data of the user saying "help" to the device.

[1290] Specific operation: The device acquires voice data and analyzes it to detect emergencies.

[1291] Data processing: If an emergency situation is detected, the device will send a notification to pre-defined emergency contacts and automatically call the nearest emergency services.

[1292] Output: Notification to emergency contacts and emergency services.

[1293] Step 7:

[1294] Providing information based on the interests of the elderly

[1295] Input: Audio data of a user saying, "Has anything interesting happened recently?"

[1296] Specific operation: The terminal acquires the voice data and sends it to the server.

[1297] Data processing: The server collects the latest relevant information based on the user's hobbies and interests and generates suggestions.

[1298] Output: Information based on the elderly person's hobbies and interests is displayed on the device or provided via voice.

[1299] Example prompt sentence:

[1300] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[1301] The above is a specific processing flow for carrying out the present invention. This system effectively realizes health management and daily support for elderly people.

[1302] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1303] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1304] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1305] [Third embodiment]

[1306] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1307] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1308] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1309] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1310] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1311] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1312] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1313] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1314] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1315] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1316] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1317] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1318] The present invention aims to provide a personalized health management system using generative AI to support the daily lives of the elderly. This system supports the daily lives of the elderly via a server and terminals, and is specifically implemented as follows.

[1319] System configuration

[1320] The system consists of the following main components:

[1321] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[1322] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[1323] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[1324] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[1325] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[1326] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[1327] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1328] Program processing

[1329] Data collection and learning

[1330] Device: Elderly people enter data about their daily lives (such as diet, exercise, and sleep), or the data is automatically collected from a wearable device.

[1331] Terminal: Sends collected data to the server.

[1332] Server: Analyzes the received data and learns about the elderly person's health condition and lifestyle habits.

[1333] Server: Generative AI learns patterns from data and builds personalized health plans.

[1334] Analysis of everyday conversations and advice

[1335] User: An elderly person speaks everyday conversation into the device (e.g., "I can't sleep lately").

[1336] Terminal: Acquires voice data and sends it to the server.

[1337] Server: Analyzes voice data, converts speech into text, and detects changes in the elderly person's health and lifestyle habits.

[1338] Server: Based on the analysis results, it generates detailed advice and sends it to the device.

[1339] Device: Providing advice to the elderly (e.g., "Try listening to the following music to help you relax")

[1340] Emergency response

[1341] User: An elderly person speaks to the device saying "Help me."

[1342] Terminal: Acquires voice data and detects emergencies.

[1343] Device: Automatically notify emergency contacts and arrange for the nearest emergency services.

[1344] Device: Providing specific guidelines for behavior to the older adult (e.g., "Take a deep breath and sit down").

[1345] Reducing loneliness and providing information

[1346] User: An elderly person talks to the device, asking, "Has anything interesting happened recently?"

[1347] Terminal: Acquires voice data and sends it to the server.

[1348] Server: Generates the latest information based on the hobbies and interests of the elderly.

[1349] Terminals: Providing information to seniors (e.g., "There's a pottery class coming up nearby").

[1350] Device: AI chatbots can initiate everyday conversations with seniors, reducing their sense of loneliness.

[1351] Specific examples

[1352] Health management plan proposals

[1353] User: An elderly person says, "My knees have been hurting lately."

[1354] Terminal: Acquires voice data and sends it to the server.

[1355] Server: Analyzes the data and compares it with past lifestyle data.

[1356] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1357] Terminal: Providing advice to seniors.

[1358] Emergency response

[1359] User: An elderly person talks to the device saying, "I feel like I'm going to fall."

[1360] Device: Detects emergencies and obtains location information.

[1361] Device: Call emergency contacts and emergency services.

[1362] Device: Communicate specific guidelines for action to the elderly.

[1363] conclusion

[1364] The system of this invention makes it possible to provide personalized support tailored to the individual needs of the elderly, reducing the burden on them and enabling them to live independently with peace of mind. It is also expected to contribute greatly to solving the aging problem of society as a whole.

[1365] The processing flow will be explained below.

[1366] Data collection and learning

[1367] Step 1:

[1368] User: Elderly people record data about their daily lives (e.g., diet, exercise, sleep) or wear a wearable device.

[1369] Step 2:

[1370] Terminal: Collects recorded life and health data.

[1371] Step 3:

[1372] Terminal: Periodically sends collected data to the server.

[1373] Step 4:

[1374] Server: Stores the received data in a database.

[1375] Step 5:

[1376] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly.

[1377] Step 6:

[1378] Server: Creates a personalized health management plan based on the data learned by the generative AI.

[1379] Analysis of everyday conversations and advice

[1380] Step 1:

[1381] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[1382] Step 2:

[1383] Terminal: Acquires voice data and sends it to the server.

[1384] Step 3:

[1385] Server: Analyzes the voice data and converts the spoken content into text.

[1386] Step 4:

[1387] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[1388] Step 5:

[1389] Server: Based on the analysis results, generates appropriate advice and sends it to the device.

[1390] Step 6:

[1391] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[1392] Emergency response

[1393] Step 1:

[1394] User: An elderly person speaks to the device saying "Help me."

[1395] Step 2:

[1396] Terminal: Acquires voice data and detects emergencies.

[1397] Step 3:

[1398] On your device: Send a notification to pre-defined emergency contacts.

[1399] Step 4:

[1400] Device: Automatically call the nearest emergency services.

[1401] Step 5:

[1402] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1403] Reducing loneliness and providing information

[1404] Step 1:

[1405] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[1406] Step 2:

[1407] Terminal: Acquires voice data and sends it to the server.

[1408] Step 3:

[1409] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[1410] Step 4:

[1411] Server: Sends the generated information to the terminal.

[1412] Step 5:

[1413] Terminal: The terminal provides the user with information such as, "It seems there's a pottery class being held at the local community center next week. Would you like to participate?"

[1414] Step 6:

[1415] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1416] conclusion

[1417] Through the above processing steps, the system of the present invention can provide personalized assistance according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[1418] Example 1

[1419] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1420] Health management for the elderly often takes a one-size-fits-all approach, without taking into account individual needs and differences in living environments. Furthermore, when elderly people live alone, it is difficult to respond appropriately to changes in their health status or emergencies in their daily lives. Furthermore, there is a lack of effective methods to reduce the sense of loneliness felt by elderly people. To address these issues, there is a need for personalized health management plans, emergency response functions, and everyday conversation support functions.

[1421] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1422] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data and daily life data to the server, means for analyzing the received data in the server and learning the elderly person's health condition and lifestyle habits, means for generating and providing a personalized health management plan using a generative AI model, means for acquiring conversation data with the elderly person and transmitting it to the server, means for converting voice data to text and performing natural language processing to detect changes in the elderly person's health condition and lifestyle habits, means for generating and providing appropriate advice based on the analysis results, means for detecting voice input in an emergency and automatically notifying an emergency contact, means for providing specific guidelines for action by voice or text, means for collecting and providing information based on the elderly person's hobbies and interests, and means for reducing the elderly person's sense of loneliness through everyday conversations via a chatbot. This allows for providing elderly people with personalized support tailored to their individual needs, improving the accuracy of health management, accelerating emergency response, and reducing the elderly person's sense of loneliness.

[1423] "Biometric data" refers to information that indicates the physical condition of an elderly person, and includes physiological indicators such as heart rate, blood pressure, body temperature, and oxygen saturation.

[1424] "Daily life data" refers to information about the activities that elderly people engage in in their daily lives, including dietary content, amount of exercise, and sleep duration.

[1425] "Server" means a computer system that receives, stores, analyzes data, and processes and provides information using a generative AI model.

[1426] A "generative AI model" is an artificial intelligence algorithm or system that generates new information based on given input data by learning from large amounts of data.

[1427] A "personalized health management plan" is an individualized health promotion and management plan created based on the health status and lifestyle habits of each elderly person.

[1428] "Voice data" refers to data containing audio signals that are recordings of what the elderly person is saying to the terminal.

[1429] "Natural language processing" is a technology that converts voice data into text format, linguistically analyzes it as a string of characters, and understands and processes the content.

[1430] "Emergencies" refer to dangerous situations or health problems faced by older adults, such as falls, difficulty breathing, or sudden changes in blood pressure.

[1431] "Emergency Contacts" refers to people or organisations that should be contacted in the event of an emergency, such as family, friends, carers or emergency services.

[1432] "Action guidelines" are instructions that show specific actions that elderly people should take in specific situations, such as taking deep breaths, sitting down, and resting.

[1433] "Hobbies and interest-based information" refers to the latest information, events and activity opportunities related to areas and activities that interest seniors.

[1434] A "chatbot" is a program that uses artificial intelligence to engage in natural dialogue with users, and can converse through text or voice.

[1435] This invention is a system for supporting the daily lives of elderly people and personalizing their health management. The system utilizes terminals and servers to effectively utilize lifestyle and health data of elderly people using generative AI models.

[1436] System Overview

[1437] This system collects and analyzes biometric and daily life data from elderly people to provide personalized health management plans and respond to emergencies. The hardware used includes wearable devices (e.g., Fitbit, Apple Watch) and internet-connected devices (e.g., smartphones, tablets). Analysis is performed using Python libraries (e.g., Pandas, Scikit-learn) and the Google Cloud Speech-to-Text API, utilizing generative AI models (e.g., GPT-4).

[1438] Explanation of program processing

[1439] Data collection

[1440] Elderly users manually enter data such as dietary habits, exercise, and sleep duration into the device, or use a wearable device to automatically collect this data, which is then sent to a server via the Internet.

[1441] Data analysis

[1442] The server analyzes the received data and learns about the elderly person's health status and lifestyle habits. It uses Python libraries Pandas and Scikit-learn to perform statistical analysis and outlier detection. It then uses a generative AI model to generate a personalized health management plan.

[1443] Analysis of everyday conversations

[1444] When an elderly user speaks into the device, the device captures this voice data and sends it to a server, which then converts the voice data into text using the Google Cloud Speech-to-Text API and performs natural language processing to detect changes in health status and lifestyle habits.

[1445] Generating Advice

[1446] The server generates appropriate advice based on the analysis results. Using the generative AI model, it creates specific health management advice (e.g., "Try listening to the following music to relax") and sends it to the device. The device then provides the advice to the elderly via voice or text.

[1447] Emergency response

[1448] When an elderly user speaks "help me" into the device, the device receives the voice data and detects an emergency. The device immediately transmits the data to the server, which then automatically notifies emergency contacts. Furthermore, the device provides specific instructions for action (e.g., "take a deep breath and sit down") via voice.

[1449] Providing information based on hobbies and interests

[1450] When an elderly user speaks to the device, asking, "Has anything interesting happened recently?", the device captures the voice data and sends it to the server. The server uses a generative AI model based on the elderly's hobbies and interests to generate the latest information. The device then provides this information to the elderly via voice or text. The chatbot function also allows for everyday conversations with the elderly, reducing their sense of loneliness.

[1451] Examples of concrete examples and prompts

[1452] Example 1: Proposing a health management plan

[1453] When an elderly user says, "My knees have been hurting lately," the device captures the voice data and sends it to the server. The server analyzes the data and compares it with past lifestyle data. Using a generative AI model, it generates the cause of the knee pain and recommends countermeasures (e.g., specific exercises). The device then provides advice to the elderly, such as, "Try the following exercises to relax."

[1454] Example 2: Emergency response

[1455] When an elderly user says to the device, "I feel like I'm going to fall," the device detects the emergency and obtains their location. The device then notifies emergency contacts and emergency services, and provides the elderly with advice such as, "Take a deep breath and sit down."

[1456] Prompt Sentence Examples

[1457] Healthcare plan: "I've been having knee pain lately. What's the best healthcare plan for this?"

[1458] Emergency response: "I feel like I'm going to collapse. What should I do?"

[1459] The system of this invention provides support tailored to the individual needs of the elderly, improves the accuracy of health management, and enables rapid response in emergencies, thereby improving the quality of life for the elderly and contributing to solving the aging problem in society as a whole.

[1460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1461] Step 1:

[1462] Data collection

[1463] Input: Manually entered data from seniors (dietary content, exercise, sleep duration) and automatically collected data from wearable devices.

[1464] How it works: The elderly user opens the application on the device and enters information about their diet, exercise, and sleep time. The wearable device also automatically collects data such as heart rate and number of steps taken.

[1465] Output: Collected life and biometric data.

[1466] Step 2:

[1467] Data transmission

[1468] Input: Collected life and biometric data.

[1469] How it works: Your device sends collected data to a server using Wi-Fi or a mobile network.

[1470] Output: The data sent to the server.

[1471] Step 3:

[1472] Data analysis

[1473] Input: Life and biometric data sent to the server.

[1474] How it works: The server uses Python libraries such as Pandas and Scikit-learn to perform statistical analysis of the data and detect outliers.

[1475] Output: Analysis results on the health status and lifestyle habits of elderly people.

[1476] Step 4:

[1477] Generate a health management plan

[1478] Input: Analysis results on health status and lifestyle habits.

[1479] How it works: The server uses a generative AI model (e.g., GPT-4) to generate a personalized health care plan based on the individual needs of the elderly person.

[1480] Output: A personalized health care plan.

[1481] Step 5:

[1482] Acquiring voice input

[1483] Input: Everyday conversations of elderly people (e.g., "I can't sleep these days").

[1484] Operation: The elderly user speaks everyday conversation into the device, and the device captures this voice data.

[1485] Output: The captured audio data.

[1486] Step 6:

[1487] Sending audio data

[1488] Input: Captured audio data.

[1489] Action: The device sends audio data to the server.

[1490] Output: The audio data sent to the server.

[1491] Step 7:

[1492] Analysis of audio data

[1493] Input: The audio data sent to the server.

[1494] How it works: The server uses the Google Cloud Speech-to-Text API to convert voice data into text, then uses natural language processing to detect changes in health and lifestyle habits.

[1495] Output: Text conversion results and analysis results of the audio data.

[1496] Step 8:

[1497] Generating Advice

[1498] Input: Text conversion results and analysis results of audio data.

[1499] How it works: The server uses a generative AI model (e.g., GPT-4) to generate appropriate advice based on the analysis results.

[1500] Output: Generated advice (e.g., "Try listening to the following music to relax")

[1501] Step 9:

[1502] Providing advice

[1503] Input: The generated advice.

[1504] How it works: The device provides the generated advice to the senior via voice or text.

[1505] Output: Advice given to the elderly.

[1506] Step 10:

[1507] Emergency voice input detection

[1508] Input: Urgent speech from an elderly person (e.g., "Help me").

[1509] Operation: When an elderly user speaks an emergency voice into the device, the device picks up the voice and detects an emergency.

[1510] Output: Emergency detection result.

[1511] Step 11:

[1512] Emergency notification

[1513] Input: Emergency detection results and location information of the elderly person.

[1514] What it does: Your device automatically calls your emergency contacts and dispatches the nearest emergency services.

[1515] Output: Notification of called emergency contacts and emergency services.

[1516] Step 12:

[1517] Providing guidelines for emergency situations

[1518] Input: Emergency detection result.

[1519] What it does: The device provides the senior with specific instructions for action via voice or text (e.g., "Take a deep breath and sit down").

[1520] Output: Provided course of action.

[1521] Step 13:

[1522] Information gathering based on hobbies and interests

[1523] Input: A request for information about a senior's hobbies and interests (e.g., "What's interesting going on these days?").

[1524] How it works: The elderly user asks questions about their hobbies and interests to the device. The device captures this voice data and sends it to the server.

[1525] Output: The audio data sent to the server.

[1526] Step 14:

[1527] Information generation

[1528] Input: A request for information about seniors' hobbies and interests.

[1529] How it works: The server uses a generative AI model (e.g., GPT-4) to generate information based on the elderly person's hobbies and interests.

[1530] Output: The information generated.

[1531] Step 15:

[1532] Providing information and conducting daily conversations

[1533] Input: Generated information.

[1534] How it works: The device provides the generated information to the elderly via voice or text, and also uses a chatbot function to engage in everyday conversations with the elderly, reducing their sense of loneliness.

[1535] Output: Information provided to the elderly and daily conversations carried out.

[1536] (Application example 1)

[1537] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1538] For seniors to maintain healthy eating habits, it is important to provide personalized meal plans based on their individual health conditions and lifestyle habits. However, manually creating such personalized plans and preparing and adjusting meals each time is extremely difficult and burdensome for seniors. Furthermore, without a system for responding quickly and appropriately in emergencies, seniors risk being put in dangerous situations. Furthermore, there is a lack of information and communication methods to reduce seniors' feelings of loneliness and enrich their daily lives.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1540] In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; and means for generating a personalized meal plan based on the elderly's health condition and lifestyle habits and ordering food based on the meal plan. This allows the elderly to easily order and receive meals appropriate for their health condition, respond quickly to emergencies, and obtain information and communication to improve the quality of their daily lives.

[1541] "Elderly" refers to people who are older and require special support and consideration in their daily lives.

[1542] "Lifestyle data" refers to information about the subject's daily life, such as their diet, amount of exercise, and sleep time.

[1543] "Health status" refers to indicators that show the normality or abnormality of an individual's physical and mental state.

[1544] "Lifestyle habits" refers to the habits and patterns of behavior that an individual engages in on a daily basis.

[1545] A "personalized health care plan" refers to a health care policy that is customized to suit the characteristics and conditions of each individual.

[1546] "Daily conversation" refers to the normal conversational activities that the subject engages in on a daily basis.

[1547] "Audio data" refers to audio recorded by a subject and stored as digital information.

[1548] "Analysis results" refers to information obtained as a result of analysis based on collected data.

[1549] "Appropriate advice" refers to advice that is most beneficial to the subject based on the analysis results.

[1550] An "emergency" is a situation in which a subject is suddenly in danger and requires a rapid response.

[1551] "Emergency contacts" refers to the people or organizations that should be notified first in an emergency.

[1552] A "guideline" refers to specific guidance or advice on how to act in a particular situation.

[1553] "Information about hobbies and interests" refers to information related to the subject's personal preferences and interests.

[1554] A "personalized meal plan" refers to meal suggestions tailored to an individual's health and lifestyle.

[1555] "Means of ordering food" refers to the methods or mechanisms for ordering ingredients and meals based on the proposed meal plan, such as through a delivery service.

[1556] This invention provides a personalized health management system using generative AI to support the lives of the elderly. The system collects and analyzes lifestyle and health data of the elderly to provide individually tailored health management and meal plans. The system also has a wide range of functions, including emergency detection and response, analysis of the elderly's daily conversations, and provision of information related to hobbies and interests.

[1557] System configuration

[1558] The system consists of the following main components:

[1559] 1. Data collection method: Hardware such as smartphones and wearable devices are used to collect lifestyle and health data from elderly people. This automatically collects information such as dietary habits, exercise levels, and sleep duration.

[1560] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits. The analysis software used is a Python-based generative AI library.

[1561] 3. Health management plan provision: A personalized health management plan is generated based on the analysis results and provided to the user. The proposed plan is notified to the user via a smartphone app.

[1562] 4. Voice data acquisition and analysis: Voice data is acquired through everyday conversations and analyzed. Voice recognition technology uses an API that converts voice into text (e.g., Google Speech-to-Text).

[1563] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly. The advice is displayed in text format on the smartphone or provided via a voice assistant.

[1564] 6. Emergency response: Detects an emergency and notifies emergency contacts. Emergency calls are made automatically via the smartphone's communication functions.

[1565] 7. Guidance: Providing specific guidance during emergencies (e.g., "Take a deep breath and sit down"), also provided in real time via the voice assistant.

[1566] 8. Information provision method: Collect and provide the latest information on the hobbies and interests of seniors. News and event information is automatically collected from the Internet.

[1567] 9. Meal plan generation and ordering: Generate a personalized meal plan based on the elderly person's health condition and lifestyle habits, and order food based on the meal plan. By linking with food delivery services, the suggested meals can be easily ordered.

[1568] Specific examples

[1569] Data collection and analysis:

[1570] Users input data using smartphones or wearable devices. The collected data is sent to a server and analyzed by a generative AI model. As a result of the analysis, a health management plan appropriate for the user's health condition is generated.

[1571] Analysis of everyday conversations and advice provided:

[1572] The user speaks to their smartphone saying, "My knees have been hurting lately." The voice data is captured and analyzed on the server. Based on the generated AI model, the results are compared with past data and the cause of the knee pain and countermeasures are provided.

[1573] Emergency Response:

[1574] When a user says "I feel like I'm going to fall," the system detects an emergency, captures their location, automatically notifies emergency contacts and emergency services, and instructs the user to "take a deep breath and sit down."

[1575] Information and loneliness relief:

[1576] When a user asks, "What's interesting lately?", the app provides the latest information based on their hobbies and interests. For example, information like "There's a pottery class opening nearby" will be displayed on the smartphone.

[1577] Meal plan suggestions and ordering:

[1578] When a user says, "I've been feeling unwell lately and have no appetite," the system analyzes the voice data and generates a meal plan tailored to their health condition. Based on the proposed plan, food is automatically ordered through a food delivery service.

[1579] Example prompt sentence:

[1580] "I've been feeling unwell lately and have no appetite."

[1581] "My knees hurt and I can't exercise"

[1582] "I think I'm having an allergic reaction, help me."

[1583] This will enable elderly people to easily order and receive meals that are appropriate for their health condition, respond quickly in emergencies, and receive information and communication that will improve the quality of their daily lives.

[1584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1585] Step 1:

[1586] Elderly people use smartphones or wearable devices to input lifestyle data, including dietary habits, exercise, and sleep duration. The input data is automatically collected by each device and centralized on the smartphone. This data is then used as input data to be sent to the server.

[1587] Step 2:

[1588] The device sends the collected life data to the server. The server receives the data and stores it in a database. This stored data is used for later analysis. The input data is the life data, and the output data is the data stored in the server.

[1589] Step 3:

[1590] The server performs analysis based on the stored data. A Python-based generative AI model analyzes the lifestyle data and learns about the elderly person's health status and lifestyle habits. This analysis identifies patterns in the data and estimates their health status. The input data is the stored lifestyle data, and the output data is the analysis results.

[1591] Step 4:

[1592] The server generates a personalized health management plan based on the generative AI model. Based on the analysis results, a plan suitable for each elderly person is automatically created. The generated health management plan is sent to the device. The input data is the analysis results, and the output data is the health management plan.

[1593] Step 5:

[1594] The device notifies the user of the generated health management plan. The health management plan is displayed to the user via a smartphone app. The user can then manage their daily health in accordance with the plan. The input data is the health management plan, and the output data is the notification to the user.

[1595] Step 6:

[1596] A user speaks to a smartphone, saying, "My knee has been hurting lately." Voice data is acquired and sent by the device to a server. The input data is the voice data, and the output data is the voice data sent to the server.

[1597] Step 7:

[1598] The server analyzes the voice data and converts it into text. To analyze the voice data, it uses speech recognition technology such as the Google Speech-to-Text API. This analysis results in data converted from voice data into text format. The input data is voice data, and the output data is text data.

[1599] Step 8:

[1600] The server uses the converted data to detect changes in the elderly person's health condition and lifestyle habits based on a generative AI model. Based on the analysis results, it generates appropriate advice and sends it to the device. The input data is text data, and the output data is the advice content.

[1601] Step 9:

[1602] The device notifies the user of the advice received from the server. The advice is displayed to the user through a smartphone app. For example, advice such as "Try the following exercise to relax" is provided. The input data is the advice content, and the output data is the notification content to the user.

[1603] Step 10:

[1604] When a user says "I'm about to fall down," the device acquires this voice data and sends it to the server. The input data is the voice data of the emergency, and the output data is the voice data sent to the server.

[1605] Step 11:

[1606] The server analyzes the received voice data and detects an emergency. By analyzing the voice data, the server confirms that an emergency has occurred. The input data is the voice data, and the output data is the emergency status determination result.

[1607] Step 12:

[1608] The server notifies the emergency contacts and takes emergency action, including notifying the nearest emergency service. The server obtains the user's location information and notifies the emergency contacts and emergency service. The input data is the emergency status determination result and location information, and the output data is the emergency call content.

[1609] Step 13:

[1610] The device provides the user with guidelines for what to do in an emergency. For example, specific guidelines such as "Take a deep breath and sit down" are provided in real time through the voice assistant. The input data is the emergency call content, and the output data is the guidelines for what to do.

[1611] Step 14:

[1612] When a user says, "Has anything interesting happened recently?", the device acquires this voice data and sends it to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[1613] Step 15:

[1614] The server analyzes the voice data, generates the latest information based on the elderly person's hobbies and interests, and sends it to the device. For example, information such as "A pottery class will be held nearby" is generated. The input data is the voice data, and the output data is the generated information.

[1615] Step 16:

[1616] The device notifies the user of the information received from the server. The latest events and news are displayed through the smartphone app. The input data is the generated information, and the output data is the notification content to the user.

[1617] Step 17:

[1618] The user says, "I've been feeling unwell lately and have no appetite," and the voice data is acquired and sent by the terminal to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[1619] Step 18:

[1620] The server analyzes the voice data and generates a meal plan based on the user's health status. Using a generative AI model, a meal plan appropriate for the user's health status and lifestyle is created and sent to the device. The input data is the voice data and analysis results, and the output data is the meal plan.

[1621] Step 19:

[1622] The terminal uses the generated meal plan to order food from a food delivery service. The order is easily completed through a smartphone app. The input data is the meal plan, and the output data is the order details.

[1623] Step 20:

[1624] The food delivery service delivers the food and the user can easily pick up the meal. The input data is the order details and the output data is the delivery status.

[1625] This allows users to easily order and receive meals that are suitable for their health condition, improving the quality of their daily lives.

[1626] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1627] This invention provides a personalized health management system that utilizes generative AI and an emotion engine to support the daily lives of the elderly. This system supports the daily lives of the elderly through a server and terminals, and is specifically implemented as follows:

[1628] System configuration

[1629] The system consists of the following main components:

[1630] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[1631] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[1632] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[1633] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[1634] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[1635] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[1636] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1637] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[1638] Program processing

[1639] Data collection and learning

[1640] Device: Elderly people enter data on their daily lives (such as diet, exercise, and sleep), or the data is collected automatically through a wearable device.

[1641] Terminal: Periodically sends collected lifestyle and health data to the server.

[1642] Server: Stores the received data in a database and analyzes it.

[1643] Server: Learns about the health status and lifestyle habits of elderly people based on collected data.

[1644] Server: Generative AI learns patterns from data and builds personalized health plans.

[1645] Analysis of everyday conversations and emotion recognition

[1646] User: An elderly person speaks to the device (e.g., "I've been feeling depressed lately").

[1647] Terminal: Acquires voice data and sends it to the server.

[1648] Server: Analyzes the voice data and converts the spoken content into text.

[1649] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[1650] Server: The emotion engine analyzes the text and voice data to recognize the emotional state of the elderly.

[1651] Server: Based on the recognized emotional state, it generates appropriate advice and support and sends it to the device.

[1652] Device: Notifies the user of the advice received via text or voice (e.g., "You seem to be feeling down. Try some breathing exercises to relax.").

[1653] Emergency response

[1654] User: An elderly person speaks to the device saying "Help me."

[1655] Terminal: Acquires voice data and detects emergencies.

[1656] On your device: Send a notification to pre-defined emergency contacts.

[1657] Device: Automatically call the nearest emergency services.

[1658] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1659] Reducing loneliness and providing information

[1660] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[1661] Terminal: Acquires voice data and sends it to the server.

[1662] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[1663] Device: Providing information to the device (e.g., "There's a pottery class coming up near me. Would you like to join?").

[1664] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1665] Server: The emotion engine recognizes the user's emotions from everyday conversations and responds appropriately.

[1666] Specific examples

[1667] Health management plan proposals

[1668] User: An elderly person says, "My knees have been hurting lately."

[1669] Terminal: Acquires voice data and sends it to the server.

[1670] Server: Analyzes the data and compares it with past lifestyle data.

[1671] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1672] Terminal: Providing advice to seniors.

[1673] Emotional awareness and mental care

[1674] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1675] Terminal: Acquires voice data and sends it to the server.

[1676] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1677] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[1678] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1679] conclusion

[1680] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[1681] The processing flow will be explained below.

[1682] Data collection and learning

[1683] Step 1:

[1684] User: Elderly people manually enter their daily lifestyle data (e.g., diet, exercise, sleep duration) or wear a wearable device.

[1685] Step 2:

[1686] Device: Collects data entered by the user and automatically collected from wearable devices.

[1687] Step 3:

[1688] Terminal: Sends collected lifestyle and health data to the server.

[1689] Step 4:

[1690] Server: Stores the received data in a database.

[1691] Step 5:

[1692] Server: Analyzes data stored in the database and uses generative AI to learn about the health status and lifestyle habits of elderly people.

[1693] Step 6:

[1694] Server: Builds a personalized health management plan based on the learning results.

[1695] Analysis of everyday conversations and emotion recognition

[1696] Step 1:

[1697] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[1698] Step 2:

[1699] Terminal: Acquires voice data and sends it to the server.

[1700] Step 3:

[1701] Server: Analyzes the voice data and converts the spoken content into text.

[1702] Step 4:

[1703] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[1704] Step 5:

[1705] Server: The emotion engine analyzes the voice data and text to recognize the emotional state of the elderly.

[1706] Step 6:

[1707] Server: Generates appropriate advice based on the analysis results and emotional state and sends it to the device.

[1708] Step 7:

[1709] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[1710] Emergency response

[1711] Step 1:

[1712] User: An elderly person speaks to the device saying "Help me."

[1713] Step 2:

[1714] Terminal: Acquires voice data and detects emergencies.

[1715] Step 3:

[1716] On your device: Send a notification to pre-defined emergency contacts.

[1717] Step 4:

[1718] Device: Automatically call the nearest emergency services.

[1719] Step 5:

[1720] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1721] Reducing loneliness and providing information

[1722] Step 1:

[1723] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[1724] Step 2:

[1725] Terminal: Acquires voice data and sends it to the server.

[1726] Step 3:

[1727] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[1728] Step 4:

[1729] Server: Sends the generated information to the terminal.

[1730] Step 5:

[1731] Terminal: The terminal provides information such as, "It seems that a pottery class will be held at the local community center next week. Would you like to participate?"

[1732] Step 6:

[1733] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1734] Emotional awareness and mental care

[1735] Step 1:

[1736] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1737] Step 2:

[1738] Terminal: Acquires voice data and sends it to the server.

[1739] Step 3:

[1740] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1741] Step 4:

[1742] Server: Generates a mental care plan (e.g., relaxation methods to improve mood) based on the recognized emotional state.

[1743] Step 5:

[1744] Server: Sends the generated mental care plan to the terminal.

[1745] Step 6:

[1746] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1747] conclusion

[1748] Through the above processing steps, the system of the present invention can provide personalized assistance and emotional support according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[1749] Example 2

[1750] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1751] Elderly people who live alone often find it difficult to manage their health and respond to emergencies in their daily lives. They are also prone to feelings of loneliness and mental anxiety, which can lead to a deterioration in their health. Current health care systems are unable to meet all of these needs. Therefore, there is a need for a system that can comprehensively support the daily lives of elderly people.

[1752] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting emergencies and notifying emergency contacts; means for providing action guidelines in emergencies; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing the elderly's emotional state from daily conversations using an emotion engine and providing appropriate support; and means for generating and providing a personalized health management plan using a generative AI model. This enables elderly people to monitor their health condition and respond quickly to emergencies, and by receiving psychological support, they can live more independently with peace of mind.

[1753] "Lifestyle data" refers to information about the activities and behaviors of elderly people in their daily lives, and specifically includes dietary content, amount of exercise, sleep duration, etc.

[1754] "Health data" refers to information relating to the health of the elderly person, and specifically includes heart rate, blood pressure, body temperature, weight, etc.

[1755] "Analysis methods" refer to methods and tools for processing collected data and analyzing the health status and lifestyle habits of older people.

[1756] A "personalized health management plan" refers to a health management plan that is customized based on the health condition and lifestyle of each elderly person.

[1757] "Voice data" refers to the voice information collected when a user speaks to a terminal, and by analyzing this information, the content of the speech can be understood.

[1758] An "emotion engine" refers to algorithms and software that recognize the emotional state of elderly people from their everyday conversations and provide appropriate support and advice.

[1759] A "generative AI model" is an artificial intelligence model that learns patterns from collected data and generates optimal health management plans and advice for seniors.

[1760] An "emergency" refers to a situation in which an elderly person is in danger or in need of help, and refers to the conditions and circumstances under which this situation can be detected quickly and appropriate action can be taken.

[1761] "Emergency Contact" refers to pre-defined contacts (such as family members or caregivers) who should be notified or contacted in the event of an emergency.

[1762] "Information on hobbies and interests" refers to information related to activities and areas of interest that older people can enjoy, and the purpose of providing this information is to improve the quality of life of older people.

[1763] The present invention provides a personalized health management system that utilizes a generative AI model and an emotion engine to support the daily lives of elderly people. This system supports the daily lives of elderly people through a server and a terminal, and is specifically implemented as follows.

[1764] System configuration

[1765] The system consists of the following main components:

[1766] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[1767] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[1768] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[1769] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[1770] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[1771] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[1772] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1773] 8. Emotion recognition means: Using an emotion engine, emotions are recognized from the elderly's everyday conversations and support is provided according to the situation.

[1774] Program processing

[1775] Data collection and learning

[1776] Device: Elderly people manually enter data about their daily lives (e.g., diet, exercise, and sleep), and also use wearable devices to automatically collect this data.

[1777] Terminal: Periodically sends collected lifestyle and health data to the server.

[1778] Server: The received data is stored in a central database and analyzed. Specific databases used include MySQL and PostgreSQL.

[1779] Server: Analyzes the stored data and applies machine learning algorithms (e.g., random forests and neural networks) using programming languages ​​such as Python to learn about the health and lifestyle habits of the elderly.

[1780] Server: The generative AI model generates a health management plan from the collected data. For example, it suggests that the user's daily walking frequency has decreased and that they should take a short walk as a daily routine.

[1781] Analysis of everyday conversations and emotion recognition

[1782] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[1783] Device: Collects voice data and sends it to the server. Use a smart speaker or smartphone with a built-in microphone.

[1784] Server: Use a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe to convert the audio data into text.

[1785] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle based on the content of the speech.

[1786] Server: Analyzes text and voice data using an emotion engine to recognize the user's emotional state. Provides positive conversations and comforting words.

[1787] Terminal: Provides appropriate advice based on the recognized emotional state (e.g., "You seem to be feeling depressed. Try some breathing exercises to relax.").

[1788] Emergency response

[1789] User: An elderly person speaks to the device saying "Help me."

[1790] Terminal: Acquires voice data and detects emergencies.

[1791] Device: Sends text message and phone call notifications to pre-defined emergency contacts.

[1792] Device: Automatically contacts the nearest emergency services, using GPS data to communicate your location.

[1793] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[1794] Reducing loneliness and providing information

[1795] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[1796] Terminal: Acquires voice data and sends it to the server.

[1797] Server: Generates the latest relevant information based on the user's hobbies and interests. Collects relevant event information and news from the Internet.

[1798] Device: Providing collected information to the user (e.g., "There's a pottery class coming up near me. Would you like to join?").

[1799] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[1800] Server: The emotion engine recognizes the user's emotional state from everyday conversation and responds appropriately.

[1801] Specific examples

[1802] Health management plan proposals

[1803] User: An elderly person says, "My knees have been hurting lately."

[1804] Terminal: Acquires voice data and sends it to the server.

[1805] Server: Analyzes the data and compares it with past lifestyle data.

[1806] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1807] Terminal: Providing advice to seniors.

[1808] Emotional awareness and mental care

[1809] User: An elderly person tells the device, "I've been feeling really lonely lately."

[1810] Terminal: Acquires voice data and sends it to the server.

[1811] Server: The emotion engine analyzes the voice data and text to recognize the user's emotional state.

[1812] Server: Generates mental health plans (e.g., relaxation techniques to improve mood).

[1813] Device: Providing mental health advice to seniors (e.g., "Try listening to the following music to help you feel calmer").

[1814] The system of the present invention enables personalized assistance and emotional support tailored to the individual needs of elderly people. This reduces the burden on elderly people and allows them to live independent lives with peace of mind. It is also expected to contribute to solving the aging problem in society as a whole.

[1815] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1816] Step 1: Data entry

[1817] User: Elderly people manually input data about their daily life (e.g., dietary details, amount of exercise, sleep time, etc.) into a dedicated application. The data input in this way includes the user's daily diet and exercise records.

[1818] Device: Data (e.g., heart rate, number of steps, sleep patterns, etc.) is automatically collected from a wearable device worn by the elderly. Biometric data from the wearable device is obtained as input.

[1819] Step 2: Send data

[1820] Device: The collected lifestyle and health data is periodically sent to the server. The data is securely transmitted using an encryption protocol. Specifically, the data is uploaded from the device to the server via the Internet.

[1821] Step 3: Save Data

[1822] Server: Stores the received data in a central database (e.g., MySQL or PostgreSQL). It takes the lifestyle and health data sent as input and adds new records to the database based on this.

[1823] Step 4: Data analysis

[1824] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly. Specifically, machine learning algorithms (e.g., random forests and neural networks) are applied using programming languages ​​such as Python. Data obtained from the database is used as input, and health status patterns are analyzed based on this.

[1825] Step 5: Generate a Health Management Plan

[1826] Server: Using the generative AI model, a personalized health management plan is generated from the learned data. Specifically, the generative AI model compares the plan with past data and outputs an appropriate health management plan. For example, instructions such as "The user's daily walking count has decreased, so suggest that they make short walks a daily routine" are included.

[1827] Step 6: Acquire audio data

[1828] User: An elderly person speaks to the device, saying, "I've been feeling depressed lately."

[1829] Terminal: The device uses a voice recognition function to acquire user speech. The input is voice data obtained through a microphone.

[1830] Step 7: Sending audio data

[1831] Terminal: Sends the acquired voice data to the server. The acquired voice data is input and is uploaded to the server.

[1832] Step 8: Transcribe speech to text

[1833] Server: To convert the audio data into text, a speech recognition service such as Google Cloud Speech-to-Text API or Amazon Transcribe is used. The input is audio data and the output is text data.

[1834] Step 9: Analyzing the speech

[1835] Server: The generation AI analyzes the text data and detects changes in the user's health condition and lifestyle based on the content of their speech. The text data is input, and the analysis results are output based on this.

[1836] Step 10: Recognize your emotional state

[1837] Server: Using an emotion engine, the server recognizes the emotional state of the elderly person from text and voice data. The input is text and voice data, and the output is the recognized emotional state.

[1838] Step 11: Emotion-based advice generation

[1839] Server: Generates appropriate advice based on the recognized emotional state. For example, advice such as "You seem to be feeling depressed, so try some breathing exercises to relax" is generated.

[1840] Step 12: Advice Notification

[1841] Terminal: Notifies the user of the generated advice. The advice is sent from the server as input and is conveyed to the user in text or voice.

[1842] Step 13: Detecting an emergency

[1843] User: An elderly person speaks to the device saying "Help me."

[1844] Terminal: Emergency situations are detected using voice recognition functionality. Voice data acquired by a microphone is used as input.

[1845] Step 14: Emergency Notification

[1846] Device: Sends notifications to pre-defined emergency contacts and automatically calls the nearest emergency services. Emergency messages are sent based on the detected emergency information as input.

[1847] Step 15: Provide emergency guidelines

[1848] Terminal: Provides the user with emergency action guidelines. Examples include instructions such as "Sit down, take a deep breath, and wait until the ambulance arrives." Input is information about the detected emergency, and based on this, action guidelines are output.

[1849] Step 16: Provide information about your hobbies and interests

[1850] User: An elderly person speaks to the device, asking, "Has anything interesting happened recently?"

[1851] Terminal: Acquires voice data and sends it to the server. Voice data obtained through a microphone is used as input.

[1852] Server: Generates relevant information based on the user's hobbies and interests. The input is text data and a hobby profile, and the output is relevant information. For example, the generated information is, "There's a pottery class being held nearby. Would you like to participate?"

[1853] Step 17: Start a conversation with your chatbot

[1854] Terminal: The AI ​​chatbot begins a conversation with the user by asking, "How was your day today?" It has a pre-defined prompt as input and starts a dialogue with the user as output.

[1855] Step 18: Emotion Recognition and Response

[1856] Server: The emotion engine recognizes the user's emotional state from everyday conversations and responds appropriately. The input is text data from the conversation, and the emotional state is output based on this. Conversations and comforting words designed to elicit positive emotions are provided.

[1857] In this way, the program processing of the system can be explained in detail with specific operations, inputs and outputs at each step.

[1858] (Application example 2)

[1859] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1860] There is a need for appropriate responses to the health management issues, loneliness, and physiological and psychological emergencies that seniors face in their daily lives. Furthermore, there is a lack of ways to provide more personalized, real-time, and effective support to seniors in physical stores. To address these issues, a system is needed that comprehensively analyzes the health status, lifestyle habits, and emotions of seniors and provides appropriate advice and support.

[1861] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; means for recognizing emotions from the elderly's daily conversations and providing support appropriate to the situation; and means for providing advice in real time using a voice assistant when the elderly receives health and lifestyle support in a physical store. This enables more personalized health management and appropriate advice and support to be provided in real time in the elderly's daily life and in support at the physical store, thereby improving the quality of life of the elderly.

[1862] "Means for acquiring lifestyle data of elderly people" refers to methods and devices for collecting information on the diet, exercise, sleep, activity level, etc. of elderly people in their daily lives.

[1863] "Means for analyzing lifestyle data and learning about the health status and lifestyle habits of elderly people" refers to algorithms and software that process collected lifestyle data and understand the health status and daily behavior patterns of elderly people.

[1864] "Means for generating and providing personalized health management plans based on health status and lifestyle habits" refers to a method or system for creating individually optimized health management plans based on analyzed data and providing them to elderly people.

[1865] "Means for capturing everyday conversations with elderly people and analyzing the audio data" refers to technologies and tools for recording the voices spoken by elderly people, converting the voices into text, and analyzing the content.

[1866] "Means for detecting changes in health condition and lifestyle habits based on analysis results and providing appropriate advice" refers to methods and systems for identifying changes in health condition and lifestyle habits from the analysis results of voice data and providing advice to elderly people based on the results.

[1867] "Means for detecting emergencies and notifying emergency contacts" refers to a mechanism that automatically detects when something abnormal occurs with an elderly person and sends a notification to pre-registered contacts.

[1868] "Means for providing guidelines for action in an emergency" refers to a method or system for providing instructions to elderly people on how to act safely in the event of an emergency.

[1869] "Means for collecting and providing information on the hobbies and interests of older people" refers to methods and technologies for collecting information on areas and activities that interest older people and providing it to them.

[1870] "Means for recognizing emotions from everyday conversations with the elderly and providing support appropriate to the situation" refers to algorithms and systems that analyze the emotional state of elderly people from everyday conversations and provide support and advice accordingly.

[1871] "Means for providing real-time advice using a voice assistant when elderly people receive health and lifestyle support in physical stores" refers to methods and technologies for using a voice assistant to provide appropriate advice on the spot when elderly people receive health consultations or lifestyle support in physical stores.

[1872] This invention is a personalized health management system for the elderly that utilizes generative AI models and emotion engines, and specific embodiments for supporting the elderly in physical stores are described below.

[1873] System configuration

[1874] The system consists of a server, terminals, and related software. The server plays a central role in managing and analyzing the elderly's lifestyle and health data and generating appropriate support plans. The terminals (smartphones, tablets, in-store kiosk terminals, etc.) are used directly by the elderly as an interface.

[1875] Hardware and software used

[1876] Hardware: Smartphones, smart tablets, in-store KIOSK terminals

[1877] Software: Python, TensorFlow, OpenCV, Dialogflow, Firebase

[1878] Data Collection and Management

[1879] The device collects health and lifestyle data (such as diet, exercise, and sleep) entered by the elderly on a daily basis, and the collected data is stored in a cloud database using Firebase.

[1880] Health management plan generation and provision

[1881] The server analyzes the collected data using TensorFlow to learn about the health status and lifestyle habits of the elderly, and then creates an individually optimized health management plan from the generated data and provides it to the elderly.

[1882] Voice data processing and emotion recognition

[1883] When an elderly person speaks to the device in everyday conversation, the voice data is captured through the device and sent to the server. The server converts the voice data into text via Dialogflow and then performs natural language processing. An emotion engine is used to recognize the elderly person's emotional state from the text and voice data.

[1884] Emergency response

[1885] If the elderly person detects any abnormality, the device will recognize the emergency and immediately send a notification to emergency contacts and store staff, enabling a prompt response.

[1886] Information provision

[1887] The server collects and provides the latest relevant information and event information based on the elderly's hobbies and interests, for example, notifying them of nearby events and health articles.

[1888] Specific examples

[1889] For example, if an elderly person says, "My knees have been hurting lately," the system will analyze their voice and compare it with past data to suggest appropriate exercises and rest methods. If it detects a decline in emotion, it will offer relaxation techniques and even contact the customer promptly in the event of an emergency.

[1890] Prompt Sentence Examples

[1891] Here are some examples of prompts for generative AI models:

[1892] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[1893] In this way, the system of the present invention can support the lifestyles and health management of the elderly, allowing them to live more safely even in physical stores.

[1894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1895] Step 1:

[1896] Collecting data on the lifestyles of the elderly

[1897] Input: Users manually input data such as daily diet, exercise, and sleep duration using a smartphone or tablet, or data is automatically collected from a wearable device.

[1898] How it works: The device collects data entered by the elderly person and data sent from wearable devices, including data from a pedometer and information from a food tracking app.

[1899] Output: The acquired lifestyle data is temporarily saved and sent to the next step.

[1900] Step 2:

[1901] Data storage and management

[1902] Input: Elderly lifestyle data obtained in step 1.

[1903] Specific operation: The device sends the acquired data to the Firebase database in the cloud.

[1904] Data processing: The submitted data is stored in the Firebase database and organized chronologically.

[1905] Output: Organized lifestyle data is stored in a database and ready for analysis.

[1906] Step 3:

[1907] Analyzing data and generating a health management plan

[1908] Input: Life data stored in Firebase database.

[1909] Specific operation: The server uses TensorFlow to analyze the collected data and learn about the health status and lifestyle habits of the elderly.

[1910] Data processing: AI models generated through data analysis assess the health status of the elderly and generate personalized health management plans.

[1911] Output: Send the generated health management plan to the terminal.

[1912] Step 4:

[1913] Acquiring everyday conversations and analyzing audio data

[1914] Input: Voice data of everyday conversations spoken by the user to the device.

[1915] Specific operation: The device picks up the voice of the elderly person using a microphone and sends the voice data to the server.

[1916] Data processing: The server uses Dialogflow to convert voice data into text and perform natural language processing.

[1917] Output: Textual audio data and analysis results.

[1918] Step 5:

[1919] Emotion recognition and advice provision

[1920] Input: Text data output in Step 4 and analysis results.

[1921] Specific operation: The server uses an emotion engine to recognize the emotional state of the elderly person from text data and voice data.

[1922] Data processing: The emotion engine analyzes the emotional state and generates appropriate advice and mental care support depending on the situation.

[1923] Output: The generated advice is sent to the terminal and notifies the user.

[1924] Step 6:

[1925] Emergency detection and response

[1926] Input: Voice data of the user saying "help" to the device.

[1927] Specific operation: The device acquires voice data and analyzes it to detect emergencies.

[1928] Data processing: If an emergency situation is detected, the device will send a notification to pre-defined emergency contacts and automatically call the nearest emergency services.

[1929] Output: Notification to emergency contacts and emergency services.

[1930] Step 7:

[1931] Providing information based on the interests of the elderly

[1932] Input: Audio data of a user saying, "Has anything interesting happened recently?"

[1933] Specific operation: The terminal acquires the voice data and sends it to the server.

[1934] Data processing: The server collects the latest relevant information based on the user's hobbies and interests and generates suggestions.

[1935] Output: Information based on the elderly person's hobbies and interests is displayed on the device or provided via voice.

[1936] Example prompt sentence:

[1937] "Generate a health plan based on your user data: {what you eat, how much you exercise, how much you sleep}. Recent voice command: 'My knee hurts.'"

[1938] The above is a specific processing flow for carrying out the present invention. This system effectively realizes health management and daily support for elderly people.

[1939] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1940] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1941] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1942] [Fourth embodiment]

[1943] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1944] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1945] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1946] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1947] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1948] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1949] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1950] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1951] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1952] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1953] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1954] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1955] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1956] The present invention aims to provide a personalized health management system using generative AI to support the daily lives of the elderly. This system supports the daily lives of the elderly via a server and terminals, and is specifically implemented as follows.

[1957] System configuration

[1958] The system consists of the following main components:

[1959] 1. Data collection method: Collecting lifestyle and health data of the elderly.

[1960] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits.

[1961] 3. Health management plan provision means: Generate and provide a personalized health management plan based on the analysis results.

[1962] 4. Voice data acquisition and analysis methods: Voice data is acquired through everyday conversations and analyzed.

[1963] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly.

[1964] 6. Emergency Response Measures: Detect emergency situations, provide appropriate course of action, and notify emergency contacts.

[1965] 7. Information provision measures: Collecting and providing information on the hobbies and interests of the elderly.

[1966] Program processing

[1967] Data collection and learning

[1968] Device: Elderly people enter data about their daily lives (such as diet, exercise, and sleep), or the data is automatically collected from a wearable device.

[1969] Terminal: Sends collected data to the server.

[1970] Server: Analyzes the received data and learns about the elderly person's health condition and lifestyle habits.

[1971] Server: Generative AI learns patterns from data and builds personalized health plans.

[1972] Analysis of everyday conversations and advice

[1973] User: An elderly person speaks everyday conversation into the device (e.g., "I can't sleep lately").

[1974] Terminal: Acquires voice data and sends it to the server.

[1975] Server: Analyzes voice data, converts speech into text, and detects changes in the elderly person's health and lifestyle habits.

[1976] Server: Based on the analysis results, it generates detailed advice and sends it to the device.

[1977] Device: Providing advice to the elderly (e.g., "Try listening to the following music to help you relax")

[1978] Emergency response

[1979] User: An elderly person speaks to the device saying "Help me."

[1980] Terminal: Acquires voice data and detects emergencies.

[1981] Device: Automatically notify emergency contacts and arrange for the nearest emergency services.

[1982] Device: Providing specific guidelines for behavior to the older adult (e.g., "Take a deep breath and sit down").

[1983] Reducing loneliness and providing information

[1984] User: An elderly person talks to the device, asking, "Has anything interesting happened recently?"

[1985] Terminal: Acquires voice data and sends it to the server.

[1986] Server: Generates the latest information based on the hobbies and interests of the elderly.

[1987] Terminals: Providing information to seniors (e.g., "There's a pottery class coming up nearby").

[1988] Device: AI chatbots can initiate everyday conversations with seniors, reducing their sense of loneliness.

[1989] Specific examples

[1990] Health management plan proposals

[1991] User: An elderly person says, "My knees have been hurting lately."

[1992] Terminal: Acquires voice data and sends it to the server.

[1993] Server: Analyzes the data and compares it with past lifestyle data.

[1994] Server: Generates causes of knee pain and recommended measures (e.g., specific exercises).

[1995] Terminal: Providing advice to seniors.

[1996] Emergency response

[1997] User: An elderly person talks to the device saying, "I feel like I'm going to fall."

[1998] Device: Detects emergencies and obtains location information.

[1999] Device: Call emergency contacts and emergency services.

[2000] Device: Communicate specific guidelines for action to the elderly.

[2001] conclusion

[2002] The system of this invention makes it possible to provide personalized support tailored to the individual needs of the elderly, reducing the burden on them and enabling them to live independently with peace of mind. It is also expected to contribute greatly to solving the aging problem of society as a whole.

[2003] The processing flow will be explained below.

[2004] Data collection and learning

[2005] Step 1:

[2006] User: Elderly people record data about their daily lives (e.g., diet, exercise, sleep) or wear a wearable device.

[2007] Step 2:

[2008] Terminal: Collects recorded life and health data.

[2009] Step 3:

[2010] Terminal: Periodically sends collected data to the server.

[2011] Step 4:

[2012] Server: Stores the received data in a database.

[2013] Step 5:

[2014] Server: Analyzes the stored data and learns about the health status and lifestyle habits of the elderly.

[2015] Step 6:

[2016] Server: Creates a personalized health management plan based on the data learned by the generative AI.

[2017] Analysis of everyday conversations and advice

[2018] Step 1:

[2019] User: An elderly person talks to the device (e.g., "I've been having back pain lately").

[2020] Step 2:

[2021] Terminal: Acquires voice data and sends it to the server.

[2022] Step 3:

[2023] Server: Analyzes the voice data and converts the spoken content into text.

[2024] Step 4:

[2025] Server: The generative AI analyzes the text and detects changes in the user's health condition and lifestyle habits.

[2026] Step 5:

[2027] Server: Based on the analysis results, generates appropriate advice and sends it to the device.

[2028] Step 6:

[2029] On your device: Notify the user via text or voice of the advice they received (e.g., "Try the following exercises to relieve your back pain").

[2030] Emergency response

[2031] Step 1:

[2032] User: An elderly person speaks to the device saying "Help me."

[2033] Step 2:

[2034] Terminal: Acquires voice data and detects emergencies.

[2035] Step 3:

[2036] On your device: Send a notification to pre-defined emergency contacts.

[2037] Step 4:

[2038] Device: Automatically call the nearest emergency services.

[2039] Step 5:

[2040] Terminal: Generates emergency action guidelines and communicates them to the user (e.g., "Sit down, take a deep breath, and wait until the ambulance arrives").

[2041] Reducing loneliness and providing information

[2042] Step 1:

[2043] User: An elderly person speaks to the device, "Has anything interesting happened recently?"

[2044] Step 2:

[2045] Terminal: Acquires voice data and sends it to the server.

[2046] Step 3:

[2047] Server: Generates up-to-date and relevant information based on the user's hobbies and interests.

[2048] Step 4:

[2049] Server: Sends the generated information to the terminal.

[2050] Step 5:

[2051] Terminal: The terminal provides the user with information such as, "It seems there's a pottery class being held at the local community center next week. Would you like to participate?"

[2052] Step 6:

[2053] Terminal: The AI ​​chatbot speaks to the user, asking, "How was your day today?" and begins a daily conversation.

[2054] conclusion

[2055] Through the above processing steps, the system of the present invention can provide personalized assistance according to the individual needs of the elderly, thereby reducing the burden on the elderly and enabling them to live independently with peace of mind.

[2056] Example 1

[2057] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2058] Health management for the elderly often takes a one-size-fits-all approach, without taking into account individual needs and differences in living environments. Furthermore, when elderly people live alone, it is difficult to respond appropriately to changes in their health status or emergencies in their daily lives. Furthermore, there is a lack of effective methods to reduce the sense of loneliness felt by elderly people. To address these issues, there is a need for personalized health management plans, emergency response functions, and everyday conversation support functions.

[2059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2060] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data and daily life data to the server, means for analyzing the received data in the server and learning the elderly person's health condition and lifestyle habits, means for generating and providing a personalized health management plan using a generative AI model, means for acquiring conversation data with the elderly person and transmitting it to the server, means for converting voice data to text and performing natural language processing to detect changes in the elderly person's health condition and lifestyle habits, means for generating and providing appropriate advice based on the analysis results, means for detecting voice input in an emergency and automatically notifying an emergency contact, means for providing specific guidelines for action by voice or text, means for collecting and providing information based on the elderly person's hobbies and interests, and means for reducing the elderly person's sense of loneliness through everyday conversations via a chatbot. This allows for providing elderly people with personalized support tailored to their individual needs, improving the accuracy of health management, accelerating emergency response, and reducing the elderly person's sense of loneliness.

[2061] "Biometric data" refers to information that indicates the physical condition of an elderly person, and includes physiological indicators such as heart rate, blood pressure, body temperature, and oxygen saturation.

[2062] "Daily life data" refers to information about the activities that elderly people engage in in their daily lives, including dietary content, amount of exercise, and sleep duration.

[2063] "Server" means a computer system that receives, stores, analyzes data, and processes and provides information using a generative AI model.

[2064] A "generative AI model" is an artificial intelligence algorithm or system that generates new information based on given input data by learning from large amounts of data.

[2065] A "personalized health management plan" is an individualized health promotion and management plan created based on the health status and lifestyle habits of each elderly person.

[2066] "Voice data" refers to data containing audio signals that are recordings of what the elderly person is saying to the terminal.

[2067] "Natural language processing" is a technology that converts voice data into text format, linguistically analyzes it as a string of characters, and understands and processes the content.

[2068] "Emergencies" refer to dangerous situations or health problems faced by older adults, such as falls, difficulty breathing, or sudden changes in blood pressure.

[2069] "Emergency Contacts" refers to people or organisations that should be contacted in the event of an emergency, such as family, friends, carers or emergency services.

[2070] "Action guidelines" are instructions that show specific actions that elderly people should take in specific situations, such as taking deep breaths, sitting down, and resting.

[2071] "Hobbies and interest-based information" refers to the latest information, events and activity opportunities related to areas and activities that interest seniors.

[2072] A "chatbot" is a program that uses artificial intelligence to engage in natural dialogue with users, and can converse through text or voice.

[2073] This invention is a system for supporting the daily lives of elderly people and personalizing their health management. The system utilizes terminals and servers to effectively utilize lifestyle and health data of elderly people using generative AI models.

[2074] System Overview

[2075] This system collects and analyzes biometric and daily life data from elderly people to provide personalized health management plans and respond to emergencies. The hardware used includes wearable devices (e.g., Fitbit, Apple Watch) and internet-connected devices (e.g., smartphones, tablets). Analysis is performed using Python libraries (e.g., Pandas, Scikit-learn) and the Google Cloud Speech-to-Text API, utilizing generative AI models (e.g., GPT-4).

[2076] Explanation of program processing

[2077] Data collection

[2078] Elderly users manually enter data such as dietary habits, exercise, and sleep duration into the device, or use a wearable device to automatically collect this data, which is then sent to a server via the Internet.

[2079] Data analysis

[2080] The server analyzes the received data and learns about the elderly person's health status and lifestyle habits. It uses Python libraries Pandas and Scikit-learn to perform statistical analysis and outlier detection. It then uses a generative AI model to generate a personalized health management plan.

[2081] Analysis of everyday conversations

[2082] When an elderly user speaks into the device, the device captures this voice data and sends it to a server, which then converts the voice data into text using the Google Cloud Speech-to-Text API and performs natural language processing to detect changes in health status and lifestyle habits.

[2083] Generating Advice

[2084] The server generates appropriate advice based on the analysis results. Using the generative AI model, it creates specific health management advice (e.g., "Try listening to the following music to relax") and sends it to the device. The device then provides the advice to the elderly via voice or text.

[2085] Emergency response

[2086] When an elderly user speaks "help me" into the device, the device receives the voice data and detects an emergency. The device immediately transmits the data to the server, which then automatically notifies emergency contacts. Furthermore, the device provides specific instructions for action (e.g., "take a deep breath and sit down") via voice.

[2087] Providing information based on hobbies and interests

[2088] When an elderly user speaks to the device, asking, "Has anything interesting happened recently?", the device captures the voice data and sends it to the server. The server uses a generative AI model based on the elderly's hobbies and interests to generate the latest information. The device then provides this information to the elderly via voice or text. The chatbot function also allows for everyday conversations with the elderly, reducing their sense of loneliness.

[2089] Examples of concrete examples and prompts

[2090] Example 1: Proposing a health management plan

[2091] When an elderly user says, "My knees have been hurting lately," the device captures the voice data and sends it to the server. The server analyzes the data and compares it with past lifestyle data. Using a generative AI model, it generates the cause of the knee pain and recommends countermeasures (e.g., specific exercises). The device then provides advice to the elderly, such as, "Try the following exercises to relax."

[2092] Example 2: Emergency response

[2093] When an elderly user says to the device, "I feel like I'm going to fall," the device detects the emergency and obtains their location. The device then notifies emergency contacts and emergency services, and provides the elderly with advice such as, "Take a deep breath and sit down."

[2094] Prompt Sentence Examples

[2095] Healthcare plan: "I've been having knee pain lately. What's the best healthcare plan for this?"

[2096] Emergency response: "I feel like I'm going to collapse. What should I do?"

[2097] The system of this invention provides support tailored to the individual needs of the elderly, improves the accuracy of health management, and enables rapid response in emergencies, thereby improving the quality of life for the elderly and contributing to solving the aging problem in society as a whole.

[2098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2099] Step 1:

[2100] Data collection

[2101] Input: Manually entered data from seniors (dietary content, exercise, sleep duration) and automatically collected data from wearable devices.

[2102] How it works: The elderly user opens the application on the device and enters information about their diet, exercise, and sleep time. The wearable device also automatically collects data such as heart rate and number of steps taken.

[2103] Output: Collected life and biometric data.

[2104] Step 2:

[2105] Data transmission

[2106] Input: Collected life and biometric data.

[2107] How it works: Your device sends collected data to a server using Wi-Fi or a mobile network.

[2108] Output: The data sent to the server.

[2109] Step 3:

[2110] Data analysis

[2111] Input: Life and biometric data sent to the server.

[2112] How it works: The server uses Python libraries such as Pandas and Scikit-learn to perform statistical analysis of the data and detect outliers.

[2113] Output: Analysis results on the health status and lifestyle habits of elderly people.

[2114] Step 4:

[2115] Generate a health management plan

[2116] Input: Analysis results on health status and lifestyle habits.

[2117] How it works: The server uses a generative AI model (e.g., GPT-4) to generate a personalized health care plan based on the individual needs of the elderly person.

[2118] Output: A personalized health care plan.

[2119] Step 5:

[2120] Acquiring voice input

[2121] Input: Everyday conversations of elderly people (e.g., "I can't sleep these days").

[2122] Operation: The elderly user speaks everyday conversation into the device, and the device captures this voice data.

[2123] Output: The captured audio data.

[2124] Step 6:

[2125] Sending audio data

[2126] Input: Captured audio data.

[2127] Action: The device sends audio data to the server.

[2128] Output: The audio data sent to the server.

[2129] Step 7:

[2130] Analysis of audio data

[2131] Input: The audio data sent to the server.

[2132] How it works: The server uses the Google Cloud Speech-to-Text API to convert voice data into text, then uses natural language processing to detect changes in health and lifestyle habits.

[2133] Output: Text conversion results and analysis results of the audio data.

[2134] Step 8:

[2135] Generating Advice

[2136] Input: Text conversion results and analysis results of audio data.

[2137] How it works: The server uses a generative AI model (e.g., GPT-4) to generate appropriate advice based on the analysis results.

[2138] Output: Generated advice (e.g., "Try listening to the following music to relax")

[2139] Step 9:

[2140] Providing advice

[2141] Input: The generated advice.

[2142] How it works: The device provides the generated advice to the senior via voice or text.

[2143] Output: Advice given to the elderly.

[2144] Step 10:

[2145] Emergency voice input detection

[2146] Input: Urgent speech from an elderly person (e.g., "Help me").

[2147] Operation: When an elderly user speaks an emergency voice into the device, the device picks up the voice and detects an emergency.

[2148] Output: Emergency detection result.

[2149] Step 11:

[2150] Emergency notification

[2151] Input: Emergency detection results and location information of the elderly person.

[2152] What it does: Your device automatically calls your emergency contacts and dispatches the nearest emergency services.

[2153] Output: Notification of called emergency contacts and emergency services.

[2154] Step 12:

[2155] Providing guidelines for emergency situations

[2156] Input: Emergency detection result.

[2157] What it does: The device provides the senior with specific instructions for action via voice or text (e.g., "Take a deep breath and sit down").

[2158] Output: Provided course of action.

[2159] Step 13:

[2160] Information gathering based on hobbies and interests

[2161] Input: A request for information about a senior's hobbies and interests (e.g., "What's interesting going on these days?").

[2162] How it works: The elderly user asks questions about their hobbies and interests to the device. The device captures this voice data and sends it to the server.

[2163] Output: The audio data sent to the server.

[2164] Step 14:

[2165] Information generation

[2166] Input: A request for information about seniors' hobbies and interests.

[2167] How it works: The server uses a generative AI model (e.g., GPT-4) to generate information based on the elderly person's hobbies and interests.

[2168] Output: The information generated.

[2169] Step 15:

[2170] Providing information and conducting daily conversations

[2171] Input: Generated information.

[2172] How it works: The device provides the generated information to the elderly via voice or text, and also uses a chatbot function to engage in everyday conversations with the elderly, reducing their sense of loneliness.

[2173] Output: Information provided to the elderly and daily conversations carried out.

[2174] (Application example 1)

[2175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2176] For seniors to maintain healthy eating habits, it is important to provide personalized meal plans based on their individual health conditions and lifestyle habits. However, manually creating such personalized plans and preparing and adjusting meals each time is extremely difficult and burdensome for seniors. Furthermore, without a system for responding quickly and appropriately in emergencies, seniors risk being put in dangerous situations. Furthermore, there is a lack of information and communication methods to reduce seniors' feelings of loneliness and enrich their daily lives.

[2177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2178] In this invention, the server includes: means for acquiring lifestyle data of the elderly; means for analyzing the lifestyle data and learning the elderly's health condition and lifestyle habits; means for generating and providing a personalized health management plan based on the elderly's health condition and lifestyle habits; means for acquiring daily conversations with the elderly and analyzing the voice data; means for detecting changes in the elderly's health condition and lifestyle habits based on the analysis results and providing appropriate advice; means for detecting an emergency and notifying an emergency contact; means for providing an emergency action plan; means for collecting and providing information on the elderly's hobbies and interests; and means for generating a personalized meal plan based on the elderly's health condition and lifestyle habits and ordering food based on the meal plan. This allows the elderly to easily order and receive meals appropriate for their health condition, respond quickly to emergencies, and obtain information and communication to improve the quality of their daily lives.

[2179] "Elderly" refers to people who are older and require special support and consideration in their daily lives.

[2180] "Lifestyle data" refers to information about the subject's daily life, such as their diet, amount of exercise, and sleep time.

[2181] "Health status" refers to indicators that show the normality or abnormality of an individual's physical and mental state.

[2182] "Lifestyle habits" refers to the habits and patterns of behavior that an individual engages in on a daily basis.

[2183] A "personalized health care plan" refers to a health care policy that is customized to suit the characteristics and conditions of each individual.

[2184] "Daily conversation" refers to the normal conversational activities that the subject engages in on a daily basis.

[2185] "Audio data" refers to audio recorded by a subject and stored as digital information.

[2186] "Analysis results" refers to information obtained as a result of analysis based on collected data.

[2187] "Appropriate advice" refers to advice that is most beneficial to the subject based on the analysis results.

[2188] An "emergency" is a situation in which a subject is suddenly in danger and requires a rapid response.

[2189] "Emergency contacts" refers to the people or organizations that should be notified first in an emergency.

[2190] A "guideline" refers to specific guidance or advice on how to act in a particular situation.

[2191] "Information about hobbies and interests" refers to information related to the subject's personal preferences and interests.

[2192] A "personalized meal plan" refers to meal suggestions tailored to an individual's health and lifestyle.

[2193] "Means of ordering food" refers to the methods or mechanisms for ordering ingredients and meals based on the proposed meal plan, such as through a delivery service.

[2194] This invention provides a personalized health management system using generative AI to support the lives of the elderly. The system collects and analyzes lifestyle and health data of the elderly to provide individually tailored health management and meal plans. The system also has a wide range of functions, including emergency detection and response, analysis of the elderly's daily conversations, and provision of information related to hobbies and interests.

[2195] System configuration

[2196] The system consists of the following main components:

[2197] 1. Data collection method: Hardware such as smartphones and wearable devices are used to collect lifestyle and health data from elderly people. This automatically collects information such as dietary habits, exercise levels, and sleep duration.

[2198] 2. Analysis method: Analyze the collected data and use a generative AI model to learn about health conditions and lifestyle habits. The analysis software used is a Python-based generative AI library.

[2199] 3. Health management plan provision: A personalized health management plan is generated based on the analysis results and provided to the user. The proposed plan is notified to the user via a smartphone app.

[2200] 4. Voice data acquisition and analysis: Voice data is acquired through everyday conversations and analyzed. Voice recognition technology uses an API that converts voice into text (e.g., Google Speech-to-Text).

[2201] 5. Advice provision method: Based on the results of voice data analysis, appropriate advice is provided to the elderly. The advice is displayed in text format on the smartphone or provided via a voice assistant.

[2202] 6. Emergency response: Detects an emergency and notifies emergency contacts. Emergency calls are made automatically via the smartphone's communication functions.

[2203] 7. Guidance: Providing specific guidance during emergencies (e.g., "Take a deep breath and sit down"), also provided in real time via the voice assistant.

[2204] 8. Information provision method: Collect and provide the latest information on the hobbies and interests of seniors. News and event information is automatically collected from the Internet.

[2205] 9. Meal plan generation and ordering: Generate a personalized meal plan based on the elderly person's health condition and lifestyle habits, and order food based on the meal plan. By linking with food delivery services, the suggested meals can be easily ordered.

[2206] Specific examples

[2207] Data collection and analysis:

[2208] Users input data using smartphones or wearable devices. The collected data is sent to a server and analyzed by a generative AI model. As a result of the analysis, a health management plan appropriate for the user's health condition is generated.

[2209] Analysis of everyday conversations and advice provided:

[2210] The user speaks to their smartphone saying, "My knees have been hurting lately." The voice data is captured and analyzed on the server. Based on the generated AI model, the results are compared with past data and the cause of the knee pain and countermeasures are provided.

[2211] Emergency Response:

[2212] When a user says "I feel like I'm going to fall," the system detects an emergency, captures their location, automatically notifies emergency contacts and emergency services, and instructs the user to "take a deep breath and sit down."

[2213] Information and loneliness relief:

[2214] When a user asks, "What's interesting lately?", the app provides the latest information based on their hobbies and interests. For example, information like "There's a pottery class opening nearby" will be displayed on the smartphone.

[2215] Meal plan suggestions and ordering:

[2216] When a user says, "I've been feeling unwell lately and have no appetite," the system analyzes the voice data and generates a meal plan tailored to their health condition. Based on the proposed plan, food is automatically ordered through a food delivery service.

[2217] Example prompt sentence:

[2218] "I've been feeling unwell lately and have no appetite."

[2219] "My knees hurt and I can't exercise"

[2220] "I think I'm having an allergic reaction, help me."

[2221] This will enable elderly people to easily order and receive meals that are appropriate for their health condition, respond quickly in emergencies, and receive information and communication that will improve the quality of their daily lives.

[2222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2223] Step 1:

[2224] Elderly people use smartphones or wearable devices to input lifestyle data, including dietary habits, exercise, and sleep duration. The input data is automatically collected by each device and centralized on the smartphone. This data is then used as input data to be sent to the server.

[2225] Step 2:

[2226] The device sends the collected life data to the server. The server receives the data and stores it in a database. This stored data is used for later analysis. The input data is the life data, and the output data is the data stored in the server.

[2227] Step 3:

[2228] The server performs analysis based on the stored data. A Python-based generative AI model analyzes the lifestyle data and learns about the elderly person's health status and lifestyle habits. This analysis identifies patterns in the data and estimates their health status. The input data is the stored lifestyle data, and the output data is the analysis results.

[2229] Step 4:

[2230] The server generates a personalized health management plan based on the generative AI model. Based on the analysis results, a plan suitable for each elderly person is automatically created. The generated health management plan is sent to the device. The input data is the analysis results, and the output data is the health management plan.

[2231] Step 5:

[2232] The device notifies the user of the generated health management plan. The health management plan is displayed to the user via a smartphone app. The user can then manage their daily health in accordance with the plan. The input data is the health management plan, and the output data is the notification to the user.

[2233] Step 6:

[2234] A user speaks to a smartphone, saying, "My knee has been hurting lately." Voice data is acquired and sent by the device to a server. The input data is the voice data, and the output data is the voice data sent to the server.

[2235] Step 7:

[2236] The server analyzes the voice data and converts it into text. To analyze the voice data, it uses speech recognition technology such as the Google Speech-to-Text API. This analysis results in data converted from voice data into text format. The input data is voice data, and the output data is text data.

[2237] Step 8:

[2238] The server uses the converted data to detect changes in the elderly person's health condition and lifestyle habits based on a generative AI model. Based on the analysis results, it generates appropriate advice and sends it to the device. The input data is text data, and the output data is the advice content.

[2239] Step 9:

[2240] The device notifies the user of the advice received from the server. The advice is displayed to the user through a smartphone app. For example, advice such as "Try the following exercise to relax" is provided. The input data is the advice content, and the output data is the notification content to the user.

[2241] Step 10:

[2242] When a user says "I'm about to fall down," the device acquires this voice data and sends it to the server. The input data is the voice data of the emergency, and the output data is the voice data sent to the server.

[2243] Step 11:

[2244] The server analyzes the received voice data and detects an emergency. By analyzing the voice data, the server confirms that an emergency has occurred. The input data is the voice data, and the output data is the emergency status determination result.

[2245] Step 12:

[2246] The server notifies the emergency contacts and takes emergency action, including notifying the nearest emergency service. The server obtains the user's location information and notifies the emergency contacts and emergency service. The input data is the emergency status determination result and location information, and the output data is the emergency call content.

[2247] Step 13:

[2248] The device provides the user with guidelines for what to do in an emergency. For example, specific guidelines such as "Take a deep breath and sit down" are provided in real time through the voice assistant. The input data is the emergency call content, and the output data is the guidelines for what to do.

[2249] Step 14:

[2250] When a user says, "Has anything interesting happened recently?", the device acquires this voice data and sends it to the server. The input data is the voice data, and the output data is the voice data sent to the server.

[2251] Step 15:

[2252] The server analyzes the voice data, generates the latest information based on the elderly person's hobbies and interests, and sends it to the device. For example, information such as "A pottery class will be held nearby" is generated. The input data is the voice data, and the output data is the generated information.

[2253] Step 16:

[2254] The device notifies the user of the information received from the server. The latest events and news are ...

Claims

1. A means of obtaining data on the lifestyles of elderly people; A means of analyzing lifestyle data and learning about the health status and lifestyle habits of elderly people; A means for generating and providing a personalized health management plan based on health status and lifestyle habits; A means for acquiring everyday conversations with elderly people and analyzing the audio data; A means to detect changes in health status and lifestyle habits based on the analysis results and provide appropriate advice, A means of detecting an emergency and notifying emergency contacts; a means of providing guidelines for action in emergencies; means of collecting and providing information about seniors' hobbies and interests; A system including:

2. 2. The system according to claim 1, further comprising means for storing the collected lifestyle data and health data in a database and using the data for analysis.

3. 2. The system according to claim 1, further comprising means for converting voice data into text and performing natural language processing to detect changes in the health status and lifestyle habits of elderly people.

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