system

Abstract

Provide a system. 【Solution means】 Means for analyzing biometric data obtained from the elderly to generate individualized health management proposals for supporting the daily life of the elderly, Means for realizing natural conversations with the elderly through voice recognition to reduce loneliness, Means for providing individualized cognitive function improvement content, Means for performing device operation and life notification based on the instructions of the elderly, Means for detecting abnormalities of the elderly and making emergency contacts, Means for collecting biometric data in real time by a mobile terminal and immediately notifying abnormalities based on preset criteria, Means for obtaining environmental information via the Internet and generating related responses, Means for automatically generating rules and puzzles and providing new challenges adjusted based on data analysis, Means for communicating with home devices capable of executing composite functions by voice instructions, A system including ​

Description

Technical Field

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

Background Art

[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 an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a society where the aging population is accelerating, the elderly have many life-related problems. Specifically, these include the burden of health management, social isolation, decline in cognitive function, inconvenience in daily life, and increased risks during emergencies. These problems are factors that reduce the quality of life (QOL) of the elderly, and means for effectively solving them are required.

Means for Solving the Problems

[0005] This invention provides a system for comprehensively supporting the lives of the elderly. This system reduces the burden of health management by incorporating means for analyzing health data acquired from the elderly and generating personalized health management suggestions. It also provides means for facilitating natural dialogue with the elderly through voice recognition, thereby reducing feelings of social isolation. Furthermore, it prevents cognitive decline by providing personalized cognitive function improvement content. To alleviate inconveniences in daily life, it includes means for controlling home appliances and providing daily reminders via voice commands to the elderly. Finally, it improves safety by providing means for detecting abnormalities in the elderly and promptly making emergency calls.

[0006] "Health data" refers to information that indicates the physical condition of elderly people, and includes numerical values ​​such as heart rate, steps taken, and body temperature.

[0007] "Personalized health management proposals" refer to a method of providing exercise programs and lifestyle improvement suggestions optimized for each individual, based on their health data.

[0008] "Speech recognition" is a technology that analyzes speech, converts it into text, and understands its content.

[0009] "Natural dialogue" refers to a form of communication that enables interaction between elderly individuals and AI agents that closely resembles human-to-human conversation.

[0010] "Reducing feelings of loneliness" refers to alleviating the sense of isolation and loneliness that elderly people experience.

[0011] "Cognitive function improvement content" refers to activities such as games and quizzes aimed at maintaining or improving the memory and thinking abilities of older adults.

[0012] "Home appliance operation" refers to controlling home appliances based on voice commands.

[0013] "Life Reminders" is a method of notifying elderly people through voice or messages so that they can remember tasks they need to do in their daily lives.

[0014] "Anomaly detection" is the process of noticing changes in the normal behavior or condition of elderly people.

[0015] An "emergency notification" is a system for quickly notifying the appropriate contacts or agencies when an emergency occurs. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Best Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a comprehensive AI system designed to support the lives of elderly people. To assist the lives of its elderly users, this system has the following functions:

[0038] First, by wearing a wearable device, users collect health data on a daily basis. The device receives this data and periodically sends it to a server. The server analyzes this data in real time and evaluates the user's health status. If an abnormality is detected, the server notifies the user via the device and suggests improvements to their health management. For example, if the user's heart rate is high, the server will advise them through the device to "take it easy today."

[0039] Next, through voice recognition, the device enables natural conversation with the user. When the user asks everyday questions or engages in casual conversation, the server generates an appropriate response, which the device then returns to the user. This reduces feelings of isolation and allows the user to enjoy the conversation. For example, if the user asks, "What's the weather like today?", the device might reply, "It's cloudy, then sunny."

[0040] To improve cognitive function, the server generates quizzes and puzzles tailored to the user based on past data. The terminal presents them to the user and sends the answers to the server. The server analyzes the results, monitors changes in cognitive function, and adjusts the content provided next time. For example, by solving a provided puzzle, a new challenge is prepared based on the user's performance.

[0041] It also features voice-activated home appliance control and a lifestyle reminder function to notify users of medication times. If a user says, "Turn on the lights," the device will operate the corresponding appliance and execute the command. Based on server settings, the device will also notify the user, "It's time to take your medication," and the user will receive a reminder.

[0042] Finally, as a crisis management function, the device constantly monitors the user's environment. If the user reports an abnormality or an unforeseen incident occurs, the device immediately notifies the server, which then notifies the appropriate emergency contacts. For example, if the device recognizes the user's voice saying "call for help," it immediately notifies registered family members or medical institutions.

[0043] These features enable the present invention to provide an environment in which elderly people can live safely and comfortably.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] When a user wears a wearable device, their physical data is recorded. The device continuously acquires data such as heart rate, steps taken, and body temperature from the device.

[0047] Step 2:

[0048] The device uploads the acquired data to the server at regular intervals. This allows the server to understand the user's latest health status.

[0049] Step 3:

[0050] The server analyzes the received data and assesses the user's health status. If an anomaly is detected by comparing it with past data, the server identifies the anomaly in real time.

[0051] Step 4:

[0052] The server generates suggestions for improving health management. These suggestions are personalized and appropriately optimized based on the user's health status. For example, if stress levels are high, relaxation might be recommended.

[0053] Step 5:

[0054] The server sends a suggestion to the terminal, and the terminal notifies the user via voice or text. The user can then adjust their actions based on this notification.

[0055] Step 6:

[0056] When a user speaks, the device performs speech recognition and analyzes the content. It sends the voice data to a server for interpretation and generates a natural-sounding response.

[0057] Step 7:

[0058] The server sends the generated response to the terminal, which then communicates it to the user via voice. This establishes a dialogue between the user and the system.

[0059] Step 8:

[0060] If a user wishes to improve their cognitive function, the server will refer to their past records to generate quizzes and puzzles best suited to them.

[0061] Step 9:

[0062] The device presents the content to the user and collects response data. The user's performance is sent to the server, which then analyzes the results.

[0063] Step 10:

[0064] Based on the analysis results, the server adjusts the content provided next time, continuously supporting the user's cognitive functions.

[0065] Step 11:

[0066] Following voice commands, the device operates home appliances. Additionally, based on a schedule set by the server, the device reminds users of medication times.

[0067] Step 12:

[0068] The terminal constantly monitors sensor information and immediately notifies the server if it detects an abnormal condition or an urgent voice input.

[0069] Step 13:

[0070] The server quickly analyzes any notifications it receives and automatically notifies registered emergency contacts or services.

[0071] Step 14:

[0072] The server continues to send follow-up instructions to the terminal after the report is made, providing support to ensure the user is safe.

[0073] (Example 1)

[0074] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0075] Elderly people face various health problems, loneliness, and cognitive decline in their daily lives. These challenges reduce their quality of life and increase their anxiety in daily living. Furthermore, inadequate systems for prompt emergency reporting and monitoring their health can lead to serious risks.

[0076] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0077] In this invention, the server includes means for generating personalized health management plans, means for facilitating natural dialogue and reducing feelings of loneliness, and means for providing content to improve cognitive function. This enables health management, a sense of security, and maintenance of cognitive function in the daily lives of elderly people.

[0078] "Biometric information" refers to physical data such as heart rate, steps taken, and sleep patterns of elderly individuals.

[0079] A "personalized health management plan" refers to specific guidance for maintaining or improving health, provided based on each elderly person's biometric information.

[0080] "Voice recognition" refers to a technology that uses a digital system to recognize the voices of elderly people and convert them into corresponding text or instructions.

[0081] "Natural dialogue" refers to communication between computer systems that takes place as naturally as conversations between humans.

[0082] "Cognitive function improvement content" refers to intellectually stimulating content such as quizzes and puzzles provided to improve the memory and judgment of elderly people.

[0083] "Appliance operation" refers to the act of controlling electrical appliances and household equipment based on the voice commands of elderly people.

[0084] "Life Reminders" refers to a function that notifies elderly people to remember their daily schedules and important matters.

[0085] The means of detecting an "abnormality" and making an emergency call refers to a function that quickly sends a warning to the outside when any physical or environmental change occurs in an elderly person.

[0086] The embodiment of the invention is a comprehensive AI system for supporting the elderly. This system assists the daily lives of elderly users and includes several key functions.

[0087] By wearing a wearable device, users can routinely acquire biometric information such as heart rate, steps taken, and sleep patterns. This device is equipped with an accelerometer and a heart rate sensor. The device periodically receives this data via Bluetooth or Wi-Fi and sends it to a server in the cloud.

[0088] The server is equipped with a data analysis program written in Python, which analyzes received biometric information in real time. The analyzed data is evaluated by an anomaly detection algorithm to monitor the user's health status. If an anomaly is detected, the server uses a generative AI model to generate personalized health management suggestions and sends them to the terminal via an API.

[0089] A device with speech recognition capabilities enables interaction with the user. Upon receiving a question from the user, the device uses speech recognition software to convert it into text. The server then generates an appropriate response using natural language processing and sends it back to the device as a text message.

[0090] The server also uses an AI model to generate quizzes and puzzles designed to improve cognitive function. A specific example of a prompt would be, "Generate a quiz to stimulate cognitive function in the elderly." The device then provides the user with an appropriate challenge and sends the results to the server.

[0091] Furthermore, the device handles appliance operation based on voice commands and provides daily reminder functions. When a user gives a voice command such as "Turn on the lights," the device controls the relevant home appliances. Based on a server schedule, the device also notifies the user by voice with reminders such as "It's time to take your medicine."

[0092] As a crisis management function, the terminal constantly monitors the user's environment and immediately notifies the server if it recognizes an emergency phrase such as "call for help." The server then notifies registered contacts, enabling a rapid response.

[0093] These features enable elderly people to live safely and comfortably, thus realizing a specific embodiment of this invention.

[0094] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0095] Step 1:

[0096] The user wears a wearable device that collects biometric information, including heart rate and steps, in real time. The input data is measured via sensors and temporarily stored within the wearable device. This data is then transmitted to a terminal via Bluetooth. The terminal stores the latest biometric information as output.

[0097] Step 2:

[0098] The device periodically transmits received biometric information to the server via Wi-Fi. The input data is biometric information transmitted from a wearable device. The device processes the data as packets and transfers them to the server using a secure communication protocol. As output, the server aggregates the latest biometric information.

[0099] Step 3:

[0100] The server executes an analysis program using Python to analyze received biometric data in real time. The input data is biometric information sent from the terminal. The server program applies an anomaly detection algorithm to extract anomaly patterns from the data. The output generates information indicating whether or not an anomaly was detected and details thereof.

[0101] Step 4:

[0102] The server uses a generative AI model to generate personalized health management plans. The input data consists of anomaly detection results and historical biometric information. The generated health management plans include suggestions derived from data-driven analysis. As output, personalized suggestions are generated and sent to the terminal.

[0103] Step 5:

[0104] The terminal receives health management suggestions from the server and notifies the user. The input data is the health management suggestion sent from the server. The terminal uses speech synthesis software to provide the user with appropriate advice via voice. As output, the user receives health suggestions in real time.

[0105] Step 6:

[0106] The user asks questions about their daily life using voice. The input data consists of the user's voice instructions. The terminal uses voice recognition software to convert the voice into text and sends it to the server. As output, the server receives the user's questions in text format.

[0107] Step 7:

[0108] The server uses natural language processing to generate appropriate responses to user questions. The input data is the user's question text from the terminal. The server analyzes the question and retrieves relevant information. As output, the generated response is sent to the terminal.

[0109] Step 8:

[0110] The terminal relays the response from the server to the user. The input data is the response sent from the server. The terminal performs speech synthesis to provide the user with a natural-sounding answer. As output, the user receives the answer to the question.

[0111] Step 9:

[0112] The server generates quizzes aimed at improving cognitive function using a generative AI model based on past data. The input data consists of the user's past cognitive performance data. The server designs new quizzes based on this data. As output, personalized questions are generated and sent to the device.

[0113] Step 10:

[0114] The terminal presents the user with a quiz and receives the answer. The input data consists of the quiz sent from the server and the user's answer. The terminal records the user's answer and prepares to send it to the server. As output, the answer data is sent to the server.

[0115] Step 11:

[0116] The server analyzes the user's response data and uses it to suggest the next content. The input data is the response information sent from the terminal. The server uses a generation AI model to adjust the content to be provided next. The adjusted content to be provided next is generated as output.

[0117] Step 12:

[0118] The user provides voice commands to operate the device. The input data is the user's voice commands. The terminal recognizes these voice commands and transmits them to the appropriate device. As an output, the device is controlled according to the user's intent.

[0119] Step 13:

[0120] The server monitors emergency phrases. The input data is the user's ambient voice. If the server detects an anomaly, it immediately prepares to notify registered contacts. As output, an emergency contact is sent to the registered relevant parties.

[0121] (Application Example 1)

[0122] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0123] Health management, reducing feelings of isolation, maintaining and improving cognitive function, and ensuring safety in the lives of the elderly are key challenges. Conventional systems are insufficient to address these individual needs, highlighting the growing need for systems that can provide multiple functions in an integrated manner. Furthermore, real-time monitoring of health status and rapid notification of abnormalities are required, but an effective solution combining these functions has not existed until now.

[0124] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0125] This invention includes a server that includes means for analyzing biometric data acquired from a user and generating personalized health management suggestions, means for facilitating natural conversation through voice recognition to reduce feelings of loneliness, means for providing personalized cognitive function improvement content, and means for collecting biometric data in real time via a mobile terminal and immediately notifying of abnormalities based on pre-set criteria. This enables multifaceted support for the lives of the elderly and the realization of a safe and comfortable living environment.

[0126] "Biometric data" refers to information about a user's body, such as heart rate and activity level, that is collected to assess their health status.

[0127] "Personalized health management suggestions" are personalized health maintenance and improvement advice generated based on the user's biometric data.

[0128] "Speech recognition" is a technology that analyzes a speaker's voice, converts it into text, understands it, and generates a response.

[0129] "Natural conversation" refers to a means of communication where users and systems can smoothly engage in everyday interactions through language, thereby deepening mutual understanding.

[0130] "Cognitive function improvement content" refers to interactive content such as quizzes and puzzles that are provided with the aim of maintaining or improving users' cognitive abilities.

[0131] A "mobile device" is a computing device that a user can carry around, and smartphones and smart glasses fall into this category.

[0132] "Instant notification" is a function that quickly informs users or registered contacts of abnormal situations, and is an important element that enables a rapid response.

[0133] "Home appliances" refer to various devices used in the home, such as home appliances and smart home devices, which can be operated by voice commands.

[0134] "Environmental information" refers to a wide range of external data, including conventional weather information and news, and aims to provide information relevant to users' lives.

[0135] The system for implementing this invention was developed to support the daily lives of elderly users. The system primarily consists of a server and terminals (smartphones and smart glasses). Details of each function are described below.

[0136] The server receives biometric data collected via Bluetooth from the user's wearable device. This data includes heart rate and step count, and is obtained using the Google Fit API or Apple HealthKit. The server analyzes this biometric data in real time using Tensorflow, and if an anomaly is detected, it notifies the user via the device using Firebase Notification. For example, if a user's heart rate rises abnormally while running, they will immediately receive advice such as "Please take a short break."

[0137] The speech recognition function uses the Google® Cloud Speech-to-Text API to convert speech into text. This allows the device to recognize user questions and commands and send them to the server. The server uses GPT to generate an appropriate response, which the device then returns to the user verbally. For example, if the user asks, "What's the weather like today?", the device will respond, "It's sunny with occasional clouds today."

[0138] Furthermore, to improve cognitive function, the server generates quizzes and puzzles using Unity based on the user's past data. The user's answers to the provided quizzes are sent to the server and analyzed. Based on the analysis results, the content provided next time is adjusted. For example, "after solving a simple math problem, a new problem is presented."

[0139] Integration with home appliances is achieved through voice commands. Using the Amazon Alexa Skills Kit or Google Assistant SDK, it is possible to control home appliances according to voice commands. For example, if a user says, "Turn off the living room lights," the lights will turn off accordingly.

[0140] Examples of prompt statements:

[0141] "The user's current heart rate is 110. Is this abnormal? How should I respond?"

[0142] "Please create appropriate brain training games for seniors."

[0143] In this way, this system provides multifaceted support for the daily lives of the elderly, offering a safe and comfortable living environment.

[0144] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0145] Step 1:

[0146] The user wears a wearable device that collects biometric data such as heart rate and steps taken. This data is transmitted to the device via Bluetooth.

[0147] Step 2:

[0148] The device transfers the received biometric data to the server using the GOOGLE FIT® API or Apple HealthKit. This data is then input into the server's data processing unit.

[0149] Step 3:

[0150] The server uses TensorFlow to analyze biometric data. It compares the data to pre-defined criteria to detect abnormalities such as heart rate, and outputs whether or not an abnormality is present as an analysis result.

[0151] Step 4:

[0152] If an anomaly is detected, the server sends a notification to the device via Firebase Notification. The device displays this notification to the user as an alert. If the user is running, the device will display a message saying, "Please take a short break."

[0153] Step 5:

[0154] The user asks "What's the weather like today?" using voice. The device converts this voice into text using the Google Cloud Speech-to-Text API and sends it to the server.

[0155] Step 6:

[0156] The server uses GPT to generate an appropriate response based on the input text. The generated text response will be "Today is sunny with occasional clouds" and will be printed to the terminal.

[0157] Step 7:

[0158] The device returns the generated response to the user as audio. This allows the user to experience a natural conversation.

[0159] Step 8:

[0160] The server generates quizzes and puzzles using Unity to improve cognitive function. The input data used at this time is information about the user's past activity history and abilities.

[0161] Step 9:

[0162] The generated quizzes and puzzles are sent to the device and displayed to the user. The user's answers are sent back from the device to the server, which analyzes the answers and adjusts the content for the next session.

[0163] Step 10:

[0164] The user gives a voice command, "Turn off the living room lights." The device converts the voice to text and sends it to the Amazon Alexa Skills Kit or Google Assistant SDK via a server.

[0165] Step 11:

[0166] The server analyzes the received command and sends operation instructions to the corresponding household device. The output confirms that the operation to turn off the electricity has been performed.

[0167] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0168] This invention is an AI system that comprehensively supports the lives of the elderly, and is characterized in that it utilizes an emotion engine to recognize the user's emotions and provides appropriate responses in accordance with those emotions.

[0169] This system collects health data through wearable devices worn daily by elderly individuals. The device receives this data in real time and sends it to a server. The server analyzes the health data and notifies the user of the information obtained as personalized health management suggestions. For example, if the server detects an increase in heart rate, the device will send a suggestion to the user such as, "We recommend you take a rest."

[0170] The emotion engine can recognize a user's emotional state by analyzing their voice and facial expression data. The device inputs this data into the emotion engine, which then analyzes the user's emotions on a server. Based on the results, the server provides the user with the most suitable relaxation program or entertainment content. For example, if the user is feeling stressed, the device might suggest, "Would you like to listen to some music?"

[0171] In addition, the emotion engine can record changes in the user's emotions over a long period. This data is used to make health management suggestions more accurate. For example, the server can refer to the emotion history and, if the user frequently feels anxious during certain times of the day, analyze the cause and make suggestions for lifestyle improvements.

[0172] Furthermore, the device can operate home appliances and remind users of medication times based on voice commands. If a user says, "Turn on the TV," the device will execute the command. It also supports the user's life by reminding them of important appointments and medication times on time.

[0173] To prepare for emergencies, the device constantly monitors the user's actions and spoken words. If an abnormal situation is detected, it promptly notifies the server, which then contacts the appropriate emergency contacts. For example, if the user says "Help," the device immediately initiates the notification process.

[0174] Thus, by incorporating an emotional engine, the present invention provides a support system that enables elderly people to live their daily lives with greater peace of mind and comfort.

[0175] The following describes the processing flow.

[0176] Step 1:

[0177] The user wears a wearable device, which continuously collects health data such as heart rate, steps taken, and body temperature.

[0178] Step 2:

[0179] The device sends the acquired health data to the server. The server begins monitoring the data in real time.

[0180] Step 3:

[0181] The server analyzes the received health data and generates personalized health management suggestions, including comparisons with past data.

[0182] Step 4:

[0183] Based on the analysis results, the server sends optimized health management suggestions to the terminal. The terminal notifies the user of this, providing on-screen displays and audio alerts.

[0184] Step 5:

[0185] When a user speaks, the device uses speech recognition to analyze the input. The device then sends this data to the server.

[0186] Step 6:

[0187] The server processes the received audio data using natural language processing to generate an appropriate response.

[0188] Step 7:

[0189] The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state. Based on this emotional state, the server selects appropriate responses and content for the user.

[0190] Step 8:

[0191] Based on the emotion recognition results, the server generates relaxation content and follow-up messages that correspond to the emotions the user is feeling.

[0192] Step 9:

[0193] The device receives a response from the server and communicates it to the user via voice. For example, a user feeling stressed might receive a message like, "Take a deep breath and relax."

[0194] Step 10:

[0195] When a user gives voice commands to operate home appliances, the terminal receives those commands and controls the appropriate appliance.

[0196] Step 11:

[0197] If a device detects an anomaly, it immediately notifies the server. The server analyzes the notification and, if necessary, automatically notifies registered emergency contacts.

[0198] Step 12:

[0199] The server integrates and manages emotional and health data, and based on long-term analysis, it regularly provides suggestions to improve users' health and well-being.

[0200] (Example 2)

[0201] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0202] In the lives of the elderly, there are demands for health management, reduction of feelings of loneliness, and rapid response to emergencies. However, current technology does not adequately provide appropriate suggestions and support based on the individual health conditions and emotions of the elderly, which is a challenge. Furthermore, it is necessary to reduce the burden on the elderly in operating home appliances based on voice commands and utilizing daily reminder functions. In addition, establishing a rapid and accurate notification system in emergencies is also important.

[0203] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0204] This invention includes a server that analyzes physiological data acquired from elderly individuals and generates personalized health management suggestions, a server that facilitates natural dialogue with elderly individuals through voice recognition to reduce feelings of loneliness, and a server that analyzes emotional states to provide relaxation programs and entertainment content. This enables individualized support tailored to the health status and emotions of elderly individuals.

[0205] "Physiological data" refers to numerical information about the physical condition of elderly people, such as heart rate, body temperature, and steps taken.

[0206] "Health management suggestions" are specific advice for maintaining or improving the health of elderly individuals, derived from the analysis of physiological data.

[0207] "Voice recognition" is a technology that allows computer systems to recognize and respond to the words spoken by elderly people.

[0208] "Natural dialogue" refers to interactions between humans and computers that closely resemble conversations between humans.

[0209] "Reducing feelings of loneliness" means alleviating the loneliness and isolation that older adults experience by providing opportunities for social connection and dialogue.

[0210] "Emotional state" refers to the mental state or mood an individual is experiencing at a particular point in time.

[0211] A "relaxation program" is a program or activity designed to alleviate users' mental tension and provide comfort and peace of mind.

[0212] "Entertainment content" refers to content such as music, movies, and games that can be enjoyed by the elderly.

[0213] "Operating home appliances" refers to controlling electrical appliances in the home through voice commands or other interfaces.

[0214] "Lifestyle notifications" refer to timely information and events necessary for daily life that are communicated to users.

[0215] "Notifying emergency contacts" refers to the action of quickly and automatically sending notifications to pre-set contacts when an elderly person finds themselves in a dangerous situation.

[0216] This invention relates to an AI system that comprehensively supports the lives of the elderly, and in particular aims to recognize the emotions of the elderly by utilizing an emotion engine and provide appropriate responses in accordance with those emotions. This system consists of a wearable device, a terminal such as a smartphone or tablet, and a server connected to the cloud.

[0217] Users collect physiological data through wearable devices they wear daily. These devices provide important health information, such as recording heart rate, body temperature, and steps taken. The devices receive this data in real time via Bluetooth or Wi-Fi and transmit it to a server in the cloud.

[0218] The server analyzes the user's health status using machine learning algorithms based on the received physiological data. One example of software used for analysis is a generative AI model that predicts health status. Based on the analysis results from the server, personalized health management suggestions are sent to the user. For example, if the heart rate is abnormally high, a notification such as "We recommend you rest for a while" will be sent via the device.

[0219] The device also periodically collects the user's voice data and inputs it into the emotion engine. The server uses this emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the results, it suggests relaxation programs and entertainment content suitable for the user, for example, by displaying options such as "Would you like to listen to music?"

[0220] The server also records user emotional data over long periods and evaluates changes in emotions based on past history. This data helps provide more accurate health management suggestions. Furthermore, the terminal operates home appliances and provides lifestyle notifications based on user voice commands. If the user says, "Turn on the TV," the terminal will execute that command.

[0221] In preparation for emergencies, the device constantly monitors the user's actions and statements, and immediately notifies the server if it detects any anomalies. The server has the capability to notify registered emergency contacts as needed. For example, if the user says "Help," the device will promptly initiate the notification process.

[0222] A concrete example of a prompt is, "Generate a sentence that identifies the emotions of an elderly person and recommends relaxing music." This prompt allows the AI ​​model to generate text data that provides appropriate music recommendations.

[0223] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0224] Step 1:

[0225] Users wear a wearable device on a daily basis. This device collects physiological data such as heart rate, body temperature, and steps taken in real time. This becomes the input data. The collected data is sent to a terminal via Bluetooth or Wi-Fi. The terminal receives the data and then sends it to a server in the cloud.

[0226] Step 2:

[0227] The server receives physiological data sent from the terminal. The input data is processed using a health analysis algorithm. This algorithm analyzes the data and evaluates the user's health status. The analyzed data generates output as health management suggestions and sends it to the terminal. For example, if the heart rate is higher than normal, it generates and outputs a suggestion such as "We recommend taking a break."

[0228] Step 3:

[0229] The device notifies the user of health management suggestions received from the server. Specifically, a suggestion message pops up on the device's display screen. The user can then adjust their actions based on these suggestions.

[0230] Step 4:

[0231] The device periodically and automatically collects the user's voice data. Voice input is sent to a server for analysis by an emotion engine. This emotion analysis processes the voice data to identify the user's emotional state. The analysis results are generated as output, such as relaxation programs or entertainment content.

[0232] Step 5:

[0233] Based on the results of the emotion analysis, the server selects a relaxation program or entertainment content suitable for the user and outputs it to the terminal. The terminal presents the suggestion to the user and displays a confirmation message such as "Do you want to play relaxation music?" as a concrete action.

[0234] Step 6:

[0235] The server records emotional data from users over a long period and uses this data to compare with past history. This input data is used to make future health management suggestions. As an output, suggestions based on specific emotional patterns are generated, contributing to improving the lifestyles of elderly people.

[0236] Step 7:

[0237] The device recognizes voice commands from the user and operates household appliances. Voice commands are processed as input, and the appliance control algorithm directly performs the action. For example, based on the command "Turn on the lights," the output is that the lighting fixture turns on.

[0238] Step 8:

[0239] The device monitors for abnormal behavior and voice in preparation for emergencies. If an abnormality is detected, the information is reported to the server. This input data is then used as output to immediately notify emergency contacts, and specific actions are taken to ensure the user's safety.

[0240] (Application Example 2)

[0241] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0242] With the increasing elderly population, the burden on caregivers, particularly in elderly care settings, is growing. Elderly individuals often experience health problems and emotional fluctuations, making prompt and individualized care essential. However, caregivers face the challenge of providing meticulous care to every elderly person amidst their busy daily schedules. Therefore, there is a need for a system that can monitor the emotional and health status of elderly individuals in real time and provide individualized care based on that information.

[0243] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0244] In this invention, the server includes means for analyzing health data and generating personalized health management suggestions, means for displaying the emotional state of elderly individuals in real time using emotion analysis and providing caregivers with appropriate response guidance, and means for detecting abnormalities and making emergency calls. This enables the provision of care tailored to each elderly person in real time, reduces the burden on caregivers, and allows elderly people to live with peace of mind.

[0245] "Health data" refers to information about the physical condition of elderly individuals, including physiological indicators such as heart rate, blood pressure, and body temperature.

[0246] "Personalized health management proposals" refer to providing specific advice and guidance for maintaining and improving the health of each elderly person, based on the results of an analysis of acquired health data.

[0247] "Voice recognition" is a technology in which a computer analyzes the voice spoken by elderly people, understands its content, and takes appropriate action.

[0248] "Emotional analysis" is a process that evaluates the current emotional state of elderly individuals based on their voice and facial expression data.

[0249] "Real-time display" means providing information about the elderly person's condition and the system in a visual and timely manner without delay.

[0250] "Providing caregivers with appropriate response guidelines" means presenting guidelines that specifically indicate what caregivers should do to support elderly individuals.

[0251] "Detecting anomalies" means identifying changes outside the normal range in the daily behaviors and health status of elderly individuals and recognizing the possibility that a problem has occurred.

[0252] An "emergency notification" is a communication process designed to quickly inform the appropriate agencies or individuals of an emergency situation.

[0253] This elderly support system combines a wearable device, smart glasses, and a server. The wearable device acquires health data such as heart rate and blood pressure in real time and sends it to the server. The server receives this data, evaluates the user's health status by comparing it with past data, and generates personalized health management suggestions. For example, if the heart rate is higher than normal, it will generate a suggestion such as, "We recommend taking a break."

[0254] Furthermore, the camera and microphone built into the smart glasses collect the elderly person's facial expressions and voice. The server uses this data to perform emotion analysis using a generative AI model. Using OpenCV and TensorFlow, the emotional state of the elderly person is analyzed from the acquired data and visualized in real time in the caregiver's field of view. Based on this information, specific action guidelines are presented to the caregiver to support how to interact with the elderly person.

[0255] When an anomaly is detected, the server immediately sends an emergency alert. For example, if an elderly person makes an emergency voice call such as "Help me," the server analyzes the information and sends a notification to a pre-designated emergency contact.

[0256] As a concrete example, if a caregiver using smart glasses in a nursing home detects a depressed mood in an elderly person's facial expression, the glasses will display instructions such as "Suggest relaxing activities." Furthermore, an example of a prompt for the generating AI model could be: "Design a care support system that recognizes the emotional state of elderly people and provides individualized suggestions. Please detail the necessary technologies and data flow."

[0257] This system is implemented by combining data analysis using Amazon Web Services with sentiment analysis functions using OpenCV and TensorFlow, enabling flexible and scalable operation.

[0258] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0259] Step 1:

[0260] Wearable devices collect health data such as heart rate and blood pressure from elderly individuals in real time. This data is transmitted to a terminal using Bluetooth. The terminal then transfers the data to a server. At this stage, physiological indicator data is acquired as input and prepared to be transmitted to the server as output.

[0261] Step 2:

[0262] The server analyzes the received health data. Specifically, it uses AWS® Lambda to process the data and compares it with historical data stored in Amazon RDS to assess the current health status. Inputs include physiological indicator data and historical health history, and output is a health status assessment result. If an abnormality is detected, it generates recommendations for the next optimal health management steps.

[0263] Step 3:

[0264] The server uses a generative AI model to generate specific health management suggestions for the user based on the evaluation results of the received health data. This prompt includes "generative AI model" and "health management suggestions." The evaluation results and generative AI model as inputs are used to produce the suggestions as output.

[0265] Step 4:

[0266] Meanwhile, the device collects voice and facial expression data from elderly individuals via smart glasses. This data is transmitted to a server in real time for emotion analysis. Voice and visual data are sent from the device to the server as input.

[0267] Step 5:

[0268] The server uses OpenCV and TensorFlow to perform emotion analysis. This allows it to analyze the emotional state of elderly individuals from voice and facial expression data, and obtain specific emotion classification results. The input consists of voice and facial expression data, and the emotional state is output as a result of the data calculations.

[0269] Step 6:

[0270] The server provides the caregiver with appropriate response guidance based on the emotional state. Visual feedback is sent to the smart glasses' display. This presents the caregiver with guidelines as visual information based on the input emotion analysis results.

[0271] Step 7:

[0272] When a user issues an emergency, the device detects it and notifies the server. The server then uses AWS SNS to immediately send the information to emergency contacts. In this step, the emergency call process is activated based on the emergency voice command as input, and a rapid notification is completed as output.

[0273] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0274] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0275] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0276] [Second Embodiment]

[0277] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0278] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0279] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0280] 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. Also, the microphone 238, the speaker 240, and the camera 42 are connected to the bus 52.

[0281] The microphone 238 receives instructions etc. from the user 20 by receiving the voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into voice data, and outputs it to the processor 46. The speaker 240 outputs voice according to an instruction from the processor 46.

[0282] The camera 42 is a small digital camera equipped with an optical system such as a lens, an aperture, and a shutter, and an imaging device such as a CMOS (Complementary Metal - Oxide - Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and images the surroundings of the user 20 (for example, an imaging range defined by an angle of view corresponding to the field of view of a general healthy person).

[0283] 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 performed in a secure state.

[0284] 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 processing program 56 is stored in the storage 32.

[0285] The specific processing program 56 is an example of the "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 operating as the specific processing unit 290 according to the specific processing program 56 executed by the processor 28 on the RAM 30.

[0286] 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 specific processing unit 290.

[0287] In the smart glasses 214, the reception and output processing is performed by the processor 46. The storage 50 stores a reception and output program 60. The processor 46 reads the reception and output program 60 from the storage 50 and executes the read reception and output program 60 on the RAM 48. The reception and output processing is realized by operating as the control unit 46A according to the reception and output program 60 executed by the processor 46 on the RAM 48.

[0288] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0289] The present invention is an integrated AI system for supporting the lives of the elderly. To assist the lives of the elderly users, this system has the following functions.

[0290] First, by the user wearing a wearable device, health data is collected daily. The terminal receives the data and periodically transmits it to the server. The server analyzes these data in real time and evaluates the user's health status. When an abnormality is detected, it notifies the user via the terminal and proposes an improvement plan for health management. For example, when the user's heart rate is high, the server gives advice such as "Let's spend the day quietly" through the terminal.

[0291] Next, through voice recognition, the device enables natural conversation with the user. When the user asks everyday questions or engages in casual conversation, the server generates an appropriate response, which the device then returns to the user. This reduces feelings of isolation and allows the user to enjoy the conversation. For example, if the user asks, "What's the weather like today?", the device might reply, "It's cloudy, then sunny."

[0292] To improve cognitive function, the server generates quizzes and puzzles tailored to the user based on past data. The terminal presents them to the user and sends the answers to the server. The server analyzes the results, monitors changes in cognitive function, and adjusts the content provided next time. For example, by solving a provided puzzle, a new challenge is prepared based on the user's performance.

[0293] It also features voice-activated home appliance control and a lifestyle reminder function to notify users of medication times. If a user says, "Turn on the lights," the device will operate the corresponding appliance and execute the command. Based on server settings, the device will also notify the user, "It's time to take your medication," and the user will receive a reminder.

[0294] Finally, as a crisis management function, the device constantly monitors the user's environment. If the user reports an abnormality or an unforeseen incident occurs, the device immediately notifies the server, which then notifies the appropriate emergency contacts. For example, if the device recognizes the user's voice saying "call for help," it immediately notifies registered family members or medical institutions.

[0295] These features enable the present invention to provide an environment in which elderly people can live safely and comfortably.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] When the user wears a wearable device, body data is recorded. The terminal continuously obtains data such as heart rate, number of steps, body temperature, etc. from the device.

[0299] Step 2:

[0300] The terminal uploads the acquired data to the server at regular time intervals. Thereby, the server can grasp the user's latest health condition.

[0301] Step 3:

[0302] The server analyzes the received data and evaluates the user's health condition. When an abnormal value is detected compared with past data, the abnormality is grasped in real time.

[0303] Step 4:

[0304] The server generates improvement proposals for health management. The proposals are individualized and appropriately optimized based on the user's health condition. For example, when stress is high, relaxation is recommended.

[0305] Step 5:

[0306] The server sends the proposal to the terminal, and the terminal notifies the user by voice or text. The user can adjust their actions based on it.

[0307] Step 6:

[0308] When the user speaks, the terminal performs voice recognition and analyzes the content. The voice data is sent to the server to interpret the meaning and generate a natural response.

[0309] Step 7:

[0310] The response generated by the server is sent to the terminal, and the terminal conveys it to the user by voice. Thereby, an interaction is established between the user and the system.

[0311] Step 8:

[0312] If a user wishes to improve their cognitive function, the server will refer to their past records to generate quizzes and puzzles best suited to them.

[0313] Step 9:

[0314] The device presents the content to the user and collects response data. The user's performance is sent to the server, which then analyzes the results.

[0315] Step 10:

[0316] Based on the analysis results, the server adjusts the content provided next time, continuously supporting the user's cognitive functions.

[0317] Step 11:

[0318] Following voice commands, the device operates home appliances. Additionally, based on a schedule set by the server, the device reminds users of medication times.

[0319] Step 12:

[0320] The terminal constantly monitors sensor information and immediately notifies the server if it detects an abnormal condition or an urgent voice input.

[0321] Step 13:

[0322] The server quickly analyzes any notifications it receives and automatically notifies registered emergency contacts or services.

[0323] Step 14:

[0324] The server continues to send follow-up instructions to the terminal after the report is made, providing support to ensure the user is safe.

[0325] (Example 1)

[0326] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] Elderly people face various health problems, loneliness, and cognitive decline in their daily lives. These challenges reduce their quality of life and increase their anxiety in daily living. Furthermore, inadequate systems for prompt emergency reporting and monitoring their health can lead to serious risks.

[0328] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0329] In this invention, the server includes means for generating personalized health management plans, means for facilitating natural dialogue and reducing feelings of loneliness, and means for providing content to improve cognitive function. This enables health management, a sense of security, and maintenance of cognitive function in the daily lives of elderly people.

[0330] "Biometric information" refers to physical data such as heart rate, steps taken, and sleep patterns of elderly individuals.

[0331] A "personalized health management plan" refers to specific guidance for maintaining or improving health, provided based on each elderly person's biometric information.

[0332] "Voice recognition" refers to a technology that uses a digital system to recognize the voices of elderly people and convert them into corresponding text or instructions.

[0333] "Natural dialogue" refers to communication between computer systems that takes place as naturally as conversations between humans.

[0334] "Cognitive function improvement content" refers to intellectually stimulating content such as quizzes and puzzles provided to improve the memory and judgment of elderly people.

[0335] "Appliance operation" refers to the act of controlling electrical appliances and household equipment based on the voice commands of elderly people.

[0336] "Life Reminders" refers to a function that notifies elderly people to remember their daily schedules and important matters.

[0337] The means of detecting an "abnormality" and making an emergency call refers to a function that quickly sends a warning to the outside when any physical or environmental change occurs in an elderly person.

[0338] The embodiment of the invention is a comprehensive AI system for supporting the elderly. This system assists the daily lives of elderly users and includes several key functions.

[0339] By wearing a wearable device, users can routinely acquire biometric information such as heart rate, steps taken, and sleep patterns. This device is equipped with an accelerometer and a heart rate sensor. The device periodically receives this data via Bluetooth or Wi-Fi and sends it to a server in the cloud.

[0340] The server is equipped with a data analysis program written in Python, which analyzes received biometric information in real time. The analyzed data is evaluated by an anomaly detection algorithm to monitor the user's health status. If an anomaly is detected, the server uses a generative AI model to generate personalized health management suggestions and sends them to the terminal via an API.

[0341] A device with speech recognition capabilities enables interaction with the user. Upon receiving a question from the user, the device uses speech recognition software to convert it into text. The server then generates an appropriate response using natural language processing and sends it back to the device as a text message.

[0342] The server also uses an AI model to generate quizzes and puzzles designed to improve cognitive function. A specific example of a prompt would be, "Generate a quiz to stimulate cognitive function in the elderly." The device then provides the user with an appropriate challenge and sends the results to the server.

[0343] Furthermore, the device handles appliance operation based on voice commands and provides daily reminder functions. When a user gives a voice command such as "Turn on the lights," the device controls the relevant home appliances. Based on a server schedule, the device also notifies the user by voice with reminders such as "It's time to take your medicine."

[0344] As a crisis management function, the terminal constantly monitors the user's environment and immediately notifies the server if it recognizes an emergency phrase such as "call for help." The server then notifies registered contacts, enabling a rapid response.

[0345] These features enable elderly people to live safely and comfortably, thus realizing a specific embodiment of this invention.

[0346] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0347] Step 1:

[0348] The user wears a wearable device that collects biometric information, including heart rate and steps, in real time. The input data is measured via sensors and temporarily stored within the wearable device. This data is then transmitted to a terminal via Bluetooth. The terminal stores the latest biometric information as output.

[0349] Step 2:

[0350] The device periodically transmits received biometric information to the server via Wi-Fi. The input data is biometric information transmitted from a wearable device. The device processes the data as packets and transfers them to the server using a secure communication protocol. As output, the server aggregates the latest biometric information.

[0351] Step 3:

[0352] The server executes an analysis program using Python to analyze received biometric data in real time. The input data is biometric information sent from the terminal. The server program applies an anomaly detection algorithm to extract anomaly patterns from the data. The output generates information indicating whether or not an anomaly was detected and details thereof.

[0353] Step 4:

[0354] The server uses a generative AI model to generate personalized health management plans. The input data consists of anomaly detection results and historical biometric information. The generated health management plans include suggestions derived from data-driven analysis. As output, personalized suggestions are generated and sent to the terminal.

[0355] Step 5:

[0356] The terminal receives health management suggestions from the server and notifies the user. The input data is the health management suggestion sent from the server. The terminal uses speech synthesis software to provide the user with appropriate advice via voice. As output, the user receives health suggestions in real time.

[0357] Step 6:

[0358] The user asks questions about their daily life using voice. The input data consists of the user's voice instructions. The terminal uses voice recognition software to convert the voice into text and sends it to the server. As output, the server receives the user's questions in text format.

[0359] Step 7:

[0360] The server uses natural language processing to generate appropriate responses to user questions. The input data is the user's question text from the terminal. The server analyzes the question and retrieves relevant information. As output, the generated response is sent to the terminal.

[0361] Step 8:

[0362] The terminal relays the response from the server to the user. The input data is the response sent from the server. The terminal performs speech synthesis to provide the user with a natural-sounding answer. As output, the user receives the answer to the question.

[0363] Step 9:

[0364] The server generates quizzes aimed at improving cognitive function using a generative AI model based on past data. The input data consists of the user's past cognitive performance data. The server designs new quizzes based on this data. As output, personalized questions are generated and sent to the device.

[0365] Step 10:

[0366] The terminal presents the user with a quiz and receives the answer. The input data consists of the quiz sent from the server and the user's answer. The terminal records the user's answer and prepares to send it to the server. As output, the answer data is sent to the server.

[0367] Step 11:

[0368] The server analyzes the user's response data and uses it to suggest the next content. The input data is the response information sent from the terminal. The server uses a generation AI model to adjust the content to be provided next. The adjusted content to be provided next is generated as output.

[0369] Step 12:

[0370] The user provides voice commands to operate the device. The input data is the user's voice commands. The terminal recognizes these voice commands and transmits them to the appropriate device. As an output, the device is controlled according to the user's intent.

[0371] Step 13:

[0372] The server monitors emergency phrases. The input data is the user's ambient voice. If the server detects an anomaly, it immediately prepares to notify registered contacts. As output, an emergency contact is sent to the registered relevant parties.

[0373] (Application Example 1)

[0374] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Health management, reducing feelings of isolation, maintaining and improving cognitive function, and ensuring safety in the lives of the elderly are key challenges. Conventional systems are insufficient to address these individual needs, highlighting the growing need for systems that can provide multiple functions in an integrated manner. Furthermore, real-time monitoring of health status and rapid notification of abnormalities are required, but an effective solution combining these functions has not existed until now.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0377] This invention includes a server that includes means for analyzing biometric data acquired from a user and generating personalized health management suggestions, means for facilitating natural conversation through voice recognition to reduce feelings of loneliness, means for providing personalized cognitive function improvement content, and means for collecting biometric data in real time via a mobile terminal and immediately notifying of abnormalities based on pre-set criteria. This enables multifaceted support for the lives of the elderly and the realization of a safe and comfortable living environment.

[0378] "Biometric data" refers to information about a user's body, such as heart rate and activity level, that is collected to assess their health status.

[0379] "Personalized health management suggestions" are personalized health maintenance and improvement advice generated based on the user's biometric data.

[0380] "Speech recognition" is a technology that analyzes a speaker's voice, converts it into text, understands it, and generates a response.

[0381] "Natural conversation" refers to a means of communication where users and systems can smoothly engage in everyday interactions through language, thereby deepening mutual understanding.

[0382] "Cognitive function improvement content" refers to interactive content such as quizzes and puzzles that are provided with the aim of maintaining or improving users' cognitive abilities.

[0383] A "mobile device" is a computing device that a user can carry around, and smartphones and smart glasses fall into this category.

[0384] "Instant notification" is a function that quickly informs users or registered contacts of abnormal situations, and is an important element that enables a rapid response.

[0385] "Home appliances" refer to various devices used in the home, such as home appliances and smart home devices, which can be operated by voice commands.

[0386] "Environmental information" refers to a wide range of external data, including conventional weather information and news, and aims to provide information relevant to users' lives.

[0387] The system for implementing this invention was developed to support the daily lives of elderly users. The system primarily consists of a server and terminals (smartphones and smart glasses). Details of each function are described below.

[0388] The server receives biometric data collected via Bluetooth from the user's wearable device. This data includes heart rate and step count, and is obtained using the Google Fit API or Apple HealthKit. The server analyzes this biometric data in real time using TensorFlow, and if an anomaly is detected, it notifies the user via the device using Firebase Notification. For example, if a user's heart rate increases abnormally while running, they will immediately receive advice such as "Please take a short break."

[0389] The speech recognition function uses the Google Cloud Speech-to-Text API to convert speech into text. This allows the device to recognize user questions and commands and send them to the server. The server uses GPT to generate an appropriate response, which the device then returns to the user verbally. For example, if the user asks, "What's the weather like today?", the device will respond, "It's sunny with occasional clouds today."

[0390] Furthermore, to improve cognitive function, the server generates quizzes and puzzles using Unity based on the user's past data. The user's answers to the provided quizzes are sent to the server and analyzed. Based on the analysis results, the content provided next time is adjusted. For example, "after solving a simple math problem, a new problem is presented."

[0391] Integration with home appliances is achieved through voice commands. Using the Amazon Alexa Skills Kit or Google Assistant SDK, it is possible to control home appliances according to voice commands. For example, if a user says, "Turn off the living room lights," the lights will turn off accordingly.

[0392] Examples of prompt statements:

[0393] "The user's current heart rate is 110. Is this abnormal? How should I respond?"

[0394] "Please create appropriate brain training games for seniors."

[0395] In this way, this system provides multifaceted support for the daily lives of the elderly, offering a safe and comfortable living environment.

[0396] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0397] Step 1:

[0398] The user wears a wearable device that collects biometric data such as heart rate and steps taken. This data is transmitted to the device via Bluetooth.

[0399] Step 2:

[0400] The device transfers the received biometric data to the server using the Google Fit API or Apple HealthKit. This data is then input into the server's data processing unit.

[0401] Step 3:

[0402] The server uses TensorFlow to analyze biometric data. It compares the data to pre-defined criteria to detect abnormalities such as heart rate, and outputs whether or not an abnormality is present as an analysis result.

[0403] Step 4:

[0404] If an anomaly is detected, the server sends a notification to the device via Firebase Notification. The device displays this notification to the user as an alert. If the user is running, the device will display a message saying, "Please take a short break."

[0405] Step 5:

[0406] The user asks "What's the weather like today?" using voice. The device converts this voice into text using the Google Cloud Speech-to-Text API and sends it to the server.

[0407] Step 6:

[0408] The server uses GPT to generate an appropriate response based on the input text. The generated text response will be "Today is sunny with occasional clouds" and will be printed to the terminal.

[0409] Step 7:

[0410] The device returns the generated response to the user as audio. This allows the user to experience a natural conversation.

[0411] Step 8:

[0412] The server generates quizzes and puzzles using Unity to improve cognitive function. The input data used at this time is information about the user's past activity history and abilities.

[0413] Step 9:

[0414] The generated quizzes and puzzles are sent to the device and displayed to the user. The user's answers are sent back from the device to the server, which analyzes the answers and adjusts the content for the next session.

[0415] Step 10:

[0416] The user gives a voice command, "Turn off the living room lights." The device converts the voice to text and sends it to the Amazon Alexa Skills Kit or Google Assistant SDK via a server.

[0417] Step 11:

[0418] The server analyzes the received command and sends operation instructions to the corresponding household device. The output confirms that the operation to turn off the electricity has been performed.

[0419] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0420] This invention is an AI system that comprehensively supports the lives of the elderly, and is characterized in that it utilizes an emotion engine to recognize the user's emotions and provides appropriate responses in accordance with those emotions.

[0421] This system collects health data through wearable devices worn daily by elderly individuals. The device receives this data in real time and sends it to a server. The server analyzes the health data and notifies the user of the information obtained as personalized health management suggestions. For example, if the server detects an increase in heart rate, the device will send a suggestion to the user such as, "We recommend you take a rest."

[0422] The emotion engine can recognize a user's emotional state by analyzing their voice and facial expression data. The device inputs this data into the emotion engine, which then analyzes the user's emotions on a server. Based on the results, the server provides the user with the most suitable relaxation program or entertainment content. For example, if the user is feeling stressed, the device might suggest, "Would you like to listen to some music?"

[0423] In addition, the emotion engine can record changes in the user's emotions over a long period. This data is used to make health management suggestions more accurate. For example, the server can refer to the emotion history and, if the user frequently feels anxious during certain times of the day, analyze the cause and make suggestions for lifestyle improvements.

[0424] Furthermore, the device can operate home appliances and remind users of medication times based on voice commands. If a user says, "Turn on the TV," the device will execute the command. It also supports the user's life by reminding them of important appointments and medication times on time.

[0425] To prepare for emergencies, the device constantly monitors the user's actions and spoken words. If an abnormal situation is detected, it promptly notifies the server, which then contacts the appropriate emergency contacts. For example, if the user says "Help," the device immediately initiates the notification process.

[0426] Thus, by incorporating an emotional engine, the present invention provides a support system that enables elderly people to live their daily lives with greater peace of mind and comfort.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The user wears a wearable device, which continuously collects health data such as heart rate, steps taken, and body temperature.

[0430] Step 2:

[0431] The device sends the acquired health data to the server. The server begins monitoring the data in real time.

[0432] Step 3:

[0433] The server analyzes the received health data and generates personalized health management suggestions, including comparisons with past data.

[0434] Step 4:

[0435] Based on the analysis results, the server sends optimized health management suggestions to the terminal. The terminal notifies the user of this, providing on-screen displays and audio alerts.

[0436] Step 5:

[0437] When a user speaks, the device uses speech recognition to analyze the input. The device then sends this data to the server.

[0438] Step 6:

[0439] The server processes the received audio data using natural language processing to generate an appropriate response.

[0440] Step 7:

[0441] The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state. Based on this emotional state, the server selects appropriate responses and content for the user.

[0442] Step 8:

[0443] Based on the emotion recognition results, the server generates relaxation content and follow-up messages that correspond to the emotions the user is feeling.

[0444] Step 9:

[0445] The device receives a response from the server and communicates it to the user via voice. For example, a user feeling stressed might receive a message like, "Take a deep breath and relax."

[0446] Step 10:

[0447] When a user gives voice commands to operate home appliances, the terminal receives those commands and controls the appropriate appliance.

[0448] Step 11:

[0449] If a device detects an anomaly, it immediately notifies the server. The server analyzes the notification and, if necessary, automatically notifies registered emergency contacts.

[0450] Step 12:

[0451] The server integrates and manages emotional and health data, and based on long-term analysis, it regularly provides suggestions to improve users' health and well-being.

[0452] (Example 2)

[0453] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0454] In the lives of the elderly, there are demands for health management, reduction of feelings of loneliness, and rapid response to emergencies. However, current technology does not adequately provide appropriate suggestions and support based on the individual health conditions and emotions of the elderly, which is a challenge. Furthermore, it is necessary to reduce the burden on the elderly in operating home appliances based on voice commands and utilizing daily reminder functions. In addition, establishing a rapid and accurate notification system in emergencies is also important.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0456] This invention includes a server that analyzes physiological data acquired from elderly individuals and generates personalized health management suggestions, a server that facilitates natural dialogue with elderly individuals through voice recognition to reduce feelings of loneliness, and a server that analyzes emotional states to provide relaxation programs and entertainment content. This enables individualized support tailored to the health status and emotions of elderly individuals.

[0457] "Physiological data" refers to numerical information about the physical condition of elderly people, such as heart rate, body temperature, and steps taken.

[0458] "Health management suggestions" are specific advice for maintaining or improving the health of elderly individuals, derived from the analysis of physiological data.

[0459] "Voice recognition" is a technology that allows computer systems to recognize and respond to the words spoken by elderly people.

[0460] "Natural dialogue" refers to interactions between humans and computers that closely resemble conversations between humans.

[0461] "Reducing feelings of loneliness" means alleviating the loneliness and isolation that older adults experience by providing opportunities for social connection and dialogue.

[0462] "Emotional state" refers to the mental state or mood an individual is experiencing at a particular point in time.

[0463] A "relaxation program" is a program or activity designed to alleviate users' mental tension and provide comfort and peace of mind.

[0464] "Entertainment content" refers to content such as music, movies, and games that can be enjoyed by the elderly.

[0465] "Operating home appliances" refers to controlling electrical appliances in the home through voice commands or other interfaces.

[0466] "Lifestyle notifications" refer to timely information and events necessary for daily life that are communicated to users.

[0467] "Notifying emergency contacts" refers to the action of quickly and automatically sending notifications to pre-set contacts when an elderly person finds themselves in a dangerous situation.

[0468] This invention relates to an AI system that comprehensively supports the lives of the elderly, and in particular aims to recognize the emotions of the elderly by utilizing an emotion engine and provide appropriate responses in accordance with those emotions. This system consists of a wearable device, a terminal such as a smartphone or tablet, and a server connected to the cloud.

[0469] Users collect physiological data through wearable devices they wear daily. These devices provide important health information, such as recording heart rate, body temperature, and steps taken. The devices receive this data in real time via Bluetooth or Wi-Fi and transmit it to a server in the cloud.

[0470] The server analyzes the user's health status using machine learning algorithms based on the received physiological data. One example of software used for analysis is a generative AI model that predicts health status. Based on the analysis results from the server, personalized health management suggestions are sent to the user. For example, if the heart rate is abnormally high, a notification such as "We recommend you rest for a while" will be sent via the device.

[0471] The device also periodically collects the user's voice data and inputs it into the emotion engine. The server uses this emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the results, it suggests relaxation programs and entertainment content suitable for the user, for example, by displaying options such as "Would you like to listen to music?"

[0472] The server also records user emotional data over long periods and evaluates changes in emotions based on past history. This data helps provide more accurate health management suggestions. Furthermore, the terminal operates home appliances and provides lifestyle notifications based on user voice commands. If the user says, "Turn on the TV," the terminal will execute that command.

[0473] In preparation for emergencies, the device constantly monitors the user's actions and statements, and immediately notifies the server if it detects any anomalies. The server has the capability to notify registered emergency contacts as needed. For example, if the user says "Help," the device will promptly initiate the notification process.

[0474] A concrete example of a prompt is, "Generate a sentence that identifies the emotions of an elderly person and recommends relaxing music." This prompt allows the AI ​​model to generate text data that provides appropriate music recommendations.

[0475] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0476] Step 1:

[0477] Users wear a wearable device on a daily basis. This device collects physiological data such as heart rate, body temperature, and steps taken in real time. This becomes the input data. The collected data is sent to a terminal via Bluetooth or Wi-Fi. The terminal receives the data and then sends it to a server in the cloud.

[0478] Step 2:

[0479] The server receives physiological data sent from the terminal. The input data is processed using a health analysis algorithm. This algorithm analyzes the data and evaluates the user's health status. The analyzed data generates output as health management suggestions and sends it to the terminal. For example, if the heart rate is higher than normal, it generates and outputs a suggestion such as "We recommend taking a break."

[0480] Step 3:

[0481] The device notifies the user of health management suggestions received from the server. Specifically, a suggestion message pops up on the device's display screen. The user can then adjust their actions based on these suggestions.

[0482] Step 4:

[0483] The device periodically and automatically collects the user's voice data. Voice input is sent to a server for analysis by an emotion engine. This emotion analysis processes the voice data to identify the user's emotional state. The analysis results are generated as output, such as relaxation programs or entertainment content.

[0484] Step 5:

[0485] Based on the results of the emotion analysis, the server selects a relaxation program or entertainment content suitable for the user and outputs it to the terminal. The terminal presents the suggestion to the user and displays a confirmation message such as "Do you want to play relaxation music?" as a concrete action.

[0486] Step 6:

[0487] The server records emotional data from users over a long period and uses this data to compare with past history. This input data is used to make future health management suggestions. As an output, suggestions based on specific emotional patterns are generated, contributing to improving the lifestyles of elderly people.

[0488] Step 7:

[0489] The device recognizes voice commands from the user and operates household appliances. Voice commands are processed as input, and the appliance control algorithm directly performs the action. For example, based on the command "Turn on the lights," the output is that the lighting fixture turns on.

[0490] Step 8:

[0491] The device monitors for abnormal behavior and voice in preparation for emergencies. If an abnormality is detected, the information is reported to the server. This input data is then used as output to immediately notify emergency contacts, and specific actions are taken to ensure the user's safety.

[0492] (Application Example 2)

[0493] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0494] With the increasing elderly population, the burden on caregivers, particularly in elderly care settings, is growing. Elderly individuals often experience health problems and emotional fluctuations, making prompt and individualized care essential. However, caregivers face the challenge of providing meticulous care to every elderly person amidst their busy daily schedules. Therefore, there is a need for a system that can monitor the emotional and health status of elderly individuals in real time and provide individualized care based on that information.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0496] In this invention, the server includes means for analyzing health data and generating personalized health management suggestions, means for displaying the emotional state of elderly individuals in real time using emotion analysis and providing caregivers with appropriate response guidance, and means for detecting abnormalities and making emergency calls. This enables the provision of care tailored to each elderly person in real time, reduces the burden on caregivers, and allows elderly people to live with peace of mind.

[0497] "Health data" refers to information about the physical condition of elderly individuals, including physiological indicators such as heart rate, blood pressure, and body temperature.

[0498] "Personalized health management proposals" refer to providing specific advice and guidance for maintaining and improving the health of each elderly person, based on the results of an analysis of acquired health data.

[0499] "Voice recognition" is a technology in which a computer analyzes the voice spoken by elderly people, understands its content, and takes appropriate action.

[0500] "Emotional analysis" is a process that evaluates the current emotional state of elderly individuals based on their voice and facial expression data.

[0501] "Real-time display" means providing information about the elderly person's condition and the system in a visual and timely manner without delay.

[0502] "Providing caregivers with appropriate response guidelines" means presenting guidelines that specifically indicate what caregivers should do to support elderly individuals.

[0503] "Detecting anomalies" means identifying changes outside the normal range in the daily behaviors and health status of elderly individuals and recognizing the possibility that a problem has occurred.

[0504] An "emergency notification" is a communication process designed to quickly inform the appropriate agencies or individuals of an emergency situation.

[0505] This elderly support system combines a wearable device, smart glasses, and a server. The wearable device acquires health data such as heart rate and blood pressure in real time and sends it to the server. The server receives this data, evaluates the user's health status by comparing it with past data, and generates personalized health management suggestions. For example, if the heart rate is higher than normal, it will generate a suggestion such as, "We recommend taking a break."

[0506] Furthermore, the camera and microphone built into the smart glasses collect the elderly person's facial expressions and voice. The server uses this data to perform emotion analysis using a generative AI model. Using OpenCV and TensorFlow, the emotional state of the elderly person is analyzed from the acquired data and visualized in real time in the caregiver's field of view. Based on this information, specific action guidelines are presented to the caregiver to support how to interact with the elderly person.

[0507] When an anomaly is detected, the server immediately sends an emergency alert. For example, if an elderly person makes an emergency voice call such as "Help me," the server analyzes the information and sends a notification to a pre-designated emergency contact.

[0508] As a concrete example, if a caregiver using smart glasses in a nursing home detects a depressed mood in an elderly person's facial expression, the glasses will display instructions such as "Suggest relaxing activities." Furthermore, an example of a prompt for the generating AI model could be: "Design a care support system that recognizes the emotional state of elderly people and provides individualized suggestions. Please detail the necessary technologies and data flow."

[0509] This system is implemented by combining data analysis using Amazon Web Services with sentiment analysis functions using OpenCV and TensorFlow, enabling flexible and scalable operation.

[0510] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0511] Step 1:

[0512] Wearable devices collect health data such as heart rate and blood pressure from elderly individuals in real time. This data is transmitted to a terminal using Bluetooth. The terminal then transfers the data to a server. At this stage, physiological indicator data is acquired as input and prepared to be transmitted to the server as output.

[0513] Step 2:

[0514] The server analyzes the received health data. Specifically, it uses AWS Lambda to process the data and compares it with historical data stored in Amazon RDS to assess the current health status. The inputs are physiological indicator data and historical health history, and the output is a health status assessment result. If an abnormality is detected, it generates recommendations for the next optimal health management steps.

[0515] Step 3:

[0516] The server uses a generative AI model to generate specific health management suggestions for the user based on the evaluation results of the received health data. This prompt includes "generative AI model" and "health management suggestions." The evaluation results and generative AI model as inputs are used to produce the suggestions as output.

[0517] Step 4:

[0518] Meanwhile, the device collects voice and facial expression data from elderly individuals via smart glasses. This data is transmitted to a server in real time for emotion analysis. Voice and visual data are sent from the device to the server as input.

[0519] Step 5:

[0520] The server uses OpenCV and TensorFlow to perform emotion analysis. This allows it to analyze the emotional state of elderly individuals from voice and facial expression data, and obtain specific emotion classification results. The input consists of voice and facial expression data, and the emotional state is output as a result of the data calculations.

[0521] Step 6:

[0522] The server provides the caregiver with appropriate response guidance based on the emotional state. Visual feedback is sent to the smart glasses' display. This presents the caregiver with guidelines as visual information based on the input emotion analysis results.

[0523] Step 7:

[0524] When a user issues an emergency, the device detects it and notifies the server. The server then uses AWS SNS to immediately send the information to emergency contacts. In this step, the emergency call process is activated based on the emergency voice command as input, and a rapid notification is completed as output.

[0525] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0526] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0527] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0528] [Third Embodiment]

[0529] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0530] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0531] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0532] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0533] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0534] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0535] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0536] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0537] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0538] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0539] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0540] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0541] This invention is a comprehensive AI system designed to support the lives of elderly people. To assist the lives of its elderly users, this system has the following functions:

[0542] First, by wearing a wearable device, users collect health data on a daily basis. The device receives this data and periodically sends it to a server. The server analyzes this data in real time and evaluates the user's health status. If an abnormality is detected, the server notifies the user via the device and suggests improvements to their health management. For example, if the user's heart rate is high, the server will advise them through the device to "take it easy today."

[0543] Next, through voice recognition, the device enables natural conversation with the user. When the user asks everyday questions or engages in casual conversation, the server generates an appropriate response, which the device then returns to the user. This reduces feelings of isolation and allows the user to enjoy the conversation. For example, if the user asks, "What's the weather like today?", the device might reply, "It's cloudy, then sunny."

[0544] To improve cognitive function, the server generates quizzes and puzzles tailored to the user based on past data. The terminal presents them to the user and sends the answers to the server. The server analyzes the results, monitors changes in cognitive function, and adjusts the content provided next time. For example, by solving a provided puzzle, a new challenge is prepared based on the user's performance.

[0545] It also features voice-activated home appliance control and a lifestyle reminder function to notify users of medication times. If a user says, "Turn on the lights," the device will operate the corresponding appliance and execute the command. Based on server settings, the device will also notify the user, "It's time to take your medication," and the user will receive a reminder.

[0546] Finally, as a crisis management function, the device constantly monitors the user's environment. If the user reports an abnormality or an unforeseen incident occurs, the device immediately notifies the server, which then notifies the appropriate emergency contacts. For example, if the device recognizes the user's voice saying "call for help," it immediately notifies registered family members or medical institutions.

[0547] These features enable the present invention to provide an environment in which elderly people can live safely and comfortably.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] When a user wears a wearable device, their physical data is recorded. The device continuously acquires data such as heart rate, steps taken, and body temperature from the device.

[0551] Step 2:

[0552] The device uploads the acquired data to the server at regular intervals. This allows the server to understand the user's latest health status.

[0553] Step 3:

[0554] The server analyzes the received data and assesses the user's health status. If an anomaly is detected by comparing it with past data, the server identifies the anomaly in real time.

[0555] Step 4:

[0556] The server generates suggestions for improving health management. These suggestions are personalized and appropriately optimized based on the user's health status. For example, if stress levels are high, relaxation might be recommended.

[0557] Step 5:

[0558] The server sends a suggestion to the terminal, and the terminal notifies the user via voice or text. The user can then adjust their actions based on this notification.

[0559] Step 6:

[0560] When a user speaks, the device performs speech recognition and analyzes the content. It sends the voice data to a server for interpretation and generates a natural-sounding response.

[0561] Step 7:

[0562] The server sends the generated response to the terminal, which then communicates it to the user via voice. This establishes a dialogue between the user and the system.

[0563] Step 8:

[0564] If a user wishes to improve their cognitive function, the server will refer to their past records to generate quizzes and puzzles best suited to them.

[0565] Step 9:

[0566] The device presents the content to the user and collects response data. The user's performance is sent to the server, which then analyzes the results.

[0567] Step 10:

[0568] Based on the analysis results, the server adjusts the content provided next time, continuously supporting the user's cognitive functions.

[0569] Step 11:

[0570] Following voice commands, the device operates home appliances. Additionally, based on a schedule set by the server, the device reminds users of medication times.

[0571] Step 12:

[0572] The terminal constantly monitors sensor information and immediately notifies the server if it detects an abnormal condition or an urgent voice input.

[0573] Step 13:

[0574] The server quickly analyzes any notifications it receives and automatically notifies registered emergency contacts or services.

[0575] Step 14:

[0576] The server continues to send follow-up instructions to the terminal after the report is made, providing support to ensure the user is safe.

[0577] (Example 1)

[0578] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] Elderly people face various health problems, loneliness, and cognitive decline in their daily lives. These challenges reduce their quality of life and increase their anxiety in daily living. Furthermore, inadequate systems for prompt emergency reporting and monitoring their health can lead to serious risks.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0581] In this invention, the server includes means for generating personalized health management plans, means for facilitating natural dialogue and reducing feelings of loneliness, and means for providing content to improve cognitive function. This enables health management, a sense of security, and maintenance of cognitive function in the daily lives of elderly people.

[0582] "Biometric information" refers to physical data such as heart rate, steps taken, and sleep patterns of elderly individuals.

[0583] A "personalized health management plan" refers to specific guidance for maintaining or improving health, provided based on each elderly person's biometric information.

[0584] "Voice recognition" refers to a technology that uses a digital system to recognize the voices of elderly people and convert them into corresponding text or instructions.

[0585] "Natural dialogue" refers to communication between computer systems that takes place as naturally as conversations between humans.

[0586] "Cognitive function improvement content" refers to intellectually stimulating content such as quizzes and puzzles provided to improve the memory and judgment of elderly people.

[0587] "Appliance operation" refers to the act of controlling electrical appliances and household equipment based on the voice commands of elderly people.

[0588] "Life Reminders" refers to a function that notifies elderly people to remember their daily schedules and important matters.

[0589] The means of detecting an "abnormality" and making an emergency call refers to a function that quickly sends a warning to the outside when any physical or environmental change occurs in an elderly person.

[0590] The embodiment of the invention is a comprehensive AI system for supporting the elderly. This system assists the daily lives of elderly users and includes several key functions.

[0591] By wearing a wearable device, users can routinely acquire biometric information such as heart rate, steps taken, and sleep patterns. This device is equipped with an accelerometer and a heart rate sensor. The device periodically receives this data via Bluetooth or Wi-Fi and sends it to a server in the cloud.

[0592] The server is equipped with a data analysis program written in Python, which analyzes received biometric information in real time. The analyzed data is evaluated by an anomaly detection algorithm to monitor the user's health status. If an anomaly is detected, the server uses a generative AI model to generate personalized health management suggestions and sends them to the terminal via an API.

[0593] A device with speech recognition capabilities enables interaction with the user. Upon receiving a question from the user, the device uses speech recognition software to convert it into text. The server then generates an appropriate response using natural language processing and sends it back to the device as a text message.

[0594] The server also uses an AI model to generate quizzes and puzzles designed to improve cognitive function. A specific example of a prompt would be, "Generate a quiz to stimulate cognitive function in the elderly." The device then provides the user with an appropriate challenge and sends the results to the server.

[0595] Furthermore, the device handles appliance operation based on voice commands and provides daily reminder functions. When a user gives a voice command such as "Turn on the lights," the device controls the relevant home appliances. Based on a server schedule, the device also notifies the user by voice with reminders such as "It's time to take your medicine."

[0596] As a crisis management function, the terminal constantly monitors the user's environment and immediately notifies the server if it recognizes an emergency phrase such as "call for help." The server then notifies registered contacts, enabling a rapid response.

[0597] These features enable elderly people to live safely and comfortably, thus realizing a specific embodiment of this invention.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1:

[0600] The user wears a wearable device that collects biometric information, including heart rate and steps, in real time. The input data is measured via sensors and temporarily stored within the wearable device. This data is then transmitted to a terminal via Bluetooth. The terminal stores the latest biometric information as output.

[0601] Step 2:

[0602] The device periodically transmits received biometric information to the server via Wi-Fi. The input data is biometric information transmitted from a wearable device. The device processes the data as packets and transfers them to the server using a secure communication protocol. As output, the server aggregates the latest biometric information.

[0603] Step 3:

[0604] The server executes an analysis program using Python to analyze received biometric data in real time. The input data is biometric information sent from the terminal. The server program applies an anomaly detection algorithm to extract anomaly patterns from the data. The output generates information indicating whether or not an anomaly was detected and details thereof.

[0605] Step 4:

[0606] The server uses a generative AI model to generate personalized health management plans. The input data consists of anomaly detection results and historical biometric information. The generated health management plans include suggestions derived from data-driven analysis. As output, personalized suggestions are generated and sent to the terminal.

[0607] Step 5:

[0608] The terminal receives health management suggestions from the server and notifies the user. The input data is the health management suggestion sent from the server. The terminal uses speech synthesis software to provide the user with appropriate advice via voice. As output, the user receives health suggestions in real time.

[0609] Step 6:

[0610] The user asks questions about their daily life using voice. The input data consists of the user's voice instructions. The terminal uses voice recognition software to convert the voice into text and sends it to the server. As output, the server receives the user's questions in text format.

[0611] Step 7:

[0612] The server uses natural language processing to generate appropriate responses to user questions. The input data is the user's question text from the terminal. The server analyzes the question and retrieves relevant information. As output, the generated response is sent to the terminal.

[0613] Step 8:

[0614] The terminal relays the response from the server to the user. The input data is the response sent from the server. The terminal performs speech synthesis to provide the user with a natural-sounding answer. As output, the user receives the answer to the question.

[0615] Step 9:

[0616] The server generates quizzes aimed at improving cognitive function using a generative AI model based on past data. The input data consists of the user's past cognitive performance data. The server designs new quizzes based on this data. As output, personalized questions are generated and sent to the device.

[0617] Step 10:

[0618] The terminal presents the user with a quiz and receives the answer. The input data consists of the quiz sent from the server and the user's answer. The terminal records the user's answer and prepares to send it to the server. As output, the answer data is sent to the server.

[0619] Step 11:

[0620] The server analyzes the user's response data and uses it to suggest the next content. The input data is the response information sent from the terminal. The server uses a generation AI model to adjust the content to be provided next. The adjusted content to be provided next is generated as output.

[0621] Step 12:

[0622] The user provides voice commands to operate the device. The input data is the user's voice commands. The terminal recognizes these voice commands and transmits them to the appropriate device. As an output, the device is controlled according to the user's intent.

[0623] Step 13:

[0624] The server monitors emergency phrases. The input data is the user's ambient voice. If the server detects an anomaly, it immediately prepares to notify registered contacts. As output, an emergency contact is sent to the registered relevant parties.

[0625] (Application Example 1)

[0626] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0627] Health management, reducing feelings of isolation, maintaining and improving cognitive function, and ensuring safety in the lives of the elderly are key challenges. Conventional systems are insufficient to address these individual needs, highlighting the growing need for systems that can provide multiple functions in an integrated manner. Furthermore, real-time monitoring of health status and rapid notification of abnormalities are required, but an effective solution combining these functions has not existed until now.

[0628] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0629] This invention includes a server that includes means for analyzing biometric data acquired from a user and generating personalized health management suggestions, means for facilitating natural conversation through voice recognition to reduce feelings of loneliness, means for providing personalized cognitive function improvement content, and means for collecting biometric data in real time via a mobile terminal and immediately notifying of abnormalities based on pre-set criteria. This enables multifaceted support for the lives of the elderly and the realization of a safe and comfortable living environment.

[0630] "Biometric data" refers to information about a user's body, such as heart rate and activity level, that is collected to assess their health status.

[0631] "Personalized health management suggestions" are personalized health maintenance and improvement advice generated based on the user's biometric data.

[0632] "Speech recognition" is a technology that analyzes a speaker's voice, converts it into text, understands it, and generates a response.

[0633] "Natural conversation" refers to a means of communication where users and systems can smoothly engage in everyday interactions through language, thereby deepening mutual understanding.

[0634] "Cognitive function improvement content" refers to interactive content such as quizzes and puzzles that are provided with the aim of maintaining or improving users' cognitive abilities.

[0635] A "mobile device" is a computing device that a user can carry around, and smartphones and smart glasses fall into this category.

[0636] "Instant notification" is a function that quickly informs users or registered contacts of abnormal situations, and is an important element that enables a rapid response.

[0637] "Home appliances" refer to various devices used in the home, such as home appliances and smart home devices, which can be operated by voice commands.

[0638] "Environmental information" refers to a wide range of external data, including conventional weather information and news, and aims to provide information relevant to users' lives.

[0639] The system for implementing this invention was developed to support the daily lives of elderly users. The system primarily consists of a server and terminals (smartphones and smart glasses). Details of each function are described below.

[0640] The server receives biometric data collected via Bluetooth from the user's wearable device. This data includes heart rate and step count, and is obtained using the Google Fit API or Apple HealthKit. The server analyzes this biometric data in real time using TensorFlow, and if an anomaly is detected, it notifies the user via the device using Firebase Notification. For example, if a user's heart rate increases abnormally while running, they will immediately receive advice such as "Please take a short break."

[0641] The speech recognition function uses the Google Cloud Speech-to-Text API to convert speech into text. This allows the device to recognize user questions and commands and send them to the server. The server uses GPT to generate an appropriate response, which the device then returns to the user verbally. For example, if the user asks, "What's the weather like today?", the device will respond, "It's sunny with occasional clouds today."

[0642] Furthermore, to improve cognitive function, the server generates quizzes and puzzles using Unity based on the user's past data. The user's answers to the provided quizzes are sent to the server and analyzed. Based on the analysis results, the content provided next time is adjusted. For example, "after solving a simple math problem, a new problem is presented."

[0643] Integration with home appliances is achieved through voice commands. Using the Amazon Alexa Skills Kit or Google Assistant SDK, it is possible to control home appliances according to voice commands. For example, if a user says, "Turn off the living room lights," the lights will turn off accordingly.

[0644] Examples of prompt statements:

[0645] "The user's current heart rate is 110. Is this abnormal? How should I respond?"

[0646] "Please create appropriate brain training games for seniors."

[0647] In this way, this system provides multifaceted support for the daily lives of the elderly, offering a safe and comfortable living environment.

[0648] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0649] Step 1:

[0650] The user wears a wearable device that collects biometric data such as heart rate and steps taken. This data is transmitted to the device via Bluetooth.

[0651] Step 2:

[0652] The device transfers the received biometric data to the server using the Google Fit API or Apple HealthKit. This data is then input into the server's data processing unit.

[0653] Step 3:

[0654] The server uses TensorFlow to analyze biometric data. It compares the data to pre-defined criteria to detect abnormalities such as heart rate, and outputs whether or not an abnormality is present as an analysis result.

[0655] Step 4:

[0656] If an anomaly is detected, the server sends a notification to the device via Firebase Notification. The device displays this notification to the user as an alert. If the user is running, the device will display a message saying, "Please take a short break."

[0657] Step 5:

[0658] The user asks "What's the weather like today?" using voice. The device converts this voice into text using the Google Cloud Speech-to-Text API and sends it to the server.

[0659] Step 6:

[0660] The server uses GPT to generate an appropriate response based on the input text. The generated text response will be "Today is sunny with occasional clouds" and will be printed to the terminal.

[0661] Step 7:

[0662] The device returns the generated response to the user as audio. This allows the user to experience a natural conversation.

[0663] Step 8:

[0664] The server generates quizzes and puzzles using Unity to improve cognitive function. The input data used at this time is information about the user's past activity history and abilities.

[0665] Step 9:

[0666] The generated quizzes and puzzles are sent to the device and displayed to the user. The user's answers are sent back from the device to the server, which analyzes the answers and adjusts the content for the next session.

[0667] Step 10:

[0668] The user gives a voice command, "Turn off the living room lights." The device converts the voice to text and sends it to the Amazon Alexa Skills Kit or Google Assistant SDK via a server.

[0669] Step 11:

[0670] The server analyzes the received command and sends operation instructions to the corresponding household device. The output confirms that the operation to turn off the electricity has been performed.

[0671] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0672] This invention is an AI system that comprehensively supports the lives of the elderly, and is characterized in that it utilizes an emotion engine to recognize the user's emotions and provides appropriate responses in accordance with those emotions.

[0673] This system collects health data through wearable devices worn daily by elderly individuals. The device receives this data in real time and sends it to a server. The server analyzes the health data and notifies the user of the information obtained as personalized health management suggestions. For example, if the server detects an increase in heart rate, the device will send a suggestion to the user such as, "We recommend you take a rest."

[0674] The emotion engine can recognize a user's emotional state by analyzing their voice and facial expression data. The device inputs this data into the emotion engine, which then analyzes the user's emotions on a server. Based on the results, the server provides the user with the most suitable relaxation program or entertainment content. For example, if the user is feeling stressed, the device might suggest, "Would you like to listen to some music?"

[0675] In addition, the emotion engine can record changes in the user's emotions over a long period. This data is used to make health management suggestions more accurate. For example, the server can refer to the emotion history and, if the user frequently feels anxious during certain times of the day, analyze the cause and make suggestions for lifestyle improvements.

[0676] Furthermore, the device can operate home appliances and remind users of medication times based on voice commands. If a user says, "Turn on the TV," the device will execute the command. It also supports the user's life by reminding them of important appointments and medication times on time.

[0677] To prepare for emergencies, the device constantly monitors the user's actions and spoken words. If an abnormal situation is detected, it promptly notifies the server, which then contacts the appropriate emergency contacts. For example, if the user says "Help," the device immediately initiates the notification process.

[0678] Thus, by incorporating an emotional engine, the present invention provides a support system that enables elderly people to live their daily lives with greater peace of mind and comfort.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The user wears a wearable device, which continuously collects health data such as heart rate, steps taken, and body temperature.

[0682] Step 2:

[0683] The device sends the acquired health data to the server. The server begins monitoring the data in real time.

[0684] Step 3:

[0685] The server analyzes the received health data and generates personalized health management suggestions, including comparisons with past data.

[0686] Step 4:

[0687] Based on the analysis results, the server sends optimized health management suggestions to the terminal. The terminal notifies the user of this, providing on-screen displays and audio alerts.

[0688] Step 5:

[0689] When a user speaks, the device uses speech recognition to analyze the input. The device then sends this data to the server.

[0690] Step 6:

[0691] The server processes the received audio data using natural language processing to generate an appropriate response.

[0692] Step 7:

[0693] The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state. Based on this emotional state, the server selects appropriate responses and content for the user.

[0694] Step 8:

[0695] Based on the emotion recognition results, the server generates relaxation content and follow-up messages that correspond to the emotions the user is feeling.

[0696] Step 9:

[0697] The device receives a response from the server and communicates it to the user via voice. For example, a user feeling stressed might receive a message like, "Take a deep breath and relax."

[0698] Step 10:

[0699] When a user gives voice commands to operate home appliances, the terminal receives those commands and controls the appropriate appliance.

[0700] Step 11:

[0701] If a device detects an anomaly, it immediately notifies the server. The server analyzes the notification and, if necessary, automatically notifies registered emergency contacts.

[0702] Step 12:

[0703] The server integrates and manages emotional and health data, and based on long-term analysis, it regularly provides suggestions to improve users' health and well-being.

[0704] (Example 2)

[0705] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0706] In the lives of the elderly, there are demands for health management, reduction of feelings of loneliness, and rapid response to emergencies. However, current technology does not adequately provide appropriate suggestions and support based on the individual health conditions and emotions of the elderly, which is a challenge. Furthermore, it is necessary to reduce the burden on the elderly in operating home appliances based on voice commands and utilizing daily reminder functions. In addition, establishing a rapid and accurate notification system in emergencies is also important.

[0707] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0708] This invention includes a server that analyzes physiological data acquired from elderly individuals and generates personalized health management suggestions, a server that facilitates natural dialogue with elderly individuals through voice recognition to reduce feelings of loneliness, and a server that analyzes emotional states to provide relaxation programs and entertainment content. This enables individualized support tailored to the health status and emotions of elderly individuals.

[0709] "Physiological data" refers to numerical information about the physical condition of elderly people, such as heart rate, body temperature, and steps taken.

[0710] "Health management suggestions" are specific advice for maintaining or improving the health of elderly individuals, derived from the analysis of physiological data.

[0711] "Voice recognition" is a technology that allows computer systems to recognize and respond to the words spoken by elderly people.

[0712] "Natural dialogue" refers to interactions between humans and computers that closely resemble conversations between humans.

[0713] "Reducing feelings of loneliness" means alleviating the loneliness and isolation that older adults experience by providing opportunities for social connection and dialogue.

[0714] "Emotional state" refers to the mental state or mood an individual is experiencing at a particular point in time.

[0715] A "relaxation program" is a program or activity designed to alleviate users' mental tension and provide comfort and peace of mind.

[0716] "Entertainment content" refers to content such as music, movies, and games that can be enjoyed by the elderly.

[0717] "Operating home appliances" refers to controlling electrical appliances in the home through voice commands or other interfaces.

[0718] "Lifestyle notifications" refer to timely information and events necessary for daily life that are communicated to users.

[0719] "Notifying emergency contacts" refers to the action of quickly and automatically sending notifications to pre-set contacts when an elderly person finds themselves in a dangerous situation.

[0720] This invention relates to an AI system that comprehensively supports the lives of the elderly, and in particular aims to recognize the emotions of the elderly by utilizing an emotion engine and provide appropriate responses in accordance with those emotions. This system consists of a wearable device, a terminal such as a smartphone or tablet, and a server connected to the cloud.

[0721] Users collect physiological data through wearable devices they wear daily. These devices provide important health information, such as recording heart rate, body temperature, and steps taken. The devices receive this data in real time via Bluetooth or Wi-Fi and transmit it to a server in the cloud.

[0722] The server analyzes the user's health status using machine learning algorithms based on the received physiological data. One example of software used for analysis is a generative AI model that predicts health status. Based on the analysis results from the server, personalized health management suggestions are sent to the user. For example, if the heart rate is abnormally high, a notification such as "We recommend you rest for a while" will be sent via the device.

[0723] The device also periodically collects the user's voice data and inputs it into the emotion engine. The server uses this emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the results, it suggests relaxation programs and entertainment content suitable for the user, for example, by displaying options such as "Would you like to listen to music?"

[0724] The server also records user emotional data over long periods and evaluates changes in emotions based on past history. This data helps provide more accurate health management suggestions. Furthermore, the terminal operates home appliances and provides lifestyle notifications based on user voice commands. If the user says, "Turn on the TV," the terminal will execute that command.

[0725] In preparation for emergencies, the device constantly monitors the user's actions and statements, and immediately notifies the server if it detects any anomalies. The server has the capability to notify registered emergency contacts as needed. For example, if the user says "Help," the device will promptly initiate the notification process.

[0726] A concrete example of a prompt is, "Generate a sentence that identifies the emotions of an elderly person and recommends relaxing music." This prompt allows the AI ​​model to generate text data that provides appropriate music recommendations.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] Users wear a wearable device on a daily basis. This device collects physiological data such as heart rate, body temperature, and steps taken in real time. This becomes the input data. The collected data is sent to a terminal via Bluetooth or Wi-Fi. The terminal receives the data and then sends it to a server in the cloud.

[0730] Step 2:

[0731] The server receives physiological data sent from the terminal. The input data is processed using a health analysis algorithm. This algorithm analyzes the data and evaluates the user's health status. The analyzed data generates output as health management suggestions and sends it to the terminal. For example, if the heart rate is higher than normal, it generates and outputs a suggestion such as "We recommend taking a break."

[0732] Step 3:

[0733] The device notifies the user of health management suggestions received from the server. Specifically, a suggestion message pops up on the device's display screen. The user can then adjust their actions based on these suggestions.

[0734] Step 4:

[0735] The device periodically and automatically collects the user's voice data. Voice input is sent to a server for analysis by an emotion engine. This emotion analysis processes the voice data to identify the user's emotional state. The analysis results are generated as output, such as relaxation programs or entertainment content.

[0736] Step 5:

[0737] Based on the results of the emotion analysis, the server selects a relaxation program or entertainment content suitable for the user and outputs it to the terminal. The terminal presents the suggestion to the user and displays a confirmation message such as "Do you want to play relaxation music?" as a concrete action.

[0738] Step 6:

[0739] The server records emotional data from users over a long period and uses this data to compare with past history. This input data is used to make future health management suggestions. As an output, suggestions based on specific emotional patterns are generated, contributing to improving the lifestyles of elderly people.

[0740] Step 7:

[0741] The device recognizes voice commands from the user and operates household appliances. Voice commands are processed as input, and the appliance control algorithm directly performs the action. For example, based on the command "Turn on the lights," the output is that the lighting fixture turns on.

[0742] Step 8:

[0743] The device monitors for abnormal behavior and voice in preparation for emergencies. If an abnormality is detected, the information is reported to the server. This input data is then used as output to immediately notify emergency contacts, and specific actions are taken to ensure the user's safety.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0746] With the increasing elderly population, the burden on caregivers, particularly in elderly care settings, is growing. Elderly individuals often experience health problems and emotional fluctuations, making prompt and individualized care essential. However, caregivers face the challenge of providing meticulous care to every elderly person amidst their busy daily schedules. Therefore, there is a need for a system that can monitor the emotional and health status of elderly individuals in real time and provide individualized care based on that information.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for analyzing health data and generating personalized health management suggestions, means for displaying the emotional state of elderly individuals in real time using emotion analysis and providing caregivers with appropriate response guidance, and means for detecting abnormalities and making emergency calls. This enables the provision of care tailored to each elderly person in real time, reduces the burden on caregivers, and allows elderly people to live with peace of mind.

[0749] "Health data" refers to information about the physical condition of elderly individuals, including physiological indicators such as heart rate, blood pressure, and body temperature.

[0750] "Personalized health management proposals" refer to providing specific advice and guidance for maintaining and improving the health of each elderly person, based on the results of an analysis of acquired health data.

[0751] "Voice recognition" is a technology in which a computer analyzes the voice spoken by elderly people, understands its content, and takes appropriate action.

[0752] "Emotional analysis" is a process that evaluates the current emotional state of elderly individuals based on their voice and facial expression data.

[0753] "Real-time display" means providing information about the elderly person's condition and the system in a visual and timely manner without delay.

[0754] "Providing caregivers with appropriate response guidelines" means presenting guidelines that specifically indicate what caregivers should do to support elderly individuals.

[0755] "Detecting anomalies" means identifying changes outside the normal range in the daily behaviors and health status of elderly individuals and recognizing the possibility that a problem has occurred.

[0756] An "emergency notification" is a communication process designed to quickly inform the appropriate agencies or individuals of an emergency situation.

[0757] This elderly support system combines a wearable device, smart glasses, and a server. The wearable device acquires health data such as heart rate and blood pressure in real time and sends it to the server. The server receives this data, evaluates the user's health status by comparing it with past data, and generates personalized health management suggestions. For example, if the heart rate is higher than normal, it will generate a suggestion such as, "We recommend taking a break."

[0758] Furthermore, the camera and microphone built into the smart glasses collect the elderly person's facial expressions and voice. The server uses this data to perform emotion analysis using a generative AI model. Using OpenCV and TensorFlow, the emotional state of the elderly person is analyzed from the acquired data and visualized in real time in the caregiver's field of view. Based on this information, specific action guidelines are presented to the caregiver to support how to interact with the elderly person.

[0759] When an anomaly is detected, the server immediately sends an emergency alert. For example, if an elderly person makes an emergency voice call such as "Help me," the server analyzes the information and sends a notification to a pre-designated emergency contact.

[0760] As a concrete example, if a caregiver using smart glasses in a nursing home detects a depressed mood in an elderly person's facial expression, the glasses will display instructions such as "Suggest relaxing activities." Furthermore, an example of a prompt for the generating AI model could be: "Design a care support system that recognizes the emotional state of elderly people and provides individualized suggestions. Please detail the necessary technologies and data flow."

[0761] This system is implemented by combining data analysis using Amazon Web Services with sentiment analysis functions using OpenCV and TensorFlow, enabling flexible and scalable operation.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] Wearable devices collect health data such as heart rate and blood pressure from elderly individuals in real time. This data is transmitted to a terminal using Bluetooth. The terminal then transfers the data to a server. At this stage, physiological indicator data is acquired as input and prepared to be transmitted to the server as output.

[0765] Step 2:

[0766] The server analyzes the received health data. Specifically, it uses AWS Lambda to process the data and compares it with historical data stored in Amazon RDS to assess the current health status. The inputs are physiological indicator data and historical health history, and the output is a health status assessment result. If an abnormality is detected, it generates recommendations for the next optimal health management steps.

[0767] Step 3:

[0768] The server uses a generative AI model to generate specific health management suggestions for the user based on the evaluation results of the received health data. This prompt includes "generative AI model" and "health management suggestions." The evaluation results and generative AI model as inputs are used to produce the suggestions as output.

[0769] Step 4:

[0770] Meanwhile, the device collects voice and facial expression data from elderly individuals via smart glasses. This data is transmitted to a server in real time for emotion analysis. Voice and visual data are sent from the device to the server as input.

[0771] Step 5:

[0772] The server uses OpenCV and TensorFlow to perform emotion analysis. This allows it to analyze the emotional state of elderly individuals from voice and facial expression data, and obtain specific emotion classification results. The input consists of voice and facial expression data, and the emotional state is output as a result of the data calculations.

[0773] Step 6:

[0774] The server provides the caregiver with appropriate response guidance based on the emotional state. Visual feedback is sent to the smart glasses' display. This presents the caregiver with guidelines as visual information based on the input emotion analysis results.

[0775] Step 7:

[0776] When a user issues an emergency, the device detects it and notifies the server. The server then uses AWS SNS to immediately send the information to emergency contacts. In this step, the emergency call process is activated based on the emergency voice command as input, and a rapid notification is completed as output.

[0777] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0778] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0779] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0780] [Fourth Embodiment]

[0781] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0782] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0783] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0784] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0785] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0786] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0787] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0788] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0789] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0790] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0791] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0792] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0793] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0794] This invention is a comprehensive AI system designed to support the lives of elderly people. To assist the lives of its elderly users, this system has the following functions:

[0795] First, by wearing a wearable device, users collect health data on a daily basis. The device receives this data and periodically sends it to a server. The server analyzes this data in real time and evaluates the user's health status. If an abnormality is detected, the server notifies the user via the device and suggests improvements to their health management. For example, if the user's heart rate is high, the server will advise them through the device to "take it easy today."

[0796] Next, through voice recognition, the device enables natural conversation with the user. When the user asks everyday questions or engages in casual conversation, the server generates an appropriate response, which the device then returns to the user. This reduces feelings of isolation and allows the user to enjoy the conversation. For example, if the user asks, "What's the weather like today?", the device might reply, "It's cloudy, then sunny."

[0797] To improve cognitive function, the server generates quizzes and puzzles tailored to the user based on past data. The terminal presents them to the user and sends the answers to the server. The server analyzes the results, monitors changes in cognitive function, and adjusts the content provided next time. For example, by solving a provided puzzle, a new challenge is prepared based on the user's performance.

[0798] It also features voice-activated home appliance control and a lifestyle reminder function to notify users of medication times. If a user says, "Turn on the lights," the device will operate the corresponding appliance and execute the command. Based on server settings, the device will also notify the user, "It's time to take your medication," and the user will receive a reminder.

[0799] Finally, as a crisis management function, the device constantly monitors the user's environment. If the user reports an abnormality or an unforeseen incident occurs, the device immediately notifies the server, which then notifies the appropriate emergency contacts. For example, if the device recognizes the user's voice saying "call for help," it immediately notifies registered family members or medical institutions.

[0800] These features enable the present invention to provide an environment in which elderly people can live safely and comfortably.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] When a user wears a wearable device, their physical data is recorded. The device continuously acquires data such as heart rate, steps taken, and body temperature from the device.

[0804] Step 2:

[0805] The device uploads the acquired data to the server at regular intervals. This allows the server to understand the user's latest health status.

[0806] Step 3:

[0807] The server analyzes the received data and assesses the user's health status. If an anomaly is detected by comparing it with past data, the server identifies the anomaly in real time.

[0808] Step 4:

[0809] The server generates suggestions for improving health management. These suggestions are personalized and appropriately optimized based on the user's health status. For example, if stress levels are high, relaxation might be recommended.

[0810] Step 5:

[0811] The server sends a suggestion to the terminal, and the terminal notifies the user via voice or text. The user can then adjust their actions based on this notification.

[0812] Step 6:

[0813] When a user speaks, the device performs speech recognition and analyzes the content. It sends the voice data to a server for interpretation and generates a natural-sounding response.

[0814] Step 7:

[0815] The server sends the generated response to the terminal, which then communicates it to the user via voice. This establishes a dialogue between the user and the system.

[0816] Step 8:

[0817] If a user wishes to improve their cognitive function, the server will refer to their past records to generate quizzes and puzzles best suited to them.

[0818] Step 9:

[0819] The device presents the content to the user and collects response data. The user's performance is sent to the server, which then analyzes the results.

[0820] Step 10:

[0821] Based on the analysis results, the server adjusts the content provided next time, continuously supporting the user's cognitive functions.

[0822] Step 11:

[0823] Following voice commands, the device operates home appliances. Additionally, based on a schedule set by the server, the device reminds users of medication times.

[0824] Step 12:

[0825] The terminal constantly monitors sensor information and immediately notifies the server if it detects an abnormal condition or an urgent voice input.

[0826] Step 13:

[0827] The server quickly analyzes any notifications it receives and automatically notifies registered emergency contacts or services.

[0828] Step 14:

[0829] The server continues to send follow-up instructions to the terminal after the report is made, providing support to ensure the user is safe.

[0830] (Example 1)

[0831] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0832] Elderly people face various health problems, loneliness, and cognitive decline in their daily lives. These challenges reduce their quality of life and increase their anxiety in daily living. Furthermore, inadequate systems for prompt emergency reporting and monitoring their health can lead to serious risks.

[0833] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0834] In this invention, the server includes means for generating personalized health management plans, means for facilitating natural dialogue and reducing feelings of loneliness, and means for providing content to improve cognitive function. This enables health management, a sense of security, and maintenance of cognitive function in the daily lives of elderly people.

[0835] "Biometric information" refers to physical data such as heart rate, steps taken, and sleep patterns of elderly individuals.

[0836] A "personalized health management plan" refers to specific guidance for maintaining or improving health, provided based on each elderly person's biometric information.

[0837] "Voice recognition" refers to a technology that uses a digital system to recognize the voices of elderly people and convert them into corresponding text or instructions.

[0838] "Natural dialogue" refers to communication between computer systems that takes place as naturally as conversations between humans.

[0839] "Cognitive function improvement content" refers to intellectually stimulating content such as quizzes and puzzles provided to improve the memory and judgment of elderly people.

[0840] "Appliance operation" refers to the act of controlling electrical appliances and household equipment based on the voice commands of elderly people.

[0841] "Life Reminders" refers to a function that notifies elderly people to remember their daily schedules and important matters.

[0842] The means of detecting an "abnormality" and making an emergency call refers to a function that quickly sends a warning to the outside when any physical or environmental change occurs in an elderly person.

[0843] The embodiment of the invention is a comprehensive AI system for supporting the elderly. This system assists the daily lives of elderly users and includes several key functions.

[0844] By wearing a wearable device, users can routinely acquire biometric information such as heart rate, steps taken, and sleep patterns. This device is equipped with an accelerometer and a heart rate sensor. The device periodically receives this data via Bluetooth or Wi-Fi and sends it to a server in the cloud.

[0845] The server is equipped with a data analysis program written in Python, which analyzes received biometric information in real time. The analyzed data is evaluated by an anomaly detection algorithm to monitor the user's health status. If an anomaly is detected, the server uses a generative AI model to generate personalized health management suggestions and sends them to the terminal via an API.

[0846] A device with speech recognition capabilities enables interaction with the user. Upon receiving a question from the user, the device uses speech recognition software to convert it into text. The server then generates an appropriate response using natural language processing and sends it back to the device as a text message.

[0847] The server also uses an AI model to generate quizzes and puzzles designed to improve cognitive function. A specific example of a prompt would be, "Generate a quiz to stimulate cognitive function in the elderly." The device then provides the user with an appropriate challenge and sends the results to the server.

[0848] Furthermore, the device handles appliance operation based on voice commands and provides daily reminder functions. When a user gives a voice command such as "Turn on the lights," the device controls the relevant home appliances. Based on a server schedule, the device also notifies the user by voice with reminders such as "It's time to take your medicine."

[0849] As a crisis management function, the terminal constantly monitors the user's environment and immediately notifies the server if it recognizes an emergency phrase such as "call for help." The server then notifies registered contacts, enabling a rapid response.

[0850] These features enable elderly people to live safely and comfortably, thus realizing a specific embodiment of this invention.

[0851] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0852] Step 1:

[0853] The user wears a wearable device that collects biometric information, including heart rate and steps, in real time. The input data is measured via sensors and temporarily stored within the wearable device. This data is then transmitted to a terminal via Bluetooth. The terminal stores the latest biometric information as output.

[0854] Step 2:

[0855] The device periodically transmits received biometric information to the server via Wi-Fi. The input data is biometric information transmitted from a wearable device. The device processes the data as packets and transfers them to the server using a secure communication protocol. As output, the server aggregates the latest biometric information.

[0856] Step 3:

[0857] The server executes an analysis program using Python to analyze received biometric data in real time. The input data is biometric information sent from the terminal. The server program applies an anomaly detection algorithm to extract anomaly patterns from the data. The output generates information indicating whether or not an anomaly was detected and details thereof.

[0858] Step 4:

[0859] The server uses a generative AI model to generate personalized health management plans. The input data consists of anomaly detection results and historical biometric information. The generated health management plans include suggestions derived from data-driven analysis. As output, personalized suggestions are generated and sent to the terminal.

[0860] Step 5:

[0861] The terminal receives health management suggestions from the server and notifies the user. The input data is the health management suggestion sent from the server. The terminal uses speech synthesis software to provide the user with appropriate advice via voice. As output, the user receives health suggestions in real time.

[0862] Step 6:

[0863] The user asks questions about their daily life using voice. The input data consists of the user's voice instructions. The terminal uses voice recognition software to convert the voice into text and sends it to the server. As output, the server receives the user's questions in text format.

[0864] Step 7:

[0865] The server uses natural language processing to generate appropriate responses to user questions. The input data is the user's question text from the terminal. The server analyzes the question and retrieves relevant information. As output, the generated response is sent to the terminal.

[0866] Step 8:

[0867] The terminal relays the response from the server to the user. The input data is the response sent from the server. The terminal performs speech synthesis to provide the user with a natural-sounding answer. As output, the user receives the answer to the question.

[0868] Step 9:

[0869] The server generates quizzes aimed at improving cognitive function using a generative AI model based on past data. The input data consists of the user's past cognitive performance data. The server designs new quizzes based on this data. As output, personalized questions are generated and sent to the device.

[0870] Step 10:

[0871] The terminal presents the user with a quiz and receives the answer. The input data consists of the quiz sent from the server and the user's answer. The terminal records the user's answer and prepares to send it to the server. As output, the answer data is sent to the server.

[0872] Step 11:

[0873] The server analyzes the user's response data and uses it to suggest the next content. The input data is the response information sent from the terminal. The server uses a generation AI model to adjust the content to be provided next. The adjusted content to be provided next is generated as output.

[0874] Step 12:

[0875] The user provides voice commands to operate the device. The input data is the user's voice commands. The terminal recognizes these voice commands and transmits them to the appropriate device. As an output, the device is controlled according to the user's intent.

[0876] Step 13:

[0877] The server monitors emergency phrases. The input data is the user's ambient voice. If the server detects an anomaly, it immediately prepares to notify registered contacts. As output, an emergency contact is sent to the registered relevant parties.

[0878] (Application Example 1)

[0879] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0880] Health management, reducing feelings of isolation, maintaining and improving cognitive function, and ensuring safety in the lives of the elderly are key challenges. Conventional systems are insufficient to address these individual needs, highlighting the growing need for systems that can provide multiple functions in an integrated manner. Furthermore, real-time monitoring of health status and rapid notification of abnormalities are required, but an effective solution combining these functions has not existed until now.

[0881] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0882] This invention includes a server that includes means for analyzing biometric data acquired from a user and generating personalized health management suggestions, means for facilitating natural conversation through voice recognition to reduce feelings of loneliness, means for providing personalized cognitive function improvement content, and means for collecting biometric data in real time via a mobile terminal and immediately notifying of abnormalities based on pre-set criteria. This enables multifaceted support for the lives of the elderly and the realization of a safe and comfortable living environment.

[0883] "Biometric data" refers to information about a user's body, such as heart rate and activity level, that is collected to assess their health status.

[0884] "Personalized health management suggestions" are personalized health maintenance and improvement advice generated based on the user's biometric data.

[0885] "Speech recognition" is a technology that analyzes a speaker's voice, converts it into text, understands it, and generates a response.

[0886] "Natural conversation" refers to a means of communication where users and systems can smoothly engage in everyday interactions through language, thereby deepening mutual understanding.

[0887] "Cognitive function improvement content" refers to interactive content such as quizzes and puzzles that are provided with the aim of maintaining or improving users' cognitive abilities.

[0888] A "mobile device" is a computing device that a user can carry around, and smartphones and smart glasses fall into this category.

[0889] "Instant notification" is a function that quickly informs users or registered contacts of abnormal situations, and is an important element that enables a rapid response.

[0890] "Home appliances" refer to various devices used in the home, such as home appliances and smart home devices, which can be operated by voice commands.

[0891] "Environmental information" refers to a wide range of external data, including conventional weather information and news, and aims to provide information relevant to users' lives.

[0892] The system for implementing this invention was developed to support the daily lives of elderly users. The system primarily consists of a server and terminals (smartphones and smart glasses). Details of each function are described below.

[0893] The server receives biometric data collected via Bluetooth from the user's wearable device. This data includes heart rate and step count, and is obtained using the Google Fit API or Apple HealthKit. The server analyzes this biometric data in real time using TensorFlow, and if an anomaly is detected, it notifies the user via the device using Firebase Notification. For example, if a user's heart rate increases abnormally while running, they will immediately receive advice such as "Please take a short break."

[0894] The speech recognition function uses the Google Cloud Speech-to-Text API to convert speech into text. This allows the device to recognize user questions and commands and send them to the server. The server uses GPT to generate an appropriate response, which the device then returns to the user verbally. For example, if the user asks, "What's the weather like today?", the device will respond, "It's sunny with occasional clouds today."

[0895] Furthermore, to improve cognitive function, the server generates quizzes and puzzles using Unity based on the user's past data. The user's answers to the provided quizzes are sent to the server and analyzed. Based on the analysis results, the content provided next time is adjusted. For example, "after solving a simple math problem, a new problem is presented."

[0896] Integration with home appliances is achieved through voice commands. Using the Amazon Alexa Skills Kit or Google Assistant SDK, it is possible to control home appliances according to voice commands. For example, if a user says, "Turn off the living room lights," the lights will turn off accordingly.

[0897] Examples of prompt statements:

[0898] "The user's current heart rate is 110. Is this abnormal? How should I respond?"

[0899] "Please create appropriate brain training games for seniors."

[0900] In this way, this system provides multifaceted support for the daily lives of the elderly, offering a safe and comfortable living environment.

[0901] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0902] Step 1:

[0903] The user wears a wearable device that collects biometric data such as heart rate and steps taken. This data is transmitted to the device via Bluetooth.

[0904] Step 2:

[0905] The device transfers the received biometric data to the server using the Google Fit API or Apple HealthKit. This data is then input into the server's data processing unit.

[0906] Step 3:

[0907] The server uses TensorFlow to analyze biometric data. It compares the data to pre-defined criteria to detect abnormalities such as heart rate, and outputs whether or not an abnormality is present as an analysis result.

[0908] Step 4:

[0909] If an anomaly is detected, the server sends a notification to the device via Firebase Notification. The device displays this notification to the user as an alert. If the user is running, the device will display a message saying, "Please take a short break."

[0910] Step 5:

[0911] The user asks "What's the weather like today?" using voice. The device converts this voice into text using the Google Cloud Speech-to-Text API and sends it to the server.

[0912] Step 6:

[0913] The server uses GPT to generate an appropriate response based on the input text. The generated text response will be "Today is sunny with occasional clouds" and will be printed to the terminal.

[0914] Step 7:

[0915] The device returns the generated response to the user as audio. This allows the user to experience a natural conversation.

[0916] Step 8:

[0917] The server generates quizzes and puzzles using Unity to improve cognitive function. The input data used at this time is information about the user's past activity history and abilities.

[0918] Step 9:

[0919] The generated quizzes and puzzles are sent to the device and displayed to the user. The user's answers are sent back from the device to the server, which analyzes the answers and adjusts the content for the next session.

[0920] Step 10:

[0921] The user gives a voice command, "Turn off the living room lights." The device converts the voice to text and sends it to the Amazon Alexa Skills Kit or Google Assistant SDK via a server.

[0922] Step 11:

[0923] The server analyzes the received command and sends operation instructions to the corresponding household device. The output confirms that the operation to turn off the electricity has been performed.

[0924] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0925] This invention is an AI system that comprehensively supports the lives of the elderly, and is characterized in that it utilizes an emotion engine to recognize the user's emotions and provides appropriate responses in accordance with those emotions.

[0926] This system collects health data through wearable devices worn daily by elderly individuals. The device receives this data in real time and sends it to a server. The server analyzes the health data and notifies the user of the information obtained as personalized health management suggestions. For example, if the server detects an increase in heart rate, the device will send a suggestion to the user such as, "We recommend you take a rest."

[0927] The emotion engine can recognize a user's emotional state by analyzing their voice and facial expression data. The device inputs this data into the emotion engine, which then analyzes the user's emotions on a server. Based on the results, the server provides the user with the most suitable relaxation program or entertainment content. For example, if the user is feeling stressed, the device might suggest, "Would you like to listen to some music?"

[0928] In addition, the emotion engine can record changes in the user's emotions over a long period. This data is used to make health management suggestions more accurate. For example, the server can refer to the emotion history and, if the user frequently feels anxious during certain times of the day, analyze the cause and make suggestions for lifestyle improvements.

[0929] Furthermore, the device can operate home appliances and remind users of medication times based on voice commands. If a user says, "Turn on the TV," the device will execute the command. It also supports the user's life by reminding them of important appointments and medication times on time.

[0930] To prepare for emergencies, the device constantly monitors the user's actions and spoken words. If an abnormal situation is detected, it promptly notifies the server, which then contacts the appropriate emergency contacts. For example, if the user says "Help," the device immediately initiates the notification process.

[0931] Thus, by incorporating an emotional engine, the present invention provides a support system that enables elderly people to live their daily lives with greater peace of mind and comfort.

[0932] The following describes the processing flow.

[0933] Step 1:

[0934] The user wears a wearable device, which continuously collects health data such as heart rate, steps taken, and body temperature.

[0935] Step 2:

[0936] The device sends the acquired health data to the server. The server begins monitoring the data in real time.

[0937] Step 3:

[0938] The server analyzes the received health data and generates personalized health management suggestions, including comparisons with past data.

[0939] Step 4:

[0940] Based on the analysis results, the server sends optimized health management suggestions to the terminal. The terminal notifies the user of this, providing on-screen displays and audio alerts.

[0941] Step 5:

[0942] When a user speaks, the device uses speech recognition to analyze the input. The device then sends this data to the server.

[0943] Step 6:

[0944] The server processes the received audio data using natural language processing to generate an appropriate response.

[0945] Step 7:

[0946] The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state. Based on this emotional state, the server selects appropriate responses and content for the user.

[0947] Step 8:

[0948] Based on the emotion recognition results, the server generates relaxation content and follow-up messages that correspond to the emotions the user is feeling.

[0949] Step 9:

[0950] The device receives a response from the server and communicates it to the user via voice. For example, a user feeling stressed might receive a message like, "Take a deep breath and relax."

[0951] Step 10:

[0952] When a user gives voice commands to operate home appliances, the terminal receives those commands and controls the appropriate appliance.

[0953] Step 11:

[0954] If a device detects an anomaly, it immediately notifies the server. The server analyzes the notification and, if necessary, automatically notifies registered emergency contacts.

[0955] Step 12:

[0956] The server integrates and manages emotional and health data, and based on long-term analysis, it regularly provides suggestions to improve users' health and well-being.

[0957] (Example 2)

[0958] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0959] In the lives of the elderly, there are demands for health management, reduction of feelings of loneliness, and rapid response to emergencies. However, current technology does not adequately provide appropriate suggestions and support based on the individual health conditions and emotions of the elderly, which is a challenge. Furthermore, it is necessary to reduce the burden on the elderly in operating home appliances based on voice commands and utilizing daily reminder functions. In addition, establishing a rapid and accurate notification system in emergencies is also important.

[0960] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0961] This invention includes a server that analyzes physiological data acquired from elderly individuals and generates personalized health management suggestions, a server that facilitates natural dialogue with elderly individuals through voice recognition to reduce feelings of loneliness, and a server that analyzes emotional states to provide relaxation programs and entertainment content. This enables individualized support tailored to the health status and emotions of elderly individuals.

[0962] "Physiological data" refers to numerical information about the physical condition of elderly people, such as heart rate, body temperature, and steps taken.

[0963] "Health management suggestions" are specific advice for maintaining or improving the health of elderly individuals, derived from the analysis of physiological data.

[0964] "Voice recognition" is a technology that allows computer systems to recognize and respond to the words spoken by elderly people.

[0965] "Natural dialogue" refers to interactions between humans and computers that closely resemble conversations between humans.

[0966] "Reducing feelings of loneliness" means alleviating the loneliness and isolation that older adults experience by providing opportunities for social connection and dialogue.

[0967] "Emotional state" refers to the mental state or mood an individual is experiencing at a particular point in time.

[0968] A "relaxation program" is a program or activity designed to alleviate users' mental tension and provide comfort and peace of mind.

[0969] "Entertainment content" refers to content such as music, movies, and games that can be enjoyed by the elderly.

[0970] "Operating home appliances" refers to controlling electrical appliances in the home through voice commands or other interfaces.

[0971] "Lifestyle notifications" refer to timely information and events necessary for daily life that are communicated to users.

[0972] "Notifying emergency contacts" refers to the action of quickly and automatically sending notifications to pre-set contacts when an elderly person finds themselves in a dangerous situation.

[0973] This invention relates to an AI system that comprehensively supports the lives of the elderly, and in particular aims to recognize the emotions of the elderly by utilizing an emotion engine and provide appropriate responses in accordance with those emotions. This system consists of a wearable device, a terminal such as a smartphone or tablet, and a server connected to the cloud.

[0974] Users collect physiological data through wearable devices they wear daily. These devices provide important health information, such as recording heart rate, body temperature, and steps taken. The devices receive this data in real time via Bluetooth or Wi-Fi and transmit it to a server in the cloud.

[0975] The server analyzes the user's health status using machine learning algorithms based on the received physiological data. One example of software used for analysis is a generative AI model that predicts health status. Based on the analysis results from the server, personalized health management suggestions are sent to the user. For example, if the heart rate is abnormally high, a notification such as "We recommend you rest for a while" will be sent via the device.

[0976] The device also periodically collects the user's voice data and inputs it into the emotion engine. The server uses this emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the results, it suggests relaxation programs and entertainment content suitable for the user, for example, by displaying options such as "Would you like to listen to music?"

[0977] The server also records user emotional data over long periods and evaluates changes in emotions based on past history. This data helps provide more accurate health management suggestions. Furthermore, the terminal operates home appliances and provides lifestyle notifications based on user voice commands. If the user says, "Turn on the TV," the terminal will execute that command.

[0978] In preparation for emergencies, the device constantly monitors the user's actions and statements, and immediately notifies the server if it detects any anomalies. The server has the capability to notify registered emergency contacts as needed. For example, if the user says "Help," the device will promptly initiate the notification process.

[0979] A concrete example of a prompt is, "Generate a sentence that identifies the emotions of an elderly person and recommends relaxing music." This prompt allows the AI ​​model to generate text data that provides appropriate music recommendations.

[0980] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0981] Step 1:

[0982] Users wear a wearable device on a daily basis. This device collects physiological data such as heart rate, body temperature, and steps taken in real time. This becomes the input data. The collected data is sent to a terminal via Bluetooth or Wi-Fi. The terminal receives the data and then sends it to a server in the cloud.

[0983] Step 2:

[0984] The server receives physiological data sent from the terminal. The input data is processed using a health analysis algorithm. This algorithm analyzes the data and evaluates the user's health status. The analyzed data generates output as health management suggestions and sends it to the terminal. For example, if the heart rate is higher than normal, it generates and outputs a suggestion such as "We recommend taking a break."

[0985] Step 3:

[0986] The device notifies the user of health management suggestions received from the server. Specifically, a suggestion message pops up on the device's display screen. The user can then adjust their actions based on these suggestions.

[0987] Step 4:

[0988] The device periodically and automatically collects the user's voice data. Voice input is sent to a server for analysis by an emotion engine. This emotion analysis processes the voice data to identify the user's emotional state. The analysis results are generated as output, such as relaxation programs or entertainment content.

[0989] Step 5:

[0990] Based on the results of the emotion analysis, the server selects a relaxation program or entertainment content suitable for the user and outputs it to the terminal. The terminal presents the suggestion to the user and displays a confirmation message such as "Do you want to play relaxation music?" as a concrete action.

[0991] Step 6:

[0992] The server records emotional data from users over a long period and uses this data to compare with past history. This input data is used to make future health management suggestions. As an output, suggestions based on specific emotional patterns are generated, contributing to improving the lifestyles of elderly people.

[0993] Step 7:

[0994] The device recognizes voice commands from the user and operates household appliances. Voice commands are processed as input, and the appliance control algorithm directly performs the action. For example, based on the command "Turn on the lights," the output is that the lighting fixture turns on.

[0995] Step 8:

[0996] The device monitors for abnormal behavior and voice in preparation for emergencies. If an abnormality is detected, the information is reported to the server. This input data is then used as output to immediately notify emergency contacts, and specific actions are taken to ensure the user's safety.

[0997] (Application Example 2)

[0998] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0999] With the increasing elderly population, the burden on caregivers, particularly in elderly care settings, is growing. Elderly individuals often experience health problems and emotional fluctuations, making prompt and individualized care essential. However, caregivers face the challenge of providing meticulous care to every elderly person amidst their busy daily schedules. Therefore, there is a need for a system that can monitor the emotional and health status of elderly individuals in real time and provide individualized care based on that information.

[1000] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1001] In this invention, the server includes means for analyzing health data and generating personalized health management suggestions, means for displaying the emotional state of elderly individuals in real time using emotion analysis and providing caregivers with appropriate response guidance, and means for detecting abnormalities and making emergency calls. This enables the provision of care tailored to each elderly person in real time, reduces the burden on caregivers, and allows elderly people to live with peace of mind.

[1002] "Health data" refers to information about the physical condition of elderly individuals, including physiological indicators such as heart rate, blood pressure, and body temperature.

[1003] "Personalized health management proposals" refer to providing specific advice and guidance for maintaining and improving the health of each elderly person, based on the results of an analysis of acquired health data.

[1004] "Voice recognition" is a technology in which a computer analyzes the voice spoken by elderly people, understands its content, and takes appropriate action.

[1005] "Emotional analysis" is a process that evaluates the current emotional state of elderly individuals based on their voice and facial expression data.

[1006] "Real-time display" means providing information about the elderly person's condition and the system in a visual and timely manner without delay.

[1007] "Providing caregivers with appropriate response guidelines" means presenting guidelines that specifically indicate what caregivers should do to support elderly individuals.

[1008] "Detecting anomalies" means identifying changes outside the normal range in the daily behaviors and health status of elderly individuals and recognizing the possibility that a problem has occurred.

[1009] An "emergency notification" is a communication process designed to quickly inform the appropriate agencies or individuals of an emergency situation.

[1010] This elderly support system combines a wearable device, smart glasses, and a server. The wearable device acquires health data such as heart rate and blood pressure in real time and sends it to the server. The server receives this data, evaluates the user's health status by comparing it with past data, and generates personalized health management suggestions. For example, if the heart rate is higher than normal, it will generate a suggestion such as, "We recommend taking a break."

[1011] Furthermore, the camera and microphone built into the smart glasses collect the elderly person's facial expressions and voice. The server uses this data to perform emotion analysis using a generative AI model. Using OpenCV and TensorFlow, the emotional state of the elderly person is analyzed from the acquired data and visualized in real time in the caregiver's field of view. Based on this information, specific action guidelines are presented to the caregiver to support how to interact with the elderly person.

[1012] When an anomaly is detected, the server immediately sends an emergency alert. For example, if an elderly person makes an emergency voice call such as "Help me," the server analyzes the information and sends a notification to a pre-designated emergency contact.

[1013] As a concrete example, if a caregiver using smart glasses in a nursing home detects a depressed mood in an elderly person's facial expression, the glasses will display instructions such as "Suggest relaxing activities." Furthermore, an example of a prompt for the generating AI model could be: "Design a care support system that recognizes the emotional state of elderly people and provides individualized suggestions. Please detail the necessary technologies and data flow."

[1014] This system is implemented by combining data analysis using Amazon Web Services with sentiment analysis functions using OpenCV and TensorFlow, enabling flexible and scalable operation.

[1015] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1016] Step 1:

[1017] Wearable devices collect health data such as heart rate and blood pressure from elderly individuals in real time. This data is transmitted to a terminal using Bluetooth. The terminal then transfers the data to a server. At this stage, physiological indicator data is acquired as input and prepared to be transmitted to the server as output.

[1018] Step 2:

[1019] The server analyzes the received health data. Specifically, it uses AWS Lambda to process the data and compares it with historical data stored in Amazon RDS to assess the current health status. The inputs are physiological indicator data and historical health history, and the output is a health status assessment result. If an abnormality is detected, it generates recommendations for the next optimal health management steps.

[1020] Step 3:

[1021] The server uses a generative AI model to generate specific health management suggestions for the user based on the evaluation results of the received health data. This prompt includes "generative AI model" and "health management suggestions." The evaluation results and generative AI model as inputs are used to produce the suggestions as output.

[1022] Step 4:

[1023] Meanwhile, the device collects voice and facial expression data from elderly individuals via smart glasses. This data is transmitted to a server in real time for emotion analysis. Voice and visual data are sent from the device to the server as input.

[1024] Step 5:

[1025] The server uses OpenCV and TensorFlow to perform emotion analysis. This allows it to analyze the emotional state of elderly individuals from voice and facial expression data, and obtain specific emotion classification results. The input consists of voice and facial expression data, and the emotional state is output as a result of the data calculations.

[1026] Step 6:

[1027] The server provides the caregiver with appropriate response guidance based on the emotional state. Visual feedback is sent to the smart glasses' display. This presents the caregiver with guidelines as visual information based on the input emotion analysis results.

[1028] Step 7:

[1029] When a user issues an emergency, the device detects it and notifies the server. The server then uses AWS SNS to immediately send the information to emergency contacts. In this step, the emergency call process is activated based on the emergency voice command as input, and a rapid notification is completed as output.

[1030] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1031] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1032] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1033] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1034] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1035] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1036] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1037] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1038] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1039] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1040] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1041] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1042] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1043] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1044] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1045] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1046] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1047] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1048] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1049] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1050] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1051] The following is further disclosed regarding the embodiments described above.

[1052] (Claim 1)

[1053] To support the daily lives of the elderly,

[1054] A means of analyzing health data obtained from the elderly and generating personalized health management suggestions,

[1055] To enable natural conversations with the elderly through voice recognition and to alleviate feelings of loneliness,

[1056] Means for providing personalized cognitive function improvement content,

[1057] A means of controlling home appliances and providing daily reminders to the elderly via voice commands,

[1058] A means of detecting abnormalities in elderly people and making emergency calls,

[1059] A system that includes this.

[1060] (Claim 2)

[1061] The system according to claim 1, characterized in that it includes a means for evaluating the health status by comparing it with past data when analyzing health data.

[1062] (Claim 3)

[1063] The system according to claim 1, characterized by comprising means for providing follow-up messages that correspond to the emotions of elderly people based on emotion analysis.

[1064] "Example 1"

[1065] (Claim 1)

[1066] To support the daily lives of the elderly,

[1067] A means for analyzing acquired biometric information and generating personalized health management plans,

[1068] To enable natural conversations with the elderly through voice recognition and to alleviate feelings of loneliness,

[1069] Means for providing personalized cognitive function improvement content,

[1070] A means of operating equipment and providing daily life reminders via voice commands,

[1071] A means of detecting anomalies and making emergency calls,

[1072] A means of monitoring biometric information in real time and immediately providing improvement suggestions when an anomaly is detected,

[1073] A means of generating cognitive tasks based on past biometric information,

[1074] A means for analyzing voice and generating responses to user inquiries,

[1075] A system that includes this.

[1076] (Claim 2)

[1077] The system according to claim 1, comprising means for analyzing abnormal patterns when evaluating a health condition by comparing health information with past information.

[1078] (Claim 3)

[1079] The system according to claim 1, comprising means for generating an appropriate response based on natural language processing.

[1080] "Application Example 1"

[1081] (Claim 1)

[1082] To support the daily lives of the elderly,

[1083] A means of analyzing biometric data obtained from elderly individuals and generating personalized health management suggestions,

[1084] A means of facilitating natural conversations with the elderly through voice recognition and reducing feelings of loneliness,

[1085] Means for providing personalized cognitive function improvement content,

[1086] A means of operating equipment and providing daily life notifications based on instructions from the elderly,

[1087] A means of detecting abnormalities in elderly people and making emergency contact,

[1088] A means of collecting biometric data in real time using a mobile terminal and immediately notifying of abnormalities based on pre-set criteria,

[1089] A means for acquiring environmental information via the internet and generating related responses,

[1090] A means of automatically generating rules and puzzles and providing new challenges adjusted based on data analysis,

[1091] A means of communicating with a home device capable of performing multiple functions by voice commands,

[1092] A system that includes this.

[1093] (Claim 2)

[1094] The system according to claim 1, characterized by having means for evaluating the biological state by comparing it with past data and making individual action suggestions.

[1095] (Claim 3)

[1096] The system according to claim 1, characterized in that it provides a quiz or puzzle adapted to the user based on the results of biometric data analysis, and includes means for customizing the content for subsequent sessions based on the results.

[1097] "Example 2 of combining an emotion engine"

[1098] (Claim 1)

[1099] To support the daily lives of the elderly,

[1100] A means of analyzing physiological data obtained from elderly people and generating personalized health management suggestions,

[1101] To enable natural conversations with the elderly through voice recognition and to alleviate feelings of loneliness,

[1102] A means of analyzing emotional states and providing relaxation programs and entertainment content,

[1103] A means of operating home appliances and providing daily life notifications via voice commands,

[1104] A means of monitoring behavior and sound, detecting anomalies, and notifying emergency contacts,

[1105] A system that includes this.

[1106] (Claim 2)

[1107] The system according to claim 1, comprising means for recording collected physiological and emotional data over a long period of time and evaluating health status and emotional changes by comparing them with past history.

[1108] (Claim 3)

[1109] The system according to claim 1, comprising means for suggesting music or videos that correspond to the emotions of an elderly person based on emotion analysis.

[1110] "Application example 2 when combining with an emotional engine"

[1111] (Claim 1)

[1112] To support the daily lives of the elderly,

[1113] A means of analyzing health data obtained from the elderly and generating personalized health management suggestions,

[1114] To enable natural conversations with the elderly through voice recognition and to alleviate feelings of loneliness,

[1115] Means for providing personalized cognitive function improvement content,

[1116] A means of controlling home appliances and providing daily reminders to the elderly via voice commands,

[1117] A means of detecting abnormalities in elderly people and making emergency calls,

[1118] A means of displaying the emotional state of elderly people in real time using emotion analysis and providing caregivers with appropriate response guidance,

[1119] A system that includes this.

[1120] (Claim 2)

[1121] The system according to claim 1, characterized in that it includes a means for evaluating the health status by comparing it with past data when analyzing health data.

[1122] (Claim 3)

[1123] The system according to claim 1, characterized by comprising means for providing follow-up messages that correspond to the emotions of elderly people based on emotion analysis. [Explanation of symbols]

[1124] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. To support the daily lives of the elderly, A means of analyzing biometric data obtained from elderly individuals and generating personalized health management suggestions, A means of facilitating natural conversations with the elderly through voice recognition and reducing feelings of loneliness, Means for providing personalized cognitive function improvement content, A means of operating equipment and providing daily life notifications based on instructions from the elderly, A means of detecting abnormalities in elderly people and making emergency contact, A means of collecting biometric data in real time using a mobile terminal and immediately notifying of abnormalities based on pre-set criteria, A means for acquiring environmental information via the internet and generating related responses, A means of automatically generating rules and puzzles and providing new challenges adjusted based on data analysis, A means of communicating with a home device capable of performing multiple functions by voice commands, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for evaluating the biological state by comparing it with past data and making individual action suggestions.

3. The system according to claim 1, characterized in that it provides a quiz or puzzle adapted to the user based on the results of biometric data analysis, and includes means for customizing the content for subsequent sessions based on the results.

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