system

Abstract

We provide the system. [Solution] A sensing device for collecting data on the daily activities of elderly people, Analytical means for analyzing collected data, A notification means that evaluates the health status based on the analysis results and generates a notification, A means of dialogue that reduces feelings of loneliness through conversation with users, A method for processing collected data in a cloud environment and performing analysis using machine learning, A means of automatically sending a communication to family members or caregivers when an anomaly is detected, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to solve the problems of social isolation and insufficient health management of the elderly in an aging society. In particular, it is necessary to efficiently monitor daily life activities and health conditions of the elderly, enable early detection of abnormalities and prompt responses, and provide an environment in which the elderly can live with peace of mind. Also, it is an important issue to reduce the sense of loneliness of the elderly with few opportunities for interpersonal communication and maintain their mental health.

Means for Solving the Problems

[0005] This invention provides a system comprising sensing means for collecting activity data of elderly people, analysis means for analyzing the collected data, notification means for evaluating health status and generating notifications based on the analysis results, and dialogue means for reducing feelings of loneliness through conversation with the user. This system, for example, uses an accelerometer and a camera to monitor the activity level and facial expressions of elderly people in detail, detects abnormalities through AI-based data analysis, and simultaneously notifies family members or caregivers in remote locations. Furthermore, it enables natural communication with elderly people through the dialogue means, supporting the maintenance of their mental health. This makes it possible to optimize health management while reducing feelings of loneliness among the elderly.

[0006] The term "elderly" refers to people who have exceeded a certain age, usually 65 years or older, and specifically includes those who have retired or are in need of long-term care.

[0007] "Daily living activity data" refers to information about the actions and movements of elderly people in their daily lives, and includes data that records activity levels such as walking, eating, and resting.

[0008] "Sensing means" refers to devices or technologies for collecting data from the physical environment, and includes methods such as sensors and monitoring devices.

[0009] "Analysis means" refers to methods and techniques for analyzing collected data, extracting meaning from information, and deriving results, and is implemented by software or hardware.

[0010] "Notification means" refers to a mechanism for transmitting abnormal or important information to designated individuals, and the technology provided in the form of alert messages or reports.

[0011] "Dialogue methods" refer to methods of information exchange between users and systems, and specifically to technologies that utilize voice or text for natural language communication. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention aims to optimize health management and address social isolation by using a system that integrates sensing, analysis, notification, and dialogue in the living environment of the elderly.

[0034] 1. Implementation of sensing means

[0035] The device uses accelerometers and cameras to capture data on the daily movements and facial expressions of elderly individuals. For example, it can record daily walking data using a pedometer and monitor emotional states by capturing facial expressions.

[0036] 2. Utilization of analytical methods

[0037] The server receives the collected data in a cloud environment and performs analysis using AI algorithms. It comprehensively assesses health status by evaluating activity levels from walking data and detecting emotional fluctuations through facial expression analysis. In particular, long-term data analysis makes it possible to identify abnormal activity patterns and emotional changes.

[0038] 3. Function of notification means

[0039] If the server detects any anomalies through analysis, it immediately sends a notification to family members or caregivers. These notifications, delivered via email or a dedicated app, ensure that important health information is received quickly and reliably. Furthermore, even in cases of minor issues, the system provides alerts to encourage preventative measures and support appropriate responses.

[0040] 4. Operation of Dialogue Methods

[0041] The device uses an AI chatbot to engage in natural conversations with the elderly. This aims to alleviate feelings of loneliness by listening to their daily anxieties and worries, and providing advice and information. For example, by offering conversations based on the elderly's hobbies or about their day's events, it creates an environment where users can feel comfortable and at ease.

[0042] As a concrete example, if an elderly person's walking volume suddenly decreases and their facial expression appears different from usual, the server analyzes the data and sends an alert to the family. The terminal then initiates a conversation with the elderly person, assessing the situation and working to stabilize their emotions. As a result, it becomes possible to support the realization of a safe and secure life by providing prompt intervention and appropriate care as needed. The invention is implemented in this form.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device uses an accelerometer and camera to monitor the movements and facial expressions of elderly individuals, acquiring this data in real time. Specifically, it records data such as daily steps taken, sitting time, and changes in facial expressions with high precision.

[0046] Step 2:

[0047] The terminal processes the acquired data and converts it to the appropriate format. This ensures data consistency and performs the necessary preprocessing for analysis. This data is then ready to be transmitted to the server via wireless communication or the internet.

[0048] Step 3:

[0049] The server stores the data received from the terminal in a cloud environment and prepares it for analysis. The stored data is then analyzed by an AI algorithm to examine the activity patterns and facial expression changes of elderly individuals.

[0050] Step 4:

[0051] The server uses AI algorithms to analyze data, assess health status, and detect anomalies. This allows it to detect sudden changes in activity levels, fixation of facial expressions, and other issues, and then comprehensively analyzes the results.

[0052] Step 5:

[0053] Based on the data analysis results, the server generates notifications for family members and caregivers as needed. These notifications are given high priority, especially in cases of anomalies, and are sent via email or a dedicated app.

[0054] Step 6:

[0055] The device initiates a conversation with the elderly, confirming their daily situation and feelings. The AI ​​chatbot not merely provides information, but alleviates feelings of loneliness through natural conversation. It offers appropriate advice regarding the stress and anxiety the user is experiencing.

[0056] Step 7:

[0057] Users can report their emotions and physical condition through interaction with the device. This information is collected again via the device and used for subsequent analysis. Ultimately, the entire system functions to make the lives of seniors safer and more fulfilling.

[0058] (Example 1)

[0059] 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."

[0060] There is a need to optimize health management and address the social isolation issues faced by older adults. In particular, the challenge lies in quickly and accurately understanding changes in daily activities and emotions, and providing the necessary care and support.

[0061] 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.

[0062] In this invention, the server includes means for acquiring data on the behavior and emotional state of elderly individuals, means for transmitting and analyzing the acquired data to the cloud, means for detecting anomalies based on the analysis results and generating and sending notifications, and means for interacting with the user using a generated AI model. This makes it possible to comprehensively understand the health status of elderly individuals and provide prompt intervention while reducing social isolation.

[0063] A "device for acquiring data on movement and emotional state" is a device that senses changes in the daily movements and emotions of elderly people and acquires that information.

[0064] A "data transmission and analysis device" is a device that transmits acquired data to the cloud and performs analysis using a generated AI model.

[0065] A "notification generation and transmission device" is a device that detects abnormalities in health conditions based on analysis results and creates and sends notifications to family members and care providers.

[0066] A "generative AI model" is an artificial intelligence model that evaluates the emotional state and behavior of elderly people through data analysis and dialogue, and provides support for necessary responses.

[0067] A "user dialogue device" is a device that facilitates natural communication with elderly people and aims to reduce feelings of loneliness through dialogue.

[0068] This invention aims to manage the health of the elderly and alleviate social isolation, and is embodied as a system combining sensors, an analysis server, a notification function, and an interactive device. The following describes how this system is configured and how it functions.

[0069] The device is equipped with an accelerometer and camera to closely observe the movements and emotional states of elderly individuals. These devices detect and record in real time movement data (e.g., number of steps and travel time) and facial expression data (e.g., smiles and level of attention). This data is optimized using advanced data compression technology and then sent to a server in the cloud for analysis.

[0070] The server receives motion and facial expression data from elderly individuals and analyzes them using a generative AI model. Here, the server uses machine learning algorithms to evaluate activity levels and emotional changes, detecting abnormal patterns. For example, if the number of steps taken on a given day is less than half the normal level, it is registered as an abnormality.

[0071] Based on the analysis results, the server generates a message to notify family members and caregivers of any anomalies indicated by the collected data. This notification is sent via email or a dedicated app and may include messages such as, "The elderly person's activity level has decreased significantly. Please check immediately."

[0072] Simultaneously, the device initiates a conversation with the elderly through an AI chatbot. This facilitates emotional support for the elderly. The conversation takes place through individually tailored questions based on the elderly person's daily experiences and interests. For example, prompts such as "How did you spend your day?" and "Can you tell me about your recent hobbies?" are provided.

[0073] Through this system, it is possible to support the health and social connections of the elderly and provide an environment in which they can live with peace of mind.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device monitors the movements and emotional state of elderly individuals in real time. Specifically, it acquires movement data such as step count and movement speed using an accelerometer and captures facial expression data using a camera. The input to this process is the data from the sensors and camera, and the output is the collected movement and facial expression data. This data is sent to the server in a compressed format.

[0077] Step 2:

[0078] The server receives motion and facial expression data transmitted from the terminal. Based on this data, it performs analysis using a generative AI model. The analysis uses machine learning algorithms to evaluate activity levels and score emotions. The input is the collected raw data, and the output is the activity level evaluation and emotion score as a result of the data analysis.

[0079] Step 3:

[0080] The server initiates a process to detect anomalies based on the analysis results. Specifically, it compares the data with past data and flags any significant changes in behavior or emotional state as an anomaly. The input is the activity level evaluation and emotional score from the analysis, and the output is the anomaly flag and its cause. This result is used to generate notifications.

[0081] Step 4:

[0082] When an anomaly flag is set, the server generates a notification message for family members or caregivers. The notification includes the nature of the anomaly and recommended actions. For example, it might generate a message such as, "Activity level has decreased. Please check." The input is information about the anomaly flag, and the output is the specific notification message.

[0083] Step 5:

[0084] The terminal initiates a conversation with the elderly user to understand the circumstances related to anomaly detection. The AI ​​chatbot guides the conversation using prompts. For example, it asks questions such as, "How have you been feeling lately?" to gather missing information. The input is the generated prompts, and the output is the feedback received from the elderly user.

[0085] Step 6:

[0086] Users accept advice and information provided through the device and incorporate it into their daily lives as needed. The device records the results and sends them to the server as feedback. The input is the user's choices and actions, and the output is feedback data. This data will be used for subsequent data analysis.

[0087] (Application Example 1)

[0088] 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."

[0089] For the elderly, understanding their health status and addressing social isolation are crucial issues. However, current technology struggles to accurately monitor the elderly's activity levels in real time and promptly notify families and caregivers of any abnormalities. Furthermore, systems that utilize collected data to provide appropriate feedback tailored to the elderly are insufficient, failing to alleviate feelings of loneliness.

[0090] 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.

[0091] In this invention, the server includes sensing means for collecting data on the daily activities of elderly people, analysis means for processing the collected data in a cloud environment and performing analysis using machine learning, and dialogue means for reducing feelings of loneliness through conversation with the user. This makes it possible to understand the health status in real time and automatically communicate any abnormalities to family members or caregivers, thereby improving the safety and quality of life of elderly people.

[0092] "Sensing means" is a general term for devices and functions used to collect real-time data on the daily activities and various environmental conditions of elderly people.

[0093] The "analysis method" refers to a system that processes collected data in a cloud environment, analyzes it using machine learning algorithms, and evaluates the health status and emotional fluctuations of elderly individuals.

[0094] "Notification means" refers to a communication function that sends notifications generated based on analysis results to family members and care providers to prompt necessary actions.

[0095] A "dialogue method" is a system designed to alleviate feelings of loneliness by engaging in continuous conversations with users and providing psychological support.

[0096] A "cloud environment" refers to virtualized computing resources used to store and analyze data via the internet.

[0097] "Machine learning" is a technology in which algorithms automatically learn patterns and rules based on collected data, and then use that knowledge to make predictions and decisions.

[0098] "Communication" is the process of reliably transmitting information to a distant location using electrical or electronic means.

[0099] The system for implementing this invention mainly consists of sensing means, analysis means, notification means, and dialogue means. Each of these components will be described in detail below.

[0100] The server works in conjunction with appropriate hardware to record the daily lives of elderly people using sensing devices. Specifically, sensors and cameras built into smartphones, wearable devices, and smart glasses are used. The data collected by these devices is then transmitted to the cloud environment in real time.

[0101] The server processes data acquired in the cloud environment using analytical tools. The analysis utilizes machine learning models based on Google Cloud Platform and Tensorflow. This allows for the analysis of activity and facial expression data from elderly individuals, evaluating their health status and emotional changes. The server also learns long-term patterns using AI algorithms, improving the accuracy of anomaly detection.

[0102] When an anomaly is detected, the notification system is automatically activated. The server uses the Twilio API to quickly send notifications to designated contacts and prompt appropriate action if immediate response is required. These notifications are sent via SMS, email, or other means.

[0103] Furthermore, the device provides a means of interaction. An AI chatbot powered by Dialogflow interacts with the user, answering questions and engaging in everyday conversations to provide emotional support. This creates an environment where users can live with peace of mind without feeling lonely.

[0104] As a concrete example, if an elderly person suddenly exhibits an unusual behavioral pattern at home, an accelerometer detects the movement and sends the data to a server. The server then analyzes the data, and if an anomaly is detected, it sends a notification to the family saying, "Some kind of anomaly has been detected. Please check." At the same time, a chatbot sends a message to the elderly person asking, "How have you been feeling lately?" An example of a prompt is the text, "Please tell me how to analyze the activity data of elderly people and detect anomalies."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The device uses built-in sensors and a camera to collect data on the daily activities of elderly users. This data includes walking patterns, movement speed, and facial expressions. Inputs include motion data from sensors and image data from the camera. This data is transmitted to the cloud environment in real time.

[0108] Step 2:

[0109] The server receives data sent to the cloud and begins processing it using analytical tools. An AI model using TensorFlow analyzes the data, detecting abnormal behavioral patterns and emotional fluctuations from facial expressions. The input is raw data sent from the terminal, and the output is an evaluation result regarding activity level and emotional state. In this process, machine learning algorithms analyze the data and apply recognition patterns to identify the user's state.

[0110] Step 3:

[0111] Based on the analysis results, the server generates a notification via a notification system if an anomaly is detected. Utilizing the Twilio API, it sends SMS or emails to pre-registered family members and care providers. The input is an anomaly alert based on the analyzed data, and the output is an emergency notification message. This allows the server to prompt a quick response.

[0112] Step 4:

[0113] The device uses Dialogflow to activate an AI chatbot to initiate a conversation with the user. When changes in emotion or abnormalities are detected, the chatbot asks the user questions about their recent physical condition and emotions. Input is analysis results from the server, and output is the conversation history and user feedback. This reduces anxiety and feelings of loneliness and ensures the user feels at ease.

[0114] 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.

[0115] This invention aims to address health management and social isolation among the elderly by using a system incorporating an emotion engine. The system's basic configuration includes means for sensing, analysis, notification, and dialogue, and further integrates an emotion engine to enable more precise emotion recognition.

[0116] 1. Method for implementing sensing means

[0117] The device continuously monitors the elderly person's activity level and facial expressions through a camera and microphone. The acquired data includes facial expressions, voice tone, and daily movement data. For example, it captures the user's facial movements and records audio while they are watching television.

[0118] 2. Function of the Emotion Engine

[0119] An emotion engine located on the server analyzes the user's emotions using data provided by sensing devices. Specifically, it utilizes facial expression analysis technology and voice analysis algorithms to recognize emotions such as smiles, surprise, and sadness. For example, when an elderly person smiles, the system records it as a positive emotion and forms a trend.

[0120] 3. Interlocking of analysis means and notification means

[0121] Based on the results from the emotion engine, the server assesses the user's health status and immediately notifies caregivers and family members if an abnormality is detected. This notification warns of the continuation of abnormal emotional patterns or a deterioration in health and is sent within the home via email or a dedicated app.

[0122] 4. Addressing Emotions in the Use of Dialogue Methods

[0123] The device utilizes feedback from its emotion engine when engaging in everyday conversations with the user through its interactive mechanisms. This allows it to select conversation topics appropriate to the user's emotions, effectively reducing feelings of loneliness. For example, if an elderly person shows a sad expression, the device will offer words of encouragement or relaxing music.

[0124] For example, if an elderly person begins to exhibit clearly unstable emotions during the day, this system can quickly detect the change, engage in appropriate dialogue, and promptly request assistance as needed. In this form, the system of the present invention can create a safe and comfortable living environment for the elderly.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The device collects data in real time from cameras and microphones installed in the elderly person's environment. Specifically, it captures facial expressions during everyday conversations and activities, and records high-quality audio data to collect detailed information.

[0128] Step 2:

[0129] The terminal temporarily stores the collected data and prepares it for transmission after compressing or encrypting it as needed. Wireless communication technology is used to transfer the data to the server.

[0130] Step 3:

[0131] The server receives the data sent from the terminal and first checks the data format. After this, it passes a portion of the data to the emotion engine to begin detailed analysis.

[0132] Step 4:

[0133] The server's emotion engine analyzes the received data to identify the user's emotional state. Specifically, it uses facial recognition algorithms to analyze subtle facial movements and voice analysis to evaluate voice tone and speed.

[0134] Step 5:

[0135] The server compiles the analysis results from the emotion engine and generates an immediate notification to family members or caregivers if an abnormal emotional pattern is detected. This notification includes details about specific emotional changes and their potential.

[0136] Step 6:

[0137] The device dynamically adjusts the content of the conversation based on the analysis results and communicates with the user through dialogue. If an elderly person shows signs of anxiety, it provides words of encouragement or soothing music.

[0138] Step 7:

[0139] Users can gain a sense of security through interaction with the device and can report their feelings and state again as needed. This information is also collected again and used for future analysis.

[0140] (Example 2)

[0141] 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".

[0142] For the elderly, not only physical health but also psychological and emotional health is important. However, it is difficult to understand their emotions from their facial expressions and tone of voice in daily life and to provide appropriate support. Furthermore, being able to react quickly in abnormal situations is crucial for a safe living environment for the elderly. This invention aims to solve these problems.

[0143] 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.

[0144] In this invention, the server includes means for preprocessing acquired emotional information, means for performing emotional analysis using the preprocessed information, and means for optimizing the conversation content based on the analysis results and sending notifications to relevant parties in the event of an anomaly. This enables accurate understanding of the emotional state of elderly people and allows for appropriate responses and notifications.

[0145] "Means of acquisition" refers to devices and methods for collecting emotional information from elderly individuals.

[0146] "Processing means" refers to devices or methods that have the function of preprocessing acquired emotional information and preparing it in a format suitable for analysis.

[0147] "Analysis means" refers to devices or methods for analyzing emotions based on pre-processed information.

[0148] "Transmission means" refers to devices or methods for sending notifications to relevant parties in the event of an anomaly based on the analysis results.

[0149] A "response tool" refers to a device or method that optimizes conversation content based on analysis results to facilitate effective communication with the elderly.

[0150] This invention is a system that understands the emotional state of elderly people and supports them in living safely and comfortably, and is mainly implemented using terminals and servers.

[0151] The device's role is to acquire emotional information from elderly individuals via its camera and microphone. Specifically, the device continuously collects facial expressions and voice tones from elderly individuals in their daily lives. This data is acquired in real time using sensor technology.

[0152] The server receives information transmitted from the terminal and performs preprocessing. Noise is removed from the audio data, and the facial expression data is normalized. This preprocessing prepares the data for analysis.

[0153] Next, an emotion engine located within the server functions as an analysis tool. The emotion engine uses machine learning techniques to identify emotional states. The algorithm used here is based on deep learning and accurately recognizes multiple emotional patterns such as smiles, surprise, and sadness.

[0154] Furthermore, based on the analysis results, the server will notify relevant parties via a transmission method if an abnormal emotional pattern is detected. This notification will be sent via email or a dedicated app.

[0155] The device also functions as a means of responding, optimizing the conversation based on the analysis results. Specifically, when an elderly person is expressing sadness, it suggests encouraging words such as, "Why don't you tell me what's wrong?" and provides relaxation music.

[0156] For example, if an elderly person begins to show clearly unstable emotions during the day, this system can quickly detect the change and request assistance while engaging in necessary dialogue. An example of a prompt to the generating AI model would be, "Please advise how to respond when an elderly person is smiling."

[0157] Based on the above, the present invention can provide more personalized support based on the emotional information of elderly people and create an environment in which they can live with peace of mind.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The device uses a camera and microphone to collect emotional information from elderly individuals. Specifically, the device continuously monitors the user's facial expressions and voice while they are in the room. Inputs are video data from the camera and audio data from the microphone, and output is this raw data.

[0161] Step 2:

[0162] The server receives video and audio data transmitted from the terminal and performs preprocessing. Specifically, the server removes background noise from the audio data and normalizes the video data by extracting each frame. The input is raw data, and the output is data converted into a format suitable for analysis.

[0163] Step 3:

[0164] The server inputs pre-processed data into the emotion engine for analysis. The emotion engine uses a deep learning model to identify emotions from facial expressions in each frame and analyzes voice tone from audio data. The input is pre-processed data, and the output is an emotion label (e.g., smile, surprise, sadness).

[0165] Step 4:

[0166] The server evaluates the health status of elderly individuals based on the analyzed emotional data. Specifically, it tracks emotional fluctuation patterns over time and generates warnings if abnormal emotional patterns are detected. The input is the emotional label and its fluctuation pattern, and the output is the health status evaluation result.

[0167] Step 5:

[0168] The server sends notifications to relevant parties as needed, based on the health assessment results. Specifically, if abnormal emotional patterns persist, it sends alerts to family members or caregivers via email or app. The input is the health assessment results, and the output is a notification message.

[0169] Step 6:

[0170] The device adjusts the communication content based on the analysis and evaluation results. Specifically, if the user expresses sadness, it will initiate a conversation such as, "Please feel free to talk about anything," and play relaxing music. The input is the analysis results and the health status evaluation results, and the output is the dialogue content and music selection.

[0171] (Application Example 2)

[0172] 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".

[0173] There are problems such as the fact that elderly people are prone to feeling socially isolated, and that their health status cannot be constantly monitored, making it difficult to respond to sudden emotional changes. Furthermore, there is a need to establish methods to quickly notify family members and caregivers of the situation and to alleviate the loneliness of the elderly through appropriate dialogue.

[0174] 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.

[0175] In this invention, the server includes sensing means for collecting information on the daily activities of elderly people, analysis means for analyzing the collected data, emotion analysis means for performing emotion analysis using voice and video in real time, and warning means for detecting abnormal emotional changes and issuing warnings to family members and caregivers. This makes it possible to closely monitor the health and emotional state of elderly people and reduce feelings of loneliness.

[0176] "Sensing methods for collecting information on the daily activities of the elderly" refers to technologies that use devices such as cameras and microphones to acquire data in order to measure the activity status of the elderly.

[0177] "Analysis means for analyzing collected data" refers to technologies that process information obtained by sensing means and analyze individual health conditions and lifestyle patterns.

[0178] "An emotion analysis method that performs emotion analysis using audio and video in real time" refers to a software algorithm or model that instantly recognizes emotions from recordings and videos and evaluates emotional states such as smiles and sadness.

[0179] A "warning mechanism that detects abnormal emotional changes and alerts family members and caregivers" is a communication technology that recognizes changes that deviate significantly from normal emotional states and quickly notifies relevant parties.

[0180] "An emotional response method for generating appropriate music and conversation" refers to a dialogue generation technology that selects and plays adaptive music and lines according to the emotional state of elderly people, with the aim of reducing feelings of loneliness.

[0181] This invention describes embodiments for carrying out this invention. The system includes sensing means that utilize a camera and microphone obtained from a terminal to acquire information on the daily activities of elderly people. Furthermore, the server analyzes this data in real time and uses emotion analysis means to recognize emotional states. For emotion analysis, the open-source image recognition library OpenCV and the PyDub library for speech analysis can be utilized. In addition, the Google Cloud Natural Language API and Microsoft® Azure® Face API are used for analyzing emotional states and forming trends.

[0182] If the server detects abnormal emotional changes as a result of analyzing the collected data, it will immediately notify relevant parties using an alert system. This alert system includes messaging services such as the Twilio API and Firebase, enabling the rapid dissemination of information to the elderly person's family and caregivers.

[0183] To facilitate smooth interaction with users, emotional response mechanisms are used to play appropriate music and generate dialogue based on the analyzed emotional state. For generating specific responses, APIs such as the Google Text-to-Speech API can be used to play music in real time, providing natural and engaging voice dialogue. For example, if an elderly person exhibits signs of distress in their daily behavior, a question like "How are you feeling today?" can be asked to gauge their mood, and relaxing music can be played to create a more comfortable living environment.

[0184] An example of a prompt message would be, "Please input the instruction 'Based on the current emotional state, determine what kind of dialogue is optimal' to the generating AI model," thereby enabling appropriate intervention by the AI.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The device uses a camera and microphone to acquire information about the daily lives of elderly people. It receives video and audio data as input, which is continuously acquired in real time. The acquired data is then sent to a server for subsequent analysis steps.

[0188] Step 2:

[0189] The server analyzes the received video data. It processes the input video data using the OpenCV library to detect the facial expressions of elderly people. It extracts facial feature points, identifies emotions such as smiles and surprise, and outputs the results as emotional state data.

[0190] Step 3:

[0191] The server analyzes the received audio data. It uses the PyDub library to perform frequency analysis on the input audio data and evaluates the tone and volume of the elderly person's voice. Based on this information, it analyzes the emotional state and outputs it as emotional state data.

[0192] Step 4:

[0193] The server integrates video and audio analysis results and uses a generating AI model to determine the overall emotional state. Based on the integrated emotional state data, it forms trends and attempts to detect abnormal patterns. Any detected anomalies become output data passed to the warning step.

[0194] Step 5:

[0195] The server sends a notification to family members or caregivers via an alert system if an abnormal emotional change is detected. The output includes information about the detected anomaly, sent via messaging services using the Twilio API or Firebase. The notification content includes details of the detected emotional state.

[0196] Step 6:

[0197] The user receives responses through a dialogue function. Based on emotional state data, the server uses the Google Text-to-Speech API to select and play appropriate music, enabling natural conversation with the elderly. Output includes audio and music playback. For example, the server might ask the elderly, "How are you feeling today?" and play relaxing music.

[0198] 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.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] 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.

[0211] 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.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention aims to optimize health management and address social isolation by using a system that integrates sensing, analysis, notification, and dialogue in the living environment of the elderly.

[0215] 1. Implementation of sensing means

[0216] The device uses accelerometers and cameras to capture data on the daily movements and facial expressions of elderly individuals. For example, it can record daily walking data using a pedometer and monitor emotional states by capturing facial expressions.

[0217] 2. Utilization of analytical methods

[0218] The server receives the collected data in a cloud environment and performs analysis using AI algorithms. It comprehensively assesses health status by evaluating activity levels from walking data and detecting emotional fluctuations through facial expression analysis. In particular, long-term data analysis makes it possible to identify abnormal activity patterns and emotional changes.

[0219] 3. Function of notification means

[0220] If the server detects any anomalies through analysis, it immediately sends a notification to family members or caregivers. These notifications, delivered via email or a dedicated app, ensure that important health information is received quickly and reliably. Furthermore, even in cases of minor issues, the system provides alerts to encourage preventative measures and support appropriate responses.

[0221] 4. Operation of Dialogue Methods

[0222] The device uses an AI chatbot to engage in natural conversations with the elderly. This aims to alleviate feelings of loneliness by listening to their daily anxieties and worries, and providing advice and information. For example, by offering conversations based on the elderly's hobbies or about their day's events, it creates an environment where users can feel comfortable and at ease.

[0223] As a concrete example, if an elderly person's walking volume suddenly decreases and their facial expression appears different from usual, the server analyzes the data and sends an alert to the family. The terminal then initiates a conversation with the elderly person, assessing the situation and working to stabilize their emotions. As a result, it becomes possible to support the realization of a safe and secure life by providing prompt intervention and appropriate care as needed. The invention is implemented in this form.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The device uses an accelerometer and camera to monitor the movements and facial expressions of elderly individuals, acquiring this data in real time. Specifically, it records data such as daily steps taken, sitting time, and changes in facial expressions with high precision.

[0227] Step 2:

[0228] The terminal processes the acquired data and converts it to the appropriate format. This ensures data consistency and performs the necessary preprocessing for analysis. This data is then ready to be transmitted to the server via wireless communication or the internet.

[0229] Step 3:

[0230] The server stores the data received from the terminal in a cloud environment and prepares it for analysis. The stored data is then analyzed by an AI algorithm to examine the activity patterns and facial expression changes of elderly individuals.

[0231] Step 4:

[0232] The server uses AI algorithms to analyze data, assess health status, and detect anomalies. This allows it to detect sudden changes in activity levels, fixation of facial expressions, and other issues, and then comprehensively analyzes the results.

[0233] Step 5:

[0234] Based on the data analysis results, the server generates notifications for family members and caregivers as needed. These notifications are given high priority, especially in cases of anomalies, and are sent via email or a dedicated app.

[0235] Step 6:

[0236] The device initiates a conversation with the elderly, confirming their daily situation and feelings. The AI ​​chatbot not merely provides information, but alleviates feelings of loneliness through natural conversation. It offers appropriate advice regarding the stress and anxiety the user is experiencing.

[0237] Step 7:

[0238] Users can report their emotions and physical condition through interaction with the device. This information is collected again via the device and used for subsequent analysis. Ultimately, the entire system functions to make the lives of seniors safer and more fulfilling.

[0239] (Example 1)

[0240] 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."

[0241] There is a need to optimize health management and address the social isolation issues faced by older adults. In particular, the challenge lies in quickly and accurately understanding changes in daily activities and emotions, and providing the necessary care and support.

[0242] 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.

[0243] In this invention, the server includes means for acquiring data on the behavior and emotional state of elderly individuals, means for transmitting and analyzing the acquired data to the cloud, means for detecting anomalies based on the analysis results and generating and sending notifications, and means for interacting with the user using a generated AI model. This makes it possible to comprehensively understand the health status of elderly individuals and provide prompt intervention while reducing social isolation.

[0244] A "device for acquiring data on movement and emotional state" is a device that senses changes in the daily movements and emotions of elderly people and acquires that information.

[0245] A "data transmission and analysis device" is a device that transmits acquired data to the cloud and performs analysis using a generated AI model.

[0246] A "notification generation and transmission device" is a device that detects abnormalities in health conditions based on analysis results and creates and sends notifications to family members and care providers.

[0247] A "generative AI model" is an artificial intelligence model that evaluates the emotional state and behavior of elderly people through data analysis and dialogue, and provides support for necessary responses.

[0248] A "user dialogue device" is a device that facilitates natural communication with elderly people and aims to reduce feelings of loneliness through dialogue.

[0249] This invention aims to manage the health of the elderly and alleviate social isolation, and is embodied as a system combining sensors, an analysis server, a notification function, and an interactive device. The following describes how this system is configured and how it functions.

[0250] The device is equipped with an accelerometer and camera to closely observe the movements and emotional states of elderly individuals. These devices detect and record in real time movement data (e.g., number of steps and travel time) and facial expression data (e.g., smiles and level of attention). This data is optimized using advanced data compression technology and then sent to a server in the cloud for analysis.

[0251] The server receives motion and facial expression data from elderly individuals and analyzes them using a generative AI model. Here, the server uses machine learning algorithms to evaluate activity levels and emotional changes, detecting abnormal patterns. For example, if the number of steps taken on a given day is less than half the normal level, it is registered as an abnormality.

[0252] Based on the analysis results, the server generates a message to notify family members and caregivers of any anomalies indicated by the collected data. This notification is sent via email or a dedicated app and may include messages such as, "The elderly person's activity level has decreased significantly. Please check immediately."

[0253] Simultaneously, the device initiates a conversation with the elderly through an AI chatbot. This facilitates emotional support for the elderly. The conversation takes place through individually tailored questions based on the elderly person's daily experiences and interests. For example, prompts such as "How did you spend your day?" and "Can you tell me about your recent hobbies?" are provided.

[0254] Through this system, it is possible to support the health and social connections of the elderly and provide an environment in which they can live with peace of mind.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The device monitors the movements and emotional state of elderly individuals in real time. Specifically, it acquires movement data such as step count and movement speed using an accelerometer and captures facial expression data using a camera. The input to this process is the data from the sensors and camera, and the output is the collected movement and facial expression data. This data is sent to the server in a compressed format.

[0258] Step 2:

[0259] The server receives motion and facial expression data transmitted from the terminal. Based on this data, it performs analysis using a generative AI model. The analysis uses machine learning algorithms to evaluate activity levels and score emotions. The input is the collected raw data, and the output is the activity level evaluation and emotion score as a result of the data analysis.

[0260] Step 3:

[0261] The server initiates a process to detect anomalies based on the analysis results. Specifically, it compares the data with past data and flags any significant changes in behavior or emotional state as an anomaly. The input is the activity level evaluation and emotional score from the analysis, and the output is the anomaly flag and its cause. This result is used to generate notifications.

[0262] Step 4:

[0263] When an anomaly flag is set, the server generates a notification message for family members or caregivers. The notification includes the nature of the anomaly and recommended actions. For example, it might generate a message such as, "Activity level has decreased. Please check." The input is information about the anomaly flag, and the output is the specific notification message.

[0264] Step 5:

[0265] The terminal initiates a conversation with the elderly user to understand the circumstances related to anomaly detection. The AI ​​chatbot guides the conversation using prompts. For example, it asks questions such as, "How have you been feeling lately?" to gather missing information. The input is the generated prompts, and the output is the feedback received from the elderly user.

[0266] Step 6:

[0267] Users accept advice and information provided through the device and incorporate it into their daily lives as needed. The device records the results and sends them to the server as feedback. The input is the user's choices and actions, and the output is feedback data. This data will be used for subsequent data analysis.

[0268] (Application Example 1)

[0269] 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."

[0270] For the elderly, understanding their health status and addressing social isolation are crucial issues. However, current technology struggles to accurately monitor the elderly's activity levels in real time and promptly notify families and caregivers of any abnormalities. Furthermore, systems that utilize collected data to provide appropriate feedback tailored to the elderly are insufficient, failing to alleviate feelings of loneliness.

[0271] 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.

[0272] In this invention, the server includes sensing means for collecting data on the daily activities of elderly people, analysis means for processing the collected data in a cloud environment and performing analysis using machine learning, and dialogue means for reducing feelings of loneliness through conversation with the user. This makes it possible to understand the health status in real time and automatically communicate any abnormalities to family members or caregivers, thereby improving the safety and quality of life of elderly people.

[0273] "Sensing means" is a general term for devices and functions used to collect real-time data on the daily activities and various environmental conditions of elderly people.

[0274] The "analysis method" refers to a system that processes collected data in a cloud environment, analyzes it using machine learning algorithms, and evaluates the health status and emotional fluctuations of elderly individuals.

[0275] "Notification means" refers to a communication function that sends notifications generated based on analysis results to family members and care providers to prompt necessary actions.

[0276] A "dialogue method" is a system designed to alleviate feelings of loneliness by engaging in continuous conversations with users and providing psychological support.

[0277] A "cloud environment" refers to virtualized computing resources used to store and analyze data via the internet.

[0278] "Machine learning" is a technology in which an algorithm automatically learns patterns and rules based on the collected data and makes predictions and judgments based on that knowledge.

[0279] "Communication" is a process for reliably transmitting information to a remote location using electrical or electronic means.

[0280] The system for implementing this invention mainly consists of a sensing means, an analysis means, a notification means, and an interaction means. Hereinafter, each component will be described in detail.

[0281] The server cooperates with appropriate hardware to record the daily life of the elderly using the sensing means. Specifically, sensors and cameras installed in smartphones, wearable devices, smart glasses, etc. are used. Also, the data collected by these devices is sent to the cloud environment in real time.

[0282] The server processes the data obtained in the cloud environment by the analysis means. For the analysis, a machine learning model using Google Cloud Platform and TensorFlow is used. Thereby, the activity data and expression data of the elderly are analyzed to evaluate the changes in their health status and emotions. The server also learns long-term patterns with AI algorithms, improving the detection accuracy of abnormalities.

[0283] When an abnormality is detected, the notification means automatically activates. The server utilizes the Twilio API to quickly send notifications to the specified contacts and prompt appropriate actions when immediate response is required. This notification is sent via SMS, email, etc.

[0284] Furthermore, the terminal provides an interaction means. An AI chatbot realized by Dialogflow acts as a user's partner, answers questions, conducts daily conversations, and provides mental support. This creates an environment where users can live comfortably without feeling lonely.

[0285] As a specific example, when an elderly person shows an abnormal behavior pattern at home suddenly, the acceleration sensor senses the movement and transmits the data to the server. After that, when the server analyzes the data and detects an abnormality, it sends a notification to the family saying, "Some abnormality has been detected. Please check." At the same time, a message asking the elderly person, "How is your recent physical condition?" is sent from the chatbot. As an example of the prompt text, there is a text saying, "Please teach me how to analyze the activity data of the elderly person and discover abnormalities."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The terminal uses the built-in sensors and camera to collect the daily life activity data of the elderly user, who is the user. This data includes walking patterns, movement speed, expressions, etc. The inputs are movement data from the sensors and image data from the camera. These data are transmitted to the cloud environment in real time.

[0289] Step 2:

[0290] The server receives the data transmitted to the cloud and starts processing with the analysis means. The AI model using TensorFlow analyzes the data and detects fluctuations in emotions from abnormal behavior patterns and expressions. The input is the raw data sent from the terminal, and the output is the evaluation results regarding the activity level and emotional state. In this process, the machine learning algorithm analyzes the data and applies the recognition pattern to identify the user's state.

[0291] Step 3:

[0292] Based on the analysis results, the server generates a notification via a notification system if an anomaly is detected. Utilizing the Twilio API, it sends SMS or emails to pre-registered family members and care providers. The input is an anomaly alert based on the analyzed data, and the output is an emergency notification message. This allows the server to prompt a quick response.

[0293] Step 4:

[0294] The device uses Dialogflow to activate an AI chatbot to initiate a conversation with the user. When changes in emotion or abnormalities are detected, the chatbot asks the user questions about their recent physical condition and emotions. Input is analysis results from the server, and output is the conversation history and user feedback. This reduces anxiety and feelings of loneliness and ensures the user feels at ease.

[0295] 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.

[0296] This invention aims to address health management and social isolation among the elderly by using a system incorporating an emotion engine. The system's basic configuration includes means for sensing, analysis, notification, and dialogue, and further integrates an emotion engine to enable more precise emotion recognition.

[0297] 1. Method for implementing sensing means

[0298] The device continuously monitors the elderly person's activity level and facial expressions through a camera and microphone. The acquired data includes facial expressions, voice tone, and daily movement data. For example, it captures the user's facial movements and records audio while they are watching television.

[0299] 2. Function of the Emotion Engine

[0300] The emotion engine arranged in the server analyzes the user's emotion by using the data provided by the sensing means. Specifically, it utilizes facial expression analysis technology and voice analysis algorithms to recognize emotions such as smiling, surprise, sadness, etc. For example, when an elderly person has a smile on their face, the system records it as a positive emotion and forms a trend.

[0301] 3. Interlocking of the analysis means and the notification means

[0302] Based on the results of the emotion engine, the server evaluates the health status and, if an abnormality is detected, immediately notifies the care provider or family. This notification warns of the continuation of abnormal emotion patterns or the deterioration of the health status and is sent within the home via email or a dedicated app.

[0303] 4. Response to emotion in the operation of the dialogue means

[0304] When conducting daily conversations with the user through the dialogue means, the terminal utilizes the feedback from the emotion engine. Thereby, it can select conversation content suitable for the user's emotion and effectively reduce the sense of loneliness. For example, when an elderly person shows a sad expression, the terminal provides encouraging words or relaxing music.

[0305] As a specific example, when a certain elderly person begins to show clearly unstable emotions during the day, this system can quickly detect the change, conduct appropriate conversations, and, if necessary, quickly request assistance. In this form, the system of the present invention can realize a safe and comfortable living environment for the elderly.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] The device collects data in real time from cameras and microphones installed in the elderly person's environment. Specifically, it captures facial expressions during everyday conversations and activities, and records high-quality audio data to collect detailed information.

[0309] Step 2:

[0310] The terminal temporarily stores the collected data and prepares it for transmission after compressing or encrypting it as needed. Wireless communication technology is used to transfer the data to the server.

[0311] Step 3:

[0312] The server receives the data sent from the terminal and first checks the data format. After this, it passes a portion of the data to the emotion engine to begin detailed analysis.

[0313] Step 4:

[0314] The server's emotion engine analyzes the received data to identify the user's emotional state. Specifically, it uses facial recognition algorithms to analyze subtle facial movements and voice analysis to evaluate voice tone and speed.

[0315] Step 5:

[0316] The server compiles the analysis results from the emotion engine and generates an immediate notification to family members or caregivers if an abnormal emotional pattern is detected. This notification includes details about specific emotional changes and their potential.

[0317] Step 6:

[0318] The device dynamically adjusts the content of the conversation based on the analysis results and communicates with the user through dialogue. If an elderly person shows signs of anxiety, it provides words of encouragement or soothing music.

[0319] Step 7:

[0320] Users can gain a sense of security through interaction with the device and can report their feelings and state again as needed. This information is also collected again and used for future analysis.

[0321] (Example 2)

[0322] 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".

[0323] For the elderly, not only physical health but also psychological and emotional health is important. However, it is difficult to understand their emotions from their facial expressions and tone of voice in daily life and to provide appropriate support. Furthermore, being able to react quickly in abnormal situations is crucial for a safe living environment for the elderly. This invention aims to solve these problems.

[0324] 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.

[0325] In this invention, the server includes means for preprocessing acquired emotional information, means for performing emotional analysis using the preprocessed information, and means for optimizing the conversation content based on the analysis results and sending notifications to relevant parties in the event of an anomaly. This enables accurate understanding of the emotional state of elderly people and allows for appropriate responses and notifications.

[0326] "Means of acquisition" refers to devices and methods for collecting emotional information from elderly individuals.

[0327] "Processing means" refers to devices or methods that have the function of preprocessing acquired emotional information and preparing it in a format suitable for analysis.

[0328] "Analysis means" refers to devices or methods for analyzing emotions based on pre-processed information.

[0329] "Transmission means" refers to devices or methods for sending notifications to relevant parties in the event of an anomaly based on the analysis results.

[0330] A "response tool" refers to a device or method that optimizes conversation content based on analysis results to facilitate effective communication with the elderly.

[0331] This invention is a system that understands the emotional state of elderly people and supports them in living safely and comfortably, and is mainly implemented using terminals and servers.

[0332] The device's role is to acquire emotional information from elderly individuals via its camera and microphone. Specifically, the device continuously collects facial expressions and voice tones from elderly individuals in their daily lives. This data is acquired in real time using sensor technology.

[0333] The server receives information transmitted from the terminal and performs preprocessing. Noise is removed from the audio data, and the facial expression data is normalized. This preprocessing prepares the data for analysis.

[0334] Next, an emotion engine located within the server functions as an analysis tool. The emotion engine uses machine learning techniques to identify emotional states. The algorithm used here is based on deep learning and accurately recognizes multiple emotional patterns such as smiles, surprise, and sadness.

[0335] Furthermore, based on the analysis results, the server will notify relevant parties via a transmission method if an abnormal emotional pattern is detected. This notification will be sent via email or a dedicated app.

[0336] The device also functions as a means of responding, optimizing the conversation based on the analysis results. Specifically, when an elderly person is expressing sadness, it suggests encouraging words such as, "Why don't you tell me what's wrong?" and provides relaxation music.

[0337] For example, if an elderly person begins to show clearly unstable emotions during the day, this system can quickly detect the change and request assistance while engaging in necessary dialogue. An example of a prompt to the generating AI model would be, "Please advise how to respond when an elderly person is smiling."

[0338] Based on the above, the present invention can provide more personalized support based on the emotional information of elderly people and create an environment in which they can live with peace of mind.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The device uses a camera and microphone to collect emotional information from elderly individuals. Specifically, the device continuously monitors the user's facial expressions and voice while they are in the room. Inputs are video data from the camera and audio data from the microphone, and output is this raw data.

[0342] Step 2:

[0343] The server receives video and audio data transmitted from the terminal and performs preprocessing. Specifically, the server removes background noise from the audio data and normalizes the video data by extracting each frame. The input is raw data, and the output is data converted into a format suitable for analysis.

[0344] Step 3:

[0345] The server inputs pre-processed data into the emotion engine for analysis. The emotion engine uses a deep learning model to identify emotions from facial expressions in each frame and analyzes voice tone from audio data. The input is pre-processed data, and the output is an emotion label (e.g., smile, surprise, sadness).

[0346] Step 4:

[0347] The server evaluates the health status of elderly individuals based on the analyzed emotional data. Specifically, it tracks emotional fluctuation patterns over time and generates warnings if abnormal emotional patterns are detected. The input is the emotional label and its fluctuation pattern, and the output is the health status evaluation result.

[0348] Step 5:

[0349] The server sends notifications to relevant parties as needed, based on the health assessment results. Specifically, if abnormal emotional patterns persist, it sends alerts to family members or caregivers via email or app. The input is the health assessment results, and the output is a notification message.

[0350] Step 6:

[0351] The device adjusts the communication content based on the analysis and evaluation results. Specifically, if the user expresses sadness, it will initiate a conversation such as, "Please feel free to talk about anything," and play relaxing music. The input is the analysis results and the health status evaluation results, and the output is the dialogue content and music selection.

[0352] (Application Example 2)

[0353] 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."

[0354] There are problems such as the fact that elderly people are prone to feeling socially isolated, and that their health status cannot be constantly monitored, making it difficult to respond to sudden emotional changes. Furthermore, there is a need to establish methods to quickly notify family members and caregivers of the situation and to alleviate the loneliness of the elderly through appropriate dialogue.

[0355] 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.

[0356] In this invention, the server includes sensing means for collecting information on the daily activities of elderly people, analysis means for analyzing the collected data, emotion analysis means for performing emotion analysis using voice and video in real time, and warning means for detecting abnormal emotional changes and issuing warnings to family members and caregivers. This makes it possible to closely monitor the health and emotional state of elderly people and reduce feelings of loneliness.

[0357] "Sensing methods for collecting information on the daily activities of the elderly" refers to technologies that use devices such as cameras and microphones to acquire data in order to measure the activity status of the elderly.

[0358] "Analysis means for analyzing collected data" refers to technologies that process information obtained by sensing means and analyze individual health conditions and lifestyle patterns.

[0359] "An emotion analysis method that performs emotion analysis using audio and video in real time" refers to a software algorithm or model that instantly recognizes emotions from recordings and videos and evaluates emotional states such as smiles and sadness.

[0360] A "warning mechanism that detects abnormal emotional changes and alerts family members and caregivers" is a communication technology that recognizes changes that deviate significantly from normal emotional states and quickly notifies relevant parties.

[0361] "An emotional response method for generating appropriate music and conversation" refers to a dialogue generation technology that selects and plays adaptive music and lines according to the emotional state of elderly people, with the aim of reducing feelings of loneliness.

[0362] This invention describes embodiments for carrying out this invention. The system includes sensing means that utilize a camera and microphone obtained from a terminal to acquire information on the daily activities of elderly people. Furthermore, the server analyzes this data in real time and uses emotion analysis means to recognize emotional states. For emotion analysis, the open-source image recognition library OpenCV and the PyDub library for speech analysis can be utilized. In addition, the Google Cloud Natural Language API and the Microsoft Azure Face API are used for analyzing emotional states and forming trends.

[0363] If the server detects abnormal emotional changes as a result of analyzing the collected data, it will immediately notify relevant parties using an alert system. This alert system includes messaging services such as the Twilio API and Firebase, enabling the rapid dissemination of information to the elderly person's family and caregivers.

[0364] To facilitate smooth interaction with users, emotional response mechanisms are used to play appropriate music and generate dialogue based on the analyzed emotional state. For generating specific responses, APIs such as the Google Text-to-Speech API can be used to play music in real time, providing natural and engaging voice dialogue. For example, if an elderly person exhibits signs of distress in their daily behavior, a question like "How are you feeling today?" can be asked to gauge their mood, and relaxing music can be played to create a more comfortable living environment.

[0365] An example of a prompt message would be, "Please input the instruction 'Based on the current emotional state, determine what kind of dialogue is optimal' to the generating AI model," thereby enabling appropriate intervention by the AI.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device uses a camera and microphone to acquire information about the daily lives of elderly people. It receives video and audio data as input, which is continuously acquired in real time. The acquired data is then sent to a server for subsequent analysis steps.

[0369] Step 2:

[0370] The server analyzes the received video data. It processes the input video data using the OpenCV library to detect the facial expressions of elderly people. It extracts facial feature points, identifies emotions such as smiles and surprise, and outputs the results as emotional state data.

[0371] Step 3:

[0372] The server analyzes the received audio data. It uses the PyDub library to perform frequency analysis on the input audio data and evaluates the tone and volume of the elderly person's voice. Based on this information, it analyzes the emotional state and outputs it as emotional state data.

[0373] Step 4:

[0374] The server integrates video and audio analysis results and uses a generating AI model to determine the overall emotional state. Based on the integrated emotional state data, it forms trends and attempts to detect abnormal patterns. Any detected anomalies become output data passed to the warning step.

[0375] Step 5:

[0376] The server sends a notification to family members or caregivers via an alert system if an abnormal emotional change is detected. The output includes information about the detected anomaly, sent via messaging services using the Twilio API or Firebase. The notification content includes details of the detected emotional state.

[0377] Step 6:

[0378] The user receives responses through a dialogue function. Based on emotional state data, the server uses the Google Text-to-Speech API to select and play appropriate music, enabling natural conversation with the elderly. Output includes audio and music playback. For example, the server might ask the elderly, "How are you feeling today?" and play relaxing music.

[0379] 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.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] 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.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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".

[0395] This invention aims to optimize health management and address social isolation by using a system that integrates sensing, analysis, notification, and dialogue in the living environment of the elderly.

[0396] 1. Implementation of sensing means

[0397] The device uses accelerometers and cameras to capture data on the daily movements and facial expressions of elderly individuals. For example, it can record daily walking data using a pedometer and monitor emotional states by capturing facial expressions.

[0398] 2. Utilization of analytical methods

[0399] The server receives the collected data in a cloud environment and performs analysis using AI algorithms. It comprehensively assesses health status by evaluating activity levels from walking data and detecting emotional fluctuations through facial expression analysis. In particular, long-term data analysis makes it possible to identify abnormal activity patterns and emotional changes.

[0400] 3. Function of notification means

[0401] If the server detects any anomalies through analysis, it immediately sends a notification to family members or caregivers. These notifications, delivered via email or a dedicated app, ensure that important health information is received quickly and reliably. Furthermore, even in cases of minor issues, the system provides alerts to encourage preventative measures and support appropriate responses.

[0402] 4. Operation of Dialogue Methods

[0403] The device uses an AI chatbot to engage in natural conversations with the elderly. This aims to alleviate feelings of loneliness by listening to their daily anxieties and worries, and providing advice and information. For example, by offering conversations based on the elderly's hobbies or about their day's events, it creates an environment where users can feel comfortable and at ease.

[0404] As a concrete example, if an elderly person's walking volume suddenly decreases and their facial expression appears different from usual, the server analyzes the data and sends an alert to the family. The terminal then initiates a conversation with the elderly person, assessing the situation and working to stabilize their emotions. As a result, it becomes possible to support the realization of a safe and secure life by providing prompt intervention and appropriate care as needed. The invention is implemented in this form.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The device uses an accelerometer and camera to monitor the movements and facial expressions of elderly individuals, acquiring this data in real time. Specifically, it records data such as daily steps taken, sitting time, and changes in facial expressions with high precision.

[0408] Step 2:

[0409] The terminal processes the acquired data and converts it to the appropriate format. This ensures data consistency and performs the necessary preprocessing for analysis. This data is then ready to be transmitted to the server via wireless communication or the internet.

[0410] Step 3:

[0411] The server stores the data received from the terminal in a cloud environment and prepares it for analysis. The stored data is then analyzed by an AI algorithm to examine the activity patterns and facial expression changes of elderly individuals.

[0412] Step 4:

[0413] The server uses AI algorithms to analyze data, assess health status, and detect anomalies. This allows it to detect sudden changes in activity levels, fixation of facial expressions, and other issues, and then comprehensively analyzes the results.

[0414] Step 5:

[0415] Based on the data analysis results, the server generates notifications for family members and caregivers as needed. These notifications are given high priority, especially in cases of anomalies, and are sent via email or a dedicated app.

[0416] Step 6:

[0417] The device initiates a conversation with the elderly, confirming their daily situation and feelings. The AI ​​chatbot not merely provides information, but alleviates feelings of loneliness through natural conversation. It offers appropriate advice regarding the stress and anxiety the user is experiencing.

[0418] Step 7:

[0419] Users can report their emotions and physical condition through interaction with the device. This information is collected again via the device and used for subsequent analysis. Ultimately, the entire system functions to make the lives of seniors safer and more fulfilling.

[0420] (Example 1)

[0421] 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."

[0422] There is a need to optimize health management and address the social isolation issues faced by older adults. In particular, the challenge lies in quickly and accurately understanding changes in daily activities and emotions, and providing the necessary care and support.

[0423] 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.

[0424] In this invention, the server includes means for acquiring data on the behavior and emotional state of elderly individuals, means for transmitting and analyzing the acquired data to the cloud, means for detecting anomalies based on the analysis results and generating and sending notifications, and means for interacting with the user using a generated AI model. This makes it possible to comprehensively understand the health status of elderly individuals and provide prompt intervention while reducing social isolation.

[0425] A "device for acquiring data on movement and emotional state" is a device that senses changes in the daily movements and emotions of elderly people and acquires that information.

[0426] A "data transmission and analysis device" is a device that transmits acquired data to the cloud and performs analysis using a generated AI model.

[0427] A "notification generation and transmission device" is a device that detects abnormalities in health conditions based on analysis results and creates and sends notifications to family members and care providers.

[0428] A "generative AI model" is an artificial intelligence model that evaluates the emotional state and behavior of elderly people through data analysis and dialogue, and provides support for necessary responses.

[0429] A "user dialogue device" is a device that facilitates natural communication with elderly people and aims to reduce feelings of loneliness through dialogue.

[0430] This invention aims to manage the health of the elderly and alleviate social isolation, and is embodied as a system combining sensors, an analysis server, a notification function, and an interactive device. The following describes how this system is configured and how it functions.

[0431] The device is equipped with an accelerometer and camera to closely observe the movements and emotional states of elderly individuals. These devices detect and record in real time movement data (e.g., number of steps and travel time) and facial expression data (e.g., smiles and level of attention). This data is optimized using advanced data compression technology and then sent to a server in the cloud for analysis.

[0432] The server receives motion and facial expression data from elderly individuals and analyzes them using a generative AI model. Here, the server uses machine learning algorithms to evaluate activity levels and emotional changes, detecting abnormal patterns. For example, if the number of steps taken on a given day is less than half the normal level, it is registered as an abnormality.

[0433] Based on the analysis results, the server generates a message to notify family members and caregivers of any anomalies indicated by the collected data. This notification is sent via email or a dedicated app and may include messages such as, "The elderly person's activity level has decreased significantly. Please check immediately."

[0434] Simultaneously, the device initiates a conversation with the elderly through an AI chatbot. This facilitates emotional support for the elderly. The conversation takes place through individually tailored questions based on the elderly person's daily experiences and interests. For example, prompts such as "How did you spend your day?" and "Can you tell me about your recent hobbies?" are provided.

[0435] Through this system, it is possible to support the health and social connections of the elderly and provide an environment in which they can live with peace of mind.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The device monitors the movements and emotional state of elderly individuals in real time. Specifically, it acquires movement data such as step count and movement speed using an accelerometer and captures facial expression data using a camera. The input to this process is the data from the sensors and camera, and the output is the collected movement and facial expression data. This data is sent to the server in a compressed format.

[0439] Step 2:

[0440] The server receives motion and facial expression data transmitted from the terminal. Based on this data, it performs analysis using a generative AI model. The analysis uses machine learning algorithms to evaluate activity levels and score emotions. The input is the collected raw data, and the output is the activity level evaluation and emotion score as a result of the data analysis.

[0441] Step 3:

[0442] The server initiates a process to detect anomalies based on the analysis results. Specifically, it compares the data with past data and flags any significant changes in behavior or emotional state as an anomaly. The input is the activity level evaluation and emotional score from the analysis, and the output is the anomaly flag and its cause. This result is used to generate notifications.

[0443] Step 4:

[0444] When an anomaly flag is set, the server generates a notification message for family members or caregivers. The notification includes the nature of the anomaly and recommended actions. For example, it might generate a message such as, "Activity level has decreased. Please check." The input is information about the anomaly flag, and the output is the specific notification message.

[0445] Step 5:

[0446] The terminal initiates a conversation with the elderly user to understand the circumstances related to anomaly detection. The AI ​​chatbot guides the conversation using prompts. For example, it asks questions such as, "How have you been feeling lately?" to gather missing information. The input is the generated prompts, and the output is the feedback received from the elderly user.

[0447] Step 6:

[0448] Users accept advice and information provided through the device and incorporate it into their daily lives as needed. The device records the results and sends them to the server as feedback. The input is the user's choices and actions, and the output is feedback data. This data will be used for subsequent data analysis.

[0449] (Application Example 1)

[0450] 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."

[0451] For the elderly, understanding their health status and addressing social isolation are crucial issues. However, current technology struggles to accurately monitor the elderly's activity levels in real time and promptly notify families and caregivers of any abnormalities. Furthermore, systems that utilize collected data to provide appropriate feedback tailored to the elderly are insufficient, failing to alleviate feelings of loneliness.

[0452] 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.

[0453] In this invention, the server includes sensing means for collecting data on the daily activities of elderly people, analysis means for processing the collected data in a cloud environment and performing analysis using machine learning, and dialogue means for reducing feelings of loneliness through conversation with the user. This makes it possible to understand the health status in real time and automatically communicate any abnormalities to family members or caregivers, thereby improving the safety and quality of life of elderly people.

[0454] "Sensing means" is a general term for devices and functions used to collect real-time data on the daily activities and various environmental conditions of elderly people.

[0455] The "analysis method" refers to a system that processes collected data in a cloud environment, analyzes it using machine learning algorithms, and evaluates the health status and emotional fluctuations of elderly individuals.

[0456] "Notification means" refers to a communication function that sends notifications generated based on analysis results to family members and care providers to prompt necessary actions.

[0457] A "dialogue method" is a system designed to alleviate feelings of loneliness by engaging in continuous conversations with users and providing psychological support.

[0458] A "cloud environment" refers to virtualized computing resources used to store and analyze data via the internet.

[0459] "Machine learning" is a technology in which algorithms automatically learn patterns and rules based on collected data, and then use that knowledge to make predictions and decisions.

[0460] "Communication" is the process of reliably transmitting information to a distant location using electrical or electronic means.

[0461] The system for implementing this invention mainly consists of sensing means, analysis means, notification means, and dialogue means. Each of these components will be described in detail below.

[0462] The server works in conjunction with appropriate hardware to record the daily lives of elderly people using sensing devices. Specifically, sensors and cameras built into smartphones, wearable devices, and smart glasses are used. The data collected by these devices is then transmitted to the cloud environment in real time.

[0463] The server processes data acquired in the cloud environment using analytical tools. This analysis utilizes machine learning models based on Google Cloud Platform and TensorFlow. This allows for the analysis of activity and facial expression data from elderly individuals to assess their health status and emotional changes. The server also learns long-term patterns using AI algorithms, improving the accuracy of anomaly detection.

[0464] When an anomaly is detected, the notification system is automatically activated. The server uses the Twilio API to quickly send notifications to designated contacts and prompt appropriate action if immediate response is required. These notifications are sent via SMS, email, or other means.

[0465] Furthermore, the device provides a means of interaction. An AI chatbot powered by Dialogflow interacts with the user, answering questions and engaging in everyday conversations to provide emotional support. This creates an environment where users can live with peace of mind without feeling lonely.

[0466] As a concrete example, if an elderly person suddenly exhibits an unusual behavioral pattern at home, an accelerometer detects the movement and sends the data to a server. The server then analyzes the data, and if an anomaly is detected, it sends a notification to the family saying, "Some kind of anomaly has been detected. Please check." At the same time, a chatbot sends a message to the elderly person asking, "How have you been feeling lately?" An example of a prompt is the text, "Please tell me how to analyze the activity data of elderly people and detect anomalies."

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The device uses built-in sensors and a camera to collect data on the daily activities of elderly users. This data includes walking patterns, movement speed, and facial expressions. Inputs include motion data from sensors and image data from the camera. This data is transmitted to the cloud environment in real time.

[0470] Step 2:

[0471] The server receives data sent to the cloud and begins processing it using analytical tools. An AI model using TensorFlow analyzes the data, detecting abnormal behavioral patterns and emotional fluctuations from facial expressions. The input is raw data sent from the terminal, and the output is an evaluation result regarding activity level and emotional state. In this process, machine learning algorithms analyze the data and apply recognition patterns to identify the user's state.

[0472] Step 3:

[0473] Based on the analysis results, the server generates a notification via a notification system if an anomaly is detected. Utilizing the Twilio API, it sends SMS or emails to pre-registered family members and care providers. The input is an anomaly alert based on the analyzed data, and the output is an emergency notification message. This allows the server to prompt a quick response.

[0474] Step 4:

[0475] The device uses Dialogflow to activate an AI chatbot to initiate a conversation with the user. When changes in emotion or abnormalities are detected, the chatbot asks the user questions about their recent physical condition and emotions. Input is analysis results from the server, and output is the conversation history and user feedback. This reduces anxiety and feelings of loneliness and ensures the user feels at ease.

[0476] 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.

[0477] This invention aims to address health management and social isolation among the elderly by using a system incorporating an emotion engine. The system's basic configuration includes means for sensing, analysis, notification, and dialogue, and further integrates an emotion engine to enable more precise emotion recognition.

[0478] 1. Method for implementing sensing means

[0479] The device continuously monitors the elderly person's activity level and facial expressions through a camera and microphone. The acquired data includes facial expressions, voice tone, and daily movement data. For example, it captures the user's facial movements and records audio while they are watching television.

[0480] 2. Function of the Emotion Engine

[0481] An emotion engine located on the server analyzes the user's emotions using data provided by sensing devices. Specifically, it utilizes facial expression analysis technology and voice analysis algorithms to recognize emotions such as smiles, surprise, and sadness. For example, when an elderly person smiles, the system records it as a positive emotion and forms a trend.

[0482] 3. Interlocking of analysis means and notification means

[0483] Based on the results from the emotion engine, the server assesses the user's health status and immediately notifies caregivers and family members if an abnormality is detected. This notification warns of the continuation of abnormal emotional patterns or a deterioration in health and is sent within the home via email or a dedicated app.

[0484] 4. Addressing Emotions in the Use of Dialogue Methods

[0485] The device utilizes feedback from its emotion engine when engaging in everyday conversations with the user through its interactive mechanisms. This allows it to select conversation topics appropriate to the user's emotions, effectively reducing feelings of loneliness. For example, if an elderly person shows a sad expression, the device will offer words of encouragement or relaxing music.

[0486] For example, if an elderly person begins to exhibit clearly unstable emotions during the day, this system can quickly detect the change, engage in appropriate dialogue, and promptly request assistance as needed. In this form, the system of the present invention can create a safe and comfortable living environment for the elderly.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The device collects data in real time from cameras and microphones installed in the elderly person's environment. Specifically, it captures facial expressions during everyday conversations and activities, and records high-quality audio data to collect detailed information.

[0490] Step 2:

[0491] The terminal temporarily stores the collected data and prepares it for transmission after compressing or encrypting it as needed. Wireless communication technology is used to transfer the data to the server.

[0492] Step 3:

[0493] The server receives the data sent from the terminal and first checks the data format. After this, it passes a portion of the data to the emotion engine to begin detailed analysis.

[0494] Step 4:

[0495] The server's emotion engine analyzes the received data to identify the user's emotional state. Specifically, it uses facial recognition algorithms to analyze subtle facial movements and voice analysis to evaluate voice tone and speed.

[0496] Step 5:

[0497] The server compiles the analysis results from the emotion engine and generates an immediate notification to family members or caregivers if an abnormal emotional pattern is detected. This notification includes details about specific emotional changes and their potential.

[0498] Step 6:

[0499] The device dynamically adjusts the content of the conversation based on the analysis results and communicates with the user through dialogue. If an elderly person shows signs of anxiety, it provides words of encouragement or soothing music.

[0500] Step 7:

[0501] Users can gain a sense of security through interaction with the device and can report their feelings and state again as needed. This information is also collected again and used for future analysis.

[0502] (Example 2)

[0503] 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."

[0504] For the elderly, not only physical health but also psychological and emotional health is important. However, it is difficult to understand their emotions from their facial expressions and tone of voice in daily life and to provide appropriate support. Furthermore, being able to react quickly in abnormal situations is crucial for a safe living environment for the elderly. This invention aims to solve these problems.

[0505] 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.

[0506] In this invention, the server includes means for preprocessing acquired emotional information, means for performing emotional analysis using the preprocessed information, and means for optimizing the conversation content based on the analysis results and sending notifications to relevant parties in the event of an anomaly. This enables accurate understanding of the emotional state of elderly people and allows for appropriate responses and notifications.

[0507] "Means of acquisition" refers to devices and methods for collecting emotional information from elderly individuals.

[0508] "Processing means" refers to devices or methods that have the function of preprocessing acquired emotional information and preparing it in a format suitable for analysis.

[0509] "Analysis means" refers to devices or methods for analyzing emotions based on pre-processed information.

[0510] "Transmission means" refers to devices or methods for sending notifications to relevant parties in the event of an anomaly based on the analysis results.

[0511] A "response tool" refers to a device or method that optimizes conversation content based on analysis results to facilitate effective communication with the elderly.

[0512] This invention is a system that understands the emotional state of elderly people and supports them in living safely and comfortably, and is mainly implemented using terminals and servers.

[0513] The device's role is to acquire emotional information from elderly individuals via its camera and microphone. Specifically, the device continuously collects facial expressions and voice tones from elderly individuals in their daily lives. This data is acquired in real time using sensor technology.

[0514] The server receives information transmitted from the terminal and performs preprocessing. Noise is removed from the audio data, and the facial expression data is normalized. This preprocessing prepares the data for analysis.

[0515] Next, an emotion engine located within the server functions as an analysis tool. The emotion engine uses machine learning techniques to identify emotional states. The algorithm used here is based on deep learning and accurately recognizes multiple emotional patterns such as smiles, surprise, and sadness.

[0516] Furthermore, based on the analysis results, the server will notify relevant parties via a transmission method if an abnormal emotional pattern is detected. This notification will be sent via email or a dedicated app.

[0517] The device also functions as a means of responding, optimizing the conversation based on the analysis results. Specifically, when an elderly person is expressing sadness, it suggests encouraging words such as, "Why don't you tell me what's wrong?" and provides relaxation music.

[0518] For example, if an elderly person begins to show clearly unstable emotions during the day, this system can quickly detect the change and request assistance while engaging in necessary dialogue. An example of a prompt to the generating AI model would be, "Please advise how to respond when an elderly person is smiling."

[0519] Based on the above, the present invention can provide more personalized support based on the emotional information of elderly people and create an environment in which they can live with peace of mind.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The device uses a camera and microphone to collect emotional information from elderly individuals. Specifically, the device continuously monitors the user's facial expressions and voice while they are in the room. Inputs are video data from the camera and audio data from the microphone, and output is this raw data.

[0523] Step 2:

[0524] The server receives video and audio data transmitted from the terminal and performs preprocessing. Specifically, the server removes background noise from the audio data and normalizes the video data by extracting each frame. The input is raw data, and the output is data converted into a format suitable for analysis.

[0525] Step 3:

[0526] The server inputs pre-processed data into the emotion engine for analysis. The emotion engine uses a deep learning model to identify emotions from facial expressions in each frame and analyzes voice tone from audio data. The input is pre-processed data, and the output is an emotion label (e.g., smile, surprise, sadness).

[0527] Step 4:

[0528] The server evaluates the health status of elderly individuals based on the analyzed emotional data. Specifically, it tracks emotional fluctuation patterns over time and generates warnings if abnormal emotional patterns are detected. The input is the emotional label and its fluctuation pattern, and the output is the health status evaluation result.

[0529] Step 5:

[0530] The server sends notifications to relevant parties as needed, based on the health assessment results. Specifically, if abnormal emotional patterns persist, it sends alerts to family members or caregivers via email or app. The input is the health assessment results, and the output is a notification message.

[0531] Step 6:

[0532] The device adjusts the communication content based on the analysis and evaluation results. Specifically, if the user expresses sadness, it will initiate a conversation such as, "Please feel free to talk about anything," and play relaxing music. The input is the analysis results and the health status evaluation results, and the output is the dialogue content and music selection.

[0533] (Application Example 2)

[0534] 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."

[0535] There are problems such as the fact that elderly people are prone to feeling socially isolated, and that their health status cannot be constantly monitored, making it difficult to respond to sudden emotional changes. Furthermore, there is a need to establish methods to quickly notify family members and caregivers of the situation and to alleviate the loneliness of the elderly through appropriate dialogue.

[0536] 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.

[0537] In this invention, the server includes sensing means for collecting information on the daily activities of elderly people, analysis means for analyzing the collected data, emotion analysis means for performing emotion analysis using voice and video in real time, and warning means for detecting abnormal emotional changes and issuing warnings to family members and caregivers. This makes it possible to closely monitor the health and emotional state of elderly people and reduce feelings of loneliness.

[0538] "Sensing methods for collecting information on the daily activities of the elderly" refers to technologies that use devices such as cameras and microphones to acquire data in order to measure the activity status of the elderly.

[0539] "Analysis means for analyzing collected data" refers to technologies that process information obtained by sensing means and analyze individual health conditions and lifestyle patterns.

[0540] "An emotion analysis method that performs emotion analysis using audio and video in real time" refers to a software algorithm or model that instantly recognizes emotions from recordings and videos and evaluates emotional states such as smiles and sadness.

[0541] A "warning mechanism that detects abnormal emotional changes and alerts family members and caregivers" is a communication technology that recognizes changes that deviate significantly from normal emotional states and quickly notifies relevant parties.

[0542] "An emotional response method for generating appropriate music and conversation" refers to a dialogue generation technology that selects and plays adaptive music and lines according to the emotional state of elderly people, with the aim of reducing feelings of loneliness.

[0543] This invention describes embodiments for carrying out this invention. The system includes sensing means that utilize a camera and microphone obtained from a terminal to acquire information on the daily activities of elderly people. Furthermore, the server analyzes this data in real time and uses emotion analysis means to recognize emotional states. For emotion analysis, the open-source image recognition library OpenCV and the PyDub library for speech analysis can be utilized. In addition, the Google Cloud Natural Language API and the Microsoft Azure Face API are used for analyzing emotional states and forming trends.

[0544] If the server detects abnormal emotional changes as a result of analyzing the collected data, it will immediately notify relevant parties using an alert system. This alert system includes messaging services such as the Twilio API and Firebase, enabling the rapid dissemination of information to the elderly person's family and caregivers.

[0545] To facilitate smooth interaction with users, emotional response mechanisms are used to play appropriate music and generate dialogue based on the analyzed emotional state. For generating specific responses, APIs such as the Google Text-to-Speech API can be used to play music in real time, providing natural and engaging voice dialogue. For example, if an elderly person exhibits signs of distress in their daily behavior, a question like "How are you feeling today?" can be asked to gauge their mood, and relaxing music can be played to create a more comfortable living environment.

[0546] An example of a prompt message would be, "Please input the instruction 'Based on the current emotional state, determine what kind of dialogue is optimal' to the generating AI model," thereby enabling appropriate intervention by the AI.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The device uses a camera and microphone to acquire information about the daily lives of elderly people. It receives video and audio data as input, which is continuously acquired in real time. The acquired data is then sent to a server for subsequent analysis steps.

[0550] Step 2:

[0551] The server analyzes the received video data. It processes the input video data using the OpenCV library to detect the facial expressions of elderly people. It extracts facial feature points, identifies emotions such as smiles and surprise, and outputs the results as emotional state data.

[0552] Step 3:

[0553] The server analyzes the received audio data. It uses the PyDub library to perform frequency analysis on the input audio data and evaluates the tone and volume of the elderly person's voice. Based on this information, it analyzes the emotional state and outputs it as emotional state data.

[0554] Step 4:

[0555] The server integrates video and audio analysis results and uses a generating AI model to determine the overall emotional state. Based on the integrated emotional state data, it forms trends and attempts to detect abnormal patterns. Any detected anomalies become output data passed to the warning step.

[0556] Step 5:

[0557] The server sends a notification to family members or caregivers via an alert system if an abnormal emotional change is detected. The output includes information about the detected anomaly, sent via messaging services using the Twilio API or Firebase. The notification content includes details of the detected emotional state.

[0558] Step 6:

[0559] The user receives responses through a dialogue function. Based on emotional state data, the server uses the Google Text-to-Speech API to select and play appropriate music, enabling natural conversation with the elderly. Output includes audio and music playback. For example, the server might ask the elderly, "How are you feeling today?" and play relaxing music.

[0560] 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.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] This invention aims to optimize health management and address social isolation by using a system that integrates sensing, analysis, notification, and dialogue in the living environment of the elderly.

[0578] 1. Implementation of sensing means

[0579] The device uses accelerometers and cameras to capture data on the daily movements and facial expressions of elderly individuals. For example, it can record daily walking data using a pedometer and monitor emotional states by capturing facial expressions.

[0580] 2. Utilization of analytical methods

[0581] The server receives the collected data in a cloud environment and performs analysis using AI algorithms. It comprehensively assesses health status by evaluating activity levels from walking data and detecting emotional fluctuations through facial expression analysis. In particular, long-term data analysis makes it possible to identify abnormal activity patterns and emotional changes.

[0582] 3. Function of notification means

[0583] If the server detects any anomalies through analysis, it immediately sends a notification to family members or caregivers. These notifications, delivered via email or a dedicated app, ensure that important health information is received quickly and reliably. Furthermore, even in cases of minor issues, the system provides alerts to encourage preventative measures and support appropriate responses.

[0584] 4. Operation of Dialogue Methods

[0585] The device uses an AI chatbot to engage in natural conversations with the elderly. This aims to alleviate feelings of loneliness by listening to their daily anxieties and worries, and providing advice and information. For example, by offering conversations based on the elderly's hobbies or about their day's events, it creates an environment where users can feel comfortable and at ease.

[0586] As a concrete example, if an elderly person's walking volume suddenly decreases and their facial expression appears different from usual, the server analyzes the data and sends an alert to the family. The terminal then initiates a conversation with the elderly person, assessing the situation and working to stabilize their emotions. As a result, it becomes possible to support the realization of a safe and secure life by providing prompt intervention and appropriate care as needed. The invention is implemented in this form.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The device uses an accelerometer and camera to monitor the movements and facial expressions of elderly individuals, acquiring this data in real time. Specifically, it records data such as daily steps taken, sitting time, and changes in facial expressions with high precision.

[0590] Step 2:

[0591] The terminal processes the acquired data and converts it to the appropriate format. This ensures data consistency and performs the necessary preprocessing for analysis. This data is then ready to be transmitted to the server via wireless communication or the internet.

[0592] Step 3:

[0593] The server stores the data received from the terminal in a cloud environment and prepares it for analysis. The stored data is then analyzed by an AI algorithm to examine the activity patterns and facial expression changes of elderly individuals.

[0594] Step 4:

[0595] The server uses AI algorithms to analyze data, assess health status, and detect anomalies. This allows it to detect sudden changes in activity levels, fixation of facial expressions, and other issues, and then comprehensively analyzes the results.

[0596] Step 5:

[0597] Based on the data analysis results, the server generates notifications for family members and caregivers as needed. These notifications are given high priority, especially in cases of anomalies, and are sent via email or a dedicated app.

[0598] Step 6:

[0599] The device initiates a conversation with the elderly, confirming their daily situation and feelings. The AI ​​chatbot not merely provides information, but alleviates feelings of loneliness through natural conversation. It offers appropriate advice regarding the stress and anxiety the user is experiencing.

[0600] Step 7:

[0601] Users can report their emotions and physical condition through interaction with the device. This information is collected again via the device and used for subsequent analysis. Ultimately, the entire system functions to make the lives of seniors safer and more fulfilling.

[0602] (Example 1)

[0603] 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".

[0604] There is a need to optimize health management and address the social isolation issues faced by older adults. In particular, the challenge lies in quickly and accurately understanding changes in daily activities and emotions, and providing the necessary care and support.

[0605] 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.

[0606] In this invention, the server includes means for acquiring data on the behavior and emotional state of elderly individuals, means for transmitting and analyzing the acquired data to the cloud, means for detecting anomalies based on the analysis results and generating and sending notifications, and means for interacting with the user using a generated AI model. This makes it possible to comprehensively understand the health status of elderly individuals and provide prompt intervention while reducing social isolation.

[0607] A "device for acquiring data on movement and emotional state" is a device that senses changes in the daily movements and emotions of elderly people and acquires that information.

[0608] A "data transmission and analysis device" is a device that transmits acquired data to the cloud and performs analysis using a generated AI model.

[0609] A "notification generation and transmission device" is a device that detects abnormalities in health conditions based on analysis results and creates and sends notifications to family members and care providers.

[0610] A "generative AI model" is an artificial intelligence model that evaluates the emotional state and behavior of elderly people through data analysis and dialogue, and provides support for necessary responses.

[0611] A "user dialogue device" is a device that facilitates natural communication with elderly people and aims to reduce feelings of loneliness through dialogue.

[0612] This invention aims to manage the health of the elderly and alleviate social isolation, and is embodied as a system combining sensors, an analysis server, a notification function, and an interactive device. The following describes how this system is configured and how it functions.

[0613] The device is equipped with an accelerometer and camera to closely observe the movements and emotional states of elderly individuals. These devices detect and record in real time movement data (e.g., number of steps and travel time) and facial expression data (e.g., smiles and level of attention). This data is optimized using advanced data compression technology and then sent to a server in the cloud for analysis.

[0614] The server receives motion and facial expression data from elderly individuals and analyzes them using a generative AI model. Here, the server uses machine learning algorithms to evaluate activity levels and emotional changes, detecting abnormal patterns. For example, if the number of steps taken on a given day is less than half the normal level, it is registered as an abnormality.

[0615] Based on the analysis results, the server generates a message to notify family members and caregivers of any anomalies indicated by the collected data. This notification is sent via email or a dedicated app and may include messages such as, "The elderly person's activity level has decreased significantly. Please check immediately."

[0616] Simultaneously, the device initiates a conversation with the elderly through an AI chatbot. This facilitates emotional support for the elderly. The conversation takes place through individually tailored questions based on the elderly person's daily experiences and interests. For example, prompts such as "How did you spend your day?" and "Can you tell me about your recent hobbies?" are provided.

[0617] Through this system, it is possible to support the health and social connections of the elderly and provide an environment in which they can live with peace of mind.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The device monitors the movements and emotional state of elderly individuals in real time. Specifically, it acquires movement data such as step count and movement speed using an accelerometer and captures facial expression data using a camera. The input to this process is the data from the sensors and camera, and the output is the collected movement and facial expression data. This data is sent to the server in a compressed format.

[0621] Step 2:

[0622] The server receives motion and facial expression data transmitted from the terminal. Based on this data, it performs analysis using a generative AI model. The analysis uses machine learning algorithms to evaluate activity levels and score emotions. The input is the collected raw data, and the output is the activity level evaluation and emotion score as a result of the data analysis.

[0623] Step 3:

[0624] The server initiates a process to detect anomalies based on the analysis results. Specifically, it compares the data with past data and flags any significant changes in behavior or emotional state as an anomaly. The input is the activity level evaluation and emotional score from the analysis, and the output is the anomaly flag and its cause. This result is used to generate notifications.

[0625] Step 4:

[0626] When an anomaly flag is set, the server generates a notification message for family members or caregivers. The notification includes the nature of the anomaly and recommended actions. For example, it might generate a message such as, "Activity level has decreased. Please check." The input is information about the anomaly flag, and the output is the specific notification message.

[0627] Step 5:

[0628] The terminal initiates a conversation with the elderly user to understand the circumstances related to anomaly detection. The AI ​​chatbot guides the conversation using prompts. For example, it asks questions such as, "How have you been feeling lately?" to gather missing information. The input is the generated prompts, and the output is the feedback received from the elderly user.

[0629] Step 6:

[0630] Users accept advice and information provided through the device and incorporate it into their daily lives as needed. The device records the results and sends them to the server as feedback. The input is the user's choices and actions, and the output is feedback data. This data will be used for subsequent data analysis.

[0631] (Application Example 1)

[0632] 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".

[0633] For the elderly, understanding their health status and addressing social isolation are crucial issues. However, current technology struggles to accurately monitor the elderly's activity levels in real time and promptly notify families and caregivers of any abnormalities. Furthermore, systems that utilize collected data to provide appropriate feedback tailored to the elderly are insufficient, failing to alleviate feelings of loneliness.

[0634] 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.

[0635] In this invention, the server includes sensing means for collecting data on the daily activities of elderly people, analysis means for processing the collected data in a cloud environment and performing analysis using machine learning, and dialogue means for reducing feelings of loneliness through conversation with the user. This makes it possible to understand the health status in real time and automatically communicate any abnormalities to family members or caregivers, thereby improving the safety and quality of life of elderly people.

[0636] "Sensing means" is a general term for devices and functions used to collect real-time data on the daily activities and various environmental conditions of elderly people.

[0637] The "analysis method" refers to a system that processes collected data in a cloud environment, analyzes it using machine learning algorithms, and evaluates the health status and emotional fluctuations of elderly individuals.

[0638] "Notification means" refers to a communication function that sends notifications generated based on analysis results to family members and care providers to prompt necessary actions.

[0639] A "dialogue method" is a system designed to alleviate feelings of loneliness by engaging in continuous conversations with users and providing psychological support.

[0640] A "cloud environment" refers to virtualized computing resources used to store and analyze data via the internet.

[0641] "Machine learning" is a technology in which algorithms automatically learn patterns and rules based on collected data, and then use that knowledge to make predictions and decisions.

[0642] "Communication" is the process of reliably transmitting information to a distant location using electrical or electronic means.

[0643] The system for implementing this invention mainly consists of sensing means, analysis means, notification means, and dialogue means. Each of these components will be described in detail below.

[0644] The server works in conjunction with appropriate hardware to record the daily lives of elderly people using sensing devices. Specifically, sensors and cameras built into smartphones, wearable devices, and smart glasses are used. The data collected by these devices is then transmitted to the cloud environment in real time.

[0645] The server processes data acquired in the cloud environment using analytical tools. This analysis utilizes machine learning models based on Google Cloud Platform and TensorFlow. This allows for the analysis of activity and facial expression data from elderly individuals to assess their health status and emotional changes. The server also learns long-term patterns using AI algorithms, improving the accuracy of anomaly detection.

[0646] When an anomaly is detected, the notification system is automatically activated. The server uses the Twilio API to quickly send notifications to designated contacts and prompt appropriate action if immediate response is required. These notifications are sent via SMS, email, or other means.

[0647] Furthermore, the device provides a means of interaction. An AI chatbot powered by Dialogflow interacts with the user, answering questions and engaging in everyday conversations to provide emotional support. This creates an environment where users can live with peace of mind without feeling lonely.

[0648] As a concrete example, if an elderly person suddenly exhibits an unusual behavioral pattern at home, an accelerometer detects the movement and sends the data to a server. The server then analyzes the data, and if an anomaly is detected, it sends a notification to the family saying, "Some kind of anomaly has been detected. Please check." At the same time, a chatbot sends a message to the elderly person asking, "How have you been feeling lately?" An example of a prompt is the text, "Please tell me how to analyze the activity data of elderly people and detect anomalies."

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The device uses built-in sensors and a camera to collect data on the daily activities of elderly users. This data includes walking patterns, movement speed, and facial expressions. Inputs include motion data from sensors and image data from the camera. This data is transmitted to the cloud environment in real time.

[0652] Step 2:

[0653] The server receives data sent to the cloud and begins processing it using analytical tools. An AI model using TensorFlow analyzes the data, detecting abnormal behavioral patterns and emotional fluctuations from facial expressions. The input is raw data sent from the terminal, and the output is an evaluation result regarding activity level and emotional state. In this process, machine learning algorithms analyze the data and apply recognition patterns to identify the user's state.

[0654] Step 3:

[0655] Based on the analysis results, the server generates a notification via a notification system if an anomaly is detected. Utilizing the Twilio API, it sends SMS or emails to pre-registered family members and care providers. The input is an anomaly alert based on the analyzed data, and the output is an emergency notification message. This allows the server to prompt a quick response.

[0656] Step 4:

[0657] The device uses Dialogflow to activate an AI chatbot to initiate a conversation with the user. When changes in emotion or abnormalities are detected, the chatbot asks the user questions about their recent physical condition and emotions. Input is analysis results from the server, and output is the conversation history and user feedback. This reduces anxiety and feelings of loneliness and ensures the user feels at ease.

[0658] 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.

[0659] This invention aims to address health management and social isolation among the elderly by using a system incorporating an emotion engine. The system's basic configuration includes means for sensing, analysis, notification, and dialogue, and further integrates an emotion engine to enable more precise emotion recognition.

[0660] 1. Method for implementing sensing means

[0661] The device continuously monitors the elderly person's activity level and facial expressions through a camera and microphone. The acquired data includes facial expressions, voice tone, and daily movement data. For example, it captures the user's facial movements and records audio while they are watching television.

[0662] 2. Function of the Emotion Engine

[0663] An emotion engine located on the server analyzes the user's emotions using data provided by sensing devices. Specifically, it utilizes facial expression analysis technology and voice analysis algorithms to recognize emotions such as smiles, surprise, and sadness. For example, when an elderly person smiles, the system records it as a positive emotion and forms a trend.

[0664] 3. Interlocking of analysis means and notification means

[0665] Based on the results from the emotion engine, the server assesses the user's health status and immediately notifies caregivers and family members if an abnormality is detected. This notification warns of the continuation of abnormal emotional patterns or a deterioration in health and is sent within the home via email or a dedicated app.

[0666] 4. Addressing Emotions in the Use of Dialogue Methods

[0667] The device utilizes feedback from its emotion engine when engaging in everyday conversations with the user through its interactive mechanisms. This allows it to select conversation topics appropriate to the user's emotions, effectively reducing feelings of loneliness. For example, if an elderly person shows a sad expression, the device will offer words of encouragement or relaxing music.

[0668] For example, if an elderly person begins to exhibit clearly unstable emotions during the day, this system can quickly detect the change, engage in appropriate dialogue, and promptly request assistance as needed. In this form, the system of the present invention can create a safe and comfortable living environment for the elderly.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The device collects data in real time from cameras and microphones installed in the elderly person's environment. Specifically, it captures facial expressions during everyday conversations and activities, and records high-quality audio data to collect detailed information.

[0672] Step 2:

[0673] The terminal temporarily stores the collected data and prepares it for transmission after compressing or encrypting it as needed. Wireless communication technology is used to transfer the data to the server.

[0674] Step 3:

[0675] The server receives the data sent from the terminal and first checks the data format. After this, it passes a portion of the data to the emotion engine to begin detailed analysis.

[0676] Step 4:

[0677] The server's emotion engine analyzes the received data to identify the user's emotional state. Specifically, it uses facial recognition algorithms to analyze subtle facial movements and voice analysis to evaluate voice tone and speed.

[0678] Step 5:

[0679] The server compiles the analysis results from the emotion engine and generates an immediate notification to family members or caregivers if an abnormal emotional pattern is detected. This notification includes details about specific emotional changes and their potential.

[0680] Step 6:

[0681] The device dynamically adjusts the content of the conversation based on the analysis results and communicates with the user through dialogue. If an elderly person shows signs of anxiety, it provides words of encouragement or soothing music.

[0682] Step 7:

[0683] Users can gain a sense of security through interaction with the device and can report their feelings and state again as needed. This information is also collected again and used for future analysis.

[0684] (Example 2)

[0685] 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".

[0686] For the elderly, not only physical health but also psychological and emotional health is important. However, it is difficult to understand their emotions from their facial expressions and tone of voice in daily life and to provide appropriate support. Furthermore, being able to react quickly in abnormal situations is crucial for a safe living environment for the elderly. This invention aims to solve these problems.

[0687] 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.

[0688] In this invention, the server includes means for preprocessing acquired emotional information, means for performing emotional analysis using the preprocessed information, and means for optimizing the conversation content based on the analysis results and sending notifications to relevant parties in the event of an anomaly. This enables accurate understanding of the emotional state of elderly people and allows for appropriate responses and notifications.

[0689] "Means of acquisition" refers to devices and methods for collecting emotional information from elderly individuals.

[0690] "Processing means" refers to devices or methods that have the function of preprocessing acquired emotional information and preparing it in a format suitable for analysis.

[0691] "Analysis means" refers to devices or methods for analyzing emotions based on pre-processed information.

[0692] "Transmission means" refers to devices or methods for sending notifications to relevant parties in the event of an anomaly based on the analysis results.

[0693] A "response tool" refers to a device or method that optimizes conversation content based on analysis results to facilitate effective communication with the elderly.

[0694] This invention is a system that understands the emotional state of elderly people and supports them in living safely and comfortably, and is mainly implemented using terminals and servers.

[0695] The device's role is to acquire emotional information from elderly individuals via its camera and microphone. Specifically, the device continuously collects facial expressions and voice tones from elderly individuals in their daily lives. This data is acquired in real time using sensor technology.

[0696] The server receives information transmitted from the terminal and performs preprocessing. Noise is removed from the audio data, and the facial expression data is normalized. This preprocessing prepares the data for analysis.

[0697] Next, an emotion engine located within the server functions as an analysis tool. The emotion engine uses machine learning techniques to identify emotional states. The algorithm used here is based on deep learning and accurately recognizes multiple emotional patterns such as smiles, surprise, and sadness.

[0698] Furthermore, based on the analysis results, the server will notify relevant parties via a transmission method if an abnormal emotional pattern is detected. This notification will be sent via email or a dedicated app.

[0699] The device also functions as a means of responding, optimizing the conversation based on the analysis results. Specifically, when an elderly person is expressing sadness, it suggests encouraging words such as, "Why don't you tell me what's wrong?" and provides relaxation music.

[0700] For example, if an elderly person begins to show clearly unstable emotions during the day, this system can quickly detect the change and request assistance while engaging in necessary dialogue. An example of a prompt to the generating AI model would be, "Please advise how to respond when an elderly person is smiling."

[0701] Based on the above, the present invention can provide more personalized support based on the emotional information of elderly people and create an environment in which they can live with peace of mind.

[0702] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0703] Step 1:

[0704] The device uses a camera and microphone to collect emotional information from elderly individuals. Specifically, the device continuously monitors the user's facial expressions and voice while they are in the room. Inputs are video data from the camera and audio data from the microphone, and output is this raw data.

[0705] Step 2:

[0706] The server receives video and audio data transmitted from the terminal and performs preprocessing. Specifically, the server removes background noise from the audio data and normalizes the video data by extracting each frame. The input is raw data, and the output is data converted into a format suitable for analysis.

[0707] Step 3:

[0708] The server inputs pre-processed data into the emotion engine for analysis. The emotion engine uses a deep learning model to identify emotions from facial expressions in each frame and analyzes voice tone from audio data. The input is pre-processed data, and the output is an emotion label (e.g., smile, surprise, sadness).

[0709] Step 4:

[0710] The server evaluates the health status of elderly individuals based on the analyzed emotional data. Specifically, it tracks emotional fluctuation patterns over time and generates warnings if abnormal emotional patterns are detected. The input is the emotional label and its fluctuation pattern, and the output is the health status evaluation result.

[0711] Step 5:

[0712] The server sends notifications to relevant parties as needed, based on the health assessment results. Specifically, if abnormal emotional patterns persist, it sends alerts to family members or caregivers via email or app. The input is the health assessment results, and the output is a notification message.

[0713] Step 6:

[0714] The device adjusts the communication content based on the analysis and evaluation results. Specifically, if the user expresses sadness, it will initiate a conversation such as, "Please feel free to talk about anything," and play relaxing music. The input is the analysis results and the health status evaluation results, and the output is the dialogue content and music selection.

[0715] (Application Example 2)

[0716] 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".

[0717] There are problems such as the fact that elderly people are prone to feeling socially isolated, and that their health status cannot be constantly monitored, making it difficult to respond to sudden emotional changes. Furthermore, there is a need to establish methods to quickly notify family members and caregivers of the situation and to alleviate the loneliness of the elderly through appropriate dialogue.

[0718] 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.

[0719] In this invention, the server includes sensing means for collecting information on the daily activities of elderly people, analysis means for analyzing the collected data, emotion analysis means for performing emotion analysis using voice and video in real time, and warning means for detecting abnormal emotional changes and issuing warnings to family members and caregivers. This makes it possible to closely monitor the health and emotional state of elderly people and reduce feelings of loneliness.

[0720] "Sensing methods for collecting information on the daily activities of the elderly" refers to technologies that use devices such as cameras and microphones to acquire data in order to measure the activity status of the elderly.

[0721] "Analysis means for analyzing collected data" refers to technologies that process information obtained by sensing means and analyze individual health conditions and lifestyle patterns.

[0722] "An emotion analysis method that performs emotion analysis using audio and video in real time" refers to a software algorithm or model that instantly recognizes emotions from recordings and videos and evaluates emotional states such as smiles and sadness.

[0723] A "warning mechanism that detects abnormal emotional changes and alerts family members and caregivers" is a communication technology that recognizes changes that deviate significantly from normal emotional states and quickly notifies relevant parties.

[0724] "An emotional response method for generating appropriate music and conversation" refers to a dialogue generation technology that selects and plays adaptive music and lines according to the emotional state of elderly people, with the aim of reducing feelings of loneliness.

[0725] This invention describes embodiments for carrying out this invention. The system includes sensing means that utilize a camera and microphone obtained from a terminal to acquire information on the daily activities of elderly people. Furthermore, the server analyzes this data in real time and uses emotion analysis means to recognize emotional states. For emotion analysis, the open-source image recognition library OpenCV and the PyDub library for speech analysis can be utilized. In addition, the Google Cloud Natural Language API and the Microsoft Azure Face API are used for analyzing emotional states and forming trends.

[0726] If the server detects abnormal emotional changes as a result of analyzing the collected data, it will immediately notify relevant parties using an alert system. This alert system includes messaging services such as the Twilio API and Firebase, enabling the rapid dissemination of information to the elderly person's family and caregivers.

[0727] To facilitate smooth interaction with users, emotional response mechanisms are used to play appropriate music and generate dialogue based on the analyzed emotional state. For generating specific responses, APIs such as the Google Text-to-Speech API can be used to play music in real time, providing natural and engaging voice dialogue. For example, if an elderly person exhibits signs of distress in their daily behavior, a question like "How are you feeling today?" can be asked to gauge their mood, and relaxing music can be played to create a more comfortable living environment.

[0728] An example of a prompt message would be, "Please input the instruction 'Based on the current emotional state, determine what kind of dialogue is optimal' to the generating AI model," thereby enabling appropriate intervention by the AI.

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The device uses a camera and microphone to acquire information about the daily lives of elderly people. It receives video and audio data as input, which is continuously acquired in real time. The acquired data is then sent to a server for subsequent analysis steps.

[0732] Step 2:

[0733] The server analyzes the received video data. It processes the input video data using the OpenCV library to detect the facial expressions of elderly people. It extracts facial feature points, identifies emotions such as smiles and surprise, and outputs the results as emotional state data.

[0734] Step 3:

[0735] The server analyzes the received audio data. It uses the PyDub library to perform frequency analysis on the input audio data and evaluates the tone and volume of the elderly person's voice. Based on this information, it analyzes the emotional state and outputs it as emotional state data.

[0736] Step 4:

[0737] The server integrates video and audio analysis results and uses a generating AI model to determine the overall emotional state. Based on the integrated emotional state data, it forms trends and attempts to detect abnormal patterns. Any detected anomalies become output data passed to the warning step.

[0738] Step 5:

[0739] The server sends a notification to family members or caregivers via an alert system if an abnormal emotional change is detected. The output includes information about the detected anomaly, sent via messaging services using the Twilio API or Firebase. The notification content includes details of the detected emotional state.

[0740] Step 6:

[0741] The user receives responses through a dialogue function. Based on emotional state data, the server uses the Google Text-to-Speech API to select and play appropriate music, enabling natural conversation with the elderly. Output includes audio and music playback. For example, the server might ask the elderly, "How are you feeling today?" and play relaxing music.

[0742] 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.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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 as being incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A sensing method for collecting data on the daily activities of elderly people,

[0766] An analytical means for analyzing the collected data,

[0767] A notification means that evaluates the health status based on the analysis results and generates a notification,

[0768] A means of dialogue that reduces feelings of loneliness through conversations with users,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the analysis means estimates an emotional state using facial expression data.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the notification means transmits a notification of an abnormality to a family member or caregiver.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A device for acquiring data on the movement and emotional state of elderly people,

[0777] A device that transmits the acquired data to the cloud for analysis,

[0778] A device that detects anomalies based on analysis results and generates and sends notifications to family members or care providers,

[0779] A device that uses a generative AI model to interact with the user,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the device estimates emotional states from facial expression data and evaluates daily activities from activity data.

[0783] (Claim 3)

[0784] The system according to claim 1, wherein when the data analysis device detects an anomaly, it initiates a dialogue using a prompt message and collects feedback.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] A sensing device for collecting data on the daily activities of elderly people,

[0788] Analytical means for analyzing collected data,

[0789] A notification means that evaluates the health status based on the analysis results and generates a notification,

[0790] A means of dialogue that reduces feelings of loneliness through conversation with users,

[0791] A method for processing collected data in a cloud environment and performing analysis using machine learning,

[0792] A means of automatically sending a communication to family members or caregivers when an anomaly is detected,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the analysis means estimates an emotional state using facial expression data.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the notification means includes prompting rapid intervention in response to the detected anomaly.

[0798] "Example 2 of combining an emotion engine"

[0799] (Claim 1)

[0800] Means for acquiring emotional information of the elderly,

[0801] Processing means for preprocessing acquired information,

[0802] An analytical means for performing emotional analysis using preprocessed information,

[0803] A means of transmitting information that evaluates health status based on analysis results and notifies in case of abnormalities,

[0804] A response means that optimizes the conversation content based on the analysis results,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the analysis means identifies an emotional state using machine learning technology.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the transmitting means transmits an abnormality warning to the relevant parties.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] A sensing method for collecting information on the daily activities of the elderly,

[0813] An analytical means for analyzing the collected data,

[0814] A notification means that evaluates the health status based on the analysis results and generates a notification,

[0815] A means of dialogue that reduces feelings of loneliness through interaction with users,

[0816] A sentiment analysis method that performs real-time sentiment analysis using audio and video,

[0817] A warning system that detects abnormal emotional changes and alerts family members and caregivers,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising an emotional response means for estimating an emotional state using facial expression data and generating appropriate music or conversation.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising a notification transmission means for identifying abnormal emotional patterns and immediately sending notifications to relevant parties. [Explanation of Symbols]

[0823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensing device for collecting data on the daily activities of elderly people, Analytical means for analyzing collected data, A notification means that evaluates the health status based on the analysis results and generates a notification, A means of dialogue that reduces feelings of loneliness through conversation with users, A method for processing collected data in a cloud environment and performing analysis using machine learning, A means of automatically sending a communication to family members or caregivers when an anomaly is detected, A system that includes this.

2. The system according to claim 1, wherein the analysis means estimates an emotional state using facial expression data.

3. The system according to claim 1, wherein the notification means includes prompting rapid intervention in response to the detected anomaly.

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