system

Abstract

We provide the system. [Solution] We monitor the behavioral patterns of the elderly. Information acquisition means for detecting anomalies, A data communication method that encrypts and transmits the collected data, A data analysis means that analyzes received data and detects anomalies using an AI algorithm, An information provision means that sends a notification based on the analysis results, A visual information presentation means that displays information in real time, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society where the number of households consisting only of the elderly is increasing, it has become a problem for families and related institutions living in remote areas to feel anxious about the daily living situation and health condition. In particular, there is a problem that the quality of monitoring cannot be ensured due to the delay in detecting abnormal situations and taking prompt actions. Also, it is important to provide effective monitoring while protecting privacy.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that enables monitoring of the behavioral patterns of elderly people living in remote locations using sensing means and detecting abnormalities. The sensing means encrypts the collected behavioral data and transmits it using communication means, and the received data is analyzed by analysis means. Based on the analysis results, notification means notifies of abnormalities and reports to local governments and relevant organizations as necessary. Furthermore, by incorporating user feedback and having the AI ​​system learn and update, highly accurate monitoring is always achieved. This provides prompt and accurate care while maintaining a high level of privacy protection.

[0006] "Remote area" refers to a geographically distant location, usually meaning that the elderly person, their family, and related organizations reside in different regions.

[0007] "Elderly people" refers to individuals who are said to have physical or social characteristics associated with their age, and in this invention, it specifically refers to individuals who require monitoring.

[0008] "Behavioral patterns" refer to consistent tendencies in an individual's daily actions and activities, including movements and habits in everyday life.

[0009] "Sensing means" are technical means for automatically detecting the environment, human actions, and conditions, and they utilize sensors and monitoring devices.

[0010] "Communication means" refers to methods and technologies for sending and receiving data, and in particular, the present invention involves securely transferring data via the internet or the like.

[0011] "Analysis means" refers to a method or system for processing and evaluating collected data and drawing conclusions according to a specific purpose.

[0012] "Notification means" refers to methods or devices for transmitting information based on analysis results to recipients, such as alerts or reports.

[0013] "User" refers to anyone who uses or accesses the system, and includes the elderly person themselves, their family, or staff of related organizations.

[0014] "Feedback" refers to information provided by users that the system uses for subsequent improvements and learning.

[0015] "Privacy" refers to a state in which the confidentiality of an individual's information and activities is protected, and in this invention, data security is given particular importance. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system for monitoring and appropriately supervising the safety and health of elderly people living in remote locations. This system consists of three elements: a terminal, a server, and a user.

[0038] The system includes a group of sensors installed in the homes of elderly individuals, which continuously monitor their daily activities and health status. For example, motion sensors detect the elderly person's movements and location, while health monitoring devices collect data such as heart rate and body temperature. This collected data is encrypted by the system and transmitted to the server in a secure state.

[0039] The server receives data sent from the terminal and securely stores it in a database. The stored data is analyzed based on an AI algorithm. The AI ​​learns normal behavior patterns and uses the results to determine whether or not there are anomalies. If this analysis detects any unusual behavior, it immediately generates an alert. The generated alerts and periodic reports are automatically sent to local governments and relevant organizations.

[0040] Users can access the system via the web or application to check the status of those being monitored in real time. Users can send feedback to their devices as needed, and the system continuously learns by incorporating this feedback into future monitoring and analysis. For example, if the frequency of an elderly person's hospital visits changes or there are changes in their health condition, the user can reflect this information in the system.

[0041] In this way, the present invention supports the lives of the elderly and provides an environment that enables quick and accurate responses to emergencies. The system is designed to allow for appropriate monitoring while protecting privacy.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device uses sensors installed in the elderly person's living space to detect movement and monitor their movements and activities within the room. This allows the system to understand their wake-up time and daily routine. The collected data is encrypted to ensure security.

[0045] Step 2:

[0046] The device sends encrypted data to the server at regular intervals. This data transmission takes place in real time to a remote server using Wi-Fi or a mobile network. Because immediacy is required, this transmission is carried out quickly.

[0047] Step 3:

[0048] The server decrypts the encrypted data received from the terminal and stores it in the database. The stored data is given an appropriate timestamp and organized for subsequent data analysis processing.

[0049] Step 4:

[0050] The server applies an AI algorithm to the information stored in the database to learn the elderly person's normal behavioral patterns. The algorithm monitors for any behavior that deviates from a defined range and detects anomalies.

[0051] Step 5:

[0052] The server immediately generates an alert if an anomaly is detected. This alert is automatically sent to relevant organizations and registered family contacts to enable a quick response. The server also periodically generates reports on health status and behavioral patterns and sends them to relevant parties.

[0053] Step 6:

[0054] Users can access the server using an application or web platform to view the latest status and past activity history of elderly individuals. Feedback entered by users is sent to the server via their devices and incorporated into the AI ​​learning process.

[0055] Step 7:

[0056] The server continuously improves the accuracy of its analysis by adding user feedback and new health information to its database and retraining its AI algorithms. This enhances its flexibility in adapting to change and improves the overall quality of monitoring.

[0057] (Example 1)

[0058] 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."

[0059] In an aging society, there is a need to efficiently manage the safety and health of elderly people living in remote areas. However, currently, there is a lack of means for the elderly themselves, their families, and relevant organizations to quickly and appropriately detect abnormal situations, raising concerns about delays in emergency response. Furthermore, the secure management of acquired information and the protection of privacy are also major challenges.

[0060] 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.

[0061] In this invention, the server includes sensing means, communication means, analysis means, notification means, access means, and update means. This enables real-time monitoring of the health status and activities of elderly individuals, and allows for rapid notification and appropriate response in the event of an anomaly. Furthermore, it enables secure data management and continuous improvement of the AI ​​model while ensuring privacy.

[0062] "Remote location" refers to a place where residences or facilities are far apart, and in this context, it means that the entity responsible for monitoring and managing the elderly person is physically located away from the area where the elderly person resides.

[0063] "Elderly people" refers to individuals who have passed their typical working-age years and usually require special consideration and support.

[0064] "Sensing means" refers to technological devices and equipment used to collect movement and biometric information from elderly individuals. Specifically, this includes motion sensors and health monitoring devices.

[0065] "Communication methods" refer to the technologies and protocols used to securely transmit collected data to a server. Specifically, this includes data encryption and data transmission technologies over the internet.

[0066] "Analysis means" refers to AI algorithms and computational processes that learn the normal behavioral patterns of elderly people based on the received data and detect abnormalities.

[0067] "Notification methods" refer to methods and technologies for informing relevant parties and organizations of anomalies based on analysis results. Specifically, this includes real-time notifications via email and social media.

[0068] "Access means" refers to interfaces and technologies that allow users to remotely access information in real time or connect to a system.

[0069] "Update methods" refer to the processes and technologies used to improve AI models based on user feedback and new data, enabling continuous learning.

[0070] This invention is a system for monitoring the safety and health of elderly people living in remote locations. The system consists of three elements: a terminal, a server, and a user.

[0071] The terminal includes a group of sensors installed in the homes of elderly people. Motion sensors detect the movements and location of the elderly, and health monitoring devices collect biometric information such as heart rate and body temperature. Examples of such hardware include IoT devices and wearable devices. The collected data is encrypted by the terminal using encryption technologies such as AES (Advanced Encryption Standard) and transmitted to the server in a secure state.

[0072] The server receives data sent from the terminal and securely stores it in the database. The software used is a database management system (DBMS). The stored data is analyzed based on a generative AI model. The AI ​​learns normal behavior patterns and uses the results to determine whether or not anomalies are present. Machine learning algorithms are used for this analysis, and Python libraries and frameworks are utilized. If unusual behavior is detected, an alert is immediately generated and notifications are sent to relevant organizations via email or SMS.

[0073] Users can access the system via a web browser or mobile application to check the status of the person being monitored in real time. The user interface is designed using technologies such as HTML5 and JavaScript (registered trademark). Users can send feedback to their devices as needed, and this information is used by the system for future monitoring and analysis, improving the learning accuracy of the generated AI model.

[0074] For example, if an elderly person's frequency of hospital visits changes or their health condition changes, the user can manually add that information to the system, and the AI ​​can incorporate it into its analysis. This allows the system to support the lives of the elderly and provide an environment that can respond quickly and accurately to abnormal situations.

[0075] An example of a prompt to input into the generated AI model would be, "Design an AI model to detect anomalies from behavioral data of elderly people." This would allow the AI ​​model to learn the daily behavioral patterns of elderly people and be designed to improve the accuracy of anomaly detection.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device uses sensors to collect real-time information on the elderly person's movements and biometric data. This input data includes location information and activity levels obtained from motion sensors, as well as heart rate and body temperature from health monitoring devices. The device temporarily stores this raw data and performs data preprocessing and anomaly detection. For example, it may set a warning flag if the heart rate exceeds a certain range.

[0079] Step 2:

[0080] The terminal encrypts the pre-processed data using AES encryption technology and securely transmits it to the server. Input data is processed as encrypted binary data and sent to the server as output using a secure communication protocol. During this process, data confidentiality is maintained using technologies such as SSL / TLS.

[0081] Step 3:

[0082] The server decrypts the received encrypted data and stores it in the database. It receives encrypted binary data as input, performs AES decryption, and then stores it as structured data via a database management system (DBMS). This enables fast and secure data storage and access.

[0083] Step 4:

[0084] The server analyzes data stored in the database using AI algorithms to determine whether or not anomalies are present. Past behavioral patterns and real-time data are used as input data, and a machine learning model performs predictive calculations. As output, if an anomaly is detected, an anomaly alert is generated. Machine learning frameworks in Python and R are used for the analysis.

[0085] Step 5:

[0086] The server generates alerts based on the results of anomaly detection and automatically sends notifications to relevant organizations and users. The generated alerts are sent via email, SMS, etc., and if it is determined that an emergency response is necessary, the alert is delivered immediately.

[0087] Step 6:

[0088] Users can access the system in real time via a web browser or mobile application to check the latest status and alert information of the elderly individuals they are monitoring. This requires an internet connection and the corresponding application. The user interface is intuitive and easy to operate thanks to the latest technology, supporting quick decision-making.

[0089] Step 7:

[0090] Users input necessary feedback into the system based on the elderly person's situation and alerts. This feedback is sent to the server and incorporated into subsequent analyses, contributing to the improvement of the AI ​​model's accuracy. The input feedback is added to the training dataset, allowing the generated AI model to continuously learn and improving the reliability of the analysis results.

[0091] (Application Example 1)

[0092] 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."

[0093] While there is a need to monitor the safety and health of the elderly in real time and to detect and respond to abnormalities early, conventional monitoring systems have limitations in terms of the immediacy of information and the provision of visual information. Therefore, there is a challenge in that it is difficult for caregivers and family members to efficiently understand the situation of the elderly and take prompt action.

[0094] 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.

[0095] In this invention, the server includes information acquisition means, data communication means, data analysis means, and visual information presentation means. This enables the real-time visual presentation of the behavior and health status of elderly people, allowing for early detection of abnormalities and rapid response.

[0096] "Information acquisition means" is a general term for devices and technologies that collect data necessary to monitor the movements and health status of elderly people.

[0097] "Data communication methods" refer to communication technologies and protocols used to securely transmit collected data to a server.

[0098] "Data analysis means" refers to technologies and devices that analyze received data and detect anomalies using AI algorithms.

[0099] "Information provision means" refers to the means of sending necessary notifications to users based on the analysis results.

[0100] "Visual information presentation means" refers to devices and technologies that display information visually in real time and convey the situation to the user intuitively.

[0101] "Data reporting means" include procedures and techniques for reporting regularly analyzed data to public institutions and related organizations.

[0102] A "data update method" is a means of updating the system's training data based on feedback provided by the operator, thereby continuously improving machine learning.

[0103] The system for implementing this invention is a device for remotely monitoring the safety and health of elderly people. It mainly consists of a group of sensors installed in the elderly person's residence, a server for data processing, and a user interface for viewing the information.

[0104] Server Processing

[0105] The server is equipped with data analysis and visual information presentation capabilities and has the ability to process various data formats. The server operates a data analysis module incorporating AI algorithms using Python and TENSORFLOW®. This module performs analysis to detect anomalies based on collected data and generates notifications as needed. The server also has communication capabilities to deliver the generated notifications to caregivers and family members in real time.

[0106] Device functions

[0107] The terminal has information acquisition and data communication means installed in the elderly person's residence. The sensor group includes motion sensors and health monitoring devices to continuously collect the elderly person's movements and biometric data. This data is encrypted and transmitted to the server via the data communication means.

[0108] User interface

[0109] Users can use devices such as smart glasses to monitor the condition of elderly individuals in real time through visual information presentation. If an abnormality is detected, they can receive immediate information via audio alerts or on the display. Furthermore, users can provide feedback through the smart glasses, which is incorporated into the system to improve AI learning.

[0110] Specific example

[0111] For example, if an elderly person performs an action that deviates from their normal daily activities, the system displays "Attention: Abnormal behavior detected" on the smart glasses, prompting the caregiver to take immediate action. This enables a quick and appropriate response.

[0112] Example of a prompt

[0113] "Your heart rate is higher than yesterday. Determine if this is abnormal and generate any necessary alerts."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The device collects data from a group of sensors (motion sensors and health monitoring devices) installed in the elderly person's residence.

[0117] The device receives real-time data from sensors as input, encrypts it using an encryption algorithm, and generates encrypted data as output. The device then sends this data to the server.

[0118] Step 2:

[0119] The server receives encrypted data sent from the terminal. It decrypts the received data and prepares to run the data analysis tools.

[0120] It receives decoded data as input, performs a format conversion before storing it in the database, and generates analysis-ready data as output.

[0121] Step 3:

[0122] The server processes the prepared data using a data analysis module. It uses AI algorithms to detect anomalies that deviate from normal behavioral patterns.

[0123] The system receives pre-prepared data for analysis as input, performs anomaly detection calculations using an AI model, and generates anomaly detection results as output.

[0124] Step 4:

[0125] Based on the anomaly detection results, the server uses information provision means to generate alerts and notify the user as needed.

[0126] The server receives anomaly detection results as input, applies alert generation logic, and generates notification information as output. The server then sends this notification information to the user's device.

[0127] Step 5:

[0128] Users receive notification information through devices such as smart glasses and can check the condition of elderly people in real time.

[0129] The system receives notification information from a server as input, displays the information using a visual display and audio functions, and generates feedback to help the user understand the situation as output.

[0130] Step 6:

[0131] Users provide feedback as needed and send that information back to their device. The system updates the AI's learning based on this feedback.

[0132] The system receives user feedback as input, performs a process to update the AI ​​model, and generates an improved learned model as output.

[0133] 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.

[0134] This invention provides a system for comprehensively monitoring the behavior and emotions of elderly people living in remote locations, enabling early detection of abnormalities and prompt response. The system consists of a terminal, a server, a user, and an emotion engine.

[0135] The device uses sensors installed in the living space to monitor the elderly person's daily activities and collect necessary data. The device is also equipped with a microphone and camera, allowing it to acquire audio and video data. This data is encrypted and transmitted to the server in a privacy-protected state.

[0136] The server receives and analyzes behavioral and emotional data transmitted from the terminal. Behavioral data is processed by an AI algorithm to determine deviations from normal behavioral patterns or any abnormalities. In this process, the AI ​​learns the elderly person's daily habits and quickly generates alerts when abnormalities occur. Furthermore, an emotion engine is used to analyze audio and video data to understand the elderly person's emotional state. The server notifies relevant organizations and family members of any detected abnormalities or emotional changes.

[0137] Users can access information provided by the server through a dedicated application or web platform. This allows users to monitor the health and emotional state of elderly individuals in real time and send feedback as needed. For example, if a user detects inconsistency in an elderly person's behavior or a significant change in their emotions, that information is immediately reflected through the system.

[0138] By further incorporating an emotion engine, the system can also monitor the mental health of older adults. For example, the emotion engine analyzes emotions from changes in voice tone and facial expressions, providing data to evaluate long-term emotional trends. This data can suggest that older adults may be experiencing problems and help determine whether intervention is necessary.

[0139] Thus, the present invention aims to provide comprehensive health management for the elderly and to offer necessary support quickly and accurately.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device uses various sensors, microphones, and cameras installed in the living space to collect data on the elderly person's daily activities, as well as audio and video data. This allows for the detection of walking patterns, conversation tone, facial expressions, and other characteristics. The collected data is immediately encrypted to ensure security.

[0143] Step 2:

[0144] The terminal sends encrypted data to the server at regular intervals. This communication is conducted remotely using the internet or a dedicated line, ensuring that the data is managed in real time.

[0145] Step 3:

[0146] The server decrypts the data received from the terminal and stores it in a database. The stored data is sorted chronologically and prepared for analysis by AI algorithms.

[0147] Step 4:

[0148] The server uses AI algorithms to analyze behavioral data and compare it to normal behavioral patterns. If there are any abnormal changes or deviations from the pattern, an alert is immediately generated.

[0149] Step 5:

[0150] The server uses an emotion engine to analyze audio and video data to infer the emotional state of elderly individuals. This involves evaluating factors such as tone of voice, word choice, and facial expressions as emotional indicators.

[0151] Step 6:

[0152] The server combines analyzed behavioral and emotional data to generate a comprehensive health status report, which is periodically sent to relevant parties. This information is accessible in real time, allowing for immediate planning of necessary interventions.

[0153] Step 7:

[0154] Users can monitor the status of elderly individuals through an application or web platform and provide feedback on any abnormalities or changes in their emotions. This feedback is collected within the system and incorporated into the learning process.

[0155] Step 8:

[0156] The server incorporates user feedback and new information, continuously learning its AI algorithms and improving analysis accuracy. This process enables personalized monitoring tailored to each elderly individual.

[0157] (Example 2)

[0158] 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".

[0159] In modern society, many elderly people live alone in remote locations, making it difficult to quickly grasp changes in their behavior and emotions. As a result, there is a possibility of delays in responding to abnormalities. In particular, there is a challenge in detecting changes in emotions and providing necessary support quickly.

[0160] 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.

[0161] In this invention, the server includes sensor means for monitoring the behavior of elderly people living in remote locations and recording their daily activities; communication means for encrypting and transmitting acquired audio and video data; analysis means for analyzing behavioral data and audio / video data using an AI algorithm to detect anomalies; means for understanding the emotional state of the elderly person based on the analysis results and evaluating it using an emotion engine; and means for notifying alert information based on the analysis results. This makes it possible to grasp changes in the behavior and emotions of elderly people in real time, detect anomalies early, and respond quickly.

[0162] A "remote location" refers to a place that is physically far away and where normal direct contact or monitoring is difficult.

[0163] "Elderly people" generally refers to people of an age group who are getting older and may require physical and mental support.

[0164] "Behavior" refers to the actions and activities of elderly people in their daily lives, including movement, eating, and sleeping.

[0165] "Monitoring" refers to the continuous and detailed observation of the behavior and emotional state of elderly individuals.

[0166] "Daily activities" refer to the normal daily activities that elderly people perform, including eating, cleaning, shopping, and going out.

[0167] "Sensor means" refers to devices and technologies for detecting the movements and environmental information of elderly people, and includes motion sensors and cameras.

[0168] "Audio and video data" refers to digital data that records audio and video information emitted by elderly individuals.

[0169] "Encryption" refers to a technology that enhances data confidentiality by transforming it to prevent unauthorized access by third parties during data transmission.

[0170] "Communication methods" refer to the technologies and protocols used to transmit data from a terminal to a server.

[0171] An "AI algorithm" refers to a computational procedure that mimics human intelligence to analyze data and perform pattern recognition and prediction.

[0172] "Analysis means" refers to hardware or software used to analyze acquired data and convert it into useful information.

[0173] "Abnormal" refers to a state that deviates from normal behavioral patterns or emotional states.

[0174] An "emotion engine" refers to a system component used to analyze emotional states from voice tone and facial expressions.

[0175] "Alert information" refers to important information that is notified to users and relevant parties when an anomaly is detected.

[0176] This invention is a system for monitoring elderly people living in remote locations and ensuring their safety and health. This system mainly consists of three main components: a terminal, a server, and a user, each component playing a specific role.

[0177] The devices are installed in the living spaces of elderly individuals and utilize the latest sensor technology to collect data on their behavior and emotions. The sensors include motion sensors, microphones, and cameras, which meticulously record the elderly individuals' daily activities. The collected data is encoded in a digital format, encrypted, and then transmitted to a server in real time. This encryption technology ensures the privacy of the elderly individuals.

[0178] The server receives data transmitted from the terminal and performs data analysis using high-performance AI algorithms. Behavioral data detects deviations from normal patterns, enabling early detection of abnormalities. Furthermore, it utilizes an emotion engine to analyze voice and video data, providing a detailed understanding of the emotional state of elderly individuals. For example, by analyzing changes in voice tone and facial expressions, it can evaluate emotional tendencies in real time. Based on these analysis results, it quickly generates alerts in the event of an anomaly, notifying relevant organizations and family members.

[0179] Users can easily access information provided by the server through a dedicated application or web platform. Using this platform, users can monitor the health status of elderly individuals in real time and provide feedback to the server as needed. For example, when a user enters the prompt "Tell me my current health status," the system retrieves the latest health information and displays it in graphs and text. This makes it possible to maintain the health and improve the quality of life for elderly individuals.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The device collects data from sensors, microphones, and cameras installed in the elderly person's living space. It acquires motion data, audio data, and video data as input. This data includes information about the elderly person's movements, conversations, and facial expressions. The collected data is encrypted in real time to protect privacy. The encrypted data is then output and sent to a server.

[0183] Step 2:

[0184] The server receives encrypted data sent from the terminal. It receives encrypted motion, audio, and video data as input. First, the data is decrypted within the server. Then, an AI algorithm is applied to analyze deviations from normal behavioral patterns and emotional states. During the data processing process, behavioral data is output through an anomaly detection algorithm based on comparison with normal patterns, and emotional data is output after tone and facial expression analysis by an emotion engine. The analyzed results are output, and if an anomaly is detected, an alert is generated.

[0185] Step 3:

[0186] Users access data from the server using a dedicated application or web platform. Users request information by entering prompts such as "Tell me about recent emotional tendencies." Based on this input, the user interface displays information about abnormal conditions and emotional tendencies as graphs and text, using the server's output. This allows users to monitor the health status of elderly individuals in real time and input necessary feedback and actions into the server.

[0187] (Application Example 2)

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

[0189] In an aging society, there is a need to comprehensively understand the health and mental state of elderly people living in remote areas, and to enable timely detection of abnormalities and prompt response. However, current methods have problems such as the risk of missing abnormalities and delays in real-time response. In particular, systems that can understand the emotional state of the elderly and allow family members and care staff to immediately check the situation are not yet adequately developed.

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

[0191] In this invention, the server includes data collection means for monitoring the behavioral patterns of individuals residing in remote locations and detecting anomalies.

[0192] A communication method for encrypting and transmitting collected activity information,

[0193] A means of analyzing emotions for analyzing mental health status,

[0194] Includes a visualization means for displaying a graphed emotional state.

[0195] This makes it possible to monitor the behavior and emotional state of elderly people in real time, and to immediately notify them of any abnormalities and visualize changes in their emotions.

[0196] "Individuals residing in remote locations" refers to people who live in a place separate from their home, and this includes elderly people in particular.

[0197] "Behavioral patterns" refer to a sequence of physical movements and activities in an individual's daily life, or to general habits.

[0198] "Data collection means" is a general term for hardware and software used to perceive information from an individual's behavior and environment and to acquire that information in digital format.

[0199] "Communication methods" refer to devices and protocols for securely and efficiently transmitting collected digital information.

[0200] "Information processing means" refers to the configuration of algorithms and computing resources for analyzing received digital data and detecting specific patterns or anomalies.

[0201] "Notification means" refers to methods and techniques for notifying relevant people or systems of specific information based on analysis results.

[0202] "Emotional analysis means" refers to a function that uses voice, facial expressions, or other relevant data to evaluate an individual's emotional changes and determine their emotional state.

[0203] "Visualization methods" refer to technologies that provide charts, graphs, and interfaces to clearly represent and visually present analyzed emotional data.

[0204] In this application example, the system configuration mainly consists of terminals, servers, and users.

[0205] The terminals are placed in the living spaces of elderly people and use sensors to monitor their behavioral patterns. The sensors perform motion detection and voice capture, and data is collected using devices such as Raspberry Pi, encrypted, and sent to a server. Encryption algorithms are used during data processing to protect data privacy.

[0206] The server receives data transmitted from the terminal and performs information processing to analyze it. This process implements an AI model using Python, and utilizes libraries such as TensorFlow and PyTorch to perform anomaly detection and sentiment analysis based on behavioral patterns. The obtained sentiment data is then graphed and provided to the user through visualization tools. This allows the user to intuitively understand the daily emotional state and activity level of elderly individuals.

[0207] Users can access this information in real time through an interface accessible from their smartphones or computers. The application uses push notifications to immediately inform users when an anomaly occurs or when a significant change in emotion is detected. These notifications are delivered using services such as Firebase Cloud Messaging (FCM).

[0208] For example, if an elderly person deviates from their normal sleep pattern, the system analyzes this information and sends a notification to the user such as, "There was an anomaly in last night's behavior pattern. Please check the situation." An example of a prompt message would be, "Analyze how the elderly person deviates from their usual behavior pattern and create a push notification message. Specific behaviors include frequent nighttime awakenings and increased unsteady walking." This allows the user to quickly assess the situation and take necessary action.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The device collects environmental and behavioral data using sensors installed in the living spaces of elderly people. It receives signals from motion and voice sensors as input and converts them into digital information. This information is encrypted using encryption algorithms such as AES to protect privacy. The output is sent to the server in an encrypted format.

[0212] Step 2:

[0213] The server receives encrypted data from the terminal and first decrypts it. Then, it performs data analysis according to each data type. The data received as input can be analyzed for behavioral patterns using a deep learning model (using TensorFlow or PyTorch) to detect anomalies. The output is a list of possible anomalies.

[0214] Step 3:

[0215] The server activates an emotion analysis engine and analyzes emotional states based on audio and video data. It supplies audio and video features as input to an AI model to detect changes in emotion. Emotional changes and long-term trends are analyzed, and a graph or report showing the emotional state is generated as output.

[0216] Step 4:

[0217] The server prepares to immediately notify the user when an anomaly is detected based on the analysis results. Upon receiving a list of anomalies as input, it creates a notification message via the Firebase Cloud Messaging (FCM) service. The output is an alert notification sent to the user's smartphone or other device.

[0218] Step 5:

[0219] Users check received notifications on their smartphones or via a web interface to stay informed about the elderly person's current condition. They receive notification content and a graph of emotional state visualized from the server as input, allowing them to intuitively assess the situation. Outputs include user intervention actions, such as making a phone call to the elderly person.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention is a system for monitoring and appropriately supervising the safety and health of elderly people living in remote locations. This system consists of three elements: a terminal, a server, and a user.

[0237] The system includes a group of sensors installed in the homes of elderly individuals, which continuously monitor their daily activities and health status. For example, motion sensors detect the elderly person's movements and location, while health monitoring devices collect data such as heart rate and body temperature. This collected data is encrypted by the system and transmitted to the server in a secure state.

[0238] The server receives data sent from the terminal and securely stores it in a database. The stored data is analyzed based on an AI algorithm. The AI ​​learns normal behavior patterns and uses the results to determine whether or not there are anomalies. If this analysis detects any unusual behavior, it immediately generates an alert. The generated alerts and periodic reports are automatically sent to local governments and relevant organizations.

[0239] Users can access the system via the web or application to check the status of those being monitored in real time. Users can send feedback to their devices as needed, and the system continuously learns by incorporating this feedback into future monitoring and analysis. For example, if the frequency of an elderly person's hospital visits changes or there are changes in their health condition, the user can reflect this information in the system.

[0240] In this way, the present invention supports the lives of the elderly and provides an environment that enables quick and accurate responses to emergencies. The system is designed to allow for appropriate monitoring while protecting privacy.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The device uses sensors installed in the elderly person's living space to detect movement and monitor their movements and activities within the room. This allows the system to understand their wake-up time and daily routine. The collected data is encrypted to ensure security.

[0244] Step 2:

[0245] The device sends encrypted data to the server at regular intervals. This data transmission takes place in real time to a remote server using Wi-Fi or a mobile network. Because immediacy is required, this transmission is carried out quickly.

[0246] Step 3:

[0247] The server decrypts the encrypted data received from the terminal and stores it in the database. The stored data is given an appropriate timestamp and organized for subsequent data analysis processing.

[0248] Step 4:

[0249] The server applies an AI algorithm to the information stored in the database to learn the elderly person's normal behavioral patterns. The algorithm monitors for any behavior that deviates from a defined range and detects anomalies.

[0250] Step 5:

[0251] The server immediately generates an alert if an anomaly is detected. This alert is automatically sent to relevant organizations and registered family contacts to enable a quick response. The server also periodically generates reports on health status and behavioral patterns and sends them to relevant parties.

[0252] Step 6:

[0253] Users can access the server using an application or web platform to view the latest status and past activity history of elderly individuals. Feedback entered by users is sent to the server via their devices and incorporated into the AI ​​learning process.

[0254] Step 7:

[0255] The server continuously improves the accuracy of its analysis by adding user feedback and new health information to its database and retraining its AI algorithms. This enhances its flexibility in adapting to change and improves the overall quality of monitoring.

[0256] (Example 1)

[0257] 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."

[0258] In an aging society, there is a need to efficiently manage the safety and health of elderly people living in remote areas. However, currently, there is a lack of means for the elderly themselves, their families, and relevant organizations to quickly and appropriately detect abnormal situations, raising concerns about delays in emergency response. Furthermore, the secure management of acquired information and the protection of privacy are also major challenges.

[0259] 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.

[0260] In this invention, the server includes sensing means, communication means, analysis means, notification means, access means, and update means. This enables real-time monitoring of the health status and activities of elderly individuals, and allows for rapid notification and appropriate response in the event of an anomaly. Furthermore, it enables secure data management and continuous improvement of the AI ​​model while ensuring privacy.

[0261] "Remote location" refers to a place where residences or facilities are far apart, and in this context, it means that the entity responsible for monitoring and managing the elderly person is physically located away from the area where the elderly person resides.

[0262] "Elderly people" refers to individuals who have passed their typical working-age years and usually require special consideration and support.

[0263] "Sensing means" refers to technological devices and equipment used to collect movement and biometric information from elderly individuals. Specifically, this includes motion sensors and health monitoring devices.

[0264] "Communication methods" refer to the technologies and protocols used to securely transmit collected data to a server. Specifically, this includes data encryption and data transmission technologies over the internet.

[0265] "Analysis means" refers to AI algorithms and computational processes that learn the normal behavioral patterns of elderly people based on the received data and detect abnormalities.

[0266] "Notification methods" refer to methods and technologies for informing relevant parties and organizations of anomalies based on analysis results. Specifically, this includes real-time notifications via email and social media.

[0267] "Access means" refers to interfaces and technologies that allow users to remotely access information in real time or connect to a system.

[0268] "Update methods" refer to the processes and technologies used to improve AI models based on user feedback and new data, enabling continuous learning.

[0269] This invention is a system for monitoring the safety and health of elderly people living in remote locations. The system consists of three elements: a terminal, a server, and a user.

[0270] The terminal includes a group of sensors installed in the homes of elderly people. Motion sensors detect the movements and location of the elderly, and health monitoring devices collect biometric information such as heart rate and body temperature. Examples of such hardware include IoT devices and wearable devices. The collected data is encrypted by the terminal using encryption technologies such as AES (Advanced Encryption Standard) and transmitted to the server in a secure state.

[0271] The server receives data sent from the terminal and securely stores it in the database. The software used is a database management system (DBMS). The stored data is analyzed based on a generative AI model. The AI ​​learns normal behavior patterns and uses the results to determine whether or not anomalies are present. Machine learning algorithms are used for this analysis, and Python libraries and frameworks are utilized. If unusual behavior is detected, an alert is immediately generated and notifications are sent to relevant organizations via email or SMS.

[0272] Users can access the system via a web browser or mobile application to check the status of the person being monitored in real time. The user interface is designed using technologies such as HTML5 and JavaScript. Users can send feedback to their devices as needed, and this information is used by the system for future monitoring and analysis, improving the learning accuracy of the generated AI model.

[0273] For example, if an elderly person's frequency of hospital visits changes or their health condition changes, the user can manually add that information to the system, and the AI ​​can incorporate it into its analysis. This allows the system to support the lives of the elderly and provide an environment that can respond quickly and accurately to abnormal situations.

[0274] An example of a prompt to input into the generated AI model would be, "Design an AI model to detect anomalies from behavioral data of elderly people." This would allow the AI ​​model to learn the daily behavioral patterns of elderly people and be designed to improve the accuracy of anomaly detection.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The terminal uses sensors to collect the actions and biometric information of the elderly in real time. This input data includes position information and activity levels obtained from motion sensors, heart rate, body temperature, etc. from a physical condition monitoring device. The terminal temporarily stores this raw data and performs preprocessing of the data and detection of outliers. For example, a process such as setting a warning flag when the heart rate exceeds a certain range is performed.

[0278] Step 2:

[0279] The terminal encrypts the preprocessed data using AES encryption technology and securely transmits it to the server. The input data is processed as encrypted binary data and transmitted to the server via a secure communication protocol as output. At this time, technologies such as SSL / TLS are used to maintain the confidentiality of the data.

[0280] Step 3:

[0281] The server decrypts the received encrypted data and stores it in the database. It receives encrypted binary data as input, performs AES decryption processing, and then stores it as structured data via a database management system (DBMS). This enables fast and secure data storage and access.

[0282] Step 4:

[0283] The server analyzes the data stored in the database using AI algorithms to determine the presence or absence of anomalies. Past behavior patterns and real-time data are used as input data, and a machine learning model performs predictive calculations. As output, an anomaly alert is generated when an anomaly is detected. Python and R machine learning frameworks are utilized for the analysis.

[0284] Step 5:

[0285] [[ID=3l]] The server generates an alert based on the result of anomaly detection and automatically sends a notification to relevant institutions and users. The generated alert is sent via email, SMS, etc., and if it is determined that immediate action is required, the alert is delivered immediately.

[0286] Step 6:

[0287] The user accesses the system in real time through a web browser or a mobile application to check the latest status of the elderly person they are monitoring and the alert information. To do this, an internet connection environment and the corresponding application are required. The user interface is intuitively operable by the latest technology and supports quick decision-making.

[0288] Step 7:

[0289] The user inputs the necessary feedback into the system based on the situation of the elderly person and the alert. This feedback is sent to the server and is reflected in subsequent analyses, contributing to the improvement of the accuracy of the AI model. The input feedback is added to the learning dataset, and the generated AI model continues to learn, resulting in an improved reliability of the analysis results.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In the context of the need to monitor the safety and health of the elderly in real time and detect and respond to anomalies at an early stage, conventional monitoring systems have limitations in the immediacy of information and visual information provision. For this reason, there is a problem that it is difficult for caregivers and family members to efficiently grasp the situation of the elderly and take prompt countermeasures.

[0293] 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.

[0294] In this invention, the server includes information acquisition means, data communication means, data analysis means, and visual information presentation means. This enables the real-time visual presentation of the behavior and health status of elderly people, allowing for early detection of abnormalities and rapid response.

[0295] "Information acquisition means" is a general term for devices and technologies that collect data necessary to monitor the movements and health status of elderly people.

[0296] "Data communication methods" refer to communication technologies and protocols used to securely transmit collected data to a server.

[0297] "Data analysis means" refers to technologies and devices that analyze received data and detect anomalies using AI algorithms.

[0298] "Information provision means" refers to the means of sending necessary notifications to users based on the analysis results.

[0299] "Visual information presentation means" refers to devices and technologies that display information visually in real time and convey the situation to the user intuitively.

[0300] "Data reporting means" include procedures and techniques for reporting regularly analyzed data to public institutions and related organizations.

[0301] A "data update method" is a means of updating the system's training data based on feedback provided by the operator, thereby continuously improving machine learning.

[0302] The system for implementing this invention is a device for remotely monitoring the safety and health of elderly people. It mainly consists of a group of sensors installed in the elderly person's residence, a server for data processing, and a user interface for viewing the information.

[0303] Server processing

[0304] The server has data analysis means and visual information presentation means, and has the function of processing various forms of data. The server uses Python and TensorFlow to operate a data analysis module incorporating AI algorithms. This module performs analysis to detect anomalies based on the collected data and generate notifications as necessary. In addition, the server has a communication function for delivering the generated notifications to caregivers and family members in real time.

[0305] Terminal functions

[0306] The terminal has information acquisition means and data communication means installed in the residence of the elderly. The sensor group includes motion sensors and body condition monitoring devices, and continuously collects the movements and biometric data of the elderly. These data are encrypted and transmitted to the server through the data communication means.

[0307] User interface

[0308] The user can use a device such as smart glasses to grasp the status of the elderly in real time through the visual information presentation means. When an anomaly is detected, information can be received immediately via an audio alert or display. Furthermore, the user can provide feedback via smart glasses, which is incorporated into the system to improve AI learning.

[0309] Specific examples

[0310] For example, when the elderly person makes an action different from normal daily activities, the system displays "Attention: Abnormal behavior detected" on the smart glasses and prompts the caregiver to respond immediately. This enables a quick and appropriate response.

[0311] Examples of prompt sentences

[0312] "Your heart rate is higher than yesterday. Determine if this is abnormal and generate any necessary alerts."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The device collects data from a group of sensors (motion sensors and health monitoring devices) installed in the elderly person's residence.

[0316] The device receives real-time data from sensors as input, encrypts it using an encryption algorithm, and generates encrypted data as output. The device then sends this data to the server.

[0317] Step 2:

[0318] The server receives encrypted data sent from the terminal. It decrypts the received data and prepares to run the data analysis tools.

[0319] It receives decoded data as input, performs a format conversion before storing it in the database, and generates analysis-ready data as output.

[0320] Step 3:

[0321] The server processes the prepared data using a data analysis module. It uses AI algorithms to detect anomalies that deviate from normal behavioral patterns.

[0322] The system receives pre-prepared data for analysis as input, performs anomaly detection calculations using an AI model, and generates anomaly detection results as output.

[0323] Step 4:

[0324] Based on the anomaly detection results, the server uses information provision means to generate alerts and notify the user as needed.

[0325] The server receives anomaly detection results as input, applies alert generation logic, and generates notification information as output. The server then sends this notification information to the user's device.

[0326] Step 5:

[0327] Users receive notification information through devices such as smart glasses and can check the condition of elderly people in real time.

[0328] The system receives notification information from a server as input, displays the information using a visual display and audio functions, and generates feedback to help the user understand the situation as output.

[0329] Step 6:

[0330] Users provide feedback as needed and send that information back to their device. The system updates the AI's learning based on this feedback.

[0331] The system receives user feedback as input, performs a process to update the AI ​​model, and generates an improved learned model as output.

[0332] 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.

[0333] This invention provides a system for comprehensively monitoring the behavior and emotions of elderly people living in remote locations, enabling early detection of abnormalities and prompt response. The system consists of a terminal, a server, a user, and an emotion engine.

[0334] The device uses sensors installed in the living space to monitor the elderly person's daily activities and collect necessary data. The device is also equipped with a microphone and camera, allowing it to acquire audio and video data. This data is encrypted and transmitted to the server in a privacy-protected state.

[0335] The server receives and analyzes behavioral and emotional data transmitted from the terminal. Behavioral data is processed by an AI algorithm to determine deviations from normal behavioral patterns or any abnormalities. In this process, the AI ​​learns the elderly person's daily habits and quickly generates alerts when abnormalities occur. Furthermore, an emotion engine is used to analyze audio and video data to understand the elderly person's emotional state. The server notifies relevant organizations and family members of any detected abnormalities or emotional changes.

[0336] Users can access information provided by the server through a dedicated application or web platform. This allows users to monitor the health and emotional state of elderly individuals in real time and send feedback as needed. For example, if a user detects inconsistency in an elderly person's behavior or a significant change in their emotions, that information is immediately reflected through the system.

[0337] By further incorporating an emotion engine, the system can also monitor the mental health of older adults. For example, the emotion engine analyzes emotions from changes in voice tone and facial expressions, providing data to evaluate long-term emotional trends. This data can suggest that older adults may be experiencing problems and help determine whether intervention is necessary.

[0338] Thus, the present invention aims to provide comprehensive health management for the elderly and to offer necessary support quickly and accurately.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The device uses various sensors, microphones, and cameras installed in the living space to collect data on the elderly person's daily activities, as well as audio and video data. This allows for the detection of walking patterns, conversation tone, facial expressions, and other characteristics. The collected data is immediately encrypted to ensure security.

[0342] Step 2:

[0343] The terminal sends encrypted data to the server at regular intervals. This communication is conducted remotely using the internet or a dedicated line, ensuring that the data is managed in real time.

[0344] Step 3:

[0345] The server decrypts the data received from the terminal and stores it in a database. The stored data is sorted chronologically and prepared for analysis by AI algorithms.

[0346] Step 4:

[0347] The server uses AI algorithms to analyze behavioral data and compare it to normal behavioral patterns. If there are any abnormal changes or deviations from the pattern, an alert is immediately generated.

[0348] Step 5:

[0349] The server uses an emotion engine to analyze audio and video data to infer the emotional state of elderly individuals. This involves evaluating factors such as tone of voice, word choice, and facial expressions as emotional indicators.

[0350] Step 6:

[0351] The server combines analyzed behavioral and emotional data to generate a comprehensive health status report, which is periodically sent to relevant parties. This information is accessible in real time, allowing for immediate planning of necessary interventions.

[0352] Step 7:

[0353] Users can monitor the status of elderly individuals through an application or web platform and provide feedback on any abnormalities or changes in their emotions. This feedback is collected within the system and incorporated into the learning process.

[0354] Step 8:

[0355] The server incorporates user feedback and new information, continuously learning its AI algorithms and improving analysis accuracy. This process enables personalized monitoring tailored to each elderly individual.

[0356] (Example 2)

[0357] 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".

[0358] In modern society, many elderly people live alone in remote locations, making it difficult to quickly grasp changes in their behavior and emotions. As a result, there is a possibility of delays in responding to abnormalities. In particular, there is a challenge in detecting changes in emotions and providing necessary support quickly.

[0359] 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.

[0360] In this invention, the server includes sensor means for monitoring the behavior of elderly people living in remote locations and recording their daily activities; communication means for encrypting and transmitting acquired audio and video data; analysis means for analyzing behavioral data and audio / video data using an AI algorithm to detect anomalies; means for understanding the emotional state of the elderly person based on the analysis results and evaluating it using an emotion engine; and means for notifying alert information based on the analysis results. This makes it possible to grasp changes in the behavior and emotions of elderly people in real time, detect anomalies early, and respond quickly.

[0361] A "remote location" refers to a place that is physically far away and where normal direct contact or monitoring is difficult.

[0362] "Elderly people" generally refers to people of an age group who are getting older and may require physical and mental support.

[0363] "Behavior" refers to the actions and activities of elderly people in their daily lives, including movement, eating, and sleeping.

[0364] "Monitoring" refers to the continuous and detailed observation of the behavior and emotional state of elderly individuals.

[0365] "Daily activities" refer to the normal daily activities that elderly people perform, including eating, cleaning, shopping, and going out.

[0366] "Sensor means" refers to devices and technologies for detecting the movements and environmental information of elderly people, and includes motion sensors and cameras.

[0367] "Audio and video data" refers to digital data that records audio and video information emitted by elderly individuals.

[0368] "Encryption" refers to a technology that enhances data confidentiality by transforming it to prevent unauthorized access by third parties during data transmission.

[0369] "Communication methods" refer to the technologies and protocols used to transmit data from a terminal to a server.

[0370] An "AI algorithm" refers to a computational procedure that mimics human intelligence to analyze data and perform pattern recognition and prediction.

[0371] "Analysis means" refers to hardware or software used to analyze acquired data and convert it into useful information.

[0372] "Abnormal" refers to a state that deviates from normal behavioral patterns or emotional states.

[0373] An "emotion engine" refers to a system component used to analyze emotional states from voice tone and facial expressions.

[0374] "Alert information" refers to important information that is notified to users and relevant parties when an anomaly is detected.

[0375] This invention is a system for monitoring elderly people living in remote locations and ensuring their safety and health. This system mainly consists of three main components: a terminal, a server, and a user, each component playing a specific role.

[0376] The devices are installed in the living spaces of elderly individuals and utilize the latest sensor technology to collect data on their behavior and emotions. The sensors include motion sensors, microphones, and cameras, which meticulously record the elderly individuals' daily activities. The collected data is encoded in a digital format, encrypted, and then transmitted to a server in real time. This encryption technology ensures the privacy of the elderly individuals.

[0377] The server receives data transmitted from the terminal and performs data analysis using high-performance AI algorithms. Behavioral data detects deviations from normal patterns, enabling early detection of abnormalities. Furthermore, it utilizes an emotion engine to analyze voice and video data, providing a detailed understanding of the emotional state of elderly individuals. For example, by analyzing changes in voice tone and facial expressions, it can evaluate emotional tendencies in real time. Based on these analysis results, it quickly generates alerts in the event of an anomaly, notifying relevant organizations and family members.

[0378] Users can easily access information provided by the server through a dedicated application or web platform. Using this platform, users can monitor the health status of elderly individuals in real time and provide feedback to the server as needed. For example, when a user enters the prompt "Tell me my current health status," the system retrieves the latest health information and displays it in graphs and text. This makes it possible to maintain the health and improve the quality of life for elderly individuals.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The device collects data from sensors, microphones, and cameras installed in the elderly person's living space. It acquires motion data, audio data, and video data as input. This data includes information about the elderly person's movements, conversations, and facial expressions. The collected data is encrypted in real time to protect privacy. The encrypted data is then output and sent to a server.

[0382] Step 2:

[0383] The server receives encrypted data sent from the terminal. It receives encrypted motion, audio, and video data as input. First, the data is decrypted within the server. Then, an AI algorithm is applied to analyze deviations from normal behavioral patterns and emotional states. During the data processing process, behavioral data is output through an anomaly detection algorithm based on comparison with normal patterns, and emotional data is output after tone and facial expression analysis by an emotion engine. The analyzed results are output, and if an anomaly is detected, an alert is generated.

[0384] Step 3:

[0385] Users access data from the server using a dedicated application or web platform. Users request information by entering prompts such as "Tell me about recent emotional tendencies." Based on this input, the user interface displays information about abnormal conditions and emotional tendencies as graphs and text, using the server's output. This allows users to monitor the health status of elderly individuals in real time and input necessary feedback and actions into the server.

[0386] (Application Example 2)

[0387] 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."

[0388] In an aging society, there is a need to comprehensively understand the health and mental state of elderly people living in remote areas, and to enable timely detection of abnormalities and prompt response. However, current methods have problems such as the risk of missing abnormalities and delays in real-time response. In particular, systems that can understand the emotional state of the elderly and allow family members and care staff to immediately check the situation are not yet adequately developed.

[0389] 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.

[0390] In this invention, the server includes data collection means for monitoring the behavioral patterns of individuals residing in remote locations and detecting anomalies.

[0391] A communication method for encrypting and transmitting collected activity information,

[0392] A means of analyzing emotions for analyzing mental health status,

[0393] Includes a visualization means for displaying a graphed emotional state.

[0394] This makes it possible to monitor the behavior and emotional state of elderly people in real time, and to immediately notify them of any abnormalities and visualize changes in their emotions.

[0395] "Individuals residing in remote locations" refers to people who live in a place separate from their home, and this includes elderly people in particular.

[0396] "Behavioral patterns" refer to a sequence of physical movements and activities in an individual's daily life, or to general habits.

[0397] "Data collection means" is a general term for hardware and software used to perceive information from an individual's behavior and environment and to acquire that information in digital format.

[0398] "Communication methods" refer to devices and protocols for securely and efficiently transmitting collected digital information.

[0399] "Information processing means" refers to the configuration of algorithms and computing resources for analyzing received digital data and detecting specific patterns or anomalies.

[0400] "Notification means" refers to methods and techniques for notifying relevant people or systems of specific information based on analysis results.

[0401] "Emotional analysis means" refers to a function that uses voice, facial expressions, or other relevant data to evaluate an individual's emotional changes and determine their emotional state.

[0402] "Visualization methods" refer to technologies that provide charts, graphs, and interfaces to clearly represent and visually present analyzed emotional data.

[0403] In this application example, the system configuration mainly consists of terminals, servers, and users.

[0404] The terminals are placed in the living spaces of elderly people and use sensors to monitor their behavioral patterns. The sensors perform motion detection and voice capture, and data is collected using devices such as Raspberry Pi, encrypted, and sent to a server. Encryption algorithms are used during data processing to protect data privacy.

[0405] The server receives data transmitted from the terminal and performs information processing to analyze it. This process implements an AI model using Python, and utilizes libraries such as TensorFlow and PyTorch to perform anomaly detection and sentiment analysis based on behavioral patterns. The obtained sentiment data is then graphed and provided to the user through visualization tools. This allows the user to intuitively understand the daily emotional state and activity level of elderly individuals.

[0406] Users can access this information in real time through an interface accessible from their smartphones or computers. The application uses push notifications to immediately inform users when an anomaly occurs or when a significant change in emotion is detected. These notifications are delivered using services such as Firebase Cloud Messaging (FCM).

[0407] For example, if an elderly person deviates from their normal sleep pattern, the system analyzes this information and sends a notification to the user such as, "There was an anomaly in last night's behavior pattern. Please check the situation." An example of a prompt message would be, "Analyze how the elderly person deviates from their usual behavior pattern and create a push notification message. Specific behaviors include frequent nighttime awakenings and increased unsteady walking." This allows the user to quickly assess the situation and take necessary action.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The device collects environmental and behavioral data using sensors installed in the living spaces of elderly people. It receives signals from motion and voice sensors as input and converts them into digital information. This information is encrypted using encryption algorithms such as AES to protect privacy. The output is sent to the server in an encrypted format.

[0411] Step 2:

[0412] The server receives encrypted data from the terminal and first decrypts it. Then, it performs data analysis according to each data type. The data received as input can be analyzed for behavioral patterns using a deep learning model (using TensorFlow or PyTorch) to detect anomalies. The output is a list of possible anomalies.

[0413] Step 3:

[0414] The server activates an emotion analysis engine and analyzes emotional states based on audio and video data. It supplies audio and video features as input to an AI model to detect changes in emotion. Emotional changes and long-term trends are analyzed, and a graph or report showing the emotional state is generated as output.

[0415] Step 4:

[0416] The server prepares to immediately notify the user when an anomaly is detected based on the analysis results. Upon receiving a list of anomalies as input, it creates a notification message via the Firebase Cloud Messaging (FCM) service. The output is an alert notification sent to the user's smartphone or other device.

[0417] Step 5:

[0418] Users check received notifications on their smartphones or via a web interface to stay informed about the elderly person's current condition. They receive notification content and a graph of emotional state visualized from the server as input, allowing them to intuitively assess the situation. Outputs include user intervention actions, such as making a phone call to the elderly person.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention is a system for monitoring and appropriately supervising the safety and health of elderly people living in remote locations. This system consists of three elements: a terminal, a server, and a user.

[0436] The system includes a group of sensors installed in the homes of elderly individuals, which continuously monitor their daily activities and health status. For example, motion sensors detect the elderly person's movements and location, while health monitoring devices collect data such as heart rate and body temperature. This collected data is encrypted by the system and transmitted to the server in a secure state.

[0437] The server receives data sent from the terminal and securely stores it in a database. The stored data is analyzed based on an AI algorithm. The AI ​​learns normal behavior patterns and uses the results to determine whether or not there are anomalies. If this analysis detects any unusual behavior, it immediately generates an alert. The generated alerts and periodic reports are automatically sent to local governments and relevant organizations.

[0438] Users can access the system via the web or application to check the status of those being monitored in real time. Users can send feedback to their devices as needed, and the system continuously learns by incorporating this feedback into future monitoring and analysis. For example, if the frequency of an elderly person's hospital visits changes or there are changes in their health condition, the user can reflect this information in the system.

[0439] In this way, the present invention supports the lives of the elderly and provides an environment that enables quick and accurate responses to emergencies. The system is designed to allow for appropriate monitoring while protecting privacy.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The device uses sensors installed in the elderly person's living space to detect movement and monitor their movements and activities within the room. This allows the system to understand their wake-up time and daily routine. The collected data is encrypted to ensure security.

[0443] Step 2:

[0444] The device sends encrypted data to the server at regular intervals. This data transmission takes place in real time to a remote server using Wi-Fi or a mobile network. Because immediacy is required, this transmission is carried out quickly.

[0445] Step 3:

[0446] The server decrypts the encrypted data received from the terminal and stores it in the database. The stored data is given an appropriate timestamp and organized for subsequent data analysis processing.

[0447] Step 4:

[0448] The server applies an AI algorithm to the information stored in the database to learn the elderly person's normal behavioral patterns. The algorithm monitors for any behavior that deviates from a defined range and detects anomalies.

[0449] Step 5:

[0450] The server immediately generates an alert if an anomaly is detected. This alert is automatically sent to relevant organizations and registered family contacts to enable a quick response. The server also periodically generates reports on health status and behavioral patterns and sends them to relevant parties.

[0451] Step 6:

[0452] Users can access the server using an application or web platform to view the latest status and past activity history of elderly individuals. Feedback entered by users is sent to the server via their devices and incorporated into the AI ​​learning process.

[0453] Step 7:

[0454] The server continuously improves the accuracy of its analysis by adding user feedback and new health information to its database and retraining its AI algorithms. This enhances its flexibility in adapting to change and improves the overall quality of monitoring.

[0455] (Example 1)

[0456] 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."

[0457] In an aging society, there is a need to efficiently manage the safety and health of elderly people living in remote areas. However, currently, there is a lack of means for the elderly themselves, their families, and relevant organizations to quickly and appropriately detect abnormal situations, raising concerns about delays in emergency response. Furthermore, the secure management of acquired information and the protection of privacy are also major challenges.

[0458] 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.

[0459] In this invention, the server includes sensing means, communication means, analysis means, notification means, access means, and update means. This enables real-time monitoring of the health status and activities of elderly individuals, and allows for rapid notification and appropriate response in the event of an anomaly. Furthermore, it enables secure data management and continuous improvement of the AI ​​model while ensuring privacy.

[0460] "Remote location" refers to a place where residences or facilities are far apart, and in this context, it means that the entity responsible for monitoring and managing the elderly person is physically located away from the area where the elderly person resides.

[0461] "Elderly people" refers to individuals who have passed their typical working-age years and usually require special consideration and support.

[0462] "Sensing means" refers to technological devices and equipment used to collect movement and biometric information from elderly individuals. Specifically, this includes motion sensors and health monitoring devices.

[0463] "Communication methods" refer to the technologies and protocols used to securely transmit collected data to a server. Specifically, this includes data encryption and data transmission technologies over the internet.

[0464] "Analysis means" refers to AI algorithms and computational processes that learn the normal behavioral patterns of elderly people based on the received data and detect abnormalities.

[0465] "Notification methods" refer to methods and technologies for informing relevant parties and organizations of anomalies based on analysis results. Specifically, this includes real-time notifications via email and social media.

[0466] "Access means" refers to interfaces and technologies that allow users to remotely access information in real time or connect to a system.

[0467] "Update methods" refer to the processes and technologies used to improve AI models based on user feedback and new data, enabling continuous learning.

[0468] This invention is a system for monitoring the safety and health of elderly people living in remote locations. The system consists of three elements: a terminal, a server, and a user.

[0469] The terminal includes a group of sensors installed in the homes of elderly people. Motion sensors detect the movements and location of the elderly, and health monitoring devices collect biometric information such as heart rate and body temperature. Examples of such hardware include IoT devices and wearable devices. The collected data is encrypted by the terminal using encryption technologies such as AES (Advanced Encryption Standard) and transmitted to the server in a secure state.

[0470] The server receives data sent from the terminal and securely stores it in the database. The software used is a database management system (DBMS). The stored data is analyzed based on a generative AI model. The AI ​​learns normal behavior patterns and uses the results to determine whether or not anomalies are present. Machine learning algorithms are used for this analysis, and Python libraries and frameworks are utilized. If unusual behavior is detected, an alert is immediately generated and notifications are sent to relevant organizations via email or SMS.

[0471] Users can access the system via a web browser or mobile application to check the status of the person being monitored in real time. The user interface is designed using technologies such as HTML5 and JavaScript. Users can send feedback to their devices as needed, and this information is used by the system for future monitoring and analysis, improving the learning accuracy of the generated AI model.

[0472] For example, if an elderly person's frequency of hospital visits changes or their health condition changes, the user can manually add that information to the system, and the AI ​​can incorporate it into its analysis. This allows the system to support the lives of the elderly and provide an environment that can respond quickly and accurately to abnormal situations.

[0473] An example of a prompt to input into the generated AI model would be, "Design an AI model to detect anomalies from behavioral data of elderly people." This would allow the AI ​​model to learn the daily behavioral patterns of elderly people and be designed to improve the accuracy of anomaly detection.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device uses sensors to collect real-time information on the elderly person's movements and biometric data. This input data includes location information and activity levels obtained from motion sensors, as well as heart rate and body temperature from health monitoring devices. The device temporarily stores this raw data and performs data preprocessing and anomaly detection. For example, it may set a warning flag if the heart rate exceeds a certain range.

[0477] Step 2:

[0478] The terminal encrypts the pre-processed data using AES encryption technology and securely transmits it to the server. Input data is processed as encrypted binary data and sent to the server as output using a secure communication protocol. During this process, data confidentiality is maintained using technologies such as SSL / TLS.

[0479] Step 3:

[0480] The server decrypts the received encrypted data and stores it in the database. It receives encrypted binary data as input, performs AES decryption, and then stores it as structured data via a database management system (DBMS). This enables fast and secure data storage and access.

[0481] Step 4:

[0482] The server analyzes data stored in the database using AI algorithms to determine whether or not anomalies are present. Past behavioral patterns and real-time data are used as input data, and a machine learning model performs predictive calculations. As output, if an anomaly is detected, an anomaly alert is generated. Machine learning frameworks in Python and R are used for the analysis.

[0483] Step 5:

[0484] The server generates alerts based on the results of anomaly detection and automatically sends notifications to relevant organizations and users. The generated alerts are sent via email, SMS, etc., and if it is determined that an emergency response is necessary, the alert is delivered immediately.

[0485] Step 6:

[0486] Users can access the system in real time via a web browser or mobile application to check the latest status and alert information of the elderly individuals they are monitoring. This requires an internet connection and the corresponding application. The user interface is intuitive and easy to operate thanks to the latest technology, supporting quick decision-making.

[0487] Step 7:

[0488] Users input necessary feedback into the system based on the elderly person's situation and alerts. This feedback is sent to the server and incorporated into subsequent analyses, contributing to the improvement of the AI ​​model's accuracy. The input feedback is added to the training dataset, allowing the generated AI model to continuously learn and improving the reliability of the analysis results.

[0489] (Application Example 1)

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

[0491] While there is a need to monitor the safety and health of the elderly in real time and to detect and respond to abnormalities early, conventional monitoring systems have limitations in terms of the immediacy of information and the provision of visual information. Therefore, there is a challenge in that it is difficult for caregivers and family members to efficiently understand the situation of the elderly and take prompt action.

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

[0493] In this invention, the server includes information acquisition means, data communication means, data analysis means, and visual information presentation means. This enables the real-time visual presentation of the behavior and health status of elderly people, allowing for early detection of abnormalities and rapid response.

[0494] "Information acquisition means" is a general term for devices and technologies that collect data necessary to monitor the movements and health status of elderly people.

[0495] "Data communication methods" refer to communication technologies and protocols used to securely transmit collected data to a server.

[0496] "Data analysis means" refers to technologies and devices that analyze received data and detect anomalies using AI algorithms.

[0497] "Information provision means" refers to the means of sending necessary notifications to users based on the analysis results.

[0498] "Visual information presentation means" refers to devices and technologies that display information visually in real time and convey the situation to the user intuitively.

[0499] "Data reporting means" include procedures and techniques for reporting regularly analyzed data to public institutions and related organizations.

[0500] A "data update method" is a means of updating the system's training data based on feedback provided by the operator, thereby continuously improving machine learning.

[0501] The system for implementing this invention is a device for remotely monitoring the safety and health of elderly people. It mainly consists of a group of sensors installed in the elderly person's residence, a server for data processing, and a user interface for viewing the information.

[0502] Server Processing

[0503] The server is equipped with data analysis and visual information presentation capabilities and has the ability to process various data formats. The server operates a data analysis module incorporating AI algorithms using Python and TensorFlow. This module performs analysis to detect anomalies based on collected data and generates notifications as needed. The server also has communication capabilities to deliver the generated notifications to caregivers and family members in real time.

[0504] Device functions

[0505] The terminal has information acquisition and data communication means installed in the elderly person's residence. The sensor group includes motion sensors and health monitoring devices to continuously collect the elderly person's movements and biometric data. This data is encrypted and transmitted to the server via the data communication means.

[0506] User interface

[0507] Users can use devices such as smart glasses to monitor the condition of elderly individuals in real time through visual information presentation. If an abnormality is detected, they can receive immediate information via audio alerts or on the display. Furthermore, users can provide feedback through the smart glasses, which is incorporated into the system to improve AI learning.

[0508] Specific example

[0509] For example, if an elderly person performs an action that deviates from their normal daily activities, the system displays "Attention: Abnormal behavior detected" on the smart glasses, prompting the caregiver to take immediate action. This enables a quick and appropriate response.

[0510] Example of a prompt

[0511] "Your heart rate is higher than yesterday. Determine if this is abnormal and generate any necessary alerts."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The device collects data from a group of sensors (motion sensors and health monitoring devices) installed in the elderly person's residence.

[0515] The device receives real-time data from sensors as input, encrypts it using an encryption algorithm, and generates encrypted data as output. The device then sends this data to the server.

[0516] Step 2:

[0517] The server receives encrypted data sent from the terminal. It decrypts the received data and prepares to run the data analysis tools.

[0518] It receives decoded data as input, performs a format conversion before storing it in the database, and generates analysis-ready data as output.

[0519] Step 3:

[0520] The server processes the prepared data using a data analysis module. It uses AI algorithms to detect anomalies that deviate from normal behavioral patterns.

[0521] The system receives pre-prepared data for analysis as input, performs anomaly detection calculations using an AI model, and generates anomaly detection results as output.

[0522] Step 4:

[0523] Based on the anomaly detection results, the server uses information provision means to generate alerts and notify the user as needed.

[0524] The server receives anomaly detection results as input, applies alert generation logic, and generates notification information as output. The server then sends this notification information to the user's device.

[0525] Step 5:

[0526] Users receive notification information through devices such as smart glasses and can check the condition of elderly people in real time.

[0527] The system receives notification information from a server as input, displays the information using a visual display and audio functions, and generates feedback to help the user understand the situation as output.

[0528] Step 6:

[0529] Users provide feedback as needed and send that information back to their device. The system updates the AI's learning based on this feedback.

[0530] The system receives user feedback as input, performs a process to update the AI ​​model, and generates an improved learned model as output.

[0531] 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.

[0532] This invention provides a system for comprehensively monitoring the behavior and emotions of elderly people living in remote locations, enabling early detection of abnormalities and prompt response. The system consists of a terminal, a server, a user, and an emotion engine.

[0533] The device uses sensors installed in the living space to monitor the elderly person's daily activities and collect necessary data. The device is also equipped with a microphone and camera, allowing it to acquire audio and video data. This data is encrypted and transmitted to the server in a privacy-protected state.

[0534] The server receives and analyzes behavioral and emotional data transmitted from the terminal. Behavioral data is processed by an AI algorithm to determine deviations from normal behavioral patterns or any abnormalities. In this process, the AI ​​learns the elderly person's daily habits and quickly generates alerts when abnormalities occur. Furthermore, an emotion engine is used to analyze audio and video data to understand the elderly person's emotional state. The server notifies relevant organizations and family members of any detected abnormalities or emotional changes.

[0535] Users can access information provided by the server through a dedicated application or web platform. This allows users to monitor the health and emotional state of elderly individuals in real time and send feedback as needed. For example, if a user detects inconsistency in an elderly person's behavior or a significant change in their emotions, that information is immediately reflected through the system.

[0536] By further incorporating an emotion engine, the system can also monitor the mental health of older adults. For example, the emotion engine analyzes emotions from changes in voice tone and facial expressions, providing data to evaluate long-term emotional trends. This data can suggest that older adults may be experiencing problems and help determine whether intervention is necessary.

[0537] Thus, the present invention aims to provide comprehensive health management for the elderly and to offer necessary support quickly and accurately.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The device uses various sensors, microphones, and cameras installed in the living space to collect data on the elderly person's daily activities, as well as audio and video data. This allows for the detection of walking patterns, conversation tone, facial expressions, and other characteristics. The collected data is immediately encrypted to ensure security.

[0541] Step 2:

[0542] The terminal sends encrypted data to the server at regular intervals. This communication is conducted remotely using the internet or a dedicated line, ensuring that the data is managed in real time.

[0543] Step 3:

[0544] The server decrypts the data received from the terminal and stores it in a database. The stored data is sorted chronologically and prepared for analysis by AI algorithms.

[0545] Step 4:

[0546] The server uses AI algorithms to analyze behavioral data and compare it to normal behavioral patterns. If there are any abnormal changes or deviations from the pattern, an alert is immediately generated.

[0547] Step 5:

[0548] The server uses an emotion engine to analyze audio and video data to infer the emotional state of elderly individuals. This involves evaluating factors such as tone of voice, word choice, and facial expressions as emotional indicators.

[0549] Step 6:

[0550] The server combines analyzed behavioral and emotional data to generate a comprehensive health status report, which is periodically sent to relevant parties. This information is accessible in real time, allowing for immediate planning of necessary interventions.

[0551] Step 7:

[0552] Users can monitor the status of elderly individuals through an application or web platform and provide feedback on any abnormalities or changes in their emotions. This feedback is collected within the system and incorporated into the learning process.

[0553] Step 8:

[0554] The server incorporates user feedback and new information, continuously learning its AI algorithms and improving analysis accuracy. This process enables personalized monitoring tailored to each elderly individual.

[0555] (Example 2)

[0556] 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."

[0557] In modern society, many elderly people live alone in remote locations, making it difficult to quickly grasp changes in their behavior and emotions. As a result, there is a possibility of delays in responding to abnormalities. In particular, there is a challenge in detecting changes in emotions and providing necessary support quickly.

[0558] 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.

[0559] In this invention, the server includes sensor means for monitoring the behavior of elderly people living in remote locations and recording their daily activities; communication means for encrypting and transmitting acquired audio and video data; analysis means for analyzing behavioral data and audio / video data using an AI algorithm to detect anomalies; means for understanding the emotional state of the elderly person based on the analysis results and evaluating it using an emotion engine; and means for notifying alert information based on the analysis results. This makes it possible to grasp changes in the behavior and emotions of elderly people in real time, detect anomalies early, and respond quickly.

[0560] A "remote location" refers to a place that is physically far away and where normal direct contact or monitoring is difficult.

[0561] "Elderly people" generally refers to people of an age group who are getting older and may require physical and mental support.

[0562] "Behavior" refers to the actions and activities of elderly people in their daily lives, including movement, eating, and sleeping.

[0563] "Monitoring" refers to the continuous and detailed observation of the behavior and emotional state of elderly individuals.

[0564] "Daily activities" refer to the normal daily activities that elderly people perform, including eating, cleaning, shopping, and going out.

[0565] "Sensor means" refers to devices and technologies for detecting the movements and environmental information of elderly people, and includes motion sensors and cameras.

[0566] "Audio and video data" refers to digital data that records audio and video information emitted by elderly individuals.

[0567] "Encryption" refers to a technology that enhances data confidentiality by transforming it to prevent unauthorized access by third parties during data transmission.

[0568] "Communication methods" refer to the technologies and protocols used to transmit data from a terminal to a server.

[0569] An "AI algorithm" refers to a computational procedure that mimics human intelligence to analyze data and perform pattern recognition and prediction.

[0570] "Analysis means" refers to hardware or software used to analyze acquired data and convert it into useful information.

[0571] "Abnormal" refers to a state that deviates from normal behavioral patterns or emotional states.

[0572] An "emotion engine" refers to a system component used to analyze emotional states from voice tone and facial expressions.

[0573] "Alert information" refers to important information that is notified to users and relevant parties when an anomaly is detected.

[0574] This invention is a system for monitoring elderly people living in remote locations and ensuring their safety and health. This system mainly consists of three main components: a terminal, a server, and a user, each component playing a specific role.

[0575] The devices are installed in the living spaces of elderly individuals and utilize the latest sensor technology to collect data on their behavior and emotions. The sensors include motion sensors, microphones, and cameras, which meticulously record the elderly individuals' daily activities. The collected data is encoded in a digital format, encrypted, and then transmitted to a server in real time. This encryption technology ensures the privacy of the elderly individuals.

[0576] The server receives data transmitted from the terminal and performs data analysis using high-performance AI algorithms. Behavioral data detects deviations from normal patterns, enabling early detection of abnormalities. Furthermore, it utilizes an emotion engine to analyze voice and video data, providing a detailed understanding of the emotional state of elderly individuals. For example, by analyzing changes in voice tone and facial expressions, it can evaluate emotional tendencies in real time. Based on these analysis results, it quickly generates alerts in the event of an anomaly, notifying relevant organizations and family members.

[0577] Users can easily access information provided by the server through a dedicated application or web platform. Using this platform, users can monitor the health status of elderly individuals in real time and provide feedback to the server as needed. For example, when a user enters the prompt "Tell me my current health status," the system retrieves the latest health information and displays it in graphs and text. This makes it possible to maintain the health and improve the quality of life for elderly individuals.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The device collects data from sensors, microphones, and cameras installed in the elderly person's living space. It acquires motion data, audio data, and video data as input. This data includes information about the elderly person's movements, conversations, and facial expressions. The collected data is encrypted in real time to protect privacy. The encrypted data is then output and sent to a server.

[0581] Step 2:

[0582] The server receives encrypted data sent from the terminal. It receives encrypted motion, audio, and video data as input. First, the data is decrypted within the server. Then, an AI algorithm is applied to analyze deviations from normal behavioral patterns and emotional states. During the data processing process, behavioral data is output through an anomaly detection algorithm based on comparison with normal patterns, and emotional data is output after tone and facial expression analysis by an emotion engine. The analyzed results are output, and if an anomaly is detected, an alert is generated.

[0583] Step 3:

[0584] Users access data from the server using a dedicated application or web platform. Users request information by entering prompts such as "Tell me about recent emotional tendencies." Based on this input, the user interface displays information about abnormal conditions and emotional tendencies as graphs and text, using the server's output. This allows users to monitor the health status of elderly individuals in real time and input necessary feedback and actions into the server.

[0585] (Application Example 2)

[0586] 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."

[0587] In an aging society, there is a need to comprehensively understand the health and mental state of elderly people living in remote areas, and to enable timely detection of abnormalities and prompt response. However, current methods have problems such as the risk of missing abnormalities and delays in real-time response. In particular, systems that can understand the emotional state of the elderly and allow family members and care staff to immediately check the situation are not yet adequately developed.

[0588] 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.

[0589] In this invention, the server includes data collection means for monitoring the behavioral patterns of individuals residing in remote locations and detecting anomalies.

[0590] A communication method for encrypting and transmitting collected activity information,

[0591] A means of analyzing emotions for analyzing mental health status,

[0592] Includes a visualization means for displaying a graphed emotional state.

[0593] This makes it possible to monitor the behavior and emotional state of elderly people in real time, and to immediately notify them of any abnormalities and visualize changes in their emotions.

[0594] "Individuals residing in remote locations" refers to people who live in a place separate from their home, and this includes elderly people in particular.

[0595] "Behavioral patterns" refer to a sequence of physical movements and activities in an individual's daily life, or to general habits.

[0596] "Data collection means" is a general term for hardware and software used to perceive information from an individual's behavior and environment and to acquire that information in digital format.

[0597] "Communication methods" refer to devices and protocols for securely and efficiently transmitting collected digital information.

[0598] "Information processing means" refers to the configuration of algorithms and computing resources for analyzing received digital data and detecting specific patterns or anomalies.

[0599] "Notification means" refers to methods and techniques for notifying relevant people or systems of specific information based on analysis results.

[0600] "Emotional analysis means" refers to a function that uses voice, facial expressions, or other relevant data to evaluate an individual's emotional changes and determine their emotional state.

[0601] "Visualization methods" refer to technologies that provide charts, graphs, and interfaces to clearly represent and visually present analyzed emotional data.

[0602] In this application example, the system configuration mainly consists of terminals, servers, and users.

[0603] The terminals are placed in the living spaces of elderly people and use sensors to monitor their behavioral patterns. The sensors perform motion detection and voice capture, and data is collected using devices such as Raspberry Pi, encrypted, and sent to a server. Encryption algorithms are used during data processing to protect data privacy.

[0604] The server receives data transmitted from the terminal and performs information processing to analyze it. This process implements an AI model using Python, and utilizes libraries such as TensorFlow and PyTorch to perform anomaly detection and sentiment analysis based on behavioral patterns. The obtained sentiment data is then graphed and provided to the user through visualization tools. This allows the user to intuitively understand the daily emotional state and activity level of elderly individuals.

[0605] Users can access this information in real time through an interface accessible from their smartphones or computers. The application uses push notifications to immediately inform users when an anomaly occurs or when a significant change in emotion is detected. These notifications are delivered using services such as Firebase Cloud Messaging (FCM).

[0606] For example, if an elderly person deviates from their normal sleep pattern, the system analyzes this information and sends a notification to the user such as, "There was an anomaly in last night's behavior pattern. Please check the situation." An example of a prompt message would be, "Analyze how the elderly person deviates from their usual behavior pattern and create a push notification message. Specific behaviors include frequent nighttime awakenings and increased unsteady walking." This allows the user to quickly assess the situation and take necessary action.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The device collects environmental and behavioral data using sensors installed in the living spaces of elderly people. It receives signals from motion and voice sensors as input and converts them into digital information. This information is encrypted using encryption algorithms such as AES to protect privacy. The output is sent to the server in an encrypted format.

[0610] Step 2:

[0611] The server receives encrypted data from the terminal and first decrypts it. Then, it performs data analysis according to each data type. The data received as input can be analyzed for behavioral patterns using a deep learning model (using TensorFlow or PyTorch) to detect anomalies. The output is a list of possible anomalies.

[0612] Step 3:

[0613] The server activates an emotion analysis engine and analyzes emotional states based on audio and video data. It supplies audio and video features as input to an AI model to detect changes in emotion. Emotional changes and long-term trends are analyzed, and a graph or report showing the emotional state is generated as output.

[0614] Step 4:

[0615] The server prepares to immediately notify the user when an anomaly is detected based on the analysis results. Upon receiving a list of anomalies as input, it creates a notification message via the Firebase Cloud Messaging (FCM) service. The output is an alert notification sent to the user's smartphone or other device.

[0616] Step 5:

[0617] Users check received notifications on their smartphones or via a web interface to stay informed about the elderly person's current condition. They receive notification content and a graph of emotional state visualized from the server as input, allowing them to intuitively assess the situation. Outputs include user intervention actions, such as making a phone call to the elderly person.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention is a system for monitoring and appropriately supervising the safety and health of elderly people living in remote locations. This system consists of three elements: a terminal, a server, and a user.

[0636] The system includes a group of sensors installed in the homes of elderly individuals, which continuously monitor their daily activities and health status. For example, motion sensors detect the elderly person's movements and location, while health monitoring devices collect data such as heart rate and body temperature. This collected data is encrypted by the system and transmitted to the server in a secure state.

[0637] The server receives data sent from the terminal and securely stores it in a database. The stored data is analyzed based on an AI algorithm. The AI ​​learns normal behavior patterns and uses the results to determine whether or not there are anomalies. If this analysis detects any unusual behavior, it immediately generates an alert. The generated alerts and periodic reports are automatically sent to local governments and relevant organizations.

[0638] Users can access the system via the web or application to check the status of those being monitored in real time. Users can send feedback to their devices as needed, and the system continuously learns by incorporating this feedback into future monitoring and analysis. For example, if the frequency of an elderly person's hospital visits changes or there are changes in their health condition, the user can reflect this information in the system.

[0639] In this way, the present invention supports the lives of the elderly and provides an environment that enables quick and accurate responses to emergencies. The system is designed to allow for appropriate monitoring while protecting privacy.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The device uses sensors installed in the elderly person's living space to detect movement and monitor their movements and activities within the room. This allows the system to understand their wake-up time and daily routine. The collected data is encrypted to ensure security.

[0643] Step 2:

[0644] The device sends encrypted data to the server at regular intervals. This data transmission takes place in real time to a remote server using Wi-Fi or a mobile network. Because immediacy is required, this transmission is carried out quickly.

[0645] Step 3:

[0646] The server decrypts the encrypted data received from the terminal and stores it in the database. The stored data is given an appropriate timestamp and organized for subsequent data analysis processing.

[0647] Step 4:

[0648] The server applies an AI algorithm to the information stored in the database to learn the elderly person's normal behavioral patterns. The algorithm monitors for any behavior that deviates from a defined range and detects anomalies.

[0649] Step 5:

[0650] The server immediately generates an alert if an anomaly is detected. This alert is automatically sent to relevant organizations and registered family contacts to enable a quick response. The server also periodically generates reports on health status and behavioral patterns and sends them to relevant parties.

[0651] Step 6:

[0652] Users can access the server using an application or web platform to view the latest status and past activity history of elderly individuals. Feedback entered by users is sent to the server via their devices and incorporated into the AI ​​learning process.

[0653] Step 7:

[0654] The server continuously improves the accuracy of its analysis by adding user feedback and new health information to its database and retraining its AI algorithms. This enhances its flexibility in adapting to change and improves the overall quality of monitoring.

[0655] (Example 1)

[0656] 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".

[0657] In an aging society, there is a need to efficiently manage the safety and health of elderly people living in remote areas. However, currently, there is a lack of means for the elderly themselves, their families, and relevant organizations to quickly and appropriately detect abnormal situations, raising concerns about delays in emergency response. Furthermore, the secure management of acquired information and the protection of privacy are also major challenges.

[0658] 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.

[0659] In this invention, the server includes sensing means, communication means, analysis means, notification means, access means, and update means. This enables real-time monitoring of the health status and activities of elderly individuals, and allows for rapid notification and appropriate response in the event of an anomaly. Furthermore, it enables secure data management and continuous improvement of the AI ​​model while ensuring privacy.

[0660] "Remote location" refers to a place where residences or facilities are far apart, and in this context, it means that the entity responsible for monitoring and managing the elderly person is physically located away from the area where the elderly person resides.

[0661] "Elderly people" refers to individuals who have passed their typical working-age years and usually require special consideration and support.

[0662] "Sensing means" refers to technological devices and equipment used to collect movement and biometric information from elderly individuals. Specifically, this includes motion sensors and health monitoring devices.

[0663] "Communication methods" refer to the technologies and protocols used to securely transmit collected data to a server. Specifically, this includes data encryption and data transmission technologies over the internet.

[0664] "Analysis means" refers to AI algorithms and computational processes that learn the normal behavioral patterns of elderly people based on the received data and detect abnormalities.

[0665] "Notification methods" refer to methods and technologies for informing relevant parties and organizations of anomalies based on analysis results. Specifically, this includes real-time notifications via email and social media.

[0666] "Access means" refers to interfaces and technologies that allow users to remotely access information in real time or connect to a system.

[0667] "Update methods" refer to the processes and technologies used to improve AI models based on user feedback and new data, enabling continuous learning.

[0668] This invention is a system for monitoring the safety and health of elderly people living in remote locations. The system consists of three elements: a terminal, a server, and a user.

[0669] The terminal includes a group of sensors installed in the homes of elderly people. Motion sensors detect the movements and location of the elderly, and health monitoring devices collect biometric information such as heart rate and body temperature. Examples of such hardware include IoT devices and wearable devices. The collected data is encrypted by the terminal using encryption technologies such as AES (Advanced Encryption Standard) and transmitted to the server in a secure state.

[0670] The server receives data sent from the terminal and securely stores it in the database. The software used is a database management system (DBMS). The stored data is analyzed based on a generative AI model. The AI ​​learns normal behavior patterns and uses the results to determine whether or not anomalies are present. Machine learning algorithms are used for this analysis, and Python libraries and frameworks are utilized. If unusual behavior is detected, an alert is immediately generated and notifications are sent to relevant organizations via email or SMS.

[0671] Users can access the system via a web browser or mobile application to check the status of the person being monitored in real time. The user interface is designed using technologies such as HTML5 and JavaScript. Users can send feedback to their devices as needed, and this information is used by the system for future monitoring and analysis, improving the learning accuracy of the generated AI model.

[0672] For example, if an elderly person's frequency of hospital visits changes or their health condition changes, the user can manually add that information to the system, and the AI ​​can incorporate it into its analysis. This allows the system to support the lives of the elderly and provide an environment that can respond quickly and accurately to abnormal situations.

[0673] An example of a prompt to input into the generated AI model would be, "Design an AI model to detect anomalies from behavioral data of elderly people." This would allow the AI ​​model to learn the daily behavioral patterns of elderly people and be designed to improve the accuracy of anomaly detection.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The device uses sensors to collect real-time information on the elderly person's movements and biometric data. This input data includes location information and activity levels obtained from motion sensors, as well as heart rate and body temperature from health monitoring devices. The device temporarily stores this raw data and performs data preprocessing and anomaly detection. For example, it may set a warning flag if the heart rate exceeds a certain range.

[0677] Step 2:

[0678] The terminal encrypts the pre-processed data using AES encryption technology and securely transmits it to the server. Input data is processed as encrypted binary data and sent to the server as output using a secure communication protocol. During this process, data confidentiality is maintained using technologies such as SSL / TLS.

[0679] Step 3:

[0680] The server decrypts the received encrypted data and stores it in the database. It receives encrypted binary data as input, performs AES decryption, and then stores it as structured data via a database management system (DBMS). This enables fast and secure data storage and access.

[0681] Step 4:

[0682] The server analyzes data stored in the database using AI algorithms to determine whether or not anomalies are present. Past behavioral patterns and real-time data are used as input data, and a machine learning model performs predictive calculations. As output, if an anomaly is detected, an anomaly alert is generated. Machine learning frameworks in Python and R are used for the analysis.

[0683] Step 5:

[0684] The server generates alerts based on the results of anomaly detection and automatically sends notifications to relevant organizations and users. The generated alerts are sent via email, SMS, etc., and if it is determined that an emergency response is necessary, the alert is delivered immediately.

[0685] Step 6:

[0686] Users can access the system in real time via a web browser or mobile application to check the latest status and alert information of the elderly individuals they are monitoring. This requires an internet connection and the corresponding application. The user interface is intuitive and easy to operate thanks to the latest technology, supporting quick decision-making.

[0687] Step 7:

[0688] Users input necessary feedback into the system based on the elderly person's situation and alerts. This feedback is sent to the server and incorporated into subsequent analyses, contributing to the improvement of the AI ​​model's accuracy. The input feedback is added to the training dataset, allowing the generated AI model to continuously learn and improving the reliability of the analysis results.

[0689] (Application Example 1)

[0690] 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".

[0691] While there is a need to monitor the safety and health of the elderly in real time and to detect and respond to abnormalities early, conventional monitoring systems have limitations in terms of the immediacy of information and the provision of visual information. Therefore, there is a challenge in that it is difficult for caregivers and family members to efficiently understand the situation of the elderly and take prompt action.

[0692] 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.

[0693] In this invention, the server includes information acquisition means, data communication means, data analysis means, and visual information presentation means. This enables the real-time visual presentation of the behavior and health status of elderly people, allowing for early detection of abnormalities and rapid response.

[0694] "Information acquisition means" is a general term for devices and technologies that collect data necessary to monitor the movements and health status of elderly people.

[0695] "Data communication methods" refer to communication technologies and protocols used to securely transmit collected data to a server.

[0696] "Data analysis means" refers to technologies and devices that analyze received data and detect anomalies using AI algorithms.

[0697] "Information provision means" refers to the means of sending necessary notifications to users based on the analysis results.

[0698] "Visual information presentation means" refers to devices and technologies that display information visually in real time and convey the situation to the user intuitively.

[0699] "Data reporting means" include procedures and techniques for reporting regularly analyzed data to public institutions and related organizations.

[0700] A "data update method" is a means of updating the system's training data based on feedback provided by the operator, thereby continuously improving machine learning.

[0701] The system for implementing this invention is a device for remotely monitoring the safety and health of elderly people. It mainly consists of a group of sensors installed in the elderly person's residence, a server for data processing, and a user interface for viewing the information.

[0702] Server Processing

[0703] The server is equipped with data analysis and visual information presentation capabilities and has the ability to process various data formats. The server operates a data analysis module incorporating AI algorithms using Python and TensorFlow. This module performs analysis to detect anomalies based on collected data and generates notifications as needed. The server also has communication capabilities to deliver the generated notifications to caregivers and family members in real time.

[0704] Device functions

[0705] The terminal has information acquisition and data communication means installed in the elderly person's residence. The sensor group includes motion sensors and health monitoring devices to continuously collect the elderly person's movements and biometric data. This data is encrypted and transmitted to the server via the data communication means.

[0706] User interface

[0707] Users can use devices such as smart glasses to monitor the condition of elderly individuals in real time through visual information presentation. If an abnormality is detected, they can receive immediate information via audio alerts or on the display. Furthermore, users can provide feedback through the smart glasses, which is incorporated into the system to improve AI learning.

[0708] Specific example

[0709] For example, if an elderly person performs an action that deviates from their normal daily activities, the system displays "Attention: Abnormal behavior detected" on the smart glasses, prompting the caregiver to take immediate action. This enables a quick and appropriate response.

[0710] Example of a prompt

[0711] "Your heart rate is higher than yesterday. Determine if this is abnormal and generate any necessary alerts."

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] The device collects data from a group of sensors (motion sensors and health monitoring devices) installed in the elderly person's residence.

[0715] The device receives real-time data from sensors as input, encrypts it using an encryption algorithm, and generates encrypted data as output. The device then sends this data to the server.

[0716] Step 2:

[0717] The server receives encrypted data sent from the terminal. It decrypts the received data and prepares to run the data analysis tools.

[0718] It receives decoded data as input, performs a format conversion before storing it in the database, and generates analysis-ready data as output.

[0719] Step 3:

[0720] The server processes the prepared data using a data analysis module. It uses AI algorithms to detect anomalies that deviate from normal behavioral patterns.

[0721] The system receives pre-prepared data for analysis as input, performs anomaly detection calculations using an AI model, and generates anomaly detection results as output.

[0722] Step 4:

[0723] Based on the anomaly detection results, the server uses information provision means to generate alerts and notify the user as needed.

[0724] The server receives anomaly detection results as input, applies alert generation logic, and generates notification information as output. The server then sends this notification information to the user's device.

[0725] Step 5:

[0726] Users receive notification information through devices such as smart glasses and can check the condition of elderly people in real time.

[0727] The system receives notification information from a server as input, displays the information using a visual display and audio functions, and generates feedback to help the user understand the situation as output.

[0728] Step 6:

[0729] Users provide feedback as needed and send that information back to their device. The system updates the AI's learning based on this feedback.

[0730] The system receives user feedback as input, performs a process to update the AI ​​model, and generates an improved learned model as output.

[0731] 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.

[0732] This invention provides a system for comprehensively monitoring the behavior and emotions of elderly people living in remote locations, enabling early detection of abnormalities and prompt response. The system consists of a terminal, a server, a user, and an emotion engine.

[0733] The device uses sensors installed in the living space to monitor the elderly person's daily activities and collect necessary data. The device is also equipped with a microphone and camera, allowing it to acquire audio and video data. This data is encrypted and transmitted to the server in a privacy-protected state.

[0734] The server receives and analyzes behavioral and emotional data transmitted from the terminal. Behavioral data is processed by an AI algorithm to determine deviations from normal behavioral patterns or any abnormalities. In this process, the AI ​​learns the elderly person's daily habits and quickly generates alerts when abnormalities occur. Furthermore, an emotion engine is used to analyze audio and video data to understand the elderly person's emotional state. The server notifies relevant organizations and family members of any detected abnormalities or emotional changes.

[0735] Users can access information provided by the server through a dedicated application or web platform. This allows users to monitor the health and emotional state of elderly individuals in real time and send feedback as needed. For example, if a user detects inconsistency in an elderly person's behavior or a significant change in their emotions, that information is immediately reflected through the system.

[0736] By further incorporating an emotion engine, the system can also monitor the mental health of older adults. For example, the emotion engine analyzes emotions from changes in voice tone and facial expressions, providing data to evaluate long-term emotional trends. This data can suggest that older adults may be experiencing problems and help determine whether intervention is necessary.

[0737] Thus, the present invention aims to provide comprehensive health management for the elderly and to offer necessary support quickly and accurately.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The device uses various sensors, microphones, and cameras installed in the living space to collect data on the elderly person's daily activities, as well as audio and video data. This allows for the detection of walking patterns, conversation tone, facial expressions, and other characteristics. The collected data is immediately encrypted to ensure security.

[0741] Step 2:

[0742] The terminal sends encrypted data to the server at regular intervals. This communication is conducted remotely using the internet or a dedicated line, ensuring that the data is managed in real time.

[0743] Step 3:

[0744] The server decrypts the data received from the terminal and stores it in a database. The stored data is sorted chronologically and prepared for analysis by AI algorithms.

[0745] Step 4:

[0746] The server uses AI algorithms to analyze behavioral data and compare it to normal behavioral patterns. If there are any abnormal changes or deviations from the pattern, an alert is immediately generated.

[0747] Step 5:

[0748] The server uses an emotion engine to analyze audio and video data to infer the emotional state of elderly individuals. This involves evaluating factors such as tone of voice, word choice, and facial expressions as emotional indicators.

[0749] Step 6:

[0750] The server combines analyzed behavioral and emotional data to generate a comprehensive health status report, which is periodically sent to relevant parties. This information is accessible in real time, allowing for immediate planning of necessary interventions.

[0751] Step 7:

[0752] Users can monitor the status of elderly individuals through an application or web platform and provide feedback on any abnormalities or changes in their emotions. This feedback is collected within the system and incorporated into the learning process.

[0753] Step 8:

[0754] The server incorporates user feedback and new information, continuously learning its AI algorithms and improving analysis accuracy. This process enables personalized monitoring tailored to each elderly individual.

[0755] (Example 2)

[0756] 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".

[0757] In modern society, many elderly people live alone in remote locations, making it difficult to quickly grasp changes in their behavior and emotions. As a result, there is a possibility of delays in responding to abnormalities. In particular, there is a challenge in detecting changes in emotions and providing necessary support quickly.

[0758] 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.

[0759] In this invention, the server includes sensor means for monitoring the behavior of elderly people living in remote locations and recording their daily activities; communication means for encrypting and transmitting acquired audio and video data; analysis means for analyzing behavioral data and audio / video data using an AI algorithm to detect anomalies; means for understanding the emotional state of the elderly person based on the analysis results and evaluating it using an emotion engine; and means for notifying alert information based on the analysis results. This makes it possible to grasp changes in the behavior and emotions of elderly people in real time, detect anomalies early, and respond quickly.

[0760] A "remote location" refers to a place that is physically far away and where normal direct contact or monitoring is difficult.

[0761] "Elderly people" generally refers to people of an age group who are getting older and may require physical and mental support.

[0762] "Behavior" refers to the actions and activities of elderly people in their daily lives, including movement, eating, and sleeping.

[0763] "Monitoring" refers to the continuous and detailed observation of the behavior and emotional state of elderly individuals.

[0764] "Daily activities" refer to the normal daily activities that elderly people perform, including eating, cleaning, shopping, and going out.

[0765] "Sensor means" refers to devices and technologies for detecting the movements and environmental information of elderly people, and includes motion sensors and cameras.

[0766] "Audio and video data" refers to digital data that records audio and video information emitted by elderly individuals.

[0767] "Encryption" refers to a technology that enhances data confidentiality by transforming it to prevent unauthorized access by third parties during data transmission.

[0768] "Communication methods" refer to the technologies and protocols used to transmit data from a terminal to a server.

[0769] An "AI algorithm" refers to a computational procedure that mimics human intelligence to analyze data and perform pattern recognition and prediction.

[0770] "Analysis means" refers to hardware or software used to analyze acquired data and convert it into useful information.

[0771] "Abnormal" refers to a state that deviates from normal behavioral patterns or emotional states.

[0772] An "emotion engine" refers to a system component used to analyze emotional states from voice tone and facial expressions.

[0773] "Alert information" refers to important information that is notified to users and relevant parties when an anomaly is detected.

[0774] This invention is a system for monitoring elderly people living in remote locations and ensuring their safety and health. This system mainly consists of three main components: a terminal, a server, and a user, each component playing a specific role.

[0775] The devices are installed in the living spaces of elderly individuals and utilize the latest sensor technology to collect data on their behavior and emotions. The sensors include motion sensors, microphones, and cameras, which meticulously record the elderly individuals' daily activities. The collected data is encoded in a digital format, encrypted, and then transmitted to a server in real time. This encryption technology ensures the privacy of the elderly individuals.

[0776] The server receives data transmitted from the terminal and performs data analysis using high-performance AI algorithms. Behavioral data detects deviations from normal patterns, enabling early detection of abnormalities. Furthermore, it utilizes an emotion engine to analyze voice and video data, providing a detailed understanding of the emotional state of elderly individuals. For example, by analyzing changes in voice tone and facial expressions, it can evaluate emotional tendencies in real time. Based on these analysis results, it quickly generates alerts in the event of an anomaly, notifying relevant organizations and family members.

[0777] Users can easily access information provided by the server through a dedicated application or web platform. Using this platform, users can monitor the health status of elderly individuals in real time and provide feedback to the server as needed. For example, when a user enters the prompt "Tell me my current health status," the system retrieves the latest health information and displays it in graphs and text. This makes it possible to maintain the health and improve the quality of life for elderly individuals.

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The device collects data from sensors, microphones, and cameras installed in the elderly person's living space. It acquires motion data, audio data, and video data as input. This data includes information about the elderly person's movements, conversations, and facial expressions. The collected data is encrypted in real time to protect privacy. The encrypted data is then output and sent to a server.

[0781] Step 2:

[0782] The server receives encrypted data sent from the terminal. It receives encrypted motion, audio, and video data as input. First, the data is decrypted within the server. Then, an AI algorithm is applied to analyze deviations from normal behavioral patterns and emotional states. During the data processing process, behavioral data is output through an anomaly detection algorithm based on comparison with normal patterns, and emotional data is output after tone and facial expression analysis by an emotion engine. The analyzed results are output, and if an anomaly is detected, an alert is generated.

[0783] Step 3:

[0784] Users access data from the server using a dedicated application or web platform. Users request information by entering prompts such as "Tell me about recent emotional tendencies." Based on this input, the user interface displays information about abnormal conditions and emotional tendencies as graphs and text, using the server's output. This allows users to monitor the health status of elderly individuals in real time and input necessary feedback and actions into the server.

[0785] (Application Example 2)

[0786] 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".

[0787] In an aging society, there is a need to comprehensively understand the health and mental state of elderly people living in remote areas, and to enable timely detection of abnormalities and prompt response. However, current methods have problems such as the risk of missing abnormalities and delays in real-time response. In particular, systems that can understand the emotional state of the elderly and allow family members and care staff to immediately check the situation are not yet adequately developed.

[0788] 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.

[0789] In this invention, the server includes data collection means for monitoring the behavioral patterns of individuals residing in remote locations and detecting anomalies.

[0790] A communication method for encrypting and transmitting collected activity information,

[0791] A means of analyzing emotions for analyzing mental health status,

[0792] Includes a visualization means for displaying a graphed emotional state.

[0793] This makes it possible to monitor the behavior and emotional state of elderly people in real time, and to immediately notify them of any abnormalities and visualize changes in their emotions.

[0794] "Individuals residing in remote locations" refers to people who live in a place separate from their home, and this includes elderly people in particular.

[0795] "Behavioral patterns" refer to a sequence of physical movements and activities in an individual's daily life, or to general habits.

[0796] "Data collection means" is a general term for hardware and software used to perceive information from an individual's behavior and environment and to acquire that information in digital format.

[0797] "Communication methods" refer to devices and protocols for securely and efficiently transmitting collected digital information.

[0798] "Information processing means" refers to the configuration of algorithms and computing resources for analyzing received digital data and detecting specific patterns or anomalies.

[0799] "Notification means" refers to methods and techniques for notifying relevant people or systems of specific information based on analysis results.

[0800] "Emotional analysis means" refers to a function that uses voice, facial expressions, or other relevant data to evaluate an individual's emotional changes and determine their emotional state.

[0801] "Visualization methods" refer to technologies that provide charts, graphs, and interfaces to clearly represent and visually present analyzed emotional data.

[0802] In this application example, the system configuration mainly consists of terminals, servers, and users.

[0803] The terminals are placed in the living spaces of elderly people and use sensors to monitor their behavioral patterns. The sensors perform motion detection and voice capture, and data is collected using devices such as Raspberry Pi, encrypted, and sent to a server. Encryption algorithms are used during data processing to protect data privacy.

[0804] The server receives data transmitted from the terminal and performs information processing to analyze it. This process implements an AI model using Python, and utilizes libraries such as TensorFlow and PyTorch to perform anomaly detection and sentiment analysis based on behavioral patterns. The obtained sentiment data is then graphed and provided to the user through visualization tools. This allows the user to intuitively understand the daily emotional state and activity level of elderly individuals.

[0805] Users can access this information in real time through an interface accessible from their smartphones or computers. The application uses push notifications to immediately inform users when an anomaly occurs or when a significant change in emotion is detected. These notifications are delivered using services such as Firebase Cloud Messaging (FCM).

[0806] For example, if an elderly person deviates from their normal sleep pattern, the system analyzes this information and sends a notification to the user such as, "There was an anomaly in last night's behavior pattern. Please check the situation." An example of a prompt message would be, "Analyze how the elderly person deviates from their usual behavior pattern and create a push notification message. Specific behaviors include frequent nighttime awakenings and increased unsteady walking." This allows the user to quickly assess the situation and take necessary action.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The device collects environmental and behavioral data using sensors installed in the living spaces of elderly people. It receives signals from motion and voice sensors as input and converts them into digital information. This information is encrypted using encryption algorithms such as AES to protect privacy. The output is sent to the server in an encrypted format.

[0810] Step 2:

[0811] The server receives encrypted data from the terminal and first decrypts it. Then, it performs data analysis according to each data type. The data received as input can be analyzed for behavioral patterns using a deep learning model (using TensorFlow or PyTorch) to detect anomalies. The output is a list of possible anomalies.

[0812] Step 3:

[0813] The server activates an emotion analysis engine and analyzes emotional states based on audio and video data. It supplies audio and video features as input to an AI model to detect changes in emotion. Emotional changes and long-term trends are analyzed, and a graph or report showing the emotional state is generated as output.

[0814] Step 4:

[0815] The server prepares to immediately notify the user when an anomaly is detected based on the analysis results. Upon receiving a list of anomalies as input, it creates a notification message via the Firebase Cloud Messaging (FCM) service. The output is an alert notification sent to the user's smartphone or other device.

[0816] Step 5:

[0817] Users check received notifications on their smartphones or via a web interface to stay informed about the elderly person's current condition. They receive notification content and a graph of emotional state visualized from the server as input, allowing them to intuitively assess the situation. Outputs include user intervention actions, such as making a phone call to the elderly person.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] We monitor the behavioral patterns of elderly people living in remote areas.

[0842] Sensing means for detecting anomalies,

[0843] A communication method for encrypting and transmitting collected behavioral data,

[0844] An analysis means for analyzing received data and detecting anomalies,

[0845] A notification means that sends a notification based on the analysis results.

[0846] Includes system.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising a reporting means for periodically reporting analyzed data to local governments and related organizations.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising an update means for continuously training the AI ​​by having the user provide feedback and the system update that data.

[0851] "Example 1"

[0852] (Claim 1)

[0853] Sensing means for monitoring the daily activities and health status of elderly people living in remote areas,

[0854] A communication method for encrypting and transmitting collected biometric information and behavioral data,

[0855] An analysis means that stores the received data in a database, analyzes it using an AI algorithm, and detects unusual behavior,

[0856] A notification means that generates and sends notifications quickly in the event of an anomaly based on the analysis results,

[0857] A means of accessing information for users in real time,

[0858] A means of updating the system to improve analysis accuracy by incorporating user feedback into the learning process.

[0859] Includes system.

[0860] (Claim 2)

[0861] The system according to claim 1, further comprising a reporting means for periodically reporting analysis results and anomaly notifications to local governments and related organizations.

[0862] (Claim 3)

[0863] The system according to claim 1, which has a function to continuously update the system's analysis criteria based on user feedback and improve the performance of the AI ​​model.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] We monitor the behavioral patterns of the elderly.

[0867] Information acquisition means for detecting anomalies,

[0868] A data communication method that encrypts and transmits the collected data,

[0869] A data analysis means that analyzes received data and detects anomalies using an AI algorithm,

[0870] An information provision means that sends a notification based on the analysis results,

[0871] A visual information presentation means that displays information in real time,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] It also includes data reporting mechanisms for regularly reporting analyzed data to public institutions and related organizations.

[0875] The system according to claim 1.

[0876] (Claim 3)

[0877] The system further includes a data updating mechanism that allows for continuous machine learning learning by having the operator provide feedback and the system update that data.

[0878] The system according to claim 1.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] Monitoring the behavior of elderly people living in remote areas,

[0882] Sensors for recording daily activities,

[0883] A communication means for encrypting and transmitting acquired audio and video data,

[0884] Behavioral data and audio / video data are analyzed using AI algorithms.

[0885] An analytical means for detecting anomalies,

[0886] A means of understanding the emotional state of elderly people based on the analysis results and evaluating it using an emotion engine,

[0887] A means of notifying alert information based on analysis results,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising a reporting means for reporting information, including periodically analyzed sentiment data, to local governments and related organizations.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising an update means for continuously training the AI ​​by allowing the user to provide feedback using prompts and the system updating the learning model based on that data.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] By monitoring the behavioral patterns of individuals residing in remote locations,

[0896] A means for collecting data to detect anomalies,

[0897] A communication method for encrypting and transmitting collected activity information,

[0898] Information processing means for analyzing received information and detecting anomalies,

[0899] A notification means that transmits a warning based on the analysis results,

[0900] A means of analyzing emotions for analyzing mental health status,

[0901] A visualization means for displaying emotional states in graph form,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising a reporting means for reporting detected abnormal conditions to external organizations or relevant parties.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising a means for updating the artificial intelligence based on feedback provided by the user. [Explanation of Symbols]

[0907] 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. We monitor the behavioral patterns of the elderly. Information acquisition means for detecting anomalies, A data communication method that encrypts and transmits the collected data, A data analysis means that analyzes received data and detects anomalies using an AI algorithm, An information provision means that sends a notification based on the analysis results, A visual information presentation means that displays information in real time, A system that includes this.

2. It also includes data reporting mechanisms for regularly reporting analyzed data to public institutions and related organizations. The system according to claim 1.

3. The system further includes a data updating mechanism that allows for continuous machine learning learning by having the operator provide feedback and the system update that data. The system according to claim 1.

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