system

Abstract

We provide the system. [Solution] A sensing device for detecting user movements, A calculation device for receiving data from the aforementioned sensing device and analyzing the operating pattern, A notification means that generates a notification when an anomaly is detected and sends it to a terminal device, The aforementioned computing device has a function to generate preventative suggestions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, the need to remotely monitor the safety of the elderly in their daily lives is increasing. However, conventional monitoring systems have a problem in that it is difficult to detect abnormalities in real time, immediately notify family members or caregivers, and make long-term proposals for improving life. As a result, there is a possibility that the prompt response in case of an emergency and the provision of preventive care will be delayed.

Means for Solving the Problems

[0005] This invention provides a system comprising a sensing device for detecting user movements, a computing device for receiving data and analyzing movement patterns, and a notification means for generating notifications and transmitting them to a terminal device when an abnormality is detected. This system can monitor the user's daily activities in real time and immediately notify family members of any abnormalities, as well as analyze the data collected by the computing device over the long term to make preventative suggestions. This enhances the safety of the elderly and reduces the burden on family members and caregivers.

[0006] "User" refers to an individual who is monitored or supported by the system.

[0007] "Movement" refers to the physical movements and actions that users perform in their daily lives.

[0008] A "sensing device" refers to a device such as a sensor or camera used to detect the movement of a user.

[0009] "Data" refers to information acquired from sensing devices, including numerical data and images related to operation and environmental conditions.

[0010] A "processing unit" is a computer device that analyzes acquired data to detect operating patterns and anomalies.

[0011] "Behavioral patterns" refer to the tendencies and characteristics of a series of actions that represent a user's normal behavior.

[0012] "Anomaly" refers to unexpected actions or behaviors that deviate significantly from the usual behavioral patterns.

[0013] "Notification" refers to the transmission of information to inform family members or caregivers when an abnormality is detected.

[0014] "Notification means" refers to a system that transmits the occurrence of an abnormality to a terminal device to inform family members or caregivers.

[0015] The "terminal device" is an electronic device such as a smartphone or a personal computer used by a user or family members to receive information.

[0016] The "preventive suggestion" refers to advice or recommendations for improving life generated by the computing device to maintain the health and safety of the user.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a non-contact anomaly detection system that improves the safety of the elderly by detecting user movements in real time and immediately notifying them of abnormalities. This system comprises a sensing device, a computing device, a notification means, and a terminal device.

[0039] The server acquires data from cameras and sensors installed in the home. Cameras record the user's location and posture as video data, while sensors collect numerical data such as room temperature and whether or not there is movement. This data is then taken to the server and prepared for analysis.

[0040] The server runs an AI algorithm to analyze the user's daily behavior patterns based on the acquired data. Based on this analysis, it compares the user's behavior patterns to normal ones to check for any abnormalities. For example, it identifies abnormalities such as falls or prolonged periods of inactivity.

[0041] If an anomaly is detected, the server creates a notification and immediately sends it to the terminal device. The terminal device is a smartphone or computer owned by the user's family or caregiver, and displays an alert such as, "A fall has been detected. Confirmation is required." This notification allows the user's family to quickly check the situation and take appropriate action.

[0042] Furthermore, the server stores the collected data over a long period and evaluates the user's health status and lifestyle patterns through AI analysis. Based on the results of this data analysis, the server can generate preventative care and lifestyle improvement suggestions and send them to the device. For example, it might suggest, "Based on recent data, lower back pain is a concern, so we recommend appropriate exercise."

[0043] In this embodiment of the present invention, it is possible to ensure the user's safety in real time and support long-term health maintenance through preventative measures. The crawling aid can improve the safety and quality of life for elderly people living alone at home.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects data in real time from sensing devices installed within the home. It acquires video data from cameras and receives environmental data such as temperature and motion detection from sensors.

[0047] Step 2:

[0048] The server preprocesses the collected raw data to make it analyzable. From the video data, it extracts the user's movements and calculates features such as the speed and direction of body movements.

[0049] Step 3:

[0050] The server applies an AI algorithm to compare the pre-processed data with normal behavioral patterns. Here, it evaluates whether abnormal behaviors in elderly individuals, such as falls or prolonged periods of immobility, are detected.

[0051] Step 4:

[0052] When an anomaly is detected, the server identifies the type and details of the anomaly. Based on this, it organizes information to notify family members or caregivers and generates a notification message.

[0053] Step 5:

[0054] The server generates a notification and sends it to the device. The device is a smartphone or computer belonging to a family member or caregiver, which receives the notification and displays the message "A fall has been detected. Confirmation is required" on the screen.

[0055] Step 6:

[0056] The user checks the notification received on their device and responds to the server as needed. Based on the response, the server issues further instructions or updates data as appropriate.

[0057] Step 7:

[0058] The server analyzes data accumulated over a long period and generates preventative suggestions. For example, based on past data and behavioral patterns, the server sends recommendations for lifestyle improvements to the device.

[0059] This processing flow allows the system to function with the aim of supporting the safety and health maintenance of the elderly.

[0060] (Example 1)

[0061] 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."

[0062] In modern times, the number of elderly people living alone is increasing, making daily safety a critical issue. Furthermore, there is a need for technology that can quickly detect and notify of abnormalities. Beyond the need for rapid response to unforeseen circumstances, the challenge lies in providing a better quality of life through long-term health monitoring and preventative health management.

[0063] 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.

[0064] In this invention, the server includes a detection means for detecting user movements non-contactually, a calculation means for receiving video data and environmental information obtained from the detection means, analyzing daily movement patterns using a generation AI, and identifying abnormalities, and a notification means for generating a notification based on the content of the identified abnormality and transmitting it to a communication device. This enables rapid abnormality detection and notification to ensure the safety of the elderly, and further enables improvement of quality of life through long-term health assessment and preventive suggestions.

[0065] A "detection means" is a device for detecting a user's movements without physical contact, and uses devices such as cameras and sensors to acquire information about the surrounding environment, as well as the user's location and posture.

[0066] "Video data" refers to digital visual information acquired by detection devices, which records the user's movements and location in detail.

[0067] "Environmental information" refers to information about the user's surrounding environment, including data such as room temperature and presence or absence of movement, which are acquired by detection means.

[0068] The "computation means" is a device that uses received video data and environmental information to analyze the user's daily movement patterns and uses generated AI to detect anomalies.

[0069] "Generative AI" refers to an artificial intelligence model used to compare a user's normal behavior with abnormal behavior, and includes algorithms for pattern recognition and analysis.

[0070] A "notification device" is a device that, when an anomaly is identified, creates a notification based on its contents and transmits it to a communication device.

[0071] This invention is a non-contact anomaly detection system for improving the safety of the elderly. The system mainly consists of the following four elements: detection means, calculation means, notification means, and communication device.

[0072] Server role:

[0073] The server acquires video data of the user's location and posture through detection means such as cameras and sensors placed in the home, and collects environmental information such as temperature and whether there is movement. This data is then transmitted to the server.

[0074] The server uses a generative AI model to analyze the user's daily activity patterns based on the collected data. This generative AI model compares normal and abnormal movements to detect situations such as falls or sudden stops. The server also stores the analyzed data and can evaluate the user's health status by analyzing long-term behavioral patterns.

[0075] Terminal role:

[0076] When an anomaly is detected, the server uses a notification system to transmit the information to a communication device. The device is a smartphone or computer owned by the user's family or caregiver, and upon receiving the notification, it is displayed as a pop-up alert. This allows family members to quickly check the situation and take necessary actions.

[0077] Specific example:

[0078] For example, if the server is monitoring a user's activity in the living room during the afternoon, and the user remains seated on the sofa watching TV for more than 30 minutes without moving, this will be detected as an abnormal inactivity. Based on this information, a notification stating "An abnormally long period of inactivity has been detected" is sent to the device, allowing family members to immediately take action to check the situation.

[0079] Example of a prompt:

[0080] "Analyze the data from the elderly monitoring system using a generated AI model to verify the fall detection algorithm. Please also generate notification messages for abnormal operation."

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server acquires video data and environmental information from cameras and sensors installed in each room of the home. Inputs include video data of the user's movements captured by the cameras, and sensor information that monitors room temperature and movement. The server receives these inputs and temporarily stores them in a database.

[0084] Step 2:

[0085] The server preprocesses the acquired raw data. This step involves noise reduction and data interpolation. The input consists of the raw video data and environmental information acquired and saved in step 1. The server uses various filtering techniques to output clean data in an analyzable format.

[0086] Step 3:

[0087] The server inputs pre-processed data into a generating AI model to analyze the user's daily movement patterns. This step utilizes a function that compares normal and abnormal movements. The input is the pre-processed data obtained in step 2. The server outputs anomaly detection alert information from the generating AI model. A specific example of this analysis is the step of detecting frames in which the user has fallen.

[0088] Step 4:

[0089] If an anomaly is detected, the server immediately generates a notification based on the information. The input is the anomaly detection result from step 3. The server generates a notification message such as "A fall has been detected. Verification is required" and prepares it as output.

[0090] Step 5:

[0091] The server sends the generated notification to the terminal via its communication function. The input is the notification message generated in step 4. The output is the server sending the notification to the terminal and its immediate display on the user's or caregiver's smartphone or computer.

[0092] Step 6:

[0093] The server stores the analyzed data for long-term health monitoring. The input is the entirety of the analyzed data from step 3. The server stores this data information in a database and outputs it in a format that can be used later to evaluate behavioral patterns and health status.

[0094] (Application Example 1)

[0095] 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."

[0096] In the living environments of the elderly, there is a need to efficiently detect and quickly respond to dangers such as falls and prolonged periods of immobility. However, conventional systems have difficulty in the immediate detection and notification of these abnormalities, and furthermore, subsequent preventative instructions and visualization of the situation are often insufficient. This invention aims to solve these problems and improve the quality of life for the elderly.

[0097] 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.

[0098] In this invention, the server includes detection means for detecting user actions, calculation means for receiving data from the detection means and analyzing operational characteristics, notification means for generating a warning when an abnormality is detected and communicating it to an information terminal, a function for generating preventative instructions using the calculation means, and display means for visualizing the analysis results obtained from the calculation means. This makes it possible not only to ensure the safety of the elderly in real time, but also to provide measures to improve their health based on data analysis.

[0099] A "detection means" is a device for non-contact sensing of a user's actions or changes in their surroundings.

[0100] A "computation means" is a device that analyzes data acquired from a detection means to detect operational characteristics and abnormalities.

[0101] A "notification device" is a device used to quickly notify relevant parties of information when an anomaly is detected.

[0102] The "preventive guidance generation function" is a function that generates advice and suggestions regarding potential health risks based on analyzed data.

[0103] A "display means" is a device for visually presenting analysis results and preventative instructions obtained from a calculation means.

[0104] The system implementing this invention is designed to ensure the safety of the elderly and aims to quickly detect abnormalities that may occur in daily life and notify relevant parties of that information.

[0105] First, the system uses cameras and sensors placed within the home to detect the user's movements and changes in the environment in real time. This data is recorded by the detection devices and transmitted to a server.

[0106] The server uses computational tools to analyze the received data. This algorithm incorporates generative AI models using software such as TENSORFLOW® and PyTorch to identify anomalies that deviate from normal operating patterns. During this process, the data is processed based on AWS® cloud infrastructure.

[0107] When an anomaly is detected, the server uses a notification system to send a notification to the smartphone or computer of family members or caregivers. This alert notification, sent via Amazon SNS, conveys the nature and urgency of the anomaly.

[0108] Furthermore, the server generates preventative instructions and provides advice to proactively prevent health risks based on data obtained from calculations. This function is visually presented as graphs and text information by the display system. For example, if recent data shows an increase in nighttime activity among the elderly, a specific example might be displayed such as, "The number of times you get up at night has increased. Consider improving your nighttime environment (lighting and bedding)."

[0109] The input to the generating AI model is a prompt such as, "Based on the nighttime activity patterns of elderly people, please suggest measures to improve their health." This enables effective health management based on data.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server acquires video and environmental data in real time from cameras and sensors installed within the home. The input for this step is raw data from the cameras and sensors, and the output is the acquired data stream. The cameras detect the location and posture of the elderly person, and the sensors measure temperature and motion.

[0113] Step 2:

[0114] The server stores the retrieved data in a database, preparing it for subsequent processing. The input to this step is the data stream retrieved in step 1, and the output is structured data in the database. The server uses AWS data storage solutions to efficiently store the data.

[0115] Step 3:

[0116] The server analyzes the stored data using computational methods. The input is structured data from a database, and the output is the analysis result. Here, a generative AI model is used to detect anomalies and analyze behavioral characteristics. This makes it possible to distinguish between normal patterns and anomalies in elderly individuals.

[0117] Step 4:

[0118] Based on the analysis results, the server immediately sends a notification using a notification system if it detects an anomaly. The input is the analyzed data, and the output is a notification message. The notification is sent to smartphones and computers via Amazon SNS.

[0119] Step 5:

[0120] The server generates preventative instructions based on long-term data analysis. The input here is accumulated historical data, and the output is suggestions for predicting health risks. The server uses a generative AI model to generate a prompt message for the user, such as "Please suggest ways to improve health based on the nocturnal behavior patterns of elderly people," and then provides suggestions.

[0121] Step 6:

[0122] The terminal displays preventative instructions and analysis results sent from the server on a display device. Input is suggestions and results from the server, and output is visual information for the user. This allows family members and caregivers of elderly individuals to understand the situation and take appropriate action.

[0123] 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.

[0124] This invention relates to a system that improves user safety and psychological well-being by detecting user actions and emotions in real time and providing appropriate notifications and suggestions. The system comprises a sensing device, a computing device, an emotion engine, notification means, and a terminal device.

[0125] The server acquires video data, including user movement information, and environmental data from sensing devices installed in the home. In addition, it uses an emotion engine to analyze facial expressions and voice from the video data to recognize the user's emotional state. This makes it possible to understand not only physical movements but also emotional changes in real time.

[0126] The server applies AI algorithms to analyze collected behavioral and emotional data to determine if there are any abnormalities and to take into account changes in emotions. If an abnormality is detected, for example, if a user falls and is also experiencing pain, the server can generate a notification that takes the detailed circumstances into account. For example, it might generate an alert such as, "There is a risk of falling, and the user is experiencing pain. Please check immediately."

[0127] The generated notification is immediately sent to the device and displayed on the smartphone or computer screen of family members or caregivers. This makes it possible to quickly confirm the user's safety and take appropriate action.

[0128] Furthermore, the server accumulates data over long periods and analyzes behavioral and emotional trends. Based on this, the server can generate and send suggestions for preventative care and lifestyle improvements to the device. For example, it might make specific suggestions such as, "You've been showing signs of stress lately. Please incorporate relaxing activities into your routine."

[0129] In this embodiment of the invention, by monitoring the user's condition from both an operational and emotional perspective, it is possible to support not only physical safety but also psychological well-being. This makes it possible to provide an environment in which elderly people can live with peace of mind.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects video and environmental data in real time from sensing devices installed within the home. The sensing devices consist of cameras and sensors that comprehensively record the user's movements and the surrounding conditions.

[0133] Step 2:

[0134] The server analyzes the user's facial expressions and voice tone from video data acquired using an emotion engine to recognize their current emotional state. This analysis is performed by an AI algorithm designed to capture changes in emotions.

[0135] Step 3:

[0136] The server uses the collected behavioral and emotional data to perform analysis using an AI algorithm. This analysis identifies normal behavioral patterns and determines whether abnormal behavior and emotional changes are occurring simultaneously.

[0137] Step 4:

[0138] When an anomaly is detected, the server creates a notification based on the nature of the anomaly and the emotional state of the user. For example, if a fall is detected along with the emotion of fear, the server will generate a notification such as, "There is a possibility of falling, and the user is feeling fear. Immediate action is required."

[0139] Step 5:

[0140] The server sends the generated notification to the device. The device is a smartphone or computer used by family members or caregivers, and the notification is displayed on these devices, allowing for immediate action.

[0141] Step 6:

[0142] Users check notifications on their devices and take necessary actions. Further instructions or assistance may be requested through responses from the user to the server.

[0143] Step 7:

[0144] The server analyzes behavioral and emotional data accumulated over a long period to extract trends. Based on these analysis results, it can generate and send suggestions for preventative care and lifestyle improvements to the user's device. For example, it might say, "We've observed an increase in stress levels in the past few weeks. We recommend getting adequate rest."

[0145] Through these processing steps, the system comprehensively supports the user's physical and psychological health.

[0146] (Example 2)

[0147] 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".

[0148] In modern society, ensuring physical safety and maintaining psychological health are crucial issues, especially for the elderly and individuals experiencing psychological anxiety. However, conventional technologies have focused on monitoring movements and notifying abnormalities, making it difficult to comprehensively understand and respond quickly to an individual's emotional state. As a result, physical accidents and psychological stress may be overlooked.

[0149] 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.

[0150] In this invention, the server includes sensing means for detecting the user's actions and emotions, calculation means for receiving data from the sensing means and analyzing the action patterns and emotional state, and notification means for detecting anomalies, generating notifications that take into account changes in emotions, and transmitting them to the terminal. This makes it possible to understand the user's actions and emotions in real time and support safety and psychological health.

[0151] "Sensing means" refers to a collection of devices for detecting the user's actions and emotions in real time, and mainly includes devices such as cameras, microphones, and sensors.

[0152] "Computation means" refers to a device or software for processing data received from sensing means and analyzing behavioral patterns and emotional states, and includes technology for identifying anomalies using AI algorithms.

[0153] "Notification means" refers to the function of a device or software that promptly transmits relevant information to the user's or related party's terminal when an anomaly is detected.

[0154] "Preventive suggestions" refer to advice and instructions aimed at improving users' lives and ensuring their safety, generated based on the accumulation and analysis of data over a long period using computational methods.

[0155] This invention is a system that detects the user's actions and emotions in real time and provides appropriate notifications and suggestions. The system consists of a sensing device, a computing device, an emotion engine, a notification means, and a terminal device.

[0156] The server receives video and environmental data acquired by sensing devices installed in the home (such as cameras and sensors). The emotion engine uses this data to analyze the user's emotional state from their facial expressions and voice. This makes it possible to recognize not only the user's physical movements but also changes in their emotions in real time.

[0157] The computing unit applies an AI algorithm to the collected motion data and emotion data. This algorithm has the function of detecting anomalies and taking into account changes in emotion. For example, if a user suddenly falls, the server recognizes this action and also detects the emotion of pain from the emotion data, and determines that an anomaly has occurred.

[0158] If an anomaly is detected, the server will immediately send a detailed notification to the device via a notification system. This notification will be displayed on the smartphone or computer screen of family members or caregivers to encourage prompt action.

[0159] Furthermore, the server accumulates data over a long period and analyzes the user's behavioral and emotional patterns. Based on this, the server generates specific suggestions for preventative care and lifestyle improvements and sends them to the user's device. An example of such a suggestion might be, "You appear to be experiencing stress recently. Please incorporate activities that help you relax."

[0160] Furthermore, by using an example of a prompt message for the generating AI model, such as "Analyze user behavior data and emotion data, and generate a notification message if an anomaly is detected," the system can take more specific actions.

[0161] This invention comprehensively supports the physical safety and psychological health of users, and can provide a safe living environment for the elderly and individuals experiencing psychological anxiety.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server acquires video and environmental data from the sensing devices. Specifically, cameras and sensors installed in the home detect movement information in real time and transmit it to the server. The input is video and environmental data from the sensing devices, and the output is a dataset for analysis.

[0165] Step 2:

[0166] The server analyzes the acquired data using an emotion engine. Specifically, it performs facial recognition from video data and determines emotions from changes in facial expressions and voice. The input is video data, and the output is data indicating the user's emotional state. An AI algorithm is used for this analysis.

[0167] Step 3:

[0168] The server analyzes motion data and emotional data using an AI algorithm to determine if an anomaly has occurred. The input is data combining motion information and emotional state, and the output is the result of the anomaly detection. Specifically, when there is a fall or sudden movement, the server determines the anomaly by combining the situation and emotions.

[0169] Step 4:

[0170] The server generates a notification message when an anomaly is detected. This notification informs family members or caregivers of the user's condition. The input is the detected anomaly, and the output is a detailed notification message. A generation AI model can be used to generate the notification message, and an example of a prompt message would be, "The user has fallen and is complaining of pain. Prompt attention is required."

[0171] Step 5:

[0172] The server sends the generated notification to the device. For example, it might be displayed in real time on a family member's smartphone or a caregiver's computer. The input is the generated notification text, and the output is the received notification from the device. This facilitates a quick response.

[0173] Step 6:

[0174] The server accumulates long-term data and analyzes behavioral and emotional trends. Input is behavioral and emotional data over a certain period, and output is the analysis of behavioral and emotional trends based on that data. Based on these results, it provides preventative suggestions to the user. Specific examples include instructions such as, "Your stress levels appear to be increasing. We suggest activities that can help you relax."

[0175] (Application Example 2)

[0176] 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 device 14 will be referred to as the "terminal."

[0177] In modern society, ensuring the safety and psychological well-being of the elderly and those living alone is a crucial issue. In particular, comprehensive monitoring is needed that considers not only physical risks such as falls and sudden illnesses, but also psychological aspects such as stress and anxiety in daily life. However, achieving this requires a system that can detect and analyze both actions and emotions in real time and respond quickly and appropriately. Currently, however, it is difficult for existing systems to analyze actions and emotions in an integrated manner, resulting in insufficient intervention and suggestions for users.

[0178] 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.

[0179] In this invention, the server includes a sensing device for detecting the user's actions and emotions, a calculation means for receiving data from the sensing device and analyzing the action patterns and emotional state, a notification means for detecting anomalies and generating and transmitting notifications that take into account the user's emotional changes to a terminal device, and a function for analyzing long-term trends in actions and emotions and generating preventative suggestions. This enables integrated monitoring of the user's physical risks and psychological health, detection of anomalies and rapid response, and even suggestions for lifestyle improvements.

[0180] A "sensing device" is hardware used to detect a user's actions and emotions, and includes sensor devices such as cameras and microphones.

[0181] "Computation means" refers to a processing device that receives data from a sensing device and analyzes the user's behavior patterns and emotional state, and includes a processor that executes AI algorithms.

[0182] A "notification mechanism" is a function that, when an anomaly is detected, generates a notification that takes into account the user's emotional changes and sends it to the terminal device.

[0183] "Emotional state" refers to the psychological state of a user as inferred from their facial expressions, tone of voice, and actions.

[0184] A "terminal device" is a device used to receive notifications transmitted from a sensing device, and includes smartphones, tablets, and other similar devices.

[0185] "Analysis of long-term trends" is a process of evaluating the patterns of change in users' actions and emotions based on accumulated action and emotion data from the past.

[0186] "Preventive suggestions" are suggestions and advice based on long-term data analysis to improve the physical and psychological health of users.

[0187] To implement this invention, a system is used that involves installing sensing devices in the user's living environment and collecting motion and emotional data. The sensing devices include sensors such as cameras and microphones, and have the function of detecting the user's motion and emotional state in real time. This data is transmitted to a server.

[0188] The server analyzes behavioral patterns and emotional states based on the received data using computational means. AI algorithms are applied to the data analysis, performing anomaly detection and emotional change analysis. A platform such as Google Cloud AI is used for this analysis.

[0189] When an anomaly is detected, the server generates a notification via a notification system that takes into account the user's emotional changes. This notification is sent to the user's terminal device, such as a smartphone or tablet. This allows for quick confirmation of the user's safety and appropriate action to be taken.

[0190] Furthermore, the server accumulates a large amount of behavioral and emotional data and analyzes trends over a long period. Based on this analysis, it can generate and deliver preventative suggestions to improve the user's physical and psychological health. This reduces risks in the user's daily life and provides a safer environment.

[0191] For example, a server could detect a fall by an elderly person, identify the emotion of pain using an emotion engine, and send a notification to the family saying, "Grandma has fallen and is in pain," to encourage a quick response. Another example of a prompt message is, "Please propose a system design for detecting falls by elderly people." This would contribute to ensuring the safety of the elderly in society.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server receives video and audio data acquired from the camera and microphone from the sensing device. This input data includes signs of the user's actions and emotions, and subsequent processing is performed based on this data.

[0195] Step 2:

[0196] The server applies AI algorithms to the received data to analyze behavioral patterns and emotional states. In particular, it uses facial recognition and voice analysis technologies to quantify the user's emotions. This data processing allows the server to determine whether the user is feeling at ease or stressed.

[0197] Step 3:

[0198] When the server detects an anomaly through motion analysis, it immediately generates a notification, taking into account changes in the user's emotional state. For example, if the analysis indicates that a user has fallen and is complaining of pain, the server generates a specific alert stating, "The user has fallen and is experiencing pain." This notification is immediately sent to the devices of family members or other relevant parties.

[0199] Step 4:

[0200] The device receives the sent notification and displays an alert to the user. If the device is the user's smartphone, it is displayed as a high-priority message via push notification to help the user respond quickly.

[0201] Step 5:

[0202] The server performs long-term trend analysis based on accumulated behavioral and emotional data. This process involves identifying patterns in behavioral changes and emotions over time, and processing the data to improve the user's living and psychological state.

[0203] Step 6:

[0204] Based on the trend analysis results, the server generates suggestions to support the user's physical and mental health. For example, if the data indicates that the user has recently been experiencing stress, it generates a suggestion such as, "You've recently shown signs of stress. Consider relaxation activities," and sends it to the device.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention relates to a non-contact anomaly detection system that improves the safety of the elderly by detecting user movements in real time and immediately notifying them of abnormalities. This system comprises a sensing device, a computing device, a notification means, and a terminal device.

[0222] The server acquires data from cameras and sensors installed in the home. Cameras record the user's location and posture as video data, while sensors collect numerical data such as room temperature and whether or not there is movement. This data is then taken to the server and prepared for analysis.

[0223] The server runs an AI algorithm to analyze the user's daily behavior patterns based on the acquired data. Based on this analysis, it compares the user's behavior patterns to normal ones to check for any abnormalities. For example, it identifies abnormalities such as falls or prolonged periods of inactivity.

[0224] If an anomaly is detected, the server creates a notification and immediately sends it to the terminal device. The terminal device is a smartphone or computer owned by the user's family or caregiver, and displays an alert such as, "A fall has been detected. Confirmation is required." This notification allows the user's family to quickly check the situation and take appropriate action.

[0225] Furthermore, the server stores the collected data over a long period and evaluates the user's health status and lifestyle patterns through AI analysis. Based on the results of this data analysis, the server can generate preventative care and lifestyle improvement suggestions and send them to the device. For example, it might suggest, "Based on recent data, lower back pain is a concern, so we recommend appropriate exercise."

[0226] In this embodiment of the present invention, it is possible to ensure the user's safety in real time and support long-term health maintenance through preventative measures. The crawling aid can improve the safety and quality of life for elderly people living alone at home.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server collects data in real time from sensing devices installed within the home. It acquires video data from cameras and receives environmental data such as temperature and motion detection from sensors.

[0230] Step 2:

[0231] The server preprocesses the collected raw data to make it analyzable. From the video data, it extracts the user's movements and calculates features such as the speed and direction of body movements.

[0232] Step 3:

[0233] The server applies an AI algorithm to compare the pre-processed data with normal behavioral patterns. Here, it evaluates whether abnormal behaviors in elderly individuals, such as falls or prolonged periods of immobility, are detected.

[0234] Step 4:

[0235] When an anomaly is detected, the server identifies the type and details of the anomaly. Based on this, it organizes information to notify family members or caregivers and generates a notification message.

[0236] Step 5:

[0237] The server generates a notification and sends it to the device. The device is a smartphone or computer belonging to a family member or caregiver, which receives the notification and displays the message "A fall has been detected. Confirmation is required" on the screen.

[0238] Step 6:

[0239] The user checks the notification received on their device and responds to the server as needed. Based on the response, the server issues further instructions or updates data as appropriate.

[0240] Step 7:

[0241] The server analyzes data accumulated over a long period and generates preventative suggestions. For example, based on past data and behavioral patterns, the server sends recommendations for lifestyle improvements to the device.

[0242] This processing flow allows the system to function with the aim of supporting the safety and health maintenance of the elderly.

[0243] (Example 1)

[0244] 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."

[0245] In modern times, the number of elderly people living alone is increasing, making daily safety a critical issue. Furthermore, there is a need for technology that can quickly detect and notify of abnormalities. Beyond the need for rapid response to unforeseen circumstances, the challenge lies in providing a better quality of life through long-term health monitoring and preventative health management.

[0246] 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.

[0247] In this invention, the server includes a detection means for detecting user movements non-contactually, a calculation means for receiving video data and environmental information obtained from the detection means, analyzing daily movement patterns using a generation AI, and identifying abnormalities, and a notification means for generating a notification based on the content of the identified abnormality and transmitting it to a communication device. This enables rapid abnormality detection and notification to ensure the safety of the elderly, and further enables improvement of quality of life through long-term health assessment and preventive suggestions.

[0248] A "detection means" is a device for detecting a user's movements without physical contact, and uses devices such as cameras and sensors to acquire information about the surrounding environment, as well as the user's location and posture.

[0249] "Video data" refers to digital visual information acquired by detection devices, which records the user's movements and location in detail.

[0250] "Environmental information" refers to information about the user's surrounding environment, including data such as room temperature and presence or absence of movement, which are acquired by detection means.

[0251] The "computation means" is a device that uses received video data and environmental information to analyze the user's daily movement patterns and uses generated AI to detect anomalies.

[0252] "Generative AI" refers to an artificial intelligence model used to compare a user's normal behavior with abnormal behavior, and includes algorithms for pattern recognition and analysis.

[0253] A "notification device" is a device that, when an anomaly is identified, creates a notification based on its contents and transmits it to a communication device.

[0254] This invention is a non-contact anomaly detection system for improving the safety of the elderly. The system mainly consists of the following four elements: detection means, calculation means, notification means, and communication device.

[0255] Server role:

[0256] The server acquires video data of the user's location and posture through detection means such as cameras and sensors placed in the home, and collects environmental information such as temperature and whether there is movement. This data is then transmitted to the server.

[0257] The server uses a generative AI model to analyze the user's daily activity patterns based on the collected data. This generative AI model compares normal and abnormal movements to detect situations such as falls or sudden stops. The server also stores the analyzed data and can evaluate the user's health status by analyzing long-term behavioral patterns.

[0258] Terminal role:

[0259] When an anomaly is detected, the server uses a notification system to transmit the information to a communication device. The device is a smartphone or computer owned by the user's family or caregiver, and upon receiving the notification, it is displayed as a pop-up alert. This allows family members to quickly check the situation and take necessary actions.

[0260] Specific example:

[0261] For example, if the server is monitoring a user's activity in the living room during the afternoon, and the user remains seated on the sofa watching TV for more than 30 minutes without moving, this will be detected as an abnormal inactivity. Based on this information, a notification stating "An abnormally long period of inactivity has been detected" is sent to the device, allowing family members to immediately take action to check the situation.

[0262] Example of a prompt:

[0263] "Analyze the data from the elderly monitoring system using a generated AI model to verify the fall detection algorithm. Please also generate notification messages for abnormal operation."

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The server acquires video data and environmental information from cameras and sensors installed in each room of the home. Inputs include video data of the user's movements captured by the cameras, and sensor information that monitors room temperature and movement. The server receives these inputs and temporarily stores them in a database.

[0267] Step 2:

[0268] The server preprocesses the acquired raw data. This step involves noise reduction and data interpolation. The input consists of the raw video data and environmental information acquired and saved in step 1. The server uses various filtering techniques to output clean data in an analyzable format.

[0269] Step 3:

[0270] The server inputs pre-processed data into a generating AI model to analyze the user's daily movement patterns. This step utilizes a function that compares normal and abnormal movements. The input is the pre-processed data obtained in step 2. The server outputs anomaly detection alert information from the generating AI model. A specific example of this analysis is the step of detecting frames in which the user has fallen.

[0271] Step 4:

[0272] If an anomaly is detected, the server immediately generates a notification based on the information. The input is the anomaly detection result from step 3. The server generates a notification message such as "A fall has been detected. Verification is required" and prepares it as output.

[0273] Step 5:

[0274] The server sends the generated notification to the terminal via its communication function. The input is the notification message generated in step 4. The output is the server sending the notification to the terminal and its immediate display on the user's or caregiver's smartphone or computer.

[0275] Step 6:

[0276] The server stores the analyzed data for long-term health monitoring. The input is the entirety of the analyzed data from step 3. The server stores this data information in a database and outputs it in a format that can be used later to evaluate behavioral patterns and health status.

[0277] (Application Example 1)

[0278] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0279] In the living environment of the elderly, it is required to efficiently detect risks such as falls and long-term operation stops and respond promptly. However, conventional systems have difficulties in the immediate detection and notification of these abnormalities, and furthermore, subsequent preventive instructions and situation visualization are often not sufficient. The present invention aims to solve these problems and improve the quality of life of the elderly.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes a detection means for detecting the actions of the user, a calculation means for receiving data from the detection means and analyzing the operation characteristics, a notification means for generating a warning when an abnormality is detected and communicating with the information terminal, a function for generating preventive instructions by the calculation means, and a display means for visualizing the analysis results obtained from the calculation means. Thereby, not only can the safety of the elderly be ensured in real time, but also it becomes possible to provide improvement measures for the health state based on data analysis.

[0282] The "detection means" is a device for non-contact sensing of the actions of the user and changes in the surroundings.

[0283] The "calculation means" is a device for analyzing the data acquired from the detection means and detecting operation characteristics and abnormalities.

[0284] The "notification means" is a device for promptly notifying relevant persons of the information when an abnormality is detected.

[0285] The "function of generating preventive instructions" is a function that creates advice and suggestions for potential health risks based on the analyzed data.

[0286] The "display means" is a device for visually presenting the analysis results and preventive instructions obtained from the calculation means.

[0287] The system for implementing this invention is designed to ensure the safety of the elderly, and aims to quickly detect abnormalities that may occur in daily life and notify the relevant parties of the information.

[0288] First, the system uses cameras and sensors placed in the home to detect the movements of the user and changes in the environment in real time. This data is recorded by the detection means and sent to the server.

[0289] The server uses calculation means to analyze the received data. This algorithm incorporates a generative AI model using software such as TensorFlow and PyTorch to identify abnormalities that deviate from normal operation patterns. At this time, the data is processed based on the AWS cloud infrastructure.

[0290] When an abnormality is detected, the server uses notification means to send a notification to the smartphones and personal computers of family members and caregivers. This alert notification sent through Amazon SNS conveys the nature and urgency of the abnormality.

[0291] Furthermore, the server generates preventive instructions and provides advice to prevent health risks based on the data obtained from the calculation means. This function is visually provided as graphs and text information by the display means. For example, if the nighttime activities of the elderly have increased based on recent data, a specific example such as "The number of times of waking up at night has increased. Please consider improving the nighttime environment (lighting and bedding)." will be displayed.

[0292] The input to the generating AI model is a prompt such as, "Based on the nighttime activity patterns of elderly people, please suggest measures to improve their health." This enables effective health management based on data.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The server acquires video and environmental data in real time from cameras and sensors installed within the home. The input for this step is raw data from the cameras and sensors, and the output is the acquired data stream. The cameras detect the location and posture of the elderly person, and the sensors measure temperature and motion.

[0296] Step 2:

[0297] The server stores the retrieved data in a database, preparing it for subsequent processing. The input to this step is the data stream retrieved in step 1, and the output is structured data in the database. The server uses AWS data storage solutions to efficiently store the data.

[0298] Step 3:

[0299] The server analyzes the stored data using computational methods. The input is structured data from a database, and the output is the analysis result. Here, a generative AI model is used to detect anomalies and analyze behavioral characteristics. This makes it possible to distinguish between normal patterns and anomalies in elderly individuals.

[0300] Step 4:

[0301] Based on the analysis results, the server immediately sends a notification using a notification system if it detects an anomaly. The input is the analyzed data, and the output is a notification message. The notification is sent to smartphones and computers via Amazon SNS.

[0302] Step 5:

[0303] The server generates preventive instructions based on long-term data analysis. The input here is the accumulated past data, and the output is a proposal for predicting health risks. The server uses a generation AI model to generate a prompt sentence for the user, "Please propose improvement measures for the health condition based on the nighttime behavior patterns of the elderly." and makes a proposal.

[0304] Step 6:

[0305] The terminal shows the preventive instructions and analysis results sent from the server on the display device. The input is the proposal and result from the server, and the output is visual information for the user. As a result, the family members and caregivers of the elderly can understand the situation and take appropriate actions.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0307] The present invention relates to a system that detects the actions and emotions of users in real time and improves the safety and psychological health of users by providing appropriate notifications and proposals. This system includes a sensing device, a computing device, an emotion engine, a notification means, and a terminal device.

[0308] The server acquires video data and environmental data including the action information of the user from the sensing device installed in the home. In addition to this, using the emotion engine, it analyzes expressions, voices, etc. from the video data to recognize the emotional state of the user. As a result, it is possible to grasp not only the physical actions but also the emotional changes in real time.

[0309] The server applies AI algorithms to analyze collected behavioral and emotional data to determine if there are any abnormalities and to take into account changes in emotions. If an abnormality is detected, for example, if a user falls and is also experiencing pain, the server can generate a notification that takes the detailed circumstances into account. For example, it might generate an alert such as, "There is a risk of falling, and the user is experiencing pain. Please check immediately."

[0310] The generated notification is immediately sent to the device and displayed on the smartphone or computer screen of family members or caregivers. This makes it possible to quickly confirm the user's safety and take appropriate action.

[0311] Furthermore, the server accumulates data over long periods and analyzes behavioral and emotional trends. Based on this, the server can generate and send suggestions for preventative care and lifestyle improvements to the device. For example, it might make specific suggestions such as, "You've been showing signs of stress lately. Please incorporate relaxing activities into your routine."

[0312] In this embodiment of the invention, by monitoring the user's condition from both an operational and emotional perspective, it is possible to support not only physical safety but also psychological well-being. This makes it possible to provide an environment in which elderly people can live with peace of mind.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The server collects video and environmental data in real time from sensing devices installed within the home. The sensing devices consist of cameras and sensors that comprehensively record the user's movements and the surrounding conditions.

[0316] Step 2:

[0317] The server analyzes the user's facial expressions and voice tone from video data acquired using an emotion engine to recognize their current emotional state. This analysis is performed by an AI algorithm designed to capture changes in emotions.

[0318] Step 3:

[0319] The server uses the collected behavioral and emotional data to perform analysis using an AI algorithm. This analysis identifies normal behavioral patterns and determines whether abnormal behavior and emotional changes are occurring simultaneously.

[0320] Step 4:

[0321] When an anomaly is detected, the server creates a notification based on the nature of the anomaly and the emotional state of the user. For example, if a fall is detected along with the emotion of fear, the server will generate a notification such as, "There is a possibility of falling, and the user is feeling fear. Immediate action is required."

[0322] Step 5:

[0323] The server sends the generated notification to the device. The device is a smartphone or computer used by family members or caregivers, and the notification is displayed on these devices, allowing for immediate action.

[0324] Step 6:

[0325] Users check notifications on their devices and take necessary actions. Further instructions or assistance may be requested through responses from the user to the server.

[0326] Step 7:

[0327] The server analyzes behavioral and emotional data accumulated over a long period to extract trends. Based on these analysis results, it can generate and send suggestions for preventative care and lifestyle improvements to the user's device. For example, it might say, "We've observed an increase in stress levels in the past few weeks. We recommend getting adequate rest."

[0328] Through these processing steps, the system comprehensively supports the user's physical and psychological health.

[0329] (Example 2)

[0330] 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".

[0331] In modern society, ensuring physical safety and maintaining psychological health are crucial issues, especially for the elderly and individuals experiencing psychological anxiety. However, conventional technologies have focused on monitoring movements and notifying abnormalities, making it difficult to comprehensively understand and respond quickly to an individual's emotional state. As a result, physical accidents and psychological stress may be overlooked.

[0332] 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.

[0333] In this invention, the server includes sensing means for detecting the user's actions and emotions, calculation means for receiving data from the sensing means and analyzing the action patterns and emotional state, and notification means for detecting anomalies, generating notifications that take into account changes in emotions, and transmitting them to the terminal. This makes it possible to understand the user's actions and emotions in real time and support safety and psychological health.

[0334] "Sensing means" refers to a collection of devices for detecting the user's actions and emotions in real time, and mainly includes devices such as cameras, microphones, and sensors.

[0335] "Computation means" refers to a device or software for processing data received from sensing means and analyzing behavioral patterns and emotional states, and includes technology for identifying anomalies using AI algorithms.

[0336] "Notification means" refers to the function of a device or software that promptly transmits relevant information to the user's or related party's terminal when an anomaly is detected.

[0337] "Preventive suggestions" refer to advice and instructions aimed at improving users' lives and ensuring their safety, generated based on the accumulation and analysis of data over a long period using computational methods.

[0338] This invention is a system that detects the user's actions and emotions in real time and provides appropriate notifications and suggestions. The system consists of a sensing device, a computing device, an emotion engine, a notification means, and a terminal device.

[0339] The server receives video and environmental data acquired by sensing devices installed in the home (such as cameras and sensors). The emotion engine uses this data to analyze the user's emotional state from their facial expressions and voice. This makes it possible to recognize not only the user's physical movements but also changes in their emotions in real time.

[0340] The computing unit applies an AI algorithm to the collected motion data and emotion data. This algorithm has the function of detecting anomalies and taking into account changes in emotion. For example, if a user suddenly falls, the server recognizes this action and also detects the emotion of pain from the emotion data, and determines that an anomaly has occurred.

[0341] If an anomaly is detected, the server will immediately send a detailed notification to the device via a notification system. This notification will be displayed on the smartphone or computer screen of family members or caregivers to encourage prompt action.

[0342] Furthermore, the server accumulates data over a long period and analyzes the user's behavioral and emotional patterns. Based on this, the server generates specific suggestions for preventative care and lifestyle improvements and sends them to the user's device. An example of such a suggestion might be, "You appear to be experiencing stress recently. Please incorporate activities that help you relax."

[0343] Furthermore, by using an example of a prompt message for the generating AI model, such as "Analyze user behavior data and emotion data, and generate a notification message if an anomaly is detected," the system can take more specific actions.

[0344] This invention comprehensively supports the physical safety and psychological health of users, and can provide a safe living environment for the elderly and individuals experiencing psychological anxiety.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server acquires video and environmental data from the sensing devices. Specifically, cameras and sensors installed in the home detect movement information in real time and transmit it to the server. The input is video and environmental data from the sensing devices, and the output is a dataset for analysis.

[0348] Step 2:

[0349] The server analyzes the acquired data using an emotion engine. Specifically, it performs facial recognition from video data and determines emotions from changes in facial expressions and voice. The input is video data, and the output is data indicating the user's emotional state. An AI algorithm is used for this analysis.

[0350] Step 3:

[0351] The server analyzes motion data and emotional data using an AI algorithm to determine if an anomaly has occurred. The input is data combining motion information and emotional state, and the output is the result of the anomaly detection. Specifically, when there is a fall or sudden movement, the server determines the anomaly by combining the situation and emotions.

[0352] Step 4:

[0353] The server generates a notification message when an anomaly is detected. This notification informs family members or caregivers of the user's condition. The input is the detected anomaly, and the output is a detailed notification message. A generation AI model can be used to generate the notification message, and an example of a prompt message would be, "The user has fallen and is complaining of pain. Prompt attention is required."

[0354] Step 5:

[0355] The server sends the generated notification to the device. For example, it might be displayed in real time on a family member's smartphone or a caregiver's computer. The input is the generated notification text, and the output is the received notification from the device. This facilitates a quick response.

[0356] Step 6:

[0357] The server accumulates long-term data and analyzes behavioral and emotional trends. Input is behavioral and emotional data over a certain period, and output is the analysis of behavioral and emotional trends based on that data. Based on these results, it provides preventative suggestions to the user. Specific examples include instructions such as, "Your stress levels appear to be increasing. We suggest activities that can help you relax."

[0358] (Application Example 2)

[0359] 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."

[0360] In modern society, ensuring the safety and psychological well-being of the elderly and those living alone is a crucial issue. In particular, comprehensive monitoring is needed that considers not only physical risks such as falls and sudden illnesses, but also psychological aspects such as stress and anxiety in daily life. However, achieving this requires a system that can detect and analyze both actions and emotions in real time and respond quickly and appropriately. Currently, however, it is difficult for existing systems to analyze actions and emotions in an integrated manner, resulting in insufficient intervention and suggestions for users.

[0361] 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.

[0362] In this invention, the server includes a sensing device for detecting the user's actions and emotions, a calculation means for receiving data from the sensing device and analyzing the action patterns and emotional state, a notification means for detecting anomalies and generating and transmitting notifications that take into account the user's emotional changes to a terminal device, and a function for analyzing long-term trends in actions and emotions and generating preventative suggestions. This enables integrated monitoring of the user's physical risks and psychological health, detection of anomalies and rapid response, and even suggestions for lifestyle improvements.

[0363] A "sensing device" is hardware used to detect a user's actions and emotions, and includes sensor devices such as cameras and microphones.

[0364] "Computation means" refers to a processing device that receives data from a sensing device and analyzes the user's behavior patterns and emotional state, and includes a processor that executes AI algorithms.

[0365] A "notification mechanism" is a function that, when an anomaly is detected, generates a notification that takes into account the user's emotional changes and sends it to the terminal device.

[0366] "Emotional state" refers to the psychological state of a user as inferred from their facial expressions, tone of voice, and actions.

[0367] A "terminal device" is a device used to receive notifications transmitted from a sensing device, and includes smartphones, tablets, and other similar devices.

[0368] "Analysis of long-term trends" is a process of evaluating the patterns of change in users' actions and emotions based on accumulated action and emotion data from the past.

[0369] "Preventive suggestions" are suggestions and advice based on long-term data analysis to improve the physical and psychological health of users.

[0370] To implement this invention, a system is used that involves installing sensing devices in the user's living environment and collecting motion and emotional data. The sensing devices include sensors such as cameras and microphones, and have the function of detecting the user's motion and emotional state in real time. This data is transmitted to a server.

[0371] The server analyzes behavioral patterns and emotional states based on the received data using computational means. AI algorithms are applied to the data analysis, performing anomaly detection and emotional change analysis. A platform like Google Cloud AI is used for this analysis.

[0372] When an anomaly is detected, the server generates a notification via a notification system that takes into account the user's emotional changes. This notification is sent to the user's terminal device, such as a smartphone or tablet. This allows for quick confirmation of the user's safety and appropriate action to be taken.

[0373] Furthermore, the server accumulates a large amount of behavioral and emotional data and analyzes trends over a long period. Based on this analysis, it can generate and deliver preventative suggestions to improve the user's physical and psychological health. This reduces risks in the user's daily life and provides a safer environment.

[0374] For example, a server could detect a fall by an elderly person, identify the emotion of pain using an emotion engine, and send a notification to the family saying, "Grandma has fallen and is in pain," to encourage a quick response. Another example of a prompt message is, "Please propose a system design for detecting falls by elderly people." This would contribute to ensuring the safety of the elderly in society.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The server receives video and audio data acquired from the camera and microphone from the sensing device. This input data includes signs of the user's actions and emotions, and subsequent processing is performed based on this data.

[0378] Step 2:

[0379] The server applies AI algorithms to the received data to analyze behavioral patterns and emotional states. In particular, it uses facial recognition and voice analysis technologies to quantify the user's emotions. This data processing allows the server to determine whether the user is feeling at ease or stressed.

[0380] Step 3:

[0381] When the server detects an anomaly through motion analysis, it immediately generates a notification, taking into account changes in the user's emotional state. For example, if the analysis indicates that a user has fallen and is complaining of pain, the server generates a specific alert stating, "The user has fallen and is experiencing pain." This notification is immediately sent to the devices of family members or other relevant parties.

[0382] Step 4:

[0383] The device receives the sent notification and displays an alert to the user. If the device is the user's smartphone, it is displayed as a high-priority message via push notification to help the user respond quickly.

[0384] Step 5:

[0385] The server performs long-term trend analysis based on accumulated behavioral and emotional data. This process involves identifying patterns in behavioral changes and emotions over time, and processing the data to improve the user's living and psychological state.

[0386] Step 6:

[0387] Based on the trend analysis results, the server generates suggestions to support the user's physical and mental health. For example, if the data indicates that the user has recently been experiencing stress, it generates a suggestion such as, "You've recently shown signs of stress. Consider relaxation activities," and sends it to the device.

[0388] 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.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] This invention relates to a non-contact anomaly detection system that improves the safety of the elderly by detecting user movements in real time and immediately notifying them of abnormalities. This system comprises a sensing device, a computing device, a notification means, and a terminal device.

[0405] The server acquires data from cameras and sensors installed in the home. Cameras record the user's location and posture as video data, while sensors collect numerical data such as room temperature and whether or not there is movement. This data is then taken to the server and prepared for analysis.

[0406] The server runs an AI algorithm to analyze the user's daily behavior patterns based on the acquired data. Based on this analysis, it compares the user's behavior patterns to normal ones to check for any abnormalities. For example, it identifies abnormalities such as falls or prolonged periods of inactivity.

[0407] If an anomaly is detected, the server creates a notification and immediately sends it to the terminal device. The terminal device is a smartphone or computer owned by the user's family or caregiver, and displays an alert such as, "A fall has been detected. Confirmation is required." This notification allows the user's family to quickly check the situation and take appropriate action.

[0408] Furthermore, the server stores the collected data over a long period and evaluates the user's health status and lifestyle patterns through AI analysis. Based on the results of this data analysis, the server can generate preventative care and lifestyle improvement suggestions and send them to the device. For example, it might suggest, "Based on recent data, lower back pain is a concern, so we recommend appropriate exercise."

[0409] In this embodiment of the present invention, it is possible to ensure the user's safety in real time and support long-term health maintenance through preventative measures. The crawling aid can improve the safety and quality of life for elderly people living alone at home.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The server collects data in real time from sensing devices installed within the home. It acquires video data from cameras and receives environmental data such as temperature and motion detection from sensors.

[0413] Step 2:

[0414] The server preprocesses the collected raw data to make it analyzable. From the video data, it extracts the user's movements and calculates features such as the speed and direction of body movements.

[0415] Step 3:

[0416] The server applies an AI algorithm to compare the pre-processed data with normal behavioral patterns. Here, it evaluates whether abnormal behaviors in elderly individuals, such as falls or prolonged periods of immobility, are detected.

[0417] Step 4:

[0418] When an anomaly is detected, the server identifies the type and details of the anomaly. Based on this, it organizes information to notify family members or caregivers and generates a notification message.

[0419] Step 5:

[0420] The server generates a notification and sends it to the device. The device is a smartphone or computer belonging to a family member or caregiver, which receives the notification and displays the message "A fall has been detected. Confirmation is required" on the screen.

[0421] Step 6:

[0422] The user checks the notification received on their device and responds to the server as needed. Based on the response, the server issues further instructions or updates data as appropriate.

[0423] Step 7:

[0424] The server analyzes data accumulated over a long period and generates preventative suggestions. For example, based on past data and behavioral patterns, the server sends recommendations for lifestyle improvements to the device.

[0425] This processing flow allows the system to function with the aim of supporting the safety and health maintenance of the elderly.

[0426] (Example 1)

[0427] 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."

[0428] In modern times, the number of elderly people living alone is increasing, making daily safety a critical issue. Furthermore, there is a need for technology that can quickly detect and notify of abnormalities. Beyond the need for rapid response to unforeseen circumstances, the challenge lies in providing a better quality of life through long-term health monitoring and preventative health management.

[0429] 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.

[0430] In this invention, the server includes a detection means for detecting user movements non-contactually, a calculation means for receiving video data and environmental information obtained from the detection means, analyzing daily movement patterns using a generation AI, and identifying abnormalities, and a notification means for generating a notification based on the content of the identified abnormality and transmitting it to a communication device. This enables rapid abnormality detection and notification to ensure the safety of the elderly, and further enables improvement of quality of life through long-term health assessment and preventive suggestions.

[0431] A "detection means" is a device for detecting a user's movements without physical contact, and uses devices such as cameras and sensors to acquire information about the surrounding environment, as well as the user's location and posture.

[0432] "Video data" refers to digital visual information acquired by detection devices, which records the user's movements and location in detail.

[0433] "Environmental information" refers to information about the user's surrounding environment, including data such as room temperature and presence or absence of movement, which are acquired by detection means.

[0434] The "computation means" is a device that uses received video data and environmental information to analyze the user's daily movement patterns and uses generated AI to detect anomalies.

[0435] "Generative AI" refers to an artificial intelligence model used to compare a user's normal behavior with abnormal behavior, and includes algorithms for pattern recognition and analysis.

[0436] A "notification device" is a device that, when an anomaly is identified, creates a notification based on its contents and transmits it to a communication device.

[0437] This invention is a non-contact anomaly detection system for improving the safety of the elderly. The system mainly consists of the following four elements: detection means, calculation means, notification means, and communication device.

[0438] Server role:

[0439] The server acquires video data of the user's location and posture through detection means such as cameras and sensors placed in the home, and collects environmental information such as temperature and whether there is movement. This data is then transmitted to the server.

[0440] The server uses a generative AI model to analyze the user's daily activity patterns based on the collected data. This generative AI model compares normal and abnormal movements to detect situations such as falls or sudden stops. The server also stores the analyzed data and can evaluate the user's health status by analyzing long-term behavioral patterns.

[0441] Terminal role:

[0442] When an anomaly is detected, the server uses a notification system to transmit the information to a communication device. The device is a smartphone or computer owned by the user's family or caregiver, and upon receiving the notification, it is displayed as a pop-up alert. This allows family members to quickly check the situation and take necessary actions.

[0443] Specific example:

[0444] For example, if the server is monitoring a user's activity in the living room during the afternoon, and the user remains seated on the sofa watching TV for more than 30 minutes without moving, this will be detected as an abnormal inactivity. Based on this information, a notification stating "An abnormally long period of inactivity has been detected" is sent to the device, allowing family members to immediately take action to check the situation.

[0445] Example of a prompt:

[0446] "Analyze the data from the elderly monitoring system using a generated AI model to verify the fall detection algorithm. Please also generate notification messages for abnormal operation."

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The server acquires video data and environmental information from cameras and sensors installed in each room of the home. Inputs include video data of the user's movements captured by the cameras, and sensor information that monitors room temperature and movement. The server receives these inputs and temporarily stores them in a database.

[0450] Step 2:

[0451] The server preprocesses the acquired raw data. This step involves noise reduction and data interpolation. The input consists of the raw video data and environmental information acquired and saved in step 1. The server uses various filtering techniques to output clean data in an analyzable format.

[0452] Step 3:

[0453] The server inputs pre-processed data into a generating AI model to analyze the user's daily movement patterns. This step utilizes a function that compares normal and abnormal movements. The input is the pre-processed data obtained in step 2. The server outputs anomaly detection alert information from the generating AI model. A specific example of this analysis is the step of detecting frames in which the user has fallen.

[0454] Step 4:

[0455] If an anomaly is detected, the server immediately generates a notification based on the information. The input is the anomaly detection result from step 3. The server generates a notification message such as "A fall has been detected. Verification is required" and prepares it as output.

[0456] Step 5:

[0457] The server sends the generated notification to the terminal via its communication function. The input is the notification message generated in step 4. The output is the server sending the notification to the terminal and its immediate display on the user's or caregiver's smartphone or computer.

[0458] Step 6:

[0459] The server stores the analyzed data for long-term health monitoring. The input is the entirety of the analyzed data from step 3. The server stores this data information in a database and outputs it in a format that can be used later to evaluate behavioral patterns and health status.

[0460] (Application Example 1)

[0461] 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."

[0462] In the living environments of the elderly, there is a need to efficiently detect and quickly respond to dangers such as falls and prolonged periods of immobility. However, conventional systems have difficulty in the immediate detection and notification of these abnormalities, and furthermore, subsequent preventative instructions and visualization of the situation are often insufficient. This invention aims to solve these problems and improve the quality of life for the elderly.

[0463] 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.

[0464] In this invention, the server includes detection means for detecting user actions, calculation means for receiving data from the detection means and analyzing operational characteristics, notification means for generating a warning when an abnormality is detected and communicating it to an information terminal, a function for generating preventative instructions using the calculation means, and display means for visualizing the analysis results obtained from the calculation means. This makes it possible not only to ensure the safety of the elderly in real time, but also to provide measures to improve their health based on data analysis.

[0465] A "detection means" is a device for non-contact sensing of a user's actions or changes in their surroundings.

[0466] A "computation means" is a device that analyzes data acquired from a detection means to detect operational characteristics and abnormalities.

[0467] A "notification device" is a device used to quickly notify relevant parties of information when an anomaly is detected.

[0468] The "preventive guidance generation function" is a function that generates advice and suggestions regarding potential health risks based on analyzed data.

[0469] A "display means" is a device for visually presenting analysis results and preventative instructions obtained from a calculation means.

[0470] The system implementing this invention is designed to ensure the safety of the elderly and aims to quickly detect abnormalities that may occur in daily life and notify relevant parties of that information.

[0471] First, the system uses cameras and sensors placed within the home to detect the user's movements and changes in the environment in real time. This data is recorded by the detection devices and transmitted to a server.

[0472] The server uses computational tools to analyze the received data. This algorithm incorporates generative AI models using software such as TensorFlow and PyTorch to identify anomalies that deviate from normal operating patterns. During this process, the data is processed on the AWS cloud infrastructure.

[0473] When an anomaly is detected, the server uses a notification system to send a notification to the smartphone or computer of family members or caregivers. This alert notification, sent via Amazon SNS, conveys the nature and urgency of the anomaly.

[0474] Furthermore, the server generates preventative instructions and provides advice to proactively prevent health risks based on data obtained from calculations. This function is visually presented as graphs and text information by the display system. For example, if recent data shows an increase in nighttime activity among the elderly, a specific example might be displayed such as, "The number of times you get up at night has increased. Consider improving your nighttime environment (lighting and bedding)."

[0475] The input to the generating AI model is a prompt such as, "Based on the nighttime activity patterns of elderly people, please suggest measures to improve their health." This enables effective health management based on data.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server acquires video and environmental data in real time from cameras and sensors installed within the home. The input for this step is raw data from the cameras and sensors, and the output is the acquired data stream. The cameras detect the location and posture of the elderly person, and the sensors measure temperature and motion.

[0479] Step 2:

[0480] The server stores the retrieved data in a database, preparing it for subsequent processing. The input to this step is the data stream retrieved in step 1, and the output is structured data in the database. The server uses AWS data storage solutions to efficiently store the data.

[0481] Step 3:

[0482] The server analyzes the stored data using computational methods. The input is structured data from a database, and the output is the analysis result. Here, a generative AI model is used to detect anomalies and analyze behavioral characteristics. This makes it possible to distinguish between normal patterns and anomalies in elderly individuals.

[0483] Step 4:

[0484] Based on the analysis results, the server immediately sends a notification using a notification system if it detects an anomaly. The input is the analyzed data, and the output is a notification message. The notification is sent to smartphones and computers via Amazon SNS.

[0485] Step 5:

[0486] The server generates preventative instructions based on long-term data analysis. The input here is accumulated historical data, and the output is suggestions for predicting health risks. The server uses a generative AI model to generate a prompt message for the user, such as "Please suggest ways to improve health based on the nocturnal behavior patterns of elderly people," and then provides suggestions.

[0487] Step 6:

[0488] The terminal displays preventative instructions and analysis results sent from the server on a display device. Input is suggestions and results from the server, and output is visual information for the user. This allows family members and caregivers of elderly individuals to understand the situation and take appropriate action.

[0489] 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.

[0490] This invention relates to a system that improves user safety and psychological well-being by detecting user actions and emotions in real time and providing appropriate notifications and suggestions. The system comprises a sensing device, a computing device, an emotion engine, notification means, and a terminal device.

[0491] The server acquires video data, including user movement information, and environmental data from sensing devices installed in the home. In addition, it uses an emotion engine to analyze facial expressions and voice from the video data to recognize the user's emotional state. This makes it possible to understand not only physical movements but also emotional changes in real time.

[0492] The server applies AI algorithms to analyze collected behavioral and emotional data to determine if there are any abnormalities and to take into account changes in emotions. If an abnormality is detected, for example, if a user falls and is also experiencing pain, the server can generate a notification that takes the detailed circumstances into account. For example, it might generate an alert such as, "There is a risk of falling, and the user is experiencing pain. Please check immediately."

[0493] The generated notification is immediately sent to the device and displayed on the smartphone or computer screen of family members or caregivers. This makes it possible to quickly confirm the user's safety and take appropriate action.

[0494] Furthermore, the server accumulates data over long periods and analyzes behavioral and emotional trends. Based on this, the server can generate and send suggestions for preventative care and lifestyle improvements to the device. For example, it might make specific suggestions such as, "You've been showing signs of stress lately. Please incorporate relaxing activities into your routine."

[0495] In this embodiment of the invention, by monitoring the user's condition from both an operational and emotional perspective, it is possible to support not only physical safety but also psychological well-being. This makes it possible to provide an environment in which elderly people can live with peace of mind.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server collects video and environmental data in real time from sensing devices installed within the home. The sensing devices consist of cameras and sensors that comprehensively record the user's movements and the surrounding conditions.

[0499] Step 2:

[0500] The server analyzes the user's facial expressions and voice tone from video data acquired using an emotion engine to recognize their current emotional state. This analysis is performed by an AI algorithm designed to capture changes in emotions.

[0501] Step 3:

[0502] The server uses the collected behavioral and emotional data to perform analysis using an AI algorithm. This analysis identifies normal behavioral patterns and determines whether abnormal behavior and emotional changes are occurring simultaneously.

[0503] Step 4:

[0504] When an anomaly is detected, the server creates a notification based on the nature of the anomaly and the emotional state of the user. For example, if a fall is detected along with the emotion of fear, the server will generate a notification such as, "There is a possibility of falling, and the user is feeling fear. Immediate action is required."

[0505] Step 5:

[0506] The server sends the generated notification to the device. The device is a smartphone or computer used by family members or caregivers, and the notification is displayed on these devices, allowing for immediate action.

[0507] Step 6:

[0508] Users check notifications on their devices and take necessary actions. Further instructions or assistance may be requested through responses from the user to the server.

[0509] Step 7:

[0510] The server analyzes behavioral and emotional data accumulated over a long period to extract trends. Based on these analysis results, it can generate and send suggestions for preventative care and lifestyle improvements to the user's device. For example, it might say, "We've observed an increase in stress levels in the past few weeks. We recommend getting adequate rest."

[0511] Through these processing steps, the system comprehensively supports the user's physical and psychological health.

[0512] (Example 2)

[0513] 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."

[0514] In modern society, ensuring physical safety and maintaining psychological health are crucial issues, especially for the elderly and individuals experiencing psychological anxiety. However, conventional technologies have focused on monitoring movements and notifying abnormalities, making it difficult to comprehensively understand and respond quickly to an individual's emotional state. As a result, physical accidents and psychological stress may be overlooked.

[0515] 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.

[0516] In this invention, the server includes sensing means for detecting the user's actions and emotions, calculation means for receiving data from the sensing means and analyzing the action patterns and emotional state, and notification means for detecting anomalies, generating notifications that take into account changes in emotions, and transmitting them to the terminal. This makes it possible to understand the user's actions and emotions in real time and support safety and psychological health.

[0517] "Sensing means" refers to a collection of devices for detecting the user's actions and emotions in real time, and mainly includes devices such as cameras, microphones, and sensors.

[0518] "Computation means" refers to a device or software for processing data received from sensing means and analyzing behavioral patterns and emotional states, and includes technology for identifying anomalies using AI algorithms.

[0519] "Notification means" refers to the function of a device or software that promptly transmits relevant information to the user's or related party's terminal when an anomaly is detected.

[0520] "Preventive suggestions" refer to advice and instructions aimed at improving users' lives and ensuring their safety, generated based on the accumulation and analysis of data over a long period using computational methods.

[0521] This invention is a system that detects the user's actions and emotions in real time and provides appropriate notifications and suggestions. The system consists of a sensing device, a computing device, an emotion engine, a notification means, and a terminal device.

[0522] The server receives video and environmental data acquired by sensing devices installed in the home (such as cameras and sensors). The emotion engine uses this data to analyze the user's emotional state from their facial expressions and voice. This makes it possible to recognize not only the user's physical movements but also changes in their emotions in real time.

[0523] The computing unit applies an AI algorithm to the collected motion data and emotion data. This algorithm has the function of detecting anomalies and taking into account changes in emotion. For example, if a user suddenly falls, the server recognizes this action and also detects the emotion of pain from the emotion data, and determines that an anomaly has occurred.

[0524] If an anomaly is detected, the server will immediately send a detailed notification to the device via a notification system. This notification will be displayed on the smartphone or computer screen of family members or caregivers to encourage prompt action.

[0525] Furthermore, the server accumulates data over a long period and analyzes the user's behavioral and emotional patterns. Based on this, the server generates specific suggestions for preventative care and lifestyle improvements and sends them to the user's device. An example of such a suggestion might be, "You appear to be experiencing stress recently. Please incorporate activities that help you relax."

[0526] Furthermore, by using an example of a prompt message for the generating AI model, such as "Analyze user behavior data and emotion data, and generate a notification message if an anomaly is detected," the system can take more specific actions.

[0527] This invention comprehensively supports the physical safety and psychological health of users, and can provide a safe living environment for the elderly and individuals experiencing psychological anxiety.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The server acquires video and environmental data from the sensing devices. Specifically, cameras and sensors installed in the home detect movement information in real time and transmit it to the server. The input is video and environmental data from the sensing devices, and the output is a dataset for analysis.

[0531] Step 2:

[0532] The server analyzes the acquired data using an emotion engine. Specifically, it performs facial recognition from video data and determines emotions from changes in facial expressions and voice. The input is video data, and the output is data indicating the user's emotional state. An AI algorithm is used for this analysis.

[0533] Step 3:

[0534] The server analyzes motion data and emotional data using an AI algorithm to determine if an anomaly has occurred. The input is data combining motion information and emotional state, and the output is the result of the anomaly detection. Specifically, when there is a fall or sudden movement, the server determines the anomaly by combining the situation and emotions.

[0535] Step 4:

[0536] The server generates a notification message when an anomaly is detected. This notification informs family members or caregivers of the user's condition. The input is the detected anomaly, and the output is a detailed notification message. A generation AI model can be used to generate the notification message, and an example of a prompt message would be, "The user has fallen and is complaining of pain. Prompt attention is required."

[0537] Step 5:

[0538] The server sends the generated notification to the device. For example, it might be displayed in real time on a family member's smartphone or a caregiver's computer. The input is the generated notification text, and the output is the received notification from the device. This facilitates a quick response.

[0539] Step 6:

[0540] The server accumulates long-term data and analyzes behavioral and emotional trends. Input is behavioral and emotional data over a certain period, and output is the analysis of behavioral and emotional trends based on that data. Based on these results, it provides preventative suggestions to the user. Specific examples include instructions such as, "Your stress levels appear to be increasing. We suggest activities that can help you relax."

[0541] (Application Example 2)

[0542] 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."

[0543] In modern society, ensuring the safety and psychological well-being of the elderly and those living alone is a crucial issue. In particular, comprehensive monitoring is needed that considers not only physical risks such as falls and sudden illnesses, but also psychological aspects such as stress and anxiety in daily life. However, achieving this requires a system that can detect and analyze both actions and emotions in real time and respond quickly and appropriately. Currently, however, it is difficult for existing systems to analyze actions and emotions in an integrated manner, resulting in insufficient intervention and suggestions for users.

[0544] 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.

[0545] In this invention, the server includes a sensing device for detecting the user's actions and emotions, a calculation means for receiving data from the sensing device and analyzing the action patterns and emotional state, a notification means for detecting anomalies and generating and transmitting notifications that take into account the user's emotional changes to a terminal device, and a function for analyzing long-term trends in actions and emotions and generating preventative suggestions. This enables integrated monitoring of the user's physical risks and psychological health, detection of anomalies and rapid response, and even suggestions for lifestyle improvements.

[0546] A "sensing device" is hardware used to detect a user's actions and emotions, and includes sensor devices such as cameras and microphones.

[0547] "Computation means" refers to a processing device that receives data from a sensing device and analyzes the user's behavior patterns and emotional state, and includes a processor that executes AI algorithms.

[0548] A "notification mechanism" is a function that, when an anomaly is detected, generates a notification that takes into account the user's emotional changes and sends it to the terminal device.

[0549] "Emotional state" refers to the psychological state of a user as inferred from their facial expressions, tone of voice, and actions.

[0550] A "terminal device" is a device used to receive notifications transmitted from a sensing device, and includes smartphones, tablets, and other similar devices.

[0551] "Analysis of long-term trends" is a process of evaluating the patterns of change in users' actions and emotions based on accumulated action and emotion data from the past.

[0552] "Preventive suggestions" are suggestions and advice based on long-term data analysis to improve the physical and psychological health of users.

[0553] To implement this invention, a system is used that involves installing sensing devices in the user's living environment and collecting motion and emotional data. The sensing devices include sensors such as cameras and microphones, and have the function of detecting the user's motion and emotional state in real time. This data is transmitted to a server.

[0554] The server analyzes behavioral patterns and emotional states based on the received data using computational means. AI algorithms are applied to the data analysis, performing anomaly detection and emotional change analysis. A platform like Google Cloud AI is used for this analysis.

[0555] When an anomaly is detected, the server generates a notification via a notification system that takes into account the user's emotional changes. This notification is sent to the user's terminal device, such as a smartphone or tablet. This allows for quick confirmation of the user's safety and appropriate action to be taken.

[0556] Furthermore, the server accumulates a large amount of behavioral and emotional data and analyzes trends over a long period. Based on this analysis, it can generate and deliver preventative suggestions to improve the user's physical and psychological health. This reduces risks in the user's daily life and provides a safer environment.

[0557] For example, a server could detect a fall by an elderly person, identify the emotion of pain using an emotion engine, and send a notification to the family saying, "Grandma has fallen and is in pain," to encourage a quick response. Another example of a prompt message is, "Please propose a system design for detecting falls by elderly people." This would contribute to ensuring the safety of the elderly in society.

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server receives video and audio data acquired from the camera and microphone from the sensing device. This input data includes signs of the user's actions and emotions, and subsequent processing is performed based on this data.

[0561] Step 2:

[0562] The server applies AI algorithms to the received data to analyze behavioral patterns and emotional states. In particular, it uses facial recognition and voice analysis technologies to quantify the user's emotions. This data processing allows the server to determine whether the user is feeling at ease or stressed.

[0563] Step 3:

[0564] When the server detects an anomaly through motion analysis, it immediately generates a notification, taking into account changes in the user's emotional state. For example, if the analysis indicates that a user has fallen and is complaining of pain, the server generates a specific alert stating, "The user has fallen and is experiencing pain." This notification is immediately sent to the devices of family members or other relevant parties.

[0565] Step 4:

[0566] The device receives the sent notification and displays an alert to the user. If the device is the user's smartphone, it is displayed as a high-priority message via push notification to help the user respond quickly.

[0567] Step 5:

[0568] The server performs long-term trend analysis based on accumulated behavioral and emotional data. This process involves identifying patterns in behavioral changes and emotions over time, and processing the data to improve the user's living and psychological state.

[0569] Step 6:

[0570] Based on the trend analysis results, the server generates suggestions to support the user's physical and mental health. For example, if the data indicates that the user has recently been experiencing stress, it generates a suggestion such as, "You've recently shown signs of stress. Consider relaxation activities," and sends it to the device.

[0571] 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.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention relates to a non-contact anomaly detection system that improves the safety of the elderly by detecting user movements in real time and immediately notifying them of abnormalities. This system comprises a sensing device, a computing device, a notification means, and a terminal device.

[0589] The server acquires data from cameras and sensors installed in the home. Cameras record the user's location and posture as video data, while sensors collect numerical data such as room temperature and whether or not there is movement. This data is then taken to the server and prepared for analysis.

[0590] The server runs an AI algorithm to analyze the user's daily behavior patterns based on the acquired data. Based on this analysis, it compares the user's behavior patterns to normal ones to check for any abnormalities. For example, it identifies abnormalities such as falls or prolonged periods of inactivity.

[0591] If an anomaly is detected, the server creates a notification and immediately sends it to the terminal device. The terminal device is a smartphone or computer owned by the user's family or caregiver, and displays an alert such as, "A fall has been detected. Confirmation is required." This notification allows the user's family to quickly check the situation and take appropriate action.

[0592] Furthermore, the server stores the collected data over a long period and evaluates the user's health status and lifestyle patterns through AI analysis. Based on the results of this data analysis, the server can generate preventative care and lifestyle improvement suggestions and send them to the device. For example, it might suggest, "Based on recent data, lower back pain is a concern, so we recommend appropriate exercise."

[0593] In this embodiment of the present invention, it is possible to ensure the user's safety in real time and support long-term health maintenance through preventative measures. The crawling aid can improve the safety and quality of life for elderly people living alone at home.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The server collects data in real time from sensing devices installed within the home. It acquires video data from cameras and receives environmental data such as temperature and motion detection from sensors.

[0597] Step 2:

[0598] The server preprocesses the collected raw data to make it analyzable. From the video data, it extracts the user's movements and calculates features such as the speed and direction of body movements.

[0599] Step 3:

[0600] The server applies an AI algorithm to compare the pre-processed data with normal behavioral patterns. Here, it evaluates whether abnormal behaviors in elderly individuals, such as falls or prolonged periods of immobility, are detected.

[0601] Step 4:

[0602] When an anomaly is detected, the server identifies the type and details of the anomaly. Based on this, it organizes information to notify family members or caregivers and generates a notification message.

[0603] Step 5:

[0604] The server generates a notification and sends it to the device. The device is a smartphone or computer belonging to a family member or caregiver, which receives the notification and displays the message "A fall has been detected. Confirmation is required" on the screen.

[0605] Step 6:

[0606] The user checks the notification received on their device and responds to the server as needed. Based on the response, the server issues further instructions or updates data as appropriate.

[0607] Step 7:

[0608] The server analyzes data accumulated over a long period and generates preventative suggestions. For example, based on past data and behavioral patterns, the server sends recommendations for lifestyle improvements to the device.

[0609] This processing flow allows the system to function with the aim of supporting the safety and health maintenance of the elderly.

[0610] (Example 1)

[0611] 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".

[0612] In modern times, the number of elderly people living alone is increasing, making daily safety a critical issue. Furthermore, there is a need for technology that can quickly detect and notify of abnormalities. Beyond the need for rapid response to unforeseen circumstances, the challenge lies in providing a better quality of life through long-term health monitoring and preventative health management.

[0613] 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.

[0614] In this invention, the server includes a detection means for detecting user movements non-contactually, a calculation means for receiving video data and environmental information obtained from the detection means, analyzing daily movement patterns using a generation AI, and identifying abnormalities, and a notification means for generating a notification based on the content of the identified abnormality and transmitting it to a communication device. This enables rapid abnormality detection and notification to ensure the safety of the elderly, and further enables improvement of quality of life through long-term health assessment and preventive suggestions.

[0615] A "detection means" is a device for detecting a user's movements without physical contact, and uses devices such as cameras and sensors to acquire information about the surrounding environment, as well as the user's location and posture.

[0616] "Video data" refers to digital visual information acquired by detection devices, which records the user's movements and location in detail.

[0617] "Environmental information" refers to information about the user's surrounding environment, including data such as room temperature and presence or absence of movement, which are acquired by detection means.

[0618] The "computation means" is a device that uses received video data and environmental information to analyze the user's daily movement patterns and uses generated AI to detect anomalies.

[0619] "Generative AI" refers to an artificial intelligence model used to compare a user's normal behavior with abnormal behavior, and includes algorithms for pattern recognition and analysis.

[0620] A "notification device" is a device that, when an anomaly is identified, creates a notification based on its contents and transmits it to a communication device.

[0621] This invention is a non-contact anomaly detection system for improving the safety of the elderly. The system mainly consists of the following four elements: detection means, calculation means, notification means, and communication device.

[0622] Server role:

[0623] The server acquires video data of the user's location and posture through detection means such as cameras and sensors placed in the home, and collects environmental information such as temperature and whether there is movement. This data is then transmitted to the server.

[0624] The server uses a generative AI model to analyze the user's daily activity patterns based on the collected data. This generative AI model compares normal and abnormal movements to detect situations such as falls or sudden stops. The server also stores the analyzed data and can evaluate the user's health status by analyzing long-term behavioral patterns.

[0625] Terminal role:

[0626] When an anomaly is detected, the server uses a notification system to transmit the information to a communication device. The device is a smartphone or computer owned by the user's family or caregiver, and upon receiving the notification, it is displayed as a pop-up alert. This allows family members to quickly check the situation and take necessary actions.

[0627] Specific example:

[0628] For example, if the server is monitoring a user's activity in the living room during the afternoon, and the user remains seated on the sofa watching TV for more than 30 minutes without moving, this will be detected as an abnormal inactivity. Based on this information, a notification stating "An abnormally long period of inactivity has been detected" is sent to the device, allowing family members to immediately take action to check the situation.

[0629] Example of a prompt:

[0630] "Analyze the data from the elderly monitoring system using a generated AI model to verify the fall detection algorithm. Please also generate notification messages for abnormal operation."

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server acquires video data and environmental information from cameras and sensors installed in each room of the home. Inputs include video data of the user's movements captured by the cameras, and sensor information that monitors room temperature and movement. The server receives these inputs and temporarily stores them in a database.

[0634] Step 2:

[0635] The server preprocesses the acquired raw data. This step involves noise reduction and data interpolation. The input consists of the raw video data and environmental information acquired and saved in step 1. The server uses various filtering techniques to output clean data in an analyzable format.

[0636] Step 3:

[0637] The server inputs pre-processed data into a generating AI model to analyze the user's daily movement patterns. This step utilizes a function that compares normal and abnormal movements. The input is the pre-processed data obtained in step 2. The server outputs anomaly detection alert information from the generating AI model. A specific example of this analysis is the step of detecting frames in which the user has fallen.

[0638] Step 4:

[0639] If an anomaly is detected, the server immediately generates a notification based on the information. The input is the anomaly detection result from step 3. The server generates a notification message such as "A fall has been detected. Verification is required" and prepares it as output.

[0640] Step 5:

[0641] The server sends the generated notification to the terminal via its communication function. The input is the notification message generated in step 4. The output is the server sending the notification to the terminal and its immediate display on the user's or caregiver's smartphone or computer.

[0642] Step 6:

[0643] The server stores the analyzed data for long-term health monitoring. The input is the entirety of the analyzed data from step 3. The server stores this data information in a database and outputs it in a format that can be used later to evaluate behavioral patterns and health status.

[0644] (Application Example 1)

[0645] 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".

[0646] In the living environments of the elderly, there is a need to efficiently detect and quickly respond to dangers such as falls and prolonged periods of immobility. However, conventional systems have difficulty in the immediate detection and notification of these abnormalities, and furthermore, subsequent preventative instructions and visualization of the situation are often insufficient. This invention aims to solve these problems and improve the quality of life for the elderly.

[0647] 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.

[0648] In this invention, the server includes detection means for detecting user actions, calculation means for receiving data from the detection means and analyzing operational characteristics, notification means for generating a warning when an abnormality is detected and communicating it to an information terminal, a function for generating preventative instructions using the calculation means, and display means for visualizing the analysis results obtained from the calculation means. This makes it possible not only to ensure the safety of the elderly in real time, but also to provide measures to improve their health based on data analysis.

[0649] A "detection means" is a device for non-contact sensing of a user's actions or changes in their surroundings.

[0650] A "computation means" is a device that analyzes data acquired from a detection means to detect operational characteristics and abnormalities.

[0651] A "notification device" is a device used to quickly notify relevant parties of information when an anomaly is detected.

[0652] The "preventive guidance generation function" is a function that generates advice and suggestions regarding potential health risks based on analyzed data.

[0653] A "display means" is a device for visually presenting analysis results and preventative instructions obtained from a calculation means.

[0654] The system implementing this invention is designed to ensure the safety of the elderly and aims to quickly detect abnormalities that may occur in daily life and notify relevant parties of that information.

[0655] First, the system uses cameras and sensors placed within the home to detect the user's movements and changes in the environment in real time. This data is recorded by the detection devices and transmitted to a server.

[0656] The server uses computational tools to analyze the received data. This algorithm incorporates generative AI models using software such as TensorFlow and PyTorch to identify anomalies that deviate from normal operating patterns. During this process, the data is processed on the AWS cloud infrastructure.

[0657] When an anomaly is detected, the server uses a notification system to send a notification to the smartphone or computer of family members or caregivers. This alert notification, sent via Amazon SNS, conveys the nature and urgency of the anomaly.

[0658] Furthermore, the server generates preventative instructions and provides advice to proactively prevent health risks based on data obtained from calculations. This function is visually presented as graphs and text information by the display system. For example, if recent data shows an increase in nighttime activity among the elderly, a specific example might be displayed such as, "The number of times you get up at night has increased. Consider improving your nighttime environment (lighting and bedding)."

[0659] The input to the generating AI model is a prompt such as, "Based on the nighttime activity patterns of elderly people, please suggest measures to improve their health." This enables effective health management based on data.

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The server acquires video and environmental data in real time from cameras and sensors installed within the home. The input for this step is raw data from the cameras and sensors, and the output is the acquired data stream. The cameras detect the location and posture of the elderly person, and the sensors measure temperature and motion.

[0663] Step 2:

[0664] The server stores the retrieved data in a database, preparing it for subsequent processing. The input to this step is the data stream retrieved in step 1, and the output is structured data in the database. The server uses AWS data storage solutions to efficiently store the data.

[0665] Step 3:

[0666] The server analyzes the stored data using computational methods. The input is structured data from a database, and the output is the analysis result. Here, a generative AI model is used to detect anomalies and analyze behavioral characteristics. This makes it possible to distinguish between normal patterns and anomalies in elderly individuals.

[0667] Step 4:

[0668] Based on the analysis results, the server immediately sends a notification using a notification system if it detects an anomaly. The input is the analyzed data, and the output is a notification message. The notification is sent to smartphones and computers via Amazon SNS.

[0669] Step 5:

[0670] The server generates preventative instructions based on long-term data analysis. The input here is accumulated historical data, and the output is suggestions for predicting health risks. The server uses a generative AI model to generate a prompt message for the user, such as "Please suggest ways to improve health based on the nocturnal behavior patterns of elderly people," and then provides suggestions.

[0671] Step 6:

[0672] The terminal displays preventative instructions and analysis results sent from the server on a display device. Input is suggestions and results from the server, and output is visual information for the user. This allows family members and caregivers of elderly individuals to understand the situation and take appropriate action.

[0673] 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.

[0674] This invention relates to a system that improves user safety and psychological well-being by detecting user actions and emotions in real time and providing appropriate notifications and suggestions. The system comprises a sensing device, a computing device, an emotion engine, notification means, and a terminal device.

[0675] The server acquires video data, including user movement information, and environmental data from sensing devices installed in the home. In addition, it uses an emotion engine to analyze facial expressions and voice from the video data to recognize the user's emotional state. This makes it possible to understand not only physical movements but also emotional changes in real time.

[0676] The server applies AI algorithms to analyze collected behavioral and emotional data to determine if there are any abnormalities and to take into account changes in emotions. If an abnormality is detected, for example, if a user falls and is also experiencing pain, the server can generate a notification that takes the detailed circumstances into account. For example, it might generate an alert such as, "There is a risk of falling, and the user is experiencing pain. Please check immediately."

[0677] The generated notification is immediately sent to the device and displayed on the smartphone or computer screen of family members or caregivers. This makes it possible to quickly confirm the user's safety and take appropriate action.

[0678] Furthermore, the server accumulates data over long periods and analyzes behavioral and emotional trends. Based on this, the server can generate and send suggestions for preventative care and lifestyle improvements to the device. For example, it might make specific suggestions such as, "You've been showing signs of stress lately. Please incorporate relaxing activities into your routine."

[0679] In this embodiment of the invention, by monitoring the user's condition from both an operational and emotional perspective, it is possible to support not only physical safety but also psychological well-being. This makes it possible to provide an environment in which elderly people can live with peace of mind.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server collects video and environmental data in real time from sensing devices installed within the home. The sensing devices consist of cameras and sensors that comprehensively record the user's movements and the surrounding conditions.

[0683] Step 2:

[0684] The server analyzes the user's facial expressions and voice tone from video data acquired using an emotion engine to recognize their current emotional state. This analysis is performed by an AI algorithm designed to capture changes in emotions.

[0685] Step 3:

[0686] The server uses the collected behavioral and emotional data to perform analysis using an AI algorithm. This analysis identifies normal behavioral patterns and determines whether abnormal behavior and emotional changes are occurring simultaneously.

[0687] Step 4:

[0688] When an anomaly is detected, the server creates a notification based on the nature of the anomaly and the emotional state of the user. For example, if a fall is detected along with the emotion of fear, the server will generate a notification such as, "There is a possibility of falling, and the user is feeling fear. Immediate action is required."

[0689] Step 5:

[0690] The server sends the generated notification to the device. The device is a smartphone or computer used by family members or caregivers, and the notification is displayed on these devices, allowing for immediate action.

[0691] Step 6:

[0692] Users check notifications on their devices and take necessary actions. Further instructions or assistance may be requested through responses from the user to the server.

[0693] Step 7:

[0694] The server analyzes behavioral and emotional data accumulated over a long period to extract trends. Based on these analysis results, it can generate and send suggestions for preventative care and lifestyle improvements to the user's device. For example, it might say, "We've observed an increase in stress levels in the past few weeks. We recommend getting adequate rest."

[0695] Through these processing steps, the system comprehensively supports the user's physical and psychological health.

[0696] (Example 2)

[0697] 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".

[0698] In modern society, ensuring physical safety and maintaining psychological health are crucial issues, especially for the elderly and individuals experiencing psychological anxiety. However, conventional technologies have focused on monitoring movements and notifying abnormalities, making it difficult to comprehensively understand and respond quickly to an individual's emotional state. As a result, physical accidents and psychological stress may be overlooked.

[0699] 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.

[0700] In this invention, the server includes sensing means for detecting the user's actions and emotions, calculation means for receiving data from the sensing means and analyzing the action patterns and emotional state, and notification means for detecting anomalies, generating notifications that take into account changes in emotions, and transmitting them to the terminal. This makes it possible to understand the user's actions and emotions in real time and support safety and psychological health.

[0701] "Sensing means" refers to a collection of devices for detecting the user's actions and emotions in real time, and mainly includes devices such as cameras, microphones, and sensors.

[0702] "Computation means" refers to a device or software for processing data received from sensing means and analyzing behavioral patterns and emotional states, and includes technology for identifying anomalies using AI algorithms.

[0703] "Notification means" refers to the function of a device or software that promptly transmits relevant information to the user's or related party's terminal when an anomaly is detected.

[0704] "Preventive suggestions" refer to advice and instructions aimed at improving users' lives and ensuring their safety, generated based on the accumulation and analysis of data over a long period using computational methods.

[0705] This invention is a system that detects the user's actions and emotions in real time and provides appropriate notifications and suggestions. The system consists of a sensing device, a computing device, an emotion engine, a notification means, and a terminal device.

[0706] The server receives video and environmental data acquired by sensing devices installed in the home (such as cameras and sensors). The emotion engine uses this data to analyze the user's emotional state from their facial expressions and voice. This makes it possible to recognize not only the user's physical movements but also changes in their emotions in real time.

[0707] The computing unit applies an AI algorithm to the collected motion data and emotion data. This algorithm has the function of detecting anomalies and taking into account changes in emotion. For example, if a user suddenly falls, the server recognizes this action and also detects the emotion of pain from the emotion data, and determines that an anomaly has occurred.

[0708] If an anomaly is detected, the server will immediately send a detailed notification to the device via a notification system. This notification will be displayed on the smartphone or computer screen of family members or caregivers to encourage prompt action.

[0709] Furthermore, the server accumulates data over a long period and analyzes the user's behavioral and emotional patterns. Based on this, the server generates specific suggestions for preventative care and lifestyle improvements and sends them to the user's device. An example of such a suggestion might be, "You appear to be experiencing stress recently. Please incorporate activities that help you relax."

[0710] Furthermore, by using an example of a prompt message for the generating AI model, such as "Analyze user behavior data and emotion data, and generate a notification message if an anomaly is detected," the system can take more specific actions.

[0711] This invention comprehensively supports the physical safety and psychological health of users, and can provide a safe living environment for the elderly and individuals experiencing psychological anxiety.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server acquires video and environmental data from the sensing devices. Specifically, cameras and sensors installed in the home detect movement information in real time and transmit it to the server. The input is video and environmental data from the sensing devices, and the output is a dataset for analysis.

[0715] Step 2:

[0716] The server analyzes the acquired data using an emotion engine. Specifically, it performs facial recognition from video data and determines emotions from changes in facial expressions and voice. The input is video data, and the output is data indicating the user's emotional state. An AI algorithm is used for this analysis.

[0717] Step 3:

[0718] The server analyzes motion data and emotional data using an AI algorithm to determine if an anomaly has occurred. The input is data combining motion information and emotional state, and the output is the result of the anomaly detection. Specifically, when there is a fall or sudden movement, the server determines the anomaly by combining the situation and emotions.

[0719] Step 4:

[0720] The server generates a notification message when an anomaly is detected. This notification informs family members or caregivers of the user's condition. The input is the detected anomaly, and the output is a detailed notification message. A generation AI model can be used to generate the notification message, and an example of a prompt message would be, "The user has fallen and is complaining of pain. Prompt attention is required."

[0721] Step 5:

[0722] The server sends the generated notification to the device. For example, it might be displayed in real time on a family member's smartphone or a caregiver's computer. The input is the generated notification text, and the output is the received notification from the device. This facilitates a quick response.

[0723] Step 6:

[0724] The server accumulates long-term data and analyzes behavioral and emotional trends. Input is behavioral and emotional data over a certain period, and output is the analysis of behavioral and emotional trends based on that data. Based on these results, it provides preventative suggestions to the user. Specific examples include instructions such as, "Your stress levels appear to be increasing. We suggest activities that can help you relax."

[0725] (Application Example 2)

[0726] 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".

[0727] In modern society, ensuring the safety and psychological well-being of the elderly and those living alone is a crucial issue. In particular, comprehensive monitoring is needed that considers not only physical risks such as falls and sudden illnesses, but also psychological aspects such as stress and anxiety in daily life. However, achieving this requires a system that can detect and analyze both actions and emotions in real time and respond quickly and appropriately. Currently, however, it is difficult for existing systems to analyze actions and emotions in an integrated manner, resulting in insufficient intervention and suggestions for users.

[0728] 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.

[0729] In this invention, the server includes a sensing device for detecting the user's actions and emotions, a calculation means for receiving data from the sensing device and analyzing the action patterns and emotional state, a notification means for detecting anomalies and generating and transmitting notifications that take into account the user's emotional changes to a terminal device, and a function for analyzing long-term trends in actions and emotions and generating preventative suggestions. This enables integrated monitoring of the user's physical risks and psychological health, detection of anomalies and rapid response, and even suggestions for lifestyle improvements.

[0730] A "sensing device" is hardware used to detect a user's actions and emotions, and includes sensor devices such as cameras and microphones.

[0731] "Computation means" refers to a processing device that receives data from a sensing device and analyzes the user's behavior patterns and emotional state, and includes a processor that executes AI algorithms.

[0732] A "notification mechanism" is a function that, when an anomaly is detected, generates a notification that takes into account the user's emotional changes and sends it to the terminal device.

[0733] "Emotional state" refers to the psychological state of a user as inferred from their facial expressions, tone of voice, and actions.

[0734] A "terminal device" is a device used to receive notifications transmitted from a sensing device, and includes smartphones, tablets, and other similar devices.

[0735] "Analysis of long-term trends" is a process of evaluating the patterns of change in users' actions and emotions based on accumulated action and emotion data from the past.

[0736] "Preventive suggestions" are suggestions and advice based on long-term data analysis to improve the physical and psychological health of users.

[0737] To implement this invention, a system is used that involves installing sensing devices in the user's living environment and collecting motion and emotional data. The sensing devices include sensors such as cameras and microphones, and have the function of detecting the user's motion and emotional state in real time. This data is transmitted to a server.

[0738] The server analyzes behavioral patterns and emotional states based on the received data using computational means. AI algorithms are applied to the data analysis, performing anomaly detection and emotional change analysis. A platform like Google Cloud AI is used for this analysis.

[0739] When an anomaly is detected, the server generates a notification via a notification system that takes into account the user's emotional changes. This notification is sent to the user's terminal device, such as a smartphone or tablet. This allows for quick confirmation of the user's safety and appropriate action to be taken.

[0740] Furthermore, the server accumulates a large amount of behavioral and emotional data and analyzes trends over a long period. Based on this analysis, it can generate and deliver preventative suggestions to improve the user's physical and psychological health. This reduces risks in the user's daily life and provides a safer environment.

[0741] For example, a server could detect a fall by an elderly person, identify the emotion of pain using an emotion engine, and send a notification to the family saying, "Grandma has fallen and is in pain," to encourage a quick response. Another example of a prompt message is, "Please propose a system design for detecting falls by elderly people." This would contribute to ensuring the safety of the elderly in society.

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The server receives video and audio data acquired from the camera and microphone from the sensing device. This input data includes signs of the user's actions and emotions, and subsequent processing is performed based on this data.

[0745] Step 2:

[0746] The server applies AI algorithms to the received data to analyze behavioral patterns and emotional states. In particular, it uses facial recognition and voice analysis technologies to quantify the user's emotions. This data processing allows the server to determine whether the user is feeling at ease or stressed.

[0747] Step 3:

[0748] When the server detects an anomaly through motion analysis, it immediately generates a notification, taking into account changes in the user's emotional state. For example, if the analysis indicates that a user has fallen and is complaining of pain, the server generates a specific alert stating, "The user has fallen and is experiencing pain." This notification is immediately sent to the devices of family members or other relevant parties.

[0749] Step 4:

[0750] The device receives the sent notification and displays an alert to the user. If the device is the user's smartphone, it is displayed as a high-priority message via push notification to help the user respond quickly.

[0751] Step 5:

[0752] The server performs long-term trend analysis based on accumulated behavioral and emotional data. This process involves identifying patterns in behavioral changes and emotions over time, and processing the data to improve the user's living and psychological state.

[0753] Step 6:

[0754] Based on the trend analysis results, the server generates suggestions to support the user's physical and mental health. For example, if the data indicates that the user has recently been experiencing stress, it generates a suggestion such as, "You've recently shown signs of stress. Consider relaxation activities," and sends it to the device.

[0755] 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.

[0756] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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."

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] A sensing device for detecting user movements,

[0779] A calculation device for receiving data from the aforementioned sensing device and analyzing the operating pattern,

[0780] A notification means that generates a notification when an anomaly is detected and sends it to a terminal device,

[0781] The aforementioned computing device has a function to generate preventative suggestions,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the sensing device is for acquiring video data and environmental data.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the computing device accumulates past operational data and performs long-term behavioral analysis.

[0787] "Example 1"

[0788] (Claim 1)

[0789] A detection means for detecting user movements without physical contact,

[0790] A computation means that receives video data and environmental information obtained from the aforementioned detection means, analyzes daily operation patterns using a generating AI, and identifies abnormalities.

[0791] A notification means that generates a notification based on the content of the anomaly when it is identified and sends it to a communication device,

[0792] Based on the analysis results, it has a function to evaluate long-term health status and generate preventative suggestions.

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the detection means acquires image information for recording the user's location and state, and environmental information for checking temperature and movement.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the calculation means stores operation information obtained from the input over a long period of time and analyzes behavioral patterns based on this information.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] A detection means for detecting user actions,

[0801] A calculation means for receiving data from the aforementioned detection means and analyzing the operating characteristics,

[0802] A notification system that generates a warning when an anomaly is detected and communicates it to an information terminal,

[0803] The calculation means has a function to generate preventative instructions,

[0804] A display means for visualizing the analysis results obtained from the calculation means,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the detection means is for acquiring visual data and surrounding information.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the calculation means accumulates past operational information and performs behavioral analysis over time.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] Sensing means for detecting the user's actions and emotions,

[0813] A calculation means for receiving data from the sensing means and analyzing the behavioral pattern and emotional state,

[0814] A notification means that detects anomalies, generates notifications that take into account emotional changes, and sends them to the terminal,

[0815] The aforementioned calculation means has the function of accumulating data over a long period of time, analyzing behavioral and emotional trends, and generating preventative suggestions.

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the sensing means acquires video data and environmental data and analyzes emotional states.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the calculation means accumulates past behavioral data and emotional data and performs long-term behavioral and emotional analysis.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A sensing device for detecting the user's actions and emotions,

[0824] A calculation means for receiving data from the aforementioned sensing device and analyzing the behavioral pattern and emotional state,

[0825] A notification means that detects anomalies, generates notifications that take into account changes in the user's emotions, and sends them to the terminal device.

[0826] The aforementioned calculation means has the function of analyzing behavioral and emotional trends over a long period of time and generating preventative suggestions.

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the sensing device is for acquiring video data and audio data.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the calculation means accumulates past operational data and emotional data and performs long-term behavioral and emotional analysis, including suggestions for improving psychological health. [Explanation of Symbols]

[0832] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensing device for detecting user movements, A calculation device for receiving data from the aforementioned sensing device and analyzing the operating pattern, A notification means that generates a notification when an anomaly is detected and sends it to a terminal device, The aforementioned computing device has a function to generate preventative suggestions, A system that includes this.

2. The system according to claim 1, wherein the sensing device is for acquiring video data and environmental data.

3. The system according to claim 1, wherein the computing device accumulates past operational data and performs long-term behavioral analysis.

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