System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining environmental data from a sensor; means for recording audio data; means for capturing video frames; means for transmitting the obtained data to a server; means for executing an artificial intelligence model to analyze the received data; means for logging the analysis results; and means for transmitting a notification if an anomaly is detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] It is difficult to continuously monitor the health and behavior of elderly people who live far away. Furthermore, the feeling of being constantly monitored can be stressful for the elderly and raise privacy concerns. There is a need for a method to solve this type of problem and allow family members to know the status of their elderly relatives with peace of mind. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems with a system that includes a means for acquiring environmental data from sensors, a means for recording audio data, a means for capturing video frames, a means for transmitting the acquired data to a server, a means for executing an artificial intelligence model to analyze the received data, a means for recording the analysis results as a log, and a means for sending a notification when an abnormality is detected. The sensors collect temperature, humidity, and motion detection data, and this data is used to noninvasively monitor the elderly person's daily life. The data is analyzed by the server, and abnormality notifications are sent as necessary, allowing family members to remotely monitor the elderly person's health condition. The logs are stored in a database and can be reviewed later, enabling long-term health management of the elderly person.

[0006] A "sensor" is a device for acquiring environmental data.

[0007] "Environmental data" is information that indicates the surrounding conditions, such as temperature, humidity, and motion detection data.

[0008] "Audio data" is sound information recorded by a microphone.

[0009] A "video frame" is a unit of visual information captured by a camera.

[0010] A "server" is a central processing unit that receives and analyzes acquired data.

[0011] An "artificial intelligence model" is a collection of algorithms for analyzing data and generating results.

[0012] "Analysis results" are the conclusions or evaluations reached after the artificial intelligence model analyzes the data.

[0013] "Log" refers to a chronological record of analysis results and other important data.

[0014] A "database" is a system for storing and managing logs.

[0015] "Notification" is a message to notify the user when an abnormality is detected. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0037] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system consists of sensors, microphones, cameras, a server, and a user terminal.

[0038] System configuration

[0039] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user.

[0040] Data collection

[0041] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is then compiled and sent to a server.

[0042] Data transmission and analysis

[0043] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0044] Logging and Notifications

[0045] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the emergency contacts that have been set up. The notification will be sent to the user via SMS or email.

[0046] User Verification

[0047] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary measures. For example, users can contact the elderly person's home to check on the situation.

[0048] Exemplary System Operation

[0049] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0050] This system allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The device collects environmental data from sensors placed in the living room, bedroom, etc. The temperature sensor measures the room temperature, the humidity sensor measures the humidity, and the motion detection sensor detects the elderly person's movements and collects this data.

[0054] Step 2:

[0055] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0056] Step 3:

[0057] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0058] Step 4:

[0059] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0060] Step 5:

[0061] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0062] Step 6:

[0063] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0064] Step 7:

[0065] The server runs an artificial intelligence model to analyze the received data. The AI ​​model analyzes the sensor data to detect abnormalities in the elderly's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0066] Step 8:

[0067] The server generates analysis results from the AI ​​model, including an assessment of the elderly person's daily behavior and health status, and details of any abnormalities detected.

[0068] Step 9:

[0069] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0070] Step 10:

[0071] If the server detects an abnormality as a result of the analysis, it will send a notification to the emergency contact via SMS or email to the configured user.

[0072] Step 11:

[0073] Users can access the server using their smartphones or PCs to check the latest logs and notifications, allowing them to understand the health status and behavioral patterns of their elderly relatives.

[0074] Step 12:

[0075] Users can check the details of any reported abnormalities and take necessary measures, such as contacting the elderly person's home and providing emergency support.

[0076] In this way, a series of processing steps are systematically executed, making it possible to ensure the health and safety of elderly people living far away.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] There is a need for an effective system for remotely and continuously monitoring the health status and behavior of elderly people. However, existing systems have difficulty in collecting, analyzing, and notifying abnormalities in real time, and are therefore unable to adequately ensure the safety of elderly people. Furthermore, there is a lack of a means for users to easily check logs and notifications. It is necessary to provide a system that solves these problems and efficiently manages the health and safety of elderly people.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video data, means for transmitting the acquired data to an information processing device, means for executing a generative AI model for analyzing the received data, means for recording the analysis results as a log on a recording medium, means for sending a notification when an abnormality is detected, and means for a user to check the log and notification on a display device. This makes it possible to continuously monitor the health condition and behavior of elderly people even from a distance and to respond quickly as necessary.

[0082] A "sensor" is a device for acquiring environmental data, and is used to measure data including temperature, humidity, and motion detection data.

[0083] "Audio data" is data used to record the voices of elderly people and surrounding environmental sounds.

[0084] "Video data" is visual information captured using a camera, including periodically taken video frames.

[0085] An "information processing device" is a terminal device for collectively transmitting data collected from devices such as sensors, microphones, and cameras.

[0086] A "generative AI model" is an artificial intelligence model used to analyze received data and generate information about the behavioral patterns and health status of elderly people.

[0087] The "recording medium" refers to a database or other storage device for storing the analysis results as a log.

[0088] "Notification" refers to information such as a warning message, SMS, or email sent to a user when an abnormality is detected.

[0089] A "display device" is a device such as a smartphone, PC, or tablet that a user uses to check logs and notifications.

[0090] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system is composed of sensors, microphones, cameras, a server, and a user terminal.

[0091] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal and configured to capture environmental data, audio data, and video frames. The terminal collects this data in real time and periodically transmits it to a server.

[0092] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The sensors measure this data in real time and send it to the device. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is compiled on the device and sent to a server at regular intervals.

[0093] The device sends the collected data to a server using a secure communication protocol such as HTTPS. The server uses a generative AI model to analyze the received data. The generative AI model receives sensor data, audio data, and video data as input and analyzes the data. As a result of the analysis, information about specific events, the elderly person's behavioral patterns, and health status is generated.

[0094] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the configured emergency contact. The notification will be sent to the user via SMS or email.

[0095] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary action. For example, users can contact the elderly's home, check the situation, and take emergency action.

[0096] Example prompt

[0097] Below are some example prompts to input to a generative AI model:

[0098] "Describe how the system would behave if a fall occurred in an elderly person's living room."

[0099] The above is an embodiment of the present invention. This system makes it possible to remotely manage the health and safety of elderly people, allowing their families to live their daily lives with peace of mind.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] Data collection

[0103] The device collects data from sensors, microphones, and cameras. The sensors measure temperature, humidity, and motion detection data and send it to the device. The microphone records audio data and sends it to the device. The camera captures video frames and sends visual information to the device.

[0104] Input: Temperature, humidity, motion data, audio data, video data

[0105] Output: Collected sensor data, audio data, and video data

[0106] Specific behavior:

[0107] Sensors capture temperature, humidity, and movement in real time and transmit the data to the device.

[0108] The microphone records environmental sounds and the elderly person's voice and transmits the data to the terminal.

[0109] The camera captures video frames at regular intervals and transmits the data to the device.

[0110] Step 2:

[0111] Data transmission

[0112] The device sends the collected data to the server at regular intervals, using HTTPS as a secure communication protocol.

[0113] Input: Collected sensor data, audio data, and video data

[0114] Output: Data sent to the server

[0115] Specific behavior:

[0116] The device stores data collected from sensors, microphones, and cameras in a buffer.

[0117] The device sends data to the server at regular intervals (e.g., every 10 seconds).

[0118] The data is securely transmitted to the server using a secure protocol.

[0119] Step 3:

[0120] Data analysis

[0121] The server runs a generative AI model to analyze the received data, which generates information about the elderly's behavioral patterns and health status.

[0122] Input: Sensor data, audio data, and video data sent from the device

[0123] Output: Analysis results of behavioral patterns and health status information

[0124] Specific behavior:

[0125] The server preprocesses the incoming data and converts it into a format suitable for the AI ​​model.

[0126] The generative AI model analyzes the preprocessed data as input.

[0127] The analysis results in information about behavioral patterns and health status.

[0128] Step 4:

[0129] Logging

[0130] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0131] Input: Behavioral patterns and health status information as analysis results

[0132] Output: Logs stored in a database

[0133] Specific behavior:

[0134] The server timestamps the analysis results.

[0135] The server stores the analysis results with a timestamp in a database.

[0136] Step 5:

[0137] Abnormal notification

[0138] If the server detects an abnormality in the analysis results, it will send a notification to the emergency contacts set up by the user via SMS or email.

[0139] Input: Anomaly detection information as analysis result

[0140] Output: A message to inform the user

[0141] Specific behavior:

[0142] The server determines whether an abnormality has been detected from the analysis results.

[0143] The server generates a notification message if an anomaly is detected.

[0144] The server sends notifications to the user via an SMS gateway or mail server.

[0145] Step 6:

[0146] User Verification

[0147] Users can access the server using their smartphones or PCs to check the latest logs and notifications. After logging in, users can view the necessary information and take emergency measures.

[0148] Input: Logs and notification information stored on the server

[0149] Output: Health status and behavioral pattern information confirmed by the user

[0150] Specific behavior:

[0151] The user accesses the server using a dedicated application or a web browser.

[0152] The user authenticates their account and logs in.

[0153] Users can view the latest logs and notifications and take action if necessary, such as contacting the elderly person's home.

[0154] (Application example 1)

[0155] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] Monitoring the health and behavior of elderly people living far away is extremely important, and a rapid response is required, especially when abnormalities in temperature or humidity or unexpected behavior occur. With conventional methods, it is difficult for family members living in remote locations to grasp the elderly's condition in real time, and abnormality detection and notification can be delayed. There is a need for a system that can solve this problem.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0158] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, means for detecting temperature or humidity abnormalities and sending a notification, means for detecting unexpected movements using a motion sensor, and means for recording abnormal events in a log file. This makes it possible to monitor the health status and behavior of elderly people living far away in real time, and to quickly notify them when an abnormality occurs and take appropriate action.

[0159] "Means for acquiring environmental data from sensors" refers to devices or mechanisms used to collect environmental information such as temperature, humidity, motion detection data, etc.

[0160] "Means for recording audio data" refers to a microphone or recording device for recording the voice of the elderly person and surrounding environmental sounds.

[0161] A "means for capturing video frames" is a device that periodically captures visual information using a camera or video equipment.

[0162] The "means for transmitting acquired data to a server" refers to a communication means for transferring data collected from sensors, microphones, and cameras to a server via a network.

[0163] "Means for executing an artificial intelligence model to analyze the received data" means a computer program that executes an AI algorithm to analyze the sensor data, audio data, and video data.

[0164] The "means for recording the analysis results as a log" is a storage and record management system for recording and saving the analyzed data over time.

[0165] "Means for sending a notification when an abnormality is detected" refers to a communication method for sending a warning to the user by SMS, email, or other means when an abnormality is detected.

[0166] "Means for detecting temperature or humidity abnormalities and sending a notification" refers to a system that detects abnormalities and sends a notification when the temperature or humidity data obtained from the sensor exceeds a set threshold.

[0167] "Means for detecting unexpected movements using motion sensors" refers to a mechanism for using motion sensors to detect unexpected movements or falls and identify abnormalities.

[0168] The "means for recording abnormal events in a log file" is a data storage mechanism for storing a detailed record of detected abnormal events in a log file.

[0169] This invention is a system for monitoring the health and behavior of elderly people living far away, and is composed of sensors, microphones, cameras, a server, and a user terminal. The program for this system and its processing content are described below.

[0170] System Configuration and Operation

[0171] 1. Data Collection Methods

[0172] The device uses sensors to acquire environmental data, including temperature, humidity, and motion detection data, a microphone to record audio data, and a camera to periodically capture video frames, all of which are then aggregated on the device.

[0173] 2. Data transmission method

[0174] The terminals send the collected data to a server, which receives the data and prepares it for analysis.

[0175] 3. Data Analysis Methods

[0176] The server uses an artificial intelligence model to analyze the data it receives. The model takes sensor data, audio data, and video data as input and performs the analysis.

[0177] 4. Logging of analysis results

[0178] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0179] 5. Anomaly detection and notification methods

[0180] If the server detects an abnormality, it will send a notification to the configured emergency contacts, which will be sent to the user via SMS or email.

[0181] 6. Abnormal event logging method

[0182] The server records the details of abnormal events in a log file, which allows you to later refer to the details of the abnormality and the time of detection.

[0183] Hardware and software used

[0184] Hardware:

[0185] Sensors: Temperature sensor, humidity sensor, motion sensor

[0186] Microphone: a device that records audio data

[0187] camera: a device that captures video frames

[0188] Server: A central computer that analyzes and stores data

[0189] software:

[0190] Python: Used for data collection, analysis, and sending notifications

[0191] Twilio: An API for sending SMS notifications

[0192] smtplib: A standard Python library for sending email notifications

[0193] Generative AI models: Artificial intelligence algorithms for analytical processing

[0194] Specific examples

[0195] For example, if the server detects a temperature below 15°C or above 30°C, or if the humidity is below 30% or above 70%, it detects an abnormality. The server then notifies emergency contacts of the abnormality via SMS or email. Furthermore, this abnormal event is recorded in a log file. This series of processes enables family members living far away to monitor the condition of their elderly relatives in real time and respond quickly when an abnormality occurs.

[0196] Prompt Sentence Examples

[0197] Below is an example of a prompt sentence to input to the generative AI model.

[0198] Example prompt:

[0199] text

[0200] Create a program that uses sensors and AI to monitor the health of elderly people living far away, and notify them by SMS and email if any abnormalities are detected. Use Python and use sensors, microphones, cameras, and a server as hardware. Also, use Twilio to send SMS and smtplib to send email. Include logic to send notifications when abnormal temperatures (below 15°C or above 30°C) or humidity (below 30% or above 70%) are detected.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] The device obtains environmental data (temperature, humidity, motion detection) from sensors. This involves reading the signals from the sensors and converting them into an appropriate format. The input is the raw data from the sensors, and the output is formatted values ​​of the temperature, humidity, and motion detection data.

[0204] Step 2:

[0205] The device uses a microphone to record audio data. The input is the audio signal from the microphone, and the output is an audio file (e.g., WAV format). The recorded audio is saved for further processing.

[0206] Step 3:

[0207] The device uses a camera to periodically capture video frames. The input is the video signal from the camera, and the output is the captured image file (e.g., JPEG format).

[0208] Step 4:

[0209] The device transmits the acquired environmental data, audio data, and video frames to the server via a communication method (e.g., Wi-Fi or Ethernet). The input is the collected data set, and the output is the data transferred to the server.

[0210] Step 5:

[0211] The server runs an artificial intelligence model to analyze the received data. The input is sensor, audio, and video data, and the output is the analysis results (e.g., an assessment of the elderly person's behavioral patterns and health status). The data is processed by AI algorithms to detect various abnormal patterns.

[0212] Step 6:

[0213] The server logs the analysis results. The input is the analysis results, and the output is a time-stamped log entry. The log is stored in a database.

[0214] Step 7:

[0215] The server sends notifications when an anomaly is detected. The anomaly detection algorithm runs and sends SMS or email notifications based on temperature or humidity exceeding thresholds or unexpected behavior. The input is the anomaly data analyzed, and the output is the notification message sent.

[0216] Step 8:

[0217] The server records abnormal events in detail in a log file. The input is the detected abnormal event, and the output is a detailed event record saved in the log file. This allows you to later refer to the content of the abnormality and the time of detection.

[0218] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0219] This invention is a system that combines a system for monitoring the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. The system is composed of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0220] System configuration

[0221] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user. Furthermore, an emotion engine that recognizes the user's emotions is built in, and the content of notifications can be adjusted based on the emotion.

[0222] Data collection

[0223] The device collects environmental data from sensors, including temperature, humidity, and motion detection data. The device also uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds. In addition, the device periodically captures video frames using a camera to collect visual information. This data is then compiled and sent to a server.

[0224] Data transmission and analysis

[0225] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0226] emotion recognition

[0227] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes audio data and video frames to recognize the user's emotions. It can also recognize emotions by taking environmental data into account. For example, it can detect changes in voice tone and facial expressions to determine whether the user is feeling stressed or relieved.

[0228] Logging and Notifications

[0229] The server records the analysis results as a log, which is time-stamped and stored in a database. If an anomaly is detected, the server sends a notification to the user's emergency contacts. The content of the notification is adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high stress, the notification will include a more detailed explanation and follow-up suggestions.

[0230] User Verification

[0231] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. In addition, if any abnormalities are reported, users can check the details and take necessary measures. For example, users can contact the elderly relative's home to check on the situation.

[0232] Exemplary System Operation

[0233] As a concrete example, consider the case where an elderly person's fall is detected. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as necessary. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0234] This system allows the health and safety of elderly people to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0235] The processing flow will be explained below.

[0236] Step 1:

[0237] The device collects environmental data from sensors placed in the living room and bedroom: a temperature sensor measures the room temperature, a humidity sensor measures the humidity, and a motion detection sensor detects the elderly person's movements and collects this data.

[0238] Step 2:

[0239] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0240] Step 3:

[0241] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0242] Step 4:

[0243] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0244] Step 5:

[0245] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0246] Step 6:

[0247] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0248] Step 7:

[0249] The server runs an artificial intelligence model to analyze the received data. The artificial intelligence model analyzes the sensor data to detect abnormalities in the elderly person's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0250] Step 8:

[0251] The server inputs the analysis results into the emotion engine, which recognizes the user's emotions from the voice data and video frames. It analyzes voice tone and facial expression changes to understand the user's emotional state.

[0252] Step 9:

[0253] The server generates notification content based on the user's emotions based on the emotion engine's recognition results. For example, if the user is feeling highly stressed, the notification content will be adjusted to include more details and follow-up suggestions.

[0254] Step 10:

[0255] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0256] Step 11:

[0257] If an anomaly is detected, the server will send a tailored notification to the emergency contacts. The notification will be sent to the user via SMS or email.

[0258] Step 12:

[0259] Users can access the server using their smartphones or PCs to check the latest logs and notifications, especially if an abnormality is reported. Users can check the details and take necessary action.

[0260] Step 13:

[0261] The user can understand the elderly person's health condition and behavioral patterns based on the logs and notifications, and contact the elderly person's home as needed. For example, if a fall is detected, the user can quickly check the situation and take emergency action.

[0262] In this way, a system incorporating an emotion engine can provide appropriate notifications that take the user's emotions into consideration, making it possible to comprehensively manage the health and safety of the elderly.

[0263] Example 2

[0264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] The present invention aims to solve the problem of the difficulty of understanding the health status and behavior of elderly people in remote locations in real time. In particular, there is a need for a system that can respond quickly to sudden changes in the health status of elderly people. There is also a need to reduce the psychological burden on users receiving notifications and encourage more appropriate responses.

[0266] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0267] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for executing an emotion engine to adjust the content of the notification via a user terminal. This makes it possible to monitor the health status and behavior of elderly people in real time even from remote locations, and to respond quickly and appropriately when an abnormality is detected.

[0268] A "sensor" is a device for acquiring environmental data such as temperature, humidity, and motion detection.

[0269] A "means for recording audio data" is a device or system that has the function of collecting audio and recording it as digital data.

[0270] "Means for capturing video frames" refers to technology that uses a device such as a camera to capture video information and store it as digital data.

[0271] "Means for transmitting data to a server" refers to a mechanism or protocol for transmitting collected data to a server via the Internet or a dedicated line.

[0272] A "means for running an artificial intelligence model" is a device or platform for running the machine learning algorithms or artificial intelligence software used to analyze the collected data.

[0273] The "means for recording the analysis results as a log" refers to a database or file system for recording and saving the analyzed data in chronological order.

[0274] "Means for sending notifications" refers to systems or communication means for sending notifications to users when an abnormality is detected, and includes email, SMS, app notifications, etc.

[0275] "Means for implementing an emotion engine" refers to artificial intelligence or analytical software that analyzes a user's emotions from collected audio and video data and uses the results to adjust the content of notifications.

[0276] This invention combines a system that monitors the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. This system consists of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0277] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically transmits it to a server.

[0278] The device pre-processes some of the collected data before sending it to the server. For example, environmental sensor data (temperature, humidity, motion detection data) is sent as is, while audio data undergoes noise filtering and video data is processed using image recognition algorithms to extract only important frames.

[0279] The server uses an artificial intelligence model to analyze the received data. The model receives sensor data, audio data, and video data as input and generates analysis results that provide information on the elderly person's behavioral patterns and health status. The analysis results are recorded as logs and stored in a database.

[0280] Additionally, the server utilizes an emotion engine to recognize the user's emotions. It analyzes audio data and video frames to understand the user's emotions. For example, it detects changes in voice tone and facial expressions to determine whether the user is feeling stressed. This information is used to adjust the content of notifications as needed.

[0281] If an anomaly is detected, the server sends a notification to the user, which is tailored based on the analysis by the emotion engine. For example, if the user is experiencing high stress, the notification may include a more detailed explanation or follow-up suggestions.

[0282] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. If necessary, they can contact the elderly's home to check on their condition and take other measures.

[0283] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server, which analyzes it and detects the fall. The analysis results are recorded as a log, and a notification is sent to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as needed. The user can check the log on their smartphone or PC and take action as needed, such as contacting the elderly person's home.

[0284] Examples of prompts include:

[0285] Describe a scenario where a monitoring system detects an elderly person's fall. The system uses sensors, microphones, and cameras. An emotion engine then analyzes the user's emotions and tailors the notification. Can you explain in detail how this system works?

[0286] This allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0288] Step 1: Data collection

[0289] The device collects environmental data from various sensors installed in the living room and bedroom, including temperature, humidity, and motion detection data, while simultaneously recording audio data using a microphone and capturing video frames using a camera.

[0290] How it works: The sensors detect temperature, humidity, and motion in real time, the microphone records audio every minute, and the camera captures a video frame every 30 seconds.

[0291] Input: Environmental data (temperature, humidity, motion detection data), audio data, video data

[0292] Output: Collected environmental data, audio data, video frames

[0293] Step 2: Data Preprocessing

[0294] The device preprocesses the collected data: environmental sensor data is processed as is, audio data is noise filtered, and video data is extracted for important frames.

[0295] Specific operation: A noise reduction algorithm is applied to the audio data to extract the important audio parts, and an image recognition algorithm is used to select only the necessary frames from the video data.

[0296] Input: Collected environmental data, audio data, and video data

[0297] Output: Preprocessed environment data, filtered audio data, extracted key video frames

[0298] Step 3: Send data

[0299] The device sends the pre-processed data to a server using an internet connection.

[0300] Specific operation: The terminal accesses the server at regular intervals and sends data in bulk.

[0301] Input: Preprocessed environmental data, audio data, video data

[0302] Output: Data sent to the server

[0303] Step 4: Data analysis

[0304] The server analyzes the received data using an artificial intelligence model to assess the elderly person's behavioral patterns and health status based on sensor, audio, and video data.

[0305] How it works: The artificial intelligence model analyzes the input data and generates a report on the elderly person's health status.

[0306] Input: Received environmental data, audio data, video data

[0307] Output: Analysis results (behavioral patterns, health status report)

[0308] Step 5: Logging

[0309] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0310] Specific operation: When the analysis results are saved in the database, they are recorded as a log along with a timestamp.

[0311] Input: Analysis results (behavioral patterns, health status report)

[0312] Output: Log data with timestamp

[0313] Step 6: Emotion Recognition

[0314] The server recognizes the user's emotions using an emotion engine, which analyzes audio and video frames to determine the user's emotions, and also takes environmental data into account.

[0315] Specific operation: The emotion engine analyzes voice tone and facial expression changes to determine whether the user is feeling stressed or relieved.

[0316] Input: Audio data, video frames, environmental data

[0317] Output: Emotion recognition results (stress level, relief level, etc.)

[0318] Step 7: Send notification

[0319] The server sends a notification to the user when an anomaly is detected. The content of the notification is adjusted based on the emotion recognition results.

[0320] Specific operation: If an abnormality is detected, the server will take into account the results of the emotion engine and send a notification to the user via a dedicated app or email.

[0321] Input: Anomaly analysis results, emotion recognition results

[0322] Output: Notification sent to the user

[0323] Step 8: User Verification

[0324] Users access the server using their smartphones or PCs to check the latest logs and notifications, and if necessary, take action such as contacting the elderly person's home.

[0325] Specific operation: The user logs in to the server's web portal or dedicated app to check the analysis results and notifications, and if necessary, contact the elderly person's home.

[0326] Input: Log data, notification content

[0327] Output: User confirmation result and corresponding action

[0328] This allows the system to continuously monitor the health and safety of the elderly remotely and respond appropriately. The introduction of an emotion engine reduces the psychological burden on users and encourages more effective responses.

[0329] (Application example 2)

[0330] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0331] Conventional systems that monitor the health of elderly people and adjust notifications based on user emotions have the problem of being difficult to respond effectively in situations where emotional changes and flexible responses are required.In addition, in food delivery sites, the emotional states of drivers and customers have a significant impact on service quality, so a system that can recognize this in real time and take appropriate action is required.

[0332] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model for analyzing the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for recognizing emotions from the acquired data using an emotion engine and adjusting the content of the notification based on the emotions. This enables flexible and appropriate responses according to the user's emotional state, thereby enabling improved customer satisfaction and service quality in food delivery services, etc.

[0333] A "sensor" is a device for acquiring environmental data, and is a piece of equipment whose role is to collect information such as temperature, humidity, and motion detection.

[0334] "Audio data" refers to data that records surrounding sounds and human voices and is stored in digital format or used for analysis.

[0335] "Video frame" refers to each frame of visual information captured by a camera, and is an individual image forming a sequence of images.

[0336] A "server" is a computer system used to process, analyze, and store data, and communicate with other devices over a network.

[0337] An "artificial intelligence model" is a computational model that includes statistical or machine learning algorithms to perform analysis on collected data.

[0338] A "log" is a file or database entry that records the results of data analysis in chronological order.

[0339] "Abnormal" refers to behavior or a state that deviates from normal patterns, especially when there is a problem with health or behavior.

[0340] A "notification" is a message or alert that sends a warning or information to the user when an abnormality is detected.

[0341] An "emotion engine" is an algorithm or system that analyzes audio data or video frames to recognize a user's emotional state.

[0342] "Emotion-based notification content adjustment" refers to the process of appropriately changing the content of notifications sent based on the user's perceived emotional state.

[0343] The system for implementing this invention monitors the health status and behavior of elderly people and incorporates a function to recognize the user's emotions using an emotion engine. The specific configuration and operation of this system will be described in detail below.

[0344] System configuration

[0345] First, sensors, microphones, and cameras are installed in places where elderly people frequently spend time, such as living rooms and bedrooms. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal periodically collects this data and transmits it to a server. Specifically, the following hardware and software are used:

[0346] Hardware: Environmental sensors including temperature, humidity, and motion sensors, a microphone for audio input, a camera for video capture, and a smartphone or computer to process the data.

[0347] Software: Terminal applications for data collection and transmission, artificial intelligence models (such as TensorFlow or Keras) for analyzing received data, and an emotion engine for emotion recognition.

[0348] Data collection and processing

[0349] The device periodically acquires environmental data from sensors. This environmental data includes temperature, humidity, and motion detection data, providing basic information for determining whether the elderly's living environment is suitable. The microphone records the elderly's voice and surrounding environmental sounds and identifies important audio events. The camera periodically captures video frames to obtain visual information.

[0350] Data transmission and analysis

[0351] The device sends collected sensor data, audio data, and video frames to a server, which uses artificial intelligence models to analyze the data. This analysis evaluates the elderly person's behavioral patterns and health status, and if any abnormalities are detected, they are logged and a notification is sent to the designated emergency contact.

[0352] Using the Emotion Engine

[0353] The server uses an emotion engine to recognize the user's emotions from the captured audio data and video frames. For example, it can determine whether the user is feeling stressed or relaxed from changes in voice tone or facial expression. This information is used to tailor the content of notifications. For example, if the user is feeling highly stressed, it can notify the support center and promptly respond.

[0354] Examples of concrete examples and prompts

[0355] As a concrete example, we will explain how this technology is applied to food delivery. The driver wears smart glasses, and while delivering, the camera and microphone monitor the driver's emotional state and the surrounding environment. The emotion engine analyzes the driver's stress level in real time, and if stress increases, it will play relaxing music or notify the support center to encourage appropriate action.

[0356] Example prompt sentence:

[0357] The user wears the smart glasses, and the emotion recognition engine analyzes the user's emotions in real time based on data collected by the camera and microphone. Use the emotion recognition model to determine emotions such as stress, happiness, and anxiety, and provide appropriate notifications and advice to the user as needed.

[0358] This invention enables flexible and appropriate responses according to the user's emotional state, thereby improving customer satisfaction and service quality, particularly in food delivery services.

[0359] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0360] Step 1:

[0361] The device collects data from sensors, microphones, and cameras, capturing environmental data (temperature, humidity, motion detection data), audio data, and video frames. During this data collection step, the device monitors the signals sent from each sensor in real time and converts them into digital data.

[0362] Step 2:

[0363] The terminal temporarily stores the collected data and then sends it to the server. The collected data is buffered for data retention and sent to the server at regular intervals. The input is various sensor data, audio data, and video data, and the output is a single data packet sent to the server.

[0364] Step 3:

[0365] The server analyzes the received data using an artificial intelligence model. Sensor data, audio data, and video data are input for analysis, and behavioral patterns and health conditions are evaluated. Specifically, temperature changes, the presence or absence of movement, audio tones, and movement patterns in the video are detected to determine whether or not there are any abnormalities. The output is the analysis results, including whether or not an abnormality was detected and behavioral pattern data.

[0366] Step 4:

[0367] The server records the analysis results as a log. The log contains the features of various data and anomaly detection results, and stores them in a database. The input to this step is the analysis results from the artificial intelligence model, and the output is a log stored in the database.

[0368] Step 5:

[0369] If an abnormality is detected, the server sends a notification to the configured emergency contact. For example, if an elderly person falls, the server notifies the user of the abnormality by sending an alert. The input is the abnormality detection result, and the output is a notification message for the user.

[0370] Step 6:

[0371] The server uses an emotion engine to recognize the user's emotions. It analyzes the captured audio data and video frames to determine whether the user is feeling stressed or relaxed. The input for the emotion engine's analysis is audio and video data, and the output is the recognition result of the user's emotional state.

[0372] Step 7:

[0373] The server tailors the notification content based on emotion. For example, if the user is experiencing high stress, the notification will include a detailed explanation and follow-up suggestions. The input is the emotion engine's recognition result, and the output is the tailored notification message.

[0374] Step 8:

[0375] Users access the server using a smartphone or PC to check the latest logs and notifications. This allows users to remotely understand the health status and behavioral patterns of elderly people and take action as needed. The input is the log and notification data stored on the server, and the output is the information displayed on the user's interface.

[0376] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0377] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0378] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0379] [Second embodiment]

[0380] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0381] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0382] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0383] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0384] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0385] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0386] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0387] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0388] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0389] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0390] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0391] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0392] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system consists of sensors, microphones, cameras, a server, and a user terminal.

[0393] System configuration

[0394] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user.

[0395] Data collection

[0396] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is then compiled and sent to a server.

[0397] Data transmission and analysis

[0398] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0399] Logging and Notifications

[0400] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the emergency contacts that have been set up. The notification will be sent to the user via SMS or email.

[0401] User Verification

[0402] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary measures. For example, users can contact the elderly person's home to check on the situation.

[0403] Exemplary System Operation

[0404] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0405] This system allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind.

[0406] The processing flow will be explained below.

[0407] Step 1:

[0408] The device collects environmental data from sensors placed in the living room, bedroom, etc. The temperature sensor measures the room temperature, the humidity sensor measures the humidity, and the motion detection sensor detects the elderly person's movements and collects this data.

[0409] Step 2:

[0410] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0411] Step 3:

[0412] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0413] Step 4:

[0414] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0415] Step 5:

[0416] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0417] Step 6:

[0418] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0419] Step 7:

[0420] The server runs an artificial intelligence model to analyze the received data. The AI ​​model analyzes the sensor data to detect abnormalities in the elderly's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0421] Step 8:

[0422] The server generates analysis results from the AI ​​model, including an assessment of the elderly person's daily behavior and health status, and details of any abnormalities detected.

[0423] Step 9:

[0424] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0425] Step 10:

[0426] If the server detects an abnormality as a result of the analysis, it will send a notification to the emergency contact via SMS or email to the configured user.

[0427] Step 11:

[0428] Users can access the server using their smartphones or PCs to check the latest logs and notifications, allowing them to understand the health status and behavioral patterns of their elderly relatives.

[0429] Step 12:

[0430] Users can check the details of any reported abnormalities and take necessary measures, such as contacting the elderly person's home and providing emergency support.

[0431] In this way, a series of processing steps are systematically executed, making it possible to ensure the health and safety of elderly people living far away.

[0432] Example 1

[0433] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0434] There is a need for an effective system for remotely and continuously monitoring the health status and behavior of elderly people. However, existing systems have difficulty in collecting, analyzing, and notifying abnormalities in real time, and are therefore unable to adequately ensure the safety of elderly people. Furthermore, there is a lack of a means for users to easily check logs and notifications. It is necessary to provide a system that solves these problems and efficiently manages the health and safety of elderly people.

[0435] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0436] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video data, means for transmitting the acquired data to an information processing device, means for executing a generative AI model for analyzing the received data, means for recording the analysis results as a log on a recording medium, means for sending a notification when an abnormality is detected, and means for a user to check the log and notification on a display device. This makes it possible to continuously monitor the health condition and behavior of elderly people even from a distance and to respond quickly as necessary.

[0437] A "sensor" is a device for acquiring environmental data, and is used to measure data including temperature, humidity, and motion detection data.

[0438] "Audio data" is data used to record the voices of elderly people and surrounding environmental sounds.

[0439] "Video data" is visual information captured using a camera, including periodically taken video frames.

[0440] An "information processing device" is a terminal device for collectively transmitting data collected from devices such as sensors, microphones, and cameras.

[0441] A "generative AI model" is an artificial intelligence model used to analyze received data and generate information about the behavioral patterns and health status of elderly people.

[0442] The "recording medium" refers to a database or other storage device for storing the analysis results as a log.

[0443] "Notification" refers to information such as a warning message, SMS, or email sent to a user when an abnormality is detected.

[0444] A "display device" is a device such as a smartphone, PC, or tablet that a user uses to check logs and notifications.

[0445] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system is composed of sensors, microphones, cameras, a server, and a user terminal.

[0446] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal and configured to capture environmental data, audio data, and video frames. The terminal collects this data in real time and periodically transmits it to a server.

[0447] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The sensors measure this data in real time and send it to the device. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is compiled on the device and sent to a server at regular intervals.

[0448] The device sends the collected data to a server using a secure communication protocol such as HTTPS. The server uses a generative AI model to analyze the received data. The generative AI model receives sensor data, audio data, and video data as input and analyzes the data. As a result of the analysis, information about specific events, the elderly person's behavioral patterns, and health status is generated.

[0449] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the configured emergency contact. The notification will be sent to the user via SMS or email.

[0450] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary action. For example, users can contact the elderly's home, check the situation, and take emergency action.

[0451] Example prompt

[0452] Below are some example prompts to input to a generative AI model:

[0453] "Describe how the system would behave if a fall occurred in an elderly person's living room."

[0454] The above is an embodiment of the present invention. This system makes it possible to remotely manage the health and safety of elderly people, allowing their families to live their daily lives with peace of mind.

[0455] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0456] Step 1:

[0457] Data collection

[0458] The device collects data from sensors, microphones, and cameras. The sensors measure temperature, humidity, and motion detection data and send it to the device. The microphone records audio data and sends it to the device. The camera captures video frames and sends visual information to the device.

[0459] Input: Temperature, humidity, motion data, audio data, video data

[0460] Output: Collected sensor data, audio data, and video data

[0461] Specific behavior:

[0462] Sensors capture temperature, humidity, and movement in real time and transmit the data to the device.

[0463] The microphone records environmental sounds and the elderly person's voice and transmits the data to the terminal.

[0464] The camera captures video frames at regular intervals and transmits the data to the device.

[0465] Step 2:

[0466] Data transmission

[0467] The device sends the collected data to the server at regular intervals, using HTTPS as a secure communication protocol.

[0468] Input: Collected sensor data, audio data, and video data

[0469] Output: Data sent to the server

[0470] Specific behavior:

[0471] The device stores data collected from sensors, microphones, and cameras in a buffer.

[0472] The device sends data to the server at regular intervals (e.g., every 10 seconds).

[0473] The data is securely transmitted to the server using a secure protocol.

[0474] Step 3:

[0475] Data analysis

[0476] The server runs a generative AI model to analyze the received data, which generates information about the elderly's behavioral patterns and health status.

[0477] Input: Sensor data, audio data, and video data sent from the device

[0478] Output: Analysis results of behavioral patterns and health status information

[0479] Specific behavior:

[0480] The server preprocesses the incoming data and converts it into a format suitable for the AI ​​model.

[0481] The generative AI model analyzes the preprocessed data as input.

[0482] The analysis results in information about behavioral patterns and health status.

[0483] Step 4:

[0484] Logging

[0485] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0486] Input: Behavioral patterns and health status information as analysis results

[0487] Output: Logs stored in a database

[0488] Specific behavior:

[0489] The server timestamps the analysis results.

[0490] The server stores the analysis results with a timestamp in a database.

[0491] Step 5:

[0492] Abnormal notification

[0493] If the server detects an abnormality in the analysis results, it will send a notification to the emergency contacts set up by the user via SMS or email.

[0494] Input: Anomaly detection information as analysis result

[0495] Output: A message to inform the user

[0496] Specific behavior:

[0497] The server determines whether an abnormality has been detected from the analysis results.

[0498] The server generates a notification message if an anomaly is detected.

[0499] The server sends notifications to the user via an SMS gateway or mail server.

[0500] Step 6:

[0501] User Verification

[0502] Users can access the server using their smartphones or PCs to check the latest logs and notifications. After logging in, users can view the necessary information and take emergency measures.

[0503] Input: Logs and notification information stored on the server

[0504] Output: Health status and behavioral pattern information confirmed by the user

[0505] Specific behavior:

[0506] The user accesses the server using a dedicated application or a web browser.

[0507] The user authenticates their account and logs in.

[0508] Users can view the latest logs and notifications and take action if necessary, such as contacting the elderly person's home.

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] Monitoring the health and behavior of elderly people living far away is extremely important, and a rapid response is required, especially when abnormalities in temperature or humidity or unexpected behavior occur. With conventional methods, it is difficult for family members living in remote locations to grasp the elderly's condition in real time, and abnormality detection and notification can be delayed. There is a need for a system that can solve this problem.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0513] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, means for detecting temperature or humidity abnormalities and sending a notification, means for detecting unexpected movements using a motion sensor, and means for recording abnormal events in a log file. This makes it possible to monitor the health status and behavior of elderly people living far away in real time, and to quickly notify them when an abnormality occurs and take appropriate action.

[0514] "Means for acquiring environmental data from sensors" refers to devices or mechanisms used to collect environmental information such as temperature, humidity, motion detection data, etc.

[0515] "Means for recording audio data" refers to a microphone or recording device for recording the voice of the elderly person and surrounding environmental sounds.

[0516] A "means for capturing video frames" is a device that periodically captures visual information using a camera or video equipment.

[0517] The "means for transmitting acquired data to a server" refers to a communication means for transferring data collected from sensors, microphones, and cameras to a server via a network.

[0518] "Means for executing an artificial intelligence model to analyze the received data" means a computer program that executes an AI algorithm to analyze the sensor data, audio data, and video data.

[0519] The "means for recording the analysis results as a log" is a storage and record management system for recording and saving the analyzed data over time.

[0520] "Means for sending a notification when an abnormality is detected" refers to a communication method for sending a warning to the user by SMS, email, or other means when an abnormality is detected.

[0521] "Means for detecting temperature or humidity abnormalities and sending a notification" refers to a system that detects abnormalities and sends a notification when the temperature or humidity data obtained from the sensor exceeds a set threshold.

[0522] "Means for detecting unexpected movements using motion sensors" refers to a mechanism for using motion sensors to detect unexpected movements or falls and identify abnormalities.

[0523] The "means for recording abnormal events in a log file" is a data storage mechanism for storing a detailed record of detected abnormal events in a log file.

[0524] This invention is a system for monitoring the health and behavior of elderly people living far away, and is composed of sensors, microphones, cameras, a server, and a user terminal. The program for this system and its processing content are described below.

[0525] System Configuration and Operation

[0526] 1. Data Collection Methods

[0527] The device uses sensors to acquire environmental data, including temperature, humidity, and motion detection data, a microphone to record audio data, and a camera to periodically capture video frames, all of which are then aggregated on the device.

[0528] 2. Data transmission method

[0529] The terminals send the collected data to a server, which receives the data and prepares it for analysis.

[0530] 3. Data Analysis Methods

[0531] The server uses an artificial intelligence model to analyze the data it receives. The model takes sensor data, audio data, and video data as input and performs the analysis.

[0532] 4. Logging of analysis results

[0533] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0534] 5. Anomaly detection and notification methods

[0535] If the server detects an abnormality, it will send a notification to the configured emergency contacts, which will be sent to the user via SMS or email.

[0536] 6. Abnormal event logging method

[0537] The server records the details of abnormal events in a log file, which allows you to later refer to the details of the abnormality and the time of detection.

[0538] Hardware and software used

[0539] Hardware:

[0540] Sensors: Temperature sensor, humidity sensor, motion sensor

[0541] Microphone: a device that records audio data

[0542] camera: a device that captures video frames

[0543] Server: A central computer that analyzes and stores data

[0544] software:

[0545] Python: Used for data collection, analysis, and sending notifications

[0546] Twilio: An API for sending SMS notifications

[0547] smtplib: A standard Python library for sending email notifications

[0548] Generative AI models: Artificial intelligence algorithms for analytical processing

[0549] Specific examples

[0550] For example, if the server detects a temperature below 15°C or above 30°C, or if the humidity is below 30% or above 70%, it detects an abnormality. The server then notifies emergency contacts of the abnormality via SMS or email. Furthermore, this abnormal event is recorded in a log file. This series of processes enables family members living far away to monitor the condition of their elderly relatives in real time and respond quickly when an abnormality occurs.

[0551] Prompt Sentence Examples

[0552] Below is an example of a prompt sentence to input to the generative AI model.

[0553] Example prompt:

[0554] text

[0555] Create a program that uses sensors and AI to monitor the health of elderly people living far away, and notify them by SMS and email if any abnormalities are detected. Use Python and use sensors, microphones, cameras, and a server as hardware. Also, use Twilio to send SMS and smtplib to send email. Include logic to send notifications when abnormal temperatures (below 15°C or above 30°C) or humidity (below 30% or above 70%) are detected.

[0556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0557] Step 1:

[0558] The device obtains environmental data (temperature, humidity, motion detection) from sensors. This involves reading the signals from the sensors and converting them into an appropriate format. The input is the raw data from the sensors, and the output is formatted values ​​of the temperature, humidity, and motion detection data.

[0559] Step 2:

[0560] The device uses a microphone to record audio data. The input is the audio signal from the microphone, and the output is an audio file (e.g., WAV format). The recorded audio is saved for further processing.

[0561] Step 3:

[0562] The device uses a camera to periodically capture video frames. The input is the video signal from the camera, and the output is the captured image file (e.g., JPEG format).

[0563] Step 4:

[0564] The device transmits the acquired environmental data, audio data, and video frames to the server via a communication method (e.g., Wi-Fi or Ethernet). The input is the collected data set, and the output is the data transferred to the server.

[0565] Step 5:

[0566] The server runs an artificial intelligence model to analyze the received data. The input is sensor, audio, and video data, and the output is the analysis results (e.g., an assessment of the elderly person's behavioral patterns and health status). The data is processed by AI algorithms to detect various abnormal patterns.

[0567] Step 6:

[0568] The server logs the analysis results. The input is the analysis results, and the output is a time-stamped log entry. The log is stored in a database.

[0569] Step 7:

[0570] The server sends notifications when an anomaly is detected. The anomaly detection algorithm runs and sends SMS or email notifications based on temperature or humidity exceeding thresholds or unexpected behavior. The input is the anomaly data analyzed, and the output is the notification message sent.

[0571] Step 8:

[0572] The server records abnormal events in detail in a log file. The input is the detected abnormal event, and the output is a detailed event record saved in the log file. This allows you to later refer to the content of the abnormality and the time of detection.

[0573] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0574] This invention is a system that combines a system for monitoring the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. The system is composed of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0575] System configuration

[0576] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user. Furthermore, an emotion engine that recognizes the user's emotions is built in, and the content of notifications can be adjusted based on the emotion.

[0577] Data collection

[0578] The device collects environmental data from sensors, including temperature, humidity, and motion detection data. The device also uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds. In addition, the device periodically captures video frames using a camera to collect visual information. This data is then compiled and sent to a server.

[0579] Data transmission and analysis

[0580] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0581] emotion recognition

[0582] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes audio data and video frames to recognize the user's emotions. It can also recognize emotions by taking environmental data into account. For example, it can detect changes in voice tone and facial expressions to determine whether the user is feeling stressed or relieved.

[0583] Logging and Notifications

[0584] The server records the analysis results as a log, which is time-stamped and stored in a database. If an anomaly is detected, the server sends a notification to the user's emergency contacts. The content of the notification is adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high stress, the notification will include a more detailed explanation and follow-up suggestions.

[0585] User Verification

[0586] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. In addition, if any abnormalities are reported, users can check the details and take necessary measures. For example, users can contact the elderly relative's home to check on the situation.

[0587] Exemplary System Operation

[0588] As a concrete example, consider the case where an elderly person's fall is detected. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as necessary. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0589] This system allows the health and safety of elderly people to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] The device collects environmental data from sensors placed in the living room and bedroom: a temperature sensor measures the room temperature, a humidity sensor measures the humidity, and a motion detection sensor detects the elderly person's movements and collects this data.

[0593] Step 2:

[0594] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0595] Step 3:

[0596] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0597] Step 4:

[0598] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0599] Step 5:

[0600] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0601] Step 6:

[0602] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0603] Step 7:

[0604] The server runs an artificial intelligence model to analyze the received data. The artificial intelligence model analyzes the sensor data to detect abnormalities in the elderly person's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0605] Step 8:

[0606] The server inputs the analysis results into the emotion engine, which recognizes the user's emotions from the voice data and video frames. It analyzes voice tone and facial expression changes to understand the user's emotional state.

[0607] Step 9:

[0608] The server generates notification content based on the user's emotions based on the emotion engine's recognition results. For example, if the user is feeling highly stressed, the notification content will be adjusted to include more details and follow-up suggestions.

[0609] Step 10:

[0610] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0611] Step 11:

[0612] If an anomaly is detected, the server will send a tailored notification to the emergency contacts. The notification will be sent to the user via SMS or email.

[0613] Step 12:

[0614] Users can access the server using their smartphones or PCs to check the latest logs and notifications, especially if an abnormality is reported. Users can check the details and take necessary action.

[0615] Step 13:

[0616] The user can understand the elderly person's health condition and behavioral patterns based on the logs and notifications, and contact the elderly person's home as needed. For example, if a fall is detected, the user can quickly check the situation and take emergency action.

[0617] In this way, a system incorporating an emotion engine can provide appropriate notifications that take the user's emotions into consideration, making it possible to comprehensively manage the health and safety of the elderly.

[0618] Example 2

[0619] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0620] The present invention aims to solve the problem of the difficulty of understanding the health status and behavior of elderly people in remote locations in real time. In particular, there is a need for a system that can respond quickly to sudden changes in the health status of elderly people. There is also a need to reduce the psychological burden on users receiving notifications and encourage more appropriate responses.

[0621] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0622] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for executing an emotion engine to adjust the content of the notification via a user terminal. This makes it possible to monitor the health status and behavior of elderly people in real time even from remote locations, and to respond quickly and appropriately when an abnormality is detected.

[0623] A "sensor" is a device for acquiring environmental data such as temperature, humidity, and motion detection.

[0624] A "means for recording audio data" is a device or system that has the function of collecting audio and recording it as digital data.

[0625] "Means for capturing video frames" refers to technology that uses a device such as a camera to capture video information and store it as digital data.

[0626] "Means for transmitting data to a server" refers to a mechanism or protocol for transmitting collected data to a server via the Internet or a dedicated line.

[0627] A "means for running an artificial intelligence model" is a device or platform for running the machine learning algorithms or artificial intelligence software used to analyze the collected data.

[0628] The "means for recording the analysis results as a log" refers to a database or file system for recording and saving the analyzed data in chronological order.

[0629] "Means for sending notifications" refers to systems or communication means for sending notifications to users when an abnormality is detected, and includes email, SMS, app notifications, etc.

[0630] "Means for implementing an emotion engine" refers to artificial intelligence or analytical software that analyzes a user's emotions from collected audio and video data and uses the results to adjust the content of notifications.

[0631] This invention combines a system that monitors the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. This system consists of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0632] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically transmits it to a server.

[0633] The device pre-processes some of the collected data before sending it to the server. For example, environmental sensor data (temperature, humidity, motion detection data) is sent as is, while audio data undergoes noise filtering and video data is processed using image recognition algorithms to extract only important frames.

[0634] The server uses an artificial intelligence model to analyze the received data. The model receives sensor data, audio data, and video data as input and generates analysis results that provide information on the elderly person's behavioral patterns and health status. The analysis results are recorded as logs and stored in a database.

[0635] Additionally, the server utilizes an emotion engine to recognize the user's emotions. It analyzes audio data and video frames to understand the user's emotions. For example, it detects changes in voice tone and facial expressions to determine whether the user is feeling stressed. This information is used to adjust the content of notifications as needed.

[0636] If an anomaly is detected, the server sends a notification to the user, which is tailored based on the analysis by the emotion engine. For example, if the user is experiencing high stress, the notification may include a more detailed explanation or follow-up suggestions.

[0637] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. If necessary, they can contact the elderly's home to check on their condition and take other measures.

[0638] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server, which analyzes it and detects the fall. The analysis results are recorded as a log, and a notification is sent to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as needed. The user can check the log on their smartphone or PC and take action as needed, such as contacting the elderly person's home.

[0639] Examples of prompts include:

[0640] Describe a scenario where a monitoring system detects an elderly person's fall. The system uses sensors, microphones, and cameras. An emotion engine then analyzes the user's emotions and tailors the notification. Can you explain in detail how this system works?

[0641] This allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0642] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0643] Step 1: Data collection

[0644] The device collects environmental data from various sensors installed in the living room and bedroom, including temperature, humidity, and motion detection data, while simultaneously recording audio data using a microphone and capturing video frames using a camera.

[0645] How it works: The sensors detect temperature, humidity, and motion in real time, the microphone records audio every minute, and the camera captures a video frame every 30 seconds.

[0646] Input: Environmental data (temperature, humidity, motion detection data), audio data, video data

[0647] Output: Collected environmental data, audio data, video frames

[0648] Step 2: Data Preprocessing

[0649] The device preprocesses the collected data: environmental sensor data is processed as is, audio data is noise filtered, and video data is extracted for important frames.

[0650] Specific operation: A noise reduction algorithm is applied to the audio data to extract the important audio parts, and an image recognition algorithm is used to select only the necessary frames from the video data.

[0651] Input: Collected environmental data, audio data, and video data

[0652] Output: Preprocessed environment data, filtered audio data, extracted key video frames

[0653] Step 3: Send data

[0654] The device sends the pre-processed data to a server using an internet connection.

[0655] Specific operation: The terminal accesses the server at regular intervals and sends data in bulk.

[0656] Input: Preprocessed environmental data, audio data, video data

[0657] Output: Data sent to the server

[0658] Step 4: Data analysis

[0659] The server analyzes the received data using an artificial intelligence model to assess the elderly person's behavioral patterns and health status based on sensor, audio, and video data.

[0660] How it works: The artificial intelligence model analyzes the input data and generates a report on the elderly person's health status.

[0661] Input: Received environmental data, audio data, video data

[0662] Output: Analysis results (behavioral patterns, health status report)

[0663] Step 5: Logging

[0664] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0665] Specific operation: When the analysis results are saved in the database, they are recorded as a log along with a timestamp.

[0666] Input: Analysis results (behavioral patterns, health status report)

[0667] Output: Log data with timestamp

[0668] Step 6: Emotion Recognition

[0669] The server recognizes the user's emotions using an emotion engine, which analyzes audio and video frames to determine the user's emotions, and also takes environmental data into account.

[0670] Specific operation: The emotion engine analyzes voice tone and facial expression changes to determine whether the user is feeling stressed or relieved.

[0671] Input: Audio data, video frames, environmental data

[0672] Output: Emotion recognition results (stress level, relief level, etc.)

[0673] Step 7: Send notification

[0674] The server sends a notification to the user when an anomaly is detected. The content of the notification is adjusted based on the emotion recognition results.

[0675] Specific operation: If an abnormality is detected, the server will take into account the results of the emotion engine and send a notification to the user via a dedicated app or email.

[0676] Input: Anomaly analysis results, emotion recognition results

[0677] Output: Notification sent to the user

[0678] Step 8: User Verification

[0679] Users access the server using their smartphones or PCs to check the latest logs and notifications, and if necessary, take action such as contacting the elderly person's home.

[0680] Specific operation: The user logs in to the server's web portal or dedicated app to check the analysis results and notifications, and if necessary, contact the elderly person's home.

[0681] Input: Log data, notification content

[0682] Output: User confirmation result and corresponding action

[0683] This allows the system to continuously monitor the health and safety of the elderly remotely and respond appropriately. The introduction of an emotion engine reduces the psychological burden on users and encourages more effective responses.

[0684] (Application example 2)

[0685] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] Conventional systems that monitor the health of elderly people and adjust notifications based on user emotions have the problem of being difficult to respond effectively in situations where emotional changes and flexible responses are required.In addition, in food delivery sites, the emotional states of drivers and customers have a significant impact on service quality, so a system that can recognize this in real time and take appropriate action is required.

[0687] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model for analyzing the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for recognizing emotions from the acquired data using an emotion engine and adjusting the content of the notification based on the emotions. This enables flexible and appropriate responses according to the user's emotional state, thereby enabling improved customer satisfaction and service quality in food delivery services, etc.

[0688] A "sensor" is a device for acquiring environmental data, and is a piece of equipment whose role is to collect information such as temperature, humidity, and motion detection.

[0689] "Audio data" refers to data that records surrounding sounds and human voices and is stored in digital format or used for analysis.

[0690] "Video frame" refers to each frame of visual information captured by a camera, and is an individual image forming a sequence of images.

[0691] A "server" is a computer system used to process, analyze, and store data, and communicate with other devices over a network.

[0692] An "artificial intelligence model" is a computational model that includes statistical or machine learning algorithms to perform analysis on collected data.

[0693] A "log" is a file or database entry that records the results of data analysis in chronological order.

[0694] "Abnormal" refers to behavior or a state that deviates from normal patterns, especially when there is a problem with health or behavior.

[0695] A "notification" is a message or alert that sends a warning or information to the user when an abnormality is detected.

[0696] An "emotion engine" is an algorithm or system that analyzes audio data or video frames to recognize a user's emotional state.

[0697] "Emotion-based notification content adjustment" refers to the process of appropriately changing the content of notifications sent based on the user's perceived emotional state.

[0698] The system for implementing this invention monitors the health status and behavior of elderly people and incorporates a function to recognize the user's emotions using an emotion engine. The specific configuration and operation of this system will be described in detail below.

[0699] System configuration

[0700] First, sensors, microphones, and cameras are installed in places where elderly people frequently spend time, such as living rooms and bedrooms. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal periodically collects this data and transmits it to a server. Specifically, the following hardware and software are used:

[0701] Hardware: Environmental sensors including temperature, humidity, and motion sensors, a microphone for audio input, a camera for video capture, and a smartphone or computer to process the data.

[0702] Software: Terminal applications for data collection and transmission, artificial intelligence models (such as TensorFlow or Keras) for analyzing received data, and an emotion engine for emotion recognition.

[0703] Data collection and processing

[0704] The device periodically acquires environmental data from sensors. This environmental data includes temperature, humidity, and motion detection data, providing basic information for determining whether the elderly's living environment is suitable. The microphone records the elderly's voice and surrounding environmental sounds and identifies important audio events. The camera periodically captures video frames to obtain visual information.

[0705] Data transmission and analysis

[0706] The device sends collected sensor data, audio data, and video frames to a server, which uses artificial intelligence models to analyze the data. This analysis evaluates the elderly person's behavioral patterns and health status, and if any abnormalities are detected, they are logged and a notification is sent to the designated emergency contact.

[0707] Using the Emotion Engine

[0708] The server uses an emotion engine to recognize the user's emotions from the captured audio data and video frames. For example, it can determine whether the user is feeling stressed or relaxed from changes in voice tone or facial expression. This information is used to tailor the content of notifications. For example, if the user is feeling highly stressed, it can notify the support center and promptly respond.

[0709] Examples of concrete examples and prompts

[0710] As a concrete example, we will explain how this technology is applied to food delivery. The driver wears smart glasses, and while delivering, the camera and microphone monitor the driver's emotional state and the surrounding environment. The emotion engine analyzes the driver's stress level in real time, and if stress increases, it will play relaxing music or notify the support center to encourage appropriate action.

[0711] Example prompt sentence:

[0712] The user wears the smart glasses, and the emotion recognition engine analyzes the user's emotions in real time based on data collected by the camera and microphone. Use the emotion recognition model to determine emotions such as stress, happiness, and anxiety, and provide appropriate notifications and advice to the user as needed.

[0713] This invention enables flexible and appropriate responses according to the user's emotional state, thereby improving customer satisfaction and service quality, particularly in food delivery services.

[0714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0715] Step 1:

[0716] The device collects data from sensors, microphones, and cameras, capturing environmental data (temperature, humidity, motion detection data), audio data, and video frames. During this data collection step, the device monitors the signals sent from each sensor in real time and converts them into digital data.

[0717] Step 2:

[0718] The terminal temporarily stores the collected data and then sends it to the server. The collected data is buffered for data retention and sent to the server at regular intervals. The input is various sensor data, audio data, and video data, and the output is a single data packet sent to the server.

[0719] Step 3:

[0720] The server analyzes the received data using an artificial intelligence model. Sensor data, audio data, and video data are input for analysis, and behavioral patterns and health conditions are evaluated. Specifically, temperature changes, the presence or absence of movement, audio tones, and movement patterns in the video are detected to determine whether or not there are any abnormalities. The output is the analysis results, including whether or not an abnormality was detected and behavioral pattern data.

[0721] Step 4:

[0722] The server records the analysis results as a log. The log contains the features of various data and anomaly detection results, and stores them in a database. The input to this step is the analysis results from the artificial intelligence model, and the output is a log stored in the database.

[0723] Step 5:

[0724] If an abnormality is detected, the server sends a notification to the configured emergency contact. For example, if an elderly person falls, the server notifies the user of the abnormality by sending an alert. The input is the abnormality detection result, and the output is a notification message for the user.

[0725] Step 6:

[0726] The server uses an emotion engine to recognize the user's emotions. It analyzes the captured audio data and video frames to determine whether the user is feeling stressed or relaxed. The input for the emotion engine's analysis is audio and video data, and the output is the recognition result of the user's emotional state.

[0727] Step 7:

[0728] The server tailors the notification content based on emotion. For example, if the user is experiencing high stress, the notification will include a detailed explanation and follow-up suggestions. The input is the emotion engine's recognition result, and the output is the tailored notification message.

[0729] Step 8:

[0730] Users access the server using a smartphone or PC to check the latest logs and notifications. This allows users to remotely understand the health status and behavioral patterns of elderly people and take action as needed. The input is the log and notification data stored on the server, and the output is the information displayed on the user's interface.

[0731] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0732] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0733] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0734] [Third embodiment]

[0735] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0736] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0737] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0738] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0739] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0740] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0741] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0742] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0743] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0744] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0745] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0746] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0747] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system consists of sensors, microphones, cameras, a server, and a user terminal.

[0748] System configuration

[0749] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user.

[0750] Data collection

[0751] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is then compiled and sent to a server.

[0752] Data transmission and analysis

[0753] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0754] Logging and Notifications

[0755] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the emergency contacts that have been set up. The notification will be sent to the user via SMS or email.

[0756] User Verification

[0757] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary measures. For example, users can contact the elderly person's home to check on the situation.

[0758] Exemplary System Operation

[0759] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0760] This system allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind.

[0761] The processing flow will be explained below.

[0762] Step 1:

[0763] The device collects environmental data from sensors placed in the living room, bedroom, etc. The temperature sensor measures the room temperature, the humidity sensor measures the humidity, and the motion detection sensor detects the elderly person's movements and collects this data.

[0764] Step 2:

[0765] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0766] Step 3:

[0767] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0768] Step 4:

[0769] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0770] Step 5:

[0771] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0772] Step 6:

[0773] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0774] Step 7:

[0775] The server runs an artificial intelligence model to analyze the received data. The AI ​​model analyzes the sensor data to detect abnormalities in the elderly's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0776] Step 8:

[0777] The server generates analysis results from the AI ​​model, including an assessment of the elderly person's daily behavior and health status, and details of any abnormalities detected.

[0778] Step 9:

[0779] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0780] Step 10:

[0781] If the server detects an abnormality as a result of the analysis, it will send a notification to the emergency contact via SMS or email to the configured user.

[0782] Step 11:

[0783] Users can access the server using their smartphones or PCs to check the latest logs and notifications, allowing them to understand the health status and behavioral patterns of their elderly relatives.

[0784] Step 12:

[0785] Users can check the details of any reported abnormalities and take necessary measures, such as contacting the elderly person's home and providing emergency support.

[0786] In this way, a series of processing steps are systematically executed, making it possible to ensure the health and safety of elderly people living far away.

[0787] Example 1

[0788] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0789] There is a need for an effective system for remotely and continuously monitoring the health status and behavior of elderly people. However, existing systems have difficulty in collecting, analyzing, and notifying abnormalities in real time, and are therefore unable to adequately ensure the safety of elderly people. Furthermore, there is a lack of a means for users to easily check logs and notifications. It is necessary to provide a system that solves these problems and efficiently manages the health and safety of elderly people.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0791] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video data, means for transmitting the acquired data to an information processing device, means for executing a generative AI model for analyzing the received data, means for recording the analysis results as a log on a recording medium, means for sending a notification when an abnormality is detected, and means for a user to check the log and notification on a display device. This makes it possible to continuously monitor the health condition and behavior of elderly people even from a distance and to respond quickly as necessary.

[0792] A "sensor" is a device for acquiring environmental data, and is used to measure data including temperature, humidity, and motion detection data.

[0793] "Audio data" is data used to record the voices of elderly people and surrounding environmental sounds.

[0794] "Video data" is visual information captured using a camera, including periodically taken video frames.

[0795] An "information processing device" is a terminal device for collectively transmitting data collected from devices such as sensors, microphones, and cameras.

[0796] A "generative AI model" is an artificial intelligence model used to analyze received data and generate information about the behavioral patterns and health status of elderly people.

[0797] The "recording medium" refers to a database or other storage device for storing the analysis results as a log.

[0798] "Notification" refers to information such as a warning message, SMS, or email sent to a user when an abnormality is detected.

[0799] A "display device" is a device such as a smartphone, PC, or tablet that a user uses to check logs and notifications.

[0800] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system is composed of sensors, microphones, cameras, a server, and a user terminal.

[0801] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal and configured to capture environmental data, audio data, and video frames. The terminal collects this data in real time and periodically transmits it to a server.

[0802] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The sensors measure this data in real time and send it to the device. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is compiled on the device and sent to a server at regular intervals.

[0803] The device sends the collected data to a server using a secure communication protocol such as HTTPS. The server uses a generative AI model to analyze the received data. The generative AI model receives sensor data, audio data, and video data as input and analyzes the data. As a result of the analysis, information about specific events, the elderly person's behavioral patterns, and health status is generated.

[0804] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the configured emergency contact. The notification will be sent to the user via SMS or email.

[0805] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary action. For example, users can contact the elderly's home, check the situation, and take emergency action.

[0806] Example prompt

[0807] Below are some example prompts to input to a generative AI model:

[0808] "Describe how the system would behave if a fall occurred in an elderly person's living room."

[0809] The above is an embodiment of the present invention. This system makes it possible to remotely manage the health and safety of elderly people, allowing their families to live their daily lives with peace of mind.

[0810] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0811] Step 1:

[0812] Data collection

[0813] The device collects data from sensors, microphones, and cameras. The sensors measure temperature, humidity, and motion detection data and send it to the device. The microphone records audio data and sends it to the device. The camera captures video frames and sends visual information to the device.

[0814] Input: Temperature, humidity, motion data, audio data, video data

[0815] Output: Collected sensor data, audio data, and video data

[0816] Specific behavior:

[0817] Sensors capture temperature, humidity, and movement in real time and transmit the data to the device.

[0818] The microphone records environmental sounds and the elderly person's voice and transmits the data to the terminal.

[0819] The camera captures video frames at regular intervals and transmits the data to the device.

[0820] Step 2:

[0821] Data transmission

[0822] The device sends the collected data to the server at regular intervals, using HTTPS as a secure communication protocol.

[0823] Input: Collected sensor data, audio data, and video data

[0824] Output: Data sent to the server

[0825] Specific behavior:

[0826] The device stores data collected from sensors, microphones, and cameras in a buffer.

[0827] The device sends data to the server at regular intervals (e.g., every 10 seconds).

[0828] The data is securely transmitted to the server using a secure protocol.

[0829] Step 3:

[0830] Data analysis

[0831] The server runs a generative AI model to analyze the received data, which generates information about the elderly's behavioral patterns and health status.

[0832] Input: Sensor data, audio data, and video data sent from the device

[0833] Output: Analysis results of behavioral patterns and health status information

[0834] Specific behavior:

[0835] The server preprocesses the incoming data and converts it into a format suitable for the AI ​​model.

[0836] The generative AI model analyzes the preprocessed data as input.

[0837] The analysis results in information about behavioral patterns and health status.

[0838] Step 4:

[0839] Logging

[0840] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0841] Input: Behavioral patterns and health status information as analysis results

[0842] Output: Logs stored in a database

[0843] Specific behavior:

[0844] The server timestamps the analysis results.

[0845] The server stores the analysis results with a timestamp in a database.

[0846] Step 5:

[0847] Abnormal notification

[0848] If the server detects an abnormality in the analysis results, it will send a notification to the emergency contacts set up by the user via SMS or email.

[0849] Input: Anomaly detection information as analysis result

[0850] Output: A message to inform the user

[0851] Specific behavior:

[0852] The server determines whether an abnormality has been detected from the analysis results.

[0853] The server generates a notification message if an anomaly is detected.

[0854] The server sends notifications to the user via an SMS gateway or mail server.

[0855] Step 6:

[0856] User Verification

[0857] Users can access the server using their smartphones or PCs to check the latest logs and notifications. After logging in, users can view the necessary information and take emergency measures.

[0858] Input: Logs and notification information stored on the server

[0859] Output: Health status and behavioral pattern information confirmed by the user

[0860] Specific behavior:

[0861] The user accesses the server using a dedicated application or a web browser.

[0862] The user authenticates their account and logs in.

[0863] Users can view the latest logs and notifications and take action if necessary, such as contacting the elderly person's home.

[0864] (Application example 1)

[0865] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0866] Monitoring the health and behavior of elderly people living far away is extremely important, and a rapid response is required, especially when abnormalities in temperature or humidity or unexpected behavior occur. With conventional methods, it is difficult for family members living in remote locations to grasp the elderly's condition in real time, and abnormality detection and notification can be delayed. There is a need for a system that can solve this problem.

[0867] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0868] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, means for detecting temperature or humidity abnormalities and sending a notification, means for detecting unexpected movements using a motion sensor, and means for recording abnormal events in a log file. This makes it possible to monitor the health status and behavior of elderly people living far away in real time, and to quickly notify them when an abnormality occurs and take appropriate action.

[0869] "Means for acquiring environmental data from sensors" refers to devices or mechanisms used to collect environmental information such as temperature, humidity, motion detection data, etc.

[0870] "Means for recording audio data" refers to a microphone or recording device for recording the voice of the elderly person and surrounding environmental sounds.

[0871] A "means for capturing video frames" is a device that periodically captures visual information using a camera or video equipment.

[0872] The "means for transmitting acquired data to a server" refers to a communication means for transferring data collected from sensors, microphones, and cameras to a server via a network.

[0873] "Means for executing an artificial intelligence model to analyze the received data" means a computer program that executes an AI algorithm to analyze the sensor data, audio data, and video data.

[0874] The "means for recording the analysis results as a log" is a storage and record management system for recording and saving the analyzed data over time.

[0875] "Means for sending a notification when an abnormality is detected" refers to a communication method for sending a warning to the user by SMS, email, or other means when an abnormality is detected.

[0876] "Means for detecting temperature or humidity abnormalities and sending a notification" refers to a system that detects abnormalities and sends a notification when the temperature or humidity data obtained from the sensor exceeds a set threshold.

[0877] "Means for detecting unexpected movements using motion sensors" refers to a mechanism for using motion sensors to detect unexpected movements or falls and identify abnormalities.

[0878] The "means for recording abnormal events in a log file" is a data storage mechanism for storing a detailed record of detected abnormal events in a log file.

[0879] This invention is a system for monitoring the health and behavior of elderly people living far away, and is composed of sensors, microphones, cameras, a server, and a user terminal. The program for this system and its processing content are described below.

[0880] System Configuration and Operation

[0881] 1. Data Collection Methods

[0882] The device uses sensors to acquire environmental data, including temperature, humidity, and motion detection data, a microphone to record audio data, and a camera to periodically capture video frames, all of which are then aggregated on the device.

[0883] 2. Data transmission method

[0884] The terminals send the collected data to a server, which receives the data and prepares it for analysis.

[0885] 3. Data Analysis Methods

[0886] The server uses an artificial intelligence model to analyze the data it receives. The model takes sensor data, audio data, and video data as input and performs the analysis.

[0887] 4. Logging of analysis results

[0888] The server records the analysis results as a log, which is time-stamped and stored in a database.

[0889] 5. Anomaly detection and notification methods

[0890] If the server detects an abnormality, it will send a notification to the configured emergency contacts, which will be sent to the user via SMS or email.

[0891] 6. Abnormal event logging method

[0892] The server records the details of abnormal events in a log file, which allows you to later refer to the details of the abnormality and the time of detection.

[0893] Hardware and software used

[0894] Hardware:

[0895] Sensors: Temperature sensor, humidity sensor, motion sensor

[0896] Microphone: a device that records audio data

[0897] camera: a device that captures video frames

[0898] Server: A central computer that analyzes and stores data

[0899] software:

[0900] Python: Used for data collection, analysis, and sending notifications

[0901] Twilio: An API for sending SMS notifications

[0902] smtplib: A standard Python library for sending email notifications

[0903] Generative AI models: Artificial intelligence algorithms for analytical processing

[0904] Specific examples

[0905] For example, if the server detects a temperature below 15°C or above 30°C, or if the humidity is below 30% or above 70%, it detects an abnormality. The server then notifies emergency contacts of the abnormality via SMS or email. Furthermore, this abnormal event is recorded in a log file. This series of processes enables family members living far away to monitor the condition of their elderly relatives in real time and respond quickly when an abnormality occurs.

[0906] Prompt Sentence Examples

[0907] Below is an example of a prompt sentence to input to the generative AI model.

[0908] Example prompt:

[0909] text

[0910] Create a program that uses sensors and AI to monitor the health of elderly people living far away, and notify them by SMS and email if any abnormalities are detected. Use Python and use sensors, microphones, cameras, and a server as hardware. Also, use Twilio to send SMS and smtplib to send email. Include logic to send notifications when abnormal temperatures (below 15°C or above 30°C) or humidity (below 30% or above 70%) are detected.

[0911] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0912] Step 1:

[0913] The device obtains environmental data (temperature, humidity, motion detection) from sensors. This involves reading the signals from the sensors and converting them into an appropriate format. The input is the raw data from the sensors, and the output is formatted values ​​of the temperature, humidity, and motion detection data.

[0914] Step 2:

[0915] The device uses a microphone to record audio data. The input is the audio signal from the microphone, and the output is an audio file (e.g., WAV format). The recorded audio is saved for further processing.

[0916] Step 3:

[0917] The device uses a camera to periodically capture video frames. The input is the video signal from the camera, and the output is the captured image file (e.g., JPEG format).

[0918] Step 4:

[0919] The device transmits the acquired environmental data, audio data, and video frames to the server via a communication method (e.g., Wi-Fi or Ethernet). The input is the collected data set, and the output is the data transferred to the server.

[0920] Step 5:

[0921] The server runs an artificial intelligence model to analyze the received data. The input is sensor, audio, and video data, and the output is the analysis results (e.g., an assessment of the elderly person's behavioral patterns and health status). The data is processed by AI algorithms to detect various abnormal patterns.

[0922] Step 6:

[0923] The server logs the analysis results. The input is the analysis results, and the output is a time-stamped log entry. The log is stored in a database.

[0924] Step 7:

[0925] The server sends notifications when an anomaly is detected. The anomaly detection algorithm runs and sends SMS or email notifications based on temperature or humidity exceeding thresholds or unexpected behavior. The input is the anomaly data analyzed, and the output is the notification message sent.

[0926] Step 8:

[0927] The server records abnormal events in detail in a log file. The input is the detected abnormal event, and the output is a detailed event record saved in the log file. This allows you to later refer to the content of the abnormality and the time of detection.

[0928] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0929] This invention is a system that combines a system for monitoring the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. The system is composed of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0930] System configuration

[0931] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user. Furthermore, an emotion engine that recognizes the user's emotions is built in, and the content of notifications can be adjusted based on the emotion.

[0932] Data collection

[0933] The device collects environmental data from sensors, including temperature, humidity, and motion detection data. The device also uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds. In addition, the device periodically captures video frames using a camera to collect visual information. This data is then compiled and sent to a server.

[0934] Data transmission and analysis

[0935] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[0936] emotion recognition

[0937] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes audio data and video frames to recognize the user's emotions. It can also recognize emotions by taking environmental data into account. For example, it can detect changes in voice tone and facial expressions to determine whether the user is feeling stressed or relieved.

[0938] Logging and Notifications

[0939] The server records the analysis results as a log, which is time-stamped and stored in a database. If an anomaly is detected, the server sends a notification to the user's emergency contacts. The content of the notification is adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high stress, the notification will include a more detailed explanation and follow-up suggestions.

[0940] User Verification

[0941] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. In addition, if any abnormalities are reported, users can check the details and take necessary measures. For example, users can contact the elderly relative's home to check on the situation.

[0942] Exemplary System Operation

[0943] As a concrete example, consider the case where an elderly person's fall is detected. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as necessary. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[0944] This system allows the health and safety of elderly people to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0945] The processing flow will be explained below.

[0946] Step 1:

[0947] The device collects environmental data from sensors placed in the living room and bedroom: a temperature sensor measures the room temperature, a humidity sensor measures the humidity, and a motion detection sensor detects the elderly person's movements and collects this data.

[0948] Step 2:

[0949] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[0950] Step 3:

[0951] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[0952] Step 4:

[0953] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[0954] Step 5:

[0955] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[0956] Step 6:

[0957] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[0958] Step 7:

[0959] The server runs an artificial intelligence model to analyze the received data. The artificial intelligence model analyzes the sensor data to detect abnormalities in the elderly person's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[0960] Step 8:

[0961] The server inputs the analysis results into the emotion engine, which recognizes the user's emotions from the voice data and video frames. It analyzes voice tone and facial expression changes to understand the user's emotional state.

[0962] Step 9:

[0963] The server generates notification content based on the user's emotions based on the emotion engine's recognition results. For example, if the user is feeling highly stressed, the notification content will be adjusted to include more details and follow-up suggestions.

[0964] Step 10:

[0965] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[0966] Step 11:

[0967] If an anomaly is detected, the server will send a tailored notification to the emergency contacts. The notification will be sent to the user via SMS or email.

[0968] Step 12:

[0969] Users can access the server using their smartphones or PCs to check the latest logs and notifications, especially if an abnormality is reported. Users can check the details and take necessary action.

[0970] Step 13:

[0971] The user can understand the elderly person's health condition and behavioral patterns based on the logs and notifications, and contact the elderly person's home as needed. For example, if a fall is detected, the user can quickly check the situation and take emergency action.

[0972] In this way, a system incorporating an emotion engine can provide appropriate notifications that take the user's emotions into consideration, making it possible to comprehensively manage the health and safety of the elderly.

[0973] Example 2

[0974] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0975] The present invention aims to solve the problem of the difficulty of understanding the health status and behavior of elderly people in remote locations in real time. In particular, there is a need for a system that can respond quickly to sudden changes in the health status of elderly people. There is also a need to reduce the psychological burden on users receiving notifications and encourage more appropriate responses.

[0976] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0977] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for executing an emotion engine to adjust the content of the notification via a user terminal. This makes it possible to monitor the health status and behavior of elderly people in real time even from remote locations, and to respond quickly and appropriately when an abnormality is detected.

[0978] A "sensor" is a device for acquiring environmental data such as temperature, humidity, and motion detection.

[0979] A "means for recording audio data" is a device or system that has the function of collecting audio and recording it as digital data.

[0980] "Means for capturing video frames" refers to technology that uses a device such as a camera to capture video information and store it as digital data.

[0981] "Means for transmitting data to a server" refers to a mechanism or protocol for transmitting collected data to a server via the Internet or a dedicated line.

[0982] A "means for running an artificial intelligence model" is a device or platform for running the machine learning algorithms or artificial intelligence software used to analyze the collected data.

[0983] The "means for recording the analysis results as a log" refers to a database or file system for recording and saving the analyzed data in chronological order.

[0984] "Means for sending notifications" refers to systems or communication means for sending notifications to users when an abnormality is detected, and includes email, SMS, app notifications, etc.

[0985] "Means for implementing an emotion engine" refers to artificial intelligence or analytical software that analyzes a user's emotions from collected audio and video data and uses the results to adjust the content of notifications.

[0986] This invention combines a system that monitors the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. This system consists of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[0987] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically transmits it to a server.

[0988] The device pre-processes some of the collected data before sending it to the server. For example, environmental sensor data (temperature, humidity, motion detection data) is sent as is, while audio data undergoes noise filtering and video data is processed using image recognition algorithms to extract only important frames.

[0989] The server uses an artificial intelligence model to analyze the received data. The model receives sensor data, audio data, and video data as input and generates analysis results that provide information on the elderly person's behavioral patterns and health status. The analysis results are recorded as logs and stored in a database.

[0990] Additionally, the server utilizes an emotion engine to recognize the user's emotions. It analyzes audio data and video frames to understand the user's emotions. For example, it detects changes in voice tone and facial expressions to determine whether the user is feeling stressed. This information is used to adjust the content of notifications as needed.

[0991] If an anomaly is detected, the server sends a notification to the user, which is tailored based on the analysis by the emotion engine. For example, if the user is experiencing high stress, the notification may include a more detailed explanation or follow-up suggestions.

[0992] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. If necessary, they can contact the elderly's home to check on their condition and take other measures.

[0993] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server, which analyzes it and detects the fall. The analysis results are recorded as a log, and a notification is sent to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as needed. The user can check the log on their smartphone or PC and take action as needed, such as contacting the elderly person's home.

[0994] Examples of prompts include:

[0995] Describe a scenario where a monitoring system detects an elderly person's fall. The system uses sensors, microphones, and cameras. An emotion engine then analyzes the user's emotions and tailors the notification. Can you explain in detail how this system works?

[0996] This allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1: Data collection

[0999] The device collects environmental data from various sensors installed in the living room and bedroom, including temperature, humidity, and motion detection data, while simultaneously recording audio data using a microphone and capturing video frames using a camera.

[1000] How it works: The sensors detect temperature, humidity, and motion in real time, the microphone records audio every minute, and the camera captures a video frame every 30 seconds.

[1001] Input: Environmental data (temperature, humidity, motion detection data), audio data, video data

[1002] Output: Collected environmental data, audio data, video frames

[1003] Step 2: Data Preprocessing

[1004] The device preprocesses the collected data: environmental sensor data is processed as is, audio data is noise filtered, and video data is extracted for important frames.

[1005] Specific operation: A noise reduction algorithm is applied to the audio data to extract the important audio parts, and an image recognition algorithm is used to select only the necessary frames from the video data.

[1006] Input: Collected environmental data, audio data, and video data

[1007] Output: Preprocessed environment data, filtered audio data, extracted key video frames

[1008] Step 3: Send data

[1009] The device sends the pre-processed data to a server using an internet connection.

[1010] Specific operation: The terminal accesses the server at regular intervals and sends data in bulk.

[1011] Input: Preprocessed environmental data, audio data, video data

[1012] Output: Data sent to the server

[1013] Step 4: Data analysis

[1014] The server analyzes the received data using an artificial intelligence model to assess the elderly person's behavioral patterns and health status based on sensor, audio, and video data.

[1015] How it works: The artificial intelligence model analyzes the input data and generates a report on the elderly person's health status.

[1016] Input: Received environmental data, audio data, video data

[1017] Output: Analysis results (behavioral patterns, health status report)

[1018] Step 5: Logging

[1019] The server records the analysis results as a log, which is time-stamped and stored in a database.

[1020] Specific operation: When the analysis results are saved in the database, they are recorded as a log along with a timestamp.

[1021] Input: Analysis results (behavioral patterns, health status report)

[1022] Output: Log data with timestamp

[1023] Step 6: Emotion Recognition

[1024] The server recognizes the user's emotions using an emotion engine, which analyzes audio and video frames to determine the user's emotions, and also takes environmental data into account.

[1025] Specific operation: The emotion engine analyzes voice tone and facial expression changes to determine whether the user is feeling stressed or relieved.

[1026] Input: Audio data, video frames, environmental data

[1027] Output: Emotion recognition results (stress level, relief level, etc.)

[1028] Step 7: Send notification

[1029] The server sends a notification to the user when an anomaly is detected. The content of the notification is adjusted based on the emotion recognition results.

[1030] Specific operation: If an abnormality is detected, the server will take into account the results of the emotion engine and send a notification to the user via a dedicated app or email.

[1031] Input: Anomaly analysis results, emotion recognition results

[1032] Output: Notification sent to the user

[1033] Step 8: User Verification

[1034] Users access the server using their smartphones or PCs to check the latest logs and notifications, and if necessary, take action such as contacting the elderly person's home.

[1035] Specific operation: The user logs in to the server's web portal or dedicated app to check the analysis results and notifications, and if necessary, contact the elderly person's home.

[1036] Input: Log data, notification content

[1037] Output: User confirmation result and corresponding action

[1038] This allows the system to continuously monitor the health and safety of the elderly remotely and respond appropriately. The introduction of an emotion engine reduces the psychological burden on users and encourages more effective responses.

[1039] (Application example 2)

[1040] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1041] Conventional systems that monitor the health of elderly people and adjust notifications based on user emotions have the problem of being difficult to respond effectively in situations where emotional changes and flexible responses are required.In addition, in food delivery sites, the emotional states of drivers and customers have a significant impact on service quality, so a system that can recognize this in real time and take appropriate action is required.

[1042] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model for analyzing the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for recognizing emotions from the acquired data using an emotion engine and adjusting the content of the notification based on the emotions. This enables flexible and appropriate responses according to the user's emotional state, thereby enabling improved customer satisfaction and service quality in food delivery services, etc.

[1043] A "sensor" is a device for acquiring environmental data, and is a piece of equipment whose role is to collect information such as temperature, humidity, and motion detection.

[1044] "Audio data" refers to data that records surrounding sounds and human voices and is stored in digital format or used for analysis.

[1045] "Video frame" refers to each frame of visual information captured by a camera, and is an individual image forming a sequence of images.

[1046] A "server" is a computer system used to process, analyze, and store data, and communicate with other devices over a network.

[1047] An "artificial intelligence model" is a computational model that includes statistical or machine learning algorithms to perform analysis on collected data.

[1048] A "log" is a file or database entry that records the results of data analysis in chronological order.

[1049] "Abnormal" refers to behavior or a state that deviates from normal patterns, especially when there is a problem with health or behavior.

[1050] A "notification" is a message or alert that sends a warning or information to the user when an abnormality is detected.

[1051] An "emotion engine" is an algorithm or system that analyzes audio data or video frames to recognize a user's emotional state.

[1052] "Emotion-based notification content adjustment" refers to the process of appropriately changing the content of notifications sent based on the user's perceived emotional state.

[1053] The system for implementing this invention monitors the health status and behavior of elderly people and incorporates a function to recognize the user's emotions using an emotion engine. The specific configuration and operation of this system will be described in detail below.

[1054] System configuration

[1055] First, sensors, microphones, and cameras are installed in places where elderly people frequently spend time, such as living rooms and bedrooms. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal periodically collects this data and transmits it to a server. Specifically, the following hardware and software are used:

[1056] Hardware: Environmental sensors including temperature, humidity, and motion sensors, a microphone for audio input, a camera for video capture, and a smartphone or computer to process the data.

[1057] Software: Terminal applications for data collection and transmission, artificial intelligence models (such as TensorFlow or Keras) for analyzing received data, and an emotion engine for emotion recognition.

[1058] Data collection and processing

[1059] The device periodically acquires environmental data from sensors. This environmental data includes temperature, humidity, and motion detection data, providing basic information for determining whether the elderly's living environment is suitable. The microphone records the elderly's voice and surrounding environmental sounds and identifies important audio events. The camera periodically captures video frames to obtain visual information.

[1060] Data transmission and analysis

[1061] The device sends collected sensor data, audio data, and video frames to a server, which uses artificial intelligence models to analyze the data. This analysis evaluates the elderly person's behavioral patterns and health status, and if any abnormalities are detected, they are logged and a notification is sent to the designated emergency contact.

[1062] Using the Emotion Engine

[1063] The server uses an emotion engine to recognize the user's emotions from the captured audio data and video frames. For example, it can determine whether the user is feeling stressed or relaxed from changes in voice tone or facial expression. This information is used to tailor the content of notifications. For example, if the user is feeling highly stressed, it can notify the support center and promptly respond.

[1064] Examples of concrete examples and prompts

[1065] As a concrete example, we will explain how this technology is applied to food delivery. The driver wears smart glasses, and while delivering, the camera and microphone monitor the driver's emotional state and the surrounding environment. The emotion engine analyzes the driver's stress level in real time, and if stress increases, it will play relaxing music or notify the support center to encourage appropriate action.

[1066] Example prompt sentence:

[1067] The user wears the smart glasses, and the emotion recognition engine analyzes the user's emotions in real time based on data collected by the camera and microphone. Use the emotion recognition model to determine emotions such as stress, happiness, and anxiety, and provide appropriate notifications and advice to the user as needed.

[1068] This invention enables flexible and appropriate responses according to the user's emotional state, thereby improving customer satisfaction and service quality, particularly in food delivery services.

[1069] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1070] Step 1:

[1071] The device collects data from sensors, microphones, and cameras, capturing environmental data (temperature, humidity, motion detection data), audio data, and video frames. During this data collection step, the device monitors the signals sent from each sensor in real time and converts them into digital data.

[1072] Step 2:

[1073] The terminal temporarily stores the collected data and then sends it to the server. The collected data is buffered for data retention and sent to the server at regular intervals. The input is various sensor data, audio data, and video data, and the output is a single data packet sent to the server.

[1074] Step 3:

[1075] The server analyzes the received data using an artificial intelligence model. Sensor data, audio data, and video data are input for analysis, and behavioral patterns and health conditions are evaluated. Specifically, temperature changes, the presence or absence of movement, audio tones, and movement patterns in the video are detected to determine whether or not there are any abnormalities. The output is the analysis results, including whether or not an abnormality was detected and behavioral pattern data.

[1076] Step 4:

[1077] The server records the analysis results as a log. The log contains the features of various data and anomaly detection results, and stores them in a database. The input to this step is the analysis results from the artificial intelligence model, and the output is a log stored in the database.

[1078] Step 5:

[1079] If an abnormality is detected, the server sends a notification to the configured emergency contact. For example, if an elderly person falls, the server notifies the user of the abnormality by sending an alert. The input is the abnormality detection result, and the output is a notification message for the user.

[1080] Step 6:

[1081] The server uses an emotion engine to recognize the user's emotions. It analyzes the captured audio data and video frames to determine whether the user is feeling stressed or relaxed. The input for the emotion engine's analysis is audio and video data, and the output is the recognition result of the user's emotional state.

[1082] Step 7:

[1083] The server tailors the notification content based on emotion. For example, if the user is experiencing high stress, the notification will include a detailed explanation and follow-up suggestions. The input is the emotion engine's recognition result, and the output is the tailored notification message.

[1084] Step 8:

[1085] Users access the server using a smartphone or PC to check the latest logs and notifications. This allows users to remotely understand the health status and behavioral patterns of elderly people and take action as needed. The input is the log and notification data stored on the server, and the output is the information displayed on the user's interface.

[1086] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1088] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1089] [Fourth embodiment]

[1090] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1091] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1093] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1094] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1097] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1098] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1099] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1101] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1102] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1103] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system consists of sensors, microphones, cameras, a server, and a user terminal.

[1104] System configuration

[1105] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user.

[1106] Data collection

[1107] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is then compiled and sent to a server.

[1108] Data transmission and analysis

[1109] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[1110] Logging and Notifications

[1111] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the emergency contacts that have been set up. The notification will be sent to the user via SMS or email.

[1112] User Verification

[1113] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary measures. For example, users can contact the elderly person's home to check on the situation.

[1114] Exemplary System Operation

[1115] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[1116] This system allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind.

[1117] The processing flow will be explained below.

[1118] Step 1:

[1119] The device collects environmental data from sensors placed in the living room, bedroom, etc. The temperature sensor measures the room temperature, the humidity sensor measures the humidity, and the motion detection sensor detects the elderly person's movements and collects this data.

[1120] Step 2:

[1121] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[1122] Step 3:

[1123] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[1124] Step 4:

[1125] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[1126] Step 5:

[1127] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[1128] Step 6:

[1129] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[1130] Step 7:

[1131] The server runs an artificial intelligence model to analyze the received data. The AI ​​model analyzes the sensor data to detect abnormalities in the elderly's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[1132] Step 8:

[1133] The server generates analysis results from the AI ​​model, including an assessment of the elderly person's daily behavior and health status, and details of any abnormalities detected.

[1134] Step 9:

[1135] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[1136] Step 10:

[1137] If the server detects an abnormality as a result of the analysis, it will send a notification to the emergency contact via SMS or email to the configured user.

[1138] Step 11:

[1139] Users can access the server using their smartphones or PCs to check the latest logs and notifications, allowing them to understand the health status and behavioral patterns of their elderly relatives.

[1140] Step 12:

[1141] Users can check the details of any reported abnormalities and take necessary measures, such as contacting the elderly person's home and providing emergency support.

[1142] In this way, a series of processing steps are systematically executed, making it possible to ensure the health and safety of elderly people living far away.

[1143] Example 1

[1144] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1145] There is a need for an effective system for remotely and continuously monitoring the health status and behavior of elderly people. However, existing systems have difficulty in collecting, analyzing, and notifying abnormalities in real time, and are therefore unable to adequately ensure the safety of elderly people. Furthermore, there is a lack of a means for users to easily check logs and notifications. It is necessary to provide a system that solves these problems and efficiently manages the health and safety of elderly people.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1147] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video data, means for transmitting the acquired data to an information processing device, means for executing a generative AI model for analyzing the received data, means for recording the analysis results as a log on a recording medium, means for sending a notification when an abnormality is detected, and means for a user to check the log and notification on a display device. This makes it possible to continuously monitor the health condition and behavior of elderly people even from a distance and to respond quickly as necessary.

[1148] A "sensor" is a device for acquiring environmental data, and is used to measure data including temperature, humidity, and motion detection data.

[1149] "Audio data" is data used to record the voices of elderly people and surrounding environmental sounds.

[1150] "Video data" is visual information captured using a camera, including periodically taken video frames.

[1151] An "information processing device" is a terminal device for collectively transmitting data collected from devices such as sensors, microphones, and cameras.

[1152] A "generative AI model" is an artificial intelligence model used to analyze received data and generate information about the behavioral patterns and health status of elderly people.

[1153] The "recording medium" refers to a database or other storage device for storing the analysis results as a log.

[1154] "Notification" refers to information such as a warning message, SMS, or email sent to a user when an abnormality is detected.

[1155] A "display device" is a device such as a smartphone, PC, or tablet that a user uses to check logs and notifications.

[1156] This invention is a system for continuously monitoring the health and behavior of elderly people living far away. The system is composed of sensors, microphones, cameras, a server, and a user terminal.

[1157] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal and configured to capture environmental data, audio data, and video frames. The terminal collects this data in real time and periodically transmits it to a server.

[1158] The device collects environmental data from sensors. The environmental data includes temperature, humidity, and motion detection data. The sensors measure this data in real time and send it to the device. The device also uses a microphone to record audio data. The recorded audio data includes the elderly person's voice and surrounding environmental sounds. In addition, the device uses a camera to periodically capture video frames to collect visual information. This data is compiled on the device and sent to a server at regular intervals.

[1159] The device sends the collected data to a server using a secure communication protocol such as HTTPS. The server uses a generative AI model to analyze the received data. The generative AI model receives sensor data, audio data, and video data as input and analyzes the data. As a result of the analysis, information about specific events, the elderly person's behavioral patterns, and health status is generated.

[1160] The server records the analysis results as a log. This log is time-stamped and saved in a database. If an abnormality is detected as a result of the analysis, the server will send a notification to the configured emergency contact. The notification will be sent to the user via SMS or email.

[1161] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows family members living far away to remotely monitor the health status and behavioral patterns of their elderly relatives. Furthermore, if any abnormalities are reported, they can check the details and take necessary action. For example, users can contact the elderly's home, check the situation, and take emergency action.

[1162] Example prompt

[1163] Below are some example prompts to input to a generative AI model:

[1164] "Describe how the system would behave if a fall occurred in an elderly person's living room."

[1165] The above is an embodiment of the present invention. This system makes it possible to remotely manage the health and safety of elderly people, allowing their families to live their daily lives with peace of mind.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1:

[1168] Data collection

[1169] The device collects data from sensors, microphones, and cameras. The sensors measure temperature, humidity, and motion detection data and send it to the device. The microphone records audio data and sends it to the device. The camera captures video frames and sends visual information to the device.

[1170] Input: Temperature, humidity, motion data, audio data, video data

[1171] Output: Collected sensor data, audio data, and video data

[1172] Specific behavior:

[1173] Sensors capture temperature, humidity, and movement in real time and transmit the data to the device.

[1174] The microphone records environmental sounds and the elderly person's voice and transmits the data to the terminal.

[1175] The camera captures video frames at regular intervals and transmits the data to the device.

[1176] Step 2:

[1177] Data transmission

[1178] The device sends the collected data to the server at regular intervals, using HTTPS as a secure communication protocol.

[1179] Input: Collected sensor data, audio data, and video data

[1180] Output: Data sent to the server

[1181] Specific behavior:

[1182] The device stores data collected from sensors, microphones, and cameras in a buffer.

[1183] The device sends data to the server at regular intervals (e.g., every 10 seconds).

[1184] The data is securely transmitted to the server using a secure protocol.

[1185] Step 3:

[1186] Data analysis

[1187] The server runs a generative AI model to analyze the received data, which generates information about the elderly's behavioral patterns and health status.

[1188] Input: Sensor data, audio data, and video data sent from the device

[1189] Output: Analysis results of behavioral patterns and health status information

[1190] Specific behavior:

[1191] The server preprocesses the incoming data and converts it into a format suitable for the AI ​​model.

[1192] The generative AI model analyzes the preprocessed data as input.

[1193] The analysis results in information about behavioral patterns and health status.

[1194] Step 4:

[1195] Logging

[1196] The server records the analysis results as a log, which is time-stamped and stored in a database.

[1197] Input: Behavioral patterns and health status information as analysis results

[1198] Output: Logs stored in a database

[1199] Specific behavior:

[1200] The server timestamps the analysis results.

[1201] The server stores the analysis results with a timestamp in a database.

[1202] Step 5:

[1203] Abnormal notification

[1204] If the server detects an abnormality in the analysis results, it will send a notification to the emergency contacts set up by the user via SMS or email.

[1205] Input: Anomaly detection information as analysis result

[1206] Output: A message to inform the user

[1207] Specific behavior:

[1208] The server determines whether an abnormality has been detected from the analysis results.

[1209] The server generates a notification message if an anomaly is detected.

[1210] The server sends notifications to the user via an SMS gateway or mail server.

[1211] Step 6:

[1212] User Verification

[1213] Users can access the server using their smartphones or PCs to check the latest logs and notifications. After logging in, users can view the necessary information and take emergency measures.

[1214] Input: Logs and notification information stored on the server

[1215] Output: Health status and behavioral pattern information confirmed by the user

[1216] Specific behavior:

[1217] The user accesses the server using a dedicated application or a web browser.

[1218] The user authenticates their account and logs in.

[1219] Users can view the latest logs and notifications and take action if necessary, such as contacting the elderly person's home.

[1220] (Application example 1)

[1221] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] Monitoring the health and behavior of elderly people living far away is extremely important, and a rapid response is required, especially when abnormalities in temperature or humidity or unexpected behavior occur. With conventional methods, it is difficult for family members living in remote locations to grasp the elderly's condition in real time, and abnormality detection and notification can be delayed. There is a need for a system that can solve this problem.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1224] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, means for detecting temperature or humidity abnormalities and sending a notification, means for detecting unexpected movements using a motion sensor, and means for recording abnormal events in a log file. This makes it possible to monitor the health status and behavior of elderly people living far away in real time, and to quickly notify them when an abnormality occurs and take appropriate action.

[1225] "Means for acquiring environmental data from sensors" refers to devices or mechanisms used to collect environmental information such as temperature, humidity, motion detection data, etc.

[1226] "Means for recording audio data" refers to a microphone or recording device for recording the voice of the elderly person and surrounding environmental sounds.

[1227] A "means for capturing video frames" is a device that periodically captures visual information using a camera or video equipment.

[1228] The "means for transmitting acquired data to a server" refers to a communication means for transferring data collected from sensors, microphones, and cameras to a server via a network.

[1229] "Means for executing an artificial intelligence model to analyze the received data" means a computer program that executes an AI algorithm to analyze the sensor data, audio data, and video data.

[1230] The "means for recording the analysis results as a log" is a storage and record management system for recording and saving the analyzed data over time.

[1231] "Means for sending a notification when an abnormality is detected" refers to a communication method for sending a warning to the user by SMS, email, or other means when an abnormality is detected.

[1232] "Means for detecting temperature or humidity abnormalities and sending a notification" refers to a system that detects abnormalities and sends a notification when the temperature or humidity data obtained from the sensor exceeds a set threshold.

[1233] "Means for detecting unexpected movements using motion sensors" refers to a mechanism for using motion sensors to detect unexpected movements or falls and identify abnormalities.

[1234] The "means for recording abnormal events in a log file" is a data storage mechanism for storing a detailed record of detected abnormal events in a log file.

[1235] This invention is a system for monitoring the health and behavior of elderly people living far away, and is composed of sensors, microphones, cameras, a server, and a user terminal. The program for this system and its processing content are described below.

[1236] System Configuration and Operation

[1237] 1. Data Collection Methods

[1238] The device uses sensors to acquire environmental data, including temperature, humidity, and motion detection data, a microphone to record audio data, and a camera to periodically capture video frames, all of which are then aggregated on the device.

[1239] 2. Data transmission method

[1240] The terminals send the collected data to a server, which receives the data and prepares it for analysis.

[1241] 3. Data Analysis Methods

[1242] The server uses an artificial intelligence model to analyze the data it receives. The model takes sensor data, audio data, and video data as input and performs the analysis.

[1243] 4. Logging of analysis results

[1244] The server records the analysis results as a log, which is time-stamped and stored in a database.

[1245] 5. Anomaly detection and notification methods

[1246] If the server detects an abnormality, it will send a notification to the configured emergency contacts, which will be sent to the user via SMS or email.

[1247] 6. Abnormal event logging method

[1248] The server records the details of abnormal events in a log file, which allows you to later refer to the details of the abnormality and the time of detection.

[1249] Hardware and software used

[1250] Hardware:

[1251] Sensors: Temperature sensor, humidity sensor, motion sensor

[1252] Microphone: a device that records audio data

[1253] camera: a device that captures video frames

[1254] Server: A central computer that analyzes and stores data

[1255] software:

[1256] Python: Used for data collection, analysis, and sending notifications

[1257] Twilio: An API for sending SMS notifications

[1258] smtplib: A standard Python library for sending email notifications

[1259] Generative AI models: Artificial intelligence algorithms for analytical processing

[1260] Specific examples

[1261] For example, if the server detects a temperature below 15°C or above 30°C, or if the humidity is below 30% or above 70%, it detects an abnormality. The server then notifies emergency contacts of the abnormality via SMS or email. Furthermore, this abnormal event is recorded in a log file. This series of processes enables family members living far away to monitor the condition of their elderly relatives in real time and respond quickly when an abnormality occurs.

[1262] Prompt Sentence Examples

[1263] Below is an example of a prompt sentence to input to the generative AI model.

[1264] Example prompt:

[1265] text

[1266] Create a program that uses sensors and AI to monitor the health of elderly people living far away, and notify them by SMS and email if any abnormalities are detected. Use Python and use sensors, microphones, cameras, and a server as hardware. Also, use Twilio to send SMS and smtplib to send email. Include logic to send notifications when abnormal temperatures (below 15°C or above 30°C) or humidity (below 30% or above 70%) are detected.

[1267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1268] Step 1:

[1269] The device obtains environmental data (temperature, humidity, motion detection) from sensors. This involves reading the signals from the sensors and converting them into an appropriate format. The input is the raw data from the sensors, and the output is formatted values ​​of the temperature, humidity, and motion detection data.

[1270] Step 2:

[1271] The device uses a microphone to record audio data. The input is the audio signal from the microphone, and the output is an audio file (e.g., WAV format). The recorded audio is saved for further processing.

[1272] Step 3:

[1273] The device uses a camera to periodically capture video frames. The input is the video signal from the camera, and the output is the captured image file (e.g., JPEG format).

[1274] Step 4:

[1275] The device transmits the acquired environmental data, audio data, and video frames to the server via a communication method (e.g., Wi-Fi or Ethernet). The input is the collected data set, and the output is the data transferred to the server.

[1276] Step 5:

[1277] The server runs an artificial intelligence model to analyze the received data. The input is sensor, audio, and video data, and the output is the analysis results (e.g., an assessment of the elderly person's behavioral patterns and health status). The data is processed by AI algorithms to detect various abnormal patterns.

[1278] Step 6:

[1279] The server logs the analysis results. The input is the analysis results, and the output is a time-stamped log entry. The log is stored in a database.

[1280] Step 7:

[1281] The server sends notifications when an anomaly is detected. The anomaly detection algorithm runs and sends SMS or email notifications based on temperature or humidity exceeding thresholds or unexpected behavior. The input is the anomaly data analyzed, and the output is the notification message sent.

[1282] Step 8:

[1283] The server records abnormal events in detail in a log file. The input is the detected abnormal event, and the output is a detailed event record saved in the log file. This allows you to later refer to the content of the abnormality and the time of detection.

[1284] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1285] This invention is a system that combines a system for monitoring the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. The system is composed of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[1286] System configuration

[1287] First, sensors, microphones, and cameras are installed in places where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically sends it to a server. The server analyzes the received data and records the analysis results as a log. If an abnormality is detected, the server sends a notification to the user. Furthermore, an emotion engine that recognizes the user's emotions is built in, and the content of notifications can be adjusted based on the emotion.

[1288] Data collection

[1289] The device collects environmental data from sensors, including temperature, humidity, and motion detection data. The device also uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds. In addition, the device periodically captures video frames using a camera to collect visual information. This data is then compiled and sent to a server.

[1290] Data transmission and analysis

[1291] The device sends the collected data to a server, which uses an artificial intelligence model to analyze the received data. The artificial intelligence model receives sensor data, audio data, and video data as input and performs analysis. The analysis results generate information about the elderly person's behavioral patterns and health status.

[1292] emotion recognition

[1293] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes audio data and video frames to recognize the user's emotions. It can also recognize emotions by taking environmental data into account. For example, it can detect changes in voice tone and facial expressions to determine whether the user is feeling stressed or relieved.

[1294] Logging and Notifications

[1295] The server records the analysis results as a log, which is time-stamped and stored in a database. If an anomaly is detected, the server sends a notification to the user's emergency contacts. The content of the notification is adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high stress, the notification will include a more detailed explanation and follow-up suggestions.

[1296] User Verification

[1297] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. In addition, if any abnormalities are reported, users can check the details and take necessary measures. For example, users can contact the elderly relative's home to check on the situation.

[1298] Exemplary System Operation

[1299] As a concrete example, consider the case where an elderly person's fall is detected. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server. The server analyzes the received data using an artificial intelligence model and determines that a fall has been detected. The server records the analysis results as a log and sends a notification to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as necessary. The user can check the log on their smartphone or PC and, if necessary, contact the elderly person's home to take emergency action.

[1300] This system allows the health and safety of elderly people to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[1301] The processing flow will be explained below.

[1302] Step 1:

[1303] The device collects environmental data from sensors placed in the living room and bedroom: a temperature sensor measures the room temperature, a humidity sensor measures the humidity, and a motion detection sensor detects the elderly person's movements and collects this data.

[1304] Step 2:

[1305] The device uses a microphone to record audio data, including the elderly person's voice and surrounding environmental sounds, which is used to detect abnormal sounds and behavior.

[1306] Step 3:

[1307] The device periodically captures video frames using a camera, recording the elderly person's behavior as visual data, which is then analyzed to detect abnormal behavior such as falls.

[1308] Step 4:

[1309] The device temporarily stores collected sensor, audio, and video data, which is then periodically prepared for transmission to a server.

[1310] Step 5:

[1311] The device transmits all collected data to a server over a secure protocol to ensure data integrity and privacy.

[1312] Step 6:

[1313] The server receives the data sent from the device, integrates it with existing data, and prepares it for analysis.

[1314] Step 7:

[1315] The server runs an artificial intelligence model to analyze the received data. The artificial intelligence model analyzes the sensor data to detect abnormalities in the elderly person's movements and environment, analyzes the audio data to identify abnormal voice patterns, and analyzes the video data to detect abnormal behavior such as falls.

[1316] Step 8:

[1317] The server inputs the analysis results into the emotion engine, which recognizes the user's emotions from the voice data and video frames. It analyzes voice tone and facial expression changes to understand the user's emotional state.

[1318] Step 9:

[1319] The server generates notification content based on the user's emotions based on the emotion engine's recognition results. For example, if the user is feeling highly stressed, the notification content will be adjusted to include more details and follow-up suggestions.

[1320] Step 10:

[1321] The server logs the analysis results. The log entries contain the analysis results and their timestamps. These logs are stored in a database.

[1322] Step 11:

[1323] If an anomaly is detected, the server will send a tailored notification to the emergency contacts. The notification will be sent to the user via SMS or email.

[1324] Step 12:

[1325] Users can access the server using their smartphones or PCs to check the latest logs and notifications, especially if an abnormality is reported. Users can check the details and take necessary action.

[1326] Step 13:

[1327] The user can understand the elderly person's health condition and behavioral patterns based on the logs and notifications, and contact the elderly person's home as needed. For example, if a fall is detected, the user can quickly check the situation and take emergency action.

[1328] In this way, a system incorporating an emotion engine can provide appropriate notifications that take the user's emotions into consideration, making it possible to comprehensively manage the health and safety of the elderly.

[1329] Example 2

[1330] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1331] The present invention aims to solve the problem of the difficulty of understanding the health status and behavior of elderly people in remote locations in real time. In particular, there is a need for a system that can respond quickly to sudden changes in the health status of elderly people. There is also a need to reduce the psychological burden on users receiving notifications and encourage more appropriate responses.

[1332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1333] In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model to analyze the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for executing an emotion engine to adjust the content of the notification via a user terminal. This makes it possible to monitor the health status and behavior of elderly people in real time even from remote locations, and to respond quickly and appropriately when an abnormality is detected.

[1334] A "sensor" is a device for acquiring environmental data such as temperature, humidity, and motion detection.

[1335] A "means for recording audio data" is a device or system that has the function of collecting audio and recording it as digital data.

[1336] "Means for capturing video frames" refers to technology that uses a device such as a camera to capture video information and store it as digital data.

[1337] "Means for transmitting data to a server" refers to a mechanism or protocol for transmitting collected data to a server via the Internet or a dedicated line.

[1338] A "means for running an artificial intelligence model" is a device or platform for running the machine learning algorithms or artificial intelligence software used to analyze the collected data.

[1339] The "means for recording the analysis results as a log" refers to a database or file system for recording and saving the analyzed data in chronological order.

[1340] "Means for sending notifications" refers to systems or communication means for sending notifications to users when an abnormality is detected, and includes email, SMS, app notifications, etc.

[1341] "Means for implementing an emotion engine" refers to artificial intelligence or analytical software that analyzes a user's emotions from collected audio and video data and uses the results to adjust the content of notifications.

[1342] This invention combines a system that monitors the health and behavior of elderly people living far away with an emotion engine that recognizes the user's emotions. This system consists of sensors, microphones, cameras, a server, an emotion engine, and a user terminal.

[1343] First, sensors, microphones, and cameras are installed in areas where elderly people frequently move, such as the living room and bedroom. These devices are connected to a terminal to capture environmental data, audio data, and video frames. The terminal collects this data and periodically transmits it to a server.

[1344] The device pre-processes some of the collected data before sending it to the server. For example, environmental sensor data (temperature, humidity, motion detection data) is sent as is, while audio data undergoes noise filtering and video data is processed using image recognition algorithms to extract only important frames.

[1345] The server uses an artificial intelligence model to analyze the received data. The model receives sensor data, audio data, and video data as input and generates analysis results that provide information on the elderly person's behavioral patterns and health status. The analysis results are recorded as logs and stored in a database.

[1346] Additionally, the server utilizes an emotion engine to recognize the user's emotions. It analyzes audio data and video frames to understand the user's emotions. For example, it detects changes in voice tone and facial expressions to determine whether the user is feeling stressed. This information is used to adjust the content of notifications as needed.

[1347] If an anomaly is detected, the server sends a notification to the user, which is tailored based on the analysis by the emotion engine. For example, if the user is experiencing high stress, the notification may include a more detailed explanation or follow-up suggestions.

[1348] Users can access the server using their smartphones or PCs to check the latest logs and notifications. This allows users to remotely monitor the health status and behavioral patterns of their elderly relatives. If necessary, they can contact the elderly's home to check on their condition and take other measures.

[1349] As a concrete example, consider the case where an elderly person is detected to have fallen. The device collects data from sensors, microphones, and cameras placed in the living room. The device sends the collected data to a server, which analyzes it and detects the fall. The analysis results are recorded as a log, and a notification is sent to the user that an abnormality has been detected. The emotion engine analyzes the user's emotions when they receive the notification and suggests follow-up actions as needed. The user can check the log on their smartphone or PC and take action as needed, such as contacting the elderly person's home.

[1350] Examples of prompts include:

[1351] Describe a scenario where a monitoring system detects an elderly person's fall. The system uses sensors, microphones, and cameras. An emotion engine then analyzes the user's emotions and tailors the notification. Can you explain in detail how this system works?

[1352] This allows the health and safety of the elderly to be managed remotely, allowing families to live their daily lives with peace of mind. The introduction of an emotion engine reduces the psychological burden on users and enables more appropriate responses.

[1353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1354] Step 1: Data collection

[1355] The device collects environmental data from various sensors installed in the living room and bedroom, including temperature, humidity, and motion detection data, while simultaneously recording audio data using a microphone and capturing video frames using a camera.

[1356] How it works: The sensors detect temperature, humidity, and motion in real time, the microphone records audio every minute, and the camera captures a video frame every 30 seconds.

[1357] Input: Environmental data (temperature, humidity, motion detection data), audio data, video data

[1358] Output: Collected environmental data, audio data, video frames

[1359] Step 2: Data Preprocessing

[1360] The device preprocesses the collected data: environmental sensor data is processed as is, audio data is noise filtered, and video data is extracted for important frames.

[1361] Specific operation: A noise reduction algorithm is applied to the audio data to extract the important audio parts, and an image recognition algorithm is used to select only the necessary frames from the video data.

[1362] Input: Collected environmental data, audio data, and video data

[1363] Output: Preprocessed environment data, filtered audio data, extracted key video frames

[1364] Step 3: Send data

[1365] The device sends the pre-processed data to a server using an internet connection.

[1366] Specific operation: The terminal accesses the server at regular intervals and sends data in bulk.

[1367] Input: Preprocessed environmental data, audio data, video data

[1368] Output: Data sent to the server

[1369] Step 4: Data analysis

[1370] The server analyzes the received data using an artificial intelligence model to assess the elderly person's behavioral patterns and health status based on sensor, audio, and video data.

[1371] How it works: The artificial intelligence model analyzes the input data and generates a report on the elderly person's health status.

[1372] Input: Received environmental data, audio data, video data

[1373] Output: Analysis results (behavioral patterns, health status report)

[1374] Step 5: Logging

[1375] The server records the analysis results as a log, which is time-stamped and stored in a database.

[1376] Specific operation: When the analysis results are saved in the database, they are recorded as a log along with a timestamp.

[1377] Input: Analysis results (behavioral patterns, health status report)

[1378] Output: Log data with timestamp

[1379] Step 6: Emotion Recognition

[1380] The server recognizes the user's emotions using an emotion engine, which analyzes audio and video frames to determine the user's emotions, and also takes environmental data into account.

[1381] Specific operation: The emotion engine analyzes voice tone and facial expression changes to determine whether the user is feeling stressed or relieved.

[1382] Input: Audio data, video frames, environmental data

[1383] Output: Emotion recognition results (stress level, relief level, etc.)

[1384] Step 7: Send notification

[1385] The server sends a notification to the user when an anomaly is detected. The content of the notification is adjusted based on the emotion recognition results.

[1386] Specific operation: If an abnormality is detected, the server will take into account the results of the emotion engine and send a notification to the user via a dedicated app or email.

[1387] Input: Anomaly analysis results, emotion recognition results

[1388] Output: Notification sent to the user

[1389] Step 8: User Verification

[1390] Users access the server using their smartphones or PCs to check the latest logs and notifications, and if necessary, take action such as contacting the elderly person's home.

[1391] Specific operation: The user logs in to the server's web portal or dedicated app to check the analysis results and notifications, and if necessary, contact the elderly person's home.

[1392] Input: Log data, notification content

[1393] Output: User confirmation result and corresponding action

[1394] This allows the system to continuously monitor the health and safety of the elderly remotely and respond appropriately. The introduction of an emotion engine reduces the psychological burden on users and encourages more effective responses.

[1395] (Application example 2)

[1396] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1397] Conventional systems that monitor the health of elderly people and adjust notifications based on user emotions have the problem of being difficult to respond effectively in situations where emotional changes and flexible responses are required.In addition, in food delivery sites, the emotional states of drivers and customers have a significant impact on service quality, so a system that can recognize this in real time and take appropriate action is required.

[1398] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring environmental data from sensors, means for recording audio data, means for capturing video frames, means for transmitting the acquired data to the server, means for executing an artificial intelligence model for analyzing the received data, means for recording the analysis results as a log, means for sending a notification when an abnormality is detected, and means for recognizing emotions from the acquired data using an emotion engine and adjusting the content of the notification based on the emotions. This enables flexible and appropriate responses according to the user's emotional state, thereby enabling improved customer satisfaction and service quality in food delivery services, etc.

[1399] A "sensor" is a device for acquiring environmental data, and is a piece of equipment whose role is to collect information such as temperature, humidity, and motion detection.

[1400] "Audio data" refers to data that records surrounding sounds and human voices and is stored in digital format or used for analysis.

[1401] "Video frame" refers to each frame of visual information captured by a camera, and is an individual image forming a sequence of images.

[1402] A "server" is a computer system used to process, analyze, and store data, and communicate with other devices over a network.

[1403] An "artificial intelligence model" is a computational model that includes statistical or machine learning algorithms to perform analysis on collected data.

[1404] A "log" is a file or database entry that records the results of data analysis in chronological order.

[1405] "Abnormal" refers to behavior or a state that deviates from normal patterns, especially when there is a problem with health or behavior.

[1406] A "notification" is a message or alert that sends a warning or information to the user when an abnormality is detected.

[1407] An "emotion engine" is an algorithm or system that analyzes audio data or video frames to recognize a user's emotional state.

[1408] "Emotion-based notification content adjustment" refers to the process of appropriately changing the content of notifications sent based on the user's perceived emotional state.

[1409] The system for implementing this invention monitors the health status and behavior of elderly people and incorporates a function to recognize the user's emotions using an emotion engine. The specific configuration and operation of this system will be described in detail below.

[1410] System configuration

[1411] First, sensors, microphones, and cameras are installed in places where elderly people frequently spend time, such as living rooms and bedrooms. These devices are connected to a terminal to acquire environmental data, audio data, and video frames. The terminal periodically collects this data and transmits it to a server. Specifically, the following hardware and software are used:

[1412] Hardware: Environmental sensors including temperature, humidity, and motion sensors, a microphone for audio input, a camera for video capture, and a smartphone or computer to process the data.

[1413] Software: Terminal applications for data collection and transmission, artificial intelligence models (such as TensorFlow or Keras) for analyzing received data, and an emotion engine for emotion recognition.

[1414] Data collection and processing

[1415] The device periodically acquires environmental data from sensors. This environmental data includes temperature, humidity, and motion detection data, providing basic information for determining whether the elderly's living environment is suitable. The microphone records the elderly's voice and surrounding environmental sounds and identifies important audio events. The camera periodically captures video frames to obtain visual information.

[1416] Data transmission and analysis

[1417] The device sends collected sensor data, audio data, and video frames to a server, which uses artificial intelligence models to analyze the data. This analysis evaluates the elderly person's behavioral patterns and health status, and if any abnormalities are detected, they are logged and a notification is sent to the designated emergency contact.

[1418] Using the Emotion Engine

[1419] The server uses an emotion engine to recognize the user's emotions from the captured audio data and video frames. For example, it can determine whether the user is feeling stressed or relaxed from changes in voice tone or facial expression. This information is used to tailor the content of notifications. For example, if the user is feeling highly stressed, it can notify the support center and promptly respond.

[1420] Examples of concrete examples and prompts

[1421] As a concrete example, we will explain how this technology is applied to food delivery. The driver wears smart glasses, and while delivering, the camera and microphone monitor the driver's emotional state and the surrounding environment. The emotion engine analyzes the driver's stress level in real time, and if stress increases, it will play relaxing music or notify the support center to encourage appropriate action.

[1422] Example prompt sentence:

[1423] The user wears the smart glasses, and the emotion recognition engine analyzes the user's emotions in real time based on data collected by the camera and microphone. Use the emotion recognition model to determine emotions such as stress, happiness, and anxiety, and provide appropriate notifications and advice to the user as needed.

[1424] This invention enables flexible and appropriate responses according to the user's emotional state, thereby improving customer satisfaction and service quality, particularly in food delivery services.

[1425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1426] Step 1:

[1427] The device collects data from sensors, microphones, and cameras, capturing environmental data (temperature, humidity, motion detection data), audio data, and video frames. During this data collection step, the device monitors the signals sent from each sensor in real time and converts them into digital data.

[1428] Step 2:

[1429] The terminal temporarily stores the collected data and then sends it to the server. The collected data is buffered for data retention and sent to the server at regular intervals. The input is various sensor data, audio data, and video data, and the output is a single data packet sent to the server.

[1430] Step 3:

[1431] The server analyzes the received data using an artificial intelligence model. Sensor data, audio data, and video data are input for analysis, and behavioral patterns and health conditions are evaluated. Specifically, temperature changes, the presence or absence of movement, audio tones, and movement patterns in the video are detected to determine whether or not there are any abnormalities. The output is the analysis results, including whether or not an abnormality was detected and behavioral pattern data.

[1432] Step 4:

[1433] The server records the analysis results as a log. The log contains the features of various data and anomaly detection results, and stores them in a database. The input to this step is the analysis results from the artificial intelligence model, and the output is a log stored in the database.

[1434] Step 5:

[1435] If an abnormality is detected, the server sends a notification to the configured emergency contact. For example, if an elderly person falls, the server notifies the user of the abnormality by sending an alert. The input is the abnormality detection result, and the output is a notification message for the user.

[1436] Step 6:

[1437] The server uses an emotion engine to recognize the user's emotions. It analyzes the captured audio data and video frames to determine whether the user is feeling stressed or relaxed. The input for the emotion engine's analysis is audio and video data, and the output is the recognition result of the user's emotional state.

[1438] Step 7:

[1439] The server tailors the notification content based on emotion. For example, if the user is experiencing high stress, the notification will include a detailed explanation and follow-up suggestions. The input is the emotion engine's recognition result, and the output is the tailored notification message.

[1440] Step 8:

[1441] Users access the server using a smartphone or PC to check the latest logs and notifications. This allows users to remotely understand the health status and behavioral patterns of elderly people and take action as needed. The input is the log and notification data stored on the server, and the output is the information displayed on the user's interface.

[1442] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1443] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1444] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1445] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1446] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1447] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1448] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1449] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1450] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1451] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1452] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1453] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1454] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1455] 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.

[1456] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1457] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1458] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1459] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1460] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1461] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1462] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1463] The following is further disclosed regarding the above embodiment.

[1464] (Claim 1)

[1465] means for acquiring environmental data from sensors;

[1466] means for recording audio data;

[1467] means for capturing video frames;

[1468] means for transmitting the acquired data to a server;

[1469] means for executing an artificial intelligence model to analyze the received data;

[1470] A means for recording the analysis results as a log;

[1471] means for sending a notification when an anomaly is detected;

[1472] A system including:

[1473] (Claim 2)

[1474] 10. The system of claim 1, wherein the environmental data comprises temperature, humidity, and motion sensing data.

[1475] (Claim 3)

[1476] 10. The system of claim 1, further comprising: means for storing the log in a database.

[1477] "Example 1"

[1478] (Claim 1)

[1479] means for acquiring environmental data from a sensor;

[1480] means for recording audio data;

[1481] a means for capturing video data;

[1482] means for transmitting the acquired data to an information processing device;

[1483] means for executing a generative AI model to analyze the received data;

[1484] a means for recording the analysis result as a log on a recording medium;

[1485] means for sending a notification when an anomaly is detected;

[1486] A means for a user to check logs and notifications on a display device;

[1487] A system including:

[1488] (Claim 2)

[1489] 10. The system of claim 1, wherein the environmental data comprises temperature, humidity, and motion sensing data.

[1490] (Claim 3)

[1491] 10. The system of claim 1, further comprising: means for storing the log in a database.

[1492] "Application Example 1"

[1493] (Claim 1)

[1494] means for acquiring environmental data from sensors;

[1495] means for recording audio data;

[1496] means for capturing video frames;

[1497] means for transmitting the acquired data to a server;

[1498] means for executing an artificial intelligence model to analyze the received data;

[1499] A means for recording the analysis results as a log;

[1500] means for sending a notification when an anomaly is detected;

[1501] means for detecting temperature or humidity anomalies and sending notifications;

[1502] a means for detecting unexpected movement using a motion sensor;

[1503] a means for recording abnormal events in a log file;

[1504] A system including:

[1505] (Claim 2)

[1506] 10. The system of claim 1, wherein the environmental data comprises temperature, humidity, and motion sensing data.

[1507] (Claim 3)

[1508] 10. The system of claim 1, further comprising: means for storing the log in a database.

[1509] "Example 2: Combining Emotion Engines"

[1510] (Claim 1)

[1511] means for acquiring environmental data from sensors;

[1512] means for recording audio data;

[1513] means for capturing video frames;

[1514] means for transmitting the acquired data to a server;

[1515] means for executing an artificial intelligence model to analyze the received data;

[1516] A means for recording the analysis results as a log;

[1517] means for sending a notification when an anomaly is detected;

[1518] means for executing an emotion engine for adjusting notification content via a user terminal;

[1519] A system including:

[1520] (Claim 2)

[1521] 10. The system of claim 1, wherein the environmental data comprises temperature, humidity, and motion sensing data.

[1522] (Claim 3)

[1523] 10. The system of claim 1, further comprising: means for storing the log in a database.

[1524] "Application example 2 when combining emotion engines"

[1525] (Claim 1)

[1526] means for acquiring environmental data from sensors;

[1527] means for recording audio data;

[1528] means for capturing video frames;

[1529] means for transmitting the acquired data to a server;

[1530] means for executing an artificial intelligence model to analyze the received data;

[1531] A means for recording the analysis results as a log;

[1532] means for sending a notification when an anomaly is detected;

[1533] a means for recognizing emotions from the acquired data using an emotion engine and adjusting notification content based on the emotions;

[1534] A system including:

[1535] (Claim 2)

[1536] 10. The system of claim 1, wherein the environmental data comprises temperature, humidity, and motion sensing data.

[1537] (Claim 3)

[1538] 10. The system of claim 1, further comprising: means for storing the log in a database. [Explanation of symbols]

[1539] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring environmental data from sensors; means for recording audio data; means for capturing video frames; means for transmitting the acquired data to a server; means for executing an artificial intelligence model to analyze the received data; A means for recording the analysis results as a log; means for sending a notification when an anomaly is detected; A system including:

2. The system of claim 1 , wherein the environmental data includes temperature, humidity, and motion sensing data.

3. The system of claim 1 further comprising means for storing the log in a database.

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