System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting vital data and environmental data of an animal using a sensor; means for receiving and storing the data transmitted from the sensor in a cloud database; means for analyzing the data and detecting an anomaly; means for generating an alert notification if an anomaly is detected; and means for providing the alert notification to a user.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] As pet species become more diverse, interest in animal health management is growing, especially for small animals. However, there is a current lack of specialized health management methods, particularly for small animals. Specifically, there is a need for effective methods to monitor animal health in real time, quickly detect abnormalities, and take appropriate measures. In addition, there is a need for a system that can collect and analyze animal vital signs and environmental data, and, if necessary, work with specialized doctors to take appropriate measures. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting vital and environmental data of an animal using sensors, a means for receiving the data transmitted from the sensors and storing it in a cloud database, a means for analyzing the data to detect abnormalities, a means for generating an alert notification when an abnormality is detected, and a means for providing the alert notification to a user. The system also includes a means for visualizing the vital and environmental data, a means for periodically compiling the data and generating a report, and a means for providing the report to the user. The system further includes a means for accepting a remote consultation request from a user, a means for sharing information with a veterinarian based on the request, and a means for providing the veterinarian's response to the user. This enables real-time monitoring of the animal's health, rapid detection of abnormalities, and appropriate response.

[0006] A "sensor" is an electronic device that measures an animal's vital and environmental data and transmits the data to an external device.

[0007] "Vital data" refers to basic physical indicators that show an animal's health, such as body temperature and heart rate.

[0008] "Environmental data" refers to indicators that indicate the state of the environment in which animals are kept, specifically temperature, humidity, etc.

[0009] A "cloud database" is a data storage environment accessible via the Internet, and is a system for collecting, storing, and managing data from multiple users and devices.

[0010] "Abnormal" refers to a state in which an animal's vital or environmental data falls outside a pre-set normal range, indicating a possible health problem.

[0011] An "alert notification" is a message sent to the user when the system detects an abnormality and notifies the user of the existence of the abnormality and its details.

[0012] "User" refers to a person who uses this system to manage the health of animals, and primarily refers to owners and managers.

[0013] "Veterinarian" refers to a medical professional who diagnoses the health of animals and provides appropriate treatment.

[0014] "Teleconsultation" refers to a method by which a user can contact a veterinarian through the system to receive health advice or a diagnosis. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system for monitoring the health of animals in real time and taking appropriate measures. Specific embodiments of the present invention will be described in detail below.

[0037] 1. Sensors and Data Collection

[0038] User

[0039] The user employs sensors that can be worn on the animal, which periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[0040] sensor

[0041] The sensor measures data at a predetermined interval and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[0042] 2. Data Receipt and Storage

[0043] server

[0044] The server receives the data sent from the sensors, and the receiving module checks the integrity of the data and stores it in a cloud database.

[0045] Each piece of data is given a timestamp and used for later analysis.

[0046] 3. Data analysis and anomaly detection

[0047] server

[0048] The server monitors the stored data in real time to determine if the animal's vital data is outside of normal ranges.

[0049] If the body temperature exceeds 39°C or the heart rate falls outside a certain range, it is detected as an abnormality.

[0050] 4. Alert Notifications

[0051] server

[0052] If an anomaly is detected, the server generates an alert message that includes the specific details of the anomaly and a recommended action.

[0053] Alert notifications are sent immediately to the user's device.

[0054] Device (app)

[0055] The terminal receives the alert notification from the server and displays an abnormality message on the user interface.

[0056] The user is notified of the abnormality message by sound, vibration, or other means.

[0057] 5. Data visualization and report generation

[0058] server

[0059] The server periodically aggregates the collected data and generates reports in the form of graphs and charts.

[0060] The report includes data variability information such as average, maximum, and minimum values.

[0061] Device (app)

[0062] The terminal receives the report sent from the server.

[0063] To present received reports to a user in a visually easy-to-understand format.

[0064] 6. Collaboration with veterinarians

[0065] User

[0066] Users contact a veterinarian through the app and submit a request to initiate a remote consultation.

[0067] server

[0068] The server receives the user's request, forwards it to the veterinarian, and provides the veterinarian with the necessary data (e.g., vital data, environmental data).

[0069] The veterinarian's response is received and transferred back to the user's terminal.

[0070] Specific examples

[0071] The operation of the system will be explained below with specific examples.

[0072] Example 1: Detecting abnormal body temperature

[0073] User

[0074] If the user's pet suddenly experiences a rise in temperature, the sensor will measure the temperature as 39.5°C.

[0075] sensor

[0076] The body temperature data measured by the sensor is sent to the server.

[0077] server

[0078] The server analyzes the received data and determines that the body temperature is above the normal range (e.g., below 39°C).

[0079] An alert message stating "body temperature is abnormally high" is immediately generated and notified to the user's terminal.

[0080] Device (app)

[0081] The device receives an alert message and displays a notification saying, "Temperature is 39.5°C. Contact your veterinarian immediately."

[0082] Example 2: Regular data reporting

[0083] server

[0084] The server aggregates temperature, heart rate, and environmental data from the past month and generates a report with average, maximum, and minimum values.

[0085] Device (app)

[0086] The terminal receives the report sent from the server and notifies the user.

[0087] Graphs and charts are used to visually display data fluctuations over the past month.

[0088] This invention makes it possible to improve the efficiency of animal health management by monitoring the health status of animals in real time, quickly detecting abnormalities and notifying the user. Furthermore, by providing a function for remote collaboration with veterinarians, this system supports prompt and appropriate medical treatment.

[0089] The processing flow will be explained below.

[0090] Specific steps of the program's processing

[0091] 1. Sensors and Data Collection

[0092] Step 1: Data collection

[0093] sensor:

[0094] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[0095] The measurement data is temporarily saved in the internal memory.

[0096] Step 2: Send data

[0097] sensor:

[0098] The temporarily stored data is periodically sent to the server via wireless communication (Bluetooth or Wi-Fi).

[0099] 2. Data Receipt and Storage

[0100] Step 3: Receiving data

[0101] server:

[0102] The server receives the data sent from the sensors.

[0103] The receiving module checks the integrity of the data and filters out any invalid data.

[0104] Step 4: Save Data

[0105] server:

[0106] The data whose integrity has been confirmed is stored in a cloud database.

[0107] Each piece of data is time-stamped and used for later analysis.

[0108] 3. Data analysis and anomaly detection

[0109] Step 5: Data analysis

[0110] server:

[0111] Runs algorithms that analyze stored data in real time.

[0112] Determine whether vital data is within normal range.

[0113] Step 6: Anomaly detection

[0114] server:

[0115] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[0116] Generate an alert with details about the anomaly detection.

[0117] 4. Alert Notifications

[0118] Step 7: Sending an alert

[0119] server:

[0120] The generated alert notification is sent to the user's terminal.

[0121] Step 8: Receiving and viewing alerts

[0122] Device (app):

[0123] Receive alert notifications and display abnormality messages on the user interface.

[0124] If necessary, the user is notified of abnormalities by sound or vibration.

[0125] 5. Data visualization and report generation

[0126] Step 9: Data Aggregation

[0127] server:

[0128] Aggregate data over a certain period of time (e.g., one month).

[0129] Calculate statistical data such as average, maximum, and minimum values.

[0130] Step 10: Generate reports

[0131] server:

[0132] Generate reports in graph and chart format based on the aggregated results.

[0133] Step 11: Submit report

[0134] server:

[0135] The generated report is sent to the user's terminal.

[0136] Step 12: Receive and view reports

[0137] Device (app):

[0138] Receive reports and notify users.

[0139] View reports in a graphical interface.

[0140] 6. Collaboration with veterinarians

[0141] Step 13: Remote consultation request

[0142] User:

[0143] Send a remote consultation request to a veterinarian through the app.

[0144] Step 14: Request forwarding

[0145] server:

[0146] Forward the user's request to a veterinarian.

[0147] Provide necessary data (e.g., vitals, environmental data) to veterinarians.

[0148] Step 15: Receive a response from your veterinarian

[0149] server:

[0150] A response from the veterinarian is received and forwarded to the user's terminal.

[0151] Step 16: Displaying the response

[0152] Device (app):

[0153] A response from the veterinarian is received and displayed on the user interface.

[0154] User

[0155] Users can check the veterinarian's advice and instructions through the app and take appropriate action.

[0156] The above are the steps in a specific embodiment of the present invention. By linking the steps together, it becomes possible to monitor the health status of animals in real time, quickly detect abnormalities, and take appropriate measures.

[0157] Example 1

[0158] 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."

[0159] Conventional animal health monitoring systems can be slow to detect abnormalities, leading to delayed appropriate responses. Continuous data collection and analysis are insufficient, and the visualization of generated reports is limited, making it difficult for users to understand intuitively. Furthermore, remote consultation with veterinarians is not easy, making it difficult to respond in emergencies. There is a need to solve these problems and streamline animal health management in real time.

[0160] 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.

[0161] In this invention, the server includes means for acquiring animal biometric and environmental data using sensors, means for receiving data transmitted from the sensors and storing it in cloud storage, means for analyzing the data and detecting abnormalities, means for generating a warning message when an abnormality is detected, means for providing the warning message to a user, means for visualizing the animal biometric and environmental data, means for periodically compiling the data and generating a report, means for providing the report to a user, means for accepting a remote consultation request from a user, means for sharing information with a veterinary professional based on the request, and means for providing the veterinary professional's response to the user. This allows a user to monitor the health of their animal in real time and quickly detect and respond to any abnormalities that may occur. Furthermore, providing the data visually makes it easier to understand, facilitating remote consultations with a veterinarian.

[0162] A "sensor" is a device for acquiring biological and environmental data of an animal.

[0163] "Biometric data" refers to data that indicates the health status of an animal, such as body temperature and heart rate.

[0164] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal exists.

[0165] "Cloud storage" is a remote server for storing and managing data over the Internet.

[0166] "Analysis" is the process of examining acquired and stored data and detecting anomalies.

[0167] "Abnormal" refers to a case where the biometric data and environmental data deviate from the set normal range.

[0168] A "warning message" is a notification that is generated when an abnormality is detected, and notifies the user of the existence of the abnormality and how to deal with it.

[0169] "Visualization" refers to displaying acquired data in a visually easy-to-understand format, such as a graph or chart.

[0170] A "report" is a document created based on periodically compiled data that shows fluctuations and trends in past data.

[0171] A "teleconsultation" is a process by which a user consults with a veterinary medical professional over the Internet.

[0172] An "animal medical professional" is a person with the expertise to diagnose and treat animal health conditions.

[0173] The present invention provides a system for monitoring the health status of animals in real time, quickly detecting abnormalities, and notifying the user. A specific embodiment of this system will be described below.

[0174] Sensors and Data Collection

[0175] User

[0176] Users use sensors that can be worn on animals that periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[0177] The sensors transmit data to a server using, for example, Bluetooth or Wi-Fi.

[0178] Data reception and storage

[0179] server

[0180] The server receives the data sent from the sensors in real time. The receiving module checks the data for integrity and for missing or outlier values. The data is then stored in cloud storage. The data is time-stamped and a unique identifier is added.

[0181] Data analysis and anomaly detection

[0182] server

[0183] The server analyzes the received data in real time. An analysis module runs to determine whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, an abnormality is detected.

[0184] Alert Notifications

[0185] server

[0186] When an abnormality is detected, the server generates a warning message that includes a specific description of the abnormality (e.g., "Temperature rises to 39.5°C") and a recommended action (e.g., "Contact a veterinary professional immediately").

[0187] A warning message is immediately sent to the user's terminal.

[0188] Device (app)

[0189] The device receives a warning message from the server and notifies the user with sound and vibration, as well as displays an abnormal message on the screen, such as "Temperature is 39.5°C. Contact a veterinary professional immediately."

[0190] Data Visualization and Report Generation

[0191] server

[0192] The server periodically aggregates the data and generates a report in the form of graphs and charts, including, for example, the average, maximum, and minimum body temperature values ​​for the past month. This report is sent to the user's device at a specified date and time.

[0193] Device (app)

[0194] The device receives the reports sent from the server and presents them to the user in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[0195] Collaboration with veterinarians

[0196] User

[0197] Users use the app to submit a request for a remote consultation with a veterinary medical professional.

[0198] server

[0199] The server receives the user's request and forwards it to the veterinary professional, providing any necessary data (e.g., biometric data, environmental data), and then forwards it back to the user's device after receiving the response from the veterinary professional.

[0200] Specific examples

[0201] The operation of the system will be explained below with specific examples.

[0202] Example 1: Detecting abnormal body temperature

[0203] User

[0204] The user's animal's temperature spikes and the sensor measures it at 39.5°C.

[0205] sensor

[0206] The sensor stores this data in its internal memory and then transmits it to a server via Bluetooth.

[0207] server

[0208] The server receives this data, and the analysis module determines that a body temperature of 39°C or higher is abnormal. A warning message is generated and sent to the user's device stating, "The body temperature is 39.5°C. Please contact a veterinary professional immediately."

[0209] Device (app)

[0210] The device receives the warning message and displays it to the user along with a sound notification.

[0211] Example 2: Regular data reporting

[0212] server

[0213] The server aggregates body temperature, heart rate, and environmental data from the past month and generates a report containing the average, maximum, and minimum values ​​for each vital data. This report is sent to the user's device at the specified date and time.

[0214] Device (app)

[0215] The device receives the report sent from the server and notifies the user, "Please check your health data report for the past month." The user can open the report and visually check the fluctuations in body temperature and heart rate data as line graphs and histograms.

[0216] Prompt Sentence Examples

[0217] Here are some examples of specific prompts:

[0218] "Describe a program that analyzes an animal's vitals sent from a sensor and sends an alert if the body temperature exceeds 39°C."

[0219] In this way, the present invention realizes a system that enables real-time health monitoring and abnormality detection, and supports rapid response.

[0220] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0221] Step 1: Sensors and Data Collection

[0222] User

[0223] The user attaches sensors to the animal, which measure the animal's body temperature, heart rate, environmental temperature, and humidity at regular intervals (e.g., every 10 minutes).

[0224] Input: User-defined measurement interval and attached sensor

[0225] Output: Body temperature, heart rate, ambient temperature, and humidity data obtained by the sensor

[0226] sensor

[0227] The sensor temporarily stores the measurement data in its internal memory and then transmits the data to a server using Bluetooth or Wi-Fi.

[0228] Input: Measured body temperature, heart rate, ambient temperature, and humidity data

[0229] Output: Data packets sent to the server

[0230] Step 2: Receiving and storing data

[0231] server

[0232] The server receives the data sent from the sensors in real time. The receiving module checks the data for consistency and verifies whether there are any missing or outlier values. The data is then stored in cloud storage.

[0233] Input: Data packets sent from the sensor

[0234] Data processing: Checking data integrity, checking for missing or outliers, adding timestamps, and adding unique identifiers

[0235] Output: Consistent data stored in cloud storage

[0236] Step 3: Data analysis and anomaly detection

[0237] server

[0238] The server analyzes the data stored in the database in real time. The analysis module determines whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, it will be detected as an abnormality.

[0239] Input: Biometric and environmental data stored in cloud storage

[0240] Data calculation: Comparison with normal range, detection of abnormal values

[0241] Output: Triggers the generation of a warning message if an anomaly is detected

[0242] Step 4: Alert Notification

[0243] server

[0244] When an anomaly is detected, the server generates a warning message, which includes the specific details of the anomaly and a recommended action, and is sent to the user's device.

[0245] Input: Trigger when an anomaly is detected

[0246] Data processing: Generating a warning message (e.g., "Temperature is 39.5°C. Contact a veterinary professional immediately.")

[0247] Output: The warning message sent to the user's terminal.

[0248] Device (app)

[0249] The user's device receives the warning message from the server and notifies the user with sound or vibration, and displays a specific abnormality message on the screen.

[0250] Input: warning message

[0251] Output: Alert sound, vibration, on-screen notification

[0252] Step 5: Data visualization and report generation

[0253] server

[0254] The server periodically aggregates the data and generates various reports, including graphs and charts showing average, maximum, and minimum body temperature and heart rate values ​​over the past month. These reports are sent to the user's device at a specified date and time.

[0255] Input: Historical data stored in cloud storage

[0256] Data calculation: Aggregation and report generation (calculating average, maximum, minimum values)

[0257] Output: Report sent to user terminal

[0258] Device (app)

[0259] The user's device receives the report sent from the server and displays it in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[0260] Input: Report from the server

[0261] Output: Reports displayed in graph and chart formats

[0262] Step 6: Work with your veterinarian

[0263] User

[0264] Users use the app to submit a request for a remote consultation with a veterinary specialist.

[0265] Input: Remote consultation request

[0266] Output: Request sent to server

[0267] server

[0268] The server receives the request from the user, forwards it to the veterinary professional, provides the necessary data (e.g., biological data, environmental data), receives the response from the veterinary professional, and forwards it back to the user's terminal.

[0269] Input: User request, corresponding data

[0270] Data processing: forwarding requests to veterinary professionals and receiving responses

[0271] Output: The response displayed on the user's terminal

[0272] (Application example 1)

[0273] 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."

[0274] While machines and robots operating in factories are required to be highly automated and efficient, there is a risk that machine failure or malfunction can have a significant impact on the entire production line. Therefore, there is a growing need for systems that can detect machine abnormalities early and take prompt action. However, current systems have issues with the accuracy of abnormality detection and the speed of notification, and also lack appropriate maintenance support and collaboration functions with specialized engineers. The objective of this invention is to provide a system that solves these problems and improves factory productivity.

[0275] 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.

[0276] In this invention, the server includes: means for collecting machine status data and environmental data using sensors; means for receiving data transmitted from the sensors and storing it in a cloud database; means for analyzing the data and detecting abnormalities; means for generating an alert notification when an abnormality is detected; means for providing the alert notification to a user device; means for visualizing the status data and environmental data; means for periodically aggregating the data and generating a report; means for providing the report to the user device; means for generating and providing machine maintenance support information when an abnormality is detected; means for accepting remote consultation requests from users; means for sharing information with specialist engineers based on the request; and means for providing the specialist engineers' responses to the user device. This enables rapid and accurate detection of abnormalities in machines and robots operating in a factory and the provision of appropriate countermeasures. Furthermore, collaboration with specialist engineers enables more advanced responses, improving the efficiency and reliability of production lines.

[0277] A "sensor" is a device for collecting machine condition and environmental data.

[0278] "Status data" refers to data relating to the operating status of machines and robots, the temperature, vibration, operating time, etc. of each part.

[0279] "Environmental data" refers to data that includes information such as temperature, humidity, and pressure in the location where the machine is installed.

[0280] "Cloud Database" means a database accessible via the Internet for storing condition and environmental data collected in real time.

[0281] "Anomaly detection" is the process of analyzing collected data and determining whether there is an abnormality in the operation or condition of a machine or robot.

[0282] An "alert notification" is a warning message that is generated when an abnormality is detected, and includes a means for providing this to a user device.

[0283] A "user device" is an information terminal such as a smartphone or tablet used by managers and engineers in a factory.

[0284] "Visualization" is the process of displaying collected data in an easy-to-understand format such as graphs or charts.

[0285] A "report" is a document that summarizes the results of aggregating data over a certain period of time, and includes statistical information such as average, maximum, and minimum values.

[0286] "Maintenance support information" is information that suggests specific repair and inspection methods based on detected abnormalities.

[0287] "Remote consultation" is a process in which managers and engineers in a factory contact specialist engineers and receive support for resolving problems from a remote location.

[0288] A "specialist engineer" is a technician with advanced knowledge and skills in the repair and maintenance of machines and robots.

[0289] The present invention is a system for monitoring abnormalities and supporting maintenance of machines and robots operating in a factory, and is specifically implemented as follows.

[0290] Sensors and Data Collection

[0291] User

[0292] Users attach multiple sensors (temperature sensors, vibration sensors, etc.) to machines or robots, which measure machine status data and environmental data in real time.

[0293] sensor

[0294] The sensor measures data at regular intervals and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[0295] Data reception and storage

[0296] server

[0297] The server receives the data sent from the sensors and stores it in a cloud database. The receiving module checks the integrity of the data and assigns a timestamp.

[0298] Data analysis and anomaly detection

[0299] server

[0300] The server monitors the stored data in real time and determines whether the machine's status data is outside of the normal range. For example, if the temperature exceeds 80°C or the vibration is outside a certain range based on the data obtained from the sensor, it will be detected as an abnormality.

[0301] Alert Notifications

[0302] server

[0303] If an anomaly is detected, the server generates an alert message, which includes the specific details of the anomaly and recommended actions. The alert notification is immediately sent to the user's device.

[0304] Device (smartphone)

[0305] The device receives an alert notification from the server and displays an abnormality message on the user interface. The abnormality message is notified to the user by sound, vibration, etc.

[0306] Data Visualization and Report Generation

[0307] server

[0308] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums, and notifies the user of any maintenance needs.

[0309] Device (smartphone)

[0310] The terminal receives the reports sent from the server and visually displays data fluctuations using graphs and charts.

[0311] Remote consultation and maintenance support

[0312] User

[0313] The user requests a remote consultation with a specialist technician via a terminal, and the request includes specific information about the machine that needs to be addressed.

[0314] server

[0315] The server receives the request, forwards it to the expert, provides the expert with the necessary data (e.g., status data, environment data), receives the expert's response, and forwards it back to the user's device.

[0316] Specific examples

[0317] For example, if the motor temperature of a factory robot exceeds 90°C, the server generates an alert message stating "Abnormality detected: Motor temperature is 90°C (time: 2023-10-01 10:00:00)" and notifies the administrator's terminal.

[0318] Prompt Sentence Examples

[0319] "Please tell me how to analyze data collected in real time by the sensors of a factory robot and notify a smartphone if an abnormality is detected. In particular, please let me know if there are any examples of implementing alert notifications using Flask and Webhooks."

[0320] This makes it possible to quickly and accurately detect abnormalities in machines and robots operating in factories and take appropriate measures. In addition, by collaborating with specialized engineers, more advanced responses are possible, improving the efficiency and reliability of production lines.

[0321] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0322] Step 1:

[0323] Sensor data collection and transmission

[0324] Sensors attached to machines or robots by users measure status data (e.g., temperature, vibration) and environmental data (e.g., temperature, humidity) at regular intervals. The collected data is sent to a server via Bluetooth or Wi-Fi.

[0325] Input: Data from sensors on machines and robots

[0326] Output: Measurement data sent to the server

[0327] Step 2:

[0328] Data reception and storage

[0329] The server receives the data sent by the sensors, checks the integrity of the data, and adds a timestamp before storing it in a database for later analysis.

[0330] Input: Data sent from the sensor

[0331] Output: Data stored in a cloud database

[0332] Step 3:

[0333] Data analysis and anomaly detection

[0334] The server monitors the stored data in real time and detects abnormal data that exceeds a predetermined threshold. For example, if temperature data exceeds 80°C or vibration data falls outside a certain range, it will be deemed abnormal.

[0335] Input: Data stored in a cloud database

[0336] Output: Anomaly detection result (normal / abnormal)

[0337] Step 4:

[0338] Generate and send alert notifications

[0339] If an anomaly is detected, the server generates an alert message containing the specific details of the anomaly and recommended actions, and immediately sends it to the user's device.

[0340] Input: Anomaly detection results

[0341] Output: The alert message sent to the user's terminal.

[0342] Step 5:

[0343] Displaying an alert message

[0344] The device (smartphone) receives the alert message sent from the server and displays an abnormality message on the user interface. The content of the alert is notified to the user by notification methods such as sound and vibration.

[0345] Input: Alert message

[0346] Output: An abnormal message displayed on the device screen, a sound or vibration notification

[0347] Step 6:

[0348] Data Visualization and Reporting

[0349] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums.

[0350] Input: Collected data

[0351] Output: Generated report

[0352] Step 7:

[0353] Report distribution and viewing

[0354] The server sends the generated report to the user's device, which receives it and displays it visually in the form of graphs and charts.

[0355] Input: Generated report

[0356] Output: A report displayed on the terminal screen

[0357] Step 8:

[0358] Sending and responding to remote consultation requests

[0359] The user requests a remote consultation from a specialist through a terminal. The server receives the request and forwards it to the specialist. The server also forwards the specialist's response back to the user's terminal.

[0360] Input: Remote consultation request from user

[0361] Output: Response from the expert

[0362] 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.

[0363] The present invention provides a more user-friendly interface by combining a system that monitors the health of animals in real time, detects abnormalities, and takes appropriate measures, with an emotion engine that recognizes the user's emotions. Specific embodiments are described in detail below.

[0364] 1. Sensors and Data Collection

[0365] User

[0366] Users use sensors that can be attached to animals, which measure the animal's body temperature, heart rate, and the temperature and humidity of the environment at regular intervals (e.g., every minute).

[0367] sensor

[0368] The sensor periodically transmits the measured data to the server via wireless communication (Bluetooth or Wi-Fi).

[0369] 2. Data Receipt and Storage

[0370] server

[0371] The server receives the data sent from the sensors, checks its integrity, and then stores it in a cloud database.

[0372] Each piece of data is given a timestamp and used for later analysis.

[0373] 3. Data analysis and anomaly detection

[0374] server

[0375] The server analyzes the stored data in real time and detects any vital data that exceeds a set threshold (e.g., body temperature above 39°C) as an abnormality.

[0376] 4. Alert Notifications

[0377] server

[0378] If an anomaly is detected, the server generates an alert message that includes specific information about the anomaly and recommended actions.

[0379] Alert notifications are sent immediately to the user's device.

[0380] Device (app)

[0381] The device receives the alert notification and displays it on the user interface. The user is notified of the abnormality message by sound, vibration, or other means.

[0382] 5. Data visualization and report generation

[0383] server

[0384] The server aggregates the data over a period of time (e.g., one month) and generates reports in the form of graphs and charts.

[0385] The report is then processed into a format that is easily understood by the user.

[0386] Device (app)

[0387] The terminal receives the report and displays it in a visually friendly format on the user interface.

[0388] 6. Collaboration with veterinarians

[0389] User

[0390] Users send a request for a remote consultation to a veterinarian through the app.

[0391] server

[0392] The server forwards the user's request to the veterinarian and provides the necessary data (e.g., vital data, environmental data).

[0393] A response from the veterinarian is received and transferred back to the user's terminal.

[0394] 7. User-friendly interface using emotion engine

[0395] Device (app)

[0396] The emotion engine analyzes emotion data from the user's facial expressions and voice to determine the user's current emotional state.

[0397] The emotion engine customizes the content or presentation of alert notifications based on the user's emotional state. For example, if the user is stressed, the message will be more friendly.

[0398] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[0399] Specific examples

[0400] The operation of the system will be explained below with specific examples.

[0401] Example 1: Abnormal body temperature detection and alert notification

[0402] sensor

[0403] The dog's temperature is measured at 39.5°C.

[0404] server

[0405] Body temperature data is received and determined to be abnormal.

[0406] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[0407] Device (app)

[0408] Receive alert notifications and notify the user with sound and vibration.

[0409] Example 2: User Emotion Recognition

[0410] User

[0411] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[0412] Device (app)

[0413] The emotion engine analyzes the emotion of anxiety and converts it into a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately."

[0414] If necessary, a remote consultation request with a veterinarian is automatically generated based on the user's concerns.

[0415] As a result, the present invention not only monitors the health status of animals in real time, but also provides an interface that takes the user's emotions into consideration, allowing for more appropriate and prompt responses.

[0416] The processing flow will be explained below.

[0417] Specific steps of the program's processing

[0418] 1. Sensors and Data Collection

[0419] Step 1: Data collection

[0420] sensor:

[0421] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[0422] The measurement data is temporarily saved in the internal memory.

[0423] Step 2: Send data

[0424] sensor:

[0425] The measured data is periodically sent to a server via wireless communication (Bluetooth or Wi-Fi).

[0426] 2. Data Receipt and Storage

[0427] Step 3: Receiving data

[0428] server:

[0429] The server receives the data sent from the sensors.

[0430] The receiving module checks the integrity of the data and filters out any invalid data.

[0431] Step 4: Save Data

[0432] server:

[0433] The data whose integrity has been confirmed is stored in a cloud database.

[0434] Each piece of data is given a timestamp and used for later analysis.

[0435] 3. Data analysis and anomaly detection

[0436] Step 5: Data analysis

[0437] server:

[0438] Runs algorithms that analyze stored data in real time.

[0439] Determine whether vital data is within normal range.

[0440] Step 6: Anomaly detection

[0441] server:

[0442] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[0443] Generate an alert with details about the anomaly detection.

[0444] 4. Alert Notifications

[0445] Step 7: Sending an alert

[0446] server:

[0447] The generated alert notification is sent to the user's terminal.

[0448] Step 8: Receiving and viewing alerts

[0449] Device (app):

[0450] Receive alert notifications and display abnormality messages on the user interface.

[0451] If necessary, the user is notified of abnormalities by sound or vibration.

[0452] 5. Data visualization and report generation

[0453] Step 9: Data Aggregation

[0454] server:

[0455] Aggregate data over a certain period of time (e.g., one month).

[0456] Calculate statistical data such as average, maximum, and minimum values.

[0457] Step 10: Generate reports

[0458] server:

[0459] Generate reports in graph and chart format based on the aggregated results.

[0460] Step 11: Submit report

[0461] server:

[0462] The generated report is sent to the user's terminal.

[0463] Step 12: Receive and view reports

[0464] Device (app):

[0465] Receive reports and notify users.

[0466] View reports in a graphical interface.

[0467] 6. Collaboration with veterinarians

[0468] Step 13: Remote consultation request

[0469] User:

[0470] Send a remote consultation request to a veterinarian through the app.

[0471] Step 14: Request forwarding

[0472] server:

[0473] Forwarding the user's request to the veterinarian and providing any necessary data (e.g., vitals, environmental data).

[0474] Step 15: Receive a response from your veterinarian

[0475] server:

[0476] A response from the veterinarian is received and forwarded to the user's terminal.

[0477] Step 16: Displaying the response

[0478] Device (app):

[0479] The response from the veterinarian is displayed to the user.

[0480] 7. User-friendly interface using emotion engine

[0481] Step 17: Collecting User Emotion Data

[0482] Device (app):

[0483] A camera and microphone are used to collect the user's facial expressions and voice.

[0484] Step 18: Sentiment Analysis

[0485] Device (app):

[0486] The emotion engine analyzes the collected data and identifies the user's emotions (e.g., joy, anger, anxiety, sadness).

[0487] Step 19: Interface Adjustments

[0488] Device (app):

[0489] Based on the analysis results, the content and presentation of the alert notification can be adjusted, for example, a more friendly message can be displayed to a user who is feeling stressed.

[0490] Step 20: Emotion-Based Teleconsultation Request

[0491] Device (app):

[0492] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[0493] For example, when a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety and changes the message to a more friendly one. If the emotion engine detects strong anxiety, it automatically generates a remote consultation request, encouraging the user to contact a veterinarian.

[0494] These steps will result in a more user-friendly health monitoring system that takes user emotions into account.

[0495] Example 2

[0496] 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."

[0497] In recent years, interest in pet health management has grown, creating a demand for systems that can monitor animals' health conditions in real time and respond quickly to abnormalities. However, existing systems do not take into account the user's emotional state when notifying them of an abnormality, which can easily cause anxiety. Furthermore, there is a lack of a way to easily visualize acquired data and present it in an easy-to-understand format. Therefore, a system is needed that provides a user-friendly, emotionally sensitive interface, as well as visualizes data and enables rapid connection to experts.

[0498] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data and environmental data of the animal using a sensor, means for receiving data transmitted from the sensor and storing it in a cloud database, means for analyzing the data in real time and detecting abnormalities, means for generating an alert notification and transmitting it to the user's terminal when an abnormality is detected, means for analyzing the user's emotional state and generating a message according to the emotional state, and means for providing a user interface through which the message is transmitted. This enables real-time monitoring of the health status and rapid response in the event of an abnormality, and by providing an alert notification that takes the user's emotions into consideration, the user can reduce anxiety and take appropriate action.

[0499] A "sensor" is a device used to collect vital and environmental data from an animal.

[0500] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as an animal's body temperature and heart rate.

[0501] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal lives.

[0502] A "cloud database" is a data storage area located on a remote server accessible via the Internet.

[0503] "Real-time analysis" means analyzing data immediately after it is sent from the sensor and detecting any abnormalities.

[0504] An "abnormality" is an abnormal situation that is detected when the vital data of an animal exceeds a preset threshold.

[0505] An "alert notification" is a message that notifies the user of a detected abnormality.

[0506] The "emotional state of the user" refers to the psychological state analyzed based on information obtained from the user's facial expressions and voice.

[0507] The "means for generating a message" is a function of the system that generates a message according to the emotional state of the user when an abnormality is detected.

[0508] "User interface" refers to the display screen and operation panel that allow the user to interact with the system.

[0509] "Means for generating reports in graph and chart format" refers to the system's functionality for compiling collected data into a format that is easy to understand visually.

[0510] "Animal health professional" refers to a health care worker with expertise in animal health care.

[0511] MODE FOR CARRYING OUT THE INVENTION

[0512] This invention is a system that monitors the health of animals in real time, detects abnormalities, and sends alerts that take into account the user's emotional state. This system is composed of sensors, a server, and a terminal (app), and the functions and roles of each part are explained in detail below.

[0513] Sensor installation and data measurement

[0514] User

[0515] The user attaches sensors to the animal, which are attached to the animal's collar or harness, and periodically (e.g., every minute) measure body temperature, heart rate, and environmental temperature and humidity.

[0516] Sending measurement data

[0517] sensor

[0518] The sensor sends the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor measures a body temperature of 38.5°C, humidity of 60%, and a heart rate of 120 bpm, and sends the data to the server every minute.

[0519] Receiving and storing data

[0520] server

[0521] The server receives the data sent from the sensors and checks the data for consistency. Specifically, it checks whether the data format is correct and whether there are any duplicates. Once the consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[0522] Data analysis and anomaly detection

[0523] server

[0524] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[0525] Generate and send alert notifications

[0526] server

[0527] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[0528] Device (app)

[0529] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[0530] Data Visualization and Reporting

[0531] server

[0532] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[0533] Device (app)

[0534] The terminal receives the report and displays it in a visually friendly format on the user interface.

[0535] Collaboration with veterinarians

[0536] User

[0537] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[0538] server

[0539] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[0540] Interface using emotion engine

[0541] Device (app)

[0542] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[0543] Specific examples

[0544] Example 1: Abnormal body temperature detection and alert notification

[0545] 1. Sensor

[0546] The dog's temperature is measured at 39.5°C.

[0547] 2. Server

[0548] Body temperature data is received and determined to be "abnormal body temperature."

[0549] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[0550] 3. Device (app)

[0551] Receives alert notifications and notifies the user with sound and vibration. Displays the alert content on the user interface.

[0552] Example 2: Recognizing user emotions

[0553] 1. Users

[0554] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[0555] 2. Device (app)

[0556] The emotion engine analyzes the user's anxiety and generates and displays a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, a remote consultation request with a veterinary professional is automatically generated based on the user's anxiety.

[0557] Example input to a generative AI model

[0558] Example prompt

[0559] "Generate an alert message when an animal's temperature is abnormally high. Also, provide an example of how to convert the message to a more friendly one if the user is feeling anxious."

[0560] In this way, the system of the present invention monitors the health status of animals in real time, responds quickly to abnormalities, and provides alert notifications that take the user's emotions into consideration, allowing the user to take appropriate action.

[0561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0562] Step 1: Sensor installation and data collection

[0563] User

[0564] The user attaches sensors to the animal, which are attached to the animal's collar or harness and measure body temperature, heart rate, and environmental temperature and humidity every minute. Specifically, the built-in sensors continuously monitor the animal's body and surrounding environment and collect measurement data.

[0565] Input: Animal body temperature, heart rate, environmental temperature and humidity

[0566] Output: Measured vital and environmental data

[0567] Step 2: Sending measurement data

[0568] sensor

[0569] The sensor periodically transmits the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor transmits data such as body temperature (38.5°C), humidity (60%), and heart rate (120 bpm) every minute.

[0570] Input: Measured vital and environmental data

[0571] Output: Data sent to the server

[0572] Step 3: Receiving and storing data

[0573] server

[0574] The server receives the data sent from the sensors and checks the data for consistency, ensuring that the data format is correct and that there are no duplicates. Once consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[0575] Input: Data sent from the sensor

[0576] Output: Data that has been checked for integrity and saved

[0577] Step 4: Data analysis and anomaly detection

[0578] server

[0579] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[0580] Input: Received data

[0581] Output: Anomaly detected and recorded information

[0582] Step 5: Generate and send alert notifications

[0583] server

[0584] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[0585] Input: Anomaly detection information

[0586] Output: The generated alert message

[0587] Device (app)

[0588] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[0589] Input: The alert message sent

[0590] Output: The alert content that is notified and displayed to the user

[0591] Step 6: Visualize the data and generate reports

[0592] server

[0593] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[0594] Input: Data for a period of time

[0595] Output: Generated report

[0596] Device (app)

[0597] The terminal receives the report and displays it in a visually friendly format on the user interface.

[0598] Input: Generated report

[0599] Output: The displayed report content

[0600] Step 7: Work with your veterinarian

[0601] User

[0602] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[0603] Input: Content of consultation request

[0604] Output: Request sent

[0605] server

[0606] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[0607] Input: The submitted request and associated data

[0608] Output: Response from veterinary medical professional

[0609] Step 8: Interface using emotion engine

[0610] Device (app)

[0611] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[0612] Input: User's facial expressions and voice

[0613] Output: Generated friendly message and remote consultation request

[0614] The above are the specific processing steps of the program of this system.

[0615] (Application example 2)

[0616] 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."

[0617] In modern food delivery services, quality control of ingredients during transportation is important. However, environmental factors such as temperature, humidity, and vibration can have a negative impact on the quality of ingredients, making real-time monitoring difficult. Another challenge is providing appropriate notification content that takes into account the emotions and stress of drivers and customers when receiving alerts.

[0618] 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 collecting information data of an object and surrounding environment data using sensors, means for receiving data transmitted from the sensors and storing the data in a cloud storage device, means for analyzing the data and detecting abnormalities, means for generating an alert notification when an abnormality is detected, means for providing the alert notification to the user, and means for analyzing the user's emotions and customizing the content or display method of the alert notification based on the emotions. This makes it possible to monitor the quality control of ingredients in real time and to provide appropriate alert notifications that take the user's emotions into consideration when an abnormality occurs.

[0619] A "sensor" is a device that measures physical data and outputs that data in electronic form.

[0620] "Object" refers to the object or environment from which data is collected or monitored.

[0621] "Information data" refers to data that contains specific information about an object.

[0622] "Ambient environment data" refers to data relating to environmental factors such as temperature, humidity, and vibrations present around an object.

[0623] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[0624] "Abnormal" refers to a data condition that exceeds a pre-established standard or threshold.

[0625] An "alert notification" is a message that warns or alerts the user when an abnormality is detected.

[0626] "Means for analyzing emotions" refers to technologies and devices for detecting and interpreting emotions from a user's facial expressions, voice, etc.

[0627] "Means of customization" refers to methods for adjusting the system's behavior and display content according to specific conditions or situations.

[0628] An "expert" is someone who has advanced knowledge or skills in a particular field.

[0629] The present invention is a system that uses sensors to collect information data on objects and their surrounding environments, stores the data in a cloud storage device, analyzes it in real time, and detects abnormalities. When an abnormality is detected, the system generates an alert notification and provides it to the user. The system also includes a function for analyzing the user's emotions and customizing the content or display method of the alert notification based on the user's emotions.

[0630] Sensors and Data Collection

[0631] The user uses sensors installed on food items or their surroundings. These sensors collect data such as temperature, humidity, and vibration, and periodically measure and transmit the data to a smartphone or delivery robot. This data is transmitted via wireless communication such as Bluetooth or Wi-Fi.

[0632] Data reception and storage

[0633] The server receives data sent from the sensors via smartphones or delivery robots. The received data is stored in a cloud storage device, and a timestamp is assigned to each piece of data. Common online storage services (e.g., AWS, Azure) are used as the cloud storage device.

[0634] Data analysis and anomaly detection

[0635] The server analyzes the data stored in the cloud storage device in real time and detects abnormalities when set thresholds are exceeded, such as when the temperature exceeds 35°C or vibration exceeds a set value. This allows for quick action to be taken before the quality of the ingredients deteriorates.

[0636] Alert Notifications

[0637] If an abnormality is detected, the server generates an alert message. This alert message is sent to the user's smartphone or delivery robot, and the user is notified by sound or vibration. The alert message contains specific information about the abnormality and recommended actions. For example, it could say, "The temperature has reached 35.5°C. Please take immediate action."

[0638] Sentiment analysis and interface customization

[0639] When a user receives an alert notification, an emotion analysis engine on the smartphone or delivery robot assists them. The emotion analysis engine analyzes emotional data from the user's facial expressions and voice, and if the user is feeling stressed, it converts it into a friendly message. Specifically, it might say something like, "Don't worry, there's no problem. Please take immediate action." This method can reduce the user's stress.

[0640] Examples and prompts

[0641] Specific examples

[0642] While the ingredients are being transported, a sensor measures the CPU temperature at 35.5°C. The emotion analysis engine recognizes the delivery driver's anxiety and displays an alert message saying, "Don't worry, we'll take action immediately."

[0643] Prompt Sentence Examples

[0644] Analyze the following sentence, determine the user's sentiment, and generate an appropriate response message:

[0645] Alert message: Temperature reached 35.5°C during transport.

[0646] User Emotion: Anxiety

[0647] An appropriate response message would be: "Don't worry, we'll fix it right away."

[0648] In this way, the present invention controls the quality of ingredients in real time and provides alert notifications that take into consideration the user's emotions when an abnormality occurs, enabling a quick and appropriate response.

[0649] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0650] Step 1:

[0651] Sensors periodically acquire information data such as temperature, humidity, and vibration, as well as data on the surrounding environment. The input is physical data from the object and the surrounding environment, and the output is electronic measurement data.

[0652] Step 2:

[0653] The terminal (smartphone or delivery robot) receives the data sent from the sensor via wireless communication (Bluetooth or Wi-Fi). The input is the measurement data from the sensor, and the output is the data stored in the terminal.

[0654] Step 3:

[0655] The data received by the device is sent to the cloud storage device. The input is the data stored on the device, and the output is the data stored in the cloud storage device. The cloud storage device is assigned a timestamp and the integrity of the data is verified.

[0656] Step 4:

[0657] The server analyzes the data stored in the cloud storage device in real time, compares it with the set threshold, and detects anomalies. The input is the data stored in the cloud storage device, and the output is the result of the anomaly detection.

[0658] Step 5:

[0659] If an anomaly is detected, the server generates an alert notification, which contains specific information about the anomaly and a recommended action. The input is the anomaly detection result, and the output is the alert message.

[0660] Step 6:

[0661] An alert notification is sent to the user's device via a smartphone or delivery robot. The input is the generated alert message, and the output is the alert notification displayed on the device. The notification is also conveyed to the user by sound or vibration.

[0662] Step 7:

[0663] The device's emotion analysis engine analyzes the user's facial expressions and voice to obtain emotional data. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0664] Step 8:

[0665] The server customizes the content or display method of the alert notification based on the analyzed emotion data. The input is the analyzed emotion data and the existing alert message, and the output is the customized alert message.

[0666] Step 9:

[0667] The customized alert message is then displayed again on the user's smartphone or the delivery robot's device. This provides an alert notification that takes into account the user's emotions. The input is the customized alert message, and the output is the final displayed alert notification.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Second embodiment]

[0672] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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."

[0684] The present invention is a system for monitoring the health of animals in real time and taking appropriate measures. Specific embodiments of the present invention will be described in detail below.

[0685] 1. Sensors and Data Collection

[0686] User

[0687] The user employs sensors that can be worn on the animal, which periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[0688] sensor

[0689] The sensor measures data at a predetermined interval and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[0690] 2. Data Receipt and Storage

[0691] server

[0692] The server receives the data sent from the sensors, and the receiving module checks the integrity of the data and stores it in a cloud database.

[0693] Each piece of data is given a timestamp and used for later analysis.

[0694] 3. Data analysis and anomaly detection

[0695] server

[0696] The server monitors the stored data in real time to determine if the animal's vital data is outside of normal ranges.

[0697] If the body temperature exceeds 39°C or the heart rate falls outside a certain range, it is detected as an abnormality.

[0698] 4. Alert Notifications

[0699] server

[0700] If an anomaly is detected, the server generates an alert message that includes the specific details of the anomaly and a recommended action.

[0701] Alert notifications are sent immediately to the user's device.

[0702] Device (app)

[0703] The terminal receives the alert notification from the server and displays an abnormality message on the user interface.

[0704] The user is notified of the abnormality message by sound, vibration, or other means.

[0705] 5. Data visualization and report generation

[0706] server

[0707] The server periodically aggregates the collected data and generates reports in the form of graphs and charts.

[0708] The report includes data variability information such as average, maximum, and minimum values.

[0709] Device (app)

[0710] The terminal receives the report sent from the server.

[0711] To present received reports to a user in a visually easy-to-understand format.

[0712] 6. Collaboration with veterinarians

[0713] User

[0714] Users contact a veterinarian through the app and submit a request to initiate a remote consultation.

[0715] server

[0716] The server receives the user's request, forwards it to the veterinarian, and provides the veterinarian with the necessary data (e.g., vital data, environmental data).

[0717] The veterinarian's response is received and transferred back to the user's terminal.

[0718] Specific examples

[0719] The operation of the system will be explained below with specific examples.

[0720] Example 1: Detecting abnormal body temperature

[0721] User

[0722] If the user's pet suddenly experiences a rise in temperature, the sensor will measure the temperature as 39.5°C.

[0723] sensor

[0724] The body temperature data measured by the sensor is sent to the server.

[0725] server

[0726] The server analyzes the received data and determines that the body temperature is above the normal range (e.g., below 39°C).

[0727] An alert message stating "body temperature is abnormally high" is immediately generated and notified to the user's terminal.

[0728] Device (app)

[0729] The device receives an alert message and displays a notification saying, "Temperature is 39.5°C. Contact your veterinarian immediately."

[0730] Example 2: Regular data reporting

[0731] server

[0732] The server aggregates temperature, heart rate, and environmental data from the past month and generates a report with average, maximum, and minimum values.

[0733] Device (app)

[0734] The terminal receives the report sent from the server and notifies the user.

[0735] Graphs and charts are used to visually display data fluctuations over the past month.

[0736] This invention makes it possible to improve the efficiency of animal health management by monitoring the health status of animals in real time, quickly detecting abnormalities and notifying the user. Furthermore, by providing a function for remote collaboration with veterinarians, this system supports prompt and appropriate medical treatment.

[0737] The processing flow will be explained below.

[0738] Specific steps of the program's processing

[0739] 1. Sensors and Data Collection

[0740] Step 1: Data collection

[0741] sensor:

[0742] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[0743] The measurement data is temporarily saved in the internal memory.

[0744] Step 2: Send data

[0745] sensor:

[0746] The temporarily stored data is periodically sent to the server via wireless communication (Bluetooth or Wi-Fi).

[0747] 2. Data Receipt and Storage

[0748] Step 3: Receiving data

[0749] server:

[0750] The server receives the data sent from the sensors.

[0751] The receiving module checks the integrity of the data and filters out any invalid data.

[0752] Step 4: Save Data

[0753] server:

[0754] The data whose integrity has been confirmed is stored in a cloud database.

[0755] Each piece of data is time-stamped and used for later analysis.

[0756] 3. Data analysis and anomaly detection

[0757] Step 5: Data analysis

[0758] server:

[0759] Runs algorithms that analyze stored data in real time.

[0760] Determine whether vital data is within normal range.

[0761] Step 6: Anomaly detection

[0762] server:

[0763] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[0764] Generate an alert with details about the anomaly detection.

[0765] 4. Alert Notifications

[0766] Step 7: Sending an alert

[0767] server:

[0768] The generated alert notification is sent to the user's terminal.

[0769] Step 8: Receiving and viewing alerts

[0770] Device (app):

[0771] Receive alert notifications and display abnormality messages on the user interface.

[0772] If necessary, the user is notified of abnormalities by sound or vibration.

[0773] 5. Data visualization and report generation

[0774] Step 9: Data Aggregation

[0775] server:

[0776] Aggregate data over a certain period of time (e.g., one month).

[0777] Calculate statistical data such as average, maximum, and minimum values.

[0778] Step 10: Generate reports

[0779] server:

[0780] Generate reports in graph and chart format based on the aggregated results.

[0781] Step 11: Submit report

[0782] server:

[0783] The generated report is sent to the user's terminal.

[0784] Step 12: Receive and view reports

[0785] Device (app):

[0786] Receive reports and notify users.

[0787] View reports in a graphical interface.

[0788] 6. Collaboration with veterinarians

[0789] Step 13: Remote consultation request

[0790] User:

[0791] Send a remote consultation request to a veterinarian through the app.

[0792] Step 14: Request forwarding

[0793] server:

[0794] Forward the user's request to a veterinarian.

[0795] Provide necessary data (e.g., vitals, environmental data) to veterinarians.

[0796] Step 15: Receive a response from your veterinarian

[0797] server:

[0798] A response from the veterinarian is received and forwarded to the user's terminal.

[0799] Step 16: Displaying the response

[0800] Device (app):

[0801] A response from the veterinarian is received and displayed on the user interface.

[0802] User

[0803] Users can check the veterinarian's advice and instructions through the app and take appropriate action.

[0804] The above are the steps in a specific embodiment of the present invention. By linking the steps together, it becomes possible to monitor the health status of animals in real time, quickly detect abnormalities, and take appropriate measures.

[0805] Example 1

[0806] 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."

[0807] Conventional animal health monitoring systems can be slow to detect abnormalities, leading to delayed appropriate responses. Continuous data collection and analysis are insufficient, and the visualization of generated reports is limited, making it difficult for users to understand intuitively. Furthermore, remote consultation with veterinarians is not easy, making it difficult to respond in emergencies. There is a need to solve these problems and streamline animal health management in real time.

[0808] 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.

[0809] In this invention, the server includes means for acquiring animal biometric and environmental data using sensors, means for receiving data transmitted from the sensors and storing it in cloud storage, means for analyzing the data and detecting abnormalities, means for generating a warning message when an abnormality is detected, means for providing the warning message to a user, means for visualizing the animal biometric and environmental data, means for periodically compiling the data and generating a report, means for providing the report to a user, means for accepting a remote consultation request from a user, means for sharing information with a veterinary professional based on the request, and means for providing the veterinary professional's response to the user. This allows a user to monitor the health of their animal in real time and quickly detect and respond to any abnormalities that may occur. Furthermore, providing the data visually makes it easier to understand, facilitating remote consultations with a veterinarian.

[0810] A "sensor" is a device for acquiring biological and environmental data of an animal.

[0811] "Biometric data" refers to data that indicates the health status of an animal, such as body temperature and heart rate.

[0812] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal exists.

[0813] "Cloud storage" is a remote server for storing and managing data over the Internet.

[0814] "Analysis" is the process of examining acquired and stored data and detecting anomalies.

[0815] "Abnormal" refers to a case where the biometric data and environmental data deviate from the set normal range.

[0816] A "warning message" is a notification that is generated when an abnormality is detected, and notifies the user of the existence of the abnormality and how to deal with it.

[0817] "Visualization" refers to displaying acquired data in a visually easy-to-understand format, such as a graph or chart.

[0818] A "report" is a document created based on periodically compiled data that shows fluctuations and trends in past data.

[0819] A "teleconsultation" is a process by which a user consults with a veterinary medical professional over the Internet.

[0820] An "animal medical professional" is a person with the expertise to diagnose and treat animal health conditions.

[0821] The present invention provides a system for monitoring the health status of animals in real time, quickly detecting abnormalities, and notifying the user. A specific embodiment of this system will be described below.

[0822] Sensors and Data Collection

[0823] User

[0824] Users use sensors that can be worn on animals that periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[0825] The sensors transmit data to a server using, for example, Bluetooth or Wi-Fi.

[0826] Data reception and storage

[0827] server

[0828] The server receives the data sent from the sensors in real time. The receiving module checks the data for integrity and for missing or outlier values. The data is then stored in cloud storage. The data is time-stamped and a unique identifier is added.

[0829] Data analysis and anomaly detection

[0830] server

[0831] The server analyzes the received data in real time. An analysis module runs to determine whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, an abnormality is detected.

[0832] Alert Notifications

[0833] server

[0834] When an abnormality is detected, the server generates a warning message that includes a specific description of the abnormality (e.g., "Temperature rises to 39.5°C") and a recommended action (e.g., "Contact a veterinary professional immediately").

[0835] A warning message is immediately sent to the user's terminal.

[0836] Device (app)

[0837] The device receives a warning message from the server and notifies the user with sound and vibration, as well as displays an abnormal message on the screen, such as "Temperature is 39.5°C. Contact a veterinary professional immediately."

[0838] Data Visualization and Report Generation

[0839] server

[0840] The server periodically aggregates the data and generates a report in the form of graphs and charts, including, for example, the average, maximum, and minimum body temperature values ​​for the past month. This report is sent to the user's device at a specified date and time.

[0841] Device (app)

[0842] The device receives the reports sent from the server and presents them to the user in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[0843] Collaboration with veterinarians

[0844] User

[0845] Users use the app to submit a request for a remote consultation with a veterinary medical professional.

[0846] server

[0847] The server receives the user's request and forwards it to the veterinary professional, providing any necessary data (e.g., biometric data, environmental data), and then forwards it back to the user's device after receiving the response from the veterinary professional.

[0848] Specific examples

[0849] The operation of the system will be explained below with specific examples.

[0850] Example 1: Detecting abnormal body temperature

[0851] User

[0852] The user's animal's temperature spikes and the sensor measures it at 39.5°C.

[0853] sensor

[0854] The sensor stores this data in its internal memory and then transmits it to a server via Bluetooth.

[0855] server

[0856] The server receives this data, and the analysis module determines that a body temperature of 39°C or higher is abnormal. A warning message is generated and sent to the user's device stating, "The body temperature is 39.5°C. Please contact a veterinary professional immediately."

[0857] Device (app)

[0858] The device receives the warning message and displays it to the user along with a sound notification.

[0859] Example 2: Regular data reporting

[0860] server

[0861] The server aggregates body temperature, heart rate, and environmental data from the past month and generates a report containing the average, maximum, and minimum values ​​for each vital data. This report is sent to the user's device at the specified date and time.

[0862] Device (app)

[0863] The device receives the report sent from the server and notifies the user, "Please check your health data report for the past month." The user can open the report and visually check the fluctuations in body temperature and heart rate data as line graphs and histograms.

[0864] Prompt Sentence Examples

[0865] Here are some examples of specific prompts:

[0866] "Describe a program that analyzes an animal's vitals sent from a sensor and sends an alert if the body temperature exceeds 39°C."

[0867] In this way, the present invention realizes a system that enables real-time health monitoring and abnormality detection, and supports rapid response.

[0868] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0869] Step 1: Sensors and Data Collection

[0870] User

[0871] The user attaches sensors to the animal, which measure the animal's body temperature, heart rate, environmental temperature, and humidity at regular intervals (e.g., every 10 minutes).

[0872] Input: User-defined measurement interval and attached sensor

[0873] Output: Body temperature, heart rate, ambient temperature, and humidity data obtained by the sensor

[0874] sensor

[0875] The sensor temporarily stores the measurement data in its internal memory and then transmits the data to a server using Bluetooth or Wi-Fi.

[0876] Input: Measured body temperature, heart rate, ambient temperature, and humidity data

[0877] Output: Data packets sent to the server

[0878] Step 2: Receiving and storing data

[0879] server

[0880] The server receives the data sent from the sensors in real time. The receiving module checks the data for consistency and verifies whether there are any missing or outlier values. The data is then stored in cloud storage.

[0881] Input: Data packets sent from the sensor

[0882] Data processing: Checking data integrity, checking for missing or outliers, adding timestamps, and adding unique identifiers

[0883] Output: Consistent data stored in cloud storage

[0884] Step 3: Data analysis and anomaly detection

[0885] server

[0886] The server analyzes the data stored in the database in real time. The analysis module determines whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, it will be detected as an abnormality.

[0887] Input: Biometric and environmental data stored in cloud storage

[0888] Data calculation: Comparison with normal range, detection of abnormal values

[0889] Output: Triggers the generation of a warning message if an anomaly is detected

[0890] Step 4: Alert Notification

[0891] server

[0892] When an anomaly is detected, the server generates a warning message, which includes the specific details of the anomaly and a recommended action, and is sent to the user's device.

[0893] Input: Trigger when an anomaly is detected

[0894] Data processing: Generating a warning message (e.g., "Temperature is 39.5°C. Contact a veterinary professional immediately.")

[0895] Output: The warning message sent to the user's terminal.

[0896] Device (app)

[0897] The user's device receives the warning message from the server and notifies the user with sound or vibration, and displays a specific abnormality message on the screen.

[0898] Input: warning message

[0899] Output: Alert sound, vibration, on-screen notification

[0900] Step 5: Data visualization and report generation

[0901] server

[0902] The server periodically aggregates the data and generates various reports, including graphs and charts showing average, maximum, and minimum body temperature and heart rate values ​​over the past month. These reports are sent to the user's device at a specified date and time.

[0903] Input: Historical data stored in cloud storage

[0904] Data calculation: Aggregation and report generation (calculating average, maximum, minimum values)

[0905] Output: Report sent to user terminal

[0906] Device (app)

[0907] The user's device receives the report sent from the server and displays it in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[0908] Input: Report from the server

[0909] Output: Reports displayed in graph and chart formats

[0910] Step 6: Work with your veterinarian

[0911] User

[0912] Users use the app to submit a request for a remote consultation with a veterinary specialist.

[0913] Input: Remote consultation request

[0914] Output: Request sent to server

[0915] server

[0916] The server receives the request from the user, forwards it to the veterinary professional, provides the necessary data (e.g., biological data, environmental data), receives the response from the veterinary professional, and forwards it back to the user's terminal.

[0917] Input: User request, corresponding data

[0918] Data processing: forwarding requests to veterinary professionals and receiving responses

[0919] Output: The response displayed on the user's terminal

[0920] (Application example 1)

[0921] 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."

[0922] While machines and robots operating in factories are required to be highly automated and efficient, there is a risk that machine failure or malfunction can have a significant impact on the entire production line. Therefore, there is a growing need for systems that can detect machine abnormalities early and take prompt action. However, current systems have issues with the accuracy of abnormality detection and the speed of notification, and also lack appropriate maintenance support and collaboration functions with specialized engineers. The objective of this invention is to provide a system that solves these problems and improves factory productivity.

[0923] 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.

[0924] In this invention, the server includes: means for collecting machine status data and environmental data using sensors; means for receiving data transmitted from the sensors and storing it in a cloud database; means for analyzing the data and detecting abnormalities; means for generating an alert notification when an abnormality is detected; means for providing the alert notification to a user device; means for visualizing the status data and environmental data; means for periodically aggregating the data and generating a report; means for providing the report to the user device; means for generating and providing machine maintenance support information when an abnormality is detected; means for accepting remote consultation requests from users; means for sharing information with specialist engineers based on the request; and means for providing the specialist engineers' responses to the user device. This enables rapid and accurate detection of abnormalities in machines and robots operating in a factory and the provision of appropriate countermeasures. Furthermore, collaboration with specialist engineers enables more advanced responses, improving the efficiency and reliability of production lines.

[0925] A "sensor" is a device for collecting machine condition and environmental data.

[0926] "Status data" refers to data relating to the operating status of machines and robots, the temperature, vibration, operating time, etc. of each part.

[0927] "Environmental data" refers to data that includes information such as temperature, humidity, and pressure in the location where the machine is installed.

[0928] "Cloud Database" means a database accessible via the Internet for storing condition and environmental data collected in real time.

[0929] "Anomaly detection" is the process of analyzing collected data and determining whether there is an abnormality in the operation or condition of a machine or robot.

[0930] An "alert notification" is a warning message that is generated when an abnormality is detected, and includes a means for providing this to a user device.

[0931] A "user device" is an information terminal such as a smartphone or tablet used by managers and engineers in a factory.

[0932] "Visualization" is the process of displaying collected data in an easy-to-understand format such as graphs or charts.

[0933] A "report" is a document that summarizes the results of aggregating data over a certain period of time, and includes statistical information such as average, maximum, and minimum values.

[0934] "Maintenance support information" is information that suggests specific repair and inspection methods based on detected abnormalities.

[0935] "Remote consultation" is a process in which managers and engineers in a factory contact specialist engineers and receive support for resolving problems from a remote location.

[0936] A "specialist engineer" is a technician with advanced knowledge and skills in the repair and maintenance of machines and robots.

[0937] The present invention is a system for monitoring abnormalities and supporting maintenance of machines and robots operating in a factory, and is specifically implemented as follows.

[0938] Sensors and Data Collection

[0939] User

[0940] Users attach multiple sensors (temperature sensors, vibration sensors, etc.) to machines or robots, which measure machine status data and environmental data in real time.

[0941] sensor

[0942] The sensor measures data at regular intervals and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[0943] Data reception and storage

[0944] server

[0945] The server receives the data sent from the sensors and stores it in a cloud database. The receiving module checks the integrity of the data and assigns a timestamp.

[0946] Data analysis and anomaly detection

[0947] server

[0948] The server monitors the stored data in real time and determines whether the machine's status data is outside of the normal range. For example, if the temperature exceeds 80°C or the vibration is outside a certain range based on the data obtained from the sensor, it will be detected as an abnormality.

[0949] Alert Notifications

[0950] server

[0951] If an anomaly is detected, the server generates an alert message, which includes the specific details of the anomaly and recommended actions. The alert notification is immediately sent to the user's device.

[0952] Device (smartphone)

[0953] The device receives an alert notification from the server and displays an abnormality message on the user interface. The abnormality message is notified to the user by sound, vibration, etc.

[0954] Data Visualization and Report Generation

[0955] server

[0956] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums, and notifies the user of any maintenance needs.

[0957] Device (smartphone)

[0958] The terminal receives the reports sent from the server and visually displays data fluctuations using graphs and charts.

[0959] Remote consultation and maintenance support

[0960] User

[0961] The user requests a remote consultation with a specialist technician via a terminal, and the request includes specific information about the machine that needs to be addressed.

[0962] server

[0963] The server receives the request, forwards it to the expert, provides the expert with the necessary data (e.g., status data, environment data), receives the expert's response, and forwards it back to the user's device.

[0964] Specific examples

[0965] For example, if the motor temperature of a factory robot exceeds 90°C, the server generates an alert message stating "Abnormality detected: Motor temperature is 90°C (time: 2023-10-01 10:00:00)" and notifies the administrator's terminal.

[0966] Prompt Sentence Examples

[0967] "Please tell me how to analyze data collected in real time by the sensors of a factory robot and notify a smartphone if an abnormality is detected. In particular, please let me know if there are any examples of implementing alert notifications using Flask and Webhooks."

[0968] This makes it possible to quickly and accurately detect abnormalities in machines and robots operating in factories and take appropriate measures. In addition, by collaborating with specialized engineers, more advanced responses are possible, improving the efficiency and reliability of production lines.

[0969] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0970] Step 1:

[0971] Sensor data collection and transmission

[0972] Sensors attached to machines or robots by users measure status data (e.g., temperature, vibration) and environmental data (e.g., temperature, humidity) at regular intervals. The collected data is sent to a server via Bluetooth or Wi-Fi.

[0973] Input: Data from sensors on machines and robots

[0974] Output: Measurement data sent to the server

[0975] Step 2:

[0976] Data reception and storage

[0977] The server receives the data sent by the sensors, checks the integrity of the data, and adds a timestamp before storing it in a database for later analysis.

[0978] Input: Data sent from the sensor

[0979] Output: Data stored in a cloud database

[0980] Step 3:

[0981] Data analysis and anomaly detection

[0982] The server monitors the stored data in real time and detects abnormal data that exceeds a predetermined threshold. For example, if temperature data exceeds 80°C or vibration data falls outside a certain range, it will be deemed abnormal.

[0983] Input: Data stored in a cloud database

[0984] Output: Anomaly detection result (normal / abnormal)

[0985] Step 4:

[0986] Generate and send alert notifications

[0987] If an anomaly is detected, the server generates an alert message containing the specific details of the anomaly and recommended actions, and immediately sends it to the user's device.

[0988] Input: Anomaly detection results

[0989] Output: The alert message sent to the user's terminal.

[0990] Step 5:

[0991] Displaying an alert message

[0992] The device (smartphone) receives the alert message sent from the server and displays an abnormality message on the user interface. The content of the alert is notified to the user by notification methods such as sound and vibration.

[0993] Input: Alert message

[0994] Output: An abnormal message displayed on the device screen, a sound or vibration notification

[0995] Step 6:

[0996] Data Visualization and Reporting

[0997] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums.

[0998] Input: Collected data

[0999] Output: Generated report

[1000] Step 7:

[1001] Report distribution and viewing

[1002] The server sends the generated report to the user's device, which receives it and displays it visually in the form of graphs and charts.

[1003] Input: Generated report

[1004] Output: A report displayed on the terminal screen

[1005] Step 8:

[1006] Sending and responding to remote consultation requests

[1007] The user requests a remote consultation from a specialist through a terminal. The server receives the request and forwards it to the specialist. The server also forwards the specialist's response back to the user's terminal.

[1008] Input: Remote consultation request from user

[1009] Output: Response from the expert

[1010] 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.

[1011] The present invention provides a more user-friendly interface by combining a system that monitors the health of animals in real time, detects abnormalities, and takes appropriate measures, with an emotion engine that recognizes the user's emotions. Specific embodiments are described in detail below.

[1012] 1. Sensors and Data Collection

[1013] User

[1014] Users use sensors that can be attached to animals, which measure the animal's body temperature, heart rate, and the temperature and humidity of the environment at regular intervals (e.g., every minute).

[1015] sensor

[1016] The sensor periodically transmits the measured data to the server via wireless communication (Bluetooth or Wi-Fi).

[1017] 2. Data Receipt and Storage

[1018] server

[1019] The server receives the data sent from the sensors, checks its integrity, and then stores it in a cloud database.

[1020] Each piece of data is given a timestamp and used for later analysis.

[1021] 3. Data analysis and anomaly detection

[1022] server

[1023] The server analyzes the stored data in real time and detects any vital data that exceeds a set threshold (e.g., body temperature above 39°C) as an abnormality.

[1024] 4. Alert Notifications

[1025] server

[1026] If an anomaly is detected, the server generates an alert message that includes specific information about the anomaly and recommended actions.

[1027] Alert notifications are sent immediately to the user's device.

[1028] Device (app)

[1029] The device receives the alert notification and displays it on the user interface. The user is notified of the abnormality message by sound, vibration, or other means.

[1030] 5. Data visualization and report generation

[1031] server

[1032] The server aggregates the data over a period of time (e.g., one month) and generates reports in the form of graphs and charts.

[1033] The report is then processed into a format that is easily understood by the user.

[1034] Device (app)

[1035] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1036] 6. Collaboration with veterinarians

[1037] User

[1038] Users send a request for a remote consultation to a veterinarian through the app.

[1039] server

[1040] The server forwards the user's request to the veterinarian and provides the necessary data (e.g., vital data, environmental data).

[1041] A response from the veterinarian is received and transferred back to the user's terminal.

[1042] 7. User-friendly interface using emotion engine

[1043] Device (app)

[1044] The emotion engine analyzes emotion data from the user's facial expressions and voice to determine the user's current emotional state.

[1045] The emotion engine customizes the content or presentation of alert notifications based on the user's emotional state. For example, if the user is stressed, the message will be more friendly.

[1046] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[1047] Specific examples

[1048] The operation of the system will be explained below with specific examples.

[1049] Example 1: Abnormal body temperature detection and alert notification

[1050] sensor

[1051] The dog's temperature is measured at 39.5°C.

[1052] server

[1053] Body temperature data is received and determined to be abnormal.

[1054] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[1055] Device (app)

[1056] Receive alert notifications and notify the user with sound and vibration.

[1057] Example 2: User Emotion Recognition

[1058] User

[1059] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[1060] Device (app)

[1061] The emotion engine analyzes the emotion of anxiety and converts it into a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately."

[1062] If necessary, a remote consultation request with a veterinarian is automatically generated based on the user's concerns.

[1063] As a result, the present invention not only monitors the health status of animals in real time, but also provides an interface that takes the user's emotions into consideration, allowing for more appropriate and prompt responses.

[1064] The processing flow will be explained below.

[1065] Specific steps of the program's processing

[1066] 1. Sensors and Data Collection

[1067] Step 1: Data collection

[1068] sensor:

[1069] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[1070] The measurement data is temporarily saved in the internal memory.

[1071] Step 2: Send data

[1072] sensor:

[1073] The measured data is periodically sent to a server via wireless communication (Bluetooth or Wi-Fi).

[1074] 2. Data Receipt and Storage

[1075] Step 3: Receiving data

[1076] server:

[1077] The server receives the data sent from the sensors.

[1078] The receiving module checks the integrity of the data and filters out any invalid data.

[1079] Step 4: Save Data

[1080] server:

[1081] The data whose integrity has been confirmed is stored in a cloud database.

[1082] Each piece of data is given a timestamp and used for later analysis.

[1083] 3. Data analysis and anomaly detection

[1084] Step 5: Data analysis

[1085] server:

[1086] Runs algorithms that analyze stored data in real time.

[1087] Determine whether vital data is within normal range.

[1088] Step 6: Anomaly detection

[1089] server:

[1090] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[1091] Generate an alert with details about the anomaly detection.

[1092] 4. Alert Notifications

[1093] Step 7: Sending an alert

[1094] server:

[1095] The generated alert notification is sent to the user's terminal.

[1096] Step 8: Receiving and viewing alerts

[1097] Device (app):

[1098] Receive alert notifications and display abnormality messages on the user interface.

[1099] If necessary, the user is notified of abnormalities by sound or vibration.

[1100] 5. Data visualization and report generation

[1101] Step 9: Data Aggregation

[1102] server:

[1103] Aggregate data over a certain period of time (e.g., one month).

[1104] Calculate statistical data such as average, maximum, and minimum values.

[1105] Step 10: Generate reports

[1106] server:

[1107] Generate reports in graph and chart format based on the aggregated results.

[1108] Step 11: Submit report

[1109] server:

[1110] The generated report is sent to the user's terminal.

[1111] Step 12: Receive and view reports

[1112] Device (app):

[1113] Receive reports and notify users.

[1114] View reports in a graphical interface.

[1115] 6. Collaboration with veterinarians

[1116] Step 13: Remote consultation request

[1117] User:

[1118] Send a remote consultation request to a veterinarian through the app.

[1119] Step 14: Request forwarding

[1120] server:

[1121] Forwarding the user's request to the veterinarian and providing any necessary data (e.g., vitals, environmental data).

[1122] Step 15: Receive a response from your veterinarian

[1123] server:

[1124] A response from the veterinarian is received and forwarded to the user's terminal.

[1125] Step 16: Displaying the response

[1126] Device (app):

[1127] The response from the veterinarian is displayed to the user.

[1128] 7. User-friendly interface using emotion engine

[1129] Step 17: Collecting User Emotion Data

[1130] Device (app):

[1131] A camera and microphone are used to collect the user's facial expressions and voice.

[1132] Step 18: Sentiment Analysis

[1133] Device (app):

[1134] The emotion engine analyzes the collected data and identifies the user's emotions (e.g., joy, anger, anxiety, sadness).

[1135] Step 19: Interface Adjustments

[1136] Device (app):

[1137] Based on the analysis results, the content and presentation of the alert notification can be adjusted, for example, a more friendly message can be displayed to a user who is feeling stressed.

[1138] Step 20: Emotion-Based Teleconsultation Request

[1139] Device (app):

[1140] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[1141] For example, when a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety and changes the message to a more friendly one. If the emotion engine detects strong anxiety, it automatically generates a remote consultation request, encouraging the user to contact a veterinarian.

[1142] These steps will result in a more user-friendly health monitoring system that takes user emotions into account.

[1143] Example 2

[1144] 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."

[1145] In recent years, interest in pet health management has grown, creating a demand for systems that can monitor animals' health conditions in real time and respond quickly to abnormalities. However, existing systems do not take into account the user's emotional state when notifying them of an abnormality, which can easily cause anxiety. Furthermore, there is a lack of a way to easily visualize acquired data and present it in an easy-to-understand format. Therefore, a system is needed that provides a user-friendly, emotionally sensitive interface, as well as visualizes data and enables rapid connection to experts.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data and environmental data of the animal using a sensor, means for receiving data transmitted from the sensor and storing it in a cloud database, means for analyzing the data in real time and detecting abnormalities, means for generating an alert notification and transmitting it to the user's terminal when an abnormality is detected, means for analyzing the user's emotional state and generating a message according to the emotional state, and means for providing a user interface through which the message is transmitted. This enables real-time monitoring of the health status and rapid response in the event of an abnormality, and by providing an alert notification that takes the user's emotions into consideration, the user can reduce anxiety and take appropriate action.

[1147] A "sensor" is a device used to collect vital and environmental data from an animal.

[1148] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as an animal's body temperature and heart rate.

[1149] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal lives.

[1150] A "cloud database" is a data storage area located on a remote server accessible via the Internet.

[1151] "Real-time analysis" means analyzing data immediately after it is sent from the sensor and detecting any abnormalities.

[1152] An "abnormality" is an abnormal situation that is detected when the vital data of an animal exceeds a preset threshold.

[1153] An "alert notification" is a message that notifies the user of a detected abnormality.

[1154] The "emotional state of the user" refers to the psychological state analyzed based on information obtained from the user's facial expressions and voice.

[1155] The "means for generating a message" is a function of the system that generates a message according to the emotional state of the user when an abnormality is detected.

[1156] "User interface" refers to the display screen and operation panel that allow the user to interact with the system.

[1157] "Means for generating reports in graph and chart format" refers to the system's functionality for compiling collected data into a format that is easy to understand visually.

[1158] "Animal health professional" refers to a health care worker with expertise in animal health care.

[1159] MODE FOR CARRYING OUT THE INVENTION

[1160] This invention is a system that monitors the health of animals in real time, detects abnormalities, and sends alerts that take into account the user's emotional state. This system is composed of sensors, a server, and a terminal (app), and the functions and roles of each part are explained in detail below.

[1161] Sensor installation and data measurement

[1162] User

[1163] The user attaches sensors to the animal, which are attached to the animal's collar or harness, and periodically (e.g., every minute) measure body temperature, heart rate, and environmental temperature and humidity.

[1164] Sending measurement data

[1165] sensor

[1166] The sensor sends the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor measures a body temperature of 38.5°C, humidity of 60%, and a heart rate of 120 bpm, and sends the data to the server every minute.

[1167] Receiving and storing data

[1168] server

[1169] The server receives the data sent from the sensors and checks the data for consistency. Specifically, it checks whether the data format is correct and whether there are any duplicates. Once the consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[1170] Data analysis and anomaly detection

[1171] server

[1172] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[1173] Generate and send alert notifications

[1174] server

[1175] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[1176] Device (app)

[1177] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[1178] Data Visualization and Reporting

[1179] server

[1180] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[1181] Device (app)

[1182] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1183] Collaboration with veterinarians

[1184] User

[1185] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[1186] server

[1187] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[1188] Interface using emotion engine

[1189] Device (app)

[1190] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[1191] Specific examples

[1192] Example 1: Abnormal body temperature detection and alert notification

[1193] 1. Sensor

[1194] The dog's temperature is measured at 39.5°C.

[1195] 2. Server

[1196] Body temperature data is received and determined to be "abnormal body temperature."

[1197] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[1198] 3. Device (app)

[1199] Receives alert notifications and notifies the user with sound and vibration. Displays the alert content on the user interface.

[1200] Example 2: Recognizing user emotions

[1201] 1. Users

[1202] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[1203] 2. Device (app)

[1204] The emotion engine analyzes the user's anxiety and generates and displays a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, a remote consultation request with a veterinary professional is automatically generated based on the user's anxiety.

[1205] Example input to a generative AI model

[1206] Example prompt

[1207] "Generate an alert message when an animal's temperature is abnormally high. Also, provide an example of how to convert the message to a more friendly one if the user is feeling anxious."

[1208] In this way, the system of the present invention monitors the health status of animals in real time, responds quickly to abnormalities, and provides alert notifications that take the user's emotions into consideration, allowing the user to take appropriate action.

[1209] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1210] Step 1: Sensor installation and data collection

[1211] User

[1212] The user attaches sensors to the animal, which are attached to the animal's collar or harness and measure body temperature, heart rate, and environmental temperature and humidity every minute. Specifically, the built-in sensors continuously monitor the animal's body and surrounding environment and collect measurement data.

[1213] Input: Animal body temperature, heart rate, environmental temperature and humidity

[1214] Output: Measured vital and environmental data

[1215] Step 2: Sending measurement data

[1216] sensor

[1217] The sensor periodically transmits the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor transmits data such as body temperature (38.5°C), humidity (60%), and heart rate (120 bpm) every minute.

[1218] Input: Measured vital and environmental data

[1219] Output: Data sent to the server

[1220] Step 3: Receiving and storing data

[1221] server

[1222] The server receives the data sent from the sensors and checks the data for consistency, ensuring that the data format is correct and that there are no duplicates. Once consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[1223] Input: Data sent from the sensor

[1224] Output: Data that has been checked for integrity and saved

[1225] Step 4: Data analysis and anomaly detection

[1226] server

[1227] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[1228] Input: Received data

[1229] Output: Anomaly detected and recorded information

[1230] Step 5: Generate and send alert notifications

[1231] server

[1232] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[1233] Input: Anomaly detection information

[1234] Output: The generated alert message

[1235] Device (app)

[1236] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[1237] Input: The alert message sent

[1238] Output: The alert content that is notified and displayed to the user

[1239] Step 6: Visualize the data and generate reports

[1240] server

[1241] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[1242] Input: Data for a period of time

[1243] Output: Generated report

[1244] Device (app)

[1245] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1246] Input: Generated report

[1247] Output: The displayed report content

[1248] Step 7: Work with your veterinarian

[1249] User

[1250] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[1251] Input: Content of consultation request

[1252] Output: Request sent

[1253] server

[1254] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[1255] Input: The submitted request and associated data

[1256] Output: Response from veterinary medical professional

[1257] Step 8: Interface using emotion engine

[1258] Device (app)

[1259] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[1260] Input: User's facial expressions and voice

[1261] Output: Generated friendly message and remote consultation request

[1262] The above are the specific processing steps of the program of this system.

[1263] (Application example 2)

[1264] 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."

[1265] In modern food delivery services, quality control of ingredients during transportation is important. However, environmental factors such as temperature, humidity, and vibration can have a negative impact on the quality of ingredients, making real-time monitoring difficult. Another challenge is providing appropriate notification content that takes into account the emotions and stress of drivers and customers when receiving alerts.

[1266] 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 collecting information data of an object and surrounding environment data using sensors, means for receiving data transmitted from the sensors and storing the data in a cloud storage device, means for analyzing the data and detecting abnormalities, means for generating an alert notification when an abnormality is detected, means for providing the alert notification to the user, and means for analyzing the user's emotions and customizing the content or display method of the alert notification based on the emotions. This makes it possible to monitor the quality control of ingredients in real time and to provide appropriate alert notifications that take the user's emotions into consideration when an abnormality occurs.

[1267] A "sensor" is a device that measures physical data and outputs that data in electronic form.

[1268] "Object" refers to the object or environment from which data is collected or monitored.

[1269] "Information data" refers to data that contains specific information about an object.

[1270] "Ambient environment data" refers to data relating to environmental factors such as temperature, humidity, and vibrations present around an object.

[1271] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[1272] "Abnormal" refers to a data condition that exceeds a pre-established standard or threshold.

[1273] An "alert notification" is a message that warns or alerts the user when an abnormality is detected.

[1274] "Means for analyzing emotions" refers to technologies and devices for detecting and interpreting emotions from a user's facial expressions, voice, etc.

[1275] "Means of customization" refers to methods for adjusting the system's behavior and display content according to specific conditions or situations.

[1276] An "expert" is someone who has advanced knowledge or skills in a particular field.

[1277] The present invention is a system that uses sensors to collect information data on objects and their surrounding environments, stores the data in a cloud storage device, analyzes it in real time, and detects abnormalities. When an abnormality is detected, the system generates an alert notification and provides it to the user. The system also includes a function for analyzing the user's emotions and customizing the content or display method of the alert notification based on the user's emotions.

[1278] Sensors and Data Collection

[1279] The user uses sensors installed on food items or their surroundings. These sensors collect data such as temperature, humidity, and vibration, and periodically measure and transmit the data to a smartphone or delivery robot. This data is transmitted via wireless communication such as Bluetooth or Wi-Fi.

[1280] Data reception and storage

[1281] The server receives data sent from the sensors via smartphones or delivery robots. The received data is stored in a cloud storage device, and a timestamp is assigned to each piece of data. Common online storage services (e.g., AWS, Azure) are used as the cloud storage device.

[1282] Data analysis and anomaly detection

[1283] The server analyzes the data stored in the cloud storage device in real time and detects abnormalities when set thresholds are exceeded, such as when the temperature exceeds 35°C or vibration exceeds a set value. This allows for quick action to be taken before the quality of the ingredients deteriorates.

[1284] Alert Notifications

[1285] If an abnormality is detected, the server generates an alert message. This alert message is sent to the user's smartphone or delivery robot, and the user is notified by sound or vibration. The alert message contains specific information about the abnormality and recommended actions. For example, it could say, "The temperature has reached 35.5°C. Please take immediate action."

[1286] Sentiment analysis and interface customization

[1287] When a user receives an alert notification, an emotion analysis engine on the smartphone or delivery robot assists them. The emotion analysis engine analyzes emotional data from the user's facial expressions and voice, and if the user is feeling stressed, it converts it into a friendly message. Specifically, it might say something like, "Don't worry, there's no problem. Please take immediate action." This method can reduce the user's stress.

[1288] Examples and prompts

[1289] Specific examples

[1290] While the ingredients are being transported, a sensor measures the CPU temperature at 35.5°C. The emotion analysis engine recognizes the delivery driver's anxiety and displays an alert message saying, "Don't worry, we'll take action immediately."

[1291] Prompt Sentence Examples

[1292] Analyze the following sentence, determine the user's sentiment, and generate an appropriate response message:

[1293] Alert message: Temperature reached 35.5°C during transport.

[1294] User Emotion: Anxiety

[1295] An appropriate response message would be: "Don't worry, we'll fix it right away."

[1296] In this way, the present invention controls the quality of ingredients in real time and provides alert notifications that take into consideration the user's emotions when an abnormality occurs, enabling a quick and appropriate response.

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Step 1:

[1299] Sensors periodically acquire information data such as temperature, humidity, and vibration, as well as data on the surrounding environment. The input is physical data from the object and the surrounding environment, and the output is electronic measurement data.

[1300] Step 2:

[1301] The terminal (smartphone or delivery robot) receives the data sent from the sensor via wireless communication (Bluetooth or Wi-Fi). The input is the measurement data from the sensor, and the output is the data stored in the terminal.

[1302] Step 3:

[1303] The data received by the device is sent to the cloud storage device. The input is the data stored on the device, and the output is the data stored in the cloud storage device. The cloud storage device is assigned a timestamp and the integrity of the data is verified.

[1304] Step 4:

[1305] The server analyzes the data stored in the cloud storage device in real time, compares it with the set threshold, and detects anomalies. The input is the data stored in the cloud storage device, and the output is the result of the anomaly detection.

[1306] Step 5:

[1307] If an anomaly is detected, the server generates an alert notification, which contains specific information about the anomaly and a recommended action. The input is the anomaly detection result, and the output is the alert message.

[1308] Step 6:

[1309] An alert notification is sent to the user's device via a smartphone or delivery robot. The input is the generated alert message, and the output is the alert notification displayed on the device. The notification is also conveyed to the user by sound or vibration.

[1310] Step 7:

[1311] The device's emotion analysis engine analyzes the user's facial expressions and voice to obtain emotional data. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[1312] Step 8:

[1313] The server customizes the content or display method of the alert notification based on the analyzed emotion data. The input is the analyzed emotion data and the existing alert message, and the output is the customized alert message.

[1314] Step 9:

[1315] The customized alert message is then displayed again on the user's smartphone or the delivery robot's device. This provides an alert notification that takes into account the user's emotions. The input is the customized alert message, and the output is the final displayed alert notification.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] [Third embodiment]

[1320] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1321] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1322] 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).

[1323] 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.

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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."

[1332] The present invention is a system for monitoring the health of animals in real time and taking appropriate measures. Specific embodiments of the present invention will be described in detail below.

[1333] 1. Sensors and Data Collection

[1334] User

[1335] The user employs sensors that can be worn on the animal, which periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[1336] sensor

[1337] The sensor measures data at a predetermined interval and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[1338] 2. Data Receipt and Storage

[1339] server

[1340] The server receives the data sent from the sensors, and the receiving module checks the integrity of the data and stores it in a cloud database.

[1341] Each piece of data is given a timestamp and used for later analysis.

[1342] 3. Data analysis and anomaly detection

[1343] server

[1344] The server monitors the stored data in real time to determine if the animal's vital data is outside of normal ranges.

[1345] If the body temperature exceeds 39°C or the heart rate falls outside a certain range, it is detected as an abnormality.

[1346] 4. Alert Notifications

[1347] server

[1348] If an anomaly is detected, the server generates an alert message that includes the specific details of the anomaly and a recommended action.

[1349] Alert notifications are sent immediately to the user's device.

[1350] Device (app)

[1351] The terminal receives the alert notification from the server and displays an abnormality message on the user interface.

[1352] The user is notified of the abnormality message by sound, vibration, or other means.

[1353] 5. Data visualization and report generation

[1354] server

[1355] The server periodically aggregates the collected data and generates reports in the form of graphs and charts.

[1356] The report includes data variability information such as average, maximum, and minimum values.

[1357] Device (app)

[1358] The terminal receives the report sent from the server.

[1359] To present received reports to a user in a visually easy-to-understand format.

[1360] 6. Collaboration with veterinarians

[1361] User

[1362] Users contact a veterinarian through the app and submit a request to initiate a remote consultation.

[1363] server

[1364] The server receives the user's request, forwards it to the veterinarian, and provides the veterinarian with the necessary data (e.g., vital data, environmental data).

[1365] The veterinarian's response is received and transferred back to the user's terminal.

[1366] Specific examples

[1367] The operation of the system will be explained below with specific examples.

[1368] Example 1: Detecting abnormal body temperature

[1369] User

[1370] If the user's pet suddenly experiences a rise in temperature, the sensor will measure the temperature as 39.5°C.

[1371] sensor

[1372] The body temperature data measured by the sensor is sent to the server.

[1373] server

[1374] The server analyzes the received data and determines that the body temperature is above the normal range (e.g., below 39°C).

[1375] An alert message stating "body temperature is abnormally high" is immediately generated and notified to the user's terminal.

[1376] Device (app)

[1377] The device receives an alert message and displays a notification saying, "Temperature is 39.5°C. Contact your veterinarian immediately."

[1378] Example 2: Regular data reporting

[1379] server

[1380] The server aggregates temperature, heart rate, and environmental data from the past month and generates a report with average, maximum, and minimum values.

[1381] Device (app)

[1382] The terminal receives the report sent from the server and notifies the user.

[1383] Graphs and charts are used to visually display data fluctuations over the past month.

[1384] This invention makes it possible to improve the efficiency of animal health management by monitoring the health status of animals in real time, quickly detecting abnormalities and notifying the user. Furthermore, by providing a function for remote collaboration with veterinarians, this system supports prompt and appropriate medical treatment.

[1385] The processing flow will be explained below.

[1386] Specific steps of the program's processing

[1387] 1. Sensors and Data Collection

[1388] Step 1: Data collection

[1389] sensor:

[1390] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[1391] The measurement data is temporarily saved in the internal memory.

[1392] Step 2: Send data

[1393] sensor:

[1394] The temporarily stored data is periodically sent to the server via wireless communication (Bluetooth or Wi-Fi).

[1395] 2. Data Receipt and Storage

[1396] Step 3: Receiving data

[1397] server:

[1398] The server receives the data sent from the sensors.

[1399] The receiving module checks the integrity of the data and filters out any invalid data.

[1400] Step 4: Save Data

[1401] server:

[1402] The data whose integrity has been confirmed is stored in a cloud database.

[1403] Each piece of data is time-stamped and used for later analysis.

[1404] 3. Data analysis and anomaly detection

[1405] Step 5: Data analysis

[1406] server:

[1407] Runs algorithms that analyze stored data in real time.

[1408] Determine whether vital data is within normal range.

[1409] Step 6: Anomaly detection

[1410] server:

[1411] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[1412] Generate an alert with details about the anomaly detection.

[1413] 4. Alert Notifications

[1414] Step 7: Sending an alert

[1415] server:

[1416] The generated alert notification is sent to the user's terminal.

[1417] Step 8: Receiving and viewing alerts

[1418] Device (app):

[1419] Receive alert notifications and display abnormality messages on the user interface.

[1420] If necessary, the user is notified of abnormalities by sound or vibration.

[1421] 5. Data visualization and report generation

[1422] Step 9: Data Aggregation

[1423] server:

[1424] Aggregate data over a certain period of time (e.g., one month).

[1425] Calculate statistical data such as average, maximum, and minimum values.

[1426] Step 10: Generate reports

[1427] server:

[1428] Generate reports in graph and chart format based on the aggregated results.

[1429] Step 11: Submit report

[1430] server:

[1431] The generated report is sent to the user's terminal.

[1432] Step 12: Receive and view reports

[1433] Device (app):

[1434] Receive reports and notify users.

[1435] View reports in a graphical interface.

[1436] 6. Collaboration with veterinarians

[1437] Step 13: Remote consultation request

[1438] User:

[1439] Send a remote consultation request to a veterinarian through the app.

[1440] Step 14: Request forwarding

[1441] server:

[1442] Forward the user's request to a veterinarian.

[1443] Provide necessary data (e.g., vitals, environmental data) to veterinarians.

[1444] Step 15: Receive a response from your veterinarian

[1445] server:

[1446] A response from the veterinarian is received and forwarded to the user's terminal.

[1447] Step 16: Displaying the response

[1448] Device (app):

[1449] A response from the veterinarian is received and displayed on the user interface.

[1450] User

[1451] Users can check the veterinarian's advice and instructions through the app and take appropriate action.

[1452] The above are the steps in a specific embodiment of the present invention. By linking the steps together, it becomes possible to monitor the health status of animals in real time, quickly detect abnormalities, and take appropriate measures.

[1453] Example 1

[1454] 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."

[1455] Conventional animal health monitoring systems can be slow to detect abnormalities, leading to delayed appropriate responses. Continuous data collection and analysis are insufficient, and the visualization of generated reports is limited, making it difficult for users to understand intuitively. Furthermore, remote consultation with veterinarians is not easy, making it difficult to respond in emergencies. There is a need to solve these problems and streamline animal health management in real time.

[1456] 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.

[1457] In this invention, the server includes means for acquiring animal biometric and environmental data using sensors, means for receiving data transmitted from the sensors and storing it in cloud storage, means for analyzing the data and detecting abnormalities, means for generating a warning message when an abnormality is detected, means for providing the warning message to a user, means for visualizing the animal biometric and environmental data, means for periodically compiling the data and generating a report, means for providing the report to a user, means for accepting a remote consultation request from a user, means for sharing information with a veterinary professional based on the request, and means for providing the veterinary professional's response to the user. This allows a user to monitor the health of their animal in real time and quickly detect and respond to any abnormalities that may occur. Furthermore, providing the data visually makes it easier to understand, facilitating remote consultations with a veterinarian.

[1458] A "sensor" is a device for acquiring biological and environmental data of an animal.

[1459] "Biometric data" refers to data that indicates the health status of an animal, such as body temperature and heart rate.

[1460] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal exists.

[1461] "Cloud storage" is a remote server for storing and managing data over the Internet.

[1462] "Analysis" is the process of examining acquired and stored data and detecting anomalies.

[1463] "Abnormal" refers to a case where the biometric data and environmental data deviate from the set normal range.

[1464] A "warning message" is a notification that is generated when an abnormality is detected, and notifies the user of the existence of the abnormality and how to deal with it.

[1465] "Visualization" refers to displaying acquired data in a visually easy-to-understand format, such as a graph or chart.

[1466] A "report" is a document created based on periodically compiled data that shows fluctuations and trends in past data.

[1467] A "teleconsultation" is a process by which a user consults with a veterinary medical professional over the Internet.

[1468] An "animal medical professional" is a person with the expertise to diagnose and treat animal health conditions.

[1469] The present invention provides a system for monitoring the health status of animals in real time, quickly detecting abnormalities, and notifying the user. A specific embodiment of this system will be described below.

[1470] Sensors and Data Collection

[1471] User

[1472] Users use sensors that can be worn on animals that periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[1473] The sensors transmit data to a server using, for example, Bluetooth or Wi-Fi.

[1474] Data reception and storage

[1475] server

[1476] The server receives the data sent from the sensors in real time. The receiving module checks the data for integrity and for missing or outlier values. The data is then stored in cloud storage. The data is time-stamped and a unique identifier is added.

[1477] Data analysis and anomaly detection

[1478] server

[1479] The server analyzes the received data in real time. An analysis module runs to determine whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, an abnormality is detected.

[1480] Alert Notifications

[1481] server

[1482] When an abnormality is detected, the server generates a warning message that includes a specific description of the abnormality (e.g., "Temperature rises to 39.5°C") and a recommended action (e.g., "Contact a veterinary professional immediately").

[1483] A warning message is immediately sent to the user's terminal.

[1484] Device (app)

[1485] The device receives a warning message from the server and notifies the user with sound and vibration, as well as displays an abnormal message on the screen, such as "Temperature is 39.5°C. Contact a veterinary professional immediately."

[1486] Data Visualization and Report Generation

[1487] server

[1488] The server periodically aggregates the data and generates a report in the form of graphs and charts, including, for example, the average, maximum, and minimum body temperature values ​​for the past month. This report is sent to the user's device at a specified date and time.

[1489] Device (app)

[1490] The device receives the reports sent from the server and presents them to the user in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[1491] Collaboration with veterinarians

[1492] User

[1493] Users use the app to submit a request for a remote consultation with a veterinary medical professional.

[1494] server

[1495] The server receives the user's request and forwards it to the veterinary professional, providing any necessary data (e.g., biometric data, environmental data), and then forwards it back to the user's device after receiving the response from the veterinary professional.

[1496] Specific examples

[1497] The operation of the system will be explained below with specific examples.

[1498] Example 1: Detecting abnormal body temperature

[1499] User

[1500] The user's animal's temperature spikes and the sensor measures it at 39.5°C.

[1501] sensor

[1502] The sensor stores this data in its internal memory and then transmits it to a server via Bluetooth.

[1503] server

[1504] The server receives this data, and the analysis module determines that a body temperature of 39°C or higher is abnormal. A warning message is generated and sent to the user's device stating, "The body temperature is 39.5°C. Please contact a veterinary professional immediately."

[1505] Device (app)

[1506] The device receives the warning message and displays it to the user along with a sound notification.

[1507] Example 2: Regular data reporting

[1508] server

[1509] The server aggregates body temperature, heart rate, and environmental data from the past month and generates a report containing the average, maximum, and minimum values ​​for each vital data. This report is sent to the user's device at the specified date and time.

[1510] Device (app)

[1511] The device receives the report sent from the server and notifies the user, "Please check your health data report for the past month." The user can open the report and visually check the fluctuations in body temperature and heart rate data as line graphs and histograms.

[1512] Prompt Sentence Examples

[1513] Here are some examples of specific prompts:

[1514] "Describe a program that analyzes an animal's vitals sent from a sensor and sends an alert if the body temperature exceeds 39°C."

[1515] In this way, the present invention realizes a system that enables real-time health monitoring and abnormality detection, and supports rapid response.

[1516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1517] Step 1: Sensors and Data Collection

[1518] User

[1519] The user attaches sensors to the animal, which measure the animal's body temperature, heart rate, environmental temperature, and humidity at regular intervals (e.g., every 10 minutes).

[1520] Input: User-defined measurement interval and attached sensor

[1521] Output: Body temperature, heart rate, ambient temperature, and humidity data obtained by the sensor

[1522] sensor

[1523] The sensor temporarily stores the measurement data in its internal memory and then transmits the data to a server using Bluetooth or Wi-Fi.

[1524] Input: Measured body temperature, heart rate, ambient temperature, and humidity data

[1525] Output: Data packets sent to the server

[1526] Step 2: Receiving and storing data

[1527] server

[1528] The server receives the data sent from the sensors in real time. The receiving module checks the data for consistency and verifies whether there are any missing or outlier values. The data is then stored in cloud storage.

[1529] Input: Data packets sent from the sensor

[1530] Data processing: Checking data integrity, checking for missing or outliers, adding timestamps, and adding unique identifiers

[1531] Output: Consistent data stored in cloud storage

[1532] Step 3: Data analysis and anomaly detection

[1533] server

[1534] The server analyzes the data stored in the database in real time. The analysis module determines whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, it will be detected as an abnormality.

[1535] Input: Biometric and environmental data stored in cloud storage

[1536] Data calculation: Comparison with normal range, detection of abnormal values

[1537] Output: Triggers the generation of a warning message if an anomaly is detected

[1538] Step 4: Alert Notification

[1539] server

[1540] When an anomaly is detected, the server generates a warning message, which includes the specific details of the anomaly and a recommended action, and is sent to the user's device.

[1541] Input: Trigger when an anomaly is detected

[1542] Data processing: Generating a warning message (e.g., "Temperature is 39.5°C. Contact a veterinary professional immediately.")

[1543] Output: The warning message sent to the user's terminal.

[1544] Device (app)

[1545] The user's device receives the warning message from the server and notifies the user with sound or vibration, and displays a specific abnormality message on the screen.

[1546] Input: warning message

[1547] Output: Alert sound, vibration, on-screen notification

[1548] Step 5: Data visualization and report generation

[1549] server

[1550] The server periodically aggregates the data and generates various reports, including graphs and charts showing average, maximum, and minimum body temperature and heart rate values ​​over the past month. These reports are sent to the user's device at a specified date and time.

[1551] Input: Historical data stored in cloud storage

[1552] Data calculation: Aggregation and report generation (calculating average, maximum, minimum values)

[1553] Output: Report sent to user terminal

[1554] Device (app)

[1555] The user's device receives the report sent from the server and displays it in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[1556] Input: Report from the server

[1557] Output: Reports displayed in graph and chart formats

[1558] Step 6: Work with your veterinarian

[1559] User

[1560] Users use the app to submit a request for a remote consultation with a veterinary specialist.

[1561] Input: Remote consultation request

[1562] Output: Request sent to server

[1563] server

[1564] The server receives the request from the user, forwards it to the veterinary professional, provides the necessary data (e.g., biological data, environmental data), receives the response from the veterinary professional, and forwards it back to the user's terminal.

[1565] Input: User request, corresponding data

[1566] Data processing: forwarding requests to veterinary professionals and receiving responses

[1567] Output: The response displayed on the user's terminal

[1568] (Application example 1)

[1569] 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."

[1570] While machines and robots operating in factories are required to be highly automated and efficient, there is a risk that machine failure or malfunction can have a significant impact on the entire production line. Therefore, there is a growing need for systems that can detect machine abnormalities early and take prompt action. However, current systems have issues with the accuracy of abnormality detection and the speed of notification, and also lack appropriate maintenance support and collaboration functions with specialized engineers. The objective of this invention is to provide a system that solves these problems and improves factory productivity.

[1571] 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.

[1572] In this invention, the server includes: means for collecting machine status data and environmental data using sensors; means for receiving data transmitted from the sensors and storing it in a cloud database; means for analyzing the data and detecting abnormalities; means for generating an alert notification when an abnormality is detected; means for providing the alert notification to a user device; means for visualizing the status data and environmental data; means for periodically aggregating the data and generating a report; means for providing the report to the user device; means for generating and providing machine maintenance support information when an abnormality is detected; means for accepting remote consultation requests from users; means for sharing information with specialist engineers based on the request; and means for providing the specialist engineers' responses to the user device. This enables rapid and accurate detection of abnormalities in machines and robots operating in a factory and the provision of appropriate countermeasures. Furthermore, collaboration with specialist engineers enables more advanced responses, improving the efficiency and reliability of production lines.

[1573] A "sensor" is a device for collecting machine condition and environmental data.

[1574] "Status data" refers to data relating to the operating status of machines and robots, the temperature, vibration, operating time, etc. of each part.

[1575] "Environmental data" refers to data that includes information such as temperature, humidity, and pressure in the location where the machine is installed.

[1576] "Cloud Database" means a database accessible via the Internet for storing condition and environmental data collected in real time.

[1577] "Anomaly detection" is the process of analyzing collected data and determining whether there is an abnormality in the operation or condition of a machine or robot.

[1578] An "alert notification" is a warning message that is generated when an abnormality is detected, and includes a means for providing this to a user device.

[1579] A "user device" is an information terminal such as a smartphone or tablet used by managers and engineers in a factory.

[1580] "Visualization" is the process of displaying collected data in an easy-to-understand format such as graphs or charts.

[1581] A "report" is a document that summarizes the results of aggregating data over a certain period of time, and includes statistical information such as average, maximum, and minimum values.

[1582] "Maintenance support information" is information that suggests specific repair and inspection methods based on detected abnormalities.

[1583] "Remote consultation" is a process in which managers and engineers in a factory contact specialist engineers and receive support for resolving problems from a remote location.

[1584] A "specialist engineer" is a technician with advanced knowledge and skills in the repair and maintenance of machines and robots.

[1585] The present invention is a system for monitoring abnormalities and supporting maintenance of machines and robots operating in a factory, and is specifically implemented as follows.

[1586] Sensors and Data Collection

[1587] User

[1588] Users attach multiple sensors (temperature sensors, vibration sensors, etc.) to machines or robots, which measure machine status data and environmental data in real time.

[1589] sensor

[1590] The sensor measures data at regular intervals and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[1591] Data reception and storage

[1592] server

[1593] The server receives the data sent from the sensors and stores it in a cloud database. The receiving module checks the integrity of the data and assigns a timestamp.

[1594] Data analysis and anomaly detection

[1595] server

[1596] The server monitors the stored data in real time and determines whether the machine's status data is outside of the normal range. For example, if the temperature exceeds 80°C or the vibration is outside a certain range based on the data obtained from the sensor, it will be detected as an abnormality.

[1597] Alert Notifications

[1598] server

[1599] If an anomaly is detected, the server generates an alert message, which includes the specific details of the anomaly and recommended actions. The alert notification is immediately sent to the user's device.

[1600] Device (smartphone)

[1601] The device receives an alert notification from the server and displays an abnormality message on the user interface. The abnormality message is notified to the user by sound, vibration, etc.

[1602] Data Visualization and Report Generation

[1603] server

[1604] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums, and notifies the user of any maintenance needs.

[1605] Device (smartphone)

[1606] The terminal receives the reports sent from the server and visually displays data fluctuations using graphs and charts.

[1607] Remote consultation and maintenance support

[1608] User

[1609] The user requests a remote consultation with a specialist technician via a terminal, and the request includes specific information about the machine that needs to be addressed.

[1610] server

[1611] The server receives the request, forwards it to the expert, provides the expert with the necessary data (e.g., status data, environment data), receives the expert's response, and forwards it back to the user's device.

[1612] Specific examples

[1613] For example, if the motor temperature of a factory robot exceeds 90°C, the server generates an alert message stating "Abnormality detected: Motor temperature is 90°C (time: 2023-10-01 10:00:00)" and notifies the administrator's terminal.

[1614] Prompt Sentence Examples

[1615] "Please tell me how to analyze data collected in real time by the sensors of a factory robot and notify a smartphone if an abnormality is detected. In particular, please let me know if there are any examples of implementing alert notifications using Flask and Webhooks."

[1616] This makes it possible to quickly and accurately detect abnormalities in machines and robots operating in factories and take appropriate measures. In addition, by collaborating with specialized engineers, more advanced responses are possible, improving the efficiency and reliability of production lines.

[1617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1618] Step 1:

[1619] Sensor data collection and transmission

[1620] Sensors attached to machines or robots by users measure status data (e.g., temperature, vibration) and environmental data (e.g., temperature, humidity) at regular intervals. The collected data is sent to a server via Bluetooth or Wi-Fi.

[1621] Input: Data from sensors on machines and robots

[1622] Output: Measurement data sent to the server

[1623] Step 2:

[1624] Data reception and storage

[1625] The server receives the data sent by the sensors, checks the integrity of the data, and adds a timestamp before storing it in a database for later analysis.

[1626] Input: Data sent from the sensor

[1627] Output: Data stored in a cloud database

[1628] Step 3:

[1629] Data analysis and anomaly detection

[1630] The server monitors the stored data in real time and detects abnormal data that exceeds a predetermined threshold. For example, if temperature data exceeds 80°C or vibration data falls outside a certain range, it will be deemed abnormal.

[1631] Input: Data stored in a cloud database

[1632] Output: Anomaly detection result (normal / abnormal)

[1633] Step 4:

[1634] Generate and send alert notifications

[1635] If an anomaly is detected, the server generates an alert message containing the specific details of the anomaly and recommended actions, and immediately sends it to the user's device.

[1636] Input: Anomaly detection results

[1637] Output: The alert message sent to the user's terminal.

[1638] Step 5:

[1639] Displaying an alert message

[1640] The device (smartphone) receives the alert message sent from the server and displays an abnormality message on the user interface. The content of the alert is notified to the user by notification methods such as sound and vibration.

[1641] Input: Alert message

[1642] Output: An abnormal message displayed on the device screen, a sound or vibration notification

[1643] Step 6:

[1644] Data Visualization and Reporting

[1645] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums.

[1646] Input: Collected data

[1647] Output: Generated report

[1648] Step 7:

[1649] Report distribution and viewing

[1650] The server sends the generated report to the user's device, which receives it and displays it visually in the form of graphs and charts.

[1651] Input: Generated report

[1652] Output: A report displayed on the terminal screen

[1653] Step 8:

[1654] Sending and responding to remote consultation requests

[1655] The user requests a remote consultation from a specialist through a terminal. The server receives the request and forwards it to the specialist. The server also forwards the specialist's response back to the user's terminal.

[1656] Input: Remote consultation request from user

[1657] Output: Response from the expert

[1658] 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.

[1659] The present invention provides a more user-friendly interface by combining a system that monitors the health of animals in real time, detects abnormalities, and takes appropriate measures, with an emotion engine that recognizes the user's emotions. Specific embodiments are described in detail below.

[1660] 1. Sensors and Data Collection

[1661] User

[1662] Users use sensors that can be attached to animals, which measure the animal's body temperature, heart rate, and the temperature and humidity of the environment at regular intervals (e.g., every minute).

[1663] sensor

[1664] The sensor periodically transmits the measured data to the server via wireless communication (Bluetooth or Wi-Fi).

[1665] 2. Data Receipt and Storage

[1666] server

[1667] The server receives the data sent from the sensors, checks its integrity, and then stores it in a cloud database.

[1668] Each piece of data is given a timestamp and used for later analysis.

[1669] 3. Data analysis and anomaly detection

[1670] server

[1671] The server analyzes the stored data in real time and detects any vital data that exceeds a set threshold (e.g., body temperature above 39°C) as an abnormality.

[1672] 4. Alert Notifications

[1673] server

[1674] If an anomaly is detected, the server generates an alert message that includes specific information about the anomaly and recommended actions.

[1675] Alert notifications are sent immediately to the user's device.

[1676] Device (app)

[1677] The device receives the alert notification and displays it on the user interface. The user is notified of the abnormality message by sound, vibration, or other means.

[1678] 5. Data visualization and report generation

[1679] server

[1680] The server aggregates the data over a period of time (e.g., one month) and generates reports in the form of graphs and charts.

[1681] The report is then processed into a format that is easily understood by the user.

[1682] Device (app)

[1683] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1684] 6. Collaboration with veterinarians

[1685] User

[1686] Users send a request for a remote consultation to a veterinarian through the app.

[1687] server

[1688] The server forwards the user's request to the veterinarian and provides the necessary data (e.g., vital data, environmental data).

[1689] A response from the veterinarian is received and transferred back to the user's terminal.

[1690] 7. User-friendly interface using emotion engine

[1691] Device (app)

[1692] The emotion engine analyzes emotion data from the user's facial expressions and voice to determine the user's current emotional state.

[1693] The emotion engine customizes the content or presentation of alert notifications based on the user's emotional state. For example, if the user is stressed, the message will be more friendly.

[1694] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[1695] Specific examples

[1696] The operation of the system will be explained below with specific examples.

[1697] Example 1: Abnormal body temperature detection and alert notification

[1698] sensor

[1699] The dog's temperature is measured at 39.5°C.

[1700] server

[1701] Body temperature data is received and determined to be abnormal.

[1702] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[1703] Device (app)

[1704] Receive alert notifications and notify the user with sound and vibration.

[1705] Example 2: User Emotion Recognition

[1706] User

[1707] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[1708] Device (app)

[1709] The emotion engine analyzes the emotion of anxiety and converts it into a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately."

[1710] If necessary, a remote consultation request with a veterinarian is automatically generated based on the user's concerns.

[1711] As a result, the present invention not only monitors the health status of animals in real time, but also provides an interface that takes the user's emotions into consideration, allowing for more appropriate and prompt responses.

[1712] The processing flow will be explained below.

[1713] Specific steps of the program's processing

[1714] 1. Sensors and Data Collection

[1715] Step 1: Data collection

[1716] sensor:

[1717] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[1718] The measurement data is temporarily saved in the internal memory.

[1719] Step 2: Send data

[1720] sensor:

[1721] The measured data is periodically sent to a server via wireless communication (Bluetooth or Wi-Fi).

[1722] 2. Data Receipt and Storage

[1723] Step 3: Receiving data

[1724] server:

[1725] The server receives the data sent from the sensors.

[1726] The receiving module checks the integrity of the data and filters out any invalid data.

[1727] Step 4: Save Data

[1728] server:

[1729] The data whose integrity has been confirmed is stored in a cloud database.

[1730] Each piece of data is given a timestamp and used for later analysis.

[1731] 3. Data analysis and anomaly detection

[1732] Step 5: Data analysis

[1733] server:

[1734] Runs algorithms that analyze stored data in real time.

[1735] Determine whether vital data is within normal range.

[1736] Step 6: Anomaly detection

[1737] server:

[1738] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[1739] Generate an alert with details about the anomaly detection.

[1740] 4. Alert Notifications

[1741] Step 7: Sending an alert

[1742] server:

[1743] The generated alert notification is sent to the user's terminal.

[1744] Step 8: Receiving and viewing alerts

[1745] Device (app):

[1746] Receive alert notifications and display abnormality messages on the user interface.

[1747] If necessary, the user is notified of abnormalities by sound or vibration.

[1748] 5. Data visualization and report generation

[1749] Step 9: Data Aggregation

[1750] server:

[1751] Aggregate data over a certain period of time (e.g., one month).

[1752] Calculate statistical data such as average, maximum, and minimum values.

[1753] Step 10: Generate reports

[1754] server:

[1755] Generate reports in graph and chart format based on the aggregated results.

[1756] Step 11: Submit report

[1757] server:

[1758] The generated report is sent to the user's terminal.

[1759] Step 12: Receive and view reports

[1760] Device (app):

[1761] Receive reports and notify users.

[1762] View reports in a graphical interface.

[1763] 6. Collaboration with veterinarians

[1764] Step 13: Remote consultation request

[1765] User:

[1766] Send a remote consultation request to a veterinarian through the app.

[1767] Step 14: Request forwarding

[1768] server:

[1769] Forwarding the user's request to the veterinarian and providing any necessary data (e.g., vitals, environmental data).

[1770] Step 15: Receive a response from your veterinarian

[1771] server:

[1772] A response from the veterinarian is received and forwarded to the user's terminal.

[1773] Step 16: Displaying the response

[1774] Device (app):

[1775] The response from the veterinarian is displayed to the user.

[1776] 7. User-friendly interface using emotion engine

[1777] Step 17: Collecting User Emotion Data

[1778] Device (app):

[1779] A camera and microphone are used to collect the user's facial expressions and voice.

[1780] Step 18: Sentiment Analysis

[1781] Device (app):

[1782] The emotion engine analyzes the collected data and identifies the user's emotions (e.g., joy, anger, anxiety, sadness).

[1783] Step 19: Interface Adjustments

[1784] Device (app):

[1785] Based on the analysis results, the content and presentation of the alert notification can be adjusted, for example, a more friendly message can be displayed to a user who is feeling stressed.

[1786] Step 20: Emotion-Based Teleconsultation Request

[1787] Device (app):

[1788] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[1789] For example, when a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety and changes the message to a more friendly one. If the emotion engine detects strong anxiety, it automatically generates a remote consultation request, encouraging the user to contact a veterinarian.

[1790] These steps will result in a more user-friendly health monitoring system that takes user emotions into account.

[1791] Example 2

[1792] 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."

[1793] In recent years, interest in pet health management has grown, creating a demand for systems that can monitor animals' health conditions in real time and respond quickly to abnormalities. However, existing systems do not take into account the user's emotional state when notifying them of an abnormality, which can easily cause anxiety. Furthermore, there is a lack of a way to easily visualize acquired data and present it in an easy-to-understand format. Therefore, a system is needed that provides a user-friendly, emotionally sensitive interface, as well as visualizes data and enables rapid connection to experts.

[1794] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data and environmental data of the animal using a sensor, means for receiving data transmitted from the sensor and storing it in a cloud database, means for analyzing the data in real time and detecting abnormalities, means for generating an alert notification and transmitting it to the user's terminal when an abnormality is detected, means for analyzing the user's emotional state and generating a message according to the emotional state, and means for providing a user interface through which the message is transmitted. This enables real-time monitoring of the health status and rapid response in the event of an abnormality, and by providing an alert notification that takes the user's emotions into consideration, the user can reduce anxiety and take appropriate action.

[1795] A "sensor" is a device used to collect vital and environmental data from an animal.

[1796] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as an animal's body temperature and heart rate.

[1797] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal lives.

[1798] A "cloud database" is a data storage area located on a remote server accessible via the Internet.

[1799] "Real-time analysis" means analyzing data immediately after it is sent from the sensor and detecting any abnormalities.

[1800] An "abnormality" is an abnormal situation that is detected when the vital data of an animal exceeds a preset threshold.

[1801] An "alert notification" is a message that notifies the user of a detected abnormality.

[1802] The "emotional state of the user" refers to the psychological state analyzed based on information obtained from the user's facial expressions and voice.

[1803] The "means for generating a message" is a function of the system that generates a message according to the emotional state of the user when an abnormality is detected.

[1804] "User interface" refers to the display screen and operation panel that allow the user to interact with the system.

[1805] "Means for generating reports in graph and chart format" refers to the system's functionality for compiling collected data into a format that is easy to understand visually.

[1806] "Animal health professional" refers to a health care worker with expertise in animal health care.

[1807] MODE FOR CARRYING OUT THE INVENTION

[1808] This invention is a system that monitors the health of animals in real time, detects abnormalities, and sends alerts that take into account the user's emotional state. This system is composed of sensors, a server, and a terminal (app), and the functions and roles of each part are explained in detail below.

[1809] Sensor installation and data measurement

[1810] User

[1811] The user attaches sensors to the animal, which are attached to the animal's collar or harness, and periodically (e.g., every minute) measure body temperature, heart rate, and environmental temperature and humidity.

[1812] Sending measurement data

[1813] sensor

[1814] The sensor sends the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor measures a body temperature of 38.5°C, humidity of 60%, and a heart rate of 120 bpm, and sends the data to the server every minute.

[1815] Receiving and storing data

[1816] server

[1817] The server receives the data sent from the sensors and checks the data for consistency. Specifically, it checks whether the data format is correct and whether there are any duplicates. Once the consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[1818] Data analysis and anomaly detection

[1819] server

[1820] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[1821] Generate and send alert notifications

[1822] server

[1823] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[1824] Device (app)

[1825] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[1826] Data Visualization and Reporting

[1827] server

[1828] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[1829] Device (app)

[1830] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1831] Collaboration with veterinarians

[1832] User

[1833] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[1834] server

[1835] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[1836] Interface using emotion engine

[1837] Device (app)

[1838] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[1839] Specific examples

[1840] Example 1: Abnormal body temperature detection and alert notification

[1841] 1. Sensor

[1842] The dog's temperature is measured at 39.5°C.

[1843] 2. Server

[1844] Body temperature data is received and determined to be "abnormal body temperature."

[1845] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[1846] 3. Device (app)

[1847] Receives alert notifications and notifies the user with sound and vibration. Displays the alert content on the user interface.

[1848] Example 2: Recognizing user emotions

[1849] 1. Users

[1850] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[1851] 2. Device (app)

[1852] The emotion engine analyzes the user's anxiety and generates and displays a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, a remote consultation request with a veterinary professional is automatically generated based on the user's anxiety.

[1853] Example input to a generative AI model

[1854] Example prompt

[1855] "Generate an alert message when an animal's temperature is abnormally high. Also, provide an example of how to convert the message to a more friendly one if the user is feeling anxious."

[1856] In this way, the system of the present invention monitors the health status of animals in real time, responds quickly to abnormalities, and provides alert notifications that take the user's emotions into consideration, allowing the user to take appropriate action.

[1857] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1858] Step 1: Sensor installation and data collection

[1859] User

[1860] The user attaches sensors to the animal, which are attached to the animal's collar or harness and measure body temperature, heart rate, and environmental temperature and humidity every minute. Specifically, the built-in sensors continuously monitor the animal's body and surrounding environment and collect measurement data.

[1861] Input: Animal body temperature, heart rate, environmental temperature and humidity

[1862] Output: Measured vital and environmental data

[1863] Step 2: Sending measurement data

[1864] sensor

[1865] The sensor periodically transmits the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor transmits data such as body temperature (38.5°C), humidity (60%), and heart rate (120 bpm) every minute.

[1866] Input: Measured vital and environmental data

[1867] Output: Data sent to the server

[1868] Step 3: Receiving and storing data

[1869] server

[1870] The server receives the data sent from the sensors and checks the data for consistency, ensuring that the data format is correct and that there are no duplicates. Once consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[1871] Input: Data sent from the sensor

[1872] Output: Data that has been checked for integrity and saved

[1873] Step 4: Data analysis and anomaly detection

[1874] server

[1875] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[1876] Input: Received data

[1877] Output: Anomaly detected and recorded information

[1878] Step 5: Generate and send alert notifications

[1879] server

[1880] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[1881] Input: Anomaly detection information

[1882] Output: The generated alert message

[1883] Device (app)

[1884] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[1885] Input: The alert message sent

[1886] Output: The alert content that is notified and displayed to the user

[1887] Step 6: Visualize the data and generate reports

[1888] server

[1889] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[1890] Input: Data for a period of time

[1891] Output: Generated report

[1892] Device (app)

[1893] The terminal receives the report and displays it in a visually friendly format on the user interface.

[1894] Input: Generated report

[1895] Output: The displayed report content

[1896] Step 7: Work with your veterinarian

[1897] User

[1898] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[1899] Input: Content of consultation request

[1900] Output: Request sent

[1901] server

[1902] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[1903] Input: The submitted request and associated data

[1904] Output: Response from veterinary medical professional

[1905] Step 8: Interface using emotion engine

[1906] Device (app)

[1907] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[1908] Input: User's facial expressions and voice

[1909] Output: Generated friendly message and remote consultation request

[1910] The above are the specific processing steps of the program of this system.

[1911] (Application example 2)

[1912] 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."

[1913] In modern food delivery services, quality control of ingredients during transportation is important. However, environmental factors such as temperature, humidity, and vibration can have a negative impact on the quality of ingredients, making real-time monitoring difficult. Another challenge is providing appropriate notification content that takes into account the emotions and stress of drivers and customers when receiving alerts.

[1914] 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 collecting information data of an object and surrounding environment data using sensors, means for receiving data transmitted from the sensors and storing the data in a cloud storage device, means for analyzing the data and detecting abnormalities, means for generating an alert notification when an abnormality is detected, means for providing the alert notification to the user, and means for analyzing the user's emotions and customizing the content or display method of the alert notification based on the emotions. This makes it possible to monitor the quality control of ingredients in real time and to provide appropriate alert notifications that take the user's emotions into consideration when an abnormality occurs.

[1915] A "sensor" is a device that measures physical data and outputs that data in electronic form.

[1916] "Object" refers to the object or environment from which data is collected or monitored.

[1917] "Information data" refers to data that contains specific information about an object.

[1918] "Ambient environment data" refers to data relating to environmental factors such as temperature, humidity, and vibrations present around an object.

[1919] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[1920] "Abnormal" refers to a data condition that exceeds a pre-established standard or threshold.

[1921] An "alert notification" is a message that warns or alerts the user when an abnormality is detected.

[1922] "Means for analyzing emotions" refers to technologies and devices for detecting and interpreting emotions from a user's facial expressions, voice, etc.

[1923] "Means of customization" refers to methods for adjusting the system's behavior and display content according to specific conditions or situations.

[1924] An "expert" is someone who has advanced knowledge or skills in a particular field.

[1925] The present invention is a system that uses sensors to collect information data on objects and their surrounding environments, stores the data in a cloud storage device, analyzes it in real time, and detects abnormalities. When an abnormality is detected, the system generates an alert notification and provides it to the user. The system also includes a function for analyzing the user's emotions and customizing the content or display method of the alert notification based on the user's emotions.

[1926] Sensors and Data Collection

[1927] The user uses sensors installed on food items or their surroundings. These sensors collect data such as temperature, humidity, and vibration, and periodically measure and transmit the data to a smartphone or delivery robot. This data is transmitted via wireless communication such as Bluetooth or Wi-Fi.

[1928] Data reception and storage

[1929] The server receives data sent from the sensors via smartphones or delivery robots. The received data is stored in a cloud storage device, and a timestamp is assigned to each piece of data. Common online storage services (e.g., AWS, Azure) are used as the cloud storage device.

[1930] Data analysis and anomaly detection

[1931] The server analyzes the data stored in the cloud storage device in real time and detects abnormalities when set thresholds are exceeded, such as when the temperature exceeds 35°C or vibration exceeds a set value. This allows for quick action to be taken before the quality of the ingredients deteriorates.

[1932] Alert Notifications

[1933] If an abnormality is detected, the server generates an alert message. This alert message is sent to the user's smartphone or delivery robot, and the user is notified by sound or vibration. The alert message contains specific information about the abnormality and recommended actions. For example, it could say, "The temperature has reached 35.5°C. Please take immediate action."

[1934] Sentiment analysis and interface customization

[1935] When a user receives an alert notification, an emotion analysis engine on the smartphone or delivery robot assists them. The emotion analysis engine analyzes emotional data from the user's facial expressions and voice, and if the user is feeling stressed, it converts it into a friendly message. Specifically, it might say something like, "Don't worry, there's no problem. Please take immediate action." This method can reduce the user's stress.

[1936] Examples and prompts

[1937] Specific examples

[1938] While the ingredients are being transported, a sensor measures the CPU temperature at 35.5°C. The emotion analysis engine recognizes the delivery driver's anxiety and displays an alert message saying, "Don't worry, we'll take action immediately."

[1939] Prompt Sentence Examples

[1940] Analyze the following sentence, determine the user's sentiment, and generate an appropriate response message:

[1941] Alert message: Temperature reached 35.5°C during transport.

[1942] User Emotion: Anxiety

[1943] An appropriate response message would be: "Don't worry, we'll fix it right away."

[1944] In this way, the present invention controls the quality of ingredients in real time and provides alert notifications that take into consideration the user's emotions when an abnormality occurs, enabling a quick and appropriate response.

[1945] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1946] Step 1:

[1947] Sensors periodically acquire information data such as temperature, humidity, and vibration, as well as data on the surrounding environment. The input is physical data from the object and the surrounding environment, and the output is electronic measurement data.

[1948] Step 2:

[1949] The terminal (smartphone or delivery robot) receives the data sent from the sensor via wireless communication (Bluetooth or Wi-Fi). The input is the measurement data from the sensor, and the output is the data stored in the terminal.

[1950] Step 3:

[1951] The data received by the device is sent to the cloud storage device. The input is the data stored on the device, and the output is the data stored in the cloud storage device. The cloud storage device is assigned a timestamp and the integrity of the data is verified.

[1952] Step 4:

[1953] The server analyzes the data stored in the cloud storage device in real time, compares it with the set threshold, and detects anomalies. The input is the data stored in the cloud storage device, and the output is the result of the anomaly detection.

[1954] Step 5:

[1955] If an anomaly is detected, the server generates an alert notification, which contains specific information about the anomaly and a recommended action. The input is the anomaly detection result, and the output is the alert message.

[1956] Step 6:

[1957] An alert notification is sent to the user's device via a smartphone or delivery robot. The input is the generated alert message, and the output is the alert notification displayed on the device. The notification is also conveyed to the user by sound or vibration.

[1958] Step 7:

[1959] The device's emotion analysis engine analyzes the user's facial expressions and voice to obtain emotional data. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[1960] Step 8:

[1961] The server customizes the content or display method of the alert notification based on the analyzed emotion data. The input is the analyzed emotion data and the existing alert message, and the output is the customized alert message.

[1962] Step 9:

[1963] The customized alert message is then displayed again on the user's smartphone or the delivery robot's device. This provides an alert notification that takes into account the user's emotions. The input is the customized alert message, and the output is the final displayed alert notification.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] [Fourth embodiment]

[1968] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1969] 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.

[1970] 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).

[1971] 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.

[1972] 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.

[1973] 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).

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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."

[1981] The present invention is a system for monitoring the health of animals in real time and taking appropriate measures. Specific embodiments of the present invention will be described in detail below.

[1982] 1. Sensors and Data Collection

[1983] User

[1984] The user employs sensors that can be worn on the animal, which periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[1985] sensor

[1986] The sensor measures data at a predetermined interval and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[1987] 2. Data Receipt and Storage

[1988] server

[1989] The server receives the data sent from the sensors, and the receiving module checks the integrity of the data and stores it in a cloud database.

[1990] Each piece of data is given a timestamp and used for later analysis.

[1991] 3. Data analysis and anomaly detection

[1992] server

[1993] The server monitors the stored data in real time to determine if the animal's vital data is outside of normal ranges.

[1994] If the body temperature exceeds 39°C or the heart rate falls outside a certain range, it is detected as an abnormality.

[1995] 4. Alert Notifications

[1996] server

[1997] If an anomaly is detected, the server generates an alert message that includes the specific details of the anomaly and a recommended action.

[1998] Alert notifications are sent immediately to the user's device.

[1999] Device (app)

[2000] The terminal receives the alert notification from the server and displays an abnormality message on the user interface.

[2001] The user is notified of the abnormality message by sound, vibration, or other means.

[2002] 5. Data visualization and report generation

[2003] server

[2004] The server periodically aggregates the collected data and generates reports in the form of graphs and charts.

[2005] The report includes data variability information such as average, maximum, and minimum values.

[2006] Device (app)

[2007] The terminal receives the report sent from the server.

[2008] To present received reports to a user in a visually easy-to-understand format.

[2009] 6. Collaboration with veterinarians

[2010] User

[2011] Users contact a veterinarian through the app and submit a request to initiate a remote consultation.

[2012] server

[2013] The server receives the user's request, forwards it to the veterinarian, and provides the veterinarian with the necessary data (e.g., vital data, environmental data).

[2014] The veterinarian's response is received and transferred back to the user's terminal.

[2015] Specific examples

[2016] The operation of the system will be explained below with specific examples.

[2017] Example 1: Detecting abnormal body temperature

[2018] User

[2019] If the user's pet suddenly experiences a rise in temperature, the sensor will measure the temperature as 39.5°C.

[2020] sensor

[2021] The body temperature data measured by the sensor is sent to the server.

[2022] server

[2023] The server analyzes the received data and determines that the body temperature is above the normal range (e.g., below 39°C).

[2024] An alert message stating "body temperature is abnormally high" is immediately generated and notified to the user's terminal.

[2025] Device (app)

[2026] The device receives an alert message and displays a notification saying, "Temperature is 39.5°C. Contact your veterinarian immediately."

[2027] Example 2: Regular data reporting

[2028] server

[2029] The server aggregates temperature, heart rate, and environmental data from the past month and generates a report with average, maximum, and minimum values.

[2030] Device (app)

[2031] The terminal receives the report sent from the server and notifies the user.

[2032] Graphs and charts are used to visually display data fluctuations over the past month.

[2033] This invention makes it possible to improve the efficiency of animal health management by monitoring the health status of animals in real time, quickly detecting abnormalities and notifying the user. Furthermore, by providing a function for remote collaboration with veterinarians, this system supports prompt and appropriate medical treatment.

[2034] The processing flow will be explained below.

[2035] Specific steps of the program's processing

[2036] 1. Sensors and Data Collection

[2037] Step 1: Data collection

[2038] sensor:

[2039] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[2040] The measurement data is temporarily saved in the internal memory.

[2041] Step 2: Send data

[2042] sensor:

[2043] The temporarily stored data is periodically sent to the server via wireless communication (Bluetooth or Wi-Fi).

[2044] 2. Data Receipt and Storage

[2045] Step 3: Receiving data

[2046] server:

[2047] The server receives the data sent from the sensors.

[2048] The receiving module checks the integrity of the data and filters out any invalid data.

[2049] Step 4: Save Data

[2050] server:

[2051] The data whose integrity has been confirmed is stored in a cloud database.

[2052] Each piece of data is time-stamped and used for later analysis.

[2053] 3. Data analysis and anomaly detection

[2054] Step 5: Data analysis

[2055] server:

[2056] Runs algorithms that analyze stored data in real time.

[2057] Determine whether vital data is within normal range.

[2058] Step 6: Anomaly detection

[2059] server:

[2060] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[2061] Generate an alert with details about the anomaly detection.

[2062] 4. Alert Notifications

[2063] Step 7: Sending an alert

[2064] server:

[2065] The generated alert notification is sent to the user's terminal.

[2066] Step 8: Receiving and viewing alerts

[2067] Device (app):

[2068] Receive alert notifications and display abnormality messages on the user interface.

[2069] If necessary, the user is notified of abnormalities by sound or vibration.

[2070] 5. Data visualization and report generation

[2071] Step 9: Data Aggregation

[2072] server:

[2073] Aggregate data over a certain period of time (e.g., one month).

[2074] Calculate statistical data such as average, maximum, and minimum values.

[2075] Step 10: Generate reports

[2076] server:

[2077] Generate reports in graph and chart format based on the aggregated results.

[2078] Step 11: Submit report

[2079] server:

[2080] The generated report is sent to the user's terminal.

[2081] Step 12: Receive and view reports

[2082] Device (app):

[2083] Receive reports and notify users.

[2084] View reports in a graphical interface.

[2085] 6. Collaboration with veterinarians

[2086] Step 13: Remote consultation request

[2087] User:

[2088] Send a remote consultation request to a veterinarian through the app.

[2089] Step 14: Request forwarding

[2090] server:

[2091] Forward the user's request to a veterinarian.

[2092] Provide necessary data (e.g., vitals, environmental data) to veterinarians.

[2093] Step 15: Receive a response from your veterinarian

[2094] server:

[2095] A response from the veterinarian is received and forwarded to the user's terminal.

[2096] Step 16: Displaying the response

[2097] Device (app):

[2098] A response from the veterinarian is received and displayed on the user interface.

[2099] User

[2100] Users can check the veterinarian's advice and instructions through the app and take appropriate action.

[2101] The above are the steps in a specific embodiment of the present invention. By linking the steps together, it becomes possible to monitor the health status of animals in real time, quickly detect abnormalities, and take appropriate measures.

[2102] Example 1

[2103] 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."

[2104] Conventional animal health monitoring systems can be slow to detect abnormalities, leading to delayed appropriate responses. Continuous data collection and analysis are insufficient, and the visualization of generated reports is limited, making it difficult for users to understand intuitively. Furthermore, remote consultation with veterinarians is not easy, making it difficult to respond in emergencies. There is a need to solve these problems and streamline animal health management in real time.

[2105] 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.

[2106] In this invention, the server includes means for acquiring animal biometric and environmental data using sensors, means for receiving data transmitted from the sensors and storing it in cloud storage, means for analyzing the data and detecting abnormalities, means for generating a warning message when an abnormality is detected, means for providing the warning message to a user, means for visualizing the animal biometric and environmental data, means for periodically compiling the data and generating a report, means for providing the report to a user, means for accepting a remote consultation request from a user, means for sharing information with a veterinary professional based on the request, and means for providing the veterinary professional's response to the user. This allows a user to monitor the health of their animal in real time and quickly detect and respond to any abnormalities that may occur. Furthermore, providing the data visually makes it easier to understand, facilitating remote consultations with a veterinarian.

[2107] A "sensor" is a device for acquiring biological and environmental data of an animal.

[2108] "Biometric data" refers to data that indicates the health status of an animal, such as body temperature and heart rate.

[2109] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal exists.

[2110] "Cloud storage" is a remote server for storing and managing data over the Internet.

[2111] "Analysis" is the process of examining acquired and stored data and detecting anomalies.

[2112] "Abnormal" refers to a case where the biometric data and environmental data deviate from the set normal range.

[2113] A "warning message" is a notification that is generated when an abnormality is detected, and notifies the user of the existence of the abnormality and how to deal with it.

[2114] "Visualization" refers to displaying acquired data in a visually easy-to-understand format, such as a graph or chart.

[2115] A "report" is a document created based on periodically compiled data that shows fluctuations and trends in past data.

[2116] A "teleconsultation" is a process by which a user consults with a veterinary medical professional over the Internet.

[2117] An "animal medical professional" is a person with the expertise to diagnose and treat animal health conditions.

[2118] The present invention provides a system for monitoring the health status of animals in real time, quickly detecting abnormalities, and notifying the user. A specific embodiment of this system will be described below.

[2119] Sensors and Data Collection

[2120] User

[2121] Users use sensors that can be worn on animals that periodically measure the animal's body temperature, heart rate, and the temperature and humidity of the animal's environment.

[2122] The sensors transmit data to a server using, for example, Bluetooth or Wi-Fi.

[2123] Data reception and storage

[2124] server

[2125] The server receives the data sent from the sensors in real time. The receiving module checks the data for integrity and for missing or outlier values. The data is then stored in cloud storage. The data is time-stamped and a unique identifier is added.

[2126] Data analysis and anomaly detection

[2127] server

[2128] The server analyzes the received data in real time. An analysis module runs to determine whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, an abnormality is detected.

[2129] Alert Notifications

[2130] server

[2131] When an abnormality is detected, the server generates a warning message that includes a specific description of the abnormality (e.g., "Temperature rises to 39.5°C") and a recommended action (e.g., "Contact a veterinary professional immediately").

[2132] A warning message is immediately sent to the user's terminal.

[2133] Device (app)

[2134] The device receives a warning message from the server and notifies the user with sound and vibration, as well as displays an abnormal message on the screen, such as "Temperature is 39.5°C. Contact a veterinary professional immediately."

[2135] Data Visualization and Report Generation

[2136] server

[2137] The server periodically aggregates the data and generates a report in the form of graphs and charts, including, for example, the average, maximum, and minimum body temperature values ​​for the past month. This report is sent to the user's device at a specified date and time.

[2138] Device (app)

[2139] The device receives the reports sent from the server and presents them to the user in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[2140] Collaboration with veterinarians

[2141] User

[2142] Users use the app to submit a request for a remote consultation with a veterinary medical professional.

[2143] server

[2144] The server receives the user's request and forwards it to the veterinary professional, providing any necessary data (e.g., biometric data, environmental data), and then forwards it back to the user's device after receiving the response from the veterinary professional.

[2145] Specific examples

[2146] The operation of the system will be explained below with specific examples.

[2147] Example 1: Detecting abnormal body temperature

[2148] User

[2149] The user's animal's temperature spikes and the sensor measures it at 39.5°C.

[2150] sensor

[2151] The sensor stores this data in its internal memory and then transmits it to a server via Bluetooth.

[2152] server

[2153] The server receives this data, and the analysis module determines that a body temperature of 39°C or higher is abnormal. A warning message is generated and sent to the user's device stating, "The body temperature is 39.5°C. Please contact a veterinary professional immediately."

[2154] Device (app)

[2155] The device receives the warning message and displays it to the user along with a sound notification.

[2156] Example 2: Regular data reporting

[2157] server

[2158] The server aggregates body temperature, heart rate, and environmental data from the past month and generates a report containing the average, maximum, and minimum values ​​for each vital data. This report is sent to the user's device at the specified date and time.

[2159] Device (app)

[2160] The device receives the report sent from the server and notifies the user, "Please check your health data report for the past month." The user can open the report and visually check the fluctuations in body temperature and heart rate data as line graphs and histograms.

[2161] Prompt Sentence Examples

[2162] Here are some examples of specific prompts:

[2163] "Describe a program that analyzes an animal's vitals sent from a sensor and sends an alert if the body temperature exceeds 39°C."

[2164] In this way, the present invention realizes a system that enables real-time health monitoring and abnormality detection, and supports rapid response.

[2165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2166] Step 1: Sensors and Data Collection

[2167] User

[2168] The user attaches sensors to the animal, which measure the animal's body temperature, heart rate, environmental temperature, and humidity at regular intervals (e.g., every 10 minutes).

[2169] Input: User-defined measurement interval and attached sensor

[2170] Output: Body temperature, heart rate, ambient temperature, and humidity data obtained by the sensor

[2171] sensor

[2172] The sensor temporarily stores the measurement data in its internal memory and then transmits the data to a server using Bluetooth or Wi-Fi.

[2173] Input: Measured body temperature, heart rate, ambient temperature, and humidity data

[2174] Output: Data packets sent to the server

[2175] Step 2: Receiving and storing data

[2176] server

[2177] The server receives the data sent from the sensors in real time. The receiving module checks the data for consistency and verifies whether there are any missing or outlier values. The data is then stored in cloud storage.

[2178] Input: Data packets sent from the sensor

[2179] Data processing: Checking data integrity, checking for missing or outliers, adding timestamps, and adding unique identifiers

[2180] Output: Consistent data stored in cloud storage

[2181] Step 3: Data analysis and anomaly detection

[2182] server

[2183] The server analyzes the data stored in the database in real time. The analysis module determines whether the animal's vital signs are within a specified normal range. For example, if the body temperature exceeds 39°C or the heart rate is outside the set normal range, it will be detected as an abnormality.

[2184] Input: Biometric and environmental data stored in cloud storage

[2185] Data calculation: Comparison with normal range, detection of abnormal values

[2186] Output: Triggers the generation of a warning message if an anomaly is detected

[2187] Step 4: Alert Notification

[2188] server

[2189] When an anomaly is detected, the server generates a warning message, which includes the specific details of the anomaly and a recommended action, and is sent to the user's device.

[2190] Input: Trigger when an anomaly is detected

[2191] Data processing: Generating a warning message (e.g., "Temperature is 39.5°C. Contact a veterinary professional immediately.")

[2192] Output: The warning message sent to the user's terminal.

[2193] Device (app)

[2194] The user's device receives the warning message from the server and notifies the user with sound or vibration, and displays a specific abnormality message on the screen.

[2195] Input: warning message

[2196] Output: Alert sound, vibration, on-screen notification

[2197] Step 5: Data visualization and report generation

[2198] server

[2199] The server periodically aggregates the data and generates various reports, including graphs and charts showing average, maximum, and minimum body temperature and heart rate values ​​over the past month. These reports are sent to the user's device at a specified date and time.

[2200] Input: Historical data stored in cloud storage

[2201] Data calculation: Aggregation and report generation (calculating average, maximum, minimum values)

[2202] Output: Report sent to user terminal

[2203] Device (app)

[2204] The user's device receives the report sent from the server and displays it in a visually understandable format, such as a line graph showing body temperature fluctuations or a histogram of heart rate.

[2205] Input: Report from the server

[2206] Output: Reports displayed in graph and chart formats

[2207] Step 6: Work with your veterinarian

[2208] User

[2209] Users use the app to submit a request for a remote consultation with a veterinary specialist.

[2210] Input: Remote consultation request

[2211] Output: Request sent to server

[2212] server

[2213] The server receives the request from the user, forwards it to the veterinary professional, provides the necessary data (e.g., biological data, environmental data), receives the response from the veterinary professional, and forwards it back to the user's terminal.

[2214] Input: User request, corresponding data

[2215] Data processing: forwarding requests to veterinary professionals and receiving responses

[2216] Output: The response displayed on the user's terminal

[2217] (Application example 1)

[2218] 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."

[2219] While machines and robots operating in factories are required to be highly automated and efficient, there is a risk that machine failure or malfunction can have a significant impact on the entire production line. Therefore, there is a growing need for systems that can detect machine abnormalities early and take prompt action. However, current systems have issues with the accuracy of abnormality detection and the speed of notification, and also lack appropriate maintenance support and collaboration functions with specialized engineers. The objective of this invention is to provide a system that solves these problems and improves factory productivity.

[2220] 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.

[2221] In this invention, the server includes: means for collecting machine status data and environmental data using sensors; means for receiving data transmitted from the sensors and storing it in a cloud database; means for analyzing the data and detecting abnormalities; means for generating an alert notification when an abnormality is detected; means for providing the alert notification to a user device; means for visualizing the status data and environmental data; means for periodically aggregating the data and generating a report; means for providing the report to the user device; means for generating and providing machine maintenance support information when an abnormality is detected; means for accepting remote consultation requests from users; means for sharing information with specialist engineers based on the request; and means for providing the specialist engineers' responses to the user device. This enables rapid and accurate detection of abnormalities in machines and robots operating in a factory and the provision of appropriate countermeasures. Furthermore, collaboration with specialist engineers enables more advanced responses, improving the efficiency and reliability of production lines.

[2222] A "sensor" is a device for collecting machine condition and environmental data.

[2223] "Status data" refers to data relating to the operating status of machines and robots, the temperature, vibration, operating time, etc. of each part.

[2224] "Environmental data" refers to data that includes information such as temperature, humidity, and pressure in the location where the machine is installed.

[2225] "Cloud Database" means a database accessible via the Internet for storing condition and environmental data collected in real time.

[2226] "Anomaly detection" is the process of analyzing collected data and determining whether there is an abnormality in the operation or condition of a machine or robot.

[2227] An "alert notification" is a warning message that is generated when an abnormality is detected, and includes a means for providing this to a user device.

[2228] A "user device" is an information terminal such as a smartphone or tablet used by managers and engineers in a factory.

[2229] "Visualization" is the process of displaying collected data in an easy-to-understand format such as graphs or charts.

[2230] A "report" is a document that summarizes the results of aggregating data over a certain period of time, and includes statistical information such as average, maximum, and minimum values.

[2231] "Maintenance support information" is information that suggests specific repair and inspection methods based on detected abnormalities.

[2232] "Remote consultation" is a process in which managers and engineers in a factory contact specialist engineers and receive support for resolving problems from a remote location.

[2233] A "specialist engineer" is a technician with advanced knowledge and skills in the repair and maintenance of machines and robots.

[2234] The present invention is a system for monitoring abnormalities and supporting maintenance of machines and robots operating in a factory, and is specifically implemented as follows.

[2235] Sensors and Data Collection

[2236] User

[2237] Users attach multiple sensors (temperature sensors, vibration sensors, etc.) to machines or robots, which measure machine status data and environmental data in real time.

[2238] sensor

[2239] The sensor measures data at regular intervals and transmits the data to a server via wireless communication (e.g., Bluetooth or Wi-Fi).

[2240] Data reception and storage

[2241] server

[2242] The server receives the data sent from the sensors and stores it in a cloud database. The receiving module checks the integrity of the data and assigns a timestamp.

[2243] Data analysis and anomaly detection

[2244] server

[2245] The server monitors the stored data in real time and determines whether the machine's status data is outside of the normal range. For example, if the temperature exceeds 80°C or the vibration is outside a certain range based on the data obtained from the sensor, it will be detected as an abnormality.

[2246] Alert Notifications

[2247] server

[2248] If an anomaly is detected, the server generates an alert message, which includes the specific details of the anomaly and recommended actions. The alert notification is immediately sent to the user's device.

[2249] Device (smartphone)

[2250] The device receives an alert notification from the server and displays an abnormality message on the user interface. The abnormality message is notified to the user by sound, vibration, etc.

[2251] Data Visualization and Report Generation

[2252] server

[2253] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums, and notifies the user of any maintenance needs.

[2254] Device (smartphone)

[2255] The terminal receives the reports sent from the server and visually displays data fluctuations using graphs and charts.

[2256] Remote consultation and maintenance support

[2257] User

[2258] The user requests a remote consultation with a specialist technician via a terminal, and the request includes specific information about the machine that needs to be addressed.

[2259] server

[2260] The server receives the request, forwards it to the expert, provides the expert with the necessary data (e.g., status data, environment data), receives the expert's response, and forwards it back to the user's device.

[2261] Specific examples

[2262] For example, if the motor temperature of a factory robot exceeds 90°C, the server generates an alert message stating "Abnormality detected: Motor temperature is 90°C (time: 2023-10-01 10:00:00)" and notifies the administrator's terminal.

[2263] Prompt Sentence Examples

[2264] "Please tell me how to analyze data collected in real time by the sensors of a factory robot and notify a smartphone if an abnormality is detected. In particular, please let me know if there are any examples of implementing alert notifications using Flask and Webhooks."

[2265] This makes it possible to quickly and accurately detect abnormalities in machines and robots operating in factories and take appropriate measures. In addition, by collaborating with specialized engineers, more advanced responses are possible, improving the efficiency and reliability of production lines.

[2266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2267] Step 1:

[2268] Sensor data collection and transmission

[2269] Sensors attached to machines or robots by users measure status data (e.g., temperature, vibration) and environmental data (e.g., temperature, humidity) at regular intervals. The collected data is sent to a server via Bluetooth or Wi-Fi.

[2270] Input: Data from sensors on machines and robots

[2271] Output: Measurement data sent to the server

[2272] Step 2:

[2273] Data reception and storage

[2274] The server receives the data sent by the sensors, checks the integrity of the data, and adds a timestamp before storing it in a database for later analysis.

[2275] Input: Data sent from the sensor

[2276] Output: Data stored in a cloud database

[2277] Step 3:

[2278] Data analysis and anomaly detection

[2279] The server monitors the stored data in real time and detects abnormal data that exceeds a predetermined threshold. For example, if temperature data exceeds 80°C or vibration data falls outside a certain range, it will be deemed abnormal.

[2280] Input: Data stored in a cloud database

[2281] Output: Anomaly detection result (normal / abnormal)

[2282] Step 4:

[2283] Generate and send alert notifications

[2284] If an anomaly is detected, the server generates an alert message containing the specific details of the anomaly and recommended actions, and immediately sends it to the user's device.

[2285] Input: Anomaly detection results

[2286] Output: The alert message sent to the user's terminal.

[2287] Step 5:

[2288] Displaying an alert message

[2289] The device (smartphone) receives the alert message sent from the server and displays an abnormality message on the user interface. The content of the alert is notified to the user by notification methods such as sound and vibration.

[2290] Input: Alert message

[2291] Output: An abnormal message displayed on the device screen, a sound or vibration notification

[2292] Step 6:

[2293] Data Visualization and Reporting

[2294] The server periodically aggregates the collected data and generates reports in the form of graphs and charts, including averages, maximums, and minimums.

[2295] Input: Collected data

[2296] Output: Generated report

[2297] Step 7:

[2298] Report distribution and viewing

[2299] The server sends the generated report to the user's device, which receives it and displays it visually in the form of graphs and charts.

[2300] Input: Generated report

[2301] Output: A report displayed on the terminal screen

[2302] Step 8:

[2303] Sending and responding to remote consultation requests

[2304] The user requests a remote consultation from a specialist through a terminal. The server receives the request and forwards it to the specialist. The server also forwards the specialist's response back to the user's terminal.

[2305] Input: Remote consultation request from user

[2306] Output: Response from the expert

[2307] 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.

[2308] The present invention provides a more user-friendly interface by combining a system that monitors the health of animals in real time, detects abnormalities, and takes appropriate measures, with an emotion engine that recognizes the user's emotions. Specific embodiments are described in detail below.

[2309] 1. Sensors and Data Collection

[2310] User

[2311] Users use sensors that can be attached to animals, which measure the animal's body temperature, heart rate, and the temperature and humidity of the environment at regular intervals (e.g., every minute).

[2312] sensor

[2313] The sensor periodically transmits the measured data to the server via wireless communication (Bluetooth or Wi-Fi).

[2314] 2. Data Receipt and Storage

[2315] server

[2316] The server receives the data sent from the sensors, checks its integrity, and then stores it in a cloud database.

[2317] Each piece of data is given a timestamp and used for later analysis.

[2318] 3. Data analysis and anomaly detection

[2319] server

[2320] The server analyzes the stored data in real time and detects any vital data that exceeds a set threshold (e.g., body temperature above 39°C) as an abnormality.

[2321] 4. Alert Notifications

[2322] server

[2323] If an anomaly is detected, the server generates an alert message that includes specific information about the anomaly and recommended actions.

[2324] Alert notifications are sent immediately to the user's device.

[2325] Device (app)

[2326] The device receives the alert notification and displays it on the user interface. The user is notified of the abnormality message by sound, vibration, or other means.

[2327] 5. Data visualization and report generation

[2328] server

[2329] The server aggregates the data over a period of time (e.g., one month) and generates reports in the form of graphs and charts.

[2330] The report is then processed into a format that is easily understood by the user.

[2331] Device (app)

[2332] The terminal receives the report and displays it in a visually friendly format on the user interface.

[2333] 6. Collaboration with veterinarians

[2334] User

[2335] Users send a request for a remote consultation to a veterinarian through the app.

[2336] server

[2337] The server forwards the user's request to the veterinarian and provides the necessary data (e.g., vital data, environmental data).

[2338] A response from the veterinarian is received and transferred back to the user's terminal.

[2339] 7. User-friendly interface using emotion engine

[2340] Device (app)

[2341] The emotion engine analyzes emotion data from the user's facial expressions and voice to determine the user's current emotional state.

[2342] The emotion engine customizes the content or presentation of alert notifications based on the user's emotional state. For example, if the user is stressed, the message will be more friendly.

[2343] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[2344] Specific examples

[2345] The operation of the system will be explained below with specific examples.

[2346] Example 1: Abnormal body temperature detection and alert notification

[2347] sensor

[2348] The dog's temperature is measured at 39.5°C.

[2349] server

[2350] Body temperature data is received and determined to be abnormal.

[2351] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[2352] Device (app)

[2353] Receive alert notifications and notify the user with sound and vibration.

[2354] Example 2: User Emotion Recognition

[2355] User

[2356] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[2357] Device (app)

[2358] The emotion engine analyzes the emotion of anxiety and converts it into a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately."

[2359] If necessary, a remote consultation request with a veterinarian is automatically generated based on the user's concerns.

[2360] As a result, the present invention not only monitors the health status of animals in real time, but also provides an interface that takes the user's emotions into consideration, allowing for more appropriate and prompt responses.

[2361] The processing flow will be explained below.

[2362] Specific steps of the program's processing

[2363] 1. Sensors and Data Collection

[2364] Step 1: Data collection

[2365] sensor:

[2366] The sensors measure the animal's body temperature, heart rate, and environmental temperature and humidity at regular intervals (e.g., every minute).

[2367] The measurement data is temporarily saved in the internal memory.

[2368] Step 2: Send data

[2369] sensor:

[2370] The measured data is periodically sent to a server via wireless communication (Bluetooth or Wi-Fi).

[2371] 2. Data Receipt and Storage

[2372] Step 3: Receiving data

[2373] server:

[2374] The server receives the data sent from the sensors.

[2375] The receiving module checks the integrity of the data and filters out any invalid data.

[2376] Step 4: Save Data

[2377] server:

[2378] The data whose integrity has been confirmed is stored in a cloud database.

[2379] Each piece of data is given a timestamp and used for later analysis.

[2380] 3. Data analysis and anomaly detection

[2381] Step 5: Data analysis

[2382] server:

[2383] Runs algorithms that analyze stored data in real time.

[2384] Determine whether vital data is within normal range.

[2385] Step 6: Anomaly detection

[2386] server:

[2387] If the data exceeds a set threshold (e.g., body temperature exceeds 39°C), it is detected as an abnormality.

[2388] Generate an alert with details about the anomaly detection.

[2389] 4. Alert Notifications

[2390] Step 7: Sending an alert

[2391] server:

[2392] The generated alert notification is sent to the user's terminal.

[2393] Step 8: Receiving and viewing alerts

[2394] Device (app):

[2395] Receive alert notifications and display abnormality messages on the user interface.

[2396] If necessary, the user is notified of abnormalities by sound or vibration.

[2397] 5. Data visualization and report generation

[2398] Step 9: Data Aggregation

[2399] server:

[2400] Aggregate data over a certain period of time (e.g., one month).

[2401] Calculate statistical data such as average, maximum, and minimum values.

[2402] Step 10: Generate reports

[2403] server:

[2404] Generate reports in graph and chart format based on the aggregated results.

[2405] Step 11: Submit report

[2406] server:

[2407] The generated report is sent to the user's terminal.

[2408] Step 12: Receive and view reports

[2409] Device (app):

[2410] Receive reports and notify users.

[2411] View reports in a graphical interface.

[2412] 6. Collaboration with veterinarians

[2413] Step 13: Remote consultation request

[2414] User:

[2415] Send a remote consultation request to a veterinarian through the app.

[2416] Step 14: Request forwarding

[2417] server:

[2418] Forwarding the user's request to the veterinarian and providing any necessary data (e.g., vitals, environmental data).

[2419] Step 15: Receive a response from your veterinarian

[2420] server:

[2421] A response from the veterinarian is received and forwarded to the user's terminal.

[2422] Step 16: Displaying the response

[2423] Device (app):

[2424] The response from the veterinarian is displayed to the user.

[2425] 7. User-friendly interface using emotion engine

[2426] Step 17: Collecting User Emotion Data

[2427] Device (app):

[2428] A camera and microphone are used to collect the user's facial expressions and voice.

[2429] Step 18: Sentiment Analysis

[2430] Device (app):

[2431] The emotion engine analyzes the collected data and identifies the user's emotions (e.g., joy, anger, anxiety, sadness).

[2432] Step 19: Interface Adjustments

[2433] Device (app):

[2434] Based on the analysis results, the content and presentation of the alert notification can be adjusted, for example, a more friendly message can be displayed to a user who is feeling stressed.

[2435] Step 20: Emotion-Based Teleconsultation Request

[2436] Device (app):

[2437] If necessary, a remote consultation request to a veterinarian is automatically generated based on the user's emotional changes.

[2438] For example, when a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety and changes the message to a more friendly one. If the emotion engine detects strong anxiety, it automatically generates a remote consultation request, encouraging the user to contact a veterinarian.

[2439] These steps will result in a more user-friendly health monitoring system that takes user emotions into account.

[2440] Example 2

[2441] 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."

[2442] In recent years, interest in pet health management has grown, creating a demand for systems that can monitor animals' health conditions in real time and respond quickly to abnormalities. However, existing systems do not take into account the user's emotional state when notifying them of an abnormality, which can easily cause anxiety. Furthermore, there is a lack of a way to easily visualize acquired data and present it in an easy-to-understand format. Therefore, a system is needed that provides a user-friendly, emotionally sensitive interface, as well as visualizes data and enables rapid connection to experts.

[2443] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data and environmental data of the animal using a sensor, means for receiving data transmitted from the sensor and storing it in a cloud database, means for analyzing the data in real time and detecting abnormalities, means for generating an alert notification and transmitting it to the user's terminal when an abnormality is detected, means for analyzing the user's emotional state and generating a message according to the emotional state, and means for providing a user interface through which the message is transmitted. This enables real-time monitoring of the health status and rapid response in the event of an abnormality, and by providing an alert notification that takes the user's emotions into consideration, the user can reduce anxiety and take appropriate action.

[2444] A "sensor" is a device used to collect vital and environmental data from an animal.

[2445] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as an animal's body temperature and heart rate.

[2446] "Environmental data" refers to data that indicates the temperature, humidity, etc. of the environment in which the animal lives.

[2447] A "cloud database" is a data storage area located on a remote server accessible via the Internet.

[2448] "Real-time analysis" means analyzing data immediately after it is sent from the sensor and detecting any abnormalities.

[2449] An "abnormality" is an abnormal situation that is detected when the vital data of an animal exceeds a preset threshold.

[2450] An "alert notification" is a message that notifies the user of a detected abnormality.

[2451] The "emotional state of the user" refers to the psychological state analyzed based on information obtained from the user's facial expressions and voice.

[2452] The "means for generating a message" is a function of the system that generates a message according to the emotional state of the user when an abnormality is detected.

[2453] "User interface" refers to the display screen and operation panel that allow the user to interact with the system.

[2454] "Means for generating reports in graph and chart format" refers to the system's functionality for compiling collected data into a format that is easy to understand visually.

[2455] "Animal health professional" refers to a health care worker with expertise in animal health care.

[2456] MODE FOR CARRYING OUT THE INVENTION

[2457] This invention is a system that monitors the health of animals in real time, detects abnormalities, and sends alerts that take into account the user's emotional state. This system is composed of sensors, a server, and a terminal (app), and the functions and roles of each part are explained in detail below.

[2458] Sensor installation and data measurement

[2459] User

[2460] The user attaches sensors to the animal, which are attached to the animal's collar or harness, and periodically (e.g., every minute) measure body temperature, heart rate, and environmental temperature and humidity.

[2461] Sending measurement data

[2462] sensor

[2463] The sensor sends the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor measures a body temperature of 38.5°C, humidity of 60%, and a heart rate of 120 bpm, and sends the data to the server every minute.

[2464] Receiving and storing data

[2465] server

[2466] The server receives the data sent from the sensors and checks the data for consistency. Specifically, it checks whether the data format is correct and whether there are any duplicates. Once the consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[2467] Data analysis and anomaly detection

[2468] server

[2469] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[2470] Generate and send alert notifications

[2471] server

[2472] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[2473] Device (app)

[2474] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[2475] Data Visualization and Reporting

[2476] server

[2477] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[2478] Device (app)

[2479] The terminal receives the report and displays it in a visually friendly format on the user interface.

[2480] Collaboration with veterinarians

[2481] User

[2482] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[2483] server

[2484] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[2485] Interface using emotion engine

[2486] Device (app)

[2487] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[2488] Specific examples

[2489] Example 1: Abnormal body temperature detection and alert notification

[2490] 1. Sensor

[2491] The dog's temperature is measured at 39.5°C.

[2492] 2. Server

[2493] Body temperature data is received and determined to be "abnormal body temperature."

[2494] An alert message stating "The body temperature is 39.5°C. Please contact a veterinarian immediately" is generated and sent to the user's device.

[2495] 3. Device (app)

[2496] Receives alert notifications and notifies the user with sound and vibration. Displays the alert content on the user interface.

[2497] Example 2: Recognizing user emotions

[2498] 1. Users

[2499] When a user checks an alert notification, the emotion engine recognizes facial expressions that indicate anxiety.

[2500] 2. Device (app)

[2501] The emotion engine analyzes the user's anxiety and generates and displays a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, a remote consultation request with a veterinary professional is automatically generated based on the user's anxiety.

[2502] Example input to a generative AI model

[2503] Example prompt

[2504] "Generate an alert message when an animal's temperature is abnormally high. Also, provide an example of how to convert the message to a more friendly one if the user is feeling anxious."

[2505] In this way, the system of the present invention monitors the health status of animals in real time, responds quickly to abnormalities, and provides alert notifications that take the user's emotions into consideration, allowing the user to take appropriate action.

[2506] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2507] Step 1: Sensor installation and data collection

[2508] User

[2509] The user attaches sensors to the animal, which are attached to the animal's collar or harness and measure body temperature, heart rate, and environmental temperature and humidity every minute. Specifically, the built-in sensors continuously monitor the animal's body and surrounding environment and collect measurement data.

[2510] Input: Animal body temperature, heart rate, environmental temperature and humidity

[2511] Output: Measured vital and environmental data

[2512] Step 2: Sending measurement data

[2513] sensor

[2514] The sensor periodically transmits the measured data to the server via Bluetooth or Wi-Fi. For example, the sensor transmits data such as body temperature (38.5°C), humidity (60%), and heart rate (120 bpm) every minute.

[2515] Input: Measured vital and environmental data

[2516] Output: Data sent to the server

[2517] Step 3: Receiving and storing data

[2518] server

[2519] The server receives the data sent from the sensors and checks the data for consistency, ensuring that the data format is correct and that there are no duplicates. Once consistency is confirmed, the data is stored in a cloud database and a timestamp is attached to each piece of data.

[2520] Input: Data sent from the sensor

[2521] Output: Data that has been checked for integrity and saved

[2522] Step 4: Data analysis and anomaly detection

[2523] server

[2524] The server analyzes the received data in real time and detects an abnormality if it exceeds a set threshold (e.g., body temperature of 39°C or higher). If an abnormality is detected, detailed information about it is recorded.

[2525] Input: Received data

[2526] Output: Anomaly detected and recorded information

[2527] Step 5: Generate and send alert notifications

[2528] server

[2529] If an abnormality is detected, the server generates a specific alert message, such as "Temperature is 39.5°C. Contact your veterinarian immediately." This message is sent to the user's device.

[2530] Input: Anomaly detection information

[2531] Output: The generated alert message

[2532] Device (app)

[2533] The device notifies the user of the received alert notification by sound or vibration, and also displays the alert content on the user interface.

[2534] Input: The alert message sent

[2535] Output: The alert content that is notified and displayed to the user

[2536] Step 6: Visualize the data and generate reports

[2537] server

[2538] The server aggregates data over a period of time (e.g., one month) and generates a report in the form of graphs and charts. For example, a graph showing the temperature trends of animals over a month can be created. The report is then processed so that it is easy for users to understand.

[2539] Input: Data for a period of time

[2540] Output: Generated report

[2541] Device (app)

[2542] The terminal receives the report and displays it in a visually friendly format on the user interface.

[2543] Input: Generated report

[2544] Output: The displayed report content

[2545] Step 7: Work with your veterinarian

[2546] User

[2547] Users can send a request for a remote consultation to a veterinary specialist through the app, for example, "My dog ​​has a high temperature, so I'd like to consult with you."

[2548] Input: Content of consultation request

[2549] Output: Request sent

[2550] server

[2551] The server forwards the user's request to the veterinary professional and provides vital and environmental data, receives the response from the veterinary professional, and forwards it to the user's device.

[2552] Input: The submitted request and associated data

[2553] Output: Response from veterinary medical professional

[2554] Step 8: Interface using emotion engine

[2555] Device (app)

[2556] The emotion engine installed in the device analyzes emotional data from the user's facial expressions and voice to determine the user's emotional state. For example, if the user is feeling anxious, the emotion engine generates a friendly message such as, "Don't worry. We recommend that you contact your veterinarian immediately." If necessary, it also automatically generates a remote consultation request with a veterinary specialist.

[2557] Input: User's facial expressions and voice

[2558] Output: Generated friendly message and remote consultation request

[2559] The above are the specific processing steps of the program of this system.

[2560] (Application example 2)

[2561] 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."

[2562] In modern food delivery services, quality control of ingredients during transportation is important. However, environmental factors such as temperature, humidity, and vibration can have a negative impact on the quality of ingredients, making real-time monitoring difficult. Another challenge is providing appropriate notification content that takes into account the emotions and stress of drivers and customers when receiving alerts.

[2563] 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 collecting information data of an object and surrounding environment data using sensors, means for receiving data transmitted from the sensors and storing the data in a cloud storage device, means for analyzing the data and detecting abnormalities, means for generating an alert notification when an abnormality is detected, means for providing the alert notification to the user, and means for analyzing the user's emotions and customizing the content or display method of the alert notification based on the emotions. This makes it possible to monitor the quality control of ingredients in real time and to provide appropriate alert notifications that take the user's emotions into consideration when an abnormality occurs.

[2564] A "sensor" is a device that measures physical data and outputs that data in electronic form.

[2565] "Object" refers to the object or environment from which data is collected or monitored.

[2566] "Information data" refers to data that contains specific information about an object.

[2567] "Ambient environment data" refers to data relating to environmental factors such as temperature, humidity, and vibrations present around an object.

[2568] "Cloud storage" is an online storage service for storing and managing data over the Internet.

[2569] "Abnormal" refers to a data condition that exceeds a pre-established standard or threshold.

[2570] An "alert notification" is a message that warns or alerts the user when an abnormality is detected.

[2571] "Means for analyzing emotions" refers to technologies and devices for detecting and interpreting emotions from a user's facial expressions, voice, etc.

[2572] "Means of customization" refers to methods for adjusting the system's behavior and display content according to specific conditions or situations.

[2573] An "expert" is someone who has advanced knowledge or skills in a particular field.

[2574] The present invention is a system that uses sensors to collect information data on objects and their surrounding environments, stores the data in a cloud storage device, analyzes it in real time, and detects abnormalities. When an abnormality is detected, the system generates an alert notification and provides it to the user. The system also includes a function for analyzing the user's emotions and customizing the content or display method of the alert notification based on the user's emotions.

[2575] Sensors and Data Collection

[2576] The user uses sensors installed on food items or their surroundings. These sensors collect data such as temperature, humidity, and vibration, and periodically measure and transmit the data to a smartphone or delivery robot. This data is transmitted via wireless communication such as Bluetooth or Wi-Fi.

[2577] Data reception and storage

[2578] The server receives data sent from the sensors via smartphones or delivery robots. The received data is stored in a cloud storage device, and a timestamp is assigned to each piece of data. Common online storage services (e.g., AWS, Azure) are used as the cloud storage device.

[2579] Data analysis and anomaly detection

[2580] The server analyzes the data stored in the cloud storage device in real time and detects abnormalities when set thresholds are exceeded, such as when the temperature exceeds 35°C or vibration exceeds a set value. This allows for quick action to be taken before the quality of the ingredients deteriorates.

[2581] Alert Notifications

[2582] If an abnormality is detected, the server generates an alert message. This alert message is sent to the user's smartphone or delivery robot, and the user is notified by sound or vibration. The alert message contains specific information about the abnormality and recommended actions. For example, it could say, "The temperature has reached 35.5°C. Please take immediate action."

[2583] Sentiment analysis and interface customization

[2584] When a user receives an alert notification, an emotion analysis engine on the smartphone or delivery robot assists them. The emotion analysis engine analyzes emotional data from the user's facial expressions and voice, and if the user is feeling stressed, it converts it into a friendly message. Specifically, it might say something like, "Don't worry, there's no problem. Please take immediate action." This method can reduce the user's stress.

[2585] Examples and prompts

[2586] Specific examples

[2587] While the ingredients are being transported, a sensor measures the CPU temperature at 35.5°C. The emotion analysis engine recognizes the delivery driver's anxiety and displays an alert message saying, "Don't worry, we'll take action immediately."

[2588] Prompt Sentence Examples

[2589] Analyze the following sentence, determine the user's sentiment, and generate an appropriate response message:

[2590] Alert message: Temperature reached 35.5°C during transport.

[2591] User Emotion: Anxiety

[2592] An appropriate response message would be: "Don't worry, we'll fix it right away."

[2593] In this way, the present invention controls the quality of ingredients in real time and provides alert notifications that take into consideration the user's emotions when an abnormality occurs, enabling a quick and appropriate response.

[2594] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2595] Step 1:

[2596] Sensors periodically acquire information data such as temperature, humidity, and vibration, as well as data on the surrounding environment. The input is physical data from the object and the surrounding environment, and the output is electronic measurement data.

[2597] Step 2:

[2598] The terminal (smartphone or delivery robot) receives the data sent from the sensor via wireless communication (Bluetooth or Wi-Fi). The input is the measurement data from the sensor, and the output is the data stored in the terminal.

[2599] Step 3:

[2600] The data received by the device is sent to the cloud storage device. The input is the data stored on the device, and the output is the data stored in the cloud storage device. The cloud storage device is assigned a timestamp and the integrity of the data is verified.

[2601] Step 4:

[2602] The server analyzes the data stored in the cloud storage device in real time, compares it with the set threshold, and detects anomalies. The input is the data stored in the cloud storage device, and the output is the result of the anomaly detection.

[2603] Step 5:

[2604] If an anomaly is detected, the server generates an alert notification, which contains specific information about the anomaly and a recommended action. The input is the anomaly detection result, and the output is the alert message.

[2605] Step 6:

[2606] An alert notification is sent to the user's device via a smartphone or delivery robot. The input is the generated alert message, and the output is the alert notification displayed on the device. The notification is also conveyed to the user by sound or vibration.

[2607] Step 7:

[2608] The device's emotion analysis engine analyzes the user's facial expressions and voice to obtain emotional data. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[2609] Step 8:

[2610] The server customizes the content or display method of the alert notification based on the analyzed emotion data. The input is the analyzed emotion data and the existing alert message, and the output is the customized alert message.

[2611] Step 9:

[2612] The customized alert message is then displayed again on the user's smartphone or the delivery robot's device. This provides an alert notification that takes into account the user's emotions. The input is the customized alert message, and the output is the final displayed alert notification.

[2613] 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.

[2614] 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.

[2615] 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.

[2616] 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.

[2617] 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.

[2618] 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.

[2619] 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).

[2620] 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.

[2621] 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 kn...

Claims

1. means for collecting vital and environmental data of the animal using sensors; means for receiving data transmitted from the sensors and storing the data in a cloud database; means for analyzing the data and detecting anomalies; means for generating an alert notification when an anomaly is detected; a means for providing an alert notification to a user; A system including:

2. a means for visualizing vital and environmental data; a means for periodically aggregating data and generating reports; means for providing the report to a user; The system of claim 1 further comprising:

3. means for accepting a remote consultation request from a user; means for sharing information with a veterinarian based on said request; means for providing the veterinarian's response to the user; The system of claim 1 further comprising:

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