System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting behavior information of a pet using a wearable device attached to the pet; means for transmitting the behavior information collected from the wearable device to a server; means for analyzing the behavior information using a generative AI model and generating an advice on a health condition of the pet in the server; and means for notifying a user device of the generated advice.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] To maintain the health of pets, it is important to accurately collect and analyze daily behavioral data. However, there is currently a lack of efficient systems for early detection of abnormalities in pets and the implementation of appropriate measures. Many pet owners rely on veterinarians or their own experience to understand their pet's health, but as a result, early-stage abnormal symptoms are often overlooked. Therefore, there is a need for a system that collects and analyzes pet behavioral data in real time and provides users with specific advice and alerts. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means. A wearable device attached to a pet is used to collect pet behavior data and send it to a server. The server then analyzes the behavior data using a generative AI model to generate specific advice about the pet's health condition and alerts in the event of an abnormality. The generated advice and alerts are sent to the user's device, allowing the user to manage the pet's health based on this information. This system allows for real-time monitoring of the pet's health condition, enabling early detection and early action.

[0006] A "wearable device" is a device that is attached to a pet and collects data on the pet's behavior.

[0007] "Behavioral data" refers to data such as the amount of exercise, rest time, heart rate, and location information of a pet.

[0008] A "server" is a main computing device that receives and analyzes data sent from a wearable device.

[0009] A "generative AI model" is an artificial intelligence system that analyzes and infers from input data, providing advice on pet health and detecting abnormalities.

[0010] "Analysis" is the process of evaluating collected behavioral data and extracting information relevant to the pet's health.

[0011] "Advice" refers to specific instructions or suggestions regarding pet health management based on the analysis results.

[0012] An "alert" is a warning message that notifies the user when an abnormality is detected in the pet's behavior data.

[0013] "User" refers to a pet owner who uses the pet care app to manage their pet's health.

[0014] A "user terminal" is a device used by a user, such as a smartphone or PC, that receives notifications. [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] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0037] Overall structure

[0038] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0039] Data collection

[0040] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0041] Data transmission

[0042] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0043] Data reception and analysis

[0044] Server: The server receives the data sent from the wearable device and stores it in a database, while preprocessing the data by filtering noise and imputing missing values.

[0045] Data Analysis and Inference

[0046] Server: The server analyzes the data using a generative AI model that assesses the amount of exercise, rest, and feeding required based on the pet's behavioral data, and also detects abnormalities in heart rate and activity patterns.

[0047] Based on the analysis results, the app generates specific advice about your pet's health. For example, if your pet isn't getting enough exercise, it might say, "Today's exercise volume hasn't reached your goal. We recommend a 30-minute walk." If your pet's heart rate is above the normal range, it might generate an alert, saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0048] User Notifications

[0049] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails.

[0050] Device: The user device will notify the user of the received notification visually or audibly. The user can then check the notification content through the application and take appropriate action.

[0051] Specific examples

[0052] Example 1: Notification of lack of exercise

[0053] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0054] 2. Terminal: Sends collected data to the server at regular intervals.

[0055] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0056] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend taking a 30-minute walk."

[0057] 5. Server: Sends advice to the user device.

[0058] 6. Terminal: The user terminal receives the notification and notifies the user.

[0059] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0060] Example 2: Detecting abnormal heart rates

[0061] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0062] 2. Terminal: Sends abnormal data to the server.

[0063] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0064] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0065] 5. Server: Sends the alert to the user device.

[0066] 6. Terminal: The user terminal receives the notification and notifies the user.

[0067] 7. User: Check the notification and consult a veterinarian immediately.

[0068] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[0072] Step 2:

[0073] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[0074] Step 3:

[0075] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[0076] Step 4:

[0077] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[0078] Step 5:

[0079] Server: The server temporarily accumulates the data received from the terminal and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processing as necessary.

[0080] Step 6:

[0081] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc., and infers the pet's health condition.

[0082] Step 7:

[0083] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0084] Step 8:

[0085] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0086] Step 9:

[0087] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0088] Step 10:

[0089] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[0090] This is the specific processing flow of the generative AI model that links a pet care app with a smart wearable device, making it possible to monitor the health of pets in real time and provide appropriate care.

[0091] Example 1

[0092] 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."

[0093] Conventional pet health management systems lack the ability to collect and analyze data in real time, making it difficult to accurately assess health conditions or detect abnormalities. Furthermore, the means of notifying users were limited, making it difficult to respond quickly in emergencies. This can delay the timing of taking appropriate action in pet health management, potentially increasing health risks.

[0094] 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.

[0095] In this invention, the server includes means for performing noise filtering and defect completion on pet behavior data, means for detecting anomalies and generating alerts based on a generative AI model, and means for notifying a user terminal of the generated advice and alerts, thereby enabling highly accurate data analysis in real time and rapid anomaly detection, allowing the user to take appropriate action in a timely manner.

[0096] A "wearable device" is a device that is attached to a pet to collect behavioral data such as the amount of exercise, rest time, heart rate, and location information.

[0097] The "server" is a computer system that receives pet behavior data, stores it in a database, and analyzes it using a generative AI model.

[0098] A "generative AI model" is a machine learning model used to analyze pet behavior data and generate health advice and abnormality alerts.

[0099] "Behavioral data" refers to data related to a pet's activities, including the amount of exercise, rest time, heart rate, and location information of the pet.

[0100] "Noise filtering" is a process that removes outliers from collected data and improves the accuracy of the data.

[0101] "Gap filling" is a process of filling in missing parts of collected data with appropriate values.

[0102] "Advice" is information that provides recommended actions to improve your pet's health based on data analyzed using a generative AI model.

[0103] An "alert" is a warning message that quickly notifies you of health abnormalities based on data analyzed using a generative AI model.

[0104] "User terminal" refers to a device for notifying a user of received advice and alerts, such as a smartphone or tablet.

[0105] "Push notification" is a communication method in which a server sends information to a user terminal in real time.

[0106] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0107] Overall structure

[0108] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0109] Data collection

[0110] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0111] Data transmission

[0112] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0113] Data reception and preprocessing

[0114] Server: The server receives the data sent from the wearable device and stores it in a database. At the same time, it preprocesses the data, performs noise filtering, and imputes missing values. For example, MySQL is used as the database system.

[0115] Data Analysis and Inference

[0116] Server: The server analyzes the data using a generative AI model. This generative AI model evaluates the amount of exercise, rest time, and whether the pet is over or underfed based on the pet's behavioral data. It also has the ability to detect abnormalities in heart rate and activity patterns. Based on the analysis results, the server generates specific advice about the pet's health. For example, if the pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0117] User Notifications

[0118] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails. For example, Firebase Cloud Messaging (FCM) is used.

[0119] Device: The user device will notify the user of the received notification visually or audibly. The user can check the notification content through the application and take appropriate action. Specifically, a message will be displayed in the smartphone's notification bar and details will be displayed on the application's notification screen. In the case of an audio notification, the smartphone speaker will read out, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0120] Specific examples

[0121] Example 1: Notification of lack of exercise

[0122] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0123] 2. Terminal: Sends collected data to the server at regular intervals.

[0124] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0125] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend that you take another 3,000 steps."

[0126] 5. Server: Sends advice to the user device.

[0127] 6. Device: The user device receives the notification and displays it in the notification bar.

[0128] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0129] Example 2: Detecting abnormal heart rates

[0130] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0131] 2. Terminal: Sends abnormal data to the server.

[0132] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0133] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0134] 5. Server: Sends the alert to the user device.

[0135] 6. Terminal: The user terminal receives the notification and gives an audio notification.

[0136] 7. User: Check the notification and consult a veterinarian immediately.

[0137] Prompt Sentence Examples

[0138] "How do you use a generative AI model to generate notifications if your pet is not getting enough exercise?"

[0139] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] Terminal: The wearable device uses sensors to collect information on the pet's activity, rest time, heart rate, and location. Input data are various measurements taken by the sensors, such as heart rate of 80 BPM, steps taken of 5,000, and rest time of 5 hours, and location information such as latitude: 35.6895, longitude: 139.6917. Output data are these behavioral data. The wearable device records data in real time and accumulates it in a buffer at regular intervals (e.g., every 5 minutes).

[0143] Step 2:

[0144] Terminal: Sends collected behavioral data to a server via the Internet. Specifically, the collected data (e.g., heart rate, number of steps, rest time, location information) is packaged into packets and sent to the server via Internet Protocol (IP). The input data is the accumulated behavioral data, and the output data is the data packets sent to the server.

[0145] Step 3:

[0146] Server: Receives the transmitted data. Specifically, the server receives data packets through a network interface and stores them in a database system (e.g., MySQL). The input data is the received behavioral data, and the output data is the behavioral data stored in the database.

[0147] Step 4:

[0148] Server: Preprocesses the behavioral data. Specifically, it performs noise filtering (removing outliers) and missing value imputation (e.g., imputing estimated values ​​based on past data). The input data is the behavioral data stored in the database, and the output data is the clean data after preprocessing.

[0149] Step 5:

[0150] Server: Analyzes the preprocessed data using a generative AI model. Specifically, clean data is input into a generative AI model (e.g., built with TensorFlow) to evaluate the pet's exercise, rest time, and dietary needs, and to detect abnormalities in heart rate and activity patterns. The input data is the preprocessed clean data, and the output data is the analysis results (e.g., advice or alerts).

[0151] Step 6:

[0152] Server: Based on the analysis results, it generates specific advice and abnormality alerts regarding the pet's health condition. For example, if the pet is not getting enough exercise, it generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is outside the normal range, it generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the analysis result of the generative AI model, and the output data is the generated advice and alerts.

[0153] Step 7:

[0154] Server: Notifies the user device of the generated advice and alerts. Specifically, information is sent to the user's smartphone or tablet using push notifications or email. For example, Firebase Cloud Messaging (FCM) is used. The input data is the generated advice and alerts, and the output data is the notified message.

[0155] Step 8:

[0156] Device: The user device notifies the user of the received notification visually or audibly. Specifically, it displays a message in the notification bar and displays details on the application's notification screen. In the case of an audio notification, it reads out through the smartphone speaker, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the received notification message, and the output data is the notification presented to the user.

[0157] Step 9:

[0158] User: The user checks the notification and takes appropriate action. For example, after checking the notification, they take their pet for a walk to increase their exercise. In case of an abnormal alert, they consult a veterinarian immediately. The input data is the notification content, and the output is the action taken by the user.

[0159] This is the specific processing flow of this system. It monitors the health condition of pets in real time and can take necessary measures promptly.

[0160] (Application example 1)

[0161] 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."

[0162] Real-time monitoring of pet health and behavior patterns is important for ensuring pet safety and maintaining their health. However, conventional systems have difficulty quickly and accurately detecting abnormalities in pet location information or behavior patterns and effectively notifying users. Furthermore, the lack of functionality to prompt users to take immediate action against abnormal behavior or health risks makes it difficult to adequately protect pet safety and health.

[0163] 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.

[0164] In this invention, the server includes means for analyzing the behavioral data using a generative AI model to generate advice and alerts regarding the pet's health condition and behavioral patterns, means for notifying the user terminal of the generated advice and alerts, and means for detecting abnormal movement patterns and behavioral patterns, thereby making it possible to prompt the user to check the safety of their pet and take security measures, and to notify the user of alerts quickly and accurately.

[0165] A "wearable device" is a device that is attached to a pet and uses various sensors to collect behavioral data and biometric information about the pet.

[0166] "Behavioral data" refers to data including biological and behavioral information such as the pet's heart rate, amount of exercise, and location information.

[0167] A "generative AI model" is a model based on artificial intelligence algorithms that analyzes collected data to evaluate behavioral patterns and health conditions.

[0168] A "server" is a computer system for receiving and analyzing data sent from a wearable device.

[0169] "Advice" refers to instructions or suggestions provided to maintain or improve your pet's health based on data analyzed by the generative AI model.

[0170] An "alert" is a notification that warns or warns the user when an abnormality is detected by the generative AI model.

[0171] A "user terminal" is a communication terminal that allows pet owners to receive and check advice and alerts, and is a device such as a smartphone or tablet.

[0172] "Abnormal movement patterns" refers to sudden or suspicious movements of pets that deviate from their normal behavior.

[0173] "Crime prevention measures" are measures that users take to ensure the safety of their pets, so that they do not get lost or are the victim of theft.

[0174] This invention is a system consisting of a wearable device attached to a pet, a server, and a user terminal. This system monitors the pet's health condition and behavioral patterns in real time and provides appropriate advice and alerts to the user to maintain the pet's safety and health.

[0175] Overall structure

[0176] The system includes the following main components:

[0177] 1. Wearable devices: These are worn by pets and collect behavioral data such as heart rate, activity, and location.

[0178] 2. Server: Receives behavioral data sent from the wearable device and analyzes it using generative AI models, generating advice and alerts on pet health and behavioral patterns.

[0179] 3. User terminal: Receives advice and alerts sent from the server and notifies the user.

[0180] Data collection

[0181] The wearable device is equipped with various sensors that collect data such as your pet's heart rate, activity level, and location in real time, and this data is sent to a server at regular intervals.

[0182] Data analysis

[0183] The server preprocesses the received behavioral data, filtering out noise and filling in missing values. It then uses a generative AI model to analyze the data and evaluate the pet's health and behavioral patterns. For example, if your pet is not getting enough exercise, it generates advice such as, "Today's exercise volume is not meeting your goal. We recommend a 30-minute walk." If the pet's heart rate is above the normal range, it generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0184] User Notifications

[0185] The generated advice and alerts are sent from the server to the user's device, which then notifies the user visually or audibly of the received notification. By checking this, the user can take appropriate action regarding their pet's health condition and behavioral patterns.

[0186] Hardware and software used

[0187] The system uses the following specific hardware and software:

[0188] Hardware: Wearable devices (e.g., FidoTrack), smartphones, tablets

[0189] Software: Python, requests library, NumPy library

[0190] Specific examples

[0191] Example 1: Notification of lack of exercise

[0192] The wearable device detects that the pet's daily step count has not reached the target value and sends the data to the server. The server analyzes the data and generates advice such as "Today's exercise volume has not reached the target. We recommend a 30-minute walk," and notifies the user's device.

[0193] Example 2: Detecting abnormal heart rates

[0194] The wearable device detects that the pet's heart rate is out of the normal range and sends the data to the server, which analyzes the data and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0195] Prompt Sentence Examples

[0196] "Use FidoTrack devices to collect your pet's behavioral data every five minutes, analyze and notify you of abnormal behavioral patterns and health conditions, and detect abnormalities in heart rate and location."

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] The wearable device collects your pet's heart rate, activity level, and location information. The input is data from various sensors, and the output is collected behavioral data. This allows you to understand your pet's real-time condition.

[0200] Step 2:

[0201] The wearable device sends the data it collects to a server at regular intervals. The input is behavioral data stored inside the wearable device, and the output is data sent to the server via the Internet. This allows the collected data to reach the server.

[0202] Step 3:

[0203] The server receives the data sent from the wearable device. The input is the behavioral data sent from the wearable device, and the output is the data stored in the server's internal database. This makes the data available for analysis.

[0204] Step 4:

[0205] The server preprocesses the data it receives, specifically by filtering noise and imputing missing values. The input is raw data stored on the server, and the output is preprocessed, clean data. This improves the quality of the data.

[0206] Step 5:

[0207] The server analyzes the data using the generative AI model. The input is pre-processed behavioral data, and the output is an assessment of the pet's health and behavioral patterns. Specifically, it evaluates the amount of exercise, rest time, and dietary information, and detects abnormal heart rate and movement patterns. This allows it to generate advice and alerts.

[0208] Step 6:

[0209] The server generates advice and alerts based on the analysis results. The input is the analysis results of the generative AI model, and the output is a specific advice or alert message. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." This provides the user with information to take appropriate action.

[0210] Step 7:

[0211] The server sends the generated advice and alerts to the user's terminal. The input is the advice and alert message, and the output is a notification sent to the user's terminal via the Internet. This allows the user to quickly understand the status of their pet.

[0212] Step 8:

[0213] The user device receives notifications and notifies them visually or audibly. The input is the notification sent from the server, and the output is the actual notification display or audio alert for the user. This allows the user to instantly know the health status of their pet or any abnormal behavior.

[0214] Step 9:

[0215] The user checks the notification content and takes appropriate action. The input is the notification content displayed on the user's device, and the output is the user's specific action (e.g., taking the pet for a walk, consulting a veterinarian). This ensures the health and safety of the pet.

[0216] 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.

[0217] The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's condition into consideration when managing the health of a pet.

[0218] Overall structure

[0219] This system consists of a wearable device attached to the pet, a server, a user terminal, and an emotion engine. The pet's behavioral data is collected from the wearable device, and emotion data is collected from the emotion engine. Both sets of data are sent to the server and analyzed by a generative AI model. Advice and alerts generated based on the analysis results are sent to the user terminal.

[0220] Data collection

[0221] Device: The wearable device collects real-time behavioral data such as exercise volume, rest time, heart rate, and location information through various sensors (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor) attached to the pet. The data is sent to a server at regular intervals.

[0222] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data.

[0223] Data transmission

[0224] Terminal: The wearable device and emotion engine each send collected behavioral data and emotion data to a server via the Internet. The data is encrypted to ensure security.

[0225] Data reception and analysis

[0226] Server: The server receives data sent from the wearable device and emotion engine and stores it in a database. The received data is first preprocessed to check for duplicates and missing values ​​and to filter noise.

[0227] Data Analysis and Inference

[0228] Server: The server analyzes the data using a generative AI model. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc. to infer the pet's health condition. It also integrates the user's emotional data for analysis. For example, if the user is feeling stressed, the server takes that information into account and adjusts the advice on pet care.

[0229] Generate advice alerts

[0230] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[0231] User Notifications

[0232] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0233] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0234] Specific examples

[0235] Example 1: Notification of lack of exercise

[0236] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0237] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[0238] 3. Terminal: Sends collected data to the server at regular intervals.

[0239] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0240] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[0241] 6. Server: Sends advice to the user terminal.

[0242] 7. Terminal: The user terminal receives the notification and notifies the user.

[0243] 8. User: Checks notification and takes pet for a walk.

[0244] Example 2: Detecting abnormal heart rates

[0245] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0246] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[0247] 3. Terminal: Sends abnormal data and emotion data to the server.

[0248] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0249] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0250] 6. Server: Sends the alert to the user device.

[0251] 7. Terminal: The user terminal receives the notification and notifies the user.

[0252] 8. User: Check the notification and consult a veterinarian immediately.

[0253] The above is an embodiment of the present invention, which makes it possible to provide appropriate care that takes into consideration not only the health condition of a pet but also the emotional state of the user.

[0254] The processing flow will be explained below.

[0255] Step 1:

[0256] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[0257] Step 2:

[0258] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[0259] Step 3:

[0260] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[0261] Step 4:

[0262] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[0263] Step 5:

[0264] Device: Using the camera and microphone on the user device, the emotion engine analyzes the user's facial expressions and voice to collect the user's emotional state (e.g., joy, stress, excitement, etc.) in real time.

[0265] Step 6:

[0266] Terminal: Collected user emotion data is sent to the server and integrated with behavioral data.

[0267] Step 7:

[0268] Server: Organizes the behavioral and emotional data received from the devices and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processes as necessary.

[0269] Step 8:

[0270] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, and dietary data to infer the pet's health condition. It also integrates the user's emotional data for analysis.

[0271] Step 9:

[0272] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume has not reached the goal. We recommend a 30-minute walk when the user is relaxed." or "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0273] Step 10:

[0274] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0275] Step 11:

[0276] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0277] Step 12:

[0278] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[0279] The above is a concrete processing flow of combining a pet care system with an emotion engine that recognizes the user's emotions. This configuration makes it possible to provide appropriate care that takes into account not only the pet's health condition but also the user's emotional state.

[0280] Example 2

[0281] 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."

[0282] Conventional pet care systems primarily monitor only the health of pets and do not consider the emotional state of the user. As a result, they are unable to provide appropriate pet care tailored to the user's emotional state, making it difficult to optimally manage the health of both the pet and the user. Another issue is that pet care advice is not appropriately adjusted when the user is stressed or fatigued.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0284] In this invention, the server includes means for collecting pet behavioral data using a wearable device attached to the pet, means for transmitting the collected data from the wearable device to the server, means for collecting user emotion data using an emotion engine installed in the user terminal, means for transmitting the user emotion data to the server, means for analyzing the behavioral data and emotion data using a generative AI model and generating advice based on the pet's health condition and the user's emotional state, and means for notifying the user terminal of the generated advice, thereby enabling more personalized pet care that takes into account not only the pet's health condition but also the user's emotional state.

[0285] "Pet" refers to an animal kept by an owner in a household.

[0286] A "wearable device" refers to an electronic device that is attached to a pet to collect behavioral and biological data.

[0287] "Behavioral data" refers to information about your pet's activities, such as its amount of exercise, heart rate, rest time, and location.

[0288] "User terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[0289] An "emotion engine" refers to software and hardware that analyzes a user's facial expressions and voice and estimates their emotional state.

[0290] "Emotion data" is information about the user's emotional state, such as relaxation, stress, fatigue, etc.

[0291] "Server" refers to a remote computer system for receiving, storing, and analyzing data over a network.

[0292] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates insights and advice.

[0293] "Advice" refers to recommended actions or instructions regarding pet care.

[0294] "Notification" refers to a message or alert that conveys information to a user terminal.

[0295] "Data transmission" refers to the act of sending collected data to other systems via the Internet.

[0296] "Data reception" refers to the act of receiving data sent from another system.

[0297] "Data preprocessing" refers to the process of checking for and organizing data duplication and noise as a preliminary step to analysis.

[0298] "Inference" refers to the act of drawing predictions or conclusions based on the results of data analysis.

[0299] "Push notification" refers to a communication method that instantly displays information on a user's device.

[0300] MODE FOR CARRYING OUT THE INVENTION

[0301] The following describes the embodiments of the present invention. The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's emotional state into consideration when managing the health of a pet. This system consists of the following main hardware and software components:

[0302] 1. Wearable devices attached to pets

[0303] 2. Server

[0304] 3. User Device

[0305] 4. Emotion Engine

[0306] Data collection

[0307] Device: The wearable device uses sensors (e.g., step count sensor, acceleration sensor, heart rate sensor, GPS sensor) to collect data on the pet's behavior. Data such as exercise volume, rest time, heart rate, and location information is collected in real time and stored in the internal memory at regular intervals.

[0308] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data. The emotion engine estimates the user's current emotional state (relaxed, stressed, tired, etc.) and temporarily stores that data.

[0309] Data transmission

[0310] Terminal: The wearable device and user terminal each encrypt the collected behavioral and emotional data and send it to the server via the Internet. A security protocol is used for data transmission to ensure data confidentiality.

[0311] Data reception and preprocessing

[0312] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is first preprocessed to remove duplicate data, filter noise, and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis.

[0313] Data Analysis and Inference

[0314] Server: The server analyzes the behavioral and emotional data using a generative AI model. The generative AI model evaluates the pet's exercise, rest time, heart rate, and dietary data to infer the pet's health condition. It also integrates and analyzes the user's emotional data to generate appropriate pet care advice. For example, advice based on the user's emotions is provided, such as recommending taking the pet for a walk when the user is relaxed.

[0315] Generate advice alerts

[0316] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the target. We recommend a 30-minute walk when the user is relaxed." If a pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[0317] User Notifications

[0318] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0319] Terminal: The user terminal receives the notification sent from the server and notifies the user visually or audibly. The user can view the notification details through the application and take necessary actions.

[0320] Specific examples

[0321] Example 1: Notification of lack of exercise

[0322] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0323] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[0324] 3. Terminal: Sends collected data to the server at regular intervals.

[0325] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0326] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[0327] 6. Server: Sends advice to the user terminal.

[0328] 7. Terminal: The user terminal receives the notification and notifies the user.

[0329] 8. User: Checks notification and takes pet for a walk.

[0330] Example 2: Detecting abnormal heart rates

[0331] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0332] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[0333] 3. Terminal: Sends abnormal data and emotion data to the server.

[0334] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0335] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0336] 6. Server: Sends the alert to the user device.

[0337] 7. Terminal: The user terminal receives the notification and notifies the user.

[0338] 8. User: Check the notification and consult a veterinarian immediately.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Step 1:

[0341] Data collection using wearable devices

[0342] Terminal: The wearable device uses a step sensor, accelerometer, heart rate sensor, and GPS sensor to obtain real-time information on the pet's activity, rest time, heart rate, and location. The device stores this data in its internal memory at regular intervals. The input data is raw data sent from the sensors, and the output data is processed behavioral data. For example, it records the number of steps a pet takes in a day and heart rate fluctuations.

[0343] Step 2:

[0344] Emotion data collection using an emotion engine

[0345] Device: The emotion engine installed on the user device uses a camera and microphone to capture the user's facial expressions and voice. From this data, the user's emotional state (relaxed, stressed, tired, etc.) is estimated and temporarily stored. The input data is the raw data sent from the camera and microphone, and the output data is analyzed emotional data. For example, it determines whether the user is smiling, angry, or stressed.

[0346] Step 3:

[0347] Data transmission from wearable devices and user terminals

[0348] Terminal: The wearable device and user terminal send the collected behavioral and emotional data to a server via the Internet. The data is encrypted to maintain security. The input data is the behavioral and emotional data stored in the internal memory, and the output data is the data sent to the server. For example, the wearable device transfers the data to a smartphone via Bluetooth, and then sends it to the server via the Internet.

[0349] Step 4:

[0350] Data reception and preprocessing by the server

[0351] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is preprocessed to remove duplicate data, filter noise, and fill in missing data. The input data is the received behavioral and emotional data, and the output data is the preprocessed, clean data. For example, it removes noise from GPS data and fills in missing parts of heart rate data.

[0352] Step 5:

[0353] Data analysis and inference using generative AI models

[0354] Server: The server analyzes the preprocessed behavioral data and emotional data using a generative AI model. The generative AI model evaluates this data in a comprehensive manner, estimates the pet's health condition, and generates pet care advice based on this information, taking the user's emotional state into account. The input data are the preprocessed behavioral data and emotional data, and the output data are the analysis and inference results. For example, if a pet is not getting enough exercise, the server recommends taking it for a walk when the user is relaxed.

[0355] Step 6:

[0356] Generate advice alerts

[0357] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately." The input data are the analysis and inference results, and the output data are specific advice and alerts.

[0358] Step 7:

[0359] Sending notifications to user devices

[0360] Server: Sends the generated advice and alerts to the user's device. The input data is the advice and alert, and the output data is the data sent to the user's device. Notifications are sent via the method selected by the user, such as push notification or email.

[0361] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. Notification details are displayed through the application. For example, a push notification is displayed on the smartphone, allowing the user to check the advice and alert details.

[0362] This allows users to view notifications and implement pet care advice.

[0363] (Application example 2)

[0364] 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."

[0365] Conventional pet care systems manage pet health by only considering pet behavioral data, and are therefore unable to provide appropriate advice or alerts when the pet owner's emotional state affects pet care. Furthermore, there are limited means to provide customized services for pets and owners in real time. To solve these problems, a new system is needed that integrates and analyzes pet behavioral data and owner emotional data, and provides pet care based on the results.

[0366] 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 pet behavioral data using a wearable device attached to a pet, means for transmitting the collected data from the wearable device to the server, means for analyzing the behavioral data using a generative AI model in the server and generating advice on the pet's health condition, means for collecting user emotion data including an emotion engine that recognizes the user's emotions, means for integrating and analyzing the emotion data from the emotion engine and the pet behavioral data, means for adjusting pet care advice based on the emotion data, means for notifying the generated advice to a user terminal, means for displaying the generated advice on smart glasses, means for promptly notifying the user of an alert when the server detects an abnormality, and means for displaying the alert in real time on a terminal including the smart glasses. This makes it possible to integrate and analyze the pet behavioral data and the user's emotional state and provide customized advice and alerts in real time.

[0367] A "wearable device" is a device that is attached to a pet and collects behavioral data such as heart rate, exercise volume, and location information.

[0368] The "server" is a computer system that stores data collected from pets and users and analyzes it using a generative AI model.

[0369] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates advice and alerts based on the pet's health and the user's emotional state.

[0370] An "emotion engine" is a device that uses a camera and microphone to analyze a user's facial expressions, voice, and behavior to collect emotional data.

[0371] "Smart glasses" are glasses-type devices worn by users that display information such as advice and alerts.

[0372] A "user terminal" is a device such as a smartphone or tablet that a user uses to receive behavioral data and advice about their pet.

[0373] "Behavioral data" refers to data related to the pet's activities, such as the pet's heart rate, amount of exercise, and location information.

[0374] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions, voice, and behavior.

[0375] "Advice" is a recommendation provided based on the analysis results of the generative AI model, depending on the pet's health condition and the user's emotional state.

[0376] An "alert" is a warning message that is provided when an abnormality is detected in the pet's behavior data or the user's emotion data.

[0377] The system of the present invention includes a wearable device attached to a pet, an emotion engine that recognizes the user's emotion, a server, and a user terminal. Specific embodiments will be described below.

[0378] Overall structure

[0379] server

[0380] The server stores pet behavior data and user emotional data, and analyzes them using a generative AI model. Specifically, the server integrates data collected by the wearable device attached to the pet with the user's emotional data collected by the emotion engine to generate advice and alerts based on the pet's health and emotional state in real time.

[0381] Wearable devices

[0382] Wearable devices include heart rate sensors, acceleration sensors, and GPS sensors to obtain location information. Data from these devices is sent to a server at regular intervals using communication methods such as Bluetooth and WiFi.

[0383] Emotion Engine

[0384] The emotion engine is software installed on the user's smartphone or tablet, which uses the camera and microphone to analyze facial expressions, voice, and behavior to collect emotional data.

[0385] User terminal

[0386] User devices are used to notify users of advice and alerts, and include smartphones, tablets, smart glasses, etc. Notifications are sent via push notifications, emails, etc.

[0387] Data collection and transmission

[0388] The wearable device collects real-time information about the pet's heart rate, activity, and location. At the same time, the emotion engine collects the user's emotional state. This data is encrypted and transmitted to a server via the internet.

[0389] Data analysis and advice generation

[0390] The server temporarily stores the received data and performs preprocessing, including removing duplicate data, imputing missing values, and filtering noise. It then analyzes the data using a generative AI model and integrates the pet's health status and the user's emotional state to generate advice and alerts.

[0391] For example, if your pet isn't getting enough exercise, the app will generate advice like, "Today's exercise volume isn't meeting your goal. We recommend a 30-minute walk when you're relaxed." If your pet's heart rate is above the normal range, the app will generate an alert saying, "Your pet's heart rate is too high. If you're feeling stressed, consult a veterinarian immediately."

[0392] User Notifications

[0393] These advice and alerts are sent from the server to the user's device, which displays the notifications in real time, allowing the user to respond immediately.

[0394] Prompt Sentence Examples

[0395] "Hi, based on your pet's current exercise level, it looks like they need a little more activity. My suggestion is that you consider this automatic ball thrower."

[0396] This invention enables the integrated analysis of pet behavior data and the user's emotional state, and provides users with customized advice and alerts in real time, aiming to provide better care for pets and their owners.

[0397] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0398] Step 1:

[0399] The wearable device terminal collects behavioral data such as the pet's heart rate, activity level, and location in real time.

[0400] Input: Data from various pet sensors

[0401] Output: Behavioral data (heart rate, exercise amount, location information)

[0402] Specific operation: Data is acquired using the heart rate sensor, acceleration sensor, and GPS sensor and stored in internal storage.

[0403] Step 2:

[0404] The emotion engine analyzes the user's facial expressions, voice, and behavior to collect emotional data.

[0405] Input: User facial, voice, and behavioral data

[0406] Output: Emotion data

[0407] Specific operations: Captures the user's facial expressions with a camera, records audio with a microphone, recognizes emotions using a machine learning model, and creates data.

[0408] Step 3:

[0409] The data collected by the device is sent to the server at regular intervals.

[0410] Input: behavioral data, emotion data

[0411] Output: Send data to the server

[0412] Specific operation: Using Bluetooth or WiFi, the encrypted data is uploaded to a server via the Internet.

[0413] Step 4:

[0414] The server preprocesses the received data, which includes removing duplicates, imputing missing values, and filtering noise.

[0415] Input: behavioral data, emotion data

[0416] Output: Preprocessed data

[0417] Specific operation: Cleanses and normalizes the data stored in the database.

[0418] Step 5:

[0419] The server uses the generated AI model to analyze the preprocessed data and integrates the pet's health condition and the user's emotional state for analysis.

[0420] Input: Preprocessed data

[0421] Output: Analysis results (health status, emotional status)

[0422] Specific behavior: Applying a generative AI model to evaluate the pet's behavioral patterns and the user's emotional data.

[0423] Step 6:

[0424] The server generates advice and alerts based on the analysis results, such as advice and alerts about lack of exercise or abnormal heart rate.

[0425] Input: Analysis results

[0426] Output: Advice, Alert

[0427] Specific behavior: The output of the generative AI model is used to generate messages based on predefined rules.

[0428] Step 7:

[0429] The server transmits the generated advice or alert to the user terminal.

[0430] Input: Advice, Alert

[0431] Output: Notification to user terminal

[0432] Specific behavior: Send advice and alerts via push notifications or emails as specified by the user.

[0433] Step 8:

[0434] The smart glasses that serve as the terminal display advice and alerts to the user.

[0435] Input: Advice, Alert

[0436] Output: What is displayed to the user

[0437] Specific operation: Displays messages in real time on the smart glasses display, providing visual feedback to the user.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Second embodiment]

[0442] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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."

[0454] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0455] Overall structure

[0456] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0457] Data collection

[0458] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0459] Data transmission

[0460] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0461] Data reception and analysis

[0462] Server: The server receives the data sent from the wearable device and stores it in a database, while preprocessing the data by filtering noise and imputing missing values.

[0463] Data Analysis and Inference

[0464] Server: The server analyzes the data using a generative AI model that assesses the amount of exercise, rest, and feeding required based on the pet's behavioral data, and also detects abnormalities in heart rate and activity patterns.

[0465] Based on the analysis results, the app generates specific advice about your pet's health. For example, if your pet isn't getting enough exercise, it might say, "Today's exercise volume hasn't reached your goal. We recommend a 30-minute walk." If your pet's heart rate is above the normal range, it might generate an alert, saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0466] User Notifications

[0467] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails.

[0468] Device: The user device will notify the user of the received notification visually or audibly. The user can then check the notification content through the application and take appropriate action.

[0469] Specific examples

[0470] Example 1: Notification of lack of exercise

[0471] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0472] 2. Terminal: Sends collected data to the server at regular intervals.

[0473] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0474] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend taking a 30-minute walk."

[0475] 5. Server: Sends advice to the user device.

[0476] 6. Terminal: The user terminal receives the notification and notifies the user.

[0477] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0478] Example 2: Detecting abnormal heart rates

[0479] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0480] 2. Terminal: Sends abnormal data to the server.

[0481] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0482] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0483] 5. Server: Sends the alert to the user device.

[0484] 6. Terminal: The user terminal receives the notification and notifies the user.

[0485] 7. User: Check the notification and consult a veterinarian immediately.

[0486] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0487] The processing flow will be explained below.

[0488] Step 1:

[0489] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[0490] Step 2:

[0491] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[0492] Step 3:

[0493] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[0494] Step 4:

[0495] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[0496] Step 5:

[0497] Server: The server temporarily accumulates the data received from the terminal and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processing as necessary.

[0498] Step 6:

[0499] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc., and infers the pet's health condition.

[0500] Step 7:

[0501] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0502] Step 8:

[0503] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0504] Step 9:

[0505] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0506] Step 10:

[0507] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[0508] This is the specific processing flow of the generative AI model that links a pet care app with a smart wearable device, making it possible to monitor the health of pets in real time and provide appropriate care.

[0509] Example 1

[0510] 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."

[0511] Conventional pet health management systems lack the ability to collect and analyze data in real time, making it difficult to accurately assess health conditions or detect abnormalities. Furthermore, the means of notifying users were limited, making it difficult to respond quickly in emergencies. This can delay the timing of taking appropriate action in pet health management, potentially increasing health risks.

[0512] 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.

[0513] In this invention, the server includes means for performing noise filtering and defect completion on pet behavior data, means for detecting anomalies and generating alerts based on a generative AI model, and means for notifying a user terminal of the generated advice and alerts, thereby enabling highly accurate data analysis in real time and rapid anomaly detection, allowing the user to take appropriate action in a timely manner.

[0514] A "wearable device" is a device that is attached to a pet to collect behavioral data such as the amount of exercise, rest time, heart rate, and location information.

[0515] The "server" is a computer system that receives pet behavior data, stores it in a database, and analyzes it using a generative AI model.

[0516] A "generative AI model" is a machine learning model used to analyze pet behavior data and generate health advice and abnormality alerts.

[0517] "Behavioral data" refers to data related to a pet's activities, including the amount of exercise, rest time, heart rate, and location information of the pet.

[0518] "Noise filtering" is a process that removes outliers from collected data and improves the accuracy of the data.

[0519] "Gap filling" is a process of filling in missing parts of collected data with appropriate values.

[0520] "Advice" is information that provides recommended actions to improve your pet's health based on data analyzed using a generative AI model.

[0521] An "alert" is a warning message that quickly notifies you of health abnormalities based on data analyzed using a generative AI model.

[0522] "User terminal" refers to a device for notifying a user of received advice and alerts, such as a smartphone or tablet.

[0523] "Push notification" is a communication method in which a server sends information to a user terminal in real time.

[0524] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0525] Overall structure

[0526] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0527] Data collection

[0528] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0529] Data transmission

[0530] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0531] Data reception and preprocessing

[0532] Server: The server receives the data sent from the wearable device and stores it in a database. At the same time, it preprocesses the data, performs noise filtering, and imputes missing values. For example, MySQL is used as the database system.

[0533] Data Analysis and Inference

[0534] Server: The server analyzes the data using a generative AI model. This generative AI model evaluates the amount of exercise, rest time, and whether the pet is over or underfed based on the pet's behavioral data. It also has the ability to detect abnormalities in heart rate and activity patterns. Based on the analysis results, the server generates specific advice about the pet's health. For example, if the pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0535] User Notifications

[0536] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails. For example, Firebase Cloud Messaging (FCM) is used.

[0537] Device: The user device will notify the user of the received notification visually or audibly. The user can check the notification content through the application and take appropriate action. Specifically, a message will be displayed in the smartphone's notification bar and details will be displayed on the application's notification screen. In the case of an audio notification, the smartphone speaker will read out, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0538] Specific examples

[0539] Example 1: Notification of lack of exercise

[0540] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0541] 2. Terminal: Sends collected data to the server at regular intervals.

[0542] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0543] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend that you take another 3,000 steps."

[0544] 5. Server: Sends advice to the user device.

[0545] 6. Device: The user device receives the notification and displays it in the notification bar.

[0546] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0547] Example 2: Detecting abnormal heart rates

[0548] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0549] 2. Terminal: Sends abnormal data to the server.

[0550] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0551] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0552] 5. Server: Sends the alert to the user device.

[0553] 6. Terminal: The user terminal receives the notification and gives an audio notification.

[0554] 7. User: Check the notification and consult a veterinarian immediately.

[0555] Prompt Sentence Examples

[0556] "How do you use a generative AI model to generate notifications if your pet is not getting enough exercise?"

[0557] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0559] Step 1:

[0560] Terminal: The wearable device uses sensors to collect information on the pet's activity, rest time, heart rate, and location. Input data are various measurements taken by the sensors, such as heart rate of 80 BPM, steps taken of 5,000, and rest time of 5 hours, and location information such as latitude: 35.6895, longitude: 139.6917. Output data are these behavioral data. The wearable device records data in real time and accumulates it in a buffer at regular intervals (e.g., every 5 minutes).

[0561] Step 2:

[0562] Terminal: Sends collected behavioral data to a server via the Internet. Specifically, the collected data (e.g., heart rate, number of steps, rest time, location information) is packaged into packets and sent to the server via Internet Protocol (IP). The input data is the accumulated behavioral data, and the output data is the data packets sent to the server.

[0563] Step 3:

[0564] Server: Receives the transmitted data. Specifically, the server receives data packets through a network interface and stores them in a database system (e.g., MySQL). The input data is the received behavioral data, and the output data is the behavioral data stored in the database.

[0565] Step 4:

[0566] Server: Preprocesses the behavioral data. Specifically, it performs noise filtering (removing outliers) and missing value imputation (e.g., imputing estimated values ​​based on past data). The input data is the behavioral data stored in the database, and the output data is the clean data after preprocessing.

[0567] Step 5:

[0568] Server: Analyzes the preprocessed data using a generative AI model. Specifically, clean data is input into a generative AI model (e.g., built with TensorFlow) to evaluate the pet's exercise, rest time, and dietary needs, and to detect abnormalities in heart rate and activity patterns. The input data is the preprocessed clean data, and the output data is the analysis results (e.g., advice or alerts).

[0569] Step 6:

[0570] Server: Based on the analysis results, it generates specific advice and abnormality alerts regarding the pet's health condition. For example, if the pet is not getting enough exercise, it generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is outside the normal range, it generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the analysis result of the generative AI model, and the output data is the generated advice and alerts.

[0571] Step 7:

[0572] Server: Notifies the user device of the generated advice and alerts. Specifically, information is sent to the user's smartphone or tablet using push notifications or email. For example, Firebase Cloud Messaging (FCM) is used. The input data is the generated advice and alerts, and the output data is the notified message.

[0573] Step 8:

[0574] Device: The user device notifies the user of the received notification visually or audibly. Specifically, it displays a message in the notification bar and displays details on the application's notification screen. In the case of an audio notification, it reads out through the smartphone speaker, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the received notification message, and the output data is the notification presented to the user.

[0575] Step 9:

[0576] User: The user checks the notification and takes appropriate action. For example, after checking the notification, they take their pet for a walk to increase their exercise. In case of an abnormal alert, they consult a veterinarian immediately. The input data is the notification content, and the output is the action taken by the user.

[0577] This is the specific processing flow of this system. It monitors the health condition of pets in real time and can take necessary measures promptly.

[0578] (Application example 1)

[0579] 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."

[0580] Real-time monitoring of pet health and behavior patterns is important for ensuring pet safety and maintaining their health. However, conventional systems have difficulty quickly and accurately detecting abnormalities in pet location information or behavior patterns and effectively notifying users. Furthermore, the lack of functionality to prompt users to take immediate action against abnormal behavior or health risks makes it difficult to adequately protect pet safety and health.

[0581] 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.

[0582] In this invention, the server includes means for analyzing the behavioral data using a generative AI model to generate advice and alerts regarding the pet's health condition and behavioral patterns, means for notifying the user terminal of the generated advice and alerts, and means for detecting abnormal movement patterns and behavioral patterns, thereby making it possible to prompt the user to check the safety of their pet and take security measures, and to notify the user of alerts quickly and accurately.

[0583] A "wearable device" is a device that is attached to a pet and uses various sensors to collect behavioral data and biometric information about the pet.

[0584] "Behavioral data" refers to data including biological and behavioral information such as the pet's heart rate, amount of exercise, and location information.

[0585] A "generative AI model" is a model based on artificial intelligence algorithms that analyzes collected data to evaluate behavioral patterns and health conditions.

[0586] A "server" is a computer system for receiving and analyzing data sent from a wearable device.

[0587] "Advice" refers to instructions or suggestions provided to maintain or improve your pet's health based on data analyzed by the generative AI model.

[0588] An "alert" is a notification that warns or warns the user when an abnormality is detected by the generative AI model.

[0589] A "user terminal" is a communication terminal that allows pet owners to receive and check advice and alerts, and is a device such as a smartphone or tablet.

[0590] "Abnormal movement patterns" refers to sudden or suspicious movements of pets that deviate from their normal behavior.

[0591] "Crime prevention measures" are measures that users take to ensure the safety of their pets, so that they do not get lost or are the victim of theft.

[0592] This invention is a system consisting of a wearable device attached to a pet, a server, and a user terminal. This system monitors the pet's health condition and behavioral patterns in real time and provides appropriate advice and alerts to the user to maintain the pet's safety and health.

[0593] Overall structure

[0594] The system includes the following main components:

[0595] 1. Wearable devices: These are worn by pets and collect behavioral data such as heart rate, activity, and location.

[0596] 2. Server: Receives behavioral data sent from the wearable device and analyzes it using generative AI models, generating advice and alerts on pet health and behavioral patterns.

[0597] 3. User terminal: Receives advice and alerts sent from the server and notifies the user.

[0598] Data collection

[0599] The wearable device is equipped with various sensors that collect data such as your pet's heart rate, activity level, and location in real time, and this data is sent to a server at regular intervals.

[0600] Data analysis

[0601] The server preprocesses the received behavioral data, filtering out noise and filling in missing values. It then uses a generative AI model to analyze the data and evaluate the pet's health and behavioral patterns. For example, if your pet is not getting enough exercise, it generates advice such as, "Today's exercise volume is not meeting your goal. We recommend a 30-minute walk." If the pet's heart rate is above the normal range, it generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0602] User Notifications

[0603] The generated advice and alerts are sent from the server to the user's device, which then notifies the user visually or audibly of the received notification. By checking this, the user can take appropriate action regarding their pet's health condition and behavioral patterns.

[0604] Hardware and software used

[0605] The system uses the following specific hardware and software:

[0606] Hardware: Wearable devices (e.g., FidoTrack), smartphones, tablets

[0607] Software: Python, requests library, NumPy library

[0608] Specific examples

[0609] Example 1: Notification of lack of exercise

[0610] The wearable device detects that the pet's daily step count has not reached the target value and sends the data to the server. The server analyzes the data and generates advice such as "Today's exercise volume has not reached the target. We recommend a 30-minute walk," and notifies the user's device.

[0611] Example 2: Detecting abnormal heart rates

[0612] The wearable device detects that the pet's heart rate is out of the normal range and sends the data to the server, which analyzes the data and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0613] Prompt Sentence Examples

[0614] "Use FidoTrack devices to collect your pet's behavioral data every five minutes, analyze and notify you of abnormal behavioral patterns and health conditions, and detect abnormalities in heart rate and location."

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Step 1:

[0617] The wearable device collects your pet's heart rate, activity level, and location information. The input is data from various sensors, and the output is collected behavioral data. This allows you to understand your pet's real-time condition.

[0618] Step 2:

[0619] The wearable device sends the data it collects to a server at regular intervals. The input is behavioral data stored inside the wearable device, and the output is data sent to the server via the Internet. This allows the collected data to reach the server.

[0620] Step 3:

[0621] The server receives the data sent from the wearable device. The input is the behavioral data sent from the wearable device, and the output is the data stored in the server's internal database. This makes the data available for analysis.

[0622] Step 4:

[0623] The server preprocesses the data it receives, specifically by filtering noise and imputing missing values. The input is raw data stored on the server, and the output is preprocessed, clean data. This improves the quality of the data.

[0624] Step 5:

[0625] The server analyzes the data using the generative AI model. The input is pre-processed behavioral data, and the output is an assessment of the pet's health and behavioral patterns. Specifically, it evaluates the amount of exercise, rest time, and dietary information, and detects abnormal heart rate and movement patterns. This allows it to generate advice and alerts.

[0626] Step 6:

[0627] The server generates advice and alerts based on the analysis results. The input is the analysis results of the generative AI model, and the output is a specific advice or alert message. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." This provides the user with information to take appropriate action.

[0628] Step 7:

[0629] The server sends the generated advice and alerts to the user's terminal. The input is the advice and alert message, and the output is a notification sent to the user's terminal via the Internet. This allows the user to quickly understand the status of their pet.

[0630] Step 8:

[0631] The user device receives notifications and notifies them visually or audibly. The input is the notification sent from the server, and the output is the actual notification display or audio alert for the user. This allows the user to instantly know the health status of their pet or any abnormal behavior.

[0632] Step 9:

[0633] The user checks the notification content and takes appropriate action. The input is the notification content displayed on the user's device, and the output is the user's specific action (e.g., taking the pet for a walk, consulting a veterinarian). This ensures the health and safety of the pet.

[0634] 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.

[0635] The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's condition into consideration when managing the health of a pet.

[0636] Overall structure

[0637] This system consists of a wearable device attached to the pet, a server, a user terminal, and an emotion engine. The pet's behavioral data is collected from the wearable device, and emotion data is collected from the emotion engine. Both sets of data are sent to the server and analyzed by a generative AI model. Advice and alerts generated based on the analysis results are sent to the user terminal.

[0638] Data collection

[0639] Device: The wearable device collects real-time behavioral data such as exercise volume, rest time, heart rate, and location information through various sensors (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor) attached to the pet. The data is sent to a server at regular intervals.

[0640] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data.

[0641] Data transmission

[0642] Terminal: The wearable device and emotion engine each send collected behavioral data and emotion data to a server via the Internet. The data is encrypted to ensure security.

[0643] Data reception and analysis

[0644] Server: The server receives data sent from the wearable device and emotion engine and stores it in a database. The received data is first preprocessed to check for duplicates and missing values ​​and to filter noise.

[0645] Data Analysis and Inference

[0646] Server: The server analyzes the data using a generative AI model. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc. to infer the pet's health condition. It also integrates the user's emotional data for analysis. For example, if the user is feeling stressed, the server takes that information into account and adjusts the advice on pet care.

[0647] Generate advice alerts

[0648] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[0649] User Notifications

[0650] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0651] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0652] Specific examples

[0653] Example 1: Notification of lack of exercise

[0654] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0655] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[0656] 3. Terminal: Sends collected data to the server at regular intervals.

[0657] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0658] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[0659] 6. Server: Sends advice to the user terminal.

[0660] 7. Terminal: The user terminal receives the notification and notifies the user.

[0661] 8. User: Checks notification and takes pet for a walk.

[0662] Example 2: Detecting abnormal heart rates

[0663] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0664] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[0665] 3. Terminal: Sends abnormal data and emotion data to the server.

[0666] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0667] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0668] 6. Server: Sends the alert to the user device.

[0669] 7. Terminal: The user terminal receives the notification and notifies the user.

[0670] 8. User: Check the notification and consult a veterinarian immediately.

[0671] The above is an embodiment of the present invention, which makes it possible to provide appropriate care that takes into consideration not only the health condition of a pet but also the emotional state of the user.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[0675] Step 2:

[0676] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[0677] Step 3:

[0678] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[0679] Step 4:

[0680] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[0681] Step 5:

[0682] Device: Using the camera and microphone on the user device, the emotion engine analyzes the user's facial expressions and voice to collect the user's emotional state (e.g., joy, stress, excitement, etc.) in real time.

[0683] Step 6:

[0684] Terminal: Collected user emotion data is sent to the server and integrated with behavioral data.

[0685] Step 7:

[0686] Server: Organizes the behavioral and emotional data received from the devices and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processes as necessary.

[0687] Step 8:

[0688] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, and dietary data to infer the pet's health condition. It also integrates the user's emotional data for analysis.

[0689] Step 9:

[0690] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume has not reached the goal. We recommend a 30-minute walk when the user is relaxed." or "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0691] Step 10:

[0692] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0693] Step 11:

[0694] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0695] Step 12:

[0696] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[0697] The above is a concrete processing flow of combining a pet care system with an emotion engine that recognizes the user's emotions. This configuration makes it possible to provide appropriate care that takes into account not only the pet's health condition but also the user's emotional state.

[0698] Example 2

[0699] 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."

[0700] Conventional pet care systems primarily monitor only the health of pets and do not consider the emotional state of the user. As a result, they are unable to provide appropriate pet care tailored to the user's emotional state, making it difficult to optimally manage the health of both the pet and the user. Another issue is that pet care advice is not appropriately adjusted when the user is stressed or fatigued.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0702] In this invention, the server includes means for collecting pet behavioral data using a wearable device attached to the pet, means for transmitting the collected data from the wearable device to the server, means for collecting user emotion data using an emotion engine installed in the user terminal, means for transmitting the user emotion data to the server, means for analyzing the behavioral data and emotion data using a generative AI model and generating advice based on the pet's health condition and the user's emotional state, and means for notifying the user terminal of the generated advice, thereby enabling more personalized pet care that takes into account not only the pet's health condition but also the user's emotional state.

[0703] "Pet" refers to an animal kept by an owner in a household.

[0704] A "wearable device" refers to an electronic device that is attached to a pet to collect behavioral and biological data.

[0705] "Behavioral data" refers to information about your pet's activities, such as its amount of exercise, heart rate, rest time, and location.

[0706] "User terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[0707] An "emotion engine" refers to software and hardware that analyzes a user's facial expressions and voice and estimates their emotional state.

[0708] "Emotion data" is information about the user's emotional state, such as relaxation, stress, fatigue, etc.

[0709] "Server" refers to a remote computer system for receiving, storing, and analyzing data over a network.

[0710] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates insights and advice.

[0711] "Advice" refers to recommended actions or instructions regarding pet care.

[0712] "Notification" refers to a message or alert that conveys information to a user terminal.

[0713] "Data transmission" refers to the act of sending collected data to other systems via the Internet.

[0714] "Data reception" refers to the act of receiving data sent from another system.

[0715] "Data preprocessing" refers to the process of checking for and organizing data duplication and noise as a preliminary step to analysis.

[0716] "Inference" refers to the act of drawing predictions or conclusions based on the results of data analysis.

[0717] "Push notification" refers to a communication method that instantly displays information on a user's device.

[0718] MODE FOR CARRYING OUT THE INVENTION

[0719] The following describes the embodiments of the present invention. The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's emotional state into consideration when managing the health of a pet. This system consists of the following main hardware and software components:

[0720] 1. Wearable devices attached to pets

[0721] 2. Server

[0722] 3. User Device

[0723] 4. Emotion Engine

[0724] Data collection

[0725] Device: The wearable device uses sensors (e.g., step count sensor, acceleration sensor, heart rate sensor, GPS sensor) to collect data on the pet's behavior. Data such as exercise volume, rest time, heart rate, and location information is collected in real time and stored in the internal memory at regular intervals.

[0726] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data. The emotion engine estimates the user's current emotional state (relaxed, stressed, tired, etc.) and temporarily stores that data.

[0727] Data transmission

[0728] Terminal: The wearable device and user terminal each encrypt the collected behavioral and emotional data and send it to the server via the Internet. A security protocol is used for data transmission to ensure data confidentiality.

[0729] Data reception and preprocessing

[0730] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is first preprocessed to remove duplicate data, filter noise, and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis.

[0731] Data Analysis and Inference

[0732] Server: The server analyzes the behavioral and emotional data using a generative AI model. The generative AI model evaluates the pet's exercise, rest time, heart rate, and dietary data to infer the pet's health condition. It also integrates and analyzes the user's emotional data to generate appropriate pet care advice. For example, advice based on the user's emotions is provided, such as recommending taking the pet for a walk when the user is relaxed.

[0733] Generate advice alerts

[0734] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the target. We recommend a 30-minute walk when the user is relaxed." If a pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[0735] User Notifications

[0736] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0737] Terminal: The user terminal receives the notification sent from the server and notifies the user visually or audibly. The user can view the notification details through the application and take necessary actions.

[0738] Specific examples

[0739] Example 1: Notification of lack of exercise

[0740] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0741] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[0742] 3. Terminal: Sends collected data to the server at regular intervals.

[0743] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0744] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[0745] 6. Server: Sends advice to the user terminal.

[0746] 7. Terminal: The user terminal receives the notification and notifies the user.

[0747] 8. User: Checks notification and takes pet for a walk.

[0748] Example 2: Detecting abnormal heart rates

[0749] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0750] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[0751] 3. Terminal: Sends abnormal data and emotion data to the server.

[0752] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0753] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[0754] 6. Server: Sends the alert to the user device.

[0755] 7. Terminal: The user terminal receives the notification and notifies the user.

[0756] 8. User: Check the notification and consult a veterinarian immediately.

[0757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0758] Step 1:

[0759] Data collection using wearable devices

[0760] Terminal: The wearable device uses a step sensor, accelerometer, heart rate sensor, and GPS sensor to obtain real-time information on the pet's activity, rest time, heart rate, and location. The device stores this data in its internal memory at regular intervals. The input data is raw data sent from the sensors, and the output data is processed behavioral data. For example, it records the number of steps a pet takes in a day and heart rate fluctuations.

[0761] Step 2:

[0762] Emotion data collection using an emotion engine

[0763] Device: The emotion engine installed on the user device uses a camera and microphone to capture the user's facial expressions and voice. From this data, the user's emotional state (relaxed, stressed, tired, etc.) is estimated and temporarily stored. The input data is the raw data sent from the camera and microphone, and the output data is analyzed emotional data. For example, it determines whether the user is smiling, angry, or stressed.

[0764] Step 3:

[0765] Data transmission from wearable devices and user terminals

[0766] Terminal: The wearable device and user terminal send the collected behavioral and emotional data to a server via the Internet. The data is encrypted to maintain security. The input data is the behavioral and emotional data stored in the internal memory, and the output data is the data sent to the server. For example, the wearable device transfers the data to a smartphone via Bluetooth, and then sends it to the server via the Internet.

[0767] Step 4:

[0768] Data reception and preprocessing by the server

[0769] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is preprocessed to remove duplicate data, filter noise, and fill in missing data. The input data is the received behavioral and emotional data, and the output data is the preprocessed, clean data. For example, it removes noise from GPS data and fills in missing parts of heart rate data.

[0770] Step 5:

[0771] Data analysis and inference using generative AI models

[0772] Server: The server analyzes the preprocessed behavioral data and emotional data using a generative AI model. The generative AI model evaluates this data in a comprehensive manner, estimates the pet's health condition, and generates pet care advice based on this information, taking the user's emotional state into account. The input data are the preprocessed behavioral data and emotional data, and the output data are the analysis and inference results. For example, if a pet is not getting enough exercise, the server recommends taking it for a walk when the user is relaxed.

[0773] Step 6:

[0774] Generate advice alerts

[0775] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately." The input data are the analysis and inference results, and the output data are specific advice and alerts.

[0776] Step 7:

[0777] Sending notifications to user devices

[0778] Server: Sends the generated advice and alerts to the user's device. The input data is the advice and alert, and the output data is the data sent to the user's device. Notifications are sent via the method selected by the user, such as push notification or email.

[0779] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. Notification details are displayed through the application. For example, a push notification is displayed on the smartphone, allowing the user to check the advice and alert details.

[0780] This allows users to view notifications and implement pet care advice.

[0781] (Application example 2)

[0782] 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."

[0783] Conventional pet care systems manage pet health by only considering pet behavioral data, and are therefore unable to provide appropriate advice or alerts when the pet owner's emotional state affects pet care. Furthermore, there are limited means to provide customized services for pets and owners in real time. To solve these problems, a new system is needed that integrates and analyzes pet behavioral data and owner emotional data, and provides pet care based on the results.

[0784] 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 pet behavioral data using a wearable device attached to a pet, means for transmitting the collected data from the wearable device to the server, means for analyzing the behavioral data using a generative AI model in the server and generating advice on the pet's health condition, means for collecting user emotion data including an emotion engine that recognizes the user's emotions, means for integrating and analyzing the emotion data from the emotion engine and the pet behavioral data, means for adjusting pet care advice based on the emotion data, means for notifying the generated advice to a user terminal, means for displaying the generated advice on smart glasses, means for promptly notifying the user of an alert when the server detects an abnormality, and means for displaying the alert in real time on a terminal including the smart glasses. This makes it possible to integrate and analyze the pet behavioral data and the user's emotional state and provide customized advice and alerts in real time.

[0785] A "wearable device" is a device that is attached to a pet and collects behavioral data such as heart rate, exercise volume, and location information.

[0786] The "server" is a computer system that stores data collected from pets and users and analyzes it using a generative AI model.

[0787] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates advice and alerts based on the pet's health and the user's emotional state.

[0788] An "emotion engine" is a device that uses a camera and microphone to analyze a user's facial expressions, voice, and behavior to collect emotional data.

[0789] "Smart glasses" are glasses-type devices worn by users that display information such as advice and alerts.

[0790] A "user terminal" is a device such as a smartphone or tablet that a user uses to receive behavioral data and advice about their pet.

[0791] "Behavioral data" refers to data related to the pet's activities, such as the pet's heart rate, amount of exercise, and location information.

[0792] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions, voice, and behavior.

[0793] "Advice" is a recommendation provided based on the analysis results of the generative AI model, depending on the pet's health condition and the user's emotional state.

[0794] An "alert" is a warning message that is provided when an abnormality is detected in the pet's behavior data or the user's emotion data.

[0795] The system of the present invention includes a wearable device attached to a pet, an emotion engine that recognizes the user's emotion, a server, and a user terminal. Specific embodiments will be described below.

[0796] Overall structure

[0797] server

[0798] The server stores pet behavior data and user emotional data, and analyzes them using a generative AI model. Specifically, the server integrates data collected by the wearable device attached to the pet with the user's emotional data collected by the emotion engine to generate advice and alerts based on the pet's health and emotional state in real time.

[0799] Wearable devices

[0800] Wearable devices include heart rate sensors, acceleration sensors, and GPS sensors to obtain location information. Data from these devices is sent to a server at regular intervals using communication methods such as Bluetooth and WiFi.

[0801] Emotion Engine

[0802] The emotion engine is software installed on the user's smartphone or tablet, which uses the camera and microphone to analyze facial expressions, voice, and behavior to collect emotional data.

[0803] User terminal

[0804] User devices are used to notify users of advice and alerts, and include smartphones, tablets, smart glasses, etc. Notifications are sent via push notifications, emails, etc.

[0805] Data collection and transmission

[0806] The wearable device collects real-time information about the pet's heart rate, activity, and location. At the same time, the emotion engine collects the user's emotional state. This data is encrypted and transmitted to a server via the internet.

[0807] Data analysis and advice generation

[0808] The server temporarily stores the received data and performs preprocessing, including removing duplicate data, imputing missing values, and filtering noise. It then analyzes the data using a generative AI model and integrates the pet's health status and the user's emotional state to generate advice and alerts.

[0809] For example, if your pet isn't getting enough exercise, the app will generate advice like, "Today's exercise volume isn't meeting your goal. We recommend a 30-minute walk when you're relaxed." If your pet's heart rate is above the normal range, the app will generate an alert saying, "Your pet's heart rate is too high. If you're feeling stressed, consult a veterinarian immediately."

[0810] User Notifications

[0811] These advice and alerts are sent from the server to the user's device, which displays the notifications in real time, allowing the user to respond immediately.

[0812] Prompt Sentence Examples

[0813] "Hi, based on your pet's current exercise level, it looks like they need a little more activity. My suggestion is that you consider this automatic ball thrower."

[0814] This invention enables the integrated analysis of pet behavior data and the user's emotional state, and provides users with customized advice and alerts in real time, aiming to provide better care for pets and their owners.

[0815] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0816] Step 1:

[0817] The wearable device terminal collects behavioral data such as the pet's heart rate, activity level, and location in real time.

[0818] Input: Data from various pet sensors

[0819] Output: Behavioral data (heart rate, exercise amount, location information)

[0820] Specific operation: Data is acquired using the heart rate sensor, acceleration sensor, and GPS sensor and stored in internal storage.

[0821] Step 2:

[0822] The emotion engine analyzes the user's facial expressions, voice, and behavior to collect emotional data.

[0823] Input: User facial, voice, and behavioral data

[0824] Output: Emotion data

[0825] Specific operations: Captures the user's facial expressions with a camera, records audio with a microphone, recognizes emotions using a machine learning model, and creates data.

[0826] Step 3:

[0827] The data collected by the device is sent to the server at regular intervals.

[0828] Input: behavioral data, emotion data

[0829] Output: Send data to the server

[0830] Specific operation: Using Bluetooth or WiFi, the encrypted data is uploaded to a server via the Internet.

[0831] Step 4:

[0832] The server preprocesses the received data, which includes removing duplicates, imputing missing values, and filtering noise.

[0833] Input: behavioral data, emotion data

[0834] Output: Preprocessed data

[0835] Specific operation: Cleanses and normalizes the data stored in the database.

[0836] Step 5:

[0837] The server uses the generated AI model to analyze the preprocessed data and integrates the pet's health condition and the user's emotional state for analysis.

[0838] Input: Preprocessed data

[0839] Output: Analysis results (health status, emotional status)

[0840] Specific behavior: Applying a generative AI model to evaluate the pet's behavioral patterns and the user's emotional data.

[0841] Step 6:

[0842] The server generates advice and alerts based on the analysis results, such as advice and alerts about lack of exercise or abnormal heart rate.

[0843] Input: Analysis results

[0844] Output: Advice, Alert

[0845] Specific behavior: The output of the generative AI model is used to generate messages based on predefined rules.

[0846] Step 7:

[0847] The server transmits the generated advice or alert to the user terminal.

[0848] Input: Advice, Alert

[0849] Output: Notification to user terminal

[0850] Specific behavior: Send advice and alerts via push notifications or emails as specified by the user.

[0851] Step 8:

[0852] The smart glasses that serve as the terminal display advice and alerts to the user.

[0853] Input: Advice, Alert

[0854] Output: What is displayed to the user

[0855] Specific operation: Displays messages in real time on the smart glasses display, providing visual feedback to the user.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0873] Overall structure

[0874] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0875] Data collection

[0876] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0877] Data transmission

[0878] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0879] Data reception and analysis

[0880] Server: The server receives the data sent from the wearable device and stores it in a database, while preprocessing the data by filtering noise and imputing missing values.

[0881] Data Analysis and Inference

[0882] Server: The server analyzes the data using a generative AI model that assesses the amount of exercise, rest, and feeding required based on the pet's behavioral data, and also detects abnormalities in heart rate and activity patterns.

[0883] Based on the analysis results, the app generates specific advice about your pet's health. For example, if your pet isn't getting enough exercise, it might say, "Today's exercise volume hasn't reached your goal. We recommend a 30-minute walk." If your pet's heart rate is above the normal range, it might generate an alert, saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0884] User Notifications

[0885] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails.

[0886] Device: The user device will notify the user of the received notification visually or audibly. The user can then check the notification content through the application and take appropriate action.

[0887] Specific examples

[0888] Example 1: Notification of lack of exercise

[0889] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0890] 2. Terminal: Sends collected data to the server at regular intervals.

[0891] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0892] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend taking a 30-minute walk."

[0893] 5. Server: Sends advice to the user device.

[0894] 6. Terminal: The user terminal receives the notification and notifies the user.

[0895] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0896] Example 2: Detecting abnormal heart rates

[0897] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0898] 2. Terminal: Sends abnormal data to the server.

[0899] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0900] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0901] 5. Server: Sends the alert to the user device.

[0902] 6. Terminal: The user terminal receives the notification and notifies the user.

[0903] 7. User: Check the notification and consult a veterinarian immediately.

[0904] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[0908] Step 2:

[0909] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[0910] Step 3:

[0911] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[0912] Step 4:

[0913] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[0914] Step 5:

[0915] Server: The server temporarily accumulates the data received from the terminal and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processing as necessary.

[0916] Step 6:

[0917] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc., and infers the pet's health condition.

[0918] Step 7:

[0919] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0920] Step 8:

[0921] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[0922] Step 9:

[0923] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[0924] Step 10:

[0925] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[0926] This is the specific processing flow of the generative AI model that links a pet care app with a smart wearable device, making it possible to monitor the health of pets in real time and provide appropriate care.

[0927] Example 1

[0928] 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."

[0929] Conventional pet health management systems lack the ability to collect and analyze data in real time, making it difficult to accurately assess health conditions or detect abnormalities. Furthermore, the means of notifying users were limited, making it difficult to respond quickly in emergencies. This can delay the timing of taking appropriate action in pet health management, potentially increasing health risks.

[0930] 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.

[0931] In this invention, the server includes means for performing noise filtering and defect completion on pet behavior data, means for detecting anomalies and generating alerts based on a generative AI model, and means for notifying a user terminal of the generated advice and alerts, thereby enabling highly accurate data analysis in real time and rapid anomaly detection, allowing the user to take appropriate action in a timely manner.

[0932] A "wearable device" is a device that is attached to a pet to collect behavioral data such as the amount of exercise, rest time, heart rate, and location information.

[0933] The "server" is a computer system that receives pet behavior data, stores it in a database, and analyzes it using a generative AI model.

[0934] A "generative AI model" is a machine learning model used to analyze pet behavior data and generate health advice and abnormality alerts.

[0935] "Behavioral data" refers to data related to a pet's activities, including the amount of exercise, rest time, heart rate, and location information of the pet.

[0936] "Noise filtering" is a process that removes outliers from collected data and improves the accuracy of the data.

[0937] "Gap filling" is a process of filling in missing parts of collected data with appropriate values.

[0938] "Advice" is information that provides recommended actions to improve your pet's health based on data analyzed using a generative AI model.

[0939] An "alert" is a warning message that quickly notifies you of health abnormalities based on data analyzed using a generative AI model.

[0940] "User terminal" refers to a device for notifying a user of received advice and alerts, such as a smartphone or tablet.

[0941] "Push notification" is a communication method in which a server sends information to a user terminal in real time.

[0942] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0943] Overall structure

[0944] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[0945] Data collection

[0946] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[0947] Data transmission

[0948] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[0949] Data reception and preprocessing

[0950] Server: The server receives the data sent from the wearable device and stores it in a database. At the same time, it preprocesses the data, performs noise filtering, and imputes missing values. For example, MySQL is used as the database system.

[0951] Data Analysis and Inference

[0952] Server: The server analyzes the data using a generative AI model. This generative AI model evaluates the amount of exercise, rest time, and whether the pet is over or underfed based on the pet's behavioral data. It also has the ability to detect abnormalities in heart rate and activity patterns. Based on the analysis results, the server generates specific advice about the pet's health. For example, if the pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0953] User Notifications

[0954] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails. For example, Firebase Cloud Messaging (FCM) is used.

[0955] Device: The user device will notify the user of the received notification visually or audibly. The user can check the notification content through the application and take appropriate action. Specifically, a message will be displayed in the smartphone's notification bar and details will be displayed on the application's notification screen. In the case of an audio notification, the smartphone speaker will read out, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0956] Specific examples

[0957] Example 1: Notification of lack of exercise

[0958] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[0959] 2. Terminal: Sends collected data to the server at regular intervals.

[0960] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0961] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend that you take another 3,000 steps."

[0962] 5. Server: Sends advice to the user device.

[0963] 6. Device: The user device receives the notification and displays it in the notification bar.

[0964] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[0965] Example 2: Detecting abnormal heart rates

[0966] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[0967] 2. Terminal: Sends abnormal data to the server.

[0968] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[0969] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[0970] 5. Server: Sends the alert to the user device.

[0971] 6. Terminal: The user terminal receives the notification and gives an audio notification.

[0972] 7. User: Check the notification and consult a veterinarian immediately.

[0973] Prompt Sentence Examples

[0974] "How do you use a generative AI model to generate notifications if your pet is not getting enough exercise?"

[0975] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[0976] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0977] Step 1:

[0978] Terminal: The wearable device uses sensors to collect information on the pet's activity, rest time, heart rate, and location. Input data are various measurements taken by the sensors, such as heart rate of 80 BPM, steps taken of 5,000, and rest time of 5 hours, and location information such as latitude: 35.6895, longitude: 139.6917. Output data are these behavioral data. The wearable device records data in real time and accumulates it in a buffer at regular intervals (e.g., every 5 minutes).

[0979] Step 2:

[0980] Terminal: Sends collected behavioral data to a server via the Internet. Specifically, the collected data (e.g., heart rate, number of steps, rest time, location information) is packaged into packets and sent to the server via Internet Protocol (IP). The input data is the accumulated behavioral data, and the output data is the data packets sent to the server.

[0981] Step 3:

[0982] Server: Receives the transmitted data. Specifically, the server receives data packets through a network interface and stores them in a database system (e.g., MySQL). The input data is the received behavioral data, and the output data is the behavioral data stored in the database.

[0983] Step 4:

[0984] Server: Preprocesses the behavioral data. Specifically, it performs noise filtering (removing outliers) and missing value imputation (e.g., imputing estimated values ​​based on past data). The input data is the behavioral data stored in the database, and the output data is the clean data after preprocessing.

[0985] Step 5:

[0986] Server: Analyzes the preprocessed data using a generative AI model. Specifically, clean data is input into a generative AI model (e.g., built with TensorFlow) to evaluate the pet's exercise, rest time, and dietary needs, and to detect abnormalities in heart rate and activity patterns. The input data is the preprocessed clean data, and the output data is the analysis results (e.g., advice or alerts).

[0987] Step 6:

[0988] Server: Based on the analysis results, it generates specific advice and abnormality alerts regarding the pet's health condition. For example, if the pet is not getting enough exercise, it generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is outside the normal range, it generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the analysis result of the generative AI model, and the output data is the generated advice and alerts.

[0989] Step 7:

[0990] Server: Notifies the user device of the generated advice and alerts. Specifically, information is sent to the user's smartphone or tablet using push notifications or email. For example, Firebase Cloud Messaging (FCM) is used. The input data is the generated advice and alerts, and the output data is the notified message.

[0991] Step 8:

[0992] Device: The user device notifies the user of the received notification visually or audibly. Specifically, it displays a message in the notification bar and displays details on the application's notification screen. In the case of an audio notification, it reads out through the smartphone speaker, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the received notification message, and the output data is the notification presented to the user.

[0993] Step 9:

[0994] User: The user checks the notification and takes appropriate action. For example, after checking the notification, they take their pet for a walk to increase their exercise. In case of an abnormal alert, they consult a veterinarian immediately. The input data is the notification content, and the output is the action taken by the user.

[0995] This is the specific processing flow of this system. It monitors the health condition of pets in real time and can take necessary measures promptly.

[0996] (Application example 1)

[0997] 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."

[0998] Real-time monitoring of pet health and behavior patterns is important for ensuring pet safety and maintaining their health. However, conventional systems have difficulty quickly and accurately detecting abnormalities in pet location information or behavior patterns and effectively notifying users. Furthermore, the lack of functionality to prompt users to take immediate action against abnormal behavior or health risks makes it difficult to adequately protect pet safety and health.

[0999] 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.

[1000] In this invention, the server includes means for analyzing the behavioral data using a generative AI model to generate advice and alerts regarding the pet's health condition and behavioral patterns, means for notifying the user terminal of the generated advice and alerts, and means for detecting abnormal movement patterns and behavioral patterns, thereby making it possible to prompt the user to check the safety of their pet and take security measures, and to notify the user of alerts quickly and accurately.

[1001] A "wearable device" is a device that is attached to a pet and uses various sensors to collect behavioral data and biometric information about the pet.

[1002] "Behavioral data" refers to data including biological and behavioral information such as the pet's heart rate, amount of exercise, and location information.

[1003] A "generative AI model" is a model based on artificial intelligence algorithms that analyzes collected data to evaluate behavioral patterns and health conditions.

[1004] A "server" is a computer system for receiving and analyzing data sent from a wearable device.

[1005] "Advice" refers to instructions or suggestions provided to maintain or improve your pet's health based on data analyzed by the generative AI model.

[1006] An "alert" is a notification that warns or warns the user when an abnormality is detected by the generative AI model.

[1007] A "user terminal" is a communication terminal that allows pet owners to receive and check advice and alerts, and is a device such as a smartphone or tablet.

[1008] "Abnormal movement patterns" refers to sudden or suspicious movements of pets that deviate from their normal behavior.

[1009] "Crime prevention measures" are measures that users take to ensure the safety of their pets, so that they do not get lost or are the victim of theft.

[1010] This invention is a system consisting of a wearable device attached to a pet, a server, and a user terminal. This system monitors the pet's health condition and behavioral patterns in real time and provides appropriate advice and alerts to the user to maintain the pet's safety and health.

[1011] Overall structure

[1012] The system includes the following main components:

[1013] 1. Wearable devices: These are worn by pets and collect behavioral data such as heart rate, activity, and location.

[1014] 2. Server: Receives behavioral data sent from the wearable device and analyzes it using generative AI models, generating advice and alerts on pet health and behavioral patterns.

[1015] 3. User terminal: Receives advice and alerts sent from the server and notifies the user.

[1016] Data collection

[1017] The wearable device is equipped with various sensors that collect data such as your pet's heart rate, activity level, and location in real time, and this data is sent to a server at regular intervals.

[1018] Data analysis

[1019] The server preprocesses the received behavioral data, filtering out noise and filling in missing values. It then uses a generative AI model to analyze the data and evaluate the pet's health and behavioral patterns. For example, if your pet is not getting enough exercise, it generates advice such as, "Today's exercise volume is not meeting your goal. We recommend a 30-minute walk." If the pet's heart rate is above the normal range, it generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1020] User Notifications

[1021] The generated advice and alerts are sent from the server to the user's device, which then notifies the user visually or audibly of the received notification. By checking this, the user can take appropriate action regarding their pet's health condition and behavioral patterns.

[1022] Hardware and software used

[1023] The system uses the following specific hardware and software:

[1024] Hardware: Wearable devices (e.g., FidoTrack), smartphones, tablets

[1025] Software: Python, requests library, NumPy library

[1026] Specific examples

[1027] Example 1: Notification of lack of exercise

[1028] The wearable device detects that the pet's daily step count has not reached the target value and sends the data to the server. The server analyzes the data and generates advice such as "Today's exercise volume has not reached the target. We recommend a 30-minute walk," and notifies the user's device.

[1029] Example 2: Detecting abnormal heart rates

[1030] The wearable device detects that the pet's heart rate is out of the normal range and sends the data to the server, which analyzes the data and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1031] Prompt Sentence Examples

[1032] "Use FidoTrack devices to collect your pet's behavioral data every five minutes, analyze and notify you of abnormal behavioral patterns and health conditions, and detect abnormalities in heart rate and location."

[1033] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1034] Step 1:

[1035] The wearable device collects your pet's heart rate, activity level, and location information. The input is data from various sensors, and the output is collected behavioral data. This allows you to understand your pet's real-time condition.

[1036] Step 2:

[1037] The wearable device sends the data it collects to a server at regular intervals. The input is behavioral data stored inside the wearable device, and the output is data sent to the server via the Internet. This allows the collected data to reach the server.

[1038] Step 3:

[1039] The server receives the data sent from the wearable device. The input is the behavioral data sent from the wearable device, and the output is the data stored in the server's internal database. This makes the data available for analysis.

[1040] Step 4:

[1041] The server preprocesses the data it receives, specifically by filtering noise and imputing missing values. The input is raw data stored on the server, and the output is preprocessed, clean data. This improves the quality of the data.

[1042] Step 5:

[1043] The server analyzes the data using the generative AI model. The input is pre-processed behavioral data, and the output is an assessment of the pet's health and behavioral patterns. Specifically, it evaluates the amount of exercise, rest time, and dietary information, and detects abnormal heart rate and movement patterns. This allows it to generate advice and alerts.

[1044] Step 6:

[1045] The server generates advice and alerts based on the analysis results. The input is the analysis results of the generative AI model, and the output is a specific advice or alert message. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." This provides the user with information to take appropriate action.

[1046] Step 7:

[1047] The server sends the generated advice and alerts to the user's terminal. The input is the advice and alert message, and the output is a notification sent to the user's terminal via the Internet. This allows the user to quickly understand the status of their pet.

[1048] Step 8:

[1049] The user device receives notifications and notifies them visually or audibly. The input is the notification sent from the server, and the output is the actual notification display or audio alert for the user. This allows the user to instantly know the health status of their pet or any abnormal behavior.

[1050] Step 9:

[1051] The user checks the notification content and takes appropriate action. The input is the notification content displayed on the user's device, and the output is the user's specific action (e.g., taking the pet for a walk, consulting a veterinarian). This ensures the health and safety of the pet.

[1052] 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.

[1053] The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's condition into consideration when managing the health of a pet.

[1054] Overall structure

[1055] This system consists of a wearable device attached to the pet, a server, a user terminal, and an emotion engine. The pet's behavioral data is collected from the wearable device, and emotion data is collected from the emotion engine. Both sets of data are sent to the server and analyzed by a generative AI model. Advice and alerts generated based on the analysis results are sent to the user terminal.

[1056] Data collection

[1057] Device: The wearable device collects real-time behavioral data such as exercise volume, rest time, heart rate, and location information through various sensors (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor) attached to the pet. The data is sent to a server at regular intervals.

[1058] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data.

[1059] Data transmission

[1060] Terminal: The wearable device and emotion engine each send collected behavioral data and emotion data to a server via the Internet. The data is encrypted to ensure security.

[1061] Data reception and analysis

[1062] Server: The server receives data sent from the wearable device and emotion engine and stores it in a database. The received data is first preprocessed to check for duplicates and missing values ​​and to filter noise.

[1063] Data Analysis and Inference

[1064] Server: The server analyzes the data using a generative AI model. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc. to infer the pet's health condition. It also integrates the user's emotional data for analysis. For example, if the user is feeling stressed, the server takes that information into account and adjusts the advice on pet care.

[1065] Generate advice alerts

[1066] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[1067] User Notifications

[1068] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1069] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[1070] Specific examples

[1071] Example 1: Notification of lack of exercise

[1072] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1073] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[1074] 3. Terminal: Sends collected data to the server at regular intervals.

[1075] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1076] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[1077] 6. Server: Sends advice to the user terminal.

[1078] 7. Terminal: The user terminal receives the notification and notifies the user.

[1079] 8. User: Checks notification and takes pet for a walk.

[1080] Example 2: Detecting abnormal heart rates

[1081] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1082] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[1083] 3. Terminal: Sends abnormal data and emotion data to the server.

[1084] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1085] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1086] 6. Server: Sends the alert to the user device.

[1087] 7. Terminal: The user terminal receives the notification and notifies the user.

[1088] 8. User: Check the notification and consult a veterinarian immediately.

[1089] The above is an embodiment of the present invention, which makes it possible to provide appropriate care that takes into consideration not only the health condition of a pet but also the emotional state of the user.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[1093] Step 2:

[1094] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[1095] Step 3:

[1096] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[1097] Step 4:

[1098] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[1099] Step 5:

[1100] Device: Using the camera and microphone on the user device, the emotion engine analyzes the user's facial expressions and voice to collect the user's emotional state (e.g., joy, stress, excitement, etc.) in real time.

[1101] Step 6:

[1102] Terminal: Collected user emotion data is sent to the server and integrated with behavioral data.

[1103] Step 7:

[1104] Server: Organizes the behavioral and emotional data received from the devices and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processes as necessary.

[1105] Step 8:

[1106] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, and dietary data to infer the pet's health condition. It also integrates the user's emotional data for analysis.

[1107] Step 9:

[1108] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume has not reached the goal. We recommend a 30-minute walk when the user is relaxed." or "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1109] Step 10:

[1110] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1111] Step 11:

[1112] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[1113] Step 12:

[1114] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[1115] The above is a concrete processing flow of combining a pet care system with an emotion engine that recognizes the user's emotions. This configuration makes it possible to provide appropriate care that takes into account not only the pet's health condition but also the user's emotional state.

[1116] Example 2

[1117] 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."

[1118] Conventional pet care systems primarily monitor only the health of pets and do not consider the emotional state of the user. As a result, they are unable to provide appropriate pet care tailored to the user's emotional state, making it difficult to optimally manage the health of both the pet and the user. Another issue is that pet care advice is not appropriately adjusted when the user is stressed or fatigued.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1120] In this invention, the server includes means for collecting pet behavioral data using a wearable device attached to the pet, means for transmitting the collected data from the wearable device to the server, means for collecting user emotion data using an emotion engine installed in the user terminal, means for transmitting the user emotion data to the server, means for analyzing the behavioral data and emotion data using a generative AI model and generating advice based on the pet's health condition and the user's emotional state, and means for notifying the user terminal of the generated advice, thereby enabling more personalized pet care that takes into account not only the pet's health condition but also the user's emotional state.

[1121] "Pet" refers to an animal kept by an owner in a household.

[1122] A "wearable device" refers to an electronic device that is attached to a pet to collect behavioral and biological data.

[1123] "Behavioral data" refers to information about your pet's activities, such as its amount of exercise, heart rate, rest time, and location.

[1124] "User terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[1125] An "emotion engine" refers to software and hardware that analyzes a user's facial expressions and voice and estimates their emotional state.

[1126] "Emotion data" is information about the user's emotional state, such as relaxation, stress, fatigue, etc.

[1127] "Server" refers to a remote computer system for receiving, storing, and analyzing data over a network.

[1128] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates insights and advice.

[1129] "Advice" refers to recommended actions or instructions regarding pet care.

[1130] "Notification" refers to a message or alert that conveys information to a user terminal.

[1131] "Data transmission" refers to the act of sending collected data to other systems via the Internet.

[1132] "Data reception" refers to the act of receiving data sent from another system.

[1133] "Data preprocessing" refers to the process of checking for and organizing data duplication and noise as a preliminary step to analysis.

[1134] "Inference" refers to the act of drawing predictions or conclusions based on the results of data analysis.

[1135] "Push notification" refers to a communication method that instantly displays information on a user's device.

[1136] MODE FOR CARRYING OUT THE INVENTION

[1137] The following describes the embodiments of the present invention. The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's emotional state into consideration when managing the health of a pet. This system consists of the following main hardware and software components:

[1138] 1. Wearable devices attached to pets

[1139] 2. Server

[1140] 3. User Device

[1141] 4. Emotion Engine

[1142] Data collection

[1143] Device: The wearable device uses sensors (e.g., step count sensor, acceleration sensor, heart rate sensor, GPS sensor) to collect data on the pet's behavior. Data such as exercise volume, rest time, heart rate, and location information is collected in real time and stored in the internal memory at regular intervals.

[1144] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data. The emotion engine estimates the user's current emotional state (relaxed, stressed, tired, etc.) and temporarily stores that data.

[1145] Data transmission

[1146] Terminal: The wearable device and user terminal each encrypt the collected behavioral and emotional data and send it to the server via the Internet. A security protocol is used for data transmission to ensure data confidentiality.

[1147] Data reception and preprocessing

[1148] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is first preprocessed to remove duplicate data, filter noise, and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis.

[1149] Data Analysis and Inference

[1150] Server: The server analyzes the behavioral and emotional data using a generative AI model. The generative AI model evaluates the pet's exercise, rest time, heart rate, and dietary data to infer the pet's health condition. It also integrates and analyzes the user's emotional data to generate appropriate pet care advice. For example, advice based on the user's emotions is provided, such as recommending taking the pet for a walk when the user is relaxed.

[1151] Generate advice alerts

[1152] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the target. We recommend a 30-minute walk when the user is relaxed." If a pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[1153] User Notifications

[1154] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1155] Terminal: The user terminal receives the notification sent from the server and notifies the user visually or audibly. The user can view the notification details through the application and take necessary actions.

[1156] Specific examples

[1157] Example 1: Notification of lack of exercise

[1158] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1159] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[1160] 3. Terminal: Sends collected data to the server at regular intervals.

[1161] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1162] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[1163] 6. Server: Sends advice to the user terminal.

[1164] 7. Terminal: The user terminal receives the notification and notifies the user.

[1165] 8. User: Checks notification and takes pet for a walk.

[1166] Example 2: Detecting abnormal heart rates

[1167] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1168] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[1169] 3. Terminal: Sends abnormal data and emotion data to the server.

[1170] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1171] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1172] 6. Server: Sends the alert to the user device.

[1173] 7. Terminal: The user terminal receives the notification and notifies the user.

[1174] 8. User: Check the notification and consult a veterinarian immediately.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] Data collection using wearable devices

[1178] Terminal: The wearable device uses a step sensor, accelerometer, heart rate sensor, and GPS sensor to obtain real-time information on the pet's activity, rest time, heart rate, and location. The device stores this data in its internal memory at regular intervals. The input data is raw data sent from the sensors, and the output data is processed behavioral data. For example, it records the number of steps a pet takes in a day and heart rate fluctuations.

[1179] Step 2:

[1180] Emotion data collection using an emotion engine

[1181] Device: The emotion engine installed on the user device uses a camera and microphone to capture the user's facial expressions and voice. From this data, the user's emotional state (relaxed, stressed, tired, etc.) is estimated and temporarily stored. The input data is the raw data sent from the camera and microphone, and the output data is analyzed emotional data. For example, it determines whether the user is smiling, angry, or stressed.

[1182] Step 3:

[1183] Data transmission from wearable devices and user terminals

[1184] Terminal: The wearable device and user terminal send the collected behavioral and emotional data to a server via the Internet. The data is encrypted to maintain security. The input data is the behavioral and emotional data stored in the internal memory, and the output data is the data sent to the server. For example, the wearable device transfers the data to a smartphone via Bluetooth, and then sends it to the server via the Internet.

[1185] Step 4:

[1186] Data reception and preprocessing by the server

[1187] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is preprocessed to remove duplicate data, filter noise, and fill in missing data. The input data is the received behavioral and emotional data, and the output data is the preprocessed, clean data. For example, it removes noise from GPS data and fills in missing parts of heart rate data.

[1188] Step 5:

[1189] Data analysis and inference using generative AI models

[1190] Server: The server analyzes the preprocessed behavioral data and emotional data using a generative AI model. The generative AI model evaluates this data in a comprehensive manner, estimates the pet's health condition, and generates pet care advice based on this information, taking the user's emotional state into account. The input data are the preprocessed behavioral data and emotional data, and the output data are the analysis and inference results. For example, if a pet is not getting enough exercise, the server recommends taking it for a walk when the user is relaxed.

[1191] Step 6:

[1192] Generate advice alerts

[1193] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately." The input data are the analysis and inference results, and the output data are specific advice and alerts.

[1194] Step 7:

[1195] Sending notifications to user devices

[1196] Server: Sends the generated advice and alerts to the user's device. The input data is the advice and alert, and the output data is the data sent to the user's device. Notifications are sent via the method selected by the user, such as push notification or email.

[1197] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. Notification details are displayed through the application. For example, a push notification is displayed on the smartphone, allowing the user to check the advice and alert details.

[1198] This allows users to view notifications and implement pet care advice.

[1199] (Application example 2)

[1200] 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."

[1201] Conventional pet care systems manage pet health by only considering pet behavioral data, and are therefore unable to provide appropriate advice or alerts when the pet owner's emotional state affects pet care. Furthermore, there are limited means to provide customized services for pets and owners in real time. To solve these problems, a new system is needed that integrates and analyzes pet behavioral data and owner emotional data, and provides pet care based on the results.

[1202] 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 pet behavioral data using a wearable device attached to a pet, means for transmitting the collected data from the wearable device to the server, means for analyzing the behavioral data using a generative AI model in the server and generating advice on the pet's health condition, means for collecting user emotion data including an emotion engine that recognizes the user's emotions, means for integrating and analyzing the emotion data from the emotion engine and the pet behavioral data, means for adjusting pet care advice based on the emotion data, means for notifying the generated advice to a user terminal, means for displaying the generated advice on smart glasses, means for promptly notifying the user of an alert when the server detects an abnormality, and means for displaying the alert in real time on a terminal including the smart glasses. This makes it possible to integrate and analyze the pet behavioral data and the user's emotional state and provide customized advice and alerts in real time.

[1203] A "wearable device" is a device that is attached to a pet and collects behavioral data such as heart rate, exercise volume, and location information.

[1204] The "server" is a computer system that stores data collected from pets and users and analyzes it using a generative AI model.

[1205] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates advice and alerts based on the pet's health and the user's emotional state.

[1206] An "emotion engine" is a device that uses a camera and microphone to analyze a user's facial expressions, voice, and behavior to collect emotional data.

[1207] "Smart glasses" are glasses-type devices worn by users that display information such as advice and alerts.

[1208] A "user terminal" is a device such as a smartphone or tablet that a user uses to receive behavioral data and advice about their pet.

[1209] "Behavioral data" refers to data related to the pet's activities, such as the pet's heart rate, amount of exercise, and location information.

[1210] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions, voice, and behavior.

[1211] "Advice" is a recommendation provided based on the analysis results of the generative AI model, depending on the pet's health condition and the user's emotional state.

[1212] An "alert" is a warning message that is provided when an abnormality is detected in the pet's behavior data or the user's emotion data.

[1213] The system of the present invention includes a wearable device attached to a pet, an emotion engine that recognizes the user's emotion, a server, and a user terminal. Specific embodiments will be described below.

[1214] Overall structure

[1215] server

[1216] The server stores pet behavior data and user emotional data, and analyzes them using a generative AI model. Specifically, the server integrates data collected by the wearable device attached to the pet with the user's emotional data collected by the emotion engine to generate advice and alerts based on the pet's health and emotional state in real time.

[1217] Wearable devices

[1218] Wearable devices include heart rate sensors, acceleration sensors, and GPS sensors to obtain location information. Data from these devices is sent to a server at regular intervals using communication methods such as Bluetooth and WiFi.

[1219] Emotion Engine

[1220] The emotion engine is software installed on the user's smartphone or tablet, which uses the camera and microphone to analyze facial expressions, voice, and behavior to collect emotional data.

[1221] User terminal

[1222] User devices are used to notify users of advice and alerts, and include smartphones, tablets, smart glasses, etc. Notifications are sent via push notifications, emails, etc.

[1223] Data collection and transmission

[1224] The wearable device collects real-time information about the pet's heart rate, activity, and location. At the same time, the emotion engine collects the user's emotional state. This data is encrypted and transmitted to a server via the internet.

[1225] Data analysis and advice generation

[1226] The server temporarily stores the received data and performs preprocessing, including removing duplicate data, imputing missing values, and filtering noise. It then analyzes the data using a generative AI model and integrates the pet's health status and the user's emotional state to generate advice and alerts.

[1227] For example, if your pet isn't getting enough exercise, the app will generate advice like, "Today's exercise volume isn't meeting your goal. We recommend a 30-minute walk when you're relaxed." If your pet's heart rate is above the normal range, the app will generate an alert saying, "Your pet's heart rate is too high. If you're feeling stressed, consult a veterinarian immediately."

[1228] User Notifications

[1229] These advice and alerts are sent from the server to the user's device, which displays the notifications in real time, allowing the user to respond immediately.

[1230] Prompt Sentence Examples

[1231] "Hi, based on your pet's current exercise level, it looks like they need a little more activity. My suggestion is that you consider this automatic ball thrower."

[1232] This invention enables the integrated analysis of pet behavior data and the user's emotional state, and provides users with customized advice and alerts in real time, aiming to provide better care for pets and their owners.

[1233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1234] Step 1:

[1235] The wearable device terminal collects behavioral data such as the pet's heart rate, activity level, and location in real time.

[1236] Input: Data from various pet sensors

[1237] Output: Behavioral data (heart rate, exercise amount, location information)

[1238] Specific operation: Data is acquired using the heart rate sensor, acceleration sensor, and GPS sensor and stored in internal storage.

[1239] Step 2:

[1240] The emotion engine analyzes the user's facial expressions, voice, and behavior to collect emotional data.

[1241] Input: User facial, voice, and behavioral data

[1242] Output: Emotion data

[1243] Specific operations: Captures the user's facial expressions with a camera, records audio with a microphone, recognizes emotions using a machine learning model, and creates data.

[1244] Step 3:

[1245] The data collected by the device is sent to the server at regular intervals.

[1246] Input: behavioral data, emotion data

[1247] Output: Send data to the server

[1248] Specific operation: Using Bluetooth or WiFi, the encrypted data is uploaded to a server via the Internet.

[1249] Step 4:

[1250] The server preprocesses the received data, which includes removing duplicates, imputing missing values, and filtering noise.

[1251] Input: behavioral data, emotion data

[1252] Output: Preprocessed data

[1253] Specific operation: Cleanses and normalizes the data stored in the database.

[1254] Step 5:

[1255] The server uses the generated AI model to analyze the preprocessed data and integrates the pet's health condition and the user's emotional state for analysis.

[1256] Input: Preprocessed data

[1257] Output: Analysis results (health status, emotional status)

[1258] Specific behavior: Applying a generative AI model to evaluate the pet's behavioral patterns and the user's emotional data.

[1259] Step 6:

[1260] The server generates advice and alerts based on the analysis results, such as advice and alerts about lack of exercise or abnormal heart rate.

[1261] Input: Analysis results

[1262] Output: Advice, Alert

[1263] Specific behavior: The output of the generative AI model is used to generate messages based on predefined rules.

[1264] Step 7:

[1265] The server transmits the generated advice or alert to the user terminal.

[1266] Input: Advice, Alert

[1267] Output: Notification to user terminal

[1268] Specific behavior: Send advice and alerts via push notifications or emails as specified by the user.

[1269] Step 8:

[1270] The smart glasses that serve as the terminal display advice and alerts to the user.

[1271] Input: Advice, Alert

[1272] Output: What is displayed to the user

[1273] Specific operation: Displays messages in real time on the smart glasses display, providing visual feedback to the user.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] [Fourth embodiment]

[1278] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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).

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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."

[1291] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1292] Overall structure

[1293] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[1294] Data collection

[1295] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[1296] Data transmission

[1297] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[1298] Data reception and analysis

[1299] Server: The server receives the data sent from the wearable device and stores it in a database, while preprocessing the data by filtering noise and imputing missing values.

[1300] Data Analysis and Inference

[1301] Server: The server analyzes the data using a generative AI model that assesses the amount of exercise, rest, and feeding required based on the pet's behavioral data, and also detects abnormalities in heart rate and activity patterns.

[1302] Based on the analysis results, the app generates specific advice about your pet's health. For example, if your pet isn't getting enough exercise, it might say, "Today's exercise volume hasn't reached your goal. We recommend a 30-minute walk." If your pet's heart rate is above the normal range, it might generate an alert, saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1303] User Notifications

[1304] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails.

[1305] Device: The user device will notify the user of the received notification visually or audibly. The user can then check the notification content through the application and take appropriate action.

[1306] Specific examples

[1307] Example 1: Notification of lack of exercise

[1308] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1309] 2. Terminal: Sends collected data to the server at regular intervals.

[1310] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1311] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend taking a 30-minute walk."

[1312] 5. Server: Sends advice to the user device.

[1313] 6. Terminal: The user terminal receives the notification and notifies the user.

[1314] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[1315] Example 2: Detecting abnormal heart rates

[1316] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1317] 2. Terminal: Sends abnormal data to the server.

[1318] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1319] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1320] 5. Server: Sends the alert to the user device.

[1321] 6. Terminal: The user terminal receives the notification and notifies the user.

[1322] 7. User: Check the notification and consult a veterinarian immediately.

[1323] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[1324] The processing flow will be explained below.

[1325] Step 1:

[1326] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[1327] Step 2:

[1328] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[1329] Step 3:

[1330] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[1331] Step 4:

[1332] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[1333] Step 5:

[1334] Server: The server temporarily accumulates the data received from the terminal and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processing as necessary.

[1335] Step 6:

[1336] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc., and infers the pet's health condition.

[1337] Step 7:

[1338] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1339] Step 8:

[1340] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1341] Step 9:

[1342] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[1343] Step 10:

[1344] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[1345] This is the specific processing flow of the generative AI model that links a pet care app with a smart wearable device, making it possible to monitor the health of pets in real time and provide appropriate care.

[1346] Example 1

[1347] 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."

[1348] Conventional pet health management systems lack the ability to collect and analyze data in real time, making it difficult to accurately assess health conditions or detect abnormalities. Furthermore, the means of notifying users were limited, making it difficult to respond quickly in emergencies. This can delay the timing of taking appropriate action in pet health management, potentially increasing health risks.

[1349] 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.

[1350] In this invention, the server includes means for performing noise filtering and defect completion on pet behavior data, means for detecting anomalies and generating alerts based on a generative AI model, and means for notifying a user terminal of the generated advice and alerts, thereby enabling highly accurate data analysis in real time and rapid anomaly detection, allowing the user to take appropriate action in a timely manner.

[1351] A "wearable device" is a device that is attached to a pet to collect behavioral data such as the amount of exercise, rest time, heart rate, and location information.

[1352] The "server" is a computer system that receives pet behavior data, stores it in a database, and analyzes it using a generative AI model.

[1353] A "generative AI model" is a machine learning model used to analyze pet behavior data and generate health advice and abnormality alerts.

[1354] "Behavioral data" refers to data related to a pet's activities, including the amount of exercise, rest time, heart rate, and location information of the pet.

[1355] "Noise filtering" is a process that removes outliers from collected data and improves the accuracy of the data.

[1356] "Gap filling" is a process of filling in missing parts of collected data with appropriate values.

[1357] "Advice" is information that provides recommended actions to improve your pet's health based on data analyzed using a generative AI model.

[1358] An "alert" is a warning message that quickly notifies you of health abnormalities based on data analyzed using a generative AI model.

[1359] "User terminal" refers to a device for notifying a user of received advice and alerts, such as a smartphone or tablet.

[1360] "Push notification" is a communication method in which a server sends information to a user terminal in real time.

[1361] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1362] Overall structure

[1363] The system consists of a wearable device attached to the pet, a server, and a user device. Pet behavior data is collected from the wearable device and sent to the server. The server analyzes the data using a generative AI model, generates advice about the pet's health and alerts in the event of abnormalities, and notifies the user device.

[1364] Data collection

[1365] Device: The wearable device uses various sensors to collect behavioral data, including the pet's activity level, rest time, heart rate, and location. The device is attached to the pet and records the data in real time. The data is sent to a server at regular intervals (e.g., every 5 minutes).

[1366] Data transmission

[1367] Device: The wearable device transmits collected behavioral data to a server via the internet. The frequency of transmission can be set by the user.

[1368] Data reception and preprocessing

[1369] Server: The server receives the data sent from the wearable device and stores it in a database. At the same time, it preprocesses the data, performs noise filtering, and imputes missing values. For example, MySQL is used as the database system.

[1370] Data Analysis and Inference

[1371] Server: The server analyzes the data using a generative AI model. This generative AI model evaluates the amount of exercise, rest time, and whether the pet is over or underfed based on the pet's behavioral data. It also has the ability to detect abnormalities in heart rate and activity patterns. Based on the analysis results, the server generates specific advice about the pet's health. For example, if the pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1372] User Notifications

[1373] Server: Sends analysis results, generated advice, and alerts to the user's device. The server notifies the user in the form of push notifications or emails. For example, Firebase Cloud Messaging (FCM) is used.

[1374] Device: The user device will notify the user of the received notification visually or audibly. The user can check the notification content through the application and take appropriate action. Specifically, a message will be displayed in the smartphone's notification bar and details will be displayed on the application's notification screen. In the case of an audio notification, the smartphone speaker will read out, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1375] Specific examples

[1376] Example 1: Notification of lack of exercise

[1377] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1378] 2. Terminal: Sends collected data to the server at regular intervals.

[1379] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1380] 4. Server: Determines the "lack of exercise" state and generates advice such as "Today's exercise volume has not reached the goal. We recommend that you take another 3,000 steps."

[1381] 5. Server: Sends advice to the user device.

[1382] 6. Device: The user device receives the notification and displays it in the notification bar.

[1383] 7. User: Check the notification and take your pet for a walk to ensure it gets some exercise.

[1384] Example 2: Detecting abnormal heart rates

[1385] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1386] 2. Terminal: Sends abnormal data to the server.

[1387] 3. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1388] 4. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1389] 5. Server: Sends the alert to the user device.

[1390] 6. Terminal: The user terminal receives the notification and gives an audio notification.

[1391] 7. User: Check the notification and consult a veterinarian immediately.

[1392] Prompt Sentence Examples

[1393] "How do you use a generative AI model to generate notifications if your pet is not getting enough exercise?"

[1394] The above is an embodiment of the present invention, which makes it possible to monitor the health condition of a pet in real time and take appropriate action early.

[1395] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1396] Step 1:

[1397] Terminal: The wearable device uses sensors to collect information on the pet's activity, rest time, heart rate, and location. Input data are various measurements taken by the sensors, such as heart rate of 80 BPM, steps taken of 5,000, and rest time of 5 hours, and location information such as latitude: 35.6895, longitude: 139.6917. Output data are these behavioral data. The wearable device records data in real time and accumulates it in a buffer at regular intervals (e.g., every 5 minutes).

[1398] Step 2:

[1399] Terminal: Sends collected behavioral data to a server via the Internet. Specifically, the collected data (e.g., heart rate, number of steps, rest time, location information) is packaged into packets and sent to the server via Internet Protocol (IP). The input data is the accumulated behavioral data, and the output data is the data packets sent to the server.

[1400] Step 3:

[1401] Server: Receives the transmitted data. Specifically, the server receives data packets through a network interface and stores them in a database system (e.g., MySQL). The input data is the received behavioral data, and the output data is the behavioral data stored in the database.

[1402] Step 4:

[1403] Server: Preprocesses the behavioral data. Specifically, it performs noise filtering (removing outliers) and missing value imputation (e.g., imputing estimated values ​​based on past data). The input data is the behavioral data stored in the database, and the output data is the clean data after preprocessing.

[1404] Step 5:

[1405] Server: Analyzes the preprocessed data using a generative AI model. Specifically, clean data is input into a generative AI model (e.g., built with TensorFlow) to evaluate the pet's exercise, rest time, and dietary needs, and to detect abnormalities in heart rate and activity patterns. The input data is the preprocessed clean data, and the output data is the analysis results (e.g., advice or alerts).

[1406] Step 6:

[1407] Server: Based on the analysis results, it generates specific advice and abnormality alerts regarding the pet's health condition. For example, if the pet is not getting enough exercise, it generates advice such as, "Today's exercise volume has not reached the goal. We recommend walking 3,000 more steps." If the heart rate is outside the normal range, it generates an alert such as, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the analysis result of the generative AI model, and the output data is the generated advice and alerts.

[1408] Step 7:

[1409] Server: Notifies the user device of the generated advice and alerts. Specifically, information is sent to the user's smartphone or tablet using push notifications or email. For example, Firebase Cloud Messaging (FCM) is used. The input data is the generated advice and alerts, and the output data is the notified message.

[1410] Step 8:

[1411] Device: The user device notifies the user of the received notification visually or audibly. Specifically, it displays a message in the notification bar and displays details on the application's notification screen. In the case of an audio notification, it reads out through the smartphone speaker, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." The input data is the received notification message, and the output data is the notification presented to the user.

[1412] Step 9:

[1413] User: The user checks the notification and takes appropriate action. For example, after checking the notification, they take their pet for a walk to increase their exercise. In case of an abnormal alert, they consult a veterinarian immediately. The input data is the notification content, and the output is the action taken by the user.

[1414] This is the specific processing flow of this system. It monitors the health condition of pets in real time and can take necessary measures promptly.

[1415] (Application example 1)

[1416] 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."

[1417] Real-time monitoring of pet health and behavior patterns is important for ensuring pet safety and maintaining their health. However, conventional systems have difficulty quickly and accurately detecting abnormalities in pet location information or behavior patterns and effectively notifying users. Furthermore, the lack of functionality to prompt users to take immediate action against abnormal behavior or health risks makes it difficult to adequately protect pet safety and health.

[1418] 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.

[1419] In this invention, the server includes means for analyzing the behavioral data using a generative AI model to generate advice and alerts regarding the pet's health condition and behavioral patterns, means for notifying the user terminal of the generated advice and alerts, and means for detecting abnormal movement patterns and behavioral patterns, thereby making it possible to prompt the user to check the safety of their pet and take security measures, and to notify the user of alerts quickly and accurately.

[1420] A "wearable device" is a device that is attached to a pet and uses various sensors to collect behavioral data and biometric information about the pet.

[1421] "Behavioral data" refers to data including biological and behavioral information such as the pet's heart rate, amount of exercise, and location information.

[1422] A "generative AI model" is a model based on artificial intelligence algorithms that analyzes collected data to evaluate behavioral patterns and health conditions.

[1423] A "server" is a computer system for receiving and analyzing data sent from a wearable device.

[1424] "Advice" refers to instructions or suggestions provided to maintain or improve your pet's health based on data analyzed by the generative AI model.

[1425] An "alert" is a notification that warns or warns the user when an abnormality is detected by the generative AI model.

[1426] A "user terminal" is a communication terminal that allows pet owners to receive and check advice and alerts, and is a device such as a smartphone or tablet.

[1427] "Abnormal movement patterns" refers to sudden or suspicious movements of pets that deviate from their normal behavior.

[1428] "Crime prevention measures" are measures that users take to ensure the safety of their pets, so that they do not get lost or are the victim of theft.

[1429] This invention is a system consisting of a wearable device attached to a pet, a server, and a user terminal. This system monitors the pet's health condition and behavioral patterns in real time and provides appropriate advice and alerts to the user to maintain the pet's safety and health.

[1430] Overall structure

[1431] The system includes the following main components:

[1432] 1. Wearable devices: These are worn by pets and collect behavioral data such as heart rate, activity, and location.

[1433] 2. Server: Receives behavioral data sent from the wearable device and analyzes it using generative AI models, generating advice and alerts on pet health and behavioral patterns.

[1434] 3. User terminal: Receives advice and alerts sent from the server and notifies the user.

[1435] Data collection

[1436] The wearable device is equipped with various sensors that collect data such as your pet's heart rate, activity level, and location in real time, and this data is sent to a server at regular intervals.

[1437] Data analysis

[1438] The server preprocesses the received behavioral data, filtering out noise and filling in missing values. It then uses a generative AI model to analyze the data and evaluate the pet's health and behavioral patterns. For example, if your pet is not getting enough exercise, it generates advice such as, "Today's exercise volume is not meeting your goal. We recommend a 30-minute walk." If the pet's heart rate is above the normal range, it generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1439] User Notifications

[1440] The generated advice and alerts are sent from the server to the user's device, which then notifies the user visually or audibly of the received notification. By checking this, the user can take appropriate action regarding their pet's health condition and behavioral patterns.

[1441] Hardware and software used

[1442] The system uses the following specific hardware and software:

[1443] Hardware: Wearable devices (e.g., FidoTrack), smartphones, tablets

[1444] Software: Python, requests library, NumPy library

[1445] Specific examples

[1446] Example 1: Notification of lack of exercise

[1447] The wearable device detects that the pet's daily step count has not reached the target value and sends the data to the server. The server analyzes the data and generates advice such as "Today's exercise volume has not reached the target. We recommend a 30-minute walk," and notifies the user's device.

[1448] Example 2: Detecting abnormal heart rates

[1449] The wearable device detects that the pet's heart rate is out of the normal range and sends the data to the server, which analyzes the data and generates an alert saying, "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible."

[1450] Prompt Sentence Examples

[1451] "Use FidoTrack devices to collect your pet's behavioral data every five minutes, analyze and notify you of abnormal behavioral patterns and health conditions, and detect abnormalities in heart rate and location."

[1452] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1453] Step 1:

[1454] The wearable device collects your pet's heart rate, activity level, and location information. The input is data from various sensors, and the output is collected behavioral data. This allows you to understand your pet's real-time condition.

[1455] Step 2:

[1456] The wearable device sends the data it collects to a server at regular intervals. The input is behavioral data stored inside the wearable device, and the output is data sent to the server via the Internet. This allows the collected data to reach the server.

[1457] Step 3:

[1458] The server receives the data sent from the wearable device. The input is the behavioral data sent from the wearable device, and the output is the data stored in the server's internal database. This makes the data available for analysis.

[1459] Step 4:

[1460] The server preprocesses the data it receives, specifically by filtering noise and imputing missing values. The input is raw data stored on the server, and the output is preprocessed, clean data. This improves the quality of the data.

[1461] Step 5:

[1462] The server analyzes the data using the generative AI model. The input is pre-processed behavioral data, and the output is an assessment of the pet's health and behavioral patterns. Specifically, it evaluates the amount of exercise, rest time, and dietary information, and detects abnormal heart rate and movement patterns. This allows it to generate advice and alerts.

[1463] Step 6:

[1464] The server generates advice and alerts based on the analysis results. The input is the analysis results of the generative AI model, and the output is a specific advice or alert message. For example, "Today's exercise volume is not meeting the goal. We recommend a 30-minute walk" or "Your pet's heart rate is too high. Please consult a veterinarian as soon as possible." This provides the user with information to take appropriate action.

[1465] Step 7:

[1466] The server sends the generated advice and alerts to the user's terminal. The input is the advice and alert message, and the output is a notification sent to the user's terminal via the Internet. This allows the user to quickly understand the status of their pet.

[1467] Step 8:

[1468] The user device receives notifications and notifies them visually or audibly. The input is the notification sent from the server, and the output is the actual notification display or audio alert for the user. This allows the user to instantly know the health status of their pet or any abnormal behavior.

[1469] Step 9:

[1470] The user checks the notification content and takes appropriate action. The input is the notification content displayed on the user's device, and the output is the user's specific action (e.g., taking the pet for a walk, consulting a veterinarian). This ensures the health and safety of the pet.

[1471] 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.

[1472] The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's condition into consideration when managing the health of a pet.

[1473] Overall structure

[1474] This system consists of a wearable device attached to the pet, a server, a user terminal, and an emotion engine. The pet's behavioral data is collected from the wearable device, and emotion data is collected from the emotion engine. Both sets of data are sent to the server and analyzed by a generative AI model. Advice and alerts generated based on the analysis results are sent to the user terminal.

[1475] Data collection

[1476] Device: The wearable device collects real-time behavioral data such as exercise volume, rest time, heart rate, and location information through various sensors (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor) attached to the pet. The data is sent to a server at regular intervals.

[1477] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data.

[1478] Data transmission

[1479] Terminal: The wearable device and emotion engine each send collected behavioral data and emotion data to a server via the Internet. The data is encrypted to ensure security.

[1480] Data reception and analysis

[1481] Server: The server receives data sent from the wearable device and emotion engine and stores it in a database. The received data is first preprocessed to check for duplicates and missing values ​​and to filter noise.

[1482] Data Analysis and Inference

[1483] Server: The server analyzes the data using a generative AI model. The generative AI model evaluates the amount of exercise, rest time, dietary data, etc. to infer the pet's health condition. It also integrates the user's emotional data for analysis. For example, if the user is feeling stressed, the server takes that information into account and adjusts the advice on pet care.

[1484] Generate advice alerts

[1485] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[1486] User Notifications

[1487] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1488] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[1489] Specific examples

[1490] Example 1: Notification of lack of exercise

[1491] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1492] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[1493] 3. Terminal: Sends collected data to the server at regular intervals.

[1494] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1495] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[1496] 6. Server: Sends advice to the user terminal.

[1497] 7. Terminal: The user terminal receives the notification and notifies the user.

[1498] 8. User: Checks notification and takes pet for a walk.

[1499] Example 2: Detecting abnormal heart rates

[1500] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1501] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[1502] 3. Terminal: Sends abnormal data and emotion data to the server.

[1503] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1504] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1505] 6. Server: Sends the alert to the user device.

[1506] 7. Terminal: The user terminal receives the notification and notifies the user.

[1507] 8. User: Check the notification and consult a veterinarian immediately.

[1508] The above is an embodiment of the present invention, which makes it possible to provide appropriate care that takes into consideration not only the health condition of a pet but also the emotional state of the user.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] User: Installs the application and performs initial setup. Specifically, the user pairs the wearable device with a smartphone or PC and inputs basic information about the owner and pet, such as their name, breed, age, and weight. The user also configures custom settings such as target exercise volume and feeding patterns.

[1512] Step 2:

[1513] Device: The wearable device collects real-time behavioral data such as the amount of exercise, rest time, heart rate, and location information through various sensors in the pet (e.g., step sensor, acceleration sensor, heart rate sensor, GPS sensor).

[1514] Step 3:

[1515] Device: Collected behavioral data is temporarily stored in a local database on the device. The data is set to be sent to the server at regular intervals (e.g., every 5 minutes).

[1516] Step 4:

[1517] Terminal: The terminal periodically reads collected data from the local database and sends it to the server via the Internet. The data is encrypted to ensure security.

[1518] Step 5:

[1519] Device: Using the camera and microphone on the user device, the emotion engine analyzes the user's facial expressions and voice to collect the user's emotional state (e.g., joy, stress, excitement, etc.) in real time.

[1520] Step 6:

[1521] Terminal: Collected user emotion data is sent to the server and integrated with behavioral data.

[1522] Step 7:

[1523] Server: Organizes the behavioral and emotional data received from the devices and stores it in a database. At the same time, it checks for duplicates and missing values ​​in the data and performs cleaning processes as necessary.

[1524] Step 8:

[1525] Server: The preprocessed data is input into the generative AI model for analysis. The generative AI model evaluates the amount of exercise, rest time, and dietary data to infer the pet's health condition. It also integrates the user's emotional data for analysis.

[1526] Step 9:

[1527] Server: Generates specific advice and necessary alerts based on the results of data analysis. For example, "Today's exercise volume has not reached the goal. We recommend a 30-minute walk when the user is relaxed." or "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1528] Step 10:

[1529] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1530] Step 11:

[1531] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. The application displays the notification details.

[1532] Step 12:

[1533] User: Check the notifications and take necessary actions, for example, take your pet for a walk if you receive a notification about lack of exercise, or consult a veterinarian immediately if you receive a notification about an abnormal heart rate.

[1534] The above is a concrete processing flow of combining a pet care system with an emotion engine that recognizes the user's emotions. This configuration makes it possible to provide appropriate care that takes into account not only the pet's health condition but also the user's emotional state.

[1535] Example 2

[1536] 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."

[1537] Conventional pet care systems primarily monitor only the health of pets and do not consider the emotional state of the user. As a result, they are unable to provide appropriate pet care tailored to the user's emotional state, making it difficult to optimally manage the health of both the pet and the user. Another issue is that pet care advice is not appropriately adjusted when the user is stressed or fatigued.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1539] In this invention, the server includes means for collecting pet behavioral data using a wearable device attached to the pet, means for transmitting the collected data from the wearable device to the server, means for collecting user emotion data using an emotion engine installed in the user terminal, means for transmitting the user emotion data to the server, means for analyzing the behavioral data and emotion data using a generative AI model and generating advice based on the pet's health condition and the user's emotional state, and means for notifying the user terminal of the generated advice, thereby enabling more personalized pet care that takes into account not only the pet's health condition but also the user's emotional state.

[1540] "Pet" refers to an animal kept by an owner in a household.

[1541] A "wearable device" refers to an electronic device that is attached to a pet to collect behavioral and biological data.

[1542] "Behavioral data" refers to information about your pet's activities, such as its amount of exercise, heart rate, rest time, and location.

[1543] "User terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[1544] An "emotion engine" refers to software and hardware that analyzes a user's facial expressions and voice and estimates their emotional state.

[1545] "Emotion data" is information about the user's emotional state, such as relaxation, stress, fatigue, etc.

[1546] "Server" refers to a remote computer system for receiving, storing, and analyzing data over a network.

[1547] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates insights and advice.

[1548] "Advice" refers to recommended actions or instructions regarding pet care.

[1549] "Notification" refers to a message or alert that conveys information to a user terminal.

[1550] "Data transmission" refers to the act of sending collected data to other systems via the Internet.

[1551] "Data reception" refers to the act of receiving data sent from another system.

[1552] "Data preprocessing" refers to the process of checking for and organizing data duplication and noise as a preliminary step to analysis.

[1553] "Inference" refers to the act of drawing predictions or conclusions based on the results of data analysis.

[1554] "Push notification" refers to a communication method that instantly displays information on a user's device.

[1555] MODE FOR CARRYING OUT THE INVENTION

[1556] The following describes the embodiments of the present invention. The present invention combines a pet care system with an emotion engine that recognizes the user's emotions, making it possible to take the user's emotional state into consideration when managing the health of a pet. This system consists of the following main hardware and software components:

[1557] 1. Wearable devices attached to pets

[1558] 2. Server

[1559] 3. User Device

[1560] 4. Emotion Engine

[1561] Data collection

[1562] Device: The wearable device uses sensors (e.g., step count sensor, acceleration sensor, heart rate sensor, GPS sensor) to collect data on the pet's behavior. Data such as exercise volume, rest time, heart rate, and location information is collected in real time and stored in the internal memory at regular intervals.

[1563] Device: The emotion engine installed in the user device uses sensors such as a camera and microphone to analyze the user's facial expressions, voice, and behavior to collect emotional data. The emotion engine estimates the user's current emotional state (relaxed, stressed, tired, etc.) and temporarily stores that data.

[1564] Data transmission

[1565] Terminal: The wearable device and user terminal each encrypt the collected behavioral and emotional data and send it to the server via the Internet. A security protocol is used for data transmission to ensure data confidentiality.

[1566] Data reception and preprocessing

[1567] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is first preprocessed to remove duplicate data, filter noise, and fill in missing data. This improves the quality of the data and increases the accuracy of the analysis.

[1568] Data Analysis and Inference

[1569] Server: The server analyzes the behavioral and emotional data using a generative AI model. The generative AI model evaluates the pet's exercise, rest time, heart rate, and dietary data to infer the pet's health condition. It also integrates and analyzes the user's emotional data to generate appropriate pet care advice. For example, advice based on the user's emotions is provided, such as recommending taking the pet for a walk when the user is relaxed.

[1570] Generate advice alerts

[1571] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet is not getting enough exercise, the server generates advice such as, "Today's exercise volume has not reached the target. We recommend a 30-minute walk when the user is relaxed." If a pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately."

[1572] User Notifications

[1573] Server: Generated advice and alerts are sent to the user's device via push notification, email, or other methods selected by the user.

[1574] Terminal: The user terminal receives the notification sent from the server and notifies the user visually or audibly. The user can view the notification details through the application and take necessary actions.

[1575] Specific examples

[1576] Example 1: Notification of lack of exercise

[1577] 1. Device: The wearable device detects that the pet's daily step count is not meeting the target.

[1578] 2. Terminal: The emotion engine collects the user's current emotional state and determines that the user is relaxed.

[1579] 3. Terminal: Sends collected data to the server at regular intervals.

[1580] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1581] 5. Server: Determines the state of inactivity and generates advice such as, "Today's exercise volume has not reached the goal. We recommend that you take a 30-minute walk when you are relaxed."

[1582] 6. Server: Sends advice to the user terminal.

[1583] 7. Terminal: The user terminal receives the notification and notifies the user.

[1584] 8. User: Checks notification and takes pet for a walk.

[1585] Example 2: Detecting abnormal heart rates

[1586] 1. Device: The wearable device detects that your pet's heart rate is outside the normal range.

[1587] 2. Device: The emotion engine collects the user's current emotional state and determines that the user is feeling stressed.

[1588] 3. Terminal: Sends abnormal data and emotion data to the server.

[1589] 4. Server: Preprocesses the received data and analyzes it with a generative AI model.

[1590] 5. Server: Determines abnormal heart rate and generates an alert saying, "Your pet's heart rate is too high. If the user is stressed, please consult a veterinarian immediately."

[1591] 6. Server: Sends the alert to the user device.

[1592] 7. Terminal: The user terminal receives the notification and notifies the user.

[1593] 8. User: Check the notification and consult a veterinarian immediately.

[1594] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1595] Step 1:

[1596] Data collection using wearable devices

[1597] Terminal: The wearable device uses a step sensor, accelerometer, heart rate sensor, and GPS sensor to obtain real-time information on the pet's activity, rest time, heart rate, and location. The device stores this data in its internal memory at regular intervals. The input data is raw data sent from the sensors, and the output data is processed behavioral data. For example, it records the number of steps a pet takes in a day and heart rate fluctuations.

[1598] Step 2:

[1599] Emotion data collection using an emotion engine

[1600] Device: The emotion engine installed on the user device uses a camera and microphone to capture the user's facial expressions and voice. From this data, the user's emotional state (relaxed, stressed, tired, etc.) is estimated and temporarily stored. The input data is the raw data sent from the camera and microphone, and the output data is analyzed emotional data. For example, it determines whether the user is smiling, angry, or stressed.

[1601] Step 3:

[1602] Data transmission from wearable devices and user terminals

[1603] Terminal: The wearable device and user terminal send the collected behavioral and emotional data to a server via the Internet. The data is encrypted to maintain security. The input data is the behavioral and emotional data stored in the internal memory, and the output data is the data sent to the server. For example, the wearable device transfers the data to a smartphone via Bluetooth, and then sends it to the server via the Internet.

[1604] Step 4:

[1605] Data reception and preprocessing by the server

[1606] Server: The server receives data sent from the wearable device and user terminal and stores it in a database. The received data is preprocessed to remove duplicate data, filter noise, and fill in missing data. The input data is the received behavioral and emotional data, and the output data is the preprocessed, clean data. For example, it removes noise from GPS data and fills in missing parts of heart rate data.

[1607] Step 5:

[1608] Data analysis and inference using generative AI models

[1609] Server: The server analyzes the preprocessed behavioral data and emotional data using a generative AI model. The generative AI model evaluates this data in a comprehensive manner, estimates the pet's health condition, and generates pet care advice based on this information, taking the user's emotional state into account. The input data are the preprocessed behavioral data and emotional data, and the output data are the analysis and inference results. For example, if a pet is not getting enough exercise, the server recommends taking it for a walk when the user is relaxed.

[1610] Step 6:

[1611] Generate advice alerts

[1612] Server: Generates specific advice and alerts based on the results of data analysis. For example, if a pet's exercise level is low, the server generates advice such as, "Today's exercise level has not reached the target. We recommend a 30-minute walk when the user is relaxed." If the pet's heart rate is above the normal range, the server generates an alert such as, "Your pet's heart rate is too high. If the user is feeling stressed, consult a veterinarian immediately." The input data are the analysis and inference results, and the output data are specific advice and alerts.

[1613] Step 7:

[1614] Sending notifications to user devices

[1615] Server: Sends the generated advice and alerts to the user's device. The input data is the advice and alert, and the output data is the data sent to the user's device. Notifications are sent via the method selected by the user, such as push notification or email.

[1616] Device: The user device receives the notification sent from the server and notifies the user visually or audibly. Notification details are displayed through the application. For example, a push notification is displayed on the smartphone, allowing the user to check the advice and alert details.

[1617] This allows users to view notifications and implement pet care advice.

[1618] (Application example 2)

[1619] 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."

[1620] Conventional pet care systems manage pet health by only considering pet behavioral data, and are therefore unable to provide appropriate advice or alerts when the pet owner's emotional state affects pet care. Furthermore, there are limited means to provide customized services for pets and owners in real time. To solve these problems, a new system is needed that integrates and analyzes pet behavioral data and owner emotional data, and provides pet care based on the results.

[1621] 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 pet behavioral data using a wearable device attached to a pet, means for transmitting the collected data from the wearable device to the server, means for analyzing the behavioral data using a generative AI model in the server and generating advice on the pet's health condition, means for collecting user emotion data including an emotion engine that recognizes the user's emotions, means for integrating and analyzing the emotion data from the emotion engine and the pet behavioral data, means for adjusting pet care advice based on the emotion data, means for notifying the generated advice to a user terminal, means for displaying the generated advice on smart glasses, means for promptly notifying the user of an alert when the server detects an abnormality, and means for displaying the alert in real time on a terminal including the smart glasses. This makes it possible to integrate and analyze the pet behavioral data and the user's emotional state and provide customized advice and alerts in real time.

[1622] A "wearable device" is a device that is attached to a pet and collects behavioral data such as heart rate, exercise volume, and location information.

[1623] The "server" is a computer system that stores data collected from pets and users and analyzes it using a generative AI model.

[1624] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates advice and alerts based on the pet's health and the user's emotional state.

[1625] An "emotion engine" is a device that uses a camera and microphone to analyze a user's facial expressions, voice, and behavior to collect emotional data.

[1626] "Smart glasses" are glasses-type devices worn by users that display information such as advice and alerts.

[1627] A "user terminal" is a device such as a smartphone or tablet that a user uses to receive behavioral data and advice about their pet.

[1628] "Behavioral data" refers to data related to the pet's activities, such as the pet's heart rate, amount of exercise, and location information.

[1629] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions, voice, and behavior.

[1630] "Advice" is a recommendation provided based on the analysis results of the generative AI model, depending on the pet's health condition and the user's emotional state.

[1631] An "alert" is a warning message that is provided when an abnormality is detected in the pet's behavior data or the user's emotion data.

[1632] The system of the present invention includes a wearable device attached to a pet, an emotion engine that recognizes the user's emotion, a server, and a user terminal. Specific embodiments will be described below.

[1633] Overall structure

[1634] server

[1635] The server stores pet behavior data and user emotional data, and analyzes them using a generative AI model. Specifically, the server integrates data collected by the wearable device attached to the pet with the user's emotional data collected by the emotion engine to generate advice and alerts based on the pet's health and emotional state in real time.

[1636] Wearable devices

[1637] Wearable devices include heart rate sensors, acceleration sensors, and GPS sensors to obtain location information. Data from these devices is sent to a server at regular intervals using communication methods such as Bluetooth and WiFi.

[1638] Emotion Engine

[1639] The emotion engine is software installed on the user's smartphone or tablet, which uses the camera and microphone to analyze facial expressions, voice, and behavior to collect emotional data.

[1640] User terminal

[1641] User devices are used to notify users of advice and alerts, and include smartphones, tablets, smart glasses, etc. Notifications are sent via push notifications, emails, etc.

[1642] Data collection and transmission

[1643] The wearable device collects real-time information about the pet's heart rate, activity, and location. At the same time, the emotion engine collects the user's emotional state. This data is encrypted and transmitted to a server via the internet.

[1644] Data analysis and advice generation

[1645] The server temporarily stores the received data and performs preprocessing, including removing duplicate data, imputing missing values, and filtering noise. It then analyzes the data using a generative AI model and integrates the pet's health status and the user's emotional state to generate advice and alerts.

[1646] For example, if your pet isn't getting enough exercise, the app will generate advice like, "Today's exercise volume isn't meeting your goal. We recommend a 30-minute walk when you're relaxed." If your pet's heart rate is above the normal range, the app will generate an alert saying, "Your pet's heart rate is too high. If you're feeling stressed, consult a veterinarian immediately."

[1647] User Notifications

[1648] These advice and alerts are sent from the server to the user's device, which displays the notifications in real time, allowing the user to respond immediately.

[1649] Prompt Sentence Examples

[1650] "Hi, based on your pet's current exercise level, it looks like they need a little more activity. My suggestion is that you consider this automatic ball thrower."

[1651] This invention enables the integrated analysis of pet behavior data and the user's emotional state, and provides users with customized advice and alerts in real time, aiming to provide better care for pets and their owners.

[1652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1653] Step 1:

[1654] The wearable device terminal collects behavioral data such as the pet's heart rate, activity level, and location in real time.

[1655] Input: Data from various pet sensors

[1656] Output: Behavioral data (heart rate, exercise amount, location information)

[1657] Specific operation: Data is acquired using the heart rate sensor, acceleration sensor, and GPS sensor and stored in internal storage.

[1658] Step 2:

[1659] The emotion engine analyzes the user's facial expressions, voice, and behavior to collect emotional data.

[1660] Input: User facial, voice, and behavioral data

[1661] Output: Emotion data

[1662] Specific operations: Captures the user's facial expressions with a camera, records audio with a microphone, recognizes emotions using a machine learning model, and creates data.

[1663] Step 3:

[1664] The data collected by the device is sent to the server at regular intervals.

[1665] Input: behavioral data, emotion data

[1666] Output: Send data to the server

[1667] Specific operation: Using Bluetooth or WiFi, the encrypted data is uploaded to a server via the Internet.

[1668] Step 4:

[1669] The server preprocesses the received data, which includes removing duplicates, imputing missing values, and filtering noise.

[1670] Input: behavioral data, emotion data

[1671] Output: Preprocessed data

[1672] Specific operation: Cleanses and normalizes the data stored in the database.

[1673] Step 5:

[1674] The server uses the generated AI model to analyze the preprocessed data and integrates the pet's health condition and the user's emotional state for analysis.

[1675] Input: Preprocessed data

[1676] Output: Analysis results (health status, emotional status)

[1677] Specific behavior: Applying a generative AI model to evaluate the pet's behavioral patterns and the user's emotional data.

[1678] Step 6:

[1679] The server generates advice and alerts based on the analysis results, such as advice and alerts about lack of exercise or abnormal heart rate.

[1680] Input: Analysis results

[1681] Output: Advice, Alert

[1682] Specific behavior: The output of the generative AI model is used to generate messages based on predefined rules.

[1683] Step 7:

[1684] The server transmits the generated advice or alert to the user terminal.

[1685] Input: Advice, Alert

[1686] Output: Notification to user terminal

[1687] Specific behavior: Send advice and alerts via push notifications or emails as specified by the user.

[1688] Step 8:

[1689] The smart glasses that serve as the terminal display advice and alerts to the user.

[1690] Input: Advice, Alert

[1691] Output: What is displayed to the user

[1692] Specific operation: Displays messages in real time on the smart glasses display, providing visual feedback to the user.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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).

[1700] 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.

[1701] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1702] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1703] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1704] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1705] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1706] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1707] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1708] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1709] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1710] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1711] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1712] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1713] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1714] The following is further disclosed regarding the above embodiment.

[1715] (Claim 1)

[1716] A means for collecting behavioral data of a pet using a wearable device attached to the pet;

[1717] means for transmitting data collected from the wearable device to a server;

[1718] means, in the server, for analyzing the behavioral data using a generative AI model to generate advice regarding the health status of the pet;

[1719] means for notifying a user terminal of the generated advice;

[1720] A system including:

[1721] (Claim 2)

[1722] A means for including heart rate, exercise amount, and location information in the pet's behavior data;

[1723] In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information;

[1724] 10. The system of claim 1, comprising:

[1725] (Claim 3)

[1726] a means for transmitting data from the wearable device to a server at regular intervals;

[1727] means for promptly notifying a user of an alert when the server detects an abnormality;

[1728] 10. The system of claim 1, comprising:

[1729] "Example 1"

[1730] (Claim 1)

[1731] A means for collecting behavioral data of a pet using a wearable device attached to the pet;

[1732] means for transmitting data collected from the wearable device to a server;

[1733] means, in the server, for analyzing the behavioral data using a generative AI model to generate advice regarding the health status of the pet;

[1734] means for performing noise filtering and defect imputation on the behavioral data;

[1735] means for detecting an anomaly based on the generated AI model and generating an alert;

[1736] means for notifying a user terminal of the generated advice and alert;

[1737] A system including:

[1738] (Claim 2)

[1739] A means for including heart rate, amount of exercise, and location information in the behavioral data;

[1740] In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information;

[1741] means for detecting abnormalities in heart rate or activity patterns in said analysis;

[1742] 10. The system of claim 1, comprising:

[1743] (Claim 3)

[1744] a means for transmitting data from the wearable device to a server at regular intervals;

[1745] means for the server to send the generated advice and alert to a user terminal via push notification or email;

[1746] means for visually or audibly notifying the user of the notification received by the user terminal;

[1747] 10. The system of claim 1, comprising:

[1748] "Application Example 1"

[1749] (Claim 1)

[1750] A means for collecting behavioral data of a pet using a wearable device attached to the pet;

[1751] means for transmitting data collected from the wearable device to a server;

[1752] means, in the server, for analyzing the behavioral data using a generative AI model to generate advice and alerts regarding the pet's health and behavioral patterns;

[1753] means for notifying a user terminal of the generated advice and alert;

[1754] A system including:

[1755] (Claim 2)

[1756] A means for including heart rate, exercise amount, and location information in the pet's behavior data;

[1757] In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information;

[1758] means for detecting abnormal movement or behavior patterns;

[1759] 10. The system of claim 1, comprising:

[1760] (Claim 3)

[1761] a means for transmitting data from the wearable device to a server at regular intervals;

[1762] means for promptly notifying a user of an alert when the server detects an abnormality;

[1763] a means for prompting a user to check safety and take crime prevention measures based on the alert;

[1764] 10. The system of claim 1, comprising:

[1765] "Example 2: Combining Emotion Engines"

[1766] (Claim 1)

[1767] A means for collecting behavioral data of a pet using a wearable device attached to the pet;

[1768] means for transmitting data collected from the wearable device to a server;

[1769] means for collecting user emotion data using an emotion engine installed in the user terminal;

[1770] means for transmitting the user's emotion data to a server;

[1771] a means for analyzing the behavioral data and emotional data using a generative AI model in the server and generating advice based on the pet's health condition and the user's emotional state;

[1772] means for notifying a user terminal of the generated advice;

[1773] A system including:

[1774] (Claim 2)

[1775] A means for including heart rate, exercise amount, and location information in the pet's behavior data;

[1776] In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information;

[1777] 10. The system of claim 1, comprising:

[1778] (Claim 3)

[1779] a means for transmitting data from the wearable device to a server at regular intervals;

[1780] means for promptly notifying a user of an alert when the server detects an abnormality;

[1781] 10. The system of claim 1, comprising:

[1782] "Application example 2 when combining emotion engines"

[1783] (Claim 1)

[1784] A means for collecting behavioral data of a pet using a wearable device attached to the pet;

[1785] means for transmitting data collected from the wearable device to a server;

[1786] means, in the server, for analyzing the behavioral data using a generative AI model to generate advice regarding the health status of the pet;

[1787] means for notifying a user terminal of the generated advice;

[1788] means for collecting user emotion data, the means comprising an emotion engine for recognizing user emotion;

[1789] means for integrating and analyzing emotion data from the emotion engine and behavior data of the pet;

[1790] a means of tailoring pet care advice based on emotional data; and

[1791] A system including:

[1792] (Claim 2)

[1793] A means for including heart rate, exercise amount, and location information in the pet's behavior data;

[1794] In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information;

[1795] means for displaying the generated advice on the smart glasses;

[1796] 10. The system of claim 1, comprising:

[1797] (Claim 3)

[1798] a means for transmitting data from the wearable device to a server at regular intervals;

[1799] means for promptly notifying a user of an alert when the server detects an abnormality;

[1800] a means for displaying the alert in real time by a terminal including smart glasses;

[1801] 10. The system of claim 1, comprising: [Explanation of symbols]

[1802] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting behavioral data of a pet using a wearable device attached to the pet; means for transmitting data collected from the wearable device to a server; means, in the server, for analyzing the behavioral data using a generative AI model to generate advice regarding the health status of the pet; means for notifying a user terminal of the generated advice; A system including:

2. A means for including heart rate, exercise amount, and location information in the pet's behavior data; In the analysis, a means for evaluating the amount of exercise, rest time, and dietary information; The system of claim 1 , comprising:

3. a means for transmitting data from the wearable device to a server at regular intervals; means for promptly notifying a user of an alert when the server detects an abnormality; The system of claim 1 , comprising:

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