System

Abstract

A system is provided.SOLUTION: Means for collecting data of an activity amount, a sleep pattern, and a food intake from an information collecting device attached to a dog; means for transmitting the collected data to an analysis server; A means for analyzing the data, a means for transmitting an analysis result to a terminal and notifying a user of the analysis result, a means for receiving an input of a question from a user or a symptom of a dog and generating an advice by a generative artificial intelligence model, a means for transmitting the generated advice to the terminal and displaying the advice to the user, and a means for providing an online consultation reservation with a veterinarian.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] Conventional dog health management systems have had the problem of being unable to effectively collect and analyze data such as a dog's activity level, sleep patterns, and food intake, and provide appropriate advice to owners in real time. Furthermore, when a dog shows abnormal symptoms, there is a lack of means to respond quickly and appropriately, and prompt communication with a veterinarian is particularly required in emergencies. The present invention aims to solve these problems by comprehensively managing a dog's health, providing appropriate advice to owners, and facilitating online consultations with a veterinarian when necessary. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means: a system including: means for collecting data on activity levels, sleep patterns, and food intake from an information collection device attached to a dog; means for transmitting the collected data to an analysis server; means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data; means for transmitting the analysis results to a terminal and notifying a user; means for receiving questions from the user and input regarding the dog's symptoms and generating advice using the generative artificial intelligence model; means for transmitting the generated advice to the terminal and displaying it to the user; and means for providing an online consultation appointment with a veterinarian.

[0006] Furthermore, the analysis server may further include means for filtering outliers and filling in missing values ​​as data preprocessing, thereby improving the accuracy of collected data. Furthermore, the analysis server may include means for identifying abnormal values ​​in activity level, sleep pattern, and food intake using a generative artificial intelligence model, thereby enabling the rapid detection of abnormalities in the dog's health condition and the provision of appropriate advice and emergency response.

[0007] An "information collection device" is a device worn by a dog to record data such as the dog's activity level, sleep patterns, and food intake.

[0008] An "analysis server" is a central processing unit that receives collected data and analyzes the data using generative artificial intelligence models.

[0009] A "generative artificial intelligence model" is an artificial intelligence algorithm that automatically analyzes and generates advice based on collected data.

[0010] A "terminal" is a device such as a smartphone or tablet used by the owner, which receives and displays notifications and advice from the analysis server.

[0011] "Data preprocessing" is the process of filtering outliers and imputing missing values ​​prior to analysis.

[0012] An "outlier" is a data point that falls outside the normal range and is determined to deviate from the usual pattern.

[0013] "Advice" is information about specific guidelines for action or areas for improvement that is generated by a generative artificial intelligence model and provided to the user.

[0014] "Online Consultation Reservation" is a reservation system that allows pet owners to consult with veterinarians online. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system for effectively collecting and analyzing health data of a dog and providing appropriate advice to a user. Hereinafter, embodiments of the present invention will be described in detail.

[0037] System Configuration

[0038] The system consists of the following main components:

[0039] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[0040] 2. Terminal (smartphone): A device used by the user that receives data collected from the information collection device and sends it to the server.

[0041] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[0042] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0043] 5. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[0044] Program processing

[0045] Data collection and transmission

[0046] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0047] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[0048] Data analysis and advice generation

[0049] 3. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[0050] 4. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[0051] 5. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[0052] User Notification and Advice Display

[0053] 6. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[0054] 7. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[0055] Online consultation

[0056] 8. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[0057] 9. The device sends the user's input to the analysis server via the Internet.

[0058] 10. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[0059] 11. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[0060] 12. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the veterinarian of their choice.

[0061] 13. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[0062] In this way, the present invention realizes a system that can collect and analyze dog health data, provide appropriate advice to the user, and facilitate online consultation with a veterinarian if necessary.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[0066] Step 2:

[0067] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[0068] Step 3:

[0069] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server.

[0070] Step 4:

[0071] The server receives the collected data, stores the data, and starts pre-processing.

[0072] Step 5:

[0073] The server performs preprocessing of the data, such as filtering outliers and imputing missing values, to create a dataset suitable for analysis.

[0074] Step 6:

[0075] The server inputs the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[0076] Step 7:

[0077] The server generates natural language advice based on the analysis results, and the AI ​​model interprets the analysis results and generates specific advice to provide to the user.

[0078] Step 8:

[0079] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and confirms that the transmission is complete.

[0080] Step 9:

[0081] The device notifies the user of the analysis results and advice, and uses the smartphone's notification function to notify the user of new information.

[0082] Step 10:

[0083] The user opens the app and reviews the analysis and advice provided, providing specific actionable information and areas for improvement.

[0084] Step 11:

[0085] The user enters the dog's symptoms and any questions they have into the application, for example, "My dog ​​is coughing," and presses the submit button.

[0086] Step 12:

[0087] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[0088] Step 13:

[0089] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[0090] Step 14:

[0091] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[0092] Step 15:

[0093] The device displays the received advice to the user. The new advice is displayed on the application screen.

[0094] Step 16:

[0095] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[0096] Step 17:

[0097] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[0098] Step 18:

[0099] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[0100] Step 19:

[0101] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[0102] Step 20:

[0103] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[0104] Example 1

[0105] 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."

[0106] Previously, systems for collecting and analyzing dog health data did not centralize the processes of data collection, analysis, and advice generation, making it difficult to provide information to users. Furthermore, a lack of preprocessing, such as outlier detection and missing value imputation, could reduce the reliability of the analysis results. Furthermore, there was a lack of comprehensive support for users to take appropriate measures for their dog's symptoms.

[0107] 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.

[0108] In this invention, the server includes means for collecting biometric data from a data collection device attached to the dog, means for transmitting the collected data to a central processing unit, means for the central processing unit to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing online consultation appointments with a veterinarian. This allows for centralized management of everything from data collection to analysis, advice provision, and online consultation appointments, enabling the user to understand the dog's health condition and easily take appropriate measures.

[0109] A "data collection device" is a device that is attached to a dog and collects biometric data.

[0110] "Biometric data" includes information such as a dog's activity level, sleep patterns, and food intake.

[0111] A "terminal" is a device used by a user to transmit biometric data to a central processing unit and receive analysis results and advice.

[0112] A "central processing unit" is a device that receives data via the internet, analyzes the data using a generative artificial intelligence model, and generates analysis results and advice.

[0113] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[0114] A "question" is an input from the user and is a question about the dog's symptoms or health condition.

[0115] "Advice" refers to instructions or advice about dog health care that is generated by a generative artificial intelligence model and provided to the user.

[0116] "Notification" is a means of transmitting information to inform the user of the analysis results and advice contents.

[0117] "Book an online consultation" is the process by which a user books a consultation with a veterinarian over the Internet.

[0118] "Abnormal value filtering" is a process of detecting and excluding values ​​that are different from normal values ​​from collected biometric data.

[0119] "Missing data value completion" is a process of filling in missing data when there are gaps in the collected biometric data.

[0120] This invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system consists of the following main components: an information collection device (data collection device), a terminal, a central processing unit (server), a generative AI model, and an online consultation reservation system.

[0121] First, the data collection device is attached to the dog and collects real-time biological data such as activity level, sleep patterns, and food intake. Specifically, this data collection device is often implemented as a smart collar.

[0122] Next, the terminal (smartphone) receives the biometric data from the information collection device via Bluetooth or WiFi. The collected data is sent to a central processing unit (server) via the Internet. The smartphone application used here has a built-in communication module for data transmission and reception.

[0123] The central processing unit (server) first preprocesses the received biometric data. Specifically, it filters out outliers and imputes missing values. This allows reliable data to be input into the generative artificial intelligence model.

[0124] Generative AI models analyze preprocessed data to detect outliers and perform trend analysis. An example of a generative AI model used is "GPT-4 (registered trademark)." An example of a prompt for executing the analysis process is "Analyze the dog's health data to identify outliers and trends."

[0125] Based on the analysis results, the central processing unit (server) generates appropriate advice and sends it in text format to the device. The device notifies the user of this advice and displays it on the application screen. For example, the advice displayed might be something like, "You've been getting less sleep recently, so try exercising less."

[0126] Furthermore, when a user inputs their dog's symptoms or questions through the application, the content is also sent to the central processing unit (server), where it is analyzed and answered by the generative AI model. For example, if a user inputs "My dog ​​is coughing," the generated advice will be "If the coughing persists, consult a veterinarian."

[0127] Finally, the terminal is equipped with an online consultation reservation system that allows users to easily make reservations with veterinarians. For example, the user can confirm the reservation by seeing a message asking, "Would you like to make an online consultation appointment tomorrow from 10:00 to 11:00?" The reservation information is sent to the central processing unit (server), and after confirmation, a reservation confirmation notice is sent to the user.

[0128] In this way, this system can centrally manage everything from data collection and analysis to providing advice and even online consultation reservations, providing users with comprehensive and efficient support for managing their dog's health.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Data collection

[0131] The terminal uses Bluetooth or WiFi to collect biometric data from a data collection device attached to the dog. Specifically, it acquires data on activity, sleep patterns, and food intake. As input, it receives real-time data from the data collection device and stores it locally. As output, the collected biometric data is stored in the terminal's memory.

[0132] Specific behavior:

[0133] The device confirms the Bluetooth connection and obtains data such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams" from the data collection device.

[0134] Step 2: Send data

[0135] The terminal transmits the collected data to a central processing unit (server) via the Internet. As input, the collected biometric data is used. As output, the data is transmitted to the server.

[0136] Specific behavior:

[0137] The terminal converts the collected data into packet format and transmits it to a server via the Internet.

[0138] Step 3: Data Preprocessing

[0139] The server preprocesses the received data, filtering outliers and imputing missing values. As input, the biometric data sent from the device is used. As output, the filtered and imputed data is obtained.

[0140] Specific behavior:

[0141] The server analyzes the received data, checks for abnormal values ​​such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams," and filters out invalid data.

[0142] If there is missing data, it is supplemented by referring to past data.

[0143] Step 4: Data analysis

[0144] The server analyzes the preprocessed data using a generative AI model. The preprocessed biometric data is provided as input. The analysis results are obtained as output. "GPT-4" is used as an example of a generative AI model.

[0145] Specific behavior:

[0146] The server inputs the prompt "Analyze the dog's health data and identify outliers and trends" into the AI ​​model and begins the analysis.

[0147] The AI ​​model generates analysis results such as "You've been sleeping less than usual recently."

[0148] Step 5: Advice Generation

[0149] The server generates advice based on the analysis results using a generative AI model. The analysis results of the AI ​​model are used as input, and the generated advice is obtained as output.

[0150] Specific behavior:

[0151] The server converts the analysis results from the generated AI model into text format and creates advice such as, "You've been getting less sleep recently, so you should reduce your exercise."

[0152] Step 6: Advice Notification

[0153] The terminal receives the advice sent from the server and notifies the user. The input is the advice data sent from the server. The output is a notification to the user.

[0154] Specific behavior:

[0155] The device uses the application's notification function to display a notification to the user saying, "You've been sleeping less recently, so you should exercise less."

[0156] Step 7: Display Advice

[0157] The terminal displays the analysis results and advice on the application screen. The input is the advice data sent from the server. The output is the advice that the user can check.

[0158] Specific behavior:

[0159] The device updates the app screen and displays the advice, "You've been getting less sleep recently, so try exercising less."

[0160] Step 8: Book an online consultation

[0161] The user inputs their dog's symptoms and questions through the application. For example, they input information like "My dog ​​is coughing." The device then sends the input to the server via the Internet.

[0162] The server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms, sends the advice to the terminal, which displays it to the user, and provides a screen where the user can schedule an online consultation with a veterinarian.

[0163] Specific behavior:

[0164] The user types "my dog ​​is coughing" into the app and submits it.

[0165] The server inputs the prompt "What should I do if my dog ​​is coughing?" into the generative AI model.

[0166] The AI ​​model generates advice such as "If the cough persists, consult a veterinarian," and the server sends it to the device.

[0167] The device displays the advice and then suggests, "Would you like to book an online consultation?"

[0168] The user confirms the reservation, and the terminal transmits the reservation information to the server.

[0169] The server checks the reservation information and notifies the user, "Your reservation has been confirmed. Your online consultation with the veterinarian will begin tomorrow at 10:00."

[0170] (Application example 1)

[0171] 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."

[0172] When it comes to managing a dog's health, it's important for owners to regularly monitor their pet's health and take prompt action if necessary. However, current methods require owners to collect and analyze data on their dog's activity levels, sleep patterns, food intake, and other factors, which takes a lot of time and effort before they can receive appropriate advice. Additionally, online consultations and appointments at physical clinics are also time-consuming, making it difficult to respond quickly.

[0173] 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.

[0174] In this invention, the server includes means for collecting data on activity level, sleep pattern, and food intake from an information collection device attached to the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it to the user, means for providing online reservations so that the user can make appointments at a physical store, means for the app to synchronize appointment information with the physical store's system, and means for generating and displaying daily and weekly health reports based on the dog's health data. This allows the server to collect and analyze the dog's health data in real time, provide prompt and appropriate advice, and easily handle online consultations and appointments at a physical store.

[0175] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[0176] The "analysis server" is a central processing unit that receives collected data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0177] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[0178] A "terminal" is a device used by a user to receive data collected from an information collection device, transmit the data to a server, and display analysis results and advice.

[0179] "Online Reservation" is a system that allows users to make appointments for medical appointments at physical stores.

[0180] A "health report" is a detailed analysis of your dog's health on a daily or weekly basis.

[0181] The present invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system is composed of an information collection device, a terminal, an analysis server, and an online reservation system.

[0182] System Configuration

[0183] Information gathering device (smart color)

[0184] The information collection device is a device worn by the dog. This device monitors and records data such as the dog's activity level, sleep patterns, and food intake 24 hours a day. For example, it has a built-in acceleration sensor, heart rate sensor, and GPS system, and transmits the data to a terminal via Bluetooth or WiFi.

[0185] Device (smartphone)

[0186] The device is a smartphone used by the user. This device transmits data collected from the smart color to an analysis server in real time, receives analysis results and advice, and notifies the user. Users can also use the app to enter questions and symptoms, and make appointments for medical examinations.

[0187] Analysis Server

[0188] The analysis server is a central processing unit that receives collected health data and analyzes it using a generative artificial intelligence model. This server first preprocesses the data, filtering out outliers and imputing missing values. It then analyzes the data using a generative AI model (e.g., a model using TENSORFLOW®) to identify abnormal patterns and health conditions. Based on the analysis results, it generates personalized health advice and sends it to the device.

[0189] Online Reservation System

[0190] Users can make appointments at pet shops and veterinary clinics through the terminal application. Reservation information is synchronized with the physical store's system in real time, allowing users to make appointments quickly. This is done using WebSocket technology.

[0191] Processing flow

[0192] 1. Data collection: The smart collar uses sensors to record your dog's activity, sleep patterns, food intake, etc. and transmits the data to your device.

[0193] 2. Data transmission: The device sends the received data to the cloud analysis server.

[0194] 3. Data Analysis: The analysis server pre-processes the collected data and then analyzes it using a generative artificial intelligence model to generate health-based advice.

[0195] 4. Notification and display: The analysis results are sent to the device, and the user can view detailed analysis results and advice.

[0196] 5. Appointment: When a user makes an appointment online, the appointment information is synchronized with the store's system.

[0197] Specific examples

[0198] For example, if a dog wearing a "smart collar" is extremely inactive, the analysis server receives the data and uses a generative AI model to detect the abnormality. Based on the analysis results, advice such as "increase walking time" is generated and sent to the device. The owner immediately receives this advice via a smartphone notification and can view detailed data analysis within the app.

[0199] Additionally, if pet owners want to make an appointment at a pet shop or veterinary clinic, they can simply select a date and time within the app and make the appointment online. This appointment information is synchronized in real time with the physical store, allowing for a prompt consultation.

[0200] Prompt Sentence Examples

[0201] To develop a pet health care assistant app, collect, analyze, and provide advice on the following:

[0202] 1. Dog activity level

[0203] 2. Sleep patterns

[0204] 3. Dietary Intake

[0205] Based on this data, please use a TensorFlow model to provide specific advice based on the health status (e.g., adjusting walking time, feeding amount, etc.). Also, please implement a consultation booking function so that users can easily book a consultation at a veterinary clinic or pet shop."

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] Data collection

[0209] The smart collar uses sensors to record your dog's activity levels, sleep patterns, food intake, etc. The smart collar collects this data in real time and transmits it to your device (smartphone) via Bluetooth or WiFi.

[0210] Inputs: Dog activity, sleep, food intake

[0211] Output: Health data sent to the device

[0212] Specific operations: Setting data recording frequency, maintaining Bluetooth or WiFi connection, and data transmission

[0213] Step 2:

[0214] Data transmission

[0215] The device then transmits the health data it receives in real time to a cloud-based analytics server via mobile data or Wi-Fi. The device may compress and anonymize the data before sending it.

[0216] Input: Health data received from smart collar

[0217] Output: Health data sent to the analysis server

[0218] Specific operations: data compression, anonymization, maintaining internet connection, data transmission

[0219] Step 3:

[0220] Data Preprocessing

[0221] The analysis server preprocesses the received data, filtering out abnormal values ​​and filling in missing values. This processing increases the reliability of the data.

[0222] Input: Health data sent from the device

[0223] Output: filtered and imputed data

[0224] Specific operations: Identifying and removing outliers, imputing missing values, normalizing data

[0225] Step 4:

[0226] Data analysis

[0227] The analytics server then inputs the pre-processed data into a generative artificial intelligence model (e.g., a TensorFlow model) for analysis. The AI ​​model identifies anomalies in activity levels, sleep patterns, and dietary intake, and generates health-based advice.

[0228] Input: Preprocessed health data

[0229] Output: Health analysis results and advice

[0230] Specific operations: Data input to AI model, analysis processing, advice generation

[0231] Step 5:

[0232] Send analysis results

[0233] The analysis server sends the analysis results and advice to the device, which receives the analysis results and notifies the user.

[0234] Input: Analysis results and advice

[0235] Output: Analysis results and advice sent to your device

[0236] Specific operations: Data transmission process, device notification system operation

[0237] Step 6:

[0238] User Notification and Display

[0239] The device notifies the user of the analysis results and provides advice, which is displayed on the application screen, allowing the user to adjust their dog's care accordingly.

[0240] Input: Analysis results and advice

[0241] Output: Analysis results and advice communicated to the user

[0242] Specific operations: Activating the notification system, updating the application screen

[0243] Step 7:

[0244] Consultation appointment

[0245] Users can make appointments at pet shops and veterinary clinics through the app, and the appointment information is synchronized in real time with the store's system.

[0246] Input: User's reservation information

[0247] Output: Reservation information synchronized with the physical store system

[0248] Specific operations: Sending reservation information, synchronizing with the physical store system

[0249] Step 8:

[0250] Health Report Generation

[0251] The analysis server generates daily and weekly health reports based on the dog's health data and sends them to the terminal, which displays the reports to the user.

[0252] Input: Health data

[0253] Output: Generated health report

[0254] Specific operations: report generation process, data transmission process, application screen update

[0255] 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.

[0256] The present invention is a system that effectively collects and analyzes health data of a dog, provides appropriate advice to a user, and is also a system that can recognize the user's emotions and reflect them in the advice. The following describes in detail an embodiment of the present invention.

[0257] System Configuration

[0258] The system consists of the following main components:

[0259] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[0260] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotional data.

[0261] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[0262] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0263] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides.

[0264] 6. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[0265] Program processing

[0266] Data collection and transmission

[0267] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0268] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[0269] 3. The device also uses voice input and facial recognition technology to recognize the user's current emotional state in order to collect emotional data.

[0270] Data analysis and advice generation

[0271] 4. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[0272] 5. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[0273] 6. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[0274] Emotion engine processing

[0275] 7. The analysis server uses the emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotional state based on the voice data and image data sent from the device.

[0276] 8. The analytics server adjusts the advice based on the user's emotional state. For example, if the user is feeling stressed, it generates gentler advice or content encouraging relaxation.

[0277] User Notification and Advice Display

[0278] 9. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[0279] 10. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[0280] Online consultation

[0281] 11. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[0282] 12. The terminal sends the user's input to the analysis server via the Internet.

[0283] 13. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[0284] 14. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[0285] 15. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the desired veterinarian.

[0286] 16. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[0287] In this way, the present invention realizes a system that not only collects and analyzes a dog's health data and provides appropriate advice to the user, but also provides advice that takes into account the user's emotional state and facilitates online consultation with a veterinarian if necessary.

[0288] The processing flow will be explained below.

[0289] Step 1:

[0290] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[0291] Step 2:

[0292] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[0293] Step 3:

[0294] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server. The sent data includes a timestamp.

[0295] Step 4:

[0296] The server receives the collected data. The server stores the data and starts pre-processing, which includes data format conversion.

[0297] Step 5:

[0298] The server pre-processes the data, filtering outliers and imputing missing values. The pre-processed data is then input into the analysis module.

[0299] Step 6:

[0300] The server feeds the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[0301] Step 7:

[0302] The server generates advice in natural language based on the analysis results, and converts the advice into text format to make it easier for the user to understand.

[0303] Step 8:

[0304] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and verifies that the transmission was successful.

[0305] Step 9:

[0306] The device receives the advice sent from the analysis server and uses the smartphone's notification function to notify the user that new information is available.

[0307] Step 10:

[0308] The device displays the analysis results and advice on the application screen, allowing the user to manage their dog's health based on the displayed information.

[0309] Step 11:

[0310] The device collects the user's emotional data. It uses voice input and facial recognition technology to recognize the user's current emotional state. For example, if the user says "I'm worried," that voice data is collected.

[0311] Step 12:

[0312] The device sends the collected emotional data to an analysis server in real time for analysis.

[0313] Step 13:

[0314] The server uses an emotion engine to analyze the user's emotional state, recognizing the emotional state based on the user's voice data and image data, and determining the type and intensity of the emotion.

[0315] Step 14:

[0316] The server tailors the advice based on the user's emotional state. For example, if the user is feeling stressed, the advice will be customized to a gentler tone and include words of encouragement.

[0317] Step 15:

[0318] The server sends the adjusted advice to the terminal, and the customized advice data is sent to the terminal via the Internet.

[0319] Step 16:

[0320] The device will again display the advice to the user, again using notifications to let the user know that new advice has been displayed.

[0321] Step 17:

[0322] The user opens the application and sees the generated advice, which is customized based on their emotional state.

[0323] Step 18:

[0324] The user enters their dog's symptoms or questions into the application, for example, "My dog ​​is coughing," and presses the submit button.

[0325] Step 19:

[0326] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[0327] Step 20:

[0328] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[0329] Step 21:

[0330] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[0331] Step 22:

[0332] The device displays the received advice to the user. The new advice is displayed on the application screen.

[0333] Step 23:

[0334] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[0335] Step 24:

[0336] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[0337] Step 25:

[0338] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[0339] Step 26:

[0340] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[0341] Step 27:

[0342] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[0343] Example 2

[0344] 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."

[0345] Conventional dog health management systems only collect and analyze data such as a dog's activity level, sleep patterns, and food intake, and do not consider the user's emotional state, which often results in inappropriate advice. Furthermore, few systems offer integrated features such as online consultation reservations, making them less convenient. The purpose of this invention is to solve these problems and provide a user-friendly health management system.

[0346] 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.

[0347] In this invention, the server includes means for preprocessing data by filtering outliers and filling in missing values, means for analyzing data using a generative AI model to identify outliers and perform trend analysis, and an emotion engine for adjusting advice based on the user's emotional state. This not only effectively collects and analyzes dog health data and provides appropriate advice to the user, but also enables advice that takes the user's emotional state into account and the ability to book online consultations in a unified manner.

[0348] The "information collection device" is a device attached to the dog that collects data such as activity levels, sleep patterns, and food intake.

[0349] A "terminal" is a device used by a user, such as a smartphone or tablet, that receives data from the information collection device and transmits it to the analysis server.

[0350] An "analysis server" is a central processing unit that receives data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0351] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[0352] The "emotion engine" is an algorithm that uses voice input and facial recognition technology to recognize a user's emotional state and adjusts advice accordingly.

[0353] The "online consultation reservation system" is a system that allows users to make reservations for online consultations with veterinarians.

[0354] "Data preprocessing" is a process performed by the analysis server, which involves filtering outliers and filling in missing data.

[0355] "Outlier detection" is performed by a generative artificial intelligence model to identify anomalous values ​​from collected data.

[0356] "Trend analysis" is a method of capturing changes in health status by analyzing long-term fluctuations and patterns in data.

[0357] "Advice" is a specific course of action or recommendation that the analysis server generates using a generative artificial intelligence model and provides to the user.

[0358] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to the user, and is also a system that can recognize the user's emotions and reflect them in the advice.

[0359] System Configuration

[0360] The system consists of the following main components:

[0361] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[0362] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotion data.

[0363] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0364] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0365] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice provided.

[0366] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[0367] Data collection and transmission

[0368] The device periodically collects data on activity, sleep patterns, and food intake from an information collection device attached to the dog via Bluetooth or Wi-Fi, allowing real-time monitoring of changes in the dog's health. The device also uses facial recognition technology to collect data on the user's emotions via voice input and a camera.

[0369] Data analysis and advice generation

[0370] The analytics server receives the data sent from the device and pre-processes it, filtering outliers and imputing missing values, preparing it for input into the generative AI model. The generative AI model then analyzes the data, detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake, and generating recommendations.

[0371] Emotion engine processing

[0372] The emotion engine is used to analyze the user's emotion data and recognize the user's emotional state. If the user is feeling stressed, the analysis server generates gentler advice or content encouraging relaxation.

[0373] User Notification and Advice Display

[0374] When the device receives advice sent from the analysis server, it notifies the user via the smartphone's notification function. The user can then adjust how they care for their dog based on the analysis results and advice displayed on the application screen.

[0375] Online consultation

[0376] Through the application, users can input their dog's symptoms and questions. For example, they can input a symptom like "My dog ​​is coughing." The device then sends this input data via the internet to an analysis server, which uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is then sent to the device and displayed to the user. The device then displays a screen for booking an online consultation with a veterinarian, allowing the user to select the desired date, time, and veterinarian.

[0377] Examples of concrete examples and prompts

[0378] As a concrete example, User A notices that his dog's activity level has decreased. Data is sent from the device to the analysis server, which detects the abnormality and generates advice to "increase walk times." At the same time, the emotion engine detects that User A is feeling stressed, so it also provides gentle advice such as "When you are feeling stressed, try to spend more time relaxing together."

[0379] An example prompt might be:

[0380] "Analyze the dog's activity data and generate advice if there are any abnormalities."

[0381] "Tailor your advice to reflect the emotions your users are feeling."

[0382] "Provide advice on dog coughing and suggest an online consultation with a veterinarian if necessary."

[0383] In this way, the present invention realizes a system that can effectively manage dog health data and provide advice that takes into account the user's emotions.

[0384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0385] Step 1:

[0386] The device uses Bluetooth or WiFi to collect data from an information collection device (smart collar) attached to the dog. The input data is the dog's activity level, sleep patterns, and food intake. The output is the collected data. Specifically, the device collects data every five minutes and stores it locally.

[0387] Step 2:

[0388] The device transmits the collected data to an analysis server in real time via the Internet. The input data is the collected data on activity, sleep patterns, and food intake. The output is the data transmitted to the analysis server. Specifically, the device converts the data into a packet format and transmits it using Wi-Fi or mobile communication.

[0389] Step 3:

[0390] The device collects emotion data using the user's voice input and facial recognition technology. The input requires image data of the user's voice and face. The output is recognized emotion data. Specifically, the device uses a microphone and camera to capture the user's voice and face, which are then analyzed using emotion recognition software.

[0391] Step 4:

[0392] The analysis server preprocesses the data received from the device. The input data is raw data on activity, sleep patterns, and food intake. The output is the preprocessed data. Specifically, it filters out outliers in the data and executes algorithms to fill in missing values.

[0393] Step 5:

[0394] The analysis server analyzes the preprocessed data using a generative AI model. The input data is preprocessed activity, sleep patterns, and food intake data. The output is the results of outlier detection and trend analysis. Specifically, the generative AI model performs pattern recognition and statistical analysis.

[0395] Step 6:

[0396] The analysis server generates advice using a generative artificial intelligence model based on the analysis results and sends the content in text format to the terminal. The input data is the analysis results, and the generated advice is obtained as output. In concrete terms, the server generates appropriate advice based on specific rules.

[0397] Step 7:

[0398] The analysis server uses an emotion engine to analyze the user's emotion data. The input data is the emotion data sent from the device. The output is the recognized emotional state. Specific operations include analyzing voice tone and facial expressions.

[0399] Step 8:

[0400] The analysis server adjusts advice based on the user's emotional state. The input data is the analyzed emotional state and the generated advice, and the output is the adjusted advice. Specific operations include modifying the advice to reflect the emotional state.

[0401] Step 9:

[0402] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The input data is the advice from the analysis server, and the output is a notification to the user. Specifically, the device displays a notification pop-up.

[0403] Step 10:

[0404] The terminal displays the analysis results and advice on the application screen. The input data is the advice sent from the analysis server, and the analysis results that are displayed to the user are obtained as output. In concrete terms, the terminal displays the data on the application's specified screen.

[0405] Step 11:

[0406] The user inputs their dog's symptoms and questions through the application. The input data is the text entered by the user, and the input question or symptom is obtained as the output. The specific operation is that the user enters text into the application form.

[0407] Step 12:

[0408] The terminal sends the user's input to the analysis server via the Internet. The input data is the text of the user's question or symptoms, and the transmission to the analysis server is completed as the output. Specifically, the terminal converts the text into packet format and sends it.

[0409] Step 13:

[0410] The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms. The input data is the text of the user's questions and symptoms, and the generated advice is obtained as the output. Specifically, the server automatically generates appropriate advice based on the content of the question.

[0411] Step 14:

[0412] The analysis server sends the generated advice to the terminal, which then displays it to the user. The input data is the generated advice, and the displayed advice is obtained as output. In specific operations, the terminal displays the advice on a specific screen of the application.

[0413] Step 15:

[0414] The terminal displays a screen for booking an online consultation with a veterinarian, allowing users to easily make a reservation. The input data is the user's desired date and time and the selection of a veterinarian, and the output is reservation information. Specifically, the terminal displays a calendar function and provides an input form.

[0415] Step 16:

[0416] The terminal sends reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user. The input data is the user's reservation information, and the output is a reservation confirmation notice. Specifically, the terminal sends the reservation data, and the server checks it and then generates and sends a notice.

[0417] (Application example 2)

[0418] 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."

[0419] Conventional pet care systems are limited to collecting and analyzing dog health data, and lack the ability to provide advice based on the owner's emotional state. Furthermore, if a user senses something is wrong, the complicated online consultation process with a veterinarian makes it difficult to provide timely and appropriate care. This can lead to inadequate management of a dog's health.

[0420] 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 data on activity level, sleep pattern, and food intake from an information collection device worn by the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for the terminal to collect emotion data and transmit it to the analysis server, means for the analysis server to analyze the user's emotional state based on the emotion data and reflect the result in advice, means for transmitting advice generated based on the analysis results and the emotion data to the terminal and notifying the user, means for receiving questions from the user and input regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing an online consultation appointment with a veterinarian. This makes it possible to provide advice that takes the user's emotional state into consideration and simplify the process of quickly consulting with a veterinarian.

[0421] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[0422] An "analytics server" is a central processing unit that receives collected data, pre-processes it, and analyzes it using generative artificial intelligence models.

[0423] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[0424] "Emotion data" is data collected from the user's voice, facial expressions, etc., and indicates the user's emotional state.

[0425] "Emotional state" is information indicating the user's emotional state, such as stress, joy, or sadness.

[0426] "Advice" refers to instructions or suggestions generated by the analysis server using a generative artificial intelligence model based on the results of data analysis and the user's emotional state.

[0427] A "terminal" is a device used by a user that receives data and emotion data collected from the information collection device and transmits them to the analysis server, such as a smartphone.

[0428] "Online consultation reservation" is a system that allows users to book a consultation with a veterinarian, and includes the ability to select a date and time and a veterinarian.

[0429] An "outlier" is data that is outside the normal range and suggests some kind of problem with the dog's health.

[0430] "Missing data imputation" is a process for filling in missing parts of collected data.

[0431] MODE FOR CARRYING OUT THE INVENTION

[0432] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to users. Furthermore, the system can recognize the user's emotions and reflect them in the advice, facilitating online consultations with veterinarians. Specific embodiments for implementing the present invention are described below.

[0433] 1. System Overview

[0434] The system mainly consists of the following components:

[0435] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[0436] 2. Terminal (smartphone): A device used by the user to receive data and emotional data collected from the information collection device and send it to the server.

[0437] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0438] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice. An example of this is OpenAI (registered trademark) GPT-4.

[0439] 5. Emotion engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides. Examples of such engines include AWS (registered trademark) Rekognition and Google (registered trademark) Cloud Speech-to-Text.

[0440] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[0441] 2. Program Processing

[0442] Data collection and transmission

[0443] The device (smartphone) uses Bluetooth or Wi-Fi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog. The collected data is sent to an analysis server in real time via an internet connection. The device also uses voice input and facial recognition technology to collect the user's emotional data and recognize the user's current emotional state.

[0444] Data analysis and advice generation

[0445] The analysis server first preprocesses the received data, filtering out outliers and filling in missing values. It then analyzes the data using a generative AI model, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake. Based on the analysis results, the analysis server takes into account the user's emotional data and generates advice, which is then sent to the device in text format.

[0446] Emotion engine processing

[0447] The analysis server uses an emotion engine to analyze the user's emotional state and reflect it in the advice it provides. For example, if the user is feeling stressed, it generates gentler advice or advice encouraging relaxation.

[0448] User Notification and Advice Display

[0449] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The user can then adjust their dog's care based on the analysis results and advice displayed on the application screen.

[0450] Online consultation

[0451] Users can input their dog's symptoms and questions through the application. For example, they can input the symptom "My dog ​​is coughing." The device sends the user's input via the Internet to an analysis server, which then uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is sent to the device and displayed to the user. For example, the advice provided might be "If the cough persists, consult a veterinarian." The device also displays a screen for booking an online consultation with a veterinarian, allowing the user to easily make an appointment.

[0452] 3. Example prompts

[0453] Examples:

[0454] If the user is stressed: "I see you're stressed. Let's take a long walk today to help you relax."

[0455] If your dog is inactive: "Your dog has been less active recently. To check its health, first review its diet and exercise."

[0456] Example prompt sentence:

[0457] "Analyze the data recently collected by the smart collar and generate recommendations about your dog's health."

[0458] "Based on the user's voice and facial expression data, determine whether the user is feeling stressed and provide appropriate advice."

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1:

[0461] Data collection

[0462] Subject: Terminal

[0463] How it works: The device uses Bluetooth or WiFi to collect data on activity, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0464] Input: Raw data from SmartColor

[0465] Output: Collected health data (activity, sleep patterns, dietary intake)

[0466] Step 2:

[0467] Emotional Data Collection

[0468] Subject: Terminal

[0469] How it works: The device uses the smartphone's camera and microphone to collect data on the user's voice and facial expressions, and then analyzes the user's emotional state based on this.

[0470] Input: User's voice data, facial expression data

[0471] Output: Parsed user emotional state data

[0472] Step 3:

[0473] Data transmission

[0474] Subject: Terminal

[0475] Operation: The device sends the collected health and emotion data to an analysis server via the Internet.

[0476] Input: Collected health data, emotional state data

[0477] Output: Data sent to the analysis server

[0478] Step 4:

[0479] Data Preprocessing

[0480] Subject: Analysis server

[0481] Operation: The analysis server preprocesses the received data, filtering outliers and imputing missing values.

[0482] Input: Received data (health data and emotion data)

[0483] Output: Preprocessed data

[0484] Step 5:

[0485] Data analysis

[0486] Subject: Analysis server

[0487] Operation: The analysis server inputs the preprocessed data into a generative AI model and performs data analysis. Specifically, it detects abnormal values ​​and performs trend analysis on activity levels, sleep patterns, and dietary intake.

[0488] Input: Preprocessed health data

[0489] Output: Analysis results (health status data)

[0490] Step 6:

[0491] Advice Generation

[0492] Subject: Analysis server

[0493] How it works: The analytics server uses a generative artificial intelligence model to generate advice based on health and emotional state data. The advice generated reflects the user's emotional state.

[0494] Input: Analysis results, emotional state data

[0495] Output: Generated advice

[0496] Step 7:

[0497] Sending and displaying advice

[0498] Subject: Analysis server

[0499] Operation: The analysis server sends the generated advice to the terminal, which receives the advice and notifies the user.

[0500] Input: Generated advice

[0501] Output: Notifications to the terminal and advice to the user

[0502] Step 8:

[0503] Book an online consultation

[0504] Subject: User

[0505] How it works: The user enters their dog's symptoms and any questions they have through the application. The device sends the user's input to the analysis server. The analysis server uses a generative AI model to generate advice for the user's questions and symptoms and sends it to the device. The user then confirms the appointment by using the online consultation appointment screen with a veterinarian.

[0506] Input: User questions and symptoms

[0507] Output: Generated advice, confirmed booking information

[0508] The above is the flow of processing of the program of the system that realizes the application example.

[0509] 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.

[0510] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0511] 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.

[0512] [Second embodiment]

[0513] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0514] 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.

[0515] 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).

[0516] 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.

[0517] 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.

[0518] 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).

[0519] 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. 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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."

[0525] The present invention provides a system for effectively collecting and analyzing health data of a dog and providing appropriate advice to a user. Hereinafter, embodiments of the present invention will be described in detail.

[0526] System Configuration

[0527] The system consists of the following main components:

[0528] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[0529] 2. Terminal (smartphone): A device used by the user that receives data collected from the information collection device and sends it to the server.

[0530] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[0531] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0532] 5. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[0533] Program processing

[0534] Data collection and transmission

[0535] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0536] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[0537] Data analysis and advice generation

[0538] 3. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[0539] 4. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[0540] 5. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[0541] User Notification and Advice Display

[0542] 6. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[0543] 7. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[0544] Online consultation

[0545] 8. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[0546] 9. The device sends the user's input to the analysis server via the Internet.

[0547] 10. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[0548] 11. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[0549] 12. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the veterinarian of their choice.

[0550] 13. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[0551] In this way, the present invention realizes a system that can collect and analyze dog health data, provide appropriate advice to the user, and facilitate online consultation with a veterinarian if necessary.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[0555] Step 2:

[0556] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[0557] Step 3:

[0558] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server.

[0559] Step 4:

[0560] The server receives the collected data, stores the data, and starts pre-processing.

[0561] Step 5:

[0562] The server performs preprocessing of the data, such as filtering outliers and imputing missing values, to create a dataset suitable for analysis.

[0563] Step 6:

[0564] The server inputs the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[0565] Step 7:

[0566] The server generates natural language advice based on the analysis results, and the AI ​​model interprets the analysis results and generates specific advice to provide to the user.

[0567] Step 8:

[0568] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and confirms that the transmission is complete.

[0569] Step 9:

[0570] The device notifies the user of the analysis results and advice, and uses the smartphone's notification function to notify the user of new information.

[0571] Step 10:

[0572] The user opens the app and reviews the analysis and advice provided, providing specific actionable information and areas for improvement.

[0573] Step 11:

[0574] The user enters the dog's symptoms and any questions they have into the application, for example, "My dog ​​is coughing," and presses the submit button.

[0575] Step 12:

[0576] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[0577] Step 13:

[0578] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[0579] Step 14:

[0580] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[0581] Step 15:

[0582] The device displays the received advice to the user. The new advice is displayed on the application screen.

[0583] Step 16:

[0584] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[0585] Step 17:

[0586] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[0587] Step 18:

[0588] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[0589] Step 19:

[0590] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[0591] Step 20:

[0592] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[0593] Example 1

[0594] 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."

[0595] Previously, systems for collecting and analyzing dog health data did not centralize the processes of data collection, analysis, and advice generation, making it difficult to provide information to users. Furthermore, a lack of preprocessing, such as outlier detection and missing value imputation, could reduce the reliability of the analysis results. Furthermore, there was a lack of comprehensive support for users to take appropriate measures for their dog's symptoms.

[0596] 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.

[0597] In this invention, the server includes means for collecting biometric data from a data collection device attached to the dog, means for transmitting the collected data to a central processing unit, means for the central processing unit to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing online consultation appointments with a veterinarian. This allows for centralized management of everything from data collection to analysis, advice provision, and online consultation appointments, enabling the user to understand the dog's health condition and easily take appropriate measures.

[0598] A "data collection device" is a device that is attached to a dog and collects biometric data.

[0599] "Biometric data" includes information such as a dog's activity level, sleep patterns, and food intake.

[0600] A "terminal" is a device used by a user to transmit biometric data to a central processing unit and receive analysis results and advice.

[0601] A "central processing unit" is a device that receives data via the internet, analyzes the data using a generative artificial intelligence model, and generates analysis results and advice.

[0602] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[0603] A "question" is an input from the user and is a question about the dog's symptoms or health condition.

[0604] "Advice" refers to instructions or advice about dog health care that is generated by a generative artificial intelligence model and provided to the user.

[0605] "Notification" is a means of transmitting information to inform the user of the analysis results and advice contents.

[0606] "Book an online consultation" is the process by which a user books a consultation with a veterinarian over the Internet.

[0607] "Abnormal value filtering" is a process of detecting and excluding values ​​that are different from normal values ​​from collected biometric data.

[0608] "Missing data value completion" is a process of filling in missing data when there are gaps in the collected biometric data.

[0609] This invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system consists of the following main components: an information collection device (data collection device), a terminal, a central processing unit (server), a generative AI model, and an online consultation reservation system.

[0610] First, the data collection device is attached to the dog and collects real-time biological data such as activity level, sleep patterns, and food intake. Specifically, this data collection device is often implemented as a smart collar.

[0611] Next, the terminal (smartphone) receives the biometric data from the information collection device via Bluetooth or WiFi. The collected data is sent to a central processing unit (server) via the Internet. The smartphone application used here has a built-in communication module for data transmission and reception.

[0612] The central processing unit (server) first preprocesses the received biometric data. Specifically, it filters out outliers and imputes missing values. This allows reliable data to be input into the generative artificial intelligence model.

[0613] Generative AI models analyze preprocessed data to detect outliers and perform trend analysis. Specific examples of generative AI models used include GPT-4. An example of a prompt for executing the analysis process is, "Analyze the dog's health data to identify outliers and trends."

[0614] Based on the analysis results, the central processing unit (server) generates appropriate advice and sends it in text format to the device. The device notifies the user of this advice and displays it on the application screen. For example, the advice displayed might be something like, "You've been getting less sleep recently, so try exercising less."

[0615] Furthermore, when a user inputs their dog's symptoms or questions through the application, the content is also sent to the central processing unit (server), where it is analyzed and answered by the generative AI model. For example, if a user inputs "My dog ​​is coughing," the generated advice will be "If the coughing persists, consult a veterinarian."

[0616] Finally, the terminal is equipped with an online consultation reservation system that allows users to easily make reservations with veterinarians. For example, the user can confirm the reservation by seeing a message asking, "Would you like to make an online consultation appointment tomorrow from 10:00 to 11:00?" The reservation information is sent to the central processing unit (server), and after confirmation, a reservation confirmation notice is sent to the user.

[0617] In this way, this system can centrally manage everything from data collection and analysis to providing advice and even online consultation reservations, providing users with comprehensive and efficient support for managing their dog's health.

[0618] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0619] Step 1: Data collection

[0620] The terminal uses Bluetooth or WiFi to collect biometric data from a data collection device attached to the dog. Specifically, it acquires data on activity, sleep patterns, and food intake. As input, it receives real-time data from the data collection device and stores it locally. As output, the collected biometric data is stored in the terminal's memory.

[0621] Specific behavior:

[0622] The device confirms the Bluetooth connection and obtains data such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams" from the data collection device.

[0623] Step 2: Send data

[0624] The terminal transmits the collected data to a central processing unit (server) via the Internet. As input, the collected biometric data is used. As output, the data is transmitted to the server.

[0625] Specific behavior:

[0626] The terminal converts the collected data into packet format and transmits it to a server via the Internet.

[0627] Step 3: Data Preprocessing

[0628] The server preprocesses the received data, filtering outliers and imputing missing values. As input, the biometric data sent from the device is used. As output, the filtered and imputed data is obtained.

[0629] Specific behavior:

[0630] The server analyzes the received data, checks for abnormal values ​​such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams," and filters out invalid data.

[0631] If there is missing data, it is supplemented by referring to past data.

[0632] Step 4: Data analysis

[0633] The server analyzes the preprocessed data using a generative AI model. The preprocessed biometric data is provided as input. The analysis results are obtained as output. "GPT-4" is used as an example of a generative AI model.

[0634] Specific behavior:

[0635] The server inputs the prompt "Analyze the dog's health data and identify outliers and trends" into the AI ​​model and begins the analysis.

[0636] The AI ​​model generates analysis results such as "You've been sleeping less than usual recently."

[0637] Step 5: Advice Generation

[0638] The server generates advice based on the analysis results using a generative AI model. The analysis results of the AI ​​model are used as input, and the generated advice is obtained as output.

[0639] Specific behavior:

[0640] The server converts the analysis results from the generated AI model into text format and creates advice such as, "You've been getting less sleep recently, so you should reduce your exercise."

[0641] Step 6: Advice Notification

[0642] The terminal receives the advice sent from the server and notifies the user. The input is the advice data sent from the server. The output is a notification to the user.

[0643] Specific behavior:

[0644] The device uses the application's notification function to display a notification to the user saying, "You've been sleeping less recently, so you should exercise less."

[0645] Step 7: Display Advice

[0646] The terminal displays the analysis results and advice on the application screen. The input is the advice data sent from the server. The output is the advice that the user can check.

[0647] Specific behavior:

[0648] The device updates the app screen and displays the advice, "You've been getting less sleep recently, so try exercising less."

[0649] Step 8: Book an online consultation

[0650] The user inputs their dog's symptoms and questions through the application. For example, they input information like "My dog ​​is coughing." The device then sends the input to the server via the Internet.

[0651] The server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms, sends the advice to the terminal, which displays it to the user, and provides a screen where the user can schedule an online consultation with a veterinarian.

[0652] Specific behavior:

[0653] The user types "my dog ​​is coughing" into the app and submits it.

[0654] The server inputs the prompt "What should I do if my dog ​​is coughing?" into the generative AI model.

[0655] The AI ​​model generates advice such as "If the cough persists, consult a veterinarian," and the server sends it to the device.

[0656] The device displays the advice and then suggests, "Would you like to book an online consultation?"

[0657] The user confirms the reservation, and the terminal transmits the reservation information to the server.

[0658] The server checks the reservation information and notifies the user, "Your reservation has been confirmed. Your online consultation with the veterinarian will begin tomorrow at 10:00."

[0659] (Application example 1)

[0660] 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."

[0661] When it comes to managing a dog's health, it's important for owners to regularly monitor their pet's health and take prompt action if necessary. However, current methods require owners to collect and analyze data on their dog's activity levels, sleep patterns, food intake, and other factors, which takes a lot of time and effort before they can receive appropriate advice. Additionally, online consultations and appointments at physical clinics are also time-consuming, making it difficult to respond quickly.

[0662] 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.

[0663] In this invention, the server includes means for collecting data on activity level, sleep pattern, and food intake from an information collection device attached to the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it to the user, means for providing online reservations so that the user can make appointments at a physical store, means for the app to synchronize appointment information with the physical store's system, and means for generating and displaying daily and weekly health reports based on the dog's health data. This allows the server to collect and analyze the dog's health data in real time, provide prompt and appropriate advice, and easily handle online consultations and appointments at a physical store.

[0664] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[0665] The "analysis server" is a central processing unit that receives collected data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0666] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[0667] A "terminal" is a device used by a user to receive data collected from an information collection device, transmit the data to a server, and display analysis results and advice.

[0668] "Online Reservation" is a system that allows users to make appointments for medical appointments at physical stores.

[0669] A "health report" is a detailed analysis of your dog's health on a daily or weekly basis.

[0670] The present invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system is composed of an information collection device, a terminal, an analysis server, and an online reservation system.

[0671] System Configuration

[0672] Information gathering device (smart color)

[0673] The information collection device is a device worn by the dog. This device monitors and records data such as the dog's activity level, sleep patterns, and food intake 24 hours a day. For example, it has a built-in acceleration sensor, heart rate sensor, and GPS system, and transmits the data to a terminal via Bluetooth or WiFi.

[0674] Device (smartphone)

[0675] The device is a smartphone used by the user. This device transmits data collected from the smart color to an analysis server in real time, receives analysis results and advice, and notifies the user. Users can also use the app to enter questions and symptoms, and make appointments for medical examinations.

[0676] Analysis Server

[0677] The analysis server is a central processing unit that receives collected health data and analyzes it using a generative artificial intelligence model. This server first preprocesses the data, filtering out outliers and imputing missing values. It then analyzes the data using a generative AI model (e.g., a model using TensorFlow) to identify abnormal patterns and health conditions. Based on the analysis results, it generates personalized health advice and sends it to the device.

[0678] Online Reservation System

[0679] Users can make appointments at pet shops and veterinary clinics through the terminal application. Reservation information is synchronized with the physical store's system in real time, allowing users to make appointments quickly. This is done using WebSocket technology.

[0680] Processing flow

[0681] 1. Data collection: The smart collar uses sensors to record your dog's activity, sleep patterns, food intake, etc. and transmits the data to your device.

[0682] 2. Data transmission: The device sends the received data to the cloud analysis server.

[0683] 3. Data Analysis: The analysis server pre-processes the collected data and then analyzes it using a generative artificial intelligence model to generate health-based advice.

[0684] 4. Notification and display: The analysis results are sent to the device, and the user can view detailed analysis results and advice.

[0685] 5. Appointment: When a user makes an appointment online, the appointment information is synchronized with the store's system.

[0686] Specific examples

[0687] For example, if a dog wearing a "smart collar" is extremely inactive, the analysis server receives the data and uses a generative AI model to detect the abnormality. Based on the analysis results, advice such as "increase walking time" is generated and sent to the device. The owner immediately receives this advice via a smartphone notification and can view detailed data analysis within the app.

[0688] Additionally, if pet owners want to make an appointment at a pet shop or veterinary clinic, they can simply select a date and time within the app and make the appointment online. This appointment information is synchronized in real time with the physical store, allowing for a prompt consultation.

[0689] Prompt Sentence Examples

[0690] To develop a pet health care assistant app, collect, analyze, and provide advice on the following:

[0691] 1. Dog activity level

[0692] 2. Sleep patterns

[0693] 3. Dietary Intake

[0694] Based on this data, please use a TensorFlow model to provide specific advice based on the health status (e.g., adjusting walking time, feeding amount, etc.). Also, please implement a consultation booking function so that users can easily book a consultation at a veterinary clinic or pet shop."

[0695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0696] Step 1:

[0697] Data collection

[0698] The smart collar uses sensors to record your dog's activity levels, sleep patterns, food intake, etc. The smart collar collects this data in real time and transmits it to your device (smartphone) via Bluetooth or WiFi.

[0699] Inputs: Dog activity, sleep, food intake

[0700] Output: Health data sent to the device

[0701] Specific operations: Setting data recording frequency, maintaining Bluetooth or WiFi connection, and data transmission

[0702] Step 2:

[0703] Data transmission

[0704] The device then transmits the health data it receives in real time to a cloud-based analytics server via mobile data or Wi-Fi. The device may compress and anonymize the data before sending it.

[0705] Input: Health data received from smart collar

[0706] Output: Health data sent to the analysis server

[0707] Specific operations: data compression, anonymization, maintaining internet connection, data transmission

[0708] Step 3:

[0709] Data Preprocessing

[0710] The analysis server preprocesses the received data, filtering out abnormal values ​​and filling in missing values. This processing increases the reliability of the data.

[0711] Input: Health data sent from the device

[0712] Output: filtered and imputed data

[0713] Specific operations: Identifying and removing outliers, imputing missing values, normalizing data

[0714] Step 4:

[0715] Data analysis

[0716] The analytics server then inputs the pre-processed data into a generative artificial intelligence model (e.g., a TensorFlow model) for analysis. The AI ​​model identifies anomalies in activity levels, sleep patterns, and dietary intake, and generates health-based advice.

[0717] Input: Preprocessed health data

[0718] Output: Health analysis results and advice

[0719] Specific operations: Data input to AI model, analysis processing, advice generation

[0720] Step 5:

[0721] Send analysis results

[0722] The analysis server sends the analysis results and advice to the device, which receives the analysis results and notifies the user.

[0723] Input: Analysis results and advice

[0724] Output: Analysis results and advice sent to your device

[0725] Specific operations: Data transmission process, device notification system operation

[0726] Step 6:

[0727] User Notification and Display

[0728] The device notifies the user of the analysis results and provides advice, which is displayed on the application screen, allowing the user to adjust their dog's care accordingly.

[0729] Input: Analysis results and advice

[0730] Output: Analysis results and advice communicated to the user

[0731] Specific operations: Activating the notification system, updating the application screen

[0732] Step 7:

[0733] Consultation appointment

[0734] Users can make appointments at pet shops and veterinary clinics through the app, and the appointment information is synchronized in real time with the store's system.

[0735] Input: User's reservation information

[0736] Output: Reservation information synchronized with the physical store system

[0737] Specific operations: Sending reservation information, synchronizing with the physical store system

[0738] Step 8:

[0739] Health Report Generation

[0740] The analysis server generates daily and weekly health reports based on the dog's health data and sends them to the terminal, which displays the reports to the user.

[0741] Input: Health data

[0742] Output: Generated health report

[0743] Specific operations: report generation process, data transmission process, application screen update

[0744] 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.

[0745] The present invention is a system that effectively collects and analyzes health data of a dog, provides appropriate advice to a user, and is also a system that can recognize the user's emotions and reflect them in the advice. The following describes in detail an embodiment of the present invention.

[0746] System Configuration

[0747] The system consists of the following main components:

[0748] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[0749] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotional data.

[0750] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[0751] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0752] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides.

[0753] 6. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[0754] Program processing

[0755] Data collection and transmission

[0756] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0757] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[0758] 3. The device also uses voice input and facial recognition technology to recognize the user's current emotional state in order to collect emotional data.

[0759] Data analysis and advice generation

[0760] 4. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[0761] 5. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[0762] 6. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[0763] Emotion engine processing

[0764] 7. The analysis server uses the emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotional state based on the voice data and image data sent from the device.

[0765] 8. The analytics server adjusts the advice based on the user's emotional state. For example, if the user is feeling stressed, it generates gentler advice or content encouraging relaxation.

[0766] User Notification and Advice Display

[0767] 9. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[0768] 10. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[0769] Online consultation

[0770] 11. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[0771] 12. The terminal sends the user's input to the analysis server via the Internet.

[0772] 13. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[0773] 14. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[0774] 15. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the desired veterinarian.

[0775] 16. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[0776] In this way, the present invention realizes a system that not only collects and analyzes a dog's health data and provides appropriate advice to the user, but also provides advice that takes into account the user's emotional state and facilitates online consultation with a veterinarian if necessary.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[0780] Step 2:

[0781] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[0782] Step 3:

[0783] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server. The sent data includes a timestamp.

[0784] Step 4:

[0785] The server receives the collected data. The server stores the data and starts pre-processing, which includes data format conversion.

[0786] Step 5:

[0787] The server pre-processes the data, filtering outliers and imputing missing values. The pre-processed data is then input into the analysis module.

[0788] Step 6:

[0789] The server feeds the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[0790] Step 7:

[0791] The server generates advice in natural language based on the analysis results, and converts the advice into text format to make it easier for the user to understand.

[0792] Step 8:

[0793] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and verifies that the transmission was successful.

[0794] Step 9:

[0795] The device receives the advice sent from the analysis server and uses the smartphone's notification function to notify the user that new information is available.

[0796] Step 10:

[0797] The device displays the analysis results and advice on the application screen, allowing the user to manage their dog's health based on the displayed information.

[0798] Step 11:

[0799] The device collects the user's emotional data. It uses voice input and facial recognition technology to recognize the user's current emotional state. For example, if the user says "I'm worried," that voice data is collected.

[0800] Step 12:

[0801] The device sends the collected emotional data to an analysis server in real time for analysis.

[0802] Step 13:

[0803] The server uses an emotion engine to analyze the user's emotional state, recognizing the emotional state based on the user's voice data and image data, and determining the type and intensity of the emotion.

[0804] Step 14:

[0805] The server tailors the advice based on the user's emotional state. For example, if the user is feeling stressed, the advice will be customized to a gentler tone and include words of encouragement.

[0806] Step 15:

[0807] The server sends the adjusted advice to the terminal, and the customized advice data is sent to the terminal via the Internet.

[0808] Step 16:

[0809] The device will again display the advice to the user, again using notifications to let the user know that new advice has been displayed.

[0810] Step 17:

[0811] The user opens the application and sees the generated advice, which is customized based on their emotional state.

[0812] Step 18:

[0813] The user enters their dog's symptoms or questions into the application, for example, "My dog ​​is coughing," and presses the submit button.

[0814] Step 19:

[0815] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[0816] Step 20:

[0817] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[0818] Step 21:

[0819] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[0820] Step 22:

[0821] The device displays the received advice to the user. The new advice is displayed on the application screen.

[0822] Step 23:

[0823] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[0824] Step 24:

[0825] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[0826] Step 25:

[0827] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[0828] Step 26:

[0829] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[0830] Step 27:

[0831] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[0832] Example 2

[0833] 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."

[0834] Conventional dog health management systems only collect and analyze data such as a dog's activity level, sleep patterns, and food intake, and do not consider the user's emotional state, which often results in inappropriate advice. Furthermore, few systems offer integrated features such as online consultation reservations, making them less convenient. The purpose of this invention is to solve these problems and provide a user-friendly health management system.

[0835] 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.

[0836] In this invention, the server includes means for preprocessing data by filtering outliers and filling in missing values, means for analyzing data using a generative AI model to identify outliers and perform trend analysis, and an emotion engine for adjusting advice based on the user's emotional state. This not only effectively collects and analyzes dog health data and provides appropriate advice to the user, but also enables advice that takes the user's emotional state into account and the ability to book online consultations in a unified manner.

[0837] The "information collection device" is a device attached to the dog that collects data such as activity levels, sleep patterns, and food intake.

[0838] A "terminal" is a device used by a user, such as a smartphone or tablet, that receives data from the information collection device and transmits it to the analysis server.

[0839] An "analysis server" is a central processing unit that receives data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0840] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[0841] The "emotion engine" is an algorithm that uses voice input and facial recognition technology to recognize a user's emotional state and adjusts advice accordingly.

[0842] The "online consultation reservation system" is a system that allows users to make reservations for online consultations with veterinarians.

[0843] "Data preprocessing" is a process performed by the analysis server, which involves filtering outliers and filling in missing data.

[0844] "Outlier detection" is performed by a generative artificial intelligence model to identify anomalous values ​​from collected data.

[0845] "Trend analysis" is a method of capturing changes in health status by analyzing long-term fluctuations and patterns in data.

[0846] "Advice" is a specific course of action or recommendation that the analysis server generates using a generative artificial intelligence model and provides to the user.

[0847] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to the user, and is also a system that can recognize the user's emotions and reflect them in the advice.

[0848] System Configuration

[0849] The system consists of the following main components:

[0850] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[0851] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotion data.

[0852] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0853] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[0854] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice provided.

[0855] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[0856] Data collection and transmission

[0857] The device periodically collects data on activity, sleep patterns, and food intake from an information collection device attached to the dog via Bluetooth or Wi-Fi, allowing real-time monitoring of changes in the dog's health. The device also uses facial recognition technology to collect data on the user's emotions via voice input and a camera.

[0858] Data analysis and advice generation

[0859] The analytics server receives the data sent from the device and pre-processes it, filtering outliers and imputing missing values, preparing it for input into the generative AI model. The generative AI model then analyzes the data, detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake, and generating recommendations.

[0860] Emotion engine processing

[0861] The emotion engine is used to analyze the user's emotion data and recognize the user's emotional state. If the user is feeling stressed, the analysis server generates gentler advice or content encouraging relaxation.

[0862] User Notification and Advice Display

[0863] When the device receives advice sent from the analysis server, it notifies the user via the smartphone's notification function. The user can then adjust how they care for their dog based on the analysis results and advice displayed on the application screen.

[0864] Online consultation

[0865] Through the application, users can input their dog's symptoms and questions. For example, they can input a symptom like "My dog ​​is coughing." The device then sends this input data via the internet to an analysis server, which uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is then sent to the device and displayed to the user. The device then displays a screen for booking an online consultation with a veterinarian, allowing the user to select the desired date, time, and veterinarian.

[0866] Examples of concrete examples and prompts

[0867] As a concrete example, User A notices that his dog's activity level has decreased. Data is sent from the device to the analysis server, which detects the abnormality and generates advice to "increase walk times." At the same time, the emotion engine detects that User A is feeling stressed, so it also provides gentle advice such as "When you are feeling stressed, try to spend more time relaxing together."

[0868] An example prompt might be:

[0869] "Analyze the dog's activity data and generate advice if there are any abnormalities."

[0870] "Tailor your advice to reflect the emotions your users are feeling."

[0871] "Provide advice on dog coughing and suggest an online consultation with a veterinarian if necessary."

[0872] In this way, the present invention realizes a system that can effectively manage dog health data and provide advice that takes into account the user's emotions.

[0873] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0874] Step 1:

[0875] The device uses Bluetooth or WiFi to collect data from an information collection device (smart collar) attached to the dog. The input data is the dog's activity level, sleep patterns, and food intake. The output is the collected data. Specifically, the device collects data every five minutes and stores it locally.

[0876] Step 2:

[0877] The device transmits the collected data to an analysis server in real time via the Internet. The input data is the collected data on activity, sleep patterns, and food intake. The output is the data transmitted to the analysis server. Specifically, the device converts the data into a packet format and transmits it using Wi-Fi or mobile communication.

[0878] Step 3:

[0879] The device collects emotion data using the user's voice input and facial recognition technology. The input requires image data of the user's voice and face. The output is recognized emotion data. Specifically, the device uses a microphone and camera to capture the user's voice and face, which are then analyzed using emotion recognition software.

[0880] Step 4:

[0881] The analysis server preprocesses the data received from the device. The input data is raw data on activity, sleep patterns, and food intake. The output is the preprocessed data. Specifically, it filters out outliers in the data and executes algorithms to fill in missing values.

[0882] Step 5:

[0883] The analysis server analyzes the preprocessed data using a generative AI model. The input data is preprocessed activity, sleep patterns, and food intake data. The output is the results of outlier detection and trend analysis. Specifically, the generative AI model performs pattern recognition and statistical analysis.

[0884] Step 6:

[0885] The analysis server generates advice using a generative artificial intelligence model based on the analysis results and sends the content in text format to the terminal. The input data is the analysis results, and the generated advice is obtained as output. In concrete terms, the server generates appropriate advice based on specific rules.

[0886] Step 7:

[0887] The analysis server uses an emotion engine to analyze the user's emotion data. The input data is the emotion data sent from the device. The output is the recognized emotional state. Specific operations include analyzing voice tone and facial expressions.

[0888] Step 8:

[0889] The analysis server adjusts advice based on the user's emotional state. The input data is the analyzed emotional state and the generated advice, and the output is the adjusted advice. Specific operations include modifying the advice to reflect the emotional state.

[0890] Step 9:

[0891] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The input data is the advice from the analysis server, and the output is a notification to the user. Specifically, the device displays a notification pop-up.

[0892] Step 10:

[0893] The terminal displays the analysis results and advice on the application screen. The input data is the advice sent from the analysis server, and the analysis results that are displayed to the user are obtained as output. In concrete terms, the terminal displays the data on the application's specified screen.

[0894] Step 11:

[0895] The user inputs their dog's symptoms and questions through the application. The input data is the text entered by the user, and the input question or symptom is obtained as the output. The specific operation is that the user enters text into the application form.

[0896] Step 12:

[0897] The terminal sends the user's input to the analysis server via the Internet. The input data is the text of the user's question or symptoms, and the transmission to the analysis server is completed as the output. Specifically, the terminal converts the text into packet format and sends it.

[0898] Step 13:

[0899] The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms. The input data is the text of the user's questions and symptoms, and the generated advice is obtained as the output. Specifically, the server automatically generates appropriate advice based on the content of the question.

[0900] Step 14:

[0901] The analysis server sends the generated advice to the terminal, which then displays it to the user. The input data is the generated advice, and the displayed advice is obtained as output. In specific operations, the terminal displays the advice on a specific screen of the application.

[0902] Step 15:

[0903] The terminal displays a screen for booking an online consultation with a veterinarian, allowing users to easily make a reservation. The input data is the user's desired date and time and the selection of a veterinarian, and the output is reservation information. Specifically, the terminal displays a calendar function and provides an input form.

[0904] Step 16:

[0905] The terminal sends reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user. The input data is the user's reservation information, and the output is a reservation confirmation notice. Specifically, the terminal sends the reservation data, and the server checks it and then generates and sends a notice.

[0906] (Application example 2)

[0907] 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."

[0908] Conventional pet care systems are limited to collecting and analyzing dog health data, and lack the ability to provide advice based on the owner's emotional state. Furthermore, if a user senses something is wrong, the complicated online consultation process with a veterinarian makes it difficult to provide timely and appropriate care. This can lead to inadequate management of a dog's health.

[0909] 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 data on activity level, sleep pattern, and food intake from an information collection device worn by the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for the terminal to collect emotion data and transmit it to the analysis server, means for the analysis server to analyze the user's emotional state based on the emotion data and reflect the result in advice, means for transmitting advice generated based on the analysis results and the emotion data to the terminal and notifying the user, means for receiving questions from the user and input regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing an online consultation appointment with a veterinarian. This makes it possible to provide advice that takes the user's emotional state into consideration and simplify the process of quickly consulting with a veterinarian.

[0910] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[0911] An "analytics server" is a central processing unit that receives collected data, pre-processes it, and analyzes it using generative artificial intelligence models.

[0912] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[0913] "Emotion data" is data collected from the user's voice, facial expressions, etc., and indicates the user's emotional state.

[0914] "Emotional state" is information indicating the user's emotional state, such as stress, joy, or sadness.

[0915] "Advice" refers to instructions or suggestions generated by the analysis server using a generative artificial intelligence model based on the results of data analysis and the user's emotional state.

[0916] A "terminal" is a device used by a user that receives data and emotion data collected from the information collection device and transmits them to the analysis server, such as a smartphone.

[0917] "Online consultation reservation" is a system that allows users to book a consultation with a veterinarian, and includes the ability to select a date and time and a veterinarian.

[0918] An "outlier" is data that is outside the normal range and suggests some kind of problem with the dog's health.

[0919] "Missing data imputation" is a process for filling in missing parts of collected data.

[0920] MODE FOR CARRYING OUT THE INVENTION

[0921] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to users. Furthermore, the system can recognize the user's emotions and reflect them in the advice, facilitating online consultations with veterinarians. Specific embodiments for implementing the present invention are described below.

[0922] 1. System Overview

[0923] The system mainly consists of the following components:

[0924] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[0925] 2. Terminal (smartphone): A device used by the user to receive data and emotional data collected from the information collection device and send it to the server.

[0926] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[0927] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice. An example of this is "OpenAI GPT-4."

[0928] 5. Emotion engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides. Examples of such engines include AWS Rekognition and Google Cloud Speech-to-Text.

[0929] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[0930] 2. Program Processing

[0931] Data collection and transmission

[0932] The device (smartphone) uses Bluetooth or Wi-Fi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog. The collected data is sent to an analysis server in real time via an internet connection. The device also uses voice input and facial recognition technology to collect the user's emotional data and recognize the user's current emotional state.

[0933] Data analysis and advice generation

[0934] The analysis server first preprocesses the received data, filtering out outliers and filling in missing values. It then analyzes the data using a generative AI model, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake. Based on the analysis results, the analysis server takes into account the user's emotional data and generates advice, which is then sent to the device in text format.

[0935] Emotion engine processing

[0936] The analysis server uses an emotion engine to analyze the user's emotional state and reflect it in the advice it provides. For example, if the user is feeling stressed, it generates gentler advice or advice encouraging relaxation.

[0937] User Notification and Advice Display

[0938] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The user can then adjust their dog's care based on the analysis results and advice displayed on the application screen.

[0939] Online consultation

[0940] Users can input their dog's symptoms and questions through the application. For example, they can input the symptom "My dog ​​is coughing." The device sends the user's input via the Internet to an analysis server, which then uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is sent to the device and displayed to the user. For example, the advice provided might be "If the cough persists, consult a veterinarian." The device also displays a screen for booking an online consultation with a veterinarian, allowing the user to easily make an appointment.

[0941] 3. Example prompts

[0942] Examples:

[0943] If the user is stressed: "I see you're stressed. Let's take a long walk today to help you relax."

[0944] If your dog is inactive: "Your dog has been less active recently. To check its health, first review its diet and exercise."

[0945] Example prompt sentence:

[0946] "Analyze the data recently collected by the smart collar and generate recommendations about your dog's health."

[0947] "Based on the user's voice and facial expression data, determine whether the user is feeling stressed and provide appropriate advice."

[0948] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0949] Step 1:

[0950] Data collection

[0951] Subject: Terminal

[0952] How it works: The device uses Bluetooth or WiFi to collect data on activity, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[0953] Input: Raw data from SmartColor

[0954] Output: Collected health data (activity, sleep patterns, dietary intake)

[0955] Step 2:

[0956] Emotional Data Collection

[0957] Subject: Terminal

[0958] How it works: The device uses the smartphone's camera and microphone to collect data on the user's voice and facial expressions, and then analyzes the user's emotional state based on this.

[0959] Input: User's voice data, facial expression data

[0960] Output: Parsed user emotional state data

[0961] Step 3:

[0962] Data transmission

[0963] Subject: Terminal

[0964] Operation: The device sends the collected health and emotion data to an analysis server via the Internet.

[0965] Input: Collected health data, emotional state data

[0966] Output: Data sent to the analysis server

[0967] Step 4:

[0968] Data Preprocessing

[0969] Subject: Analysis server

[0970] Operation: The analysis server preprocesses the received data, filtering outliers and imputing missing values.

[0971] Input: Received data (health data and emotion data)

[0972] Output: Preprocessed data

[0973] Step 5:

[0974] Data analysis

[0975] Subject: Analysis server

[0976] Operation: The analysis server inputs the preprocessed data into a generative AI model and performs data analysis. Specifically, it detects abnormal values ​​and performs trend analysis on activity levels, sleep patterns, and dietary intake.

[0977] Input: Preprocessed health data

[0978] Output: Analysis results (health status data)

[0979] Step 6:

[0980] Advice Generation

[0981] Subject: Analysis server

[0982] How it works: The analytics server uses a generative artificial intelligence model to generate advice based on health and emotional state data. The advice generated reflects the user's emotional state.

[0983] Input: Analysis results, emotional state data

[0984] Output: Generated advice

[0985] Step 7:

[0986] Sending and displaying advice

[0987] Subject: Analysis server

[0988] Operation: The analysis server sends the generated advice to the terminal, which receives the advice and notifies the user.

[0989] Input: Generated advice

[0990] Output: Notifications to the terminal and advice to the user

[0991] Step 8:

[0992] Book an online consultation

[0993] Subject: User

[0994] How it works: The user enters their dog's symptoms and any questions they have through the application. The device sends the user's input to the analysis server. The analysis server uses a generative AI model to generate advice for the user's questions and symptoms and sends it to the device. The user then confirms the appointment by using the online consultation appointment screen with a veterinarian.

[0995] Input: User questions and symptoms

[0996] Output: Generated advice, confirmed booking information

[0997] The above is the flow of processing of the program of the system that realizes the application example.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] [Third embodiment]

[1002] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1003] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1004] 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).

[1005] 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.

[1006] 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.

[1007] 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).

[1008] 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. 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.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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."

[1014] The present invention provides a system for effectively collecting and analyzing health data of a dog and providing appropriate advice to a user. Hereinafter, embodiments of the present invention will be described in detail.

[1015] System Configuration

[1016] The system consists of the following main components:

[1017] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[1018] 2. Terminal (smartphone): A device used by the user that receives data collected from the information collection device and sends it to the server.

[1019] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[1020] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1021] 5. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[1022] Program processing

[1023] Data collection and transmission

[1024] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1025] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[1026] Data analysis and advice generation

[1027] 3. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[1028] 4. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[1029] 5. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[1030] User Notification and Advice Display

[1031] 6. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[1032] 7. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[1033] Online consultation

[1034] 8. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[1035] 9. The device sends the user's input to the analysis server via the Internet.

[1036] 10. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[1037] 11. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[1038] 12. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the veterinarian of their choice.

[1039] 13. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[1040] In this way, the present invention realizes a system that can collect and analyze dog health data, provide appropriate advice to the user, and facilitate online consultation with a veterinarian if necessary.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[1044] Step 2:

[1045] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[1046] Step 3:

[1047] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server.

[1048] Step 4:

[1049] The server receives the collected data, stores the data, and starts pre-processing.

[1050] Step 5:

[1051] The server performs preprocessing of the data, such as filtering outliers and imputing missing values, to create a dataset suitable for analysis.

[1052] Step 6:

[1053] The server inputs the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[1054] Step 7:

[1055] The server generates natural language advice based on the analysis results, and the AI ​​model interprets the analysis results and generates specific advice to provide to the user.

[1056] Step 8:

[1057] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and confirms that the transmission is complete.

[1058] Step 9:

[1059] The device notifies the user of the analysis results and advice, and uses the smartphone's notification function to notify the user of new information.

[1060] Step 10:

[1061] The user opens the app and reviews the analysis and advice provided, providing specific actionable information and areas for improvement.

[1062] Step 11:

[1063] The user enters the dog's symptoms and any questions they have into the application, for example, "My dog ​​is coughing," and presses the submit button.

[1064] Step 12:

[1065] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[1066] Step 13:

[1067] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[1068] Step 14:

[1069] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[1070] Step 15:

[1071] The device displays the received advice to the user. The new advice is displayed on the application screen.

[1072] Step 16:

[1073] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[1074] Step 17:

[1075] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[1076] Step 18:

[1077] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[1078] Step 19:

[1079] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[1080] Step 20:

[1081] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[1082] Example 1

[1083] 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."

[1084] Previously, systems for collecting and analyzing dog health data did not centralize the processes of data collection, analysis, and advice generation, making it difficult to provide information to users. Furthermore, a lack of preprocessing, such as outlier detection and missing value imputation, could reduce the reliability of the analysis results. Furthermore, there was a lack of comprehensive support for users to take appropriate measures for their dog's symptoms.

[1085] 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.

[1086] In this invention, the server includes means for collecting biometric data from a data collection device attached to the dog, means for transmitting the collected data to a central processing unit, means for the central processing unit to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing online consultation appointments with a veterinarian. This allows for centralized management of everything from data collection to analysis, advice provision, and online consultation appointments, enabling the user to understand the dog's health condition and easily take appropriate measures.

[1087] A "data collection device" is a device that is attached to a dog and collects biometric data.

[1088] "Biometric data" includes information such as a dog's activity level, sleep patterns, and food intake.

[1089] A "terminal" is a device used by a user to transmit biometric data to a central processing unit and receive analysis results and advice.

[1090] A "central processing unit" is a device that receives data via the internet, analyzes the data using a generative artificial intelligence model, and generates analysis results and advice.

[1091] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[1092] A "question" is an input from the user and is a question about the dog's symptoms or health condition.

[1093] "Advice" refers to instructions or advice about dog health care that is generated by a generative artificial intelligence model and provided to the user.

[1094] "Notification" is a means of transmitting information to inform the user of the analysis results and advice contents.

[1095] "Book an online consultation" is the process by which a user books a consultation with a veterinarian over the Internet.

[1096] "Abnormal value filtering" is a process of detecting and excluding values ​​that are different from normal values ​​from collected biometric data.

[1097] "Missing data value completion" is a process of filling in missing data when there are gaps in the collected biometric data.

[1098] This invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system consists of the following main components: an information collection device (data collection device), a terminal, a central processing unit (server), a generative AI model, and an online consultation reservation system.

[1099] First, the data collection device is attached to the dog and collects real-time biological data such as activity level, sleep patterns, and food intake. Specifically, this data collection device is often implemented as a smart collar.

[1100] Next, the terminal (smartphone) receives the biometric data from the information collection device via Bluetooth or WiFi. The collected data is sent to a central processing unit (server) via the Internet. The smartphone application used here has a built-in communication module for data transmission and reception.

[1101] The central processing unit (server) first preprocesses the received biometric data. Specifically, it filters out outliers and imputes missing values. This allows reliable data to be input into the generative artificial intelligence model.

[1102] Generative AI models analyze preprocessed data to detect outliers and perform trend analysis. Specific examples of generative AI models used include GPT-4. An example of a prompt for executing the analysis process is, "Analyze the dog's health data to identify outliers and trends."

[1103] Based on the analysis results, the central processing unit (server) generates appropriate advice and sends it in text format to the device. The device notifies the user of this advice and displays it on the application screen. For example, the advice displayed might be something like, "You've been getting less sleep recently, so try exercising less."

[1104] Furthermore, when a user inputs their dog's symptoms or questions through the application, the content is also sent to the central processing unit (server), where it is analyzed and answered by the generative AI model. For example, if a user inputs "My dog ​​is coughing," the generated advice will be "If the coughing persists, consult a veterinarian."

[1105] Finally, the terminal is equipped with an online consultation reservation system that allows users to easily make reservations with veterinarians. For example, the user can confirm the reservation by seeing a message asking, "Would you like to make an online consultation appointment tomorrow from 10:00 to 11:00?" The reservation information is sent to the central processing unit (server), and after confirmation, a reservation confirmation notice is sent to the user.

[1106] In this way, this system can centrally manage everything from data collection and analysis to providing advice and even online consultation reservations, providing users with comprehensive and efficient support for managing their dog's health.

[1107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1108] Step 1: Data collection

[1109] The terminal uses Bluetooth or WiFi to collect biometric data from a data collection device attached to the dog. Specifically, it acquires data on activity, sleep patterns, and food intake. As input, it receives real-time data from the data collection device and stores it locally. As output, the collected biometric data is stored in the terminal's memory.

[1110] Specific behavior:

[1111] The device confirms the Bluetooth connection and obtains data such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams" from the data collection device.

[1112] Step 2: Send data

[1113] The terminal transmits the collected data to a central processing unit (server) via the Internet. As input, the collected biometric data is used. As output, the data is transmitted to the server.

[1114] Specific behavior:

[1115] The terminal converts the collected data into packet format and transmits it to a server via the Internet.

[1116] Step 3: Data Preprocessing

[1117] The server preprocesses the received data, filtering outliers and imputing missing values. As input, the biometric data sent from the device is used. As output, the filtered and imputed data is obtained.

[1118] Specific behavior:

[1119] The server analyzes the received data, checks for abnormal values ​​such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams," and filters out invalid data.

[1120] If there is missing data, it is supplemented by referring to past data.

[1121] Step 4: Data analysis

[1122] The server analyzes the preprocessed data using a generative AI model. The preprocessed biometric data is provided as input. The analysis results are obtained as output. "GPT-4" is used as an example of a generative AI model.

[1123] Specific behavior:

[1124] The server inputs the prompt "Analyze the dog's health data and identify outliers and trends" into the AI ​​model and begins the analysis.

[1125] The AI ​​model generates analysis results such as "You've been sleeping less than usual recently."

[1126] Step 5: Advice Generation

[1127] The server generates advice based on the analysis results using a generative AI model. The analysis results of the AI ​​model are used as input, and the generated advice is obtained as output.

[1128] Specific behavior:

[1129] The server converts the analysis results from the generated AI model into text format and creates advice such as, "You've been getting less sleep recently, so you should reduce your exercise."

[1130] Step 6: Advice Notification

[1131] The terminal receives the advice sent from the server and notifies the user. The input is the advice data sent from the server. The output is a notification to the user.

[1132] Specific behavior:

[1133] The device uses the application's notification function to display a notification to the user saying, "You've been sleeping less recently, so you should exercise less."

[1134] Step 7: Display Advice

[1135] The terminal displays the analysis results and advice on the application screen. The input is the advice data sent from the server. The output is the advice that the user can check.

[1136] Specific behavior:

[1137] The device updates the app screen and displays the advice, "You've been getting less sleep recently, so try exercising less."

[1138] Step 8: Book an online consultation

[1139] The user inputs their dog's symptoms and questions through the application. For example, they input information like "My dog ​​is coughing." The device then sends the input to the server via the Internet.

[1140] The server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms, sends the advice to the terminal, which displays it to the user, and provides a screen where the user can schedule an online consultation with a veterinarian.

[1141] Specific behavior:

[1142] The user types "my dog ​​is coughing" into the app and submits it.

[1143] The server inputs the prompt "What should I do if my dog ​​is coughing?" into the generative AI model.

[1144] The AI ​​model generates advice such as "If the cough persists, consult a veterinarian," and the server sends it to the device.

[1145] The device displays the advice and then suggests, "Would you like to book an online consultation?"

[1146] The user confirms the reservation, and the terminal transmits the reservation information to the server.

[1147] The server checks the reservation information and notifies the user, "Your reservation has been confirmed. Your online consultation with the veterinarian will begin tomorrow at 10:00."

[1148] (Application example 1)

[1149] 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."

[1150] When it comes to managing a dog's health, it's important for owners to regularly monitor their pet's health and take prompt action if necessary. However, current methods require owners to collect and analyze data on their dog's activity levels, sleep patterns, food intake, and other factors, which takes a lot of time and effort before they can receive appropriate advice. Additionally, online consultations and appointments at physical clinics are also time-consuming, making it difficult to respond quickly.

[1151] 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.

[1152] In this invention, the server includes means for collecting data on activity level, sleep pattern, and food intake from an information collection device attached to the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it to the user, means for providing online reservations so that the user can make appointments at a physical store, means for the app to synchronize appointment information with the physical store's system, and means for generating and displaying daily and weekly health reports based on the dog's health data. This allows the server to collect and analyze the dog's health data in real time, provide prompt and appropriate advice, and easily handle online consultations and appointments at a physical store.

[1153] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[1154] The "analysis server" is a central processing unit that receives collected data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1155] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[1156] A "terminal" is a device used by a user to receive data collected from an information collection device, transmit the data to a server, and display analysis results and advice.

[1157] "Online Reservation" is a system that allows users to make appointments for medical appointments at physical stores.

[1158] A "health report" is a detailed analysis of your dog's health on a daily or weekly basis.

[1159] The present invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system is composed of an information collection device, a terminal, an analysis server, and an online reservation system.

[1160] System Configuration

[1161] Information gathering device (smart color)

[1162] The information collection device is a device worn by the dog. This device monitors and records data such as the dog's activity level, sleep patterns, and food intake 24 hours a day. For example, it has a built-in acceleration sensor, heart rate sensor, and GPS system, and transmits the data to a terminal via Bluetooth or WiFi.

[1163] Device (smartphone)

[1164] The device is a smartphone used by the user. This device transmits data collected from the smart color to an analysis server in real time, receives analysis results and advice, and notifies the user. Users can also use the app to enter questions and symptoms, and make appointments for medical examinations.

[1165] Analysis Server

[1166] The analysis server is a central processing unit that receives collected health data and analyzes it using a generative artificial intelligence model. This server first preprocesses the data, filtering out outliers and imputing missing values. It then analyzes the data using a generative AI model (e.g., a model using TensorFlow) to identify abnormal patterns and health conditions. Based on the analysis results, it generates personalized health advice and sends it to the device.

[1167] Online Reservation System

[1168] Users can make appointments at pet shops and veterinary clinics through the terminal application. Reservation information is synchronized with the physical store's system in real time, allowing users to make appointments quickly. This is done using WebSocket technology.

[1169] Processing flow

[1170] 1. Data collection: The smart collar uses sensors to record your dog's activity, sleep patterns, food intake, etc. and transmits the data to your device.

[1171] 2. Data transmission: The device sends the received data to the cloud analysis server.

[1172] 3. Data Analysis: The analysis server pre-processes the collected data and then analyzes it using a generative artificial intelligence model to generate health-based advice.

[1173] 4. Notification and display: The analysis results are sent to the device, and the user can view detailed analysis results and advice.

[1174] 5. Appointment: When a user makes an appointment online, the appointment information is synchronized with the store's system.

[1175] Specific examples

[1176] For example, if a dog wearing a "smart collar" is extremely inactive, the analysis server receives the data and uses a generative AI model to detect the abnormality. Based on the analysis results, advice such as "increase walking time" is generated and sent to the device. The owner immediately receives this advice via a smartphone notification and can view detailed data analysis within the app.

[1177] Additionally, if pet owners want to make an appointment at a pet shop or veterinary clinic, they can simply select a date and time within the app and make the appointment online. This appointment information is synchronized in real time with the physical store, allowing for a prompt consultation.

[1178] Prompt Sentence Examples

[1179] To develop a pet health care assistant app, collect, analyze, and provide advice on the following:

[1180] 1. Dog activity level

[1181] 2. Sleep patterns

[1182] 3. Dietary Intake

[1183] Based on this data, please use a TensorFlow model to provide specific advice based on the health status (e.g., adjusting walking time, feeding amount, etc.). Also, please implement a consultation booking function so that users can easily book a consultation at a veterinary clinic or pet shop."

[1184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1185] Step 1:

[1186] Data collection

[1187] The smart collar uses sensors to record your dog's activity levels, sleep patterns, food intake, etc. The smart collar collects this data in real time and transmits it to your device (smartphone) via Bluetooth or WiFi.

[1188] Inputs: Dog activity, sleep, food intake

[1189] Output: Health data sent to the device

[1190] Specific operations: Setting data recording frequency, maintaining Bluetooth or WiFi connection, and data transmission

[1191] Step 2:

[1192] Data transmission

[1193] The device then transmits the health data it receives in real time to a cloud-based analytics server via mobile data or Wi-Fi. The device may compress and anonymize the data before sending it.

[1194] Input: Health data received from smart collar

[1195] Output: Health data sent to the analysis server

[1196] Specific operations: data compression, anonymization, maintaining internet connection, data transmission

[1197] Step 3:

[1198] Data Preprocessing

[1199] The analysis server preprocesses the received data, filtering out abnormal values ​​and filling in missing values. This processing increases the reliability of the data.

[1200] Input: Health data sent from the device

[1201] Output: filtered and imputed data

[1202] Specific operations: Identifying and removing outliers, imputing missing values, normalizing data

[1203] Step 4:

[1204] Data analysis

[1205] The analytics server then inputs the pre-processed data into a generative artificial intelligence model (e.g., a TensorFlow model) for analysis. The AI ​​model identifies anomalies in activity levels, sleep patterns, and dietary intake, and generates health-based advice.

[1206] Input: Preprocessed health data

[1207] Output: Health analysis results and advice

[1208] Specific operations: Data input to AI model, analysis processing, advice generation

[1209] Step 5:

[1210] Send analysis results

[1211] The analysis server sends the analysis results and advice to the device, which receives the analysis results and notifies the user.

[1212] Input: Analysis results and advice

[1213] Output: Analysis results and advice sent to your device

[1214] Specific operations: Data transmission process, device notification system operation

[1215] Step 6:

[1216] User Notification and Display

[1217] The device notifies the user of the analysis results and provides advice, which is displayed on the application screen, allowing the user to adjust their dog's care accordingly.

[1218] Input: Analysis results and advice

[1219] Output: Analysis results and advice communicated to the user

[1220] Specific operations: Activating the notification system, updating the application screen

[1221] Step 7:

[1222] Consultation appointment

[1223] Users can make appointments at pet shops and veterinary clinics through the app, and the appointment information is synchronized in real time with the store's system.

[1224] Input: User's reservation information

[1225] Output: Reservation information synchronized with the physical store system

[1226] Specific operations: Sending reservation information, synchronizing with the physical store system

[1227] Step 8:

[1228] Health Report Generation

[1229] The analysis server generates daily and weekly health reports based on the dog's health data and sends them to the terminal, which displays the reports to the user.

[1230] Input: Health data

[1231] Output: Generated health report

[1232] Specific operations: report generation process, data transmission process, application screen update

[1233] 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.

[1234] The present invention is a system that effectively collects and analyzes health data of a dog, provides appropriate advice to a user, and is also a system that can recognize the user's emotions and reflect them in the advice. The following describes in detail an embodiment of the present invention.

[1235] System Configuration

[1236] The system consists of the following main components:

[1237] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[1238] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotional data.

[1239] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[1240] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1241] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides.

[1242] 6. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[1243] Program processing

[1244] Data collection and transmission

[1245] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1246] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[1247] 3. The device also uses voice input and facial recognition technology to recognize the user's current emotional state in order to collect emotional data.

[1248] Data analysis and advice generation

[1249] 4. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[1250] 5. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[1251] 6. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[1252] Emotion engine processing

[1253] 7. The analysis server uses the emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotional state based on the voice data and image data sent from the device.

[1254] 8. The analytics server adjusts the advice based on the user's emotional state. For example, if the user is feeling stressed, it generates gentler advice or content encouraging relaxation.

[1255] User Notification and Advice Display

[1256] 9. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[1257] 10. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[1258] Online consultation

[1259] 11. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[1260] 12. The terminal sends the user's input to the analysis server via the Internet.

[1261] 13. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[1262] 14. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[1263] 15. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the desired veterinarian.

[1264] 16. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[1265] In this way, the present invention realizes a system that not only collects and analyzes a dog's health data and provides appropriate advice to the user, but also provides advice that takes into account the user's emotional state and facilitates online consultation with a veterinarian if necessary.

[1266] The processing flow will be explained below.

[1267] Step 1:

[1268] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[1269] Step 2:

[1270] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[1271] Step 3:

[1272] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server. The sent data includes a timestamp.

[1273] Step 4:

[1274] The server receives the collected data. The server stores the data and starts pre-processing, which includes data format conversion.

[1275] Step 5:

[1276] The server pre-processes the data, filtering outliers and imputing missing values. The pre-processed data is then input into the analysis module.

[1277] Step 6:

[1278] The server feeds the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[1279] Step 7:

[1280] The server generates advice in natural language based on the analysis results, and converts the advice into text format to make it easier for the user to understand.

[1281] Step 8:

[1282] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and verifies that the transmission was successful.

[1283] Step 9:

[1284] The device receives the advice sent from the analysis server and uses the smartphone's notification function to notify the user that new information is available.

[1285] Step 10:

[1286] The device displays the analysis results and advice on the application screen, allowing the user to manage their dog's health based on the displayed information.

[1287] Step 11:

[1288] The device collects the user's emotional data. It uses voice input and facial recognition technology to recognize the user's current emotional state. For example, if the user says "I'm worried," that voice data is collected.

[1289] Step 12:

[1290] The device sends the collected emotional data to an analysis server in real time for analysis.

[1291] Step 13:

[1292] The server uses an emotion engine to analyze the user's emotional state, recognizing the emotional state based on the user's voice data and image data, and determining the type and intensity of the emotion.

[1293] Step 14:

[1294] The server tailors the advice based on the user's emotional state. For example, if the user is feeling stressed, the advice will be customized to a gentler tone and include words of encouragement.

[1295] Step 15:

[1296] The server sends the adjusted advice to the terminal, and the customized advice data is sent to the terminal via the Internet.

[1297] Step 16:

[1298] The device will again display the advice to the user, again using notifications to let the user know that new advice has been displayed.

[1299] Step 17:

[1300] The user opens the application and sees the generated advice, which is customized based on their emotional state.

[1301] Step 18:

[1302] The user enters their dog's symptoms or questions into the application, for example, "My dog ​​is coughing," and presses the submit button.

[1303] Step 19:

[1304] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[1305] Step 20:

[1306] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[1307] Step 21:

[1308] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[1309] Step 22:

[1310] The device displays the received advice to the user. The new advice is displayed on the application screen.

[1311] Step 23:

[1312] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[1313] Step 24:

[1314] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[1315] Step 25:

[1316] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[1317] Step 26:

[1318] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[1319] Step 27:

[1320] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[1321] Example 2

[1322] 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."

[1323] Conventional dog health management systems only collect and analyze data such as a dog's activity level, sleep patterns, and food intake, and do not consider the user's emotional state, which often results in inappropriate advice. Furthermore, few systems offer integrated features such as online consultation reservations, making them less convenient. The purpose of this invention is to solve these problems and provide a user-friendly health management system.

[1324] 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.

[1325] In this invention, the server includes means for preprocessing data by filtering outliers and filling in missing values, means for analyzing data using a generative AI model to identify outliers and perform trend analysis, and an emotion engine for adjusting advice based on the user's emotional state. This not only effectively collects and analyzes dog health data and provides appropriate advice to the user, but also enables advice that takes the user's emotional state into account and the ability to book online consultations in a unified manner.

[1326] The "information collection device" is a device attached to the dog that collects data such as activity levels, sleep patterns, and food intake.

[1327] A "terminal" is a device used by a user, such as a smartphone or tablet, that receives data from the information collection device and transmits it to the analysis server.

[1328] An "analysis server" is a central processing unit that receives data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1329] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[1330] The "emotion engine" is an algorithm that uses voice input and facial recognition technology to recognize a user's emotional state and adjusts advice accordingly.

[1331] The "online consultation reservation system" is a system that allows users to make reservations for online consultations with veterinarians.

[1332] "Data preprocessing" is a process performed by the analysis server, which involves filtering outliers and filling in missing data.

[1333] "Outlier detection" is performed by a generative artificial intelligence model to identify anomalous values ​​from collected data.

[1334] "Trend analysis" is a method of capturing changes in health status by analyzing long-term fluctuations and patterns in data.

[1335] "Advice" is a specific course of action or recommendation that the analysis server generates using a generative artificial intelligence model and provides to the user.

[1336] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to the user, and is also a system that can recognize the user's emotions and reflect them in the advice.

[1337] System Configuration

[1338] The system consists of the following main components:

[1339] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[1340] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotion data.

[1341] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1342] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1343] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice provided.

[1344] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[1345] Data collection and transmission

[1346] The device periodically collects data on activity, sleep patterns, and food intake from an information collection device attached to the dog via Bluetooth or Wi-Fi, allowing real-time monitoring of changes in the dog's health. The device also uses facial recognition technology to collect data on the user's emotions via voice input and a camera.

[1347] Data analysis and advice generation

[1348] The analytics server receives the data sent from the device and pre-processes it, filtering outliers and imputing missing values, preparing it for input into the generative AI model. The generative AI model then analyzes the data, detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake, and generating recommendations.

[1349] Emotion engine processing

[1350] The emotion engine is used to analyze the user's emotion data and recognize the user's emotional state. If the user is feeling stressed, the analysis server generates gentler advice or content encouraging relaxation.

[1351] User Notification and Advice Display

[1352] When the device receives advice sent from the analysis server, it notifies the user via the smartphone's notification function. The user can then adjust how they care for their dog based on the analysis results and advice displayed on the application screen.

[1353] Online consultation

[1354] Through the application, users can input their dog's symptoms and questions. For example, they can input a symptom like "My dog ​​is coughing." The device then sends this input data via the internet to an analysis server, which uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is then sent to the device and displayed to the user. The device then displays a screen for booking an online consultation with a veterinarian, allowing the user to select the desired date, time, and veterinarian.

[1355] Examples of concrete examples and prompts

[1356] As a concrete example, User A notices that his dog's activity level has decreased. Data is sent from the device to the analysis server, which detects the abnormality and generates advice to "increase walk times." At the same time, the emotion engine detects that User A is feeling stressed, so it also provides gentle advice such as "When you are feeling stressed, try to spend more time relaxing together."

[1357] An example prompt might be:

[1358] "Analyze the dog's activity data and generate advice if there are any abnormalities."

[1359] "Tailor your advice to reflect the emotions your users are feeling."

[1360] "Provide advice on dog coughing and suggest an online consultation with a veterinarian if necessary."

[1361] In this way, the present invention realizes a system that can effectively manage dog health data and provide advice that takes into account the user's emotions.

[1362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1363] Step 1:

[1364] The device uses Bluetooth or WiFi to collect data from an information collection device (smart collar) attached to the dog. The input data is the dog's activity level, sleep patterns, and food intake. The output is the collected data. Specifically, the device collects data every five minutes and stores it locally.

[1365] Step 2:

[1366] The device transmits the collected data to an analysis server in real time via the Internet. The input data is the collected data on activity, sleep patterns, and food intake. The output is the data transmitted to the analysis server. Specifically, the device converts the data into a packet format and transmits it using Wi-Fi or mobile communication.

[1367] Step 3:

[1368] The device collects emotion data using the user's voice input and facial recognition technology. The input requires image data of the user's voice and face. The output is recognized emotion data. Specifically, the device uses a microphone and camera to capture the user's voice and face, which are then analyzed using emotion recognition software.

[1369] Step 4:

[1370] The analysis server preprocesses the data received from the device. The input data is raw data on activity, sleep patterns, and food intake. The output is the preprocessed data. Specifically, it filters out outliers in the data and executes algorithms to fill in missing values.

[1371] Step 5:

[1372] The analysis server analyzes the preprocessed data using a generative AI model. The input data is preprocessed activity, sleep patterns, and food intake data. The output is the results of outlier detection and trend analysis. Specifically, the generative AI model performs pattern recognition and statistical analysis.

[1373] Step 6:

[1374] The analysis server generates advice using a generative artificial intelligence model based on the analysis results and sends the content in text format to the terminal. The input data is the analysis results, and the generated advice is obtained as output. In concrete terms, the server generates appropriate advice based on specific rules.

[1375] Step 7:

[1376] The analysis server uses an emotion engine to analyze the user's emotion data. The input data is the emotion data sent from the device. The output is the recognized emotional state. Specific operations include analyzing voice tone and facial expressions.

[1377] Step 8:

[1378] The analysis server adjusts advice based on the user's emotional state. The input data is the analyzed emotional state and the generated advice, and the output is the adjusted advice. Specific operations include modifying the advice to reflect the emotional state.

[1379] Step 9:

[1380] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The input data is the advice from the analysis server, and the output is a notification to the user. Specifically, the device displays a notification pop-up.

[1381] Step 10:

[1382] The terminal displays the analysis results and advice on the application screen. The input data is the advice sent from the analysis server, and the analysis results that are displayed to the user are obtained as output. In concrete terms, the terminal displays the data on the application's specified screen.

[1383] Step 11:

[1384] The user inputs their dog's symptoms and questions through the application. The input data is the text entered by the user, and the input question or symptom is obtained as the output. The specific operation is that the user enters text into the application form.

[1385] Step 12:

[1386] The terminal sends the user's input to the analysis server via the Internet. The input data is the text of the user's question or symptoms, and the transmission to the analysis server is completed as the output. Specifically, the terminal converts the text into packet format and sends it.

[1387] Step 13:

[1388] The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms. The input data is the text of the user's questions and symptoms, and the generated advice is obtained as the output. Specifically, the server automatically generates appropriate advice based on the content of the question.

[1389] Step 14:

[1390] The analysis server sends the generated advice to the terminal, which then displays it to the user. The input data is the generated advice, and the displayed advice is obtained as output. In specific operations, the terminal displays the advice on a specific screen of the application.

[1391] Step 15:

[1392] The terminal displays a screen for booking an online consultation with a veterinarian, allowing users to easily make a reservation. The input data is the user's desired date and time and the selection of a veterinarian, and the output is reservation information. Specifically, the terminal displays a calendar function and provides an input form.

[1393] Step 16:

[1394] The terminal sends reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user. The input data is the user's reservation information, and the output is a reservation confirmation notice. Specifically, the terminal sends the reservation data, and the server checks it and then generates and sends a notice.

[1395] (Application example 2)

[1396] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1397] Conventional pet care systems are limited to collecting and analyzing dog health data, and lack the ability to provide advice based on the owner's emotional state. Furthermore, if a user senses something is wrong, the complicated online consultation process with a veterinarian makes it difficult to provide timely and appropriate care. This can lead to inadequate management of a dog's health.

[1398] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on activity level, sleep pattern, and food intake from an information collection device worn by the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for the terminal to collect emotion data and transmit it to the analysis server, means for the analysis server to analyze the user's emotional state based on the emotion data and reflect the result in advice, means for transmitting advice generated based on the analysis results and the emotion data to the terminal and notifying the user, means for receiving questions from the user and input regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing an online consultation appointment with a veterinarian. This makes it possible to provide advice that takes the user's emotional state into consideration and simplify the process of quickly consulting with a veterinarian.

[1399] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[1400] An "analytics server" is a central processing unit that receives collected data, pre-processes it, and analyzes it using generative artificial intelligence models.

[1401] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[1402] "Emotion data" is data collected from the user's voice, facial expressions, etc., and indicates the user's emotional state.

[1403] "Emotional state" is information indicating the user's emotional state, such as stress, joy, or sadness.

[1404] "Advice" refers to instructions or suggestions generated by the analysis server using a generative artificial intelligence model based on the results of data analysis and the user's emotional state.

[1405] A "terminal" is a device used by a user that receives data and emotion data collected from the information collection device and transmits them to the analysis server, such as a smartphone.

[1406] "Online consultation reservation" is a system that allows users to book a consultation with a veterinarian, and includes the ability to select a date and time and a veterinarian.

[1407] An "outlier" is data that is outside the normal range and suggests some kind of problem with the dog's health.

[1408] "Missing data imputation" is a process for filling in missing parts of collected data.

[1409] MODE FOR CARRYING OUT THE INVENTION

[1410] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to users. Furthermore, the system can recognize the user's emotions and reflect them in the advice, facilitating online consultations with veterinarians. Specific embodiments for implementing the present invention are described below.

[1411] 1. System Overview

[1412] The system mainly consists of the following components:

[1413] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[1414] 2. Terminal (smartphone): A device used by the user to receive data and emotional data collected from the information collection device and send it to the server.

[1415] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1416] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice. An example of this is "OpenAI GPT-4."

[1417] 5. Emotion engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides. Examples of such engines include AWS Rekognition and Google Cloud Speech-to-Text.

[1418] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[1419] 2. Program Processing

[1420] Data collection and transmission

[1421] The device (smartphone) uses Bluetooth or Wi-Fi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog. The collected data is sent to an analysis server in real time via an internet connection. The device also uses voice input and facial recognition technology to collect the user's emotional data and recognize the user's current emotional state.

[1422] Data analysis and advice generation

[1423] The analysis server first preprocesses the received data, filtering out outliers and filling in missing values. It then analyzes the data using a generative AI model, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake. Based on the analysis results, the analysis server takes into account the user's emotional data and generates advice, which is then sent to the device in text format.

[1424] Emotion engine processing

[1425] The analysis server uses an emotion engine to analyze the user's emotional state and reflect it in the advice it provides. For example, if the user is feeling stressed, it generates gentler advice or advice encouraging relaxation.

[1426] User Notification and Advice Display

[1427] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The user can then adjust their dog's care based on the analysis results and advice displayed on the application screen.

[1428] Online consultation

[1429] Users can input their dog's symptoms and questions through the application. For example, they can input the symptom "My dog ​​is coughing." The device sends the user's input via the Internet to an analysis server, which then uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is sent to the device and displayed to the user. For example, the advice provided might be "If the cough persists, consult a veterinarian." The device also displays a screen for booking an online consultation with a veterinarian, allowing the user to easily make an appointment.

[1430] 3. Example prompts

[1431] Examples:

[1432] If the user is stressed: "I see you're stressed. Let's take a long walk today to help you relax."

[1433] If your dog is inactive: "Your dog has been less active recently. To check its health, first review its diet and exercise."

[1434] Example prompt sentence:

[1435] "Analyze the data recently collected by the smart collar and generate recommendations about your dog's health."

[1436] "Based on the user's voice and facial expression data, determine whether the user is feeling stressed and provide appropriate advice."

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] Data collection

[1440] Subject: Terminal

[1441] How it works: The device uses Bluetooth or WiFi to collect data on activity, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1442] Input: Raw data from SmartColor

[1443] Output: Collected health data (activity, sleep patterns, dietary intake)

[1444] Step 2:

[1445] Emotional Data Collection

[1446] Subject: Terminal

[1447] How it works: The device uses the smartphone's camera and microphone to collect data on the user's voice and facial expressions, and then analyzes the user's emotional state based on this.

[1448] Input: User's voice data, facial expression data

[1449] Output: Parsed user emotional state data

[1450] Step 3:

[1451] Data transmission

[1452] Subject: Terminal

[1453] Operation: The device sends the collected health and emotion data to an analysis server via the Internet.

[1454] Input: Collected health data, emotional state data

[1455] Output: Data sent to the analysis server

[1456] Step 4:

[1457] Data Preprocessing

[1458] Subject: Analysis server

[1459] Operation: The analysis server preprocesses the received data, filtering outliers and imputing missing values.

[1460] Input: Received data (health data and emotion data)

[1461] Output: Preprocessed data

[1462] Step 5:

[1463] Data analysis

[1464] Subject: Analysis server

[1465] Operation: The analysis server inputs the preprocessed data into a generative AI model and performs data analysis. Specifically, it detects abnormal values ​​and performs trend analysis on activity levels, sleep patterns, and dietary intake.

[1466] Input: Preprocessed health data

[1467] Output: Analysis results (health status data)

[1468] Step 6:

[1469] Advice Generation

[1470] Subject: Analysis server

[1471] How it works: The analytics server uses a generative artificial intelligence model to generate advice based on health and emotional state data. The advice generated reflects the user's emotional state.

[1472] Input: Analysis results, emotional state data

[1473] Output: Generated advice

[1474] Step 7:

[1475] Sending and displaying advice

[1476] Subject: Analysis server

[1477] Operation: The analysis server sends the generated advice to the terminal, which receives the advice and notifies the user.

[1478] Input: Generated advice

[1479] Output: Notifications to the terminal and advice to the user

[1480] Step 8:

[1481] Book an online consultation

[1482] Subject: User

[1483] How it works: The user enters their dog's symptoms and any questions they have through the application. The device sends the user's input to the analysis server. The analysis server uses a generative AI model to generate advice for the user's questions and symptoms and sends it to the device. The user then confirms the appointment by using the online consultation appointment screen with a veterinarian.

[1484] Input: User questions and symptoms

[1485] Output: Generated advice, confirmed booking information

[1486] The above is the flow of processing of the program of the system that realizes the application example.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] [Fourth embodiment]

[1491] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1492] 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.

[1493] 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).

[1494] 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.

[1495] 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.

[1496] 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).

[1497] 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. 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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."

[1504] The present invention provides a system for effectively collecting and analyzing health data of a dog and providing appropriate advice to a user. Hereinafter, embodiments of the present invention will be described in detail.

[1505] System Configuration

[1506] The system consists of the following main components:

[1507] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[1508] 2. Terminal (smartphone): A device used by the user that receives data collected from the information collection device and sends it to the server.

[1509] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[1510] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1511] 5. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[1512] Program processing

[1513] Data collection and transmission

[1514] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1515] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[1516] Data analysis and advice generation

[1517] 3. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[1518] 4. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[1519] 5. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[1520] User Notification and Advice Display

[1521] 6. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[1522] 7. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[1523] Online consultation

[1524] 8. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[1525] 9. The device sends the user's input to the analysis server via the Internet.

[1526] 10. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[1527] 11. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[1528] 12. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the veterinarian of their choice.

[1529] 13. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[1530] In this way, the present invention realizes a system that can collect and analyze dog health data, provide appropriate advice to the user, and facilitate online consultation with a veterinarian if necessary.

[1531] The processing flow will be explained below.

[1532] Step 1:

[1533] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[1534] Step 2:

[1535] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[1536] Step 3:

[1537] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server.

[1538] Step 4:

[1539] The server receives the collected data, stores the data, and starts pre-processing.

[1540] Step 5:

[1541] The server performs preprocessing of the data, such as filtering outliers and imputing missing values, to create a dataset suitable for analysis.

[1542] Step 6:

[1543] The server inputs the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[1544] Step 7:

[1545] The server generates natural language advice based on the analysis results, and the AI ​​model interprets the analysis results and generates specific advice to provide to the user.

[1546] Step 8:

[1547] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and confirms that the transmission is complete.

[1548] Step 9:

[1549] The device notifies the user of the analysis results and advice, and uses the smartphone's notification function to notify the user of new information.

[1550] Step 10:

[1551] The user opens the app and reviews the analysis and advice provided, providing specific actionable information and areas for improvement.

[1552] Step 11:

[1553] The user enters the dog's symptoms and any questions they have into the application, for example, "My dog ​​is coughing," and presses the submit button.

[1554] Step 12:

[1555] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[1556] Step 13:

[1557] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[1558] Step 14:

[1559] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[1560] Step 15:

[1561] The device displays the received advice to the user. The new advice is displayed on the application screen.

[1562] Step 16:

[1563] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[1564] Step 17:

[1565] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[1566] Step 18:

[1567] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[1568] Step 19:

[1569] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[1570] Step 20:

[1571] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[1572] Example 1

[1573] 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."

[1574] Previously, systems for collecting and analyzing dog health data did not centralize the processes of data collection, analysis, and advice generation, making it difficult to provide information to users. Furthermore, a lack of preprocessing, such as outlier detection and missing value imputation, could reduce the reliability of the analysis results. Furthermore, there was a lack of comprehensive support for users to take appropriate measures for their dog's symptoms.

[1575] 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.

[1576] In this invention, the server includes means for collecting biometric data from a data collection device attached to the dog, means for transmitting the collected data to a central processing unit, means for the central processing unit to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing online consultation appointments with a veterinarian. This allows for centralized management of everything from data collection to analysis, advice provision, and online consultation appointments, enabling the user to understand the dog's health condition and easily take appropriate measures.

[1577] A "data collection device" is a device that is attached to a dog and collects biometric data.

[1578] "Biometric data" includes information such as a dog's activity level, sleep patterns, and food intake.

[1579] A "terminal" is a device used by a user to transmit biometric data to a central processing unit and receive analysis results and advice.

[1580] A "central processing unit" is a device that receives data via the internet, analyzes the data using a generative artificial intelligence model, and generates analysis results and advice.

[1581] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[1582] A "question" is an input from the user and is a question about the dog's symptoms or health condition.

[1583] "Advice" refers to instructions or advice about dog health care that is generated by a generative artificial intelligence model and provided to the user.

[1584] "Notification" is a means of transmitting information to inform the user of the analysis results and advice contents.

[1585] "Book an online consultation" is the process by which a user books a consultation with a veterinarian over the Internet.

[1586] "Abnormal value filtering" is a process of detecting and excluding values ​​that are different from normal values ​​from collected biometric data.

[1587] "Missing data value completion" is a process of filling in missing data when there are gaps in the collected biometric data.

[1588] This invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system consists of the following main components: an information collection device (data collection device), a terminal, a central processing unit (server), a generative AI model, and an online consultation reservation system.

[1589] First, the data collection device is attached to the dog and collects real-time biological data such as activity level, sleep patterns, and food intake. Specifically, this data collection device is often implemented as a smart collar.

[1590] Next, the terminal (smartphone) receives the biometric data from the information collection device via Bluetooth or WiFi. The collected data is sent to a central processing unit (server) via the Internet. The smartphone application used here has a built-in communication module for data transmission and reception.

[1591] The central processing unit (server) first preprocesses the received biometric data. Specifically, it filters out outliers and imputes missing values. This allows reliable data to be input into the generative artificial intelligence model.

[1592] Generative AI models analyze preprocessed data to detect outliers and perform trend analysis. Specific examples of generative AI models used include GPT-4. An example of a prompt for executing the analysis process is, "Analyze the dog's health data to identify outliers and trends."

[1593] Based on the analysis results, the central processing unit (server) generates appropriate advice and sends it in text format to the device. The device notifies the user of this advice and displays it on the application screen. For example, the advice displayed might be something like, "You've been getting less sleep recently, so try exercising less."

[1594] Furthermore, when a user inputs their dog's symptoms or questions through the application, the content is also sent to the central processing unit (server), where it is analyzed and answered by the generative AI model. For example, if a user inputs "My dog ​​is coughing," the generated advice will be "If the coughing persists, consult a veterinarian."

[1595] Finally, the terminal is equipped with an online consultation reservation system that allows users to easily make reservations with veterinarians. For example, the user can confirm the reservation by seeing a message asking, "Would you like to make an online consultation appointment tomorrow from 10:00 to 11:00?" The reservation information is sent to the central processing unit (server), and after confirmation, a reservation confirmation notice is sent to the user.

[1596] In this way, this system can centrally manage everything from data collection and analysis to providing advice and even online consultation reservations, providing users with comprehensive and efficient support for managing their dog's health.

[1597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1598] Step 1: Data collection

[1599] The terminal uses Bluetooth or WiFi to collect biometric data from a data collection device attached to the dog. Specifically, it acquires data on activity, sleep patterns, and food intake. As input, it receives real-time data from the data collection device and stores it locally. As output, the collected biometric data is stored in the terminal's memory.

[1600] Specific behavior:

[1601] The device confirms the Bluetooth connection and obtains data such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams" from the data collection device.

[1602] Step 2: Send data

[1603] The terminal transmits the collected data to a central processing unit (server) via the Internet. As input, the collected biometric data is used. As output, the data is transmitted to the server.

[1604] Specific behavior:

[1605] The terminal converts the collected data into packet format and transmits it to a server via the Internet.

[1606] Step 3: Data Preprocessing

[1607] The server preprocesses the received data, filtering outliers and imputing missing values. As input, the biometric data sent from the device is used. As output, the filtered and imputed data is obtained.

[1608] Specific behavior:

[1609] The server analyzes the received data, checks for abnormal values ​​such as "activity level: 3000 steps," "sleep pattern: 8 hours," and "food intake: 50 grams," and filters out invalid data.

[1610] If there is missing data, it is supplemented by referring to past data.

[1611] Step 4: Data analysis

[1612] The server analyzes the preprocessed data using a generative AI model. The preprocessed biometric data is provided as input. The analysis results are obtained as output. "GPT-4" is used as an example of a generative AI model.

[1613] Specific behavior:

[1614] The server inputs the prompt "Analyze the dog's health data and identify outliers and trends" into the AI ​​model and begins the analysis.

[1615] The AI ​​model generates analysis results such as "You've been sleeping less than usual recently."

[1616] Step 5: Advice Generation

[1617] The server generates advice based on the analysis results using a generative AI model. The analysis results of the AI ​​model are used as input, and the generated advice is obtained as output.

[1618] Specific behavior:

[1619] The server converts the analysis results from the generated AI model into text format and creates advice such as, "You've been getting less sleep recently, so you should reduce your exercise."

[1620] Step 6: Advice Notification

[1621] The terminal receives the advice sent from the server and notifies the user. The input is the advice data sent from the server. The output is a notification to the user.

[1622] Specific behavior:

[1623] The device uses the application's notification function to display a notification to the user saying, "You've been sleeping less recently, so you should exercise less."

[1624] Step 7: Display Advice

[1625] The terminal displays the analysis results and advice on the application screen. The input is the advice data sent from the server. The output is the advice that the user can check.

[1626] Specific behavior:

[1627] The device updates the app screen and displays the advice, "You've been getting less sleep recently, so try exercising less."

[1628] Step 8: Book an online consultation

[1629] The user inputs their dog's symptoms and questions through the application. For example, they input information like "My dog ​​is coughing." The device then sends the input to the server via the Internet.

[1630] The server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms, sends the advice to the terminal, which displays it to the user, and provides a screen where the user can schedule an online consultation with a veterinarian.

[1631] Specific behavior:

[1632] The user types "my dog ​​is coughing" into the app and submits it.

[1633] The server inputs the prompt "What should I do if my dog ​​is coughing?" into the generative AI model.

[1634] The AI ​​model generates advice such as "If the cough persists, consult a veterinarian," and the server sends it to the device.

[1635] The device displays the advice and then suggests, "Would you like to book an online consultation?"

[1636] The user confirms the reservation, and the terminal transmits the reservation information to the server.

[1637] The server checks the reservation information and notifies the user, "Your reservation has been confirmed. Your online consultation with the veterinarian will begin tomorrow at 10:00."

[1638] (Application example 1)

[1639] 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."

[1640] When it comes to managing a dog's health, it's important for owners to regularly monitor their pet's health and take prompt action if necessary. However, current methods require owners to collect and analyze data on their dog's activity levels, sleep patterns, food intake, and other factors, which takes a lot of time and effort before they can receive appropriate advice. Additionally, online consultations and appointments at physical clinics are also time-consuming, making it difficult to respond quickly.

[1641] 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.

[1642] In this invention, the server includes means for collecting data on activity level, sleep pattern, and food intake from an information collection device attached to the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for transmitting the analysis results to a terminal and notifying the user, means for receiving questions and input from the user regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it to the user, means for providing online reservations so that the user can make appointments at a physical store, means for the app to synchronize appointment information with the physical store's system, and means for generating and displaying daily and weekly health reports based on the dog's health data. This allows the server to collect and analyze the dog's health data in real time, provide prompt and appropriate advice, and easily handle online consultations and appointments at a physical store.

[1643] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[1644] The "analysis server" is a central processing unit that receives collected data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1645] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[1646] A "terminal" is a device used by a user to receive data collected from an information collection device, transmit the data to a server, and display analysis results and advice.

[1647] "Online Reservation" is a system that allows users to make appointments for medical appointments at physical stores.

[1648] A "health report" is a detailed analysis of your dog's health on a daily or weekly basis.

[1649] The present invention is a system for effectively collecting and analyzing dog health data and providing appropriate advice to users. This system is composed of an information collection device, a terminal, an analysis server, and an online reservation system.

[1650] System Configuration

[1651] Information gathering device (smart color)

[1652] The information collection device is a device worn by the dog. This device monitors and records data such as the dog's activity level, sleep patterns, and food intake 24 hours a day. For example, it has a built-in acceleration sensor, heart rate sensor, and GPS system, and transmits the data to a terminal via Bluetooth or WiFi.

[1653] Device (smartphone)

[1654] The device is a smartphone used by the user. This device transmits data collected from the smart color to an analysis server in real time, receives analysis results and advice, and notifies the user. Users can also use the app to enter questions and symptoms, and make appointments for medical examinations.

[1655] Analysis Server

[1656] The analysis server is a central processing unit that receives collected health data and analyzes it using a generative artificial intelligence model. This server first preprocesses the data, filtering out outliers and imputing missing values. It then analyzes the data using a generative AI model (e.g., a model using TensorFlow) to identify abnormal patterns and health conditions. Based on the analysis results, it generates personalized health advice and sends it to the device.

[1657] Online Reservation System

[1658] Users can make appointments at pet shops and veterinary clinics through the terminal application. Reservation information is synchronized with the physical store's system in real time, allowing users to make appointments quickly. This is done using WebSocket technology.

[1659] Processing flow

[1660] 1. Data collection: The smart collar uses sensors to record your dog's activity, sleep patterns, food intake, etc. and transmits the data to your device.

[1661] 2. Data transmission: The device sends the received data to the cloud analysis server.

[1662] 3. Data Analysis: The analysis server pre-processes the collected data and then analyzes it using a generative artificial intelligence model to generate health-based advice.

[1663] 4. Notification and display: The analysis results are sent to the device, and the user can view detailed analysis results and advice.

[1664] 5. Appointment: When a user makes an appointment online, the appointment information is synchronized with the store's system.

[1665] Specific examples

[1666] For example, if a dog wearing a "smart collar" is extremely inactive, the analysis server receives the data and uses a generative AI model to detect the abnormality. Based on the analysis results, advice such as "increase walking time" is generated and sent to the device. The owner immediately receives this advice via a smartphone notification and can view detailed data analysis within the app.

[1667] Additionally, if pet owners want to make an appointment at a pet shop or veterinary clinic, they can simply select a date and time within the app and make the appointment online. This appointment information is synchronized in real time with the physical store, allowing for a prompt consultation.

[1668] Prompt Sentence Examples

[1669] To develop a pet health care assistant app, collect, analyze, and provide advice on the following:

[1670] 1. Dog activity level

[1671] 2. Sleep patterns

[1672] 3. Dietary Intake

[1673] Based on this data, please use a TensorFlow model to provide specific advice based on the health status (e.g., adjusting walking time, feeding amount, etc.). Also, please implement a consultation booking function so that users can easily book a consultation at a veterinary clinic or pet shop."

[1674] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1675] Step 1:

[1676] Data collection

[1677] The smart collar uses sensors to record your dog's activity levels, sleep patterns, food intake, etc. The smart collar collects this data in real time and transmits it to your device (smartphone) via Bluetooth or WiFi.

[1678] Inputs: Dog activity, sleep, food intake

[1679] Output: Health data sent to the device

[1680] Specific operations: Setting data recording frequency, maintaining Bluetooth or WiFi connection, and data transmission

[1681] Step 2:

[1682] Data transmission

[1683] The device then transmits the health data it receives in real time to a cloud-based analytics server via mobile data or Wi-Fi. The device may compress and anonymize the data before sending it.

[1684] Input: Health data received from smart collar

[1685] Output: Health data sent to the analysis server

[1686] Specific operations: data compression, anonymization, maintaining internet connection, data transmission

[1687] Step 3:

[1688] Data Preprocessing

[1689] The analysis server preprocesses the received data, filtering out abnormal values ​​and filling in missing values. This processing increases the reliability of the data.

[1690] Input: Health data sent from the device

[1691] Output: filtered and imputed data

[1692] Specific operations: Identifying and removing outliers, imputing missing values, normalizing data

[1693] Step 4:

[1694] Data analysis

[1695] The analytics server then inputs the pre-processed data into a generative artificial intelligence model (e.g., a TensorFlow model) for analysis. The AI ​​model identifies anomalies in activity levels, sleep patterns, and dietary intake, and generates health-based advice.

[1696] Input: Preprocessed health data

[1697] Output: Health analysis results and advice

[1698] Specific operations: Data input to AI model, analysis processing, advice generation

[1699] Step 5:

[1700] Send analysis results

[1701] The analysis server sends the analysis results and advice to the device, which receives the analysis results and notifies the user.

[1702] Input: Analysis results and advice

[1703] Output: Analysis results and advice sent to your device

[1704] Specific operations: Data transmission process, device notification system operation

[1705] Step 6:

[1706] User Notification and Display

[1707] The device notifies the user of the analysis results and provides advice, which is displayed on the application screen, allowing the user to adjust their dog's care accordingly.

[1708] Input: Analysis results and advice

[1709] Output: Analysis results and advice communicated to the user

[1710] Specific operations: Activating the notification system, updating the application screen

[1711] Step 7:

[1712] Consultation appointment

[1713] Users can make appointments at pet shops and veterinary clinics through the app, and the appointment information is synchronized in real time with the store's system.

[1714] Input: User's reservation information

[1715] Output: Reservation information synchronized with the physical store system

[1716] Specific operations: Sending reservation information, synchronizing with the physical store system

[1717] Step 8:

[1718] Health Report Generation

[1719] The analysis server generates daily and weekly health reports based on the dog's health data and sends them to the terminal, which displays the reports to the user.

[1720] Input: Health data

[1721] Output: Generated health report

[1722] Specific operations: report generation process, data transmission process, application screen update

[1723] 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.

[1724] The present invention is a system that effectively collects and analyzes health data of a dog, provides appropriate advice to a user, and is also a system that can recognize the user's emotions and reflect them in the advice. The following describes in detail an embodiment of the present invention.

[1725] System Configuration

[1726] The system consists of the following main components:

[1727] 1. Information collection device (smart collar): A device worn by a dog that collects data such as activity levels, sleep patterns, and food intake.

[1728] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotional data.

[1729] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to the user.

[1730] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1731] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides.

[1732] 6. Online Consultation Booking System: A system that allows users to book online consultations with veterinarians.

[1733] Program processing

[1734] Data collection and transmission

[1735] 1. The device (smartphone) uses Bluetooth or WiFi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1736] 2. The device sends the collected data to an analysis server in real time using an internet connection.

[1737] 3. The device also uses voice input and facial recognition technology to recognize the user's current emotional state in order to collect emotional data.

[1738] Data analysis and advice generation

[1739] 4. The analysis server first preprocesses the received data, filtering outliers and imputing missing values.

[1740] 5. The analytics server uses generative AI models to analyze the pre-processed data, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and food intake.

[1741] 6. The analysis server generates advice based on the analysis results using a generative artificial intelligence model and sends the advice in text format to the terminal.

[1742] Emotion engine processing

[1743] 7. The analysis server uses the emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotional state based on the voice data and image data sent from the device.

[1744] 8. The analytics server adjusts the advice based on the user's emotional state. For example, if the user is feeling stressed, it generates gentler advice or content encouraging relaxation.

[1745] User Notification and Advice Display

[1746] 9. The device receives the advice sent from the analysis server and notifies the user. The notification is performed using the smartphone's notification function.

[1747] 10. The device displays the analysis results and advice on the application screen, allowing the user to adjust their dog's care accordingly.

[1748] Online consultation

[1749] 11. Users can enter their dog's symptoms or questions through the application. For example, they can enter a symptom like "My dog ​​is coughing."

[1750] 12. The terminal sends the user's input to the analysis server via the Internet.

[1751] 13. The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms.

[1752] 14. The analysis server sends the generated advice to the terminal, which displays it to the user. For example, the advice may be "If the cough persists, consult a veterinarian."

[1753] 15. The device displays an online consultation appointment screen with a veterinarian, allowing the user to easily make an appointment. The user can select a date and time and confirm the appointment with the desired veterinarian.

[1754] 16. The terminal sends the reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user.

[1755] In this way, the present invention realizes a system that not only collects and analyzes a dog's health data and provides appropriate advice to the user, but also provides advice that takes into account the user's emotional state and facilitates online consultation with a veterinarian if necessary.

[1756] The processing flow will be explained below.

[1757] Step 1:

[1758] The device connects via Bluetooth or WiFi to a smart collar worn by the dog, which records data such as the dog's activity level, sleep patterns, and food intake.

[1759] Step 2:

[1760] The terminal acquires data from the information collection device. The terminal periodically reads the data from the collection device and stores it in its local memory.

[1761] Step 3:

[1762] The device sends the acquired data to the analysis server. The device uses an internet connection to send the data to the analysis server. The sent data includes a timestamp.

[1763] Step 4:

[1764] The server receives the collected data. The server stores the data and starts pre-processing, which includes data format conversion.

[1765] Step 5:

[1766] The server pre-processes the data, filtering outliers and imputing missing values. The pre-processed data is then input into the analysis module.

[1767] Step 6:

[1768] The server feeds the pre-processed data into a generative artificial intelligence model, which then analyzes the data to detect outliers and perform trend analysis based on activity levels, sleep patterns, and dietary intake.

[1769] Step 7:

[1770] The server generates advice in natural language based on the analysis results, and converts the advice into text format to make it easier for the user to understand.

[1771] Step 8:

[1772] The server sends the generated advice and analysis results to the device, then sends the data to the device via the Internet and verifies that the transmission was successful.

[1773] Step 9:

[1774] The device receives the advice sent from the analysis server and uses the smartphone's notification function to notify the user that new information is available.

[1775] Step 10:

[1776] The device displays the analysis results and advice on the application screen, allowing the user to manage their dog's health based on the displayed information.

[1777] Step 11:

[1778] The device collects the user's emotional data. It uses voice input and facial recognition technology to recognize the user's current emotional state. For example, if the user says "I'm worried," that voice data is collected.

[1779] Step 12:

[1780] The device sends the collected emotional data to an analysis server in real time for analysis.

[1781] Step 13:

[1782] The server uses an emotion engine to analyze the user's emotional state, recognizing the emotional state based on the user's voice data and image data, and determining the type and intensity of the emotion.

[1783] Step 14:

[1784] The server tailors the advice based on the user's emotional state. For example, if the user is feeling stressed, the advice will be customized to a gentler tone and include words of encouragement.

[1785] Step 15:

[1786] The server sends the adjusted advice to the terminal, and the customized advice data is sent to the terminal via the Internet.

[1787] Step 16:

[1788] The device will again display the advice to the user, again using notifications to let the user know that new advice has been displayed.

[1789] Step 17:

[1790] The user opens the application and sees the generated advice, which is customized based on their emotional state.

[1791] Step 18:

[1792] The user enters their dog's symptoms or questions into the application, for example, "My dog ​​is coughing," and presses the submit button.

[1793] Step 19:

[1794] The device sends the user's input to an analysis server, which then sends data about the user's questions and symptoms to the server via the Internet.

[1795] Step 20:

[1796] The server generates advice using a generative artificial intelligence model. Based on the information received from the user, the AI ​​model creates appropriate advice.

[1797] Step 21:

[1798] The server sends the generated advice to the terminal. The advice data is sent to the terminal via the Internet.

[1799] Step 22:

[1800] The device displays the received advice to the user. The new advice is displayed on the application screen.

[1801] Step 23:

[1802] The device displays a screen for booking an online consultation with a veterinarian, providing information on available time slots and veterinarians to make it easier for users to book an appointment.

[1803] Step 24:

[1804] The user selects the desired veterinarian and date and time to confirm the online consultation appointment.

[1805] Step 25:

[1806] The terminal sends the reservation information to the analysis server, and the reservation details are sent to the server via the Internet.

[1807] Step 26:

[1808] The server checks the reservation details and sends a reservation confirmation notice to the terminal.

[1809] Step 27:

[1810] The terminal displays a reservation confirmation to the user, informing the user that the reservation is complete.

[1811] Example 2

[1812] 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."

[1813] Conventional dog health management systems only collect and analyze data such as a dog's activity level, sleep patterns, and food intake, and do not consider the user's emotional state, which often results in inappropriate advice. Furthermore, few systems offer integrated features such as online consultation reservations, making them less convenient. The purpose of this invention is to solve these problems and provide a user-friendly health management system.

[1814] 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.

[1815] In this invention, the server includes means for preprocessing data by filtering outliers and filling in missing values, means for analyzing data using a generative AI model to identify outliers and perform trend analysis, and an emotion engine for adjusting advice based on the user's emotional state. This not only effectively collects and analyzes dog health data and provides appropriate advice to the user, but also enables advice that takes the user's emotional state into account and the ability to book online consultations in a unified manner.

[1816] The "information collection device" is a device attached to the dog that collects data such as activity levels, sleep patterns, and food intake.

[1817] A "terminal" is a device used by a user, such as a smartphone or tablet, that receives data from the information collection device and transmits it to the analysis server.

[1818] An "analysis server" is a central processing unit that receives data via the Internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1819] A "generative artificial intelligence model" is an algorithm that analyzes data, detects outliers, and generates advice.

[1820] The "emotion engine" is an algorithm that uses voice input and facial recognition technology to recognize a user's emotional state and adjusts advice accordingly.

[1821] The "online consultation reservation system" is a system that allows users to make reservations for online consultations with veterinarians.

[1822] "Data preprocessing" is a process performed by the analysis server, which involves filtering outliers and filling in missing data.

[1823] "Outlier detection" is performed by a generative artificial intelligence model to identify anomalous values ​​from collected data.

[1824] "Trend analysis" is a method of capturing changes in health status by analyzing long-term fluctuations and patterns in data.

[1825] "Advice" is a specific course of action or recommendation that the analysis server generates using a generative artificial intelligence model and provides to the user.

[1826] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to the user, and is also a system that can recognize the user's emotions and reflect them in the advice.

[1827] System Configuration

[1828] The system consists of the following main components:

[1829] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[1830] 2. Terminal (smartphone): A device used by the user to receive data collected from the information collection device and send it to the server. It also has the function of collecting user emotion data.

[1831] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1832] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice.

[1833] 5. Emotion Engine: An algorithm that recognizes the user's emotional state and reflects it in the advice provided.

[1834] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[1835] Data collection and transmission

[1836] The device periodically collects data on activity, sleep patterns, and food intake from an information collection device attached to the dog via Bluetooth or Wi-Fi, allowing real-time monitoring of changes in the dog's health. The device also uses facial recognition technology to collect data on the user's emotions via voice input and a camera.

[1837] Data analysis and advice generation

[1838] The analytics server receives the data sent from the device and pre-processes it, filtering outliers and imputing missing values, preparing it for input into the generative AI model. The generative AI model then analyzes the data, detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake, and generating recommendations.

[1839] Emotion engine processing

[1840] The emotion engine is used to analyze the user's emotion data and recognize the user's emotional state. If the user is feeling stressed, the analysis server generates gentler advice or content encouraging relaxation.

[1841] User Notification and Advice Display

[1842] When the device receives advice sent from the analysis server, it notifies the user via the smartphone's notification function. The user can then adjust how they care for their dog based on the analysis results and advice displayed on the application screen.

[1843] Online consultation

[1844] Through the application, users can input their dog's symptoms and questions. For example, they can input a symptom like "My dog ​​is coughing." The device then sends this input data via the internet to an analysis server, which uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is then sent to the device and displayed to the user. The device then displays a screen for booking an online consultation with a veterinarian, allowing the user to select the desired date, time, and veterinarian.

[1845] Examples of concrete examples and prompts

[1846] As a concrete example, User A notices that his dog's activity level has decreased. Data is sent from the device to the analysis server, which detects the abnormality and generates advice to "increase walk times." At the same time, the emotion engine detects that User A is feeling stressed, so it also provides gentle advice such as "When you are feeling stressed, try to spend more time relaxing together."

[1847] An example prompt might be:

[1848] "Analyze the dog's activity data and generate advice if there are any abnormalities."

[1849] "Tailor your advice to reflect the emotions your users are feeling."

[1850] "Provide advice on dog coughing and suggest an online consultation with a veterinarian if necessary."

[1851] In this way, the present invention realizes a system that can effectively manage dog health data and provide advice that takes into account the user's emotions.

[1852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1853] Step 1:

[1854] The device uses Bluetooth or WiFi to collect data from an information collection device (smart collar) attached to the dog. The input data is the dog's activity level, sleep patterns, and food intake. The output is the collected data. Specifically, the device collects data every five minutes and stores it locally.

[1855] Step 2:

[1856] The device transmits the collected data to an analysis server in real time via the Internet. The input data is the collected data on activity, sleep patterns, and food intake. The output is the data transmitted to the analysis server. Specifically, the device converts the data into a packet format and transmits it using Wi-Fi or mobile communication.

[1857] Step 3:

[1858] The device collects emotion data using the user's voice input and facial recognition technology. The input requires image data of the user's voice and face. The output is recognized emotion data. Specifically, the device uses a microphone and camera to capture the user's voice and face, which are then analyzed using emotion recognition software.

[1859] Step 4:

[1860] The analysis server preprocesses the data received from the device. The input data is raw data on activity, sleep patterns, and food intake. The output is the preprocessed data. Specifically, it filters out outliers in the data and executes algorithms to fill in missing values.

[1861] Step 5:

[1862] The analysis server analyzes the preprocessed data using a generative AI model. The input data is preprocessed activity, sleep patterns, and food intake data. The output is the results of outlier detection and trend analysis. Specifically, the generative AI model performs pattern recognition and statistical analysis.

[1863] Step 6:

[1864] The analysis server generates advice using a generative artificial intelligence model based on the analysis results and sends the content in text format to the terminal. The input data is the analysis results, and the generated advice is obtained as output. In concrete terms, the server generates appropriate advice based on specific rules.

[1865] Step 7:

[1866] The analysis server uses an emotion engine to analyze the user's emotion data. The input data is the emotion data sent from the device. The output is the recognized emotional state. Specific operations include analyzing voice tone and facial expressions.

[1867] Step 8:

[1868] The analysis server adjusts advice based on the user's emotional state. The input data is the analyzed emotional state and the generated advice, and the output is the adjusted advice. Specific operations include modifying the advice to reflect the emotional state.

[1869] Step 9:

[1870] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The input data is the advice from the analysis server, and the output is a notification to the user. Specifically, the device displays a notification pop-up.

[1871] Step 10:

[1872] The terminal displays the analysis results and advice on the application screen. The input data is the advice sent from the analysis server, and the analysis results that are displayed to the user are obtained as output. In concrete terms, the terminal displays the data on the application's specified screen.

[1873] Step 11:

[1874] The user inputs their dog's symptoms and questions through the application. The input data is the text entered by the user, and the input question or symptom is obtained as the output. The specific operation is that the user enters text into the application form.

[1875] Step 12:

[1876] The terminal sends the user's input to the analysis server via the Internet. The input data is the text of the user's question or symptoms, and the transmission to the analysis server is completed as the output. Specifically, the terminal converts the text into packet format and sends it.

[1877] Step 13:

[1878] The analysis server uses a generative artificial intelligence model to generate advice for the user's questions and symptoms. The input data is the text of the user's questions and symptoms, and the generated advice is obtained as the output. Specifically, the server automatically generates appropriate advice based on the content of the question.

[1879] Step 14:

[1880] The analysis server sends the generated advice to the terminal, which then displays it to the user. The input data is the generated advice, and the displayed advice is obtained as output. In specific operations, the terminal displays the advice on a specific screen of the application.

[1881] Step 15:

[1882] The terminal displays a screen for booking an online consultation with a veterinarian, allowing users to easily make a reservation. The input data is the user's desired date and time and the selection of a veterinarian, and the output is reservation information. Specifically, the terminal displays a calendar function and provides an input form.

[1883] Step 16:

[1884] The terminal sends reservation information to the analysis server, which then checks the reservation details and sends a reservation confirmation notice to the user. The input data is the user's reservation information, and the output is a reservation confirmation notice. Specifically, the terminal sends the reservation data, and the server checks it and then generates and sends a notice.

[1885] (Application example 2)

[1886] 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."

[1887] Conventional pet care systems are limited to collecting and analyzing dog health data, and lack the ability to provide advice based on the owner's emotional state. Furthermore, if a user senses something is wrong, the complicated online consultation process with a veterinarian makes it difficult to provide timely and appropriate care. This can lead to inadequate management of a dog's health.

[1888] 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 data on activity level, sleep pattern, and food intake from an information collection device worn by the dog, means for transmitting the collected data to an analysis server, means for the analysis server to input the collected data into a generative artificial intelligence model and analyze the data, means for the terminal to collect emotion data and transmit it to the analysis server, means for the analysis server to analyze the user's emotional state based on the emotion data and reflect the result in advice, means for transmitting advice generated based on the analysis results and the emotion data to the terminal and notifying the user, means for receiving questions from the user and input regarding the dog's symptoms and generating advice using the generative artificial intelligence model, means for transmitting the generated advice to the terminal and displaying it to the user, and means for providing an online consultation appointment with a veterinarian. This makes it possible to provide advice that takes the user's emotional state into consideration and simplify the process of quickly consulting with a veterinarian.

[1889] The "information collection device" is a device that is attached to the dog and collects data such as activity levels, sleep patterns, and food intake.

[1890] An "analytics server" is a central processing unit that receives collected data, pre-processes it, and analyzes it using generative artificial intelligence models.

[1891] A "generative artificial intelligence model" is an algorithm that analyzes collected data, detects outliers, and generates advice.

[1892] "Emotion data" is data collected from the user's voice, facial expressions, etc., and indicates the user's emotional state.

[1893] "Emotional state" is information indicating the user's emotional state, such as stress, joy, or sadness.

[1894] "Advice" refers to instructions or suggestions generated by the analysis server using a generative artificial intelligence model based on the results of data analysis and the user's emotional state.

[1895] A "terminal" is a device used by a user that receives data and emotion data collected from the information collection device and transmits them to the analysis server, such as a smartphone.

[1896] "Online consultation reservation" is a system that allows users to book a consultation with a veterinarian, and includes the ability to select a date and time and a veterinarian.

[1897] An "outlier" is data that is outside the normal range and suggests some kind of problem with the dog's health.

[1898] "Missing data imputation" is a process for filling in missing parts of collected data.

[1899] MODE FOR CARRYING OUT THE INVENTION

[1900] The present invention is a system that effectively collects and analyzes dog health data and provides appropriate advice to users. Furthermore, the system can recognize the user's emotions and reflect them in the advice, facilitating online consultations with veterinarians. Specific embodiments for implementing the present invention are described below.

[1901] 1. System Overview

[1902] The system mainly consists of the following components:

[1903] 1. Information collection device (smart collar): A device attached to a dog that collects data such as activity levels, sleep patterns, and food intake.

[1904] 2. Terminal (smartphone): A device used by the user to receive data and emotional data collected from the information collection device and send it to the server.

[1905] 3. Analysis Server: A central processing unit that receives data via the internet, analyzes the data using generative artificial intelligence models, and provides advice to users.

[1906] 4. Generative AI model: An algorithm that analyzes data, detects outliers, and generates advice. An example of this is "OpenAI GPT-4."

[1907] 5. Emotion engine: An algorithm that recognizes the user's emotional state and reflects it in the advice it provides. Examples of such engines include AWS Rekognition and Google Cloud Speech-to-Text.

[1908] 6. Online consultation booking system: A system that allows users to book online consultations with veterinarians.

[1909] 2. Program Processing

[1910] Data collection and transmission

[1911] The device (smartphone) uses Bluetooth or Wi-Fi to collect data such as activity levels, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog. The collected data is sent to an analysis server in real time via an internet connection. The device also uses voice input and facial recognition technology to collect the user's emotional data and recognize the user's current emotional state.

[1912] Data analysis and advice generation

[1913] The analysis server first preprocesses the received data, filtering out outliers and filling in missing values. It then analyzes the data using a generative AI model, specifically detecting outliers and analyzing trends based on activity levels, sleep patterns, and dietary intake. Based on the analysis results, the analysis server takes into account the user's emotional data and generates advice, which is then sent to the device in text format.

[1914] Emotion engine processing

[1915] The analysis server uses an emotion engine to analyze the user's emotional state and reflect it in the advice it provides. For example, if the user is feeling stressed, it generates gentler advice or advice encouraging relaxation.

[1916] User Notification and Advice Display

[1917] The device receives the advice sent from the analysis server and notifies the user using the smartphone's notification function. The user can then adjust their dog's care based on the analysis results and advice displayed on the application screen.

[1918] Online consultation

[1919] Users can input their dog's symptoms and questions through the application. For example, they can input the symptom "My dog ​​is coughing." The device sends the user's input via the Internet to an analysis server, which then uses a generative artificial intelligence model to generate advice for the user's question or symptoms. The generated advice is sent to the device and displayed to the user. For example, the advice provided might be "If the cough persists, consult a veterinarian." The device also displays a screen for booking an online consultation with a veterinarian, allowing the user to easily make an appointment.

[1920] 3. Example prompts

[1921] Examples:

[1922] If the user is stressed: "I see you're stressed. Let's take a long walk today to help you relax."

[1923] If your dog is inactive: "Your dog has been less active recently. To check its health, first review its diet and exercise."

[1924] Example prompt sentence:

[1925] "Analyze the data recently collected by the smart collar and generate recommendations about your dog's health."

[1926] "Based on the user's voice and facial expression data, determine whether the user is feeling stressed and provide appropriate advice."

[1927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1928] Step 1:

[1929] Data collection

[1930] Subject: Terminal

[1931] How it works: The device uses Bluetooth or WiFi to collect data on activity, sleep patterns, and food intake from an information collection device (smart collar) attached to the dog.

[1932] Input: Raw data from SmartColor

[1933] Output: Collected health data (activity, sleep patterns, dietary intake)

[1934] Step 2:

[1935] Emotional Data Collection

[1936] Subject: Terminal

[1937] How it works: The device uses the smartphone's camera and microphone to collect data on the user's voice and facial expressions, and then analyzes the user's emotional state based on this.

[1938] Input: User's voice data, facial expression data

[1939] Output: Parsed user emotional state data

[1940] Step 3:

[1941] Data transmission

[1942] Subject: Terminal

[1943] Operation: The device sends the collected health and emotion data to an analysis server via the Internet.

[1944] Input: Collected health data, emotional state data

[1945] Output: Data sent to the analysis server

[1946] Step 4:

[1947] Data Preprocessing

[1948] Subject: Analysis server

[1949] Operation: The analysis server preprocesses the received data, filtering outliers and imputing missing values.

[1950] Input: Received data (health data and emotion data)

[1951] Output: Preprocessed data

[1952] Step 5:

[1953] Data analysis

[1954] Subject: Analysis server

[1955] Operation: The analysis server inputs the preprocessed data into a generative AI model and performs data analysis. Specifically, it detects abnormal values ​​and performs trend analysis on activity levels, sleep patterns, and dietary intake.

[1956] Input: Preprocessed health data

[1957] Output: Analysis results (health status data)

[1958] Step 6:

[1959] Advice Generation

[1960] Subject: Analysis server

[1961] How it works: The analytics server uses a generative artificial intelligence model to generate advice based on health and emotional state data. The advice generated reflects the user's emotional state.

[1962] Input: Analysis results, emotional state data

[1963] Output: Generated advice

[1964] Step 7:

[1965] Sending and displaying advice

[1966] Subject: Analysis server

[1967] Operation: The analysis server sends the generated advice to the terminal, which receives the advice and notifies the user.

[1968] Input: Generated advice

[1969] Output: Notifications to the terminal and advice to the user

[1970] Step 8:

[1971] Book an online consultation

[1972] Subject: User

[1973] How it works: The user enters their dog's symptoms and any questions they have through the application. The device sends the user's input to the analysis server. The analysis server uses a generative AI model to generate advice for the user's questions and symptoms and sends it to the device. The user then confirms the appointment by using the online consultation appointment screen with a veterinarian.

[1974] Input: User questions and symptoms

[1975] Output: Generated advice, confirmed booking information

[1976] The above is the flow of processing of the program of the system that realizes the application example.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] FIG. 9 illustrates 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 behaviors 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.

[1982] 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.

[1983] 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).

[1984] 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.

[1985] 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."

[1986] 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.

[1987] 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).

[1988] 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.

[1989] 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...

Claims

1. A means for collecting data on activity, sleep patterns, and food intake from an information collection device attached to the dog; means for transmitting the collected data to an analysis server; An analysis server inputs the collected data into a generative artificial intelligence model and analyzes the data; means for transmitting the analysis result to a terminal and notifying the user; a means for receiving input from a user regarding a question or a symptom of the dog and generating advice using a generative artificial intelligence model; means for transmitting the generated advice to a terminal and displaying the advice to a user; A system including a means for providing online consultation appointments with veterinarians.

2. The system according to claim 1 , wherein the analysis server further comprises means for filtering outliers and imputing missing values ​​of data as preprocessing of the data.

3. 10. The system of claim 1, wherein the analysis server further comprises means for identifying outliers in activity, sleep patterns, and dietary intake using generative artificial intelligence models.

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