System

Abstract

A system is provided.SOLUTION: A system, comprising: a microchip, configured to collect health information of a companion animal; a server, configured to analyze the collected health information by using a generative AI model; and a client, configured to send an analysis result to a client of a user for display.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] When it comes to pet health management, it is difficult for owners to detect abnormalities early on because pets cannot complain about their poor health. Current microchips only record individual identification numbers and owner information, but are unable to collect or manage health-related data. For this reason, there is a need for a system that can constantly monitor pet health, detect abnormalities early, and take appropriate measures. [Means for solving the problem]

[0005] The present invention provides a system that includes a microchip for collecting pet health data, a server for analyzing the collected data using a generative AI model, and a terminal for transmitting and displaying the analysis results to a user's terminal. Specifically, the microchip collects body temperature, pulse rate, blood data, and exercise volume, and the server detects abnormal values ​​and generates corresponding advice, thereby constantly monitoring the pet's health condition and enabling early detection of illness and appropriate countermeasures.

[0006] A "microchip" is a small electronic device that is implanted in a pet's body to collect health data such as body temperature, pulse rate, blood data, and activity levels.

[0007] A "generative AI model" is an artificial intelligence algorithm that analyzes collected health data, detects outliers, and generates corresponding advice.

[0008] The "server" is a computer system that stores and manages collected health data, analyzes the data using a generative AI model, and transmits the results to the device.

[0009] A "terminal" is a device that a user uses to check the analysis results of their health data, and includes smartphones, tablets, etc.

[0010] "Health data" refers to information that indicates the pet's health condition, such as body temperature, pulse rate, blood data, and amount of exercise.

[0011] An "outlier" is a value in health data that is outside the normal range and suggests a health problem.

[0012] "Advice" is specific instructions or suggestions for managing your pet's health and responding to abnormalities that are issued by the generative AI model based on the analysis results.

[0013] A "user" is a pet owner who uses the pet health management system to monitor the health status of their pet and take any necessary measures. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The pet health management system of the present invention is composed of a microchip attached to a pet, a server for analyzing data, and a user's terminal for displaying the analysis results. This system collects pet health data (body temperature, pulse rate, blood data, amount of exercise, etc.) in real time and analyzes it using a generative AI model, allowing for efficient health management of pets.

[0036] Data collection

[0037] server:

[0038] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[0039] Examples:

[0040] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then store it in a database as "Temperature: 38.5°C."

[0041] Data analysis

[0042] server:

[0043] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[0044] Examples:

[0045] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0046] Data Display

[0047] Device (user's smartphone):

[0048] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[0049] Examples:

[0050] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[0051] Providing advice

[0052] server:

[0053] Based on the analysis results of the generative AI model, specific advice such as dietary recommendations and medication suggestions is generated and sent to the user's device. The server compares the user's pet's current status with past data and provides information to support optimal health management.

[0054] Examples:

[0055] If your pet's activity level is low, a message like "Your pet is not getting enough exercise. Add 30 minutes of walks each day to increase the amount of exercise your pet gets each day" will be generated.

[0056] Collaboration with veterinarians

[0057] User:

[0058] If you suspect your pet is unwell, you can send the data to a veterinarian via the app and consult with them. Users simply select the data they need and press the send button to send the data to their veterinarian.

[0059] Device:

[0060] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[0061] Examples:

[0062] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0063] As described above, the pet health management system of the present invention constantly monitors the health status of pets through data collected using microchips and analysis by a generative AI model, supporting early detection of illness and appropriate health management.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[0067] Specific behavior:

[0068] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[0069] Step 2:

[0070] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[0071] Specific behavior:

[0072] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[0073] Step 3:

[0074] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[0075] Specific behavior:

[0076] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[0077] Step 4:

[0078] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[0079] Specific behavior:

[0080] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[0081] Step 5:

[0082] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[0083] Specific behavior:

[0084] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[0085] Step 6:

[0086] Users can use the smartphone app to check their pet's health data and advice, and can also check past data and advice history.

[0087] Specific behavior:

[0088] The user opens the smartphone app to check Pet A's latest health data and advice, as well as a graph of its temperature data over the past week.

[0089] Step 7:

[0090] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[0091] Specific behavior:

[0092] The user taps the "Contact Vet" button within the app, selects the most recent pulse data, and presses the send button.

[0093] Step 8:

[0094] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[0095] Specific behavior:

[0096] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[0097] In this way, specific actions are performed sequentially at each step, making it a system that monitors the health of pets and allows necessary measures to be taken promptly.

[0098] Example 1

[0099] 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."

[0100] In recent years, the importance of accurately and quickly understanding a pet's daily health condition has become increasingly important in pet health management. However, conventional methods require owners to manually collect and analyze data, which not only takes a great deal of time and effort, but also carries the risk of missing or incorrect data. Another issue is the difficulty of detecting abnormalities in pets early and taking appropriate action. Furthermore, there are insufficient means for sharing information about a pet's health condition with a veterinarian, making it difficult to respond quickly in emergencies.

[0101] 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.

[0102] In this invention, the server includes a data collection means, a data analysis means, a data display means, an advice provision means, and a communication means. This allows for efficient and accurate collection of pet health data and analysis using a generative AI model, enabling early detection of abnormalities and prompt provision of appropriate advice. Furthermore, pet owners can easily share data with veterinarians, enabling prompt response in emergencies.

[0103] "Data collection means" refers to a means of collecting health data such as body temperature, pulse rate, blood data, and exercise volume using devices or sensors attached to the pet.

[0104] "Data analysis means" refers to the means of analyzing collected health data using technologies such as generative AI models to detect abnormal values ​​and evaluate health status.

[0105] The "data display means" is a means for displaying the analysis results on the user's terminal, allowing the user to easily check the health condition of their pet.

[0106] The "advice providing means" is a means for generating advice regarding health management and treatment based on the analyzed data and notifying the user of the advice.

[0107] "Communication means" refers to a means of data communication for sharing health data and analysis results with a server or veterinarian.

[0108] A "generative AI model" is a model that uses artificial intelligence technology to detect and analyze outliers in health data and generate appropriate advice.

[0109] This invention relates to a system that efficiently and accurately collects, analyzes, displays, notifies, and shares pet health data. The pet health management system of the present invention is composed of a data collection device to be attached to a pet, a server that processes the analyzed data, and a terminal that displays the analysis results and advice.

[0110] The operation of the system is as follows.

[0111] Data collection

[0112] server

[0113] The microchip, which serves as a data collection tool, measures the pet's health data (body temperature, pulse, blood data, amount of exercise, etc.) at regular intervals and sends the data to a server. The server temporarily stores the measured data and stores it in a database for each pet. For example, the microchip measures body temperature once an hour and stores the data in the database as "Body temperature: 38.5°C."

[0114] Data analysis

[0115] server

[0116] The server sends the stored data to the generative AI model to detect outliers. The generative AI model compares past data with current data to assess the health status. If an abnormality is detected, the generative AI model generates corresponding advice. For example, if the body temperature exceeds 40.0°C, the model generates advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately."

[0117] Data Display

[0118] Device (user's smartphone)

[0119] Users receive the analysis results and advice sent from the server via a smartphone app. The app displays their pet's latest health data and advice. It also stores past data and displays trends. For example, it might display "Latest body temperature: 38.5°C" or "Advice: Your pet's temperature is high, so caution is advised," and users can also view a graph of their pet's body temperature data over the past week.

[0120] Providing advice

[0121] server

[0122] Based on the analysis results of the generative AI model, the server generates specific advice (such as diet, exercise, and medication suggestions) and notifies the user's device. For example, if the amount of exercise is low, a message such as "Your pet is not getting enough exercise. Please take an extra 30 minutes of walks every day to increase the amount of exercise."

[0123] Collaboration with veterinarians

[0124] User

[0125] If users suspect their pet is not feeling well, they can send data to a veterinarian through the app. Simply select the data you need and press the send button to send the data to your veterinarian. For example, if you feel your pet's pulse rate is abnormal, tap the "Contact a Vet" button on the app, select and send the latest pulse data and trend data from the past week. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0126] Example prompts to input to the generative AI model

[0127] "Analyze pet temperature data and generate advice when the temperature exceeds 40.0°C."

[0128] "Based on your exercise data from the past week, determine whether you are lacking in exercise and generate the necessary advice."

[0129] As described above, the pet health management system of this invention constantly monitors the health of pets by collecting data from microchips, analyzing the data using a generative AI model, displaying the data on the user's terminal, providing appropriate health management advice, and collaborating with veterinarians, thereby supporting early detection of illness and appropriate health management.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Data measurement by data collection method

[0133] The server is programmed to have the microchip, which is the data collection means, measure the pet's body temperature, pulse, blood data, exercise amount, etc. at regular intervals. The measurement interval setting is included as input, and the measured health data is obtained as output.

[0134] Specifically, the microchip measures your pet's body temperature every hour and captures the data in the format "Body temperature: 38.5℃."

[0135] Step 2:

[0136] Sending data to the server

[0137] The microchip transmits the measured data to the server. The input of the transmission is the measured data and the server address to which it is to be transmitted. The output of the transmission is the data correctly stored on the server.

[0138] Specifically, the microchip transmits the collected data over a secure communication channel to a server, which receives it.

[0139] Step 3:

[0140] Temporary storage of data by the server

[0141] The server temporarily stores the received data and stores it in a database for each pet. The received data is the input, and the health data stored in the database is the output.

[0142] Specifically, the server stores the data in the database for each pet's ID as "body temperature: 38.5℃."

[0143] Step 4:

[0144] Data Preprocessing

[0145] The server pre-processes the stored data for analysis: the input is the health data read from the database, and the output is the pre-processed data.

[0146] Specifically, the server collects time series data and converts it into a format that is easy to analyze.

[0147] Step 5:

[0148] Sending data to a generative AI model

[0149] The server sends the preprocessed data to the generative AI model, which has the preprocessed data as input and the data passed to the generative AI model as output.

[0150] Specifically, the server sends data to an API endpoint on the cloud server.

[0151] Step 6:

[0152] Data analysis with generative AI models

[0153] The generative AI model performs analysis based on the data it receives, taking preprocessed data as input and generating analysis results and advice as output.

[0154] Specifically, the generative AI model compares data with historical trends, detects outliers, and generates advice.

[0155] Step 7:

[0156] Server sends analysis results

[0157] The server sends the analysis results and advice received from the generative AI model to the user's device. The received analysis results are the input, and the data sent to the user's device is the output.

[0158] Specifically, the server sends the analysis results as a notification to the user's smartphone.

[0159] Step 8:

[0160] Displaying analysis results on your device

[0161] The user opens the smartphone app and checks the analysis results. The input is data from the server, and the output is the analysis results displayed on the app.

[0162] Specifically, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful."

[0163] Step 9:

[0164] Advice Notice

[0165] The server sends advice based on the analysis results to the user's device. The generated advice is the input, and the advice sent to the user is the output.

[0166] Specifically, the server sends a message such as, "Your pet needs more exercise. Please add 30 minutes of walks each day to increase your daily exercise time."

[0167] Step 10:

[0168] Collaboration with veterinarians

[0169] Users send health data to their veterinarians through the app, with the data sent to the veterinarian as input and the data sent to the veterinarian as output.

[0170] Specifically, the user taps the "Contact a Vet" button, selects and sends the necessary data, and the veterinarian provides advice based on the received data.

[0171] (Application example 1)

[0172] 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."

[0173] Conventional pet health management systems only collect and analyze pet health data, leaving users with little means to properly manage their pet's health based on that data. It's also difficult to instantly check a pet's health status and provide appropriate advice in a physical store. To solve this problem, a system is needed that analyzes collected data using a generative AI model and instantly provides appropriate advice.

[0174] 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.

[0175] In this invention, the server includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results to a user's input / output device, and application means for instantly checking the pet's health condition and providing advice to customers at the physical store. This makes it possible to quickly check the pet's health condition at the physical store and provide appropriate advice.

[0176] "Pet health data" refers to various measurement data that indicate the pet's health condition, such as the pet's body temperature, pulse rate, vital signs, and amount of exercise.

[0177] A "microchip" is a small electronic device that is placed inside or on a pet's body to measure and collect health data.

[0178] "Information processing device" is a general term for hardware and software such as computers and servers used to analyze collected data using generative AI models.

[0179] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and analyzes collected health data and detects outliers.

[0180] "Input / output devices" are devices such as displays, smartphones, and tablets that present analysis results to users.

[0181] An "information display device" is a device for visually displaying analysis results to a user, and is synonymous with an input / output device.

[0182] "Application means" refers to software or applications that allow customers to instantly check the health status of pets in physical stores and provide appropriate advice to them.

[0183] A "physical store" is a physical store that provides pet health check services and is a location where dedicated terminals and data receiving devices are installed.

[0184] "Customers" refer to pet owners and users who visit physical stores with their pets.

[0185] The present invention is a system for managing the health of pets, and includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results on the user's input / output device, and an application means for instantly checking the health status of pets in physical stores and providing advice to customers.

[0186] 1. Data Collection

[0187] microchip

[0188] The microchip is attached to or inside the pet's body and measures and collects health data such as body temperature, pulse, vital signs, and activity level. When the pet visits the store, this data is collected automatically by bringing it close to a dedicated data receiving device. Specifically, the data transmitted from the microchip is captured by the receiving device and transferred to an information processing device.

[0189] 2. Data Analysis

[0190] Information processing device

[0191] The information processing device receives the collected health data and analyzes it using a generative AI model (e.g., TensorFlow or PyTorch). The specific steps for data analysis are as follows:

[0192] 1. Data preprocessing: Converting collected data into a format suitable for the generative AI model.

[0193] 2. Outlier detection: The generative AI model compares data such as body temperature and pulse rate with past data to determine whether they are within normal ranges.

[0194] 3. Advice Generation: If an abnormal value is detected, the AI ​​will generate appropriate health care advice, for example, "The temperature is abnormally high, so give the pet a bath to cool it down and contact the vet immediately."

[0195] 3. Data display

[0196] Information display device (application means)

[0197] The information display device displays the analysis results and generated advice to the user. When used in a physical store, store staff can check the analysis results using their smartphones or tablets and provide appropriate advice to customers. Specifically, the application displays "Latest body temperature: 38.5°C, Advice: Temperature is high, caution is required." Past data is also displayed, making it easy to check trends in the pet's health.

[0198] 4. Examples of applications in physical stores

[0199] The software and hardware required to instantly check pet health in physical stores and provide advice to customers consists of the following:

[0200] Dedicated terminal and data receiver: Receives data from your pet's microchip.

[0201] Information processing device: Analyzes collected data using a generative AI model.

[0202] Smartphones and tablets: Display analysis results and advice.

[0203] Examples of prompt statements

[0204] Below are some examples of prompts that can be input to a generative AI model, which can improve the model's accuracy.

[0205] Example prompt:

[0206] How do I train a generative AI model to analyze my pet's temperature, pulse, and activity data and detect outliers?

[0207] For example, how can you detect abnormalities when the latest body temperature data is high, and what kind of health management advice can be generated?

[0208] This system makes it possible to quickly check the health status of pets and provide appropriate advice, making pet health management more efficient.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Data reception

[0212] The microchip transmits pet health data (body temperature, pulse, vital signs, and activity level) to a dedicated data receiving device. The input is health data collected from inside or on the pet's body, and the output is the raw data received by the data receiving device. The server temporarily stores this data for subsequent processing.

[0213] Step 2:

[0214] Data Transfer

[0215] The temporarily stored health data is transferred from the receiving device to an information processing device (server). At this stage, the input is raw data from the data receiving device, and the output is data transferred to the information processing device. The server converts this raw data into a format that can be analyzed by the generative AI model.

[0216] Step 3:

[0217] Data Preprocessing

[0218] The server preprocesses the received raw data into a format suitable for analysis. Specifically, it standardizes the raw data and extracts the necessary features. The input is the raw data transferred in the previous step, and the output is the preprocessed data that is used as input to the generative AI model. Outliers and missing values ​​are also imputed at this stage.

[0219] Step 4:

[0220] Data analysis

[0221] The server inputs the preprocessed data into a generative AI model (e.g., TensorFlow, PyTorch) for analysis. The model compares it with past data to detect outliers and generate corresponding advice. The input is the preprocessed data, and the output is the analysis results and advice.

[0222] Step 5:

[0223] Sending analysis results

[0224] The server sends the generated analysis results and advice to an information display device (such as a smartphone or tablet). The input is the analysis results, and the output is the analysis results and advice sent to the user's device.

[0225] Step 6:

[0226] Displaying the results

[0227] The user's device displays the received analysis results and advice. Specifically, it displays health data such as the latest body temperature and pulse rate, as well as advice created by the generative AI model. The input is the analysis results and advice sent from the server, and the output is a display that the user can visually confirm.

[0228] Step 7:

[0229] Providing advice to customers

[0230] Based on the displayed analysis results and advice, staff at the physical store provide pet owners with appropriate health care advice. The input is the information displayed on the terminal, and the output is oral and written advice to the owner. Specifically, this includes practical advice such as "If the pet's temperature is abnormally high, give it a bath to cool it down."

[0231] 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.

[0232] The pet health management system of the present invention comprises a microchip attached to the pet, a server for analyzing data, a user terminal for displaying the analysis results, and an emotion engine for recognizing the user's emotions. This system efficiently manages the pet's health by collecting pet health data (body temperature, pulse rate, blood data, exercise volume, etc.) in real time and analyzing it using a generative AI model. Furthermore, the emotion engine analyzes the user's emotions and optimizes health management advice.

[0233] Data collection

[0234] server:

[0235] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[0236] Examples:

[0237] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then receive it and store it in a database as "Temperature: 38.5°C."

[0238] Data analysis

[0239] server:

[0240] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[0241] Examples:

[0242] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0243] Data Display

[0244] Device (user's smartphone):

[0245] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[0246] Examples:

[0247] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[0248] Optimizing advice delivery

[0249] Emotion Engine:

[0250] It recognizes the user's emotions and customizes notifications and advice based on the analysis results. The emotion engine collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to the server. The server then analyzes this emotional data in conjunction with a generative AI model.

[0251] server:

[0252] The emotion engine uses the emotional data it recognizes to customize the content and notification method of advice. For example, if the user is feeling stressed, the advice will be provided in simpler and more gentle language.

[0253] Examples:

[0254] If the emotion engine determines that the user is feeling unusually stressed, the server will generate and display advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0255] Collaboration with veterinarians

[0256] User:

[0257] If the user suspects that their pet is unwell, they can send the data to a veterinarian via the app for consultation. The user selects the necessary data and gives instructions to send it.

[0258] Device:

[0259] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[0260] Examples:

[0261] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0262] As described above, the pet health management system of the present invention constantly monitors the health status of pets by using an emotion engine in addition to data collected using a microchip and analysis by a generative AI model, supporting early detection of illness and appropriate health management. The emotion engine analyzes the user's emotions and customizes advice based on them, realizing a user-friendly interface.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[0266] Specific behavior:

[0267] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[0268] Step 2:

[0269] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[0270] Specific behavior:

[0271] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[0272] Step 3:

[0273] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[0274] Specific behavior:

[0275] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[0276] Step 4:

[0277] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[0278] Specific behavior:

[0279] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[0280] Step 5:

[0281] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[0282] Specific behavior:

[0283] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[0284] Step 6:

[0285] The emotion engine recognizes the user's emotions, collects data such as the user's facial expressions, voice, and touch operations, and sends it to the server.

[0286] Specific behavior:

[0287] The emotion engine captures the user's facial expression with a camera, and if it determines that the user is feeling stressed, it sends the data to the server.

[0288] Step 7:

[0289] The server sends the emotion data from the emotion engine to the generative AI model, which customizes the advice content and notification method. The generative AI model generates advice based on the user's emotions.

[0290] Specific behavior:

[0291] The server sends "emotion data: stress" to the generative AI model. The generative AI model determines that "the user is feeling stressed, so give gentle advice," and generates the advice, "The user's body temperature is a little high, but please stay calm. Give them some water and move them to a cooler place."

[0292] Step 8:

[0293] The server transmits customized advice based on the emotion engine to the terminal, and the terminal displays the advice.

[0294] Specific behavior:

[0295] The server then sends customized advice to the user's smartphone, which displays, "Advice: The temperature is a little high, but please stay calm. Give the pet some water and move it to a cooler place."

[0296] Step 9:

[0297] Users can use a smartphone app to view their pet's health data and receive customized advice. Users can also view past data and advice history.

[0298] Specific behavior:

[0299] The user opens the smartphone app to view Pet A's latest health data and personalized advice, as well as a graph of its temperature data over the past week.

[0300] Step 10:

[0301] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[0302] Specific behavior:

[0303] The user taps the "Contact Vet" button in the app, selects the latest pulse data, and then presses the send button to send the data to the veterinarian via the server.

[0304] Step 11:

[0305] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[0306] Specific behavior:

[0307] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[0308] In this way, specific actions are performed sequentially at each step, the system monitors the pet's health condition, and provides customized advice according to the user's emotions.

[0309] Example 2

[0310] 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."

[0311] Pet health management is an important issue in modern times. However, constantly monitoring pet health and responding quickly and appropriately requires advanced technology. Conventional systems take time to analyze collected data, which often results in delayed advice when an abnormality is discovered. Furthermore, advice provided does not take the user's emotions into consideration, which can be stressful for both the user and the pet. Furthermore, the inability to smoothly collaborate with experts makes it difficult to respond quickly.

[0312] 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.

[0313] In this invention, the server includes a sensor for collecting biometric data of the pet, a storage device for temporarily storing the collected data, an analysis device for analyzing the collected data using a generative AI model, a communication device for transmitting the analysis results to the user's information terminal, an emotion recognition device for recognizing the user's emotions, and an adaptation device for adaptively customizing the analysis results and advice based on the emotion recognition data. This makes it possible to monitor the pet's health condition in real time and provide prompt and appropriate advice when an abnormality is detected. Furthermore, because the advice is customized according to the user's emotions, stress can be reduced. Furthermore, seamless collaboration with experts enables prompt response.

[0314] A "sensor" is a device for collecting biological data from pets.

[0315] A "storage device" is a device for temporarily storing collected data.

[0316] An "analysis device" is a device for analyzing collected data using a generative AI model.

[0317] The "communication device" is a device for transmitting the analysis results to the user's information terminal.

[0318] An "emotion recognition device" is a device for recognizing a user's emotions.

[0319] An "adaptive device" is a device for adaptively customizing analysis results and advice based on emotion recognition data.

[0320] A "user's information terminal" is a device through which a user receives data displays and notifications.

[0321] An "expert" is a person or institution with specialized knowledge regarding pet health.

[0322] MODE FOR CARRYING OUT THE INVENTION

[0323] The pet health management system of the present invention includes a sensor attached to a pet, a server for analyzing data, a user's information terminal for displaying the analysis results, and an emotion recognition device for recognizing the user's emotions.

[0324] Sensor

[0325] The sensor is used to collect pet biological data (body temperature, pulse, blood data, amount of exercise, etc.). The sensor collects data at regular intervals and has the function of wirelessly transmitting the data to a server. Specifically, the sensor measures body temperature once an hour and transmits the resulting data, "Body temperature: 38.5°C," to the server.

[0326] server

[0327] The server temporarily stores the data received from the sensor and analyzes it using the generative AI model. The server analyzes the data, and if an abnormality is detected, it generates appropriate health management advice. For example, if the collected body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The generated advice is sent to the user's information terminal via a communication device.

[0328] User's information terminal

[0329] The user's information terminal is a device such as a smartphone or tablet, which receives and displays the analysis results and advice sent from the server. Users can check this data through the application. The application also stores past health data and has a function that allows users to visually check trends. For example, when a user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so caution is advised." It also displays a graph of body temperature data from the past week.

[0330] emotion recognition device

[0331] The emotion recognition device recognizes the user's emotions and uses this information to analyze data and provide advice. The device collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to a server. The server then uses this emotional data to connect with a generative AI model and customizes the analysis results, advice content, and notification method. For example, if the server determines that the user is feeling stressed, it will provide gentle advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0332] Collaboration with veterinarians

[0333] If a user feels that their pet is not feeling well, they can send data to a veterinarian through the app and consult with them. The user taps the "Contact Veterinarian" button on the app, selects the data they want to send (for example, the latest pulse data and trend data from the past week), and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[0334] In this way, by combining sensors, a server, a user's information terminal, and an emotion recognition device, the pet health management system can efficiently manage the pet's health and provide notifications and advice that take the user's emotions into consideration. As a result, it is possible to protect the health of pets while also realizing stress-free health management for users.

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] Step 1: Data collection

[0337] The server periodically receives biological data from the pet's sensor. It takes data such as body temperature and pulse rate sent from the sensor as input and saves the data in a temporary file. Specifically, the sensor measures body temperature once an hour and sends the data to the server as "Body temperature: 38.5°C." The server stores the received data in a database for each pet.

[0338] Step 2: Data analysis

[0339] The server sends the data stored in the temporary file to the generative AI model for analysis. Specifically, biological data such as body temperature and pulse rate are passed as input to the generative AI model, which detects abnormal values ​​and generates appropriate advice. For example, if the body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The server records this advice and prepares to send it to the user's information terminal.

[0340] Step 3: Data display

[0341] The user's device receives the analysis results and advice from the server. The analysis results and advice are received as input and displayed to the user as output. Specifically, when the user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so be careful." It also displays a graph of body temperature data from the past week.

[0342] Step 4: Emotion recognition and advice optimization

[0343] The server receives the user's emotional data from the emotion recognition device. It receives the emotional data as input and analyzes it to determine the user's stress level and emotional state. Specifically, the emotion recognition device analyzes the user's facial expressions and voice and sends the data to the server. The server adjusts the content of advice based on the emotional data, and can provide advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0344] Step 5: Work with your veterinarian

[0345] When a user feels that their pet is not feeling well, the user sends data to a veterinarian through the app. The health data selected by the user as input (for example, the latest pulse data and trend data for the past week) is sent to the server, which then forwards the data to the veterinarian. Specifically, the user taps the "Contact Vet" button on the app, selects the data to send, and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[0346] (Application example 2)

[0347] 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."

[0348] Conventional pet health management systems are specialized in monitoring only the health status of pets and do not provide advice that takes into account the owner's emotions or stress level. Furthermore, health data abnormality detection and notification are performed using a standardized method, making it difficult to provide appropriate responses for each user. The present invention aims to solve this problem and provide a more user-friendly and effective health management system.

[0349] 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 and analyzing user emotion data, means for notifying and providing advice to the user when an abnormality is detected, and means for analyzing the collected data with a generative AI model. This makes it possible to provide personalized advice that takes the owner's emotions into consideration.

[0350] "Pet health data" refers to information about a pet's biological status, such as body temperature, pulse rate, blood data, and amount of exercise.

[0351] A "microchip" is a small electronic device that is attached to a pet and has the ability to collect and transmit health data about the pet.

[0352] A "generative AI model" is a type of artificial intelligence that learns patterns based on large amounts of data, and then analyzes and generates specific information from that data.

[0353] A "server" is a computer system that collects, analyzes, stores, and communicates data over a network.

[0354] A "user's device" is an electronic device used by a user (for example, a smartphone or tablet) that has the function of displaying analysis results and notifications sent from the server.

[0355] The "emotion engine" is a system for collecting and analyzing emotional data from users' facial expressions, voice, touch operations, etc.

[0356] An "outlier" is a value in a pet's health data that is considered to deviate from the normal range.

[0357] "Notifications" are information or alerts sent from the system to the user's device, containing important information or warnings.

[0358] "Advice" is specific instructions or recommendations for action suggested to users based on collected and analyzed data.

[0359] In the embodiment of the present invention, a system for collecting and analyzing health data of pets and providing appropriate advice to users will be described.

[0360] At the heart of the system is a microchip attached to your pet, which collects real-time health data such as your pet's temperature, pulse, blood pressure, and activity level, and periodically transmits this data to a server where it is temporarily stored.

[0361] The server analyzes the collected data using a generative AI model. The generative AI model compares the health data with past data and detects outliers. When an abnormality is detected, it generates appropriate health management advice. For example, if a pet's temperature is higher than normal, the generative AI model will generate specific advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0362] Furthermore, the emotion engine collects and analyzes the user's emotional data. The user's emotional data is collected from facial expressions, voice, touch operations, etc. The server uses this emotional data to analyze the user's stress level and emotional state. Based on this information, the server customizes the content of notifications and advice and provides the advice in an appropriate format to the user. For example, if the user is feeling stressed, the advice content is provided in simple and gentle language.

[0363] The analysis results and advice sent from the server are displayed on the user's device, such as a smartphone. Through the app, users can check their pet's latest health data in real time, as well as receive advice when abnormalities are detected. Past data history is also displayed, allowing users to understand trends in their pet's health.

[0364] For example, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful." It also displays a graph of the body temperature data from the past week, allowing users to visually check changes in their condition.

[0365] Additionally, if users suspect their pet is not feeling well, they can directly send data to a veterinarian through the app for consultation. Users can select the data they need and issue a transmission command. For example, if their pet's pulse rate shows an abnormal value, they can tap the "Contact a Vet" button, select and send the latest pulse data and trend data from the past week. Based on the received data, the veterinarian will determine whether immediate medical attention is necessary and provide appropriate advice to the owner.

[0366] An example prompt for a generative AI model might look something like this:

[0367] "Use the following user and sensor data to perform anomaly detection and sentiment analysis and generate advice.

[0368] User Data:

[0369] ID: user123

[0370] Facial expression: neutral

[0371] Tone of voice: calm

[0372] Sensor Data:

[0373] Temperature: 22.5℃

[0374] Detects motion: True

[0375] Door Open / Close: False

[0376] Historical Data:

[0377] Temperature: 21.5℃

[0378] Detects motion: False

[0379] Door Open / Close: False

[0380] Expected output:

[0381] Abnormal: True

[0382] Tip: The motion sensor has detected an abnormality. If you are out, please lock the door.

[0383] Emotion analysis result: calm”

[0384] In this way, pet health management systems can provide more accurate and personalized health care advice, taking into account the conditions of both the pet and the owner.

[0385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0386] Step 1:

[0387] The microchip collects pet health data. Specifically, the microchip measures body temperature, pulse, blood data, and movement volume, and periodically transmits the data to a server. The microchip receives health data measured by sensors as input and transmits them to the server as output.

[0388] Step 2:

[0389] The server temporarily stores the received data. As input, it receives the health data transmitted from the microchip and stores it in a database. As output, the stored data is used in the next analysis step.

[0390] Step 3:

[0391] The server analyzes the collected health data using a generative AI model. The saved health data is supplied as input to the generative AI model to detect anomalies in the data. If an anomaly is detected as a result of data analysis, health management advice is generated. The output is the presence or absence of an anomaly and the generated advice.

[0392] Step 4:

[0393] The emotion engine collects and analyzes the user's emotional data. Emotional data such as facial expressions, voice, and touch operations are collected from the user's device (smartphone) and sent to the server. The user's emotional data is received as input and analyzed by the emotion engine. The user's emotional state is obtained as the output of the analysis.

[0394] Step 5:

[0395] The server customizes advice based on the analysis results and the user's emotional state. It receives anomaly data and the user's emotional state as input, and determines the content of notifications and advice based on the analysis results of the generative AI model and emotion engine. The output is personalized advice.

[0396] Step 6:

[0397] The server notifies the user's device of the generated advice. As input, it receives customized advice and sends it to the user's smartphone via the notification system. As output, the advice is displayed on the user's device.

[0398] Step 7:

[0399] The user reviews the advice provided and sends the data to the veterinarian if necessary. The user selects the health data to send to the veterinarian through the app and issues a transmission instruction. The app receives the selected health data as input and sends it to the veterinarian through the server. As an output, the veterinarian provides appropriate advice and examination instructions based on the data received.

[0400] Through these steps, the pet health management system can collect health data in real time, detect abnormalities, and provide personalized advice based on the user's emotions.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Second embodiment]

[0405] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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."

[0417] The pet health management system of the present invention is composed of a microchip attached to a pet, a server for analyzing data, and a user's terminal for displaying the analysis results. This system collects pet health data (body temperature, pulse rate, blood data, amount of exercise, etc.) in real time and analyzes it using a generative AI model, allowing for efficient health management of pets.

[0418] Data collection

[0419] server:

[0420] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[0421] Examples:

[0422] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then store it in a database as "Temperature: 38.5°C."

[0423] Data analysis

[0424] server:

[0425] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[0426] Examples:

[0427] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0428] Data Display

[0429] Device (user's smartphone):

[0430] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[0431] Examples:

[0432] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[0433] Providing advice

[0434] server:

[0435] Based on the analysis results of the generative AI model, specific advice such as dietary recommendations and medication suggestions is generated and sent to the user's device. The server compares the user's pet's current status with past data and provides information to support optimal health management.

[0436] Examples:

[0437] If your pet's activity level is low, a message like "Your pet is not getting enough exercise. Add 30 minutes of walks each day to increase the amount of exercise your pet gets each day" will be generated.

[0438] Collaboration with veterinarians

[0439] User:

[0440] If you suspect your pet is unwell, you can send the data to a veterinarian via the app and consult with them. Users simply select the data they need and press the send button to send the data to their veterinarian.

[0441] Device:

[0442] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[0443] Examples:

[0444] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0445] As described above, the pet health management system of the present invention constantly monitors the health status of pets through data collected using microchips and analysis by a generative AI model, supporting early detection of illness and appropriate health management.

[0446] The processing flow will be explained below.

[0447] Step 1:

[0448] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[0449] Specific behavior:

[0450] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[0451] Step 2:

[0452] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[0453] Specific behavior:

[0454] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[0455] Step 3:

[0456] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[0457] Specific behavior:

[0458] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[0459] Step 4:

[0460] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[0461] Specific behavior:

[0462] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[0463] Step 5:

[0464] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[0465] Specific behavior:

[0466] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[0467] Step 6:

[0468] Users can use the smartphone app to check their pet's health data and advice, and can also check past data and advice history.

[0469] Specific behavior:

[0470] The user opens the smartphone app to check Pet A's latest health data and advice, as well as a graph of its temperature data over the past week.

[0471] Step 7:

[0472] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[0473] Specific behavior:

[0474] The user taps the "Contact Vet" button within the app, selects the most recent pulse data, and presses the send button.

[0475] Step 8:

[0476] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[0477] Specific behavior:

[0478] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[0479] In this way, specific actions are performed sequentially at each step, making it a system that monitors the health of pets and allows necessary measures to be taken promptly.

[0480] Example 1

[0481] 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."

[0482] In recent years, the importance of accurately and quickly understanding a pet's daily health condition has become increasingly important in pet health management. However, conventional methods require owners to manually collect and analyze data, which not only takes a great deal of time and effort, but also carries the risk of missing or incorrect data. Another issue is the difficulty of detecting abnormalities in pets early and taking appropriate action. Furthermore, there are insufficient means for sharing information about a pet's health condition with a veterinarian, making it difficult to respond quickly in emergencies.

[0483] 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.

[0484] In this invention, the server includes a data collection means, a data analysis means, a data display means, an advice provision means, and a communication means. This allows for efficient and accurate collection of pet health data and analysis using a generative AI model, enabling early detection of abnormalities and prompt provision of appropriate advice. Furthermore, pet owners can easily share data with veterinarians, enabling prompt response in emergencies.

[0485] "Data collection means" refers to a means of collecting health data such as body temperature, pulse rate, blood data, and exercise volume using devices or sensors attached to the pet.

[0486] "Data analysis means" refers to the means of analyzing collected health data using technologies such as generative AI models to detect abnormal values ​​and evaluate health status.

[0487] The "data display means" is a means for displaying the analysis results on the user's terminal, allowing the user to easily check the health condition of their pet.

[0488] The "advice providing means" is a means for generating advice regarding health management and treatment based on the analyzed data and notifying the user of the advice.

[0489] "Communication means" refers to a means of data communication for sharing health data and analysis results with a server or veterinarian.

[0490] A "generative AI model" is a model that uses artificial intelligence technology to detect and analyze outliers in health data and generate appropriate advice.

[0491] This invention relates to a system that efficiently and accurately collects, analyzes, displays, notifies, and shares pet health data. The pet health management system of the present invention is composed of a data collection device to be attached to a pet, a server that processes the analyzed data, and a terminal that displays the analysis results and advice.

[0492] The operation of the system is as follows.

[0493] Data collection

[0494] server

[0495] The microchip, which serves as a data collection tool, measures the pet's health data (body temperature, pulse, blood data, amount of exercise, etc.) at regular intervals and sends the data to a server. The server temporarily stores the measured data and stores it in a database for each pet. For example, the microchip measures body temperature once an hour and stores the data in the database as "Body temperature: 38.5°C."

[0496] Data analysis

[0497] server

[0498] The server sends the stored data to the generative AI model to detect outliers. The generative AI model compares past data with current data to assess the health status. If an abnormality is detected, the generative AI model generates corresponding advice. For example, if the body temperature exceeds 40.0°C, the model generates advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately."

[0499] Data Display

[0500] Device (user's smartphone)

[0501] Users receive the analysis results and advice sent from the server via a smartphone app. The app displays their pet's latest health data and advice. It also stores past data and displays trends. For example, it might display "Latest body temperature: 38.5°C" or "Advice: Your pet's temperature is high, so caution is advised," and users can also view a graph of their pet's body temperature data over the past week.

[0502] Providing advice

[0503] server

[0504] Based on the analysis results of the generative AI model, the server generates specific advice (such as diet, exercise, and medication suggestions) and notifies the user's device. For example, if the amount of exercise is low, a message such as "Your pet is not getting enough exercise. Please take an extra 30 minutes of walks every day to increase the amount of exercise."

[0505] Collaboration with veterinarians

[0506] User

[0507] If users suspect their pet is not feeling well, they can send data to a veterinarian through the app. Simply select the data you need and press the send button to send the data to your veterinarian. For example, if you feel your pet's pulse rate is abnormal, tap the "Contact a Vet" button on the app, select and send the latest pulse data and trend data from the past week. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0508] Example prompts to input to the generative AI model

[0509] "Analyze pet temperature data and generate advice when the temperature exceeds 40.0°C."

[0510] "Based on your exercise data from the past week, determine whether you are lacking in exercise and generate the necessary advice."

[0511] As described above, the pet health management system of this invention constantly monitors the health of pets by collecting data from microchips, analyzing the data using a generative AI model, displaying the data on the user's terminal, providing appropriate health management advice, and collaborating with veterinarians, thereby supporting early detection of illness and appropriate health management.

[0512] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0513] Step 1:

[0514] Data measurement by data collection method

[0515] The server is programmed to have the microchip, which is the data collection means, measure the pet's body temperature, pulse, blood data, exercise amount, etc. at regular intervals. The measurement interval setting is included as input, and the measured health data is obtained as output.

[0516] Specifically, the microchip measures your pet's body temperature every hour and captures the data in the format "Body temperature: 38.5℃."

[0517] Step 2:

[0518] Sending data to the server

[0519] The microchip transmits the measured data to the server. The input of the transmission is the measured data and the server address to which it is to be transmitted. The output of the transmission is the data correctly stored on the server.

[0520] Specifically, the microchip transmits the collected data over a secure communication channel to a server, which receives it.

[0521] Step 3:

[0522] Temporary storage of data by the server

[0523] The server temporarily stores the received data and stores it in a database for each pet. The received data is the input, and the health data stored in the database is the output.

[0524] Specifically, the server stores the data in the database for each pet's ID as "body temperature: 38.5℃."

[0525] Step 4:

[0526] Data Preprocessing

[0527] The server pre-processes the stored data for analysis: the input is the health data read from the database, and the output is the pre-processed data.

[0528] Specifically, the server collects time series data and converts it into a format that is easy to analyze.

[0529] Step 5:

[0530] Sending data to a generative AI model

[0531] The server sends the preprocessed data to the generative AI model, which has the preprocessed data as input and the data passed to the generative AI model as output.

[0532] Specifically, the server sends data to an API endpoint on the cloud server.

[0533] Step 6:

[0534] Data analysis with generative AI models

[0535] The generative AI model performs analysis based on the data it receives, taking preprocessed data as input and generating analysis results and advice as output.

[0536] Specifically, the generative AI model compares data with historical trends, detects outliers, and generates advice.

[0537] Step 7:

[0538] Server sends analysis results

[0539] The server sends the analysis results and advice received from the generative AI model to the user's device. The received analysis results are the input, and the data sent to the user's device is the output.

[0540] Specifically, the server sends the analysis results as a notification to the user's smartphone.

[0541] Step 8:

[0542] Displaying analysis results on your device

[0543] The user opens the smartphone app and checks the analysis results. The input is data from the server, and the output is the analysis results displayed on the app.

[0544] Specifically, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful."

[0545] Step 9:

[0546] Advice Notice

[0547] The server sends advice based on the analysis results to the user's device. The generated advice is the input, and the advice sent to the user is the output.

[0548] Specifically, the server sends a message such as, "Your pet needs more exercise. Please add 30 minutes of walks each day to increase your daily exercise time."

[0549] Step 10:

[0550] Collaboration with veterinarians

[0551] Users send health data to their veterinarians through the app, with the data sent to the veterinarian as input and the data sent to the veterinarian as output.

[0552] Specifically, the user taps the "Contact a Vet" button, selects and sends the necessary data, and the veterinarian provides advice based on the received data.

[0553] (Application example 1)

[0554] 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."

[0555] Conventional pet health management systems only collect and analyze pet health data, leaving users with little means to properly manage their pet's health based on that data. It's also difficult to instantly check a pet's health status and provide appropriate advice in a physical store. To solve this problem, a system is needed that analyzes collected data using a generative AI model and instantly provides appropriate advice.

[0556] 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.

[0557] In this invention, the server includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results to a user's input / output device, and application means for instantly checking the pet's health condition and providing advice to customers at the physical store. This makes it possible to quickly check the pet's health condition at the physical store and provide appropriate advice.

[0558] "Pet health data" refers to various measurement data that indicate the pet's health condition, such as the pet's body temperature, pulse rate, vital signs, and amount of exercise.

[0559] A "microchip" is a small electronic device that is placed inside or on a pet's body to measure and collect health data.

[0560] "Information processing device" is a general term for hardware and software such as computers and servers used to analyze collected data using generative AI models.

[0561] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and analyzes collected health data and detects outliers.

[0562] "Input / output devices" are devices such as displays, smartphones, and tablets that present analysis results to users.

[0563] An "information display device" is a device for visually displaying analysis results to a user, and is synonymous with an input / output device.

[0564] "Application means" refers to software or applications that allow customers to instantly check the health status of pets in physical stores and provide appropriate advice to them.

[0565] A "physical store" is a physical store that provides pet health check services and is a location where dedicated terminals and data receiving devices are installed.

[0566] "Customers" refer to pet owners and users who visit physical stores with their pets.

[0567] The present invention is a system for managing the health of pets, and includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results on the user's input / output device, and an application means for instantly checking the health status of pets in physical stores and providing advice to customers.

[0568] 1. Data Collection

[0569] microchip

[0570] The microchip is attached to or inside the pet's body and measures and collects health data such as body temperature, pulse, vital signs, and activity level. When the pet visits the store, this data is collected automatically by bringing it close to a dedicated data receiving device. Specifically, the data transmitted from the microchip is captured by the receiving device and transferred to an information processing device.

[0571] 2. Data Analysis

[0572] Information processing device

[0573] The information processing device receives the collected health data and analyzes it using a generative AI model (e.g., TensorFlow or PyTorch). The specific steps for data analysis are as follows:

[0574] 1. Data preprocessing: Converting collected data into a format suitable for the generative AI model.

[0575] 2. Outlier detection: The generative AI model compares data such as body temperature and pulse rate with past data to determine whether they are within normal ranges.

[0576] 3. Advice Generation: If an abnormal value is detected, the AI ​​will generate appropriate health care advice, for example, "The temperature is abnormally high, so give the pet a bath to cool it down and contact the vet immediately."

[0577] 3. Data display

[0578] Information display device (application means)

[0579] The information display device displays the analysis results and generated advice to the user. When used in a physical store, store staff can check the analysis results using their smartphones or tablets and provide appropriate advice to customers. Specifically, the application displays "Latest body temperature: 38.5°C, Advice: Temperature is high, caution is required." Past data is also displayed, making it easy to check trends in the pet's health.

[0580] 4. Examples of applications in physical stores

[0581] The software and hardware required to instantly check pet health in physical stores and provide advice to customers consists of the following:

[0582] Dedicated terminal and data receiver: Receives data from your pet's microchip.

[0583] Information processing device: Analyzes collected data using a generative AI model.

[0584] Smartphones and tablets: Display analysis results and advice.

[0585] Examples of prompt statements

[0586] Below are some examples of prompts that can be input to a generative AI model, which can improve the model's accuracy.

[0587] Example prompt:

[0588] How do I train a generative AI model to analyze my pet's temperature, pulse, and activity data and detect outliers?

[0589] For example, how can you detect abnormalities when the latest body temperature data is high, and what kind of health management advice can be generated?

[0590] This system makes it possible to quickly check the health status of pets and provide appropriate advice, making pet health management more efficient.

[0591] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0592] Step 1:

[0593] Data reception

[0594] The microchip transmits pet health data (body temperature, pulse, vital signs, and activity level) to a dedicated data receiving device. The input is health data collected from inside or on the pet's body, and the output is the raw data received by the data receiving device. The server temporarily stores this data for subsequent processing.

[0595] Step 2:

[0596] Data Transfer

[0597] The temporarily stored health data is transferred from the receiving device to an information processing device (server). At this stage, the input is raw data from the data receiving device, and the output is data transferred to the information processing device. The server converts this raw data into a format that can be analyzed by the generative AI model.

[0598] Step 3:

[0599] Data Preprocessing

[0600] The server preprocesses the received raw data into a format suitable for analysis. Specifically, it standardizes the raw data and extracts the necessary features. The input is the raw data transferred in the previous step, and the output is the preprocessed data that is used as input to the generative AI model. Outliers and missing values ​​are also imputed at this stage.

[0601] Step 4:

[0602] Data analysis

[0603] The server inputs the preprocessed data into a generative AI model (e.g., TensorFlow, PyTorch) for analysis. The model compares it with past data to detect outliers and generate corresponding advice. The input is the preprocessed data, and the output is the analysis results and advice.

[0604] Step 5:

[0605] Sending analysis results

[0606] The server sends the generated analysis results and advice to an information display device (such as a smartphone or tablet). The input is the analysis results, and the output is the analysis results and advice sent to the user's device.

[0607] Step 6:

[0608] Displaying the results

[0609] The user's device displays the received analysis results and advice. Specifically, it displays health data such as the latest body temperature and pulse rate, as well as advice created by the generative AI model. The input is the analysis results and advice sent from the server, and the output is a display that the user can visually confirm.

[0610] Step 7:

[0611] Providing advice to customers

[0612] Based on the displayed analysis results and advice, staff at the physical store provide pet owners with appropriate health care advice. The input is the information displayed on the terminal, and the output is oral and written advice to the owner. Specifically, this includes practical advice such as "If the pet's temperature is abnormally high, give it a bath to cool it down."

[0613] 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.

[0614] The pet health management system of the present invention comprises a microchip attached to the pet, a server for analyzing data, a user terminal for displaying the analysis results, and an emotion engine for recognizing the user's emotions. This system efficiently manages the pet's health by collecting pet health data (body temperature, pulse rate, blood data, exercise volume, etc.) in real time and analyzing it using a generative AI model. Furthermore, the emotion engine analyzes the user's emotions and optimizes health management advice.

[0615] Data collection

[0616] server:

[0617] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[0618] Examples:

[0619] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then receive it and store it in a database as "Temperature: 38.5°C."

[0620] Data analysis

[0621] server:

[0622] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[0623] Examples:

[0624] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0625] Data Display

[0626] Device (user's smartphone):

[0627] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[0628] Examples:

[0629] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[0630] Optimizing advice delivery

[0631] Emotion Engine:

[0632] It recognizes the user's emotions and customizes notifications and advice based on the analysis results. The emotion engine collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to the server. The server then analyzes this emotional data in conjunction with a generative AI model.

[0633] server:

[0634] The emotion engine uses the emotional data it recognizes to customize the content and notification method of advice. For example, if the user is feeling stressed, the advice will be provided in simpler and more gentle language.

[0635] Examples:

[0636] If the emotion engine determines that the user is feeling unusually stressed, the server will generate and display advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0637] Collaboration with veterinarians

[0638] User:

[0639] If the user suspects that their pet is unwell, they can send the data to a veterinarian via the app for consultation. The user selects the necessary data and gives instructions to send it.

[0640] Device:

[0641] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[0642] Examples:

[0643] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0644] As described above, the pet health management system of the present invention constantly monitors the health status of pets by using an emotion engine in addition to data collected using a microchip and analysis by a generative AI model, supporting early detection of illness and appropriate health management. The emotion engine analyzes the user's emotions and customizes advice based on them, realizing a user-friendly interface.

[0645] The processing flow will be explained below.

[0646] Step 1:

[0647] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[0648] Specific behavior:

[0649] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[0650] Step 2:

[0651] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[0652] Specific behavior:

[0653] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[0654] Step 3:

[0655] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[0656] Specific behavior:

[0657] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[0658] Step 4:

[0659] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[0660] Specific behavior:

[0661] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[0662] Step 5:

[0663] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[0664] Specific behavior:

[0665] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[0666] Step 6:

[0667] The emotion engine recognizes the user's emotions, collects data such as the user's facial expressions, voice, and touch operations, and sends it to the server.

[0668] Specific behavior:

[0669] The emotion engine captures the user's facial expression with a camera, and if it determines that the user is feeling stressed, it sends the data to the server.

[0670] Step 7:

[0671] The server sends the emotion data from the emotion engine to the generative AI model, which customizes the advice content and notification method. The generative AI model generates advice based on the user's emotions.

[0672] Specific behavior:

[0673] The server sends "emotion data: stress" to the generative AI model. The generative AI model determines that "the user is feeling stressed, so give gentle advice," and generates the advice, "The user's body temperature is a little high, but please stay calm. Give them some water and move them to a cooler place."

[0674] Step 8:

[0675] The server transmits customized advice based on the emotion engine to the terminal, and the terminal displays the advice.

[0676] Specific behavior:

[0677] The server then sends customized advice to the user's smartphone, which displays, "Advice: The temperature is a little high, but please stay calm. Give the pet some water and move it to a cooler place."

[0678] Step 9:

[0679] Users can use a smartphone app to view their pet's health data and receive customized advice. Users can also view past data and advice history.

[0680] Specific behavior:

[0681] The user opens the smartphone app to view Pet A's latest health data and personalized advice, as well as a graph of its temperature data over the past week.

[0682] Step 10:

[0683] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[0684] Specific behavior:

[0685] The user taps the "Contact Vet" button in the app, selects the latest pulse data, and then presses the send button to send the data to the veterinarian via the server.

[0686] Step 11:

[0687] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[0688] Specific behavior:

[0689] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[0690] In this way, specific actions are performed sequentially at each step, the system monitors the pet's health condition, and provides customized advice according to the user's emotions.

[0691] Example 2

[0692] 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."

[0693] Pet health management is an important issue in modern times. However, constantly monitoring pet health and responding quickly and appropriately requires advanced technology. Conventional systems take time to analyze collected data, which often results in delayed advice when an abnormality is discovered. Furthermore, advice provided does not take the user's emotions into consideration, which can be stressful for both the user and the pet. Furthermore, the inability to smoothly collaborate with experts makes it difficult to respond quickly.

[0694] 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.

[0695] In this invention, the server includes a sensor for collecting biometric data of the pet, a storage device for temporarily storing the collected data, an analysis device for analyzing the collected data using a generative AI model, a communication device for transmitting the analysis results to the user's information terminal, an emotion recognition device for recognizing the user's emotions, and an adaptation device for adaptively customizing the analysis results and advice based on the emotion recognition data. This makes it possible to monitor the pet's health condition in real time and provide prompt and appropriate advice when an abnormality is detected. Furthermore, because the advice is customized according to the user's emotions, stress can be reduced. Furthermore, seamless collaboration with experts enables prompt response.

[0696] A "sensor" is a device for collecting biological data from pets.

[0697] A "storage device" is a device for temporarily storing collected data.

[0698] An "analysis device" is a device for analyzing collected data using a generative AI model.

[0699] The "communication device" is a device for transmitting the analysis results to the user's information terminal.

[0700] An "emotion recognition device" is a device for recognizing a user's emotions.

[0701] An "adaptive device" is a device for adaptively customizing analysis results and advice based on emotion recognition data.

[0702] A "user's information terminal" is a device through which a user receives data displays and notifications.

[0703] An "expert" is a person or institution with specialized knowledge regarding pet health.

[0704] MODE FOR CARRYING OUT THE INVENTION

[0705] The pet health management system of the present invention includes a sensor attached to a pet, a server for analyzing data, a user's information terminal for displaying the analysis results, and an emotion recognition device for recognizing the user's emotions.

[0706] Sensor

[0707] The sensor is used to collect pet biological data (body temperature, pulse, blood data, amount of exercise, etc.). The sensor collects data at regular intervals and has the function of wirelessly transmitting the data to a server. Specifically, the sensor measures body temperature once an hour and transmits the resulting data, "Body temperature: 38.5°C," to the server.

[0708] server

[0709] The server temporarily stores the data received from the sensor and analyzes it using the generative AI model. The server analyzes the data, and if an abnormality is detected, it generates appropriate health management advice. For example, if the collected body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The generated advice is sent to the user's information terminal via a communication device.

[0710] User's information terminal

[0711] The user's information terminal is a device such as a smartphone or tablet, which receives and displays the analysis results and advice sent from the server. Users can check this data through the application. The application also stores past health data and has a function that allows users to visually check trends. For example, when a user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so caution is advised." It also displays a graph of body temperature data from the past week.

[0712] emotion recognition device

[0713] The emotion recognition device recognizes the user's emotions and uses this information to analyze data and provide advice. The device collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to a server. The server then uses this emotional data to connect with a generative AI model and customizes the analysis results, advice content, and notification method. For example, if the server determines that the user is feeling stressed, it will provide gentle advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0714] Collaboration with veterinarians

[0715] If a user feels that their pet is not feeling well, they can send data to a veterinarian through the app and consult with them. The user taps the "Contact Veterinarian" button on the app, selects the data they want to send (for example, the latest pulse data and trend data from the past week), and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[0716] In this way, by combining sensors, a server, a user's information terminal, and an emotion recognition device, the pet health management system can efficiently manage the pet's health and provide notifications and advice that take the user's emotions into consideration. As a result, it is possible to protect the health of pets while also realizing stress-free health management for users.

[0717] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0718] Step 1: Data collection

[0719] The server periodically receives biological data from the pet's sensor. It takes data such as body temperature and pulse rate sent from the sensor as input and saves the data in a temporary file. Specifically, the sensor measures body temperature once an hour and sends the data to the server as "Body temperature: 38.5°C." The server stores the received data in a database for each pet.

[0720] Step 2: Data analysis

[0721] The server sends the data stored in the temporary file to the generative AI model for analysis. Specifically, biological data such as body temperature and pulse rate are passed as input to the generative AI model, which detects abnormal values ​​and generates appropriate advice. For example, if the body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The server records this advice and prepares to send it to the user's information terminal.

[0722] Step 3: Data display

[0723] The user's device receives the analysis results and advice from the server. The analysis results and advice are received as input and displayed to the user as output. Specifically, when the user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so be careful." It also displays a graph of body temperature data from the past week.

[0724] Step 4: Emotion recognition and advice optimization

[0725] The server receives the user's emotional data from the emotion recognition device. It receives the emotional data as input and analyzes it to determine the user's stress level and emotional state. Specifically, the emotion recognition device analyzes the user's facial expressions and voice and sends the data to the server. The server adjusts the content of advice based on the emotional data, and can provide advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[0726] Step 5: Work with your veterinarian

[0727] When a user feels that their pet is not feeling well, the user sends data to a veterinarian through the app. The health data selected by the user as input (for example, the latest pulse data and trend data for the past week) is sent to the server, which then forwards the data to the veterinarian. Specifically, the user taps the "Contact Vet" button on the app, selects the data to send, and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[0728] (Application example 2)

[0729] 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."

[0730] Conventional pet health management systems are specialized in monitoring only the health status of pets and do not provide advice that takes into account the owner's emotions or stress level. Furthermore, health data abnormality detection and notification are performed using a standardized method, making it difficult to provide appropriate responses for each user. The present invention aims to solve this problem and provide a more user-friendly and effective health management system.

[0731] 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 and analyzing user emotion data, means for notifying and providing advice to the user when an abnormality is detected, and means for analyzing the collected data with a generative AI model. This makes it possible to provide personalized advice that takes the owner's emotions into consideration.

[0732] "Pet health data" refers to information about a pet's biological status, such as body temperature, pulse rate, blood data, and amount of exercise.

[0733] A "microchip" is a small electronic device that is attached to a pet and has the ability to collect and transmit health data about the pet.

[0734] A "generative AI model" is a type of artificial intelligence that learns patterns based on large amounts of data, and then analyzes and generates specific information from that data.

[0735] A "server" is a computer system that collects, analyzes, stores, and communicates data over a network.

[0736] A "user's device" is an electronic device used by a user (for example, a smartphone or tablet) that has the function of displaying analysis results and notifications sent from the server.

[0737] The "emotion engine" is a system for collecting and analyzing emotional data from users' facial expressions, voice, touch operations, etc.

[0738] An "outlier" is a value in a pet's health data that is considered to deviate from the normal range.

[0739] "Notifications" are information or alerts sent from the system to the user's device, containing important information or warnings.

[0740] "Advice" is specific instructions or recommendations for action suggested to users based on collected and analyzed data.

[0741] In the embodiment of the present invention, a system for collecting and analyzing health data of pets and providing appropriate advice to users will be described.

[0742] At the heart of the system is a microchip attached to your pet, which collects real-time health data such as your pet's temperature, pulse, blood pressure, and activity level, and periodically transmits this data to a server where it is temporarily stored.

[0743] The server analyzes the collected data using a generative AI model. The generative AI model compares the health data with past data and detects outliers. When an abnormality is detected, it generates appropriate health management advice. For example, if a pet's temperature is higher than normal, the generative AI model will generate specific advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0744] Furthermore, the emotion engine collects and analyzes the user's emotional data. The user's emotional data is collected from facial expressions, voice, touch operations, etc. The server uses this emotional data to analyze the user's stress level and emotional state. Based on this information, the server customizes the content of notifications and advice and provides the advice in an appropriate format to the user. For example, if the user is feeling stressed, the advice content is provided in simple and gentle language.

[0745] The analysis results and advice sent from the server are displayed on the user's device, such as a smartphone. Through the app, users can check their pet's latest health data in real time, as well as receive advice when abnormalities are detected. Past data history is also displayed, allowing users to understand trends in their pet's health.

[0746] For example, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful." It also displays a graph of the body temperature data from the past week, allowing users to visually check changes in their condition.

[0747] Additionally, if users suspect their pet is not feeling well, they can directly send data to a veterinarian through the app for consultation. Users can select the data they need and issue a transmission command. For example, if their pet's pulse rate shows an abnormal value, they can tap the "Contact a Vet" button, select and send the latest pulse data and trend data from the past week. Based on the received data, the veterinarian will determine whether immediate medical attention is necessary and provide appropriate advice to the owner.

[0748] An example prompt for a generative AI model might look something like this:

[0749] "Use the following user and sensor data to perform anomaly detection and sentiment analysis and generate advice.

[0750] User Data:

[0751] ID: user123

[0752] Facial expression: neutral

[0753] Tone of voice: calm

[0754] Sensor Data:

[0755] Temperature: 22.5℃

[0756] Detects motion: True

[0757] Door Open / Close: False

[0758] Historical Data:

[0759] Temperature: 21.5℃

[0760] Detects motion: False

[0761] Door Open / Close: False

[0762] Expected output:

[0763] Abnormal: True

[0764] Tip: The motion sensor has detected an abnormality. If you are out, please lock the door.

[0765] Emotion analysis result: calm”

[0766] In this way, pet health management systems can provide more accurate and personalized health care advice, taking into account the conditions of both the pet and the owner.

[0767] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0768] Step 1:

[0769] The microchip collects pet health data. Specifically, the microchip measures body temperature, pulse, blood data, and movement volume, and periodically transmits the data to a server. The microchip receives health data measured by sensors as input and transmits them to the server as output.

[0770] Step 2:

[0771] The server temporarily stores the received data. As input, it receives the health data transmitted from the microchip and stores it in a database. As output, the stored data is used in the next analysis step.

[0772] Step 3:

[0773] The server analyzes the collected health data using a generative AI model. The saved health data is supplied as input to the generative AI model to detect anomalies in the data. If an anomaly is detected as a result of data analysis, health management advice is generated. The output is the presence or absence of an anomaly and the generated advice.

[0774] Step 4:

[0775] The emotion engine collects and analyzes the user's emotional data. Emotional data such as facial expressions, voice, and touch operations are collected from the user's device (smartphone) and sent to the server. The user's emotional data is received as input and analyzed by the emotion engine. The user's emotional state is obtained as the output of the analysis.

[0776] Step 5:

[0777] The server customizes advice based on the analysis results and the user's emotional state. It receives anomaly data and the user's emotional state as input, and determines the content of notifications and advice based on the analysis results of the generative AI model and emotion engine. The output is personalized advice.

[0778] Step 6:

[0779] The server notifies the user's device of the generated advice. As input, it receives customized advice and sends it to the user's smartphone via the notification system. As output, the advice is displayed on the user's device.

[0780] Step 7:

[0781] The user reviews the advice provided and sends the data to the veterinarian if necessary. The user selects the health data to send to the veterinarian through the app and issues a transmission instruction. The app receives the selected health data as input and sends it to the veterinarian through the server. As an output, the veterinarian provides appropriate advice and examination instructions based on the data received.

[0782] Through these steps, the pet health management system can collect health data in real time, detect abnormalities, and provide personalized advice based on the user's emotions.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] [Third embodiment]

[0787] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0788] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0789] 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).

[0790] 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.

[0791] 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.

[0792] 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).

[0793] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] The pet health management system of the present invention is composed of a microchip attached to a pet, a server for analyzing data, and a user's terminal for displaying the analysis results. This system collects pet health data (body temperature, pulse rate, blood data, amount of exercise, etc.) in real time and analyzes it using a generative AI model, allowing for efficient health management of pets.

[0800] Data collection

[0801] server:

[0802] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[0803] Examples:

[0804] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then store it in a database as "Temperature: 38.5°C."

[0805] Data analysis

[0806] server:

[0807] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[0808] Examples:

[0809] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[0810] Data Display

[0811] Device (user's smartphone):

[0812] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[0813] Examples:

[0814] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[0815] Providing advice

[0816] server:

[0817] Based on the analysis results of the generative AI model, specific advice such as dietary recommendations and medication suggestions is generated and sent to the user's device. The server compares the user's pet's current status with past data and provides information to support optimal health management.

[0818] Examples:

[0819] If your pet's activity level is low, a message like "Your pet is not getting enough exercise. Add 30 minutes of walks each day to increase the amount of exercise your pet gets each day" will be generated.

[0820] Collaboration with veterinarians

[0821] User:

[0822] If you suspect your pet is unwell, you can send the data to a veterinarian via the app and consult with them. Users simply select the data they need and press the send button to send the data to their veterinarian.

[0823] Device:

[0824] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[0825] Examples:

[0826] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0827] As described above, the pet health management system of the present invention constantly monitors the health status of pets through data collected using microchips and analysis by a generative AI model, supporting early detection of illness and appropriate health management.

[0828] The processing flow will be explained below.

[0829] Step 1:

[0830] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[0831] Specific behavior:

[0832] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[0833] Step 2:

[0834] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[0835] Specific behavior:

[0836] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[0837] Step 3:

[0838] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[0839] Specific behavior:

[0840] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[0841] Step 4:

[0842] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[0843] Specific behavior:

[0844] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[0845] Step 5:

[0846] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[0847] Specific behavior:

[0848] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[0849] Step 6:

[0850] Users can use the smartphone app to check their pet's health data and advice, and can also check past data and advice history.

[0851] Specific behavior:

[0852] The user opens the smartphone app to check Pet A's latest health data and advice, as well as a graph of its temperature data over the past week.

[0853] Step 7:

[0854] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[0855] Specific behavior:

[0856] The user taps the "Contact Vet" button within the app, selects the most recent pulse data, and presses the send button.

[0857] Step 8:

[0858] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[0859] Specific behavior:

[0860] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[0861] In this way, specific actions are performed sequentially at each step, making it a system that monitors the health of pets and allows necessary measures to be taken promptly.

[0862] Example 1

[0863] 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."

[0864] In recent years, the importance of accurately and quickly understanding a pet's daily health condition has become increasingly important in pet health management. However, conventional methods require owners to manually collect and analyze data, which not only takes a great deal of time and effort, but also carries the risk of missing or incorrect data. Another issue is the difficulty of detecting abnormalities in pets early and taking appropriate action. Furthermore, there are insufficient means for sharing information about a pet's health condition with a veterinarian, making it difficult to respond quickly in emergencies.

[0865] 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.

[0866] In this invention, the server includes a data collection means, a data analysis means, a data display means, an advice provision means, and a communication means. This allows for efficient and accurate collection of pet health data and analysis using a generative AI model, enabling early detection of abnormalities and prompt provision of appropriate advice. Furthermore, pet owners can easily share data with veterinarians, enabling prompt response in emergencies.

[0867] "Data collection means" refers to a means of collecting health data such as body temperature, pulse rate, blood data, and exercise volume using devices or sensors attached to the pet.

[0868] "Data analysis means" refers to the means of analyzing collected health data using technologies such as generative AI models to detect abnormal values ​​and evaluate health status.

[0869] The "data display means" is a means for displaying the analysis results on the user's terminal, allowing the user to easily check the health condition of their pet.

[0870] The "advice providing means" is a means for generating advice regarding health management and treatment based on the analyzed data and notifying the user of the advice.

[0871] "Communication means" refers to a means of data communication for sharing health data and analysis results with a server or veterinarian.

[0872] A "generative AI model" is a model that uses artificial intelligence technology to detect and analyze outliers in health data and generate appropriate advice.

[0873] This invention relates to a system that efficiently and accurately collects, analyzes, displays, notifies, and shares pet health data. The pet health management system of the present invention is composed of a data collection device to be attached to a pet, a server that processes the analyzed data, and a terminal that displays the analysis results and advice.

[0874] The operation of the system is as follows.

[0875] Data collection

[0876] server

[0877] The microchip, which serves as a data collection tool, measures the pet's health data (body temperature, pulse, blood data, amount of exercise, etc.) at regular intervals and sends the data to a server. The server temporarily stores the measured data and stores it in a database for each pet. For example, the microchip measures body temperature once an hour and stores the data in the database as "Body temperature: 38.5°C."

[0878] Data analysis

[0879] server

[0880] The server sends the stored data to the generative AI model to detect outliers. The generative AI model compares past data with current data to assess the health status. If an abnormality is detected, the generative AI model generates corresponding advice. For example, if the body temperature exceeds 40.0°C, the model generates advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately."

[0881] Data Display

[0882] Device (user's smartphone)

[0883] Users receive the analysis results and advice sent from the server via a smartphone app. The app displays their pet's latest health data and advice. It also stores past data and displays trends. For example, it might display "Latest body temperature: 38.5°C" or "Advice: Your pet's temperature is high, so caution is advised," and users can also view a graph of their pet's body temperature data over the past week.

[0884] Providing advice

[0885] server

[0886] Based on the analysis results of the generative AI model, the server generates specific advice (such as diet, exercise, and medication suggestions) and notifies the user's device. For example, if the amount of exercise is low, a message such as "Your pet is not getting enough exercise. Please take an extra 30 minutes of walks every day to increase the amount of exercise."

[0887] Collaboration with veterinarians

[0888] User

[0889] If users suspect their pet is not feeling well, they can send data to a veterinarian through the app. Simply select the data you need and press the send button to send the data to your veterinarian. For example, if you feel your pet's pulse rate is abnormal, tap the "Contact a Vet" button on the app, select and send the latest pulse data and trend data from the past week. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[0890] Example prompts to input to the generative AI model

[0891] "Analyze pet temperature data and generate advice when the temperature exceeds 40.0°C."

[0892] "Based on your exercise data from the past week, determine whether you are lacking in exercise and generate the necessary advice."

[0893] As described above, the pet health management system of this invention constantly monitors the health of pets by collecting data from microchips, analyzing the data using a generative AI model, displaying the data on the user's terminal, providing appropriate health management advice, and collaborating with veterinarians, thereby supporting early detection of illness and appropriate health management.

[0894] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0895] Step 1:

[0896] Data measurement by data collection method

[0897] The server is programmed to have the microchip, which is the data collection means, measure the pet's body temperature, pulse, blood data, exercise amount, etc. at regular intervals. The measurement interval setting is included as input, and the measured health data is obtained as output.

[0898] Specifically, the microchip measures your pet's body temperature every hour and captures the data in the format "Body temperature: 38.5℃."

[0899] Step 2:

[0900] Sending data to the server

[0901] The microchip transmits the measured data to the server. The input of the transmission is the measured data and the server address to which it is to be transmitted. The output of the transmission is the data correctly stored on the server.

[0902] Specifically, the microchip transmits the collected data over a secure communication channel to a server, which receives it.

[0903] Step 3:

[0904] Temporary storage of data by the server

[0905] The server temporarily stores the received data and stores it in a database for each pet. The received data is the input, and the health data stored in the database is the output.

[0906] Specifically, the server stores the data in the database for each pet's ID as "body temperature: 38.5℃."

[0907] Step 4:

[0908] Data Preprocessing

[0909] The server pre-processes the stored data for analysis: the input is the health data read from the database, and the output is the pre-processed data.

[0910] Specifically, the server collects time series data and converts it into a format that is easy to analyze.

[0911] Step 5:

[0912] Sending data to a generative AI model

[0913] The server sends the preprocessed data to the generative AI model, which has the preprocessed data as input and the data passed to the generative AI model as output.

[0914] Specifically, the server sends data to an API endpoint on the cloud server.

[0915] Step 6:

[0916] Data analysis with generative AI models

[0917] The generative AI model performs analysis based on the data it receives, taking preprocessed data as input and generating analysis results and advice as output.

[0918] Specifically, the generative AI model compares data with historical trends, detects outliers, and generates advice.

[0919] Step 7:

[0920] Server sends analysis results

[0921] The server sends the analysis results and advice received from the generative AI model to the user's device. The received analysis results are the input, and the data sent to the user's device is the output.

[0922] Specifically, the server sends the analysis results as a notification to the user's smartphone.

[0923] Step 8:

[0924] Displaying analysis results on your device

[0925] The user opens the smartphone app and checks the analysis results. The input is data from the server, and the output is the analysis results displayed on the app.

[0926] Specifically, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful."

[0927] Step 9:

[0928] Advice Notice

[0929] The server sends advice based on the analysis results to the user's device. The generated advice is the input, and the advice sent to the user is the output.

[0930] Specifically, the server sends a message such as, "Your pet needs more exercise. Please add 30 minutes of walks each day to increase your daily exercise time."

[0931] Step 10:

[0932] Collaboration with veterinarians

[0933] Users send health data to their veterinarians through the app, with the data sent to the veterinarian as input and the data sent to the veterinarian as output.

[0934] Specifically, the user taps the "Contact a Vet" button, selects and sends the necessary data, and the veterinarian provides advice based on the received data.

[0935] (Application example 1)

[0936] 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."

[0937] Conventional pet health management systems only collect and analyze pet health data, leaving users with little means to properly manage their pet's health based on that data. It's also difficult to instantly check a pet's health status and provide appropriate advice in a physical store. To solve this problem, a system is needed that analyzes collected data using a generative AI model and instantly provides appropriate advice.

[0938] 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.

[0939] In this invention, the server includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results to a user's input / output device, and application means for instantly checking the pet's health condition and providing advice to customers at the physical store. This makes it possible to quickly check the pet's health condition at the physical store and provide appropriate advice.

[0940] "Pet health data" refers to various measurement data that indicate the pet's health condition, such as the pet's body temperature, pulse rate, vital signs, and amount of exercise.

[0941] A "microchip" is a small electronic device that is placed inside or on a pet's body to measure and collect health data.

[0942] "Information processing device" is a general term for hardware and software such as computers and servers used to analyze collected data using generative AI models.

[0943] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and analyzes collected health data and detects outliers.

[0944] "Input / output devices" are devices such as displays, smartphones, and tablets that present analysis results to users.

[0945] An "information display device" is a device for visually displaying analysis results to a user, and is synonymous with an input / output device.

[0946] "Application means" refers to software or applications that allow customers to instantly check the health status of pets in physical stores and provide appropriate advice to them.

[0947] A "physical store" is a physical store that provides pet health check services and is a location where dedicated terminals and data receiving devices are installed.

[0948] "Customers" refer to pet owners and users who visit physical stores with their pets.

[0949] The present invention is a system for managing the health of pets, and includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results on the user's input / output device, and an application means for instantly checking the health status of pets in physical stores and providing advice to customers.

[0950] 1. Data Collection

[0951] microchip

[0952] The microchip is attached to or inside the pet's body and measures and collects health data such as body temperature, pulse, vital signs, and activity level. When the pet visits the store, this data is collected automatically by bringing it close to a dedicated data receiving device. Specifically, the data transmitted from the microchip is captured by the receiving device and transferred to an information processing device.

[0953] 2. Data Analysis

[0954] Information processing device

[0955] The information processing device receives the collected health data and analyzes it using a generative AI model (e.g., TensorFlow or PyTorch). The specific steps for data analysis are as follows:

[0956] 1. Data preprocessing: Converting collected data into a format suitable for the generative AI model.

[0957] 2. Outlier detection: The generative AI model compares data such as body temperature and pulse rate with past data to determine whether they are within normal ranges.

[0958] 3. Advice Generation: If an abnormal value is detected, the AI ​​will generate appropriate health care advice, for example, "The temperature is abnormally high, so give the pet a bath to cool it down and contact the vet immediately."

[0959] 3. Data display

[0960] Information display device (application means)

[0961] The information display device displays the analysis results and generated advice to the user. When used in a physical store, store staff can check the analysis results using their smartphones or tablets and provide appropriate advice to customers. Specifically, the application displays "Latest body temperature: 38.5°C, Advice: Temperature is high, caution is required." Past data is also displayed, making it easy to check trends in the pet's health.

[0962] 4. Examples of applications in physical stores

[0963] The software and hardware required to instantly check pet health in physical stores and provide advice to customers consists of the following:

[0964] Dedicated terminal and data receiver: Receives data from your pet's microchip.

[0965] Information processing device: Analyzes collected data using a generative AI model.

[0966] Smartphones and tablets: Display analysis results and advice.

[0967] Examples of prompt statements

[0968] Below are some examples of prompts that can be input to a generative AI model, which can improve the model's accuracy.

[0969] Example prompt:

[0970] How do I train a generative AI model to analyze my pet's temperature, pulse, and activity data and detect outliers?

[0971] For example, how can you detect abnormalities when the latest body temperature data is high, and what kind of health management advice can be generated?

[0972] This system makes it possible to quickly check the health status of pets and provide appropriate advice, making pet health management more efficient.

[0973] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0974] Step 1:

[0975] Data reception

[0976] The microchip transmits pet health data (body temperature, pulse, vital signs, and activity level) to a dedicated data receiving device. The input is health data collected from inside or on the pet's body, and the output is the raw data received by the data receiving device. The server temporarily stores this data for subsequent processing.

[0977] Step 2:

[0978] Data Transfer

[0979] The temporarily stored health data is transferred from the receiving device to an information processing device (server). At this stage, the input is raw data from the data receiving device, and the output is data transferred to the information processing device. The server converts this raw data into a format that can be analyzed by the generative AI model.

[0980] Step 3:

[0981] Data Preprocessing

[0982] The server preprocesses the received raw data into a format suitable for analysis. Specifically, it standardizes the raw data and extracts the necessary features. The input is the raw data transferred in the previous step, and the output is the preprocessed data that is used as input to the generative AI model. Outliers and missing values ​​are also imputed at this stage.

[0983] Step 4:

[0984] Data analysis

[0985] The server inputs the preprocessed data into a generative AI model (e.g., TensorFlow, PyTorch) for analysis. The model compares it with past data to detect outliers and generate corresponding advice. The input is the preprocessed data, and the output is the analysis results and advice.

[0986] Step 5:

[0987] Sending analysis results

[0988] The server sends the generated analysis results and advice to an information display device (such as a smartphone or tablet). The input is the analysis results, and the output is the analysis results and advice sent to the user's device.

[0989] Step 6:

[0990] Displaying the results

[0991] The user's device displays the received analysis results and advice. Specifically, it displays health data such as the latest body temperature and pulse rate, as well as advice created by the generative AI model. The input is the analysis results and advice sent from the server, and the output is a display that the user can visually confirm.

[0992] Step 7:

[0993] Providing advice to customers

[0994] Based on the displayed analysis results and advice, staff at the physical store provide pet owners with appropriate health care advice. The input is the information displayed on the terminal, and the output is oral and written advice to the owner. Specifically, this includes practical advice such as "If the pet's temperature is abnormally high, give it a bath to cool it down."

[0995] 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.

[0996] The pet health management system of the present invention comprises a microchip attached to the pet, a server for analyzing data, a user terminal for displaying the analysis results, and an emotion engine for recognizing the user's emotions. This system efficiently manages the pet's health by collecting pet health data (body temperature, pulse rate, blood data, exercise volume, etc.) in real time and analyzing it using a generative AI model. Furthermore, the emotion engine analyzes the user's emotions and optimizes health management advice.

[0997] Data collection

[0998] server:

[0999] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[1000] Examples:

[1001] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then receive it and store it in a database as "Temperature: 38.5°C."

[1002] Data analysis

[1003] server:

[1004] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[1005] Examples:

[1006] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[1007] Data Display

[1008] Device (user's smartphone):

[1009] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[1010] Examples:

[1011] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[1012] Optimizing advice delivery

[1013] Emotion Engine:

[1014] It recognizes the user's emotions and customizes notifications and advice based on the analysis results. The emotion engine collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to the server. The server then analyzes this emotional data in conjunction with a generative AI model.

[1015] server:

[1016] The emotion engine uses the emotional data it recognizes to customize the content and notification method of advice. For example, if the user is feeling stressed, the advice will be provided in simpler and more gentle language.

[1017] Examples:

[1018] If the emotion engine determines that the user is feeling unusually stressed, the server will generate and display advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1019] Collaboration with veterinarians

[1020] User:

[1021] If the user suspects that their pet is unwell, they can send the data to a veterinarian via the app for consultation. The user selects the necessary data and gives instructions to send it.

[1022] Device:

[1023] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[1024] Examples:

[1025] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[1026] As described above, the pet health management system of the present invention constantly monitors the health status of pets by using an emotion engine in addition to data collected using a microchip and analysis by a generative AI model, supporting early detection of illness and appropriate health management. The emotion engine analyzes the user's emotions and customizes advice based on them, realizing a user-friendly interface.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[1030] Specific behavior:

[1031] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[1032] Step 2:

[1033] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[1034] Specific behavior:

[1035] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[1036] Step 3:

[1037] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[1038] Specific behavior:

[1039] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[1040] Step 4:

[1041] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[1042] Specific behavior:

[1043] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[1044] Step 5:

[1045] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[1046] Specific behavior:

[1047] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[1048] Step 6:

[1049] The emotion engine recognizes the user's emotions, collects data such as the user's facial expressions, voice, and touch operations, and sends it to the server.

[1050] Specific behavior:

[1051] The emotion engine captures the user's facial expression with a camera, and if it determines that the user is feeling stressed, it sends the data to the server.

[1052] Step 7:

[1053] The server sends the emotion data from the emotion engine to the generative AI model, which customizes the advice content and notification method. The generative AI model generates advice based on the user's emotions.

[1054] Specific behavior:

[1055] The server sends "emotion data: stress" to the generative AI model. The generative AI model determines that "the user is feeling stressed, so give gentle advice," and generates the advice, "The user's body temperature is a little high, but please stay calm. Give them some water and move them to a cooler place."

[1056] Step 8:

[1057] The server transmits customized advice based on the emotion engine to the terminal, and the terminal displays the advice.

[1058] Specific behavior:

[1059] The server then sends customized advice to the user's smartphone, which displays, "Advice: The temperature is a little high, but please stay calm. Give the pet some water and move it to a cooler place."

[1060] Step 9:

[1061] Users can use a smartphone app to view their pet's health data and receive customized advice. Users can also view past data and advice history.

[1062] Specific behavior:

[1063] The user opens the smartphone app to view Pet A's latest health data and personalized advice, as well as a graph of its temperature data over the past week.

[1064] Step 10:

[1065] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[1066] Specific behavior:

[1067] The user taps the "Contact Vet" button in the app, selects the latest pulse data, and then presses the send button to send the data to the veterinarian via the server.

[1068] Step 11:

[1069] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[1070] Specific behavior:

[1071] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[1072] In this way, specific actions are performed sequentially at each step, the system monitors the pet's health condition, and provides customized advice according to the user's emotions.

[1073] Example 2

[1074] 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."

[1075] Pet health management is an important issue in modern times. However, constantly monitoring pet health and responding quickly and appropriately requires advanced technology. Conventional systems take time to analyze collected data, which often results in delayed advice when an abnormality is discovered. Furthermore, advice provided does not take the user's emotions into consideration, which can be stressful for both the user and the pet. Furthermore, the inability to smoothly collaborate with experts makes it difficult to respond quickly.

[1076] 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.

[1077] In this invention, the server includes a sensor for collecting biometric data of the pet, a storage device for temporarily storing the collected data, an analysis device for analyzing the collected data using a generative AI model, a communication device for transmitting the analysis results to the user's information terminal, an emotion recognition device for recognizing the user's emotions, and an adaptation device for adaptively customizing the analysis results and advice based on the emotion recognition data. This makes it possible to monitor the pet's health condition in real time and provide prompt and appropriate advice when an abnormality is detected. Furthermore, because the advice is customized according to the user's emotions, stress can be reduced. Furthermore, seamless collaboration with experts enables prompt response.

[1078] A "sensor" is a device for collecting biological data from pets.

[1079] A "storage device" is a device for temporarily storing collected data.

[1080] An "analysis device" is a device for analyzing collected data using a generative AI model.

[1081] The "communication device" is a device for transmitting the analysis results to the user's information terminal.

[1082] An "emotion recognition device" is a device for recognizing a user's emotions.

[1083] An "adaptive device" is a device for adaptively customizing analysis results and advice based on emotion recognition data.

[1084] A "user's information terminal" is a device through which a user receives data displays and notifications.

[1085] An "expert" is a person or institution with specialized knowledge regarding pet health.

[1086] MODE FOR CARRYING OUT THE INVENTION

[1087] The pet health management system of the present invention includes a sensor attached to a pet, a server for analyzing data, a user's information terminal for displaying the analysis results, and an emotion recognition device for recognizing the user's emotions.

[1088] Sensor

[1089] The sensor is used to collect pet biological data (body temperature, pulse, blood data, amount of exercise, etc.). The sensor collects data at regular intervals and has the function of wirelessly transmitting the data to a server. Specifically, the sensor measures body temperature once an hour and transmits the resulting data, "Body temperature: 38.5°C," to the server.

[1090] server

[1091] The server temporarily stores the data received from the sensor and analyzes it using the generative AI model. The server analyzes the data, and if an abnormality is detected, it generates appropriate health management advice. For example, if the collected body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The generated advice is sent to the user's information terminal via a communication device.

[1092] User's information terminal

[1093] The user's information terminal is a device such as a smartphone or tablet, which receives and displays the analysis results and advice sent from the server. Users can check this data through the application. The application also stores past health data and has a function that allows users to visually check trends. For example, when a user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so caution is advised." It also displays a graph of body temperature data from the past week.

[1094] emotion recognition device

[1095] The emotion recognition device recognizes the user's emotions and uses this information to analyze data and provide advice. The device collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to a server. The server then uses this emotional data to connect with a generative AI model and customizes the analysis results, advice content, and notification method. For example, if the server determines that the user is feeling stressed, it will provide gentle advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1096] Collaboration with veterinarians

[1097] If a user feels that their pet is not feeling well, they can send data to a veterinarian through the app and consult with them. The user taps the "Contact Veterinarian" button on the app, selects the data they want to send (for example, the latest pulse data and trend data from the past week), and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[1098] In this way, by combining sensors, a server, a user's information terminal, and an emotion recognition device, the pet health management system can efficiently manage the pet's health and provide notifications and advice that take the user's emotions into consideration. As a result, it is possible to protect the health of pets while also realizing stress-free health management for users.

[1099] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1100] Step 1: Data collection

[1101] The server periodically receives biological data from the pet's sensor. It takes data such as body temperature and pulse rate sent from the sensor as input and saves the data in a temporary file. Specifically, the sensor measures body temperature once an hour and sends the data to the server as "Body temperature: 38.5°C." The server stores the received data in a database for each pet.

[1102] Step 2: Data analysis

[1103] The server sends the data stored in the temporary file to the generative AI model for analysis. Specifically, biological data such as body temperature and pulse rate are passed as input to the generative AI model, which detects abnormal values ​​and generates appropriate advice. For example, if the body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The server records this advice and prepares to send it to the user's information terminal.

[1104] Step 3: Data display

[1105] The user's device receives the analysis results and advice from the server. The analysis results and advice are received as input and displayed to the user as output. Specifically, when the user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so be careful." It also displays a graph of body temperature data from the past week.

[1106] Step 4: Emotion recognition and advice optimization

[1107] The server receives the user's emotional data from the emotion recognition device. It receives the emotional data as input and analyzes it to determine the user's stress level and emotional state. Specifically, the emotion recognition device analyzes the user's facial expressions and voice and sends the data to the server. The server adjusts the content of advice based on the emotional data, and can provide advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1108] Step 5: Work with your veterinarian

[1109] When a user feels that their pet is not feeling well, the user sends data to a veterinarian through the app. The health data selected by the user as input (for example, the latest pulse data and trend data for the past week) is sent to the server, which then forwards the data to the veterinarian. Specifically, the user taps the "Contact Vet" button on the app, selects the data to send, and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[1110] (Application example 2)

[1111] 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."

[1112] Conventional pet health management systems are specialized in monitoring only the health status of pets and do not provide advice that takes into account the owner's emotions or stress level. Furthermore, health data abnormality detection and notification are performed using a standardized method, making it difficult to provide appropriate responses for each user. The present invention aims to solve this problem and provide a more user-friendly and effective health management system.

[1113] 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 and analyzing user emotion data, means for notifying and providing advice to the user when an abnormality is detected, and means for analyzing the collected data with a generative AI model. This makes it possible to provide personalized advice that takes the owner's emotions into consideration.

[1114] "Pet health data" refers to information about a pet's biological status, such as body temperature, pulse rate, blood data, and amount of exercise.

[1115] A "microchip" is a small electronic device that is attached to a pet and has the ability to collect and transmit health data about the pet.

[1116] A "generative AI model" is a type of artificial intelligence that learns patterns based on large amounts of data, and then analyzes and generates specific information from that data.

[1117] A "server" is a computer system that collects, analyzes, stores, and communicates data over a network.

[1118] A "user's device" is an electronic device used by a user (for example, a smartphone or tablet) that has the function of displaying analysis results and notifications sent from the server.

[1119] The "emotion engine" is a system for collecting and analyzing emotional data from users' facial expressions, voice, touch operations, etc.

[1120] An "outlier" is a value in a pet's health data that is considered to deviate from the normal range.

[1121] "Notifications" are information or alerts sent from the system to the user's device, containing important information or warnings.

[1122] "Advice" is specific instructions or recommendations for action suggested to users based on collected and analyzed data.

[1123] In the embodiment of the present invention, a system for collecting and analyzing health data of pets and providing appropriate advice to users will be described.

[1124] At the heart of the system is a microchip attached to your pet, which collects real-time health data such as your pet's temperature, pulse, blood pressure, and activity level, and periodically transmits this data to a server where it is temporarily stored.

[1125] The server analyzes the collected data using a generative AI model. The generative AI model compares the health data with past data and detects outliers. When an abnormality is detected, it generates appropriate health management advice. For example, if a pet's temperature is higher than normal, the generative AI model will generate specific advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[1126] Furthermore, the emotion engine collects and analyzes the user's emotional data. The user's emotional data is collected from facial expressions, voice, touch operations, etc. The server uses this emotional data to analyze the user's stress level and emotional state. Based on this information, the server customizes the content of notifications and advice and provides the advice in an appropriate format to the user. For example, if the user is feeling stressed, the advice content is provided in simple and gentle language.

[1127] The analysis results and advice sent from the server are displayed on the user's device, such as a smartphone. Through the app, users can check their pet's latest health data in real time, as well as receive advice when abnormalities are detected. Past data history is also displayed, allowing users to understand trends in their pet's health.

[1128] For example, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful." It also displays a graph of the body temperature data from the past week, allowing users to visually check changes in their condition.

[1129] Additionally, if users suspect their pet is not feeling well, they can directly send data to a veterinarian through the app for consultation. Users can select the data they need and issue a transmission command. For example, if their pet's pulse rate shows an abnormal value, they can tap the "Contact a Vet" button, select and send the latest pulse data and trend data from the past week. Based on the received data, the veterinarian will determine whether immediate medical attention is necessary and provide appropriate advice to the owner.

[1130] An example prompt for a generative AI model might look something like this:

[1131] "Use the following user and sensor data to perform anomaly detection and sentiment analysis and generate advice.

[1132] User Data:

[1133] ID: user123

[1134] Facial expression: neutral

[1135] Tone of voice: calm

[1136] Sensor Data:

[1137] Temperature: 22.5℃

[1138] Detects motion: True

[1139] Door Open / Close: False

[1140] Historical Data:

[1141] Temperature: 21.5℃

[1142] Detects motion: False

[1143] Door Open / Close: False

[1144] Expected output:

[1145] Abnormal: True

[1146] Tip: The motion sensor has detected an abnormality. If you are out, please lock the door.

[1147] Emotion analysis result: calm”

[1148] In this way, pet health management systems can provide more accurate and personalized health care advice, taking into account the conditions of both the pet and the owner.

[1149] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1150] Step 1:

[1151] The microchip collects pet health data. Specifically, the microchip measures body temperature, pulse, blood data, and movement volume, and periodically transmits the data to a server. The microchip receives health data measured by sensors as input and transmits them to the server as output.

[1152] Step 2:

[1153] The server temporarily stores the received data. As input, it receives the health data transmitted from the microchip and stores it in a database. As output, the stored data is used in the next analysis step.

[1154] Step 3:

[1155] The server analyzes the collected health data using a generative AI model. The saved health data is supplied as input to the generative AI model to detect anomalies in the data. If an anomaly is detected as a result of data analysis, health management advice is generated. The output is the presence or absence of an anomaly and the generated advice.

[1156] Step 4:

[1157] The emotion engine collects and analyzes the user's emotional data. Emotional data such as facial expressions, voice, and touch operations are collected from the user's device (smartphone) and sent to the server. The user's emotional data is received as input and analyzed by the emotion engine. The user's emotional state is obtained as the output of the analysis.

[1158] Step 5:

[1159] The server customizes advice based on the analysis results and the user's emotional state. It receives anomaly data and the user's emotional state as input, and determines the content of notifications and advice based on the analysis results of the generative AI model and emotion engine. The output is personalized advice.

[1160] Step 6:

[1161] The server notifies the user's device of the generated advice. As input, it receives customized advice and sends it to the user's smartphone via the notification system. As output, the advice is displayed on the user's device.

[1162] Step 7:

[1163] The user reviews the advice provided and sends the data to the veterinarian if necessary. The user selects the health data to send to the veterinarian through the app and issues a transmission instruction. The app receives the selected health data as input and sends it to the veterinarian through the server. As an output, the veterinarian provides appropriate advice and examination instructions based on the data received.

[1164] Through these steps, the pet health management system can collect health data in real time, detect abnormalities, and provide personalized advice based on the user's emotions.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] [Fourth embodiment]

[1169] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1170] 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.

[1171] 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).

[1172] 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.

[1173] 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.

[1174] 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).

[1175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] 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.

[1181] 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."

[1182] The pet health management system of the present invention is composed of a microchip attached to a pet, a server for analyzing data, and a user's terminal for displaying the analysis results. This system collects pet health data (body temperature, pulse rate, blood data, amount of exercise, etc.) in real time and analyzes it using a generative AI model, allowing for efficient health management of pets.

[1183] Data collection

[1184] server:

[1185] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[1186] Examples:

[1187] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then store it in a database as "Temperature: 38.5°C."

[1188] Data analysis

[1189] server:

[1190] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[1191] Examples:

[1192] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[1193] Data Display

[1194] Device (user's smartphone):

[1195] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[1196] Examples:

[1197] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[1198] Providing advice

[1199] server:

[1200] Based on the analysis results of the generative AI model, specific advice such as dietary recommendations and medication suggestions is generated and sent to the user's device. The server compares the user's pet's current status with past data and provides information to support optimal health management.

[1201] Examples:

[1202] If your pet's activity level is low, a message like "Your pet is not getting enough exercise. Add 30 minutes of walks each day to increase the amount of exercise your pet gets each day" will be generated.

[1203] Collaboration with veterinarians

[1204] User:

[1205] If you suspect your pet is unwell, you can send the data to a veterinarian via the app and consult with them. Users simply select the data they need and press the send button to send the data to their veterinarian.

[1206] Device:

[1207] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[1208] Examples:

[1209] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[1210] As described above, the pet health management system of the present invention constantly monitors the health status of pets through data collected using microchips and analysis by a generative AI model, supporting early detection of illness and appropriate health management.

[1211] The processing flow will be explained below.

[1212] Step 1:

[1213] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[1214] Specific behavior:

[1215] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[1216] Step 2:

[1217] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[1218] Specific behavior:

[1219] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[1220] Step 3:

[1221] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[1222] Specific behavior:

[1223] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[1224] Step 4:

[1225] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[1226] Specific behavior:

[1227] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[1228] Step 5:

[1229] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[1230] Specific behavior:

[1231] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[1232] Step 6:

[1233] Users can use the smartphone app to check their pet's health data and advice, and can also check past data and advice history.

[1234] Specific behavior:

[1235] The user opens the smartphone app to check Pet A's latest health data and advice, as well as a graph of its temperature data over the past week.

[1236] Step 7:

[1237] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[1238] Specific behavior:

[1239] The user taps the "Contact Vet" button within the app, selects the most recent pulse data, and presses the send button.

[1240] Step 8:

[1241] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[1242] Specific behavior:

[1243] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[1244] In this way, specific actions are performed sequentially at each step, making it a system that monitors the health of pets and allows necessary measures to be taken promptly.

[1245] Example 1

[1246] 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."

[1247] In recent years, the importance of accurately and quickly understanding a pet's daily health condition has become increasingly important in pet health management. However, conventional methods require owners to manually collect and analyze data, which not only takes a great deal of time and effort, but also carries the risk of missing or incorrect data. Another issue is the difficulty of detecting abnormalities in pets early and taking appropriate action. Furthermore, there are insufficient means for sharing information about a pet's health condition with a veterinarian, making it difficult to respond quickly in emergencies.

[1248] 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.

[1249] In this invention, the server includes a data collection means, a data analysis means, a data display means, an advice provision means, and a communication means. This allows for efficient and accurate collection of pet health data and analysis using a generative AI model, enabling early detection of abnormalities and prompt provision of appropriate advice. Furthermore, pet owners can easily share data with veterinarians, enabling prompt response in emergencies.

[1250] "Data collection means" refers to a means of collecting health data such as body temperature, pulse rate, blood data, and exercise volume using devices or sensors attached to the pet.

[1251] "Data analysis means" refers to the means of analyzing collected health data using technologies such as generative AI models to detect abnormal values ​​and evaluate health status.

[1252] The "data display means" is a means for displaying the analysis results on the user's terminal, allowing the user to easily check the health condition of their pet.

[1253] The "advice providing means" is a means for generating advice regarding health management and treatment based on the analyzed data and notifying the user of the advice.

[1254] "Communication means" refers to a means of data communication for sharing health data and analysis results with a server or veterinarian.

[1255] A "generative AI model" is a model that uses artificial intelligence technology to detect and analyze outliers in health data and generate appropriate advice.

[1256] This invention relates to a system that efficiently and accurately collects, analyzes, displays, notifies, and shares pet health data. The pet health management system of the present invention is composed of a data collection device to be attached to a pet, a server that processes the analyzed data, and a terminal that displays the analysis results and advice.

[1257] The operation of the system is as follows.

[1258] Data collection

[1259] server

[1260] The microchip, which serves as a data collection tool, measures the pet's health data (body temperature, pulse, blood data, amount of exercise, etc.) at regular intervals and sends the data to a server. The server temporarily stores the measured data and stores it in a database for each pet. For example, the microchip measures body temperature once an hour and stores the data in the database as "Body temperature: 38.5°C."

[1261] Data analysis

[1262] server

[1263] The server sends the stored data to the generative AI model to detect outliers. The generative AI model compares past data with current data to assess the health status. If an abnormality is detected, the generative AI model generates corresponding advice. For example, if the body temperature exceeds 40.0°C, the model generates advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately."

[1264] Data Display

[1265] Device (user's smartphone)

[1266] Users receive the analysis results and advice sent from the server via a smartphone app. The app displays their pet's latest health data and advice. It also stores past data and displays trends. For example, it might display "Latest body temperature: 38.5°C" or "Advice: Your pet's temperature is high, so caution is advised," and users can also view a graph of their pet's body temperature data over the past week.

[1267] Providing advice

[1268] server

[1269] Based on the analysis results of the generative AI model, the server generates specific advice (such as diet, exercise, and medication suggestions) and notifies the user's device. For example, if the amount of exercise is low, a message such as "Your pet is not getting enough exercise. Please take an extra 30 minutes of walks every day to increase the amount of exercise."

[1270] Collaboration with veterinarians

[1271] User

[1272] If users suspect their pet is not feeling well, they can send data to a veterinarian through the app. Simply select the data you need and press the send button to send the data to your veterinarian. For example, if you feel your pet's pulse rate is abnormal, tap the "Contact a Vet" button on the app, select and send the latest pulse data and trend data from the past week. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[1273] Example prompts to input to the generative AI model

[1274] "Analyze pet temperature data and generate advice when the temperature exceeds 40.0°C."

[1275] "Based on your exercise data from the past week, determine whether you are lacking in exercise and generate the necessary advice."

[1276] As described above, the pet health management system of this invention constantly monitors the health of pets by collecting data from microchips, analyzing the data using a generative AI model, displaying the data on the user's terminal, providing appropriate health management advice, and collaborating with veterinarians, thereby supporting early detection of illness and appropriate health management.

[1277] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1278] Step 1:

[1279] Data measurement by data collection method

[1280] The server is programmed to have the microchip, which is the data collection means, measure the pet's body temperature, pulse, blood data, exercise amount, etc. at regular intervals. The measurement interval setting is included as input, and the measured health data is obtained as output.

[1281] Specifically, the microchip measures your pet's body temperature every hour and captures the data in the format "Body temperature: 38.5℃."

[1282] Step 2:

[1283] Sending data to the server

[1284] The microchip transmits the measured data to the server. The input of the transmission is the measured data and the server address to which it is to be transmitted. The output of the transmission is the data correctly stored on the server.

[1285] Specifically, the microchip transmits the collected data over a secure communication channel to a server, which receives it.

[1286] Step 3:

[1287] Temporary storage of data by the server

[1288] The server temporarily stores the received data and stores it in a database for each pet. The received data is the input, and the health data stored in the database is the output.

[1289] Specifically, the server stores the data in the database for each pet's ID as "body temperature: 38.5℃."

[1290] Step 4:

[1291] Data Preprocessing

[1292] The server pre-processes the stored data for analysis: the input is the health data read from the database, and the output is the pre-processed data.

[1293] Specifically, the server collects time series data and converts it into a format that is easy to analyze.

[1294] Step 5:

[1295] Sending data to a generative AI model

[1296] The server sends the preprocessed data to the generative AI model, which has the preprocessed data as input and the data passed to the generative AI model as output.

[1297] Specifically, the server sends data to an API endpoint on the cloud server.

[1298] Step 6:

[1299] Data analysis with generative AI models

[1300] The generative AI model performs analysis based on the data it receives, taking preprocessed data as input and generating analysis results and advice as output.

[1301] Specifically, the generative AI model compares data with historical trends, detects outliers, and generates advice.

[1302] Step 7:

[1303] Server sends analysis results

[1304] The server sends the analysis results and advice received from the generative AI model to the user's device. The received analysis results are the input, and the data sent to the user's device is the output.

[1305] Specifically, the server sends the analysis results as a notification to the user's smartphone.

[1306] Step 8:

[1307] Displaying analysis results on your device

[1308] The user opens the smartphone app and checks the analysis results. The input is data from the server, and the output is the analysis results displayed on the app.

[1309] Specifically, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful."

[1310] Step 9:

[1311] Advice Notice

[1312] The server sends advice based on the analysis results to the user's device. The generated advice is the input, and the advice sent to the user is the output.

[1313] Specifically, the server sends a message such as, "Your pet needs more exercise. Please add 30 minutes of walks each day to increase your daily exercise time."

[1314] Step 10:

[1315] Collaboration with veterinarians

[1316] Users send health data to their veterinarians through the app, with the data sent to the veterinarian as input and the data sent to the veterinarian as output.

[1317] Specifically, the user taps the "Contact a Vet" button, selects and sends the necessary data, and the veterinarian provides advice based on the received data.

[1318] (Application example 1)

[1319] 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."

[1320] Conventional pet health management systems only collect and analyze pet health data, leaving users with little means to properly manage their pet's health based on that data. It's also difficult to instantly check a pet's health status and provide appropriate advice in a physical store. To solve this problem, a system is needed that analyzes collected data using a generative AI model and instantly provides appropriate advice.

[1321] 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.

[1322] In this invention, the server includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results to a user's input / output device, and application means for instantly checking the pet's health condition and providing advice to customers at the physical store. This makes it possible to quickly check the pet's health condition at the physical store and provide appropriate advice.

[1323] "Pet health data" refers to various measurement data that indicate the pet's health condition, such as the pet's body temperature, pulse rate, vital signs, and amount of exercise.

[1324] A "microchip" is a small electronic device that is placed inside or on a pet's body to measure and collect health data.

[1325] "Information processing device" is a general term for hardware and software such as computers and servers used to analyze collected data using generative AI models.

[1326] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and analyzes collected health data and detects outliers.

[1327] "Input / output devices" are devices such as displays, smartphones, and tablets that present analysis results to users.

[1328] An "information display device" is a device for visually displaying analysis results to a user, and is synonymous with an input / output device.

[1329] "Application means" refers to software or applications that allow customers to instantly check the health status of pets in physical stores and provide appropriate advice to them.

[1330] A "physical store" is a physical store that provides pet health check services and is a location where dedicated terminals and data receiving devices are installed.

[1331] "Customers" refer to pet owners and users who visit physical stores with their pets.

[1332] The present invention is a system for managing the health of pets, and includes a microchip for collecting pet health data, an information processing device for analyzing the collected data using a generative AI model, an information display device for transmitting and displaying the analysis results on the user's input / output device, and an application means for instantly checking the health status of pets in physical stores and providing advice to customers.

[1333] 1. Data Collection

[1334] microchip

[1335] The microchip is attached to or inside the pet's body and measures and collects health data such as body temperature, pulse, vital signs, and activity level. When the pet visits the store, this data is collected automatically by bringing it close to a dedicated data receiving device. Specifically, the data transmitted from the microchip is captured by the receiving device and transferred to an information processing device.

[1336] 2. Data Analysis

[1337] Information processing device

[1338] The information processing device receives the collected health data and analyzes it using a generative AI model (e.g., TensorFlow or PyTorch). The specific steps for data analysis are as follows:

[1339] 1. Data preprocessing: Converting collected data into a format suitable for the generative AI model.

[1340] 2. Outlier detection: The generative AI model compares data such as body temperature and pulse rate with past data to determine whether they are within normal ranges.

[1341] 3. Advice Generation: If an abnormal value is detected, the AI ​​will generate appropriate health care advice, for example, "The temperature is abnormally high, so give the pet a bath to cool it down and contact the vet immediately."

[1342] 3. Data display

[1343] Information display device (application means)

[1344] The information display device displays the analysis results and generated advice to the user. When used in a physical store, store staff can check the analysis results using their smartphones or tablets and provide appropriate advice to customers. Specifically, the application displays "Latest body temperature: 38.5°C, Advice: Temperature is high, caution is required." Past data is also displayed, making it easy to check trends in the pet's health.

[1345] 4. Examples of applications in physical stores

[1346] The software and hardware required to instantly check pet health in physical stores and provide advice to customers consists of the following:

[1347] Dedicated terminal and data receiver: Receives data from your pet's microchip.

[1348] Information processing device: Analyzes collected data using a generative AI model.

[1349] Smartphones and tablets: Display analysis results and advice.

[1350] Examples of prompt statements

[1351] Below are some examples of prompts that can be input to a generative AI model, which can improve the model's accuracy.

[1352] Example prompt:

[1353] How do I train a generative AI model to analyze my pet's temperature, pulse, and activity data and detect outliers?

[1354] For example, how can you detect abnormalities when the latest body temperature data is high, and what kind of health management advice can be generated?

[1355] This system makes it possible to quickly check the health status of pets and provide appropriate advice, making pet health management more efficient.

[1356] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1357] Step 1:

[1358] Data reception

[1359] The microchip transmits pet health data (body temperature, pulse, vital signs, and activity level) to a dedicated data receiving device. The input is health data collected from inside or on the pet's body, and the output is the raw data received by the data receiving device. The server temporarily stores this data for subsequent processing.

[1360] Step 2:

[1361] Data Transfer

[1362] The temporarily stored health data is transferred from the receiving device to an information processing device (server). At this stage, the input is raw data from the data receiving device, and the output is data transferred to the information processing device. The server converts this raw data into a format that can be analyzed by the generative AI model.

[1363] Step 3:

[1364] Data Preprocessing

[1365] The server preprocesses the received raw data into a format suitable for analysis. Specifically, it standardizes the raw data and extracts the necessary features. The input is the raw data transferred in the previous step, and the output is the preprocessed data that is used as input to the generative AI model. Outliers and missing values ​​are also imputed at this stage.

[1366] Step 4:

[1367] Data analysis

[1368] The server inputs the preprocessed data into a generative AI model (e.g., TensorFlow, PyTorch) for analysis. The model compares it with past data to detect outliers and generate corresponding advice. The input is the preprocessed data, and the output is the analysis results and advice.

[1369] Step 5:

[1370] Sending analysis results

[1371] The server sends the generated analysis results and advice to an information display device (such as a smartphone or tablet). The input is the analysis results, and the output is the analysis results and advice sent to the user's device.

[1372] Step 6:

[1373] Displaying the results

[1374] The user's device displays the received analysis results and advice. Specifically, it displays health data such as the latest body temperature and pulse rate, as well as advice created by the generative AI model. The input is the analysis results and advice sent from the server, and the output is a display that the user can visually confirm.

[1375] Step 7:

[1376] Providing advice to customers

[1377] Based on the displayed analysis results and advice, staff at the physical store provide pet owners with appropriate health care advice. The input is the information displayed on the terminal, and the output is oral and written advice to the owner. Specifically, this includes practical advice such as "If the pet's temperature is abnormally high, give it a bath to cool it down."

[1378] 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.

[1379] The pet health management system of the present invention comprises a microchip attached to the pet, a server for analyzing data, a user terminal for displaying the analysis results, and an emotion engine for recognizing the user's emotions. This system efficiently manages the pet's health by collecting pet health data (body temperature, pulse rate, blood data, exercise volume, etc.) in real time and analyzing it using a generative AI model. Furthermore, the emotion engine analyzes the user's emotions and optimizes health management advice.

[1380] Data collection

[1381] server:

[1382] The data is periodically received from the microchip and temporarily stored. For example, the microchip measures the pet's temperature at regular intervals and sends the data to the server. The server then stores this data in a database for each pet.

[1383] Examples:

[1384] Your pet will measure its temperature once an hour, and the microchip will send the data to a server, which will then receive it and store it in a database as "Temperature: 38.5°C."

[1385] Data analysis

[1386] server:

[1387] The collected data is sent to a generative AI model, which analyzes it to detect abnormalities. The generative AI model compares the collected data, such as body temperature and pulse rate, with past data to determine whether it deviates from the normal range. If an abnormality is detected, it generates appropriate health management advice.

[1388] Examples:

[1389] If the body temperature is higher than normal, for example, above 40.0°C, the generative AI model will generate the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[1390] Data Display

[1391] Device (user's smartphone):

[1392] Users receive analysis results and advice from the server via a smartphone app. The app displays their pet's latest health data for easy review. The app also stores past data and has the ability to display trends.

[1393] Examples:

[1394] When a user opens the app, they will see the message "Latest temperature: 38.5°C" and "Advice: Your temperature is high, so please be careful." It also shows a graph of your temperature data over the past week.

[1395] Optimizing advice delivery

[1396] Emotion Engine:

[1397] It recognizes the user's emotions and customizes notifications and advice based on the analysis results. The emotion engine collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to the server. The server then analyzes this emotional data in conjunction with a generative AI model.

[1398] server:

[1399] The emotion engine uses the emotional data it recognizes to customize the content and notification method of advice. For example, if the user is feeling stressed, the advice will be provided in simpler and more gentle language.

[1400] Examples:

[1401] If the emotion engine determines that the user is feeling unusually stressed, the server will generate and display advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1402] Collaboration with veterinarians

[1403] User:

[1404] If the user suspects that their pet is unwell, they can send the data to a veterinarian via the app for consultation. The user selects the necessary data and gives instructions to send it.

[1405] Device:

[1406] It has a function to select and send data to a veterinarian. When the user presses the send button, the selected data is sent to the veterinarian via the server.

[1407] Examples:

[1408] If a user feels that their pet's pulse rate is abnormal, they can tap the "Contact a Veterinarian" button on the app, select the necessary data (for example, the most recent pulse rate data and trend data from the past week) and send it. The veterinarian will contact the owner based on the received data and advise whether immediate medical attention is required.

[1409] As described above, the pet health management system of the present invention constantly monitors the health status of pets by using an emotion engine in addition to data collected using a microchip and analysis by a generative AI model, supporting early detection of illness and appropriate health management. The emotion engine analyzes the user's emotions and customizes advice based on them, realizing a user-friendly interface.

[1410] The processing flow will be explained below.

[1411] Step 1:

[1412] The server periodically receives data from the microchip, which measures the pet's temperature, pulse, blood data, and activity level, and then transmits this data to the server.

[1413] Specific behavior:

[1414] The microchip measures the pet's temperature at 38.5°C and sends the data to a server, which receives and temporarily stores the data.

[1415] Step 2:

[1416] The server stores the collected data in a database, which is divided into separate databases for each pet, and manages the latest and cumulative data for each pet.

[1417] Specific behavior:

[1418] The server stores the received temperature data in the database for "Pet A" as "Temperature: 38.5°C." Pulse data and exercise data are also stored in the same way.

[1419] Step 3:

[1420] The server retrieves the latest data from the database and sends it to the generative AI model, which analyzes the data and checks for any anomalies.

[1421] Specific behavior:

[1422] The server sends Pet A's latest body temperature, pulse, blood data, and movement volume to the AI ​​model. The AI ​​model analyzes the body temperature of 38.5°C and considers whether it is within the normal range.

[1423] Step 4:

[1424] The generative AI model sends the analysis results back to the server and generates the necessary advice, including specific suggestions and warnings for health management.

[1425] Specific behavior:

[1426] The generative AI model determines that a body temperature of 38.5°C is normal and returns the advice, "Your current body temperature is within the normal range. There is no problem." to the server.

[1427] Step 5:

[1428] The server sends the analysis results and advice to the user's device, which receives them and displays them in the app.

[1429] Specific behavior:

[1430] The server sends the analysis results and advice to the user's smartphone, which displays the message, "Body temperature: 38.5°C. Advice: Your current body temperature is within the normal range. There is no problem."

[1431] Step 6:

[1432] The emotion engine recognizes the user's emotions, collects data such as the user's facial expressions, voice, and touch operations, and sends it to the server.

[1433] Specific behavior:

[1434] The emotion engine captures the user's facial expression with a camera, and if it determines that the user is feeling stressed, it sends the data to the server.

[1435] Step 7:

[1436] The server sends the emotion data from the emotion engine to the generative AI model, which customizes the advice content and notification method. The generative AI model generates advice based on the user's emotions.

[1437] Specific behavior:

[1438] The server sends "emotion data: stress" to the generative AI model. The generative AI model determines that "the user is feeling stressed, so give gentle advice," and generates the advice, "The user's body temperature is a little high, but please stay calm. Give them some water and move them to a cooler place."

[1439] Step 8:

[1440] The server transmits customized advice based on the emotion engine to the terminal, and the terminal displays the advice.

[1441] Specific behavior:

[1442] The server then sends customized advice to the user's smartphone, which displays, "Advice: The temperature is a little high, but please stay calm. Give the pet some water and move it to a cooler place."

[1443] Step 9:

[1444] Users can use a smartphone app to view their pet's health data and receive customized advice. Users can also view past data and advice history.

[1445] Specific behavior:

[1446] The user opens the smartphone app to view Pet A's latest health data and personalized advice, as well as a graph of its temperature data over the past week.

[1447] Step 10:

[1448] If the user detects something abnormal with their pet, the data is sent to a veterinarian through the app. The user selects the data to send and gives instructions to send it.

[1449] Specific behavior:

[1450] The user taps the "Contact Vet" button in the app, selects the latest pulse data, and then presses the send button to send the data to the veterinarian via the server.

[1451] Step 11:

[1452] The server, following the user's instructions, sends the selected data to the veterinarian, who then uses the received data to make a diagnosis and provide advice.

[1453] Specific behavior:

[1454] The server then emails the pulse data selected by the user to the veterinarian, who then receives the data and sends back a message to the user advising them to see the animal immediately.

[1455] In this way, specific actions are performed sequentially at each step, the system monitors the pet's health condition, and provides customized advice according to the user's emotions.

[1456] Example 2

[1457] 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."

[1458] Pet health management is an important issue in modern times. However, constantly monitoring pet health and responding quickly and appropriately requires advanced technology. Conventional systems take time to analyze collected data, which often results in delayed advice when an abnormality is discovered. Furthermore, advice provided does not take the user's emotions into consideration, which can be stressful for both the user and the pet. Furthermore, the inability to smoothly collaborate with experts makes it difficult to respond quickly.

[1459] 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.

[1460] In this invention, the server includes a sensor for collecting biometric data of the pet, a storage device for temporarily storing the collected data, an analysis device for analyzing the collected data using a generative AI model, a communication device for transmitting the analysis results to the user's information terminal, an emotion recognition device for recognizing the user's emotions, and an adaptation device for adaptively customizing the analysis results and advice based on the emotion recognition data. This makes it possible to monitor the pet's health condition in real time and provide prompt and appropriate advice when an abnormality is detected. Furthermore, because the advice is customized according to the user's emotions, stress can be reduced. Furthermore, seamless collaboration with experts enables prompt response.

[1461] A "sensor" is a device for collecting biological data from pets.

[1462] A "storage device" is a device for temporarily storing collected data.

[1463] An "analysis device" is a device for analyzing collected data using a generative AI model.

[1464] The "communication device" is a device for transmitting the analysis results to the user's information terminal.

[1465] An "emotion recognition device" is a device for recognizing a user's emotions.

[1466] An "adaptive device" is a device for adaptively customizing analysis results and advice based on emotion recognition data.

[1467] A "user's information terminal" is a device through which a user receives data displays and notifications.

[1468] An "expert" is a person or institution with specialized knowledge regarding pet health.

[1469] MODE FOR CARRYING OUT THE INVENTION

[1470] The pet health management system of the present invention includes a sensor attached to a pet, a server for analyzing data, a user's information terminal for displaying the analysis results, and an emotion recognition device for recognizing the user's emotions.

[1471] Sensor

[1472] The sensor is used to collect pet biological data (body temperature, pulse, blood data, amount of exercise, etc.). The sensor collects data at regular intervals and has the function of wirelessly transmitting the data to a server. Specifically, the sensor measures body temperature once an hour and transmits the resulting data, "Body temperature: 38.5°C," to the server.

[1473] server

[1474] The server temporarily stores the data received from the sensor and analyzes it using the generative AI model. The server analyzes the data, and if an abnormality is detected, it generates appropriate health management advice. For example, if the collected body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The generated advice is sent to the user's information terminal via a communication device.

[1475] User's information terminal

[1476] The user's information terminal is a device such as a smartphone or tablet, which receives and displays the analysis results and advice sent from the server. Users can check this data through the application. The application also stores past health data and has a function that allows users to visually check trends. For example, when a user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so caution is advised." It also displays a graph of body temperature data from the past week.

[1477] emotion recognition device

[1478] The emotion recognition device recognizes the user's emotions and uses this information to analyze data and provide advice. The device collects emotional data from the user's facial expressions, voice, touch operations, etc. and sends it to a server. The server then uses this emotional data to connect with a generative AI model and customizes the analysis results, advice content, and notification method. For example, if the server determines that the user is feeling stressed, it will provide gentle advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1479] Collaboration with veterinarians

[1480] If a user feels that their pet is not feeling well, they can send data to a veterinarian through the app and consult with them. The user taps the "Contact Veterinarian" button on the app, selects the data they want to send (for example, the latest pulse data and trend data from the past week), and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[1481] In this way, by combining sensors, a server, a user's information terminal, and an emotion recognition device, the pet health management system can efficiently manage the pet's health and provide notifications and advice that take the user's emotions into consideration. As a result, it is possible to protect the health of pets while also realizing stress-free health management for users.

[1482] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1483] Step 1: Data collection

[1484] The server periodically receives biological data from the pet's sensor. It takes data such as body temperature and pulse rate sent from the sensor as input and saves the data in a temporary file. Specifically, the sensor measures body temperature once an hour and sends the data to the server as "Body temperature: 38.5°C." The server stores the received data in a database for each pet.

[1485] Step 2: Data analysis

[1486] The server sends the data stored in the temporary file to the generative AI model for analysis. Specifically, biological data such as body temperature and pulse rate are passed as input to the generative AI model, which detects abnormal values ​​and generates appropriate advice. For example, if the body temperature data exceeds 40.0°C, the generative AI model generates the advice, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact a veterinarian immediately." The server records this advice and prepares to send it to the user's information terminal.

[1487] Step 3: Data display

[1488] The user's device receives the analysis results and advice from the server. The analysis results and advice are received as input and displayed to the user as output. Specifically, when the user opens the app, it displays "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so be careful." It also displays a graph of body temperature data from the past week.

[1489] Step 4: Emotion recognition and advice optimization

[1490] The server receives the user's emotional data from the emotion recognition device. It receives the emotional data as input and analyzes it to determine the user's stress level and emotional state. Specifically, the emotion recognition device analyzes the user's facial expressions and voice and sends the data to the server. The server adjusts the content of advice based on the emotional data, and can provide advice such as, "Your pet's temperature is a little high. Give it some water and move it to a cooler place. Please stay calm."

[1491] Step 5: Work with your veterinarian

[1492] When a user feels that their pet is not feeling well, the user sends data to a veterinarian through the app. The health data selected by the user as input (for example, the latest pulse data and trend data for the past week) is sent to the server, which then forwards the data to the veterinarian. Specifically, the user taps the "Contact Vet" button on the app, selects the data to send, and sends it. The server then sends this data to the veterinarian, who then contacts the owner based on the received data and advises them whether a medical examination is necessary.

[1493] (Application example 2)

[1494] 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."

[1495] Conventional pet health management systems are specialized in monitoring only the health status of pets and do not provide advice that takes into account the owner's emotions or stress level. Furthermore, health data abnormality detection and notification are performed using a standardized method, making it difficult to provide appropriate responses for each user. The present invention aims to solve this problem and provide a more user-friendly and effective health management system.

[1496] 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 and analyzing user emotion data, means for notifying and providing advice to the user when an abnormality is detected, and means for analyzing the collected data with a generative AI model. This makes it possible to provide personalized advice that takes the owner's emotions into consideration.

[1497] "Pet health data" refers to information about a pet's biological status, such as body temperature, pulse rate, blood data, and amount of exercise.

[1498] A "microchip" is a small electronic device that is attached to a pet and has the ability to collect and transmit health data about the pet.

[1499] A "generative AI model" is a type of artificial intelligence that learns patterns based on large amounts of data, and then analyzes and generates specific information from that data.

[1500] A "server" is a computer system that collects, analyzes, stores, and communicates data over a network.

[1501] A "user's device" is an electronic device used by a user (for example, a smartphone or tablet) that has the function of displaying analysis results and notifications sent from the server.

[1502] The "emotion engine" is a system for collecting and analyzing emotional data from users' facial expressions, voice, touch operations, etc.

[1503] An "outlier" is a value in a pet's health data that is considered to deviate from the normal range.

[1504] "Notifications" are information or alerts sent from the system to the user's device, containing important information or warnings.

[1505] "Advice" is specific instructions or recommendations for action suggested to users based on collected and analyzed data.

[1506] In the embodiment of the present invention, a system for collecting and analyzing health data of pets and providing appropriate advice to users will be described.

[1507] At the heart of the system is a microchip attached to your pet, which collects real-time health data such as your pet's temperature, pulse, blood pressure, and activity level, and periodically transmits this data to a server where it is temporarily stored.

[1508] The server analyzes the collected data using a generative AI model. The generative AI model compares the health data with past data and detects outliers. When an abnormality is detected, it generates appropriate health management advice. For example, if a pet's temperature is higher than normal, the generative AI model will generate specific advice such as, "Your pet's temperature is abnormally high. Give it a bath to cool it down and contact your veterinarian immediately."

[1509] Furthermore, the emotion engine collects and analyzes the user's emotional data. The user's emotional data is collected from facial expressions, voice, touch operations, etc. The server uses this emotional data to analyze the user's stress level and emotional state. Based on this information, the server customizes the content of notifications and advice and provides the advice in an appropriate format to the user. For example, if the user is feeling stressed, the advice content is provided in simple and gentle language.

[1510] The analysis results and advice sent from the server are displayed on the user's device, such as a smartphone. Through the app, users can check their pet's latest health data in real time, as well as receive advice when abnormalities are detected. Past data history is also displayed, allowing users to understand trends in their pet's health.

[1511] For example, when a user opens the app, it displays the message "Latest body temperature: 38.5°C" and "Advice: Your body temperature is high, so please be careful." It also displays a graph of the body temperature data from the past week, allowing users to visually check changes in their condition.

[1512] Additionally, if users suspect their pet is not feeling well, they can directly send data to a veterinarian through the app for consultation. Users can select the data they need and issue a transmission command. For example, if their pet's pulse rate shows an abnormal value, they can tap the "Contact a Vet" button, select and send the latest pulse data and trend data from the past week. Based on the received data, the veterinarian will determine whether immediate medical attention is necessary and provide appropriate advice to the owner.

[1513] An example prompt for a generative AI model might look something like this:

[1514] "Use the following user and sensor data to perform anomaly detection and sentiment analysis and generate advice.

[1515] User Data:

[1516] ID: user123

[1517] Facial expression: neutral

[1518] Tone of voice: calm

[1519] Sensor Data:

[1520] Temperature: 22.5℃

[1521] Detects motion: True

[1522] Door Open / Close: False

[1523] Historical Data:

[1524] Temperature: 21.5℃

[1525] Detects motion: False

[1526] Door Open / Close: False

[1527] Expected output:

[1528] Abnormal: True

[1529] Tip: The motion sensor has detected an abnormality. If you are out, please lock the door.

[1530] Emotion analysis result: calm”

[1531] In this way, pet health management systems can provide more accurate and personalized health care advice, taking into account the conditions of both the pet and the owner.

[1532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1533] Step 1:

[1534] The microchip collects pet health data. Specifically, the microchip measures body temperature, pulse, blood data, and movement volume, and periodically transmits the data to a server. The microchip receives health data measured by sensors as input and transmits them to the server as output.

[1535] Step 2:

[1536] The server temporarily stores the received data. As input, it receives the health data transmitted from the microchip and stores it in a database. As output, the stored data is used in the next analysis step.

[1537] Step 3:

[1538] The server analyzes the collected health data using a generative AI model. The saved health data is supplied as input to the generative AI model to detect anomalies in the data. If an anomaly is detected as a result of data analysis, health management advice is generated. The output is the presence or absence of an anomaly and the generated advice.

[1539] Step 4:

[1540] The emotion engine collects and analyzes the user's emotional data. Emotional data such as facial expressions, voice, and touch operations are collected from the user's device (smartphone) and sent to the server. The user's emotional data is received as input and analyzed by the emotion engine. The user's emotional state is obtained as the output of the analysis.

[1541] Step 5:

[1542] The server customizes advice based on the analysis results and the user's emotional state. It receives anomaly data and the user's emotional state as input, and determines the content of notifications and advice based on the analysis results of the generative AI model and emotion engine. The output is personalized advice.

[1543] Step 6:

[1544] The server notifies the user's device of the generated advice. As input, it receives customized advice and sends it to the user's smartphone via the notification system. As output, the advice is displayed on the user's device.

[1545] Step 7:

[1546] The user reviews the advice provided and sends the data to the veterinarian if necessary. The user selects the health data to send to the veterinarian through the app and issues a transmission instruction. The app receives the selected health data as input and sends it to the veterinarian through the server. As an output, the veterinarian provides appropriate advice and examination instructions based on the data received.

[1547] Through these steps, the pet health management system can collect health data in real time, detect abnormalities, and provide personalized advice based on the user's emotions.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1553] 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.

[1554] 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).

[1555] 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.

[1556] 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."

[1557] 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.

[1558] 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).

[1559] 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.

[1560] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1561] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1562] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1563] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1564] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1565] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1566] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1567] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1568] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1569] The following is further disclosed regarding the above embodiment.

[1570] (Claim 1)

[1571] Microchips to collect pet health data and

[1572] A server for analyzing the collected data using a generative AI model;

[1573] a terminal for transmitting and displaying the analysis results to a user's terminal;

[1574] A system including:

[1575] (Claim 2)

[1576] The system of claim 1, wherein the microchip collects body temperature, pulse, blood data, and exercise volume.

[1577] (Claim 3)

[1578] The system of claim 1 , wherein the server detects outliers and generates corresponding advice.

[1579] "Example 1"

[1580] (Claim 1)

[1581] a data collection means for collecting pet health data;

[1582] a data analysis means for analyzing the collected data;

[1583] data display means for displaying the analysis results;

[1584] an advice providing means for notifying the owner of health care advice;

[1585] a communication means for transmitting the data to a veterinarian;

[1586] A system including:

[1587] (Claim 2)

[1588] 10. The system of claim 1, wherein the data collection means collects body temperature, pulse, blood data, and exercise volume.

[1589] (Claim 3)

[1590] The system of claim 1, wherein the data analysis means uses a generative AI model to detect outliers and generate corresponding advice.

[1591] "Application Example 1"

[1592] (Claim 1)

[1593] Microchips to collect pet health data and

[1594] an information processing device for analyzing the collected data using a generative AI model;

[1595] an information display device for transmitting and displaying the analysis results on a user's input / output device;

[1596] An application means to instantly check pet health status and provide advice to customers in physical stores;

[1597] A system including:

[1598] (Claim 2)

[1599] The system of claim 1, wherein the microchip collects body temperature, pulse rate, vital signs, and activity levels.

[1600] (Claim 3)

[1601] The system of claim 1 , wherein the information processing device detects an abnormal value and generates a corresponding advice.

[1602] "Example 2: Combining Emotion Engines"

[1603] (Claim 1)

[1604] a sensor for collecting biological data of the pet;

[1605] a storage device for temporarily storing the collected data;

[1606] an analysis device for analyzing the collected data using a generative AI model;

[1607] a communication device for transmitting the analysis results to a user's information terminal;

[1608] an emotion recognition device for recognizing an emotion of a user;

[1609] an adaptation device for adaptively customizing analysis results and advice based on emotion recognition data;

[1610] A system including:

[1611] (Claim 2)

[1612] The system of claim 1, wherein the sensors collect body temperature, pulse, blood data, and movement data.

[1613] (Claim 3)

[1614] The system of claim 1 , wherein the analysis device detects abnormal values ​​and generates appropriate health management advice.

[1615] (Claim 4)

[1616] 10. The system of claim 1, wherein the adaptive device customizes the content and notification method of the advice based on the user's emotional data.

[1617] (Claim 5)

[1618] 2. The system according to claim 1, wherein the information terminal has a function of transmitting pet health data to a specialist based on a user's operation.

[1619] "Application example 2 when combining emotion engines"

[1620] (Claim 1)

[1621] Microchips to collect pet health data and

[1622] A server for analyzing the collected data using a generative AI model;

[1623] a terminal for transmitting and displaying the analysis results to a user's terminal;

[1624] an emotion engine that collects and analyzes user emotion data;

[1625] means for providing notification and advice to the user when an anomaly is detected;

[1626] A system including:

[1627] (Claim 2)

[1628] 10. The system of claim 1, wherein the microchip collects body temperature, pulse, blood data, and exercise volume.

[1629] (Claim 3)

[1630] 10. The system of claim 1, wherein the server detects outliers and generates corresponding advice. [Explanation of symbols]

[1631] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. Microchips to collect pet health data and A server for analyzing the collected data using a generative AI model; a terminal for transmitting and displaying the analysis results to a user's terminal; A system including:

2. The system of claim 1, wherein the microchip collects body temperature, pulse, blood data, and exercise volume.

3. The system of claim 1 , wherein the server detects outliers and generates corresponding advice.

Images