system

Abstract

Provide a system. A pet health monitoring system comprising: an imaging means for detecting a motion and generating image data; a sensor means for measuring body temperature, activity level, sleep level, and location information; A measuring device to measure dosage, pH balance and weight; a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means; an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly; a notification means for notifying a user of an abnormality detected by the abnormality detection means; display means for displaying said data to a user; A system including:

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] Efficient health management for pets is difficult, and many owners are slow to notice when their pets are unwell. Furthermore, pets cannot verbally communicate their health conditions, making it unrealistic for owners to monitor their pets 24 / 7. It is particularly important to continuously monitor abnormalities in body temperature, daily activity levels, sleep levels, and litter box usage, as well as to quickly locate pets if they get lost. It is necessary to provide an effective system that solves these problems. [Means for solving the problem]

[0005] The present invention provides a system for monitoring the health condition of a pet, which includes an imaging means for detecting movement and generating image data, a sensor means for measuring body temperature, activity level, amount of sleep and location information, a measurement means for measuring usage, pH balance and weight, a data management means for receiving and storing data transmitted from the imaging means, sensor means and measurement means, anomaly detection means for analyzing data stored in the data management means and detecting abnormalities, a notification means for notifying a user of an abnormality detected by the anomaly detection means, and a display means for displaying the data to the user.

[0006] The system allows owners to monitor their pets' health in real time, receive instant notifications if any abnormalities are detected, provide advice on exercise and food quantity and quality, and quickly locate pets if they get lost.

[0007] The "photography means" is a device for detecting the movements of the pet and generating image data.

[0008] The "sensor means" is a device for measuring the pet's body temperature, activity level, sleep level, and location information.

[0009] "Measuring means" refers to a device for measuring a pet's litter box usage, pH balance, and weight.

[0010] The "data management means" is a system for receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[0011] The "abnormality detection means" is a system that analyzes the data stored in the data management means and detects whether there is an abnormality in the pet's health condition.

[0012] The "notification means" is a system for notifying the user of an abnormality detected by the abnormality detection means.

[0013] A "display means" is a device or system for visually displaying data and notifications regarding the pet's health status to a user. [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 present invention relates to a system for monitoring the health condition of a pet, which includes: an imaging device for detecting the behavior of the pet and generating image data; a sensor device for measuring the pet's body temperature, activity level, sleep level, and location information; a measuring device for measuring the pet's litter box usage, pH balance, and weight; a data management device for receiving and storing data transmitted from the imaging device, sensor device, and measuring device; an anomaly detection device for analyzing the data stored in the data management device and detecting anomalies; a notification device for notifying a user of an anomaly detected by the anomaly detection device; and a display device for displaying the data to the user.

[0036] A specific example for implementing this system will now be described.

[0037] Data collection and transmission

[0038] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[0039] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[0040] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[0041] Receiving and analyzing data

[0042] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[0043] The server analyzes the stored data to learn your pet's normal behavior patterns and compares current data with past data to detect anomalies.

[0044] Alert generation and user notification

[0045] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[0046] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[0047] Data display and advice

[0048] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[0049] Location tracking when lost

[0050] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[0051] Specific examples

[0052] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0053] In this way, the present invention allows for effective monitoring of the health status of pets and rapid response when abnormalities are detected.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[0057] Step 2:

[0058] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[0059] Step 3:

[0060] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[0061] Step 4:

[0062] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[0063] Step 5:

[0064] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0065] Step 6:

[0066] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[0067] Step 7:

[0068] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[0069] Step 8:

[0070] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[0071] Step 9:

[0072] Users can launch the app to view alert notifications and other health data, allowing them to quickly recognize any abnormalities in their pets and take appropriate measures.

[0073] Step 10:

[0074] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[0075] Step 11:

[0076] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[0077] Step 12:

[0078] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[0079] Step 13:

[0080] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[0081] Example 1

[0082] 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."

[0083] There is a demand for continuous, real-time monitoring of pet health conditions and prompt notification to users when abnormalities are detected. However, conventional systems are limited to collecting and notifying users of specific data items (such as body temperature and activity level), making comprehensive health management of pets difficult. Furthermore, delays in notification of abnormalities can make it difficult to respond quickly. Furthermore, conventional systems lacked features such as advice provision and location tracking, resulting in a lack of comprehensive support for pet health and safety management.

[0084] 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.

[0085] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measurement that measures usage, pH balance, and weight; a data management system that receives and stores data transmitted from the camera, sensor, and measurement systems; an anomaly detection system that analyzes the data stored in the data management system and detects abnormalities; a notification system that notifies the user of abnormalities detected by the anomaly detection system; a display system that displays the data to the user; a system that receives real-time location information and displays it to the user; a system that generates advice regarding exercise and diet based on the collected data and provides it to the user; and a system that generates anomaly alert messages using a generative AI model. This allows for comprehensive and real-time monitoring and management of pet health, and allows for prompt notification and appropriate advice when an abnormality is detected. Furthermore, tracking location information facilitates pet safety management.

[0086] The "photography means" is a device that detects the movements of the pet and generates image data.

[0087] The "sensor means" is a device that measures the pet's body temperature, activity level, sleep level, and location information.

[0088] The "measuring means" is a device that measures the pet's litter box usage, pH balance, and weight.

[0089] The "data management means" is a device that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[0090] The "abnormality detection means" is a device that analyzes the data stored in the data management means and detects abnormalities.

[0091] The "notification means" is a device that notifies the user of an abnormality detected by the abnormality detection means.

[0092] A "display means" is a device that displays the data to a user.

[0093] The "means for receiving real-time location information and displaying it to the user" is a device that receives real-time location information of a pet and displays that information to the user.

[0094] The "means for generating advice regarding the amount of exercise and diet based on collected data and providing it to the user" is a device that generates advice regarding the amount of exercise and diet based on collected data and provides that advice to the user.

[0095] "Means for generating an abnormality alert statement using a generative AI model" refers to a device that generates an alert statement regarding an abnormality using a generative AI model.

[0096] The present invention is a comprehensive system for monitoring the health of pets and quickly detecting and notifying abnormalities. The system is composed of the following elements:

[0097] 1. Filming Method

[0098] This system includes a camera that detects pet movements and generates image data. The camera is installed in a room, generates image data at regular intervals, and sends it to a server via a network. This camera can be a commercially available surveillance camera.

[0099] 2. Sensor means

[0100] The system includes a collar that measures your pet's body temperature, activity level, sleep level, and location. The collar is attached to your pet's neck and measures data in real time, periodically sending it to a server. The collar is equipped with a body temperature sensor, an acceleration sensor, and a GPS module.

[0101] 3. Measurement methods

[0102] This includes toilets that measure pet toilet usage, pH balance, and weight. The toilet measures this data every time the pet uses it and sends it to a server via the network. This toilet is equipped with a weight sensor and a pH sensor.

[0103] 4. Data Management Measures

[0104] The server receives the data sent from the camera, collar, and toilet and stores it in a database, specifically using a database system such as MySQL (registered trademark) or PostgreSQL.

[0105] 5. Anomaly Detection Methods

[0106] The server analyzes the stored data and detects anomalies using anomaly detection algorithms or machine learning models (e.g., Python libraries Scikit-learn and TENSORFLOW (registered trademark)).

[0107] 6. Means of notification

[0108] If an anomaly is detected, the server generates an anomaly alert and notifies the user through an application, such as a mobile app with notification functionality for Android or iOS.

[0109] 7. Display means

[0110] The server visualizes the collected data and displays it to the user through an app, which can be a web app or a mobile app's dashboard function.

[0111] 8. A means of receiving and displaying real-time location information to the user

[0112] The server receives real-time location information transmitted from the collar and displays it to the user, allowing them to quickly locate their pet if it gets lost.

[0113] 9. Means for generating and providing advice to users regarding exercise and diet based on collected data

[0114] The server generates advice on exercise and diet based on the collected data, and provides this advice to the user through the app.

[0115] 10. How to generate anomaly alerts using generative AI models

[0116] The server uses a generative AI model to generate an alert message based on the detected anomaly. For example, by inputting a prompt message such as "Please generate a notification message when my pet's temperature is too high" into a generative AI model (such as GPT-3 (registered trademark)), the server generates an appropriate notification message.

[0117] Specific examples

[0118] For example, if the collar device measures a pet's temperature as 39.5°C and sends that data to the server, the server will determine that this is outside the normal body temperature range (e.g., 38.0°C to 39.0°C) and generate an abnormality alert. The generated alert is sent from the server to the app, and the user will receive a notification such as: "Your pet's temperature is too high. Please consult your veterinarian."

[0119] Prompt Sentence Examples

[0120] "Generate a notification message if your pet's temperature is too high"

[0121] "Please create an alert statement for when the toilet's pH balance is abnormal."

[0122] "Please provide an example of a location notification for when a pet gets lost."

[0123] In this way, the present invention allows for comprehensive monitoring of the health status of pets and allows for rapid response if an abnormality is detected.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The device collects data

[0127] Specific operation: A camera installed in the room captures the pet's movements at regular intervals and generates image data. The collar also measures the pet's temperature, activity level, sleep level, and location, while the toilet measures usage, pH balance, and weight.

[0128] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage

[0129] Output: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0130] Step 2:

[0131] The device sends the data to the server

[0132] Specific operation: All devices (camera, collar, and toilet) send the collected data to a server via a network. For example, the camera sends image data to the server, and the collar sends body temperature and activity data to the server.

[0133] Input: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0134] Output: Various data sent to the server

[0135] Step 3:

[0136] The server receives and stores the data

[0137] Specific operation: The server receives data sent from the terminal via the network and stores it in a database. Specifically, it stores the data using MySQL or PostgreSQL.

[0138] Input: Various data sent from the terminal

[0139] Output: Various data stored in the database

[0140] Step 4:

[0141] The server analyzes the stored data

[0142] How it works: The server retrieves data stored in the database and analyzes it using anomaly detection algorithms and machine learning models, such as Scikit-learn and TensorFlow.

[0143] Input: Various data stored in the database

[0144] Output: Analysis results, anomaly detection results

[0145] Step 5:

[0146] If the server detects an anomaly, it generates an anomaly alert.

[0147] Specific operation: Based on the analysis results, if an abnormality is detected, such as if the pet's body temperature is outside the normal range, the server will generate an abnormality alert. For example, it will generate an alert message such as "Temperature is too high." A generative AI model (such as GPT-3) can be used in this process.

[0148] Input: Analysis results, anomaly detection results

[0149] Output: Abnormal alert statement

[0150] Step 6:

[0151] The server notifies the user of an abnormality alert.

[0152] Specific operation: The generated abnormality alert is notified to the user via the application from the server. The user receives a notification on their smartphone app and can check the situation.

[0153] Input: Abnormal alert statement

[0154] Output: User notification

[0155] Step 7:

[0156] The server generates advice based on the collected data and provides it to the user.

[0157] How it works: The server analyzes the collected data and generates advice on exercise and diet for pets. These advice are provided to users through an application. Generative AI models may also be used.

[0158] Input: Analysis results, collected data

[0159] Output: Advice on exercise and diet

[0160] Step 8:

[0161] The server displays the collected data to the user.

[0162] Specific operation: The server visualizes the collected data and displays it to the user through an application. For example, it can display the progress of activity levels and sleep patterns in graph form.

[0163] Input: Analysis results, collected data

[0164] Output: Data visualization information displayed to the user

[0165] Step 9:

[0166] Displaying real-time location information to users

[0167] Specific operation: The server receives the real-time GPS location information sent by the collar and displays it to the user through the application, allowing the user to know the current location of their pet at any time.

[0168] Input: Real-time location

[0169] Output: Location displayed to the user

[0170] In this way, the system of the present invention can comprehensively monitor the health status of pets and quickly notify users if any abnormalities are detected. Furthermore, by providing advice on exercise and diet, as well as real-time location information, the system supports the health and safety management of pets.

[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 data such as pet body temperature and activity levels, and lack the mechanisms for quickly responding when abnormalities are detected in real time. Furthermore, they lack sufficient functionality for tracking a pet's location if it gets lost, or for providing detailed health information based on the collected data. Particularly when managing pets in brick-and-mortar stores, store staff are required to quickly grasp the pet's health status and take appropriate action, but no such system existed.

[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 camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measuring device that measures usage, pH balance, and weight; a data management device that receives and stores data transmitted from the camera, sensor, and measuring device; an anomaly detection device that analyzes the data stored in the data management device and detects abnormalities; a notification device that notifies the user of abnormalities detected by the anomaly detection device; a display device that analyzes collected data and provides the user with detailed health information; and a tracking device that tracks the location of the pet if it gets lost. This allows for real-time monitoring of the pet's health and for prompt notification to a store staff member if an abnormality is detected. Furthermore, even if the pet gets lost, the location can be quickly identified and detailed health information can be provided to the customer.

[0176] The "photography means for detecting movements and generating image data" is a device for capturing the movements of a pet and generating image data based on the movements.

[0177] The "sensor means for measuring body temperature, activity level, sleep level and location information" is a sensor device for measuring a pet's body temperature, activity level, sleep level and location information in real time.

[0178] "Measuring means for measuring usage, pH balance and weight" refers to a device for measuring the amount of toilet used by a pet, the pH balance of urine and the weight of the pet.

[0179] The "data management means" is a system for receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[0180] The "abnormality detection means" is a function for analyzing data stored in the data management means and detecting values ​​or states that are outside the normal range.

[0181] The "notification means" is a communication system for notifying the user of an abnormality detected by the abnormality detection means, and is a device or application for sending alerts and messages.

[0182] The "display means" refers to a device or interface for visually displaying the collected data and analysis results to the user.

[0183] The "tracking means" is a system that tracks the location of a pet in real time if the pet gets lost and notifies the user of the location.

[0184] A "server" is a central processing unit that receives, stores, analyzes, notifies, and displays data, and is a device that manages the entire system in cooperation with various means.

[0185] The present invention is a system for monitoring the health of pets, detecting and notifying abnormalities, and is composed of various devices and programs, including a photographing means, a sensor means, a measuring means, a data management means, an abnormality detection means, a notification means, a display means, and a tracking means.

[0186] Specific system configuration

[0187] Data collection

[0188] The camera serving as the terminal is installed in the pet shop, and captures the movements of the pet at regular intervals to generate image data.

[0189] The sensor terminal is attached to the pet's collar and measures body temperature, activity level, sleep level, and location information in real time.

[0190] The terminal measuring device is installed in the pet's toilet and measures the amount used, the pH balance of the urine, and the pet's weight.

[0191] Data reception and analysis

[0192] The server continuously receives various data and stores it in a database. The server processes HTTP requests using Python and the requests module.

[0193] The stored data is analyzed and anomalies are detected using an anomaly detection algorithm, which is an analytical method based on a generative AI model.

[0194] User Notification and Data Display

[0195] If an abnormality is detected, the server generates an abnormality alert and sends the alert to the user via the notification means.

[0196] Users can receive real-time notifications of abnormalities using their smartphones.

[0197] The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[0198] Specific operation examples

[0199] For example, if a pet's temperature is measured at 39.5 degrees, this data is sent to the server. The server determines that this is outside the normal temperature range and generates an abnormality alert saying, "Your pet's temperature is too high. Please consult a veterinarian." This alert is then sent to the user via their smartphone.

[0200] Prompt Sentence Examples

[0201] "Latest data from your pet's collar: Temperature: 39.5°C Activity: 15 Please detect any abnormalities and generate appropriate notification messages."

[0202] This system allows real-time monitoring of the health status of pets in pet shops, allowing for swift action if an abnormality occurs. In addition, detailed health information can be provided to customers when they purchase a pet, allowing them to purchase with peace of mind.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] Data collection

[0206] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0207] Behavior: The camera captures your pet's movements and generates image data. Sensors also measure your pet's body temperature, activity level, sleep level, and location. Furthermore, a measuring device measures your pet's toilet usage, urine pH balance, and weight.

[0208] Output: Each piece of data collected is generated.

[0209] Step 2:

[0210] Data transmission

[0211] Input: collected image data, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0212] How it works: Cameras, sensors, and measuring devices send their data to a server.

[0213] Output: The data arrives at the server.

[0214] Step 3:

[0215] Data reception

[0216] Input: Data sent from cameras, sensors, and measurement devices

[0217] How it works: The server receives each piece of data and stores it in a database.

[0218] Output: Each data stored in the database is obtained.

[0219] Step 4:

[0220] Data analysis

[0221] Input: Image data stored in the database, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0222] How it works: The server analyzes the stored data and uses generative AI models to detect anomalies.

[0223] Output: A judgment result is obtained as to whether or not there is an abnormality.

[0224] Step 5:

[0225] Generate anomaly notifications

[0226] Input: Abnormality judgment result

[0227] How it works: If an anomaly is detected, the server generates an anomaly alert.

[0228] Output: An anomaly alert is generated.

[0229] Step 6:

[0230] User Notification

[0231] Input: Generated anomaly alert

[0232] Operation: The server sends an abnormality alert to the smartphone via the notification means.

[0233] Output: An abnormality alert is displayed on the user's smartphone.

[0234] Step 7:

[0235] Data display and advice generation

[0236] Input: Parsed data stored in a database

[0237] Operation: The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[0238] Output: Data and advice are displayed on the user's smartphone.

[0239] Step 8:

[0240] Location Tracking

[0241] Input: Real-time location information sent from the sensor

[0242] How it works: The server tracks the location of the pet and notifies the user if the pet gets lost.

[0243] Output: Real-time location information is displayed on the user's smartphone.

[0244] 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.

[0245] The present invention relates to a system for monitoring the health condition of a pet. The system includes an imaging means for detecting the behavior of the pet and generating image data, a sensor means for measuring the pet's body temperature, activity level, sleep level, and location information, a measurement means for measuring the pet's litter box usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the imaging means, sensor means, and measurement means, anomaly detection means for analyzing the data stored in the data management means and detecting anomalies, a notification means for notifying a user of an anomaly detected by the anomaly detection means, a display means for displaying the data to the user, and an emotion engine for recognizing the user's emotions.

[0246] Data collection and transmission

[0247] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[0248] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[0249] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[0250] Receiving and analyzing data

[0251] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[0252] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0253] Alert generation and user notification

[0254] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[0255] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[0256] Data display and advice

[0257] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[0258] Location tracking when lost

[0259] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[0260] Emotion Engine Functions

[0261] The server is equipped with an emotion engine for recognizing the user's emotions, which analyzes the user's facial expressions and tone of voice to recognize the user's current emotional state.

[0262] Based on the recognized emotion, the server can tailor pet care suggestions, for example, calming notifications and advice about the pet's health if the user is feeling stressed.

[0263] Specific examples

[0264] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0265] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[0266] In this way, the present invention allows for effective monitoring of the pet's health and, when an abnormality is detected, a response that takes into account the user's emotional state.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[0270] Step 2:

[0271] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[0272] Step 3:

[0273] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[0274] Step 4:

[0275] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[0276] Step 5:

[0277] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0278] Step 6:

[0279] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[0280] Step 7:

[0281] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[0282] Step 8:

[0283] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[0284] Step 9:

[0285] When a user launches the app, the app uses the camera and microphone to capture the user's facial expressions and tone of voice.

[0286] Step 10:

[0287] The server analyzes the user's facial expressions and tone of voice through an emotion engine to recognize the user's current emotional state, for example, determining whether the user is feeling stressed.

[0288] Step 11:

[0289] The server then adjusts the wording of notifications and advice about the pet's health based on the recognized emotion. For example, if the user is feeling stressed, the notification content will be changed to a stress-reducing message such as "Don't worry, but be careful as your pet has a high temperature."

[0290] Step 12:

[0291] The server sends tailored notifications and advice to the app, which the user can then view.

[0292] Step 13:

[0293] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[0294] Step 14:

[0295] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[0296] Step 15:

[0297] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[0298] Step 16:

[0299] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[0300] Example 2

[0301] 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."

[0302] Conventional pet health monitoring systems are capable of measuring various health indicators of pets and collecting data. However, these systems have difficulty in providing effective notifications to users. In particular, they can be stressful for users because they provide uniform notifications without considering the user's emotional state. Furthermore, in actual use, there is a need for a system that can comprehensively manage data from individual devices, detect abnormalities, and take appropriate action.

[0303] 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.

[0304] In this invention, the server includes a photographing means for detecting movements and generating image data, a sensor means for measuring body temperature, activity level, sleep level, and location information, a measuring means for measuring usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the photographing means, the sensor means, and the measuring means, an abnormality detection means for analyzing the data stored in the data management means and detecting abnormalities, a notification means for notifying the user of abnormalities detected by the abnormality detection means, a display means for displaying the data to the user, and an emotion recognition means for recognizing the user's emotions and adjusting the content of the notification. This makes it possible to effectively monitor the health condition of a pet and, when an abnormality is detected, to provide a notification that takes into account the user's emotional state.

[0305] The "photography means" is a device that has the function of detecting the movements of the pet and generating image data.

[0306] The "sensor means" is a device that has the function of measuring the pet's body temperature, activity level, sleep level, and location information.

[0307] "Measuring means" refers to a device that has the function of measuring the amount of pet use, pH balance, and weight.

[0308] The "data management means" is a device that has the function of receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[0309] The "abnormality detection means" is a device that has the function of analyzing the data stored in the data management means and detecting abnormalities.

[0310] The "notification means" is a device having a function of notifying the user of an abnormality detected by the abnormality detection means.

[0311] A "display means" is a device that has the function of displaying data to a user.

[0312] The "emotion recognition means" is a device that has the function of recognizing the user's emotions and adjusting the notification content.

[0313] This invention relates to a system that monitors the health condition of pets and notifies the user when an abnormality is detected. This system measures and collects pet behavior, health indicators, and location information, and analyzes this data to detect abnormalities in the pet and notify the user in a way that takes into account the user's emotional state.

[0314] This system consists of the following elements:

[0315] Filming method

[0316] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals to generate image data. For example, a "general web camera" is used for this camera, and image data in JPEG format is generated. The generated image data is then sent to the server via the network.

[0317] Sensor Means

[0318] The collar, which serves as the terminal, is attached to the pet's neck and measures body temperature, activity, sleep, and location information in real time. This collar uses a "pet wearable device," for example, and the measured data is periodically sent to a server.

[0319] Measurement methods

[0320] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and the measured data is sent to a server via a network.

[0321] Data Management Measures

[0322] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database, typically using MySQL or PostgreSQL.

[0323] Data analysis

[0324] The server analyzes the stored data and learns the pet's normal behavior patterns and baseline values. For analysis, it uses data analysis libraries such as "Pandas" and "NumPy." It can calculate the pet's normal body temperature range and average activity level from past data.

[0325] Anomaly detection

[0326] The server compares the analyzed data with real-time data to detect anomalies, for example, if a pet's temperature exceeds the normal range, it generates an anomaly alert.

[0327] User Notifications

[0328] The server sends the generated anomaly alert to the app, which notifies the user. The app then displays a push notification on the user's smartphone and suggests specific measures to address the anomaly.

[0329] Data Display and Advice

[0330] The server analyzes the collected data and generates advice on the pet's health, such as the amount of exercise and the quantity and quality of food, which is then provided to the user via the app.

[0331] emotion recognition

[0332] The server uses an "emotion recognition API" to recognize the user's emotions. The app analyzes the user's facial expressions and tone of voice to recognize their current emotional state. Based on the recognized emotion, the server adjusts the notification content.

[0333] Specific examples

[0334] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0335] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[0336] Prompt statement

[0337] Prompt sentence for the AI ​​model to generate a specific example:

[0338] Describe a system that monitors the health of pets. Data is collected from cameras, collars, and litter boxes, and analyzed on a server to detect abnormalities. If an abnormality is detected, the system notifies the user and uses an emotion engine to provide notifications that take the user's emotions into account.

[0339] In this way, the present invention effectively monitors the health of a pet and allows for a response that takes into account the user's emotional state when an abnormality is detected.

[0340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0341] Step 1: Data collection

[0342] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating JPEG image data. The collected image data is sent from the camera to a server via a network. The collar, which is the terminal, measures the pet's body temperature, activity level, sleep level, and location information in real time, and temporarily stores each piece of data in its internal storage. The toilet, which is the terminal, measures the amount of toilet use, pH balance, and weight each time the pet uses the toilet, and generates the data.

[0343] Step 2: Send data

[0344] The devices (camera, collar, and toilet) periodically send the collected data to a server. JPEG image data generated by the camera, sensor data measured by the collar, and data measured by the toilet are transmitted via wireless communication such as Wi-Fi. The input here is the data generated by each device, and the output is the data sent to the server.

[0345] Step 3: Receiving and storing data

[0346] The server constantly receives data sent from the camera, collar, and toilet, and stores it in a database. The server manages the database using, for example, MySQL or PostgreSQL, and classifies and stores the data from each device along with a timestamp. The input is the data sent from each device, and the output is the data stored in the database.

[0347] Step 4: Data analysis

[0348] The server uses Python data analysis libraries such as "Pandas" and "NumPy" to analyze the stored data. It learns the pet's normal behavior patterns and standard values ​​based on past data, and calculates, for example, the normal body temperature range and average activity level. The input is the past data stored in the database, and the output is various standard values ​​as the analysis results.

[0349] Step 5: Anomaly detection

[0350] The server compares the analyzed data with real-time data to detect abnormalities. For example, if a pet's body temperature is outside the normal range, it will be recognized as an abnormality and generate an abnormality alert. The input is the analyzed reference value and real-time data, and the output is an abnormality alert.

[0351] Step 6: User Notification

[0352] If an abnormality is detected, the server sends an abnormality alert to the app and notifies the user. The abnormality alert includes specific details of the abnormality and countermeasures. For example, a notification may be sent saying, "Your pet's temperature is too high. Please consult a veterinarian." The input is the generated abnormality alert, and the output is the notification sent to the app.

[0353] Step 7: Data display and advice

[0354] The server generates advice on the amount of exercise and the quantity and quality of food for the pet's health based on the collected and analyzed data. This advice is provided to the user through the app. The input is the data stored in the database and the analysis results, and the output is the advice displayed on the app.

[0355] Step 8: Emotion recognition and notification adjustment

[0356] The server uses an "emotion recognition API" to recognize the user's emotions and analyzes the user's facial expressions and tone of voice obtained through the app. Based on the user's emotional state, the server adjusts the notification content. For example, if the user is feeling stressed, the notification wording will be changed to a more gentle one. The input is the user's facial expression and voice data, and the output is the adjusted notification content.

[0357] The above is the flow of processing by the system, and details of the specific operations performed at each step.

[0358] (Application example 2)

[0359] 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."

[0360] It is necessary to provide a system that can effectively and comprehensively monitor the health status of pets and respond quickly when abnormalities occur. It is also necessary to provide notifications and advice on pet health management taking into account the user's emotional state. Such a system can reduce pet health risks and ease the burden on users.

[0361] The specific processing by the specific 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 a photographing means that detects movement and generates image data, a sensor means that measures body temperature, activity level, sleep level, and location information, and a measuring means that measures usage, pH balance, and weight. This makes it possible to comprehensively monitor the pet's health and quickly notify the user when an abnormality is detected. In addition, by using an emotion engine that recognizes the user's emotional state and adjusts the content of notifications and advice, it is possible to reduce the user's stress and encourage appropriate responses.

[0362] The "photography means for detecting the operation and generating image data" is a device used to monitor the operation of a factory robot and generate data in the form of images.

[0363] The "sensor means for measuring body temperature, activity level, sleep level and location information" refers to a group of devices for measuring the body temperature, activity level during work, time spent stopped and current location of a factory robot.

[0364] "Measuring means for measuring usage amount, pH balance and weight" refers to a group of devices that measure a certain usage amount, pH balance and weight of a liquid related to a factory robot.

[0365] The "data management means" is a system including devices and software for receiving and storing data transmitted from the above-mentioned photographing means, sensor means, and measurement means.

[0366] The "abnormality detection means" refers to a device and software that analyzes the data stored in the data management means and detects abnormalities that deviate from the normal operating range.

[0367] The "notification means" is a device and system for notifying the user of an abnormality detected by the abnormality detection means.

[0368] "Display means" refers to a device for visually displaying data to a user, and includes, for example, a display or a monitor.

[0369] The "emotion engine" is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[0370] The system for implementing this invention is configured to monitor the operating status and maintenance status of factory robots and to respond quickly when an abnormality is detected. The system is mainly composed of the following hardware and software.

[0371] Hardware

[0372] 1. Photography Method:

[0373] It includes a camera for monitoring the operation of a factory robot, which detects the movement and generates image data.

[0374] 2. Sensor means:

[0375] Temperature sensors that measure the body temperature of factory robots

[0376] Vibration sensor that measures activity

[0377] A sensor that measures sleep amount (time inactive)

[0378] Location sensors that measure location information

[0379] 3. Measurement methods:

[0380] It includes sensors that measure usage, Ph balance, and weight.

[0381] software

[0382] 1. Data Management Measures:

[0383] The system includes a server that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[0384] 2. Anomaly detection methods:

[0385] It includes software that analyzes stored data and detects anomalies that deviate from normal operating ranges.

[0386] 3. Means of notification:

[0387] This is a system for notifying a user of an abnormality detected by an abnormality detection means.

[0388] 4. Display means:

[0389] It includes a display or monitor for visually displaying data to the user.

[0390] 5. Emotion Engine:

[0391] It is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[0392] Processing flow

[0393] The server constantly receives data sent from the imaging means, sensor means, and measurement means and stores it in a database. Based on the stored data, it learns the factory robot's normal operating patterns and reference values. For example, it calculates the normal body temperature range and average activity level based on past data. If an abnormality is detected, the server generates an abnormality alert and promptly notifies the user via the notification means.

[0394] Specific examples

[0395] For example, if a factory robot's temperature sensor detects a temperature of 80 degrees, the server will determine this and generate an abnormality alert. This alert will be sent to the user via the notification system as a message such as "The robot's temperature is abnormal. Please cool it down." Furthermore, if the emotion engine detects stress in the user when receiving the alert, it can change the content of the notification to a gentler one such as "Please do not panic. However, the robot's temperature is high, so please be careful."

[0396] Prompt Sentence Examples

[0397] To recognize the user's emotions and notify them with an appropriate message, we use an emotion engine. When notifying, we analyze the user's emotional state, and if a specific emotion (e.g., stress) is detected, we change the message to a calmer one. Can you please give us an example program for this implementation?

[0398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0399] Step 1:

[0400] A camera (photographing means) detects the movement of the factory robot and generates image data.

[0401] Input: Camera detects factory robot movement.

[0402] Data processing: Analyzes the movements captured by the camera and generates image data.

[0403] Output: The generated image data is sent to the server.

[0404] Step 2:

[0405] The sensor means measures the temperature, activity level, sleep level, and location information of the factory robot.

[0406] Input: The temperature sensor measures the factory robot's temperature, the vibration sensor measures activity, the inactivity sensor measures sleep, and the location sensor obtains its current location.

[0407] Data processing: Organize the data collected by each sensor and convert it into a format that can be sent to the server.

[0408] Output: The measured body temperature, activity level, sleep level, and location information are sent to the server.

[0409] Step 3:

[0410] The measuring device measures the usage, pH balance and weight of the factory robot.

[0411] Inputs: Usage sensor measures the usage of the factory robot, pH balance sensor measures the pH balance of the liquid, weight sensor measures the weight of the robot.

[0412] Data Processing: Collected usage, pH balance and weight data is compiled and converted into a format to be sent to the server.

[0413] Output: Sends measured usage, pH balance, and weight data to the server.

[0414] Step 4:

[0415] The server stores the received data in a database.

[0416] Input: Various data sent from photography means, sensor means, and measurement means.

[0417] Data processing: The received data is converted into an appropriate data format and recorded in the database.

[0418] Output: The various saved data is stored in a database.

[0419] Step 5:

[0420] The server learns normal ranges and benchmark values ​​based on the stored data.

[0421] Input: Past and current factory robot data stored in a database.

[0422] Data processing: Analysis software performs statistical analysis to calculate the robot's normal operating range and reference values.

[0423] Output: Sets the calculated baseline or normal operating range.

[0424] Step 6:

[0425] The server detects an anomaly.

[0426] Input: Current data and learned baseline.

[0427] Data processing: Determine whether the current data deviates from the baseline and generate an alert if an anomaly is detected.

[0428] Output: Generated anomaly alerts.

[0429] Step 7:

[0430] The server notifies the user of the abnormality.

[0431] Input: The generated anomaly alert.

[0432] Data processing: Converts the alert content into a format that can be notified to the user.

[0433] Output: A notification message to the user.

[0434] Step 8:

[0435] The server recognizes the user's emotional state.

[0436] Input: User's facial and voice data.

[0437] Data processing: The emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[0438] Output: Adjust notification content based on the perceived emotional state.

[0439] Step 9:

[0440] The server displays the data to the user.

[0441] Input: Data and analysis results stored in a database.

[0442] Data processing: The data is formatted into an appropriate format and displayed on the user's display or monitor.

[0443] Output: Visually displayed data and analysis results.

[0444] 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.

[0445] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] 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.

[0447] [Second embodiment]

[0448] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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."

[0460] The present invention relates to a system for monitoring the health condition of a pet, which includes: an imaging device for detecting the behavior of the pet and generating image data; a sensor device for measuring the pet's body temperature, activity level, sleep level, and location information; a measuring device for measuring the pet's litter box usage, pH balance, and weight; a data management device for receiving and storing data transmitted from the imaging device, sensor device, and measuring device; an anomaly detection device for analyzing the data stored in the data management device and detecting anomalies; a notification device for notifying a user of an anomaly detected by the anomaly detection device; and a display device for displaying the data to the user.

[0461] A specific example for implementing this system will now be described.

[0462] Data collection and transmission

[0463] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[0464] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[0465] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[0466] Receiving and analyzing data

[0467] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[0468] The server analyzes the stored data to learn your pet's normal behavior patterns and compares current data with past data to detect anomalies.

[0469] Alert generation and user notification

[0470] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[0471] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[0472] Data display and advice

[0473] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[0474] Location tracking when lost

[0475] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[0476] Specific examples

[0477] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0478] In this way, the present invention allows for effective monitoring of the health status of pets and rapid response when abnormalities are detected.

[0479] The processing flow will be explained below.

[0480] Step 1:

[0481] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[0482] Step 2:

[0483] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[0484] Step 3:

[0485] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[0486] Step 4:

[0487] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[0488] Step 5:

[0489] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0490] Step 6:

[0491] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[0492] Step 7:

[0493] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[0494] Step 8:

[0495] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[0496] Step 9:

[0497] Users can launch the app to view alert notifications and other health data, allowing them to quickly recognize any abnormalities in their pets and take appropriate measures.

[0498] Step 10:

[0499] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[0500] Step 11:

[0501] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[0502] Step 12:

[0503] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[0504] Step 13:

[0505] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[0506] Example 1

[0507] 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."

[0508] There is a demand for continuous, real-time monitoring of pet health conditions and prompt notification to users when abnormalities are detected. However, conventional systems are limited to collecting and notifying users of specific data items (such as body temperature and activity level), making comprehensive health management of pets difficult. Furthermore, delays in notification of abnormalities can make it difficult to respond quickly. Furthermore, conventional systems lacked features such as advice provision and location tracking, resulting in a lack of comprehensive support for pet health and safety management.

[0509] 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.

[0510] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measurement that measures usage, pH balance, and weight; a data management system that receives and stores data transmitted from the camera, sensor, and measurement systems; an anomaly detection system that analyzes the data stored in the data management system and detects abnormalities; a notification system that notifies the user of abnormalities detected by the anomaly detection system; a display system that displays the data to the user; a system that receives real-time location information and displays it to the user; a system that generates advice regarding exercise and diet based on the collected data and provides it to the user; and a system that generates anomaly alert messages using a generative AI model. This allows for comprehensive and real-time monitoring and management of pet health, and allows for prompt notification and appropriate advice when an abnormality is detected. Furthermore, tracking location information facilitates pet safety management.

[0511] The "photography means" is a device that detects the movements of the pet and generates image data.

[0512] The "sensor means" is a device that measures the pet's body temperature, activity level, sleep level, and location information.

[0513] The "measuring means" is a device that measures the pet's litter box usage, pH balance, and weight.

[0514] The "data management means" is a device that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[0515] The "abnormality detection means" is a device that analyzes the data stored in the data management means and detects abnormalities.

[0516] The "notification means" is a device that notifies the user of an abnormality detected by the abnormality detection means.

[0517] A "display means" is a device that displays the data to a user.

[0518] The "means for receiving real-time location information and displaying it to the user" is a device that receives real-time location information of a pet and displays that information to the user.

[0519] The "means for generating advice regarding the amount of exercise and diet based on collected data and providing it to the user" is a device that generates advice regarding the amount of exercise and diet based on collected data and provides that advice to the user.

[0520] "Means for generating an abnormality alert statement using a generative AI model" refers to a device that generates an alert statement regarding an abnormality using a generative AI model.

[0521] The present invention is a comprehensive system for monitoring the health of pets and quickly detecting and notifying abnormalities. The system is composed of the following elements:

[0522] 1. Filming Method

[0523] This system includes a camera that detects pet movements and generates image data. The camera is installed in a room, generates image data at regular intervals, and sends it to a server via a network. This camera can be a commercially available surveillance camera.

[0524] 2. Sensor means

[0525] The system includes a collar that measures your pet's body temperature, activity level, sleep level, and location. The collar is attached to your pet's neck and measures data in real time, periodically sending it to a server. The collar is equipped with a body temperature sensor, an acceleration sensor, and a GPS module.

[0526] 3. Measurement methods

[0527] This includes toilets that measure pet toilet usage, pH balance, and weight. The toilet measures this data every time the pet uses it and sends it to a server via the network. This toilet is equipped with a weight sensor and a pH sensor.

[0528] 4. Data Management Measures

[0529] The server receives the data sent from the camera, collar, and litter box and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[0530] 5. Anomaly Detection Methods

[0531] The server analyzes the stored data and detects anomalies using anomaly detection algorithms or machine learning models (such as the Python libraries Scikit-learn and TensorFlow).

[0532] 6. Means of notification

[0533] If an anomaly is detected, the server generates an anomaly alert and notifies the user through an application, such as a mobile app with notification functionality for Android or iOS.

[0534] 7. Display means

[0535] The server visualizes the collected data and displays it to the user through an app, which can be a web app or a mobile app's dashboard function.

[0536] 8. A means of receiving and displaying real-time location information to the user

[0537] The server receives real-time location information transmitted from the collar and displays it to the user, allowing them to quickly locate their pet if it gets lost.

[0538] 9. Means for generating and providing advice to users regarding exercise and diet based on collected data

[0539] The server generates advice on exercise and diet based on the collected data, and provides this advice to the user through the app.

[0540] 10. How to generate anomaly alerts using generative AI models

[0541] The server uses a generative AI model to generate an alert message based on the detected anomaly. For example, by inputting a prompt message such as "Please generate a notification message when my pet's temperature is too high" into a generative AI model (such as GPT-3), the server can generate an appropriate notification message.

[0542] Specific examples

[0543] For example, if the collar device measures a pet's temperature as 39.5°C and sends that data to the server, the server will determine that this is outside the normal body temperature range (e.g., 38.0°C to 39.0°C) and generate an abnormality alert. The generated alert is sent from the server to the app, and the user will receive a notification such as: "Your pet's temperature is too high. Please consult your veterinarian."

[0544] Prompt Sentence Examples

[0545] "Generate a notification message if your pet's temperature is too high"

[0546] "Please create an alert statement for when the toilet's pH balance is abnormal."

[0547] "Please provide an example of a location notification for when a pet gets lost."

[0548] In this way, the present invention allows for comprehensive monitoring of the health status of pets and allows for rapid response if an abnormality is detected.

[0549] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0550] Step 1:

[0551] The device collects data

[0552] Specific operation: A camera installed in the room captures the pet's movements at regular intervals and generates image data. The collar also measures the pet's temperature, activity level, sleep level, and location, while the toilet measures usage, pH balance, and weight.

[0553] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage

[0554] Output: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0555] Step 2:

[0556] The device sends the data to the server

[0557] Specific operation: All devices (camera, collar, and toilet) send the collected data to a server via a network. For example, the camera sends image data to the server, and the collar sends body temperature and activity data to the server.

[0558] Input: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0559] Output: Various data sent to the server

[0560] Step 3:

[0561] The server receives and stores the data

[0562] Specific operation: The server receives data sent from the terminal via the network and stores it in a database. Specifically, it stores the data using MySQL or PostgreSQL.

[0563] Input: Various data sent from the terminal

[0564] Output: Various data stored in the database

[0565] Step 4:

[0566] The server analyzes the stored data

[0567] How it works: The server retrieves data stored in the database and analyzes it using anomaly detection algorithms and machine learning models, such as Scikit-learn and TensorFlow.

[0568] Input: Various data stored in the database

[0569] Output: Analysis results, anomaly detection results

[0570] Step 5:

[0571] If the server detects an anomaly, it generates an anomaly alert.

[0572] Specific operation: Based on the analysis results, if an abnormality is detected, such as if the pet's body temperature is outside the normal range, the server will generate an abnormality alert. For example, it will generate an alert message such as "Temperature is too high." A generative AI model (such as GPT-3) can be used in this process.

[0573] Input: Analysis results, anomaly detection results

[0574] Output: Abnormal alert statement

[0575] Step 6:

[0576] The server notifies the user of an abnormality alert.

[0577] Specific operation: The generated abnormality alert is notified to the user via the application from the server. The user receives a notification on their smartphone app and can check the situation.

[0578] Input: Abnormal alert statement

[0579] Output: User notification

[0580] Step 7:

[0581] The server generates advice based on the collected data and provides it to the user.

[0582] How it works: The server analyzes the collected data and generates advice on exercise and diet for pets. These advice are provided to users through an application. Generative AI models may also be used.

[0583] Input: Analysis results, collected data

[0584] Output: Advice on exercise and diet

[0585] Step 8:

[0586] The server displays the collected data to the user.

[0587] Specific operation: The server visualizes the collected data and displays it to the user through an application. For example, it can display the progress of activity levels and sleep patterns in graph form.

[0588] Input: Analysis results, collected data

[0589] Output: Data visualization information displayed to the user

[0590] Step 9:

[0591] Displaying real-time location information to users

[0592] Specific operation: The server receives the real-time GPS location information sent by the collar and displays it to the user through the application, allowing the user to know the current location of their pet at any time.

[0593] Input: Real-time location

[0594] Output: Location displayed to the user

[0595] In this way, the system of the present invention can comprehensively monitor the health status of pets and quickly notify users if any abnormalities are detected. Furthermore, by providing advice on exercise and diet, as well as real-time location information, the system supports the health and safety management of pets.

[0596] (Application example 1)

[0597] 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."

[0598] Conventional pet health management systems only collect data such as pet body temperature and activity levels, and lack the mechanisms for quickly responding when abnormalities are detected in real time. Furthermore, they lack sufficient functionality for tracking a pet's location if it gets lost, or for providing detailed health information based on the collected data. Particularly when managing pets in brick-and-mortar stores, store staff are required to quickly grasp the pet's health status and take appropriate action, but no such system existed.

[0599] 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.

[0600] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measuring device that measures usage, pH balance, and weight; a data management device that receives and stores data transmitted from the camera, sensor, and measuring device; an anomaly detection device that analyzes the data stored in the data management device and detects abnormalities; a notification device that notifies the user of abnormalities detected by the anomaly detection device; a display device that analyzes collected data and provides the user with detailed health information; and a tracking device that tracks the location of the pet if it gets lost. This allows for real-time monitoring of the pet's health and for prompt notification to a store staff member if an abnormality is detected. Furthermore, even if the pet gets lost, the location can be quickly identified and detailed health information can be provided to the customer.

[0601] The "photography means for detecting movements and generating image data" is a device for capturing the movements of a pet and generating image data based on the movements.

[0602] The "sensor means for measuring body temperature, activity level, sleep level and location information" is a sensor device for measuring a pet's body temperature, activity level, sleep level and location information in real time.

[0603] "Measuring means for measuring usage, pH balance and weight" refers to a device for measuring the amount of toilet used by a pet, the pH balance of urine and the weight of the pet.

[0604] The "data management means" is a system for receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[0605] The "abnormality detection means" is a function for analyzing data stored in the data management means and detecting values ​​or states that are outside the normal range.

[0606] The "notification means" is a communication system for notifying the user of an abnormality detected by the abnormality detection means, and is a device or application for sending alerts and messages.

[0607] The "display means" refers to a device or interface for visually displaying the collected data and analysis results to the user.

[0608] The "tracking means" is a system that tracks the location of a pet in real time if the pet gets lost and notifies the user of the location.

[0609] A "server" is a central processing unit that receives, stores, analyzes, notifies, and displays data, and is a device that manages the entire system in cooperation with various means.

[0610] The present invention is a system for monitoring the health of pets, detecting and notifying abnormalities, and is composed of various devices and programs, including a photographing means, a sensor means, a measuring means, a data management means, an abnormality detection means, a notification means, a display means, and a tracking means.

[0611] Specific system configuration

[0612] Data collection

[0613] The camera serving as the terminal is installed in the pet shop, and captures the movements of the pet at regular intervals to generate image data.

[0614] The sensor terminal is attached to the pet's collar and measures body temperature, activity level, sleep level, and location information in real time.

[0615] The terminal measuring device is installed in the pet's toilet and measures the amount used, the pH balance of the urine, and the pet's weight.

[0616] Data reception and analysis

[0617] The server continuously receives various data and stores it in a database. The server processes HTTP requests using Python and the requests module.

[0618] The stored data is analyzed and anomalies are detected using an anomaly detection algorithm, which is an analytical method based on a generative AI model.

[0619] User Notification and Data Display

[0620] If an abnormality is detected, the server generates an abnormality alert and sends the alert to the user via the notification means.

[0621] Users can receive real-time notifications of abnormalities using their smartphones.

[0622] The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[0623] Specific operation examples

[0624] For example, if a pet's temperature is measured at 39.5 degrees, this data is sent to the server. The server determines that this is outside the normal temperature range and generates an abnormality alert saying, "Your pet's temperature is too high. Please consult a veterinarian." This alert is then sent to the user via their smartphone.

[0625] Prompt Sentence Examples

[0626] "Latest data from your pet's collar: Temperature: 39.5°C Activity: 15 Please detect any abnormalities and generate appropriate notification messages."

[0627] This system allows real-time monitoring of the health status of pets in pet shops, allowing for swift action if an abnormality occurs. In addition, detailed health information can be provided to customers when they purchase a pet, allowing them to purchase with peace of mind.

[0628] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0629] Step 1:

[0630] Data collection

[0631] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0632] Behavior: The camera captures your pet's movements and generates image data. Sensors also measure your pet's body temperature, activity level, sleep level, and location. Furthermore, a measuring device measures your pet's toilet usage, urine pH balance, and weight.

[0633] Output: Each piece of data collected is generated.

[0634] Step 2:

[0635] Data transmission

[0636] Input: collected image data, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0637] How it works: Cameras, sensors, and measuring devices send their data to a server.

[0638] Output: The data arrives at the server.

[0639] Step 3:

[0640] Data reception

[0641] Input: Data sent from cameras, sensors, and measurement devices

[0642] How it works: The server receives each piece of data and stores it in a database.

[0643] Output: Each data stored in the database is obtained.

[0644] Step 4:

[0645] Data analysis

[0646] Input: Image data stored in the database, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[0647] How it works: The server analyzes the stored data and uses generative AI models to detect anomalies.

[0648] Output: A judgment result is obtained as to whether or not there is an abnormality.

[0649] Step 5:

[0650] Generate anomaly notifications

[0651] Input: Abnormality judgment result

[0652] How it works: If an anomaly is detected, the server generates an anomaly alert.

[0653] Output: An anomaly alert is generated.

[0654] Step 6:

[0655] User Notification

[0656] Input: Generated anomaly alert

[0657] Operation: The server sends an abnormality alert to the smartphone via the notification means.

[0658] Output: An abnormality alert is displayed on the user's smartphone.

[0659] Step 7:

[0660] Data display and advice generation

[0661] Input: Parsed data stored in a database

[0662] Operation: The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[0663] Output: Data and advice are displayed on the user's smartphone.

[0664] Step 8:

[0665] Location Tracking

[0666] Input: Real-time location information sent from the sensor

[0667] How it works: The server tracks the location of the pet and notifies the user if the pet gets lost.

[0668] Output: Real-time location information is displayed on the user's smartphone.

[0669] 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.

[0670] The present invention relates to a system for monitoring the health condition of a pet. The system includes an imaging means for detecting the behavior of the pet and generating image data, a sensor means for measuring the pet's body temperature, activity level, sleep level, and location information, a measurement means for measuring the pet's litter box usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the imaging means, sensor means, and measurement means, anomaly detection means for analyzing the data stored in the data management means and detecting anomalies, a notification means for notifying a user of an anomaly detected by the anomaly detection means, a display means for displaying the data to the user, and an emotion engine for recognizing the user's emotions.

[0671] Data collection and transmission

[0672] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[0673] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[0674] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[0675] Receiving and analyzing data

[0676] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[0677] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0678] Alert generation and user notification

[0679] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[0680] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[0681] Data display and advice

[0682] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[0683] Location tracking when lost

[0684] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[0685] Emotion Engine Functions

[0686] The server is equipped with an emotion engine for recognizing the user's emotions, which analyzes the user's facial expressions and tone of voice to recognize the user's current emotional state.

[0687] Based on the recognized emotion, the server can tailor pet care suggestions, for example, calming notifications and advice about the pet's health if the user is feeling stressed.

[0688] Specific examples

[0689] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0690] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[0691] In this way, the present invention allows for effective monitoring of the pet's health and, when an abnormality is detected, a response that takes into account the user's emotional state.

[0692] The processing flow will be explained below.

[0693] Step 1:

[0694] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[0695] Step 2:

[0696] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[0697] Step 3:

[0698] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[0699] Step 4:

[0700] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[0701] Step 5:

[0702] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0703] Step 6:

[0704] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[0705] Step 7:

[0706] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[0707] Step 8:

[0708] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[0709] Step 9:

[0710] When a user launches the app, the app uses the camera and microphone to capture the user's facial expressions and tone of voice.

[0711] Step 10:

[0712] The server analyzes the user's facial expressions and tone of voice through an emotion engine to recognize the user's current emotional state, for example, determining whether the user is feeling stressed.

[0713] Step 11:

[0714] The server then adjusts the wording of notifications and advice about the pet's health based on the recognized emotion. For example, if the user is feeling stressed, the notification content will be changed to a stress-reducing message such as "Don't worry, but be careful as your pet has a high temperature."

[0715] Step 12:

[0716] The server sends tailored notifications and advice to the app, which the user can then view.

[0717] Step 13:

[0718] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[0719] Step 14:

[0720] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[0721] Step 15:

[0722] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[0723] Step 16:

[0724] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[0725] Example 2

[0726] 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."

[0727] Conventional pet health monitoring systems are capable of measuring various health indicators of pets and collecting data. However, these systems have difficulty in providing effective notifications to users. In particular, they can be stressful for users because they provide uniform notifications without considering the user's emotional state. Furthermore, in actual use, there is a need for a system that can comprehensively manage data from individual devices, detect abnormalities, and take appropriate action.

[0728] 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.

[0729] In this invention, the server includes a photographing means for detecting movements and generating image data, a sensor means for measuring body temperature, activity level, sleep level, and location information, a measuring means for measuring usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the photographing means, the sensor means, and the measuring means, an abnormality detection means for analyzing the data stored in the data management means and detecting abnormalities, a notification means for notifying the user of abnormalities detected by the abnormality detection means, a display means for displaying the data to the user, and an emotion recognition means for recognizing the user's emotions and adjusting the content of the notification. This makes it possible to effectively monitor the health condition of a pet and, when an abnormality is detected, to provide a notification that takes into account the user's emotional state.

[0730] The "photography means" is a device that has the function of detecting the movements of the pet and generating image data.

[0731] The "sensor means" is a device that has the function of measuring the pet's body temperature, activity level, sleep level, and location information.

[0732] "Measuring means" refers to a device that has the function of measuring the amount of pet use, pH balance, and weight.

[0733] The "data management means" is a device that has the function of receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[0734] The "abnormality detection means" is a device that has the function of analyzing the data stored in the data management means and detecting abnormalities.

[0735] The "notification means" is a device having a function of notifying the user of an abnormality detected by the abnormality detection means.

[0736] A "display means" is a device that has the function of displaying data to a user.

[0737] The "emotion recognition means" is a device that has the function of recognizing the user's emotions and adjusting the notification content.

[0738] This invention relates to a system that monitors the health condition of pets and notifies the user when an abnormality is detected. This system measures and collects pet behavior, health indicators, and location information, and analyzes this data to detect abnormalities in the pet and notify the user in a way that takes into account the user's emotional state.

[0739] This system consists of the following elements:

[0740] Filming method

[0741] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals to generate image data. For example, a "general web camera" is used for this camera, and image data in JPEG format is generated. The generated image data is then sent to the server via the network.

[0742] Sensor Means

[0743] The collar, which serves as the terminal, is attached to the pet's neck and measures body temperature, activity, sleep, and location information in real time. This collar uses a "pet wearable device," for example, and the measured data is periodically sent to a server.

[0744] Measurement methods

[0745] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and the measured data is sent to a server via a network.

[0746] Data Management Measures

[0747] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database, typically using MySQL or PostgreSQL.

[0748] Data analysis

[0749] The server analyzes the stored data and learns the pet's normal behavior patterns and baseline values. For analysis, it uses data analysis libraries such as "Pandas" and "NumPy." It can calculate the pet's normal body temperature range and average activity level from past data.

[0750] Anomaly detection

[0751] The server compares the analyzed data with real-time data to detect anomalies, for example, if a pet's temperature exceeds the normal range, it generates an anomaly alert.

[0752] User Notifications

[0753] The server sends the generated anomaly alert to the app, which notifies the user. The app then displays a push notification on the user's smartphone and suggests specific measures to address the anomaly.

[0754] Data Display and Advice

[0755] The server analyzes the collected data and generates advice on the pet's health, such as the amount of exercise and the quantity and quality of food, which is then provided to the user via the app.

[0756] emotion recognition

[0757] The server uses an "emotion recognition API" to recognize the user's emotions. The app analyzes the user's facial expressions and tone of voice to recognize their current emotional state. Based on the recognized emotion, the server adjusts the notification content.

[0758] Specific examples

[0759] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0760] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[0761] Prompt statement

[0762] Prompt sentence for the AI ​​model to generate a specific example:

[0763] Describe a system that monitors the health of pets. Data is collected from cameras, collars, and litter boxes, and analyzed on a server to detect abnormalities. If an abnormality is detected, the system notifies the user and uses an emotion engine to provide notifications that take the user's emotions into account.

[0764] In this way, the present invention effectively monitors the health of a pet and allows for a response that takes into account the user's emotional state when an abnormality is detected.

[0765] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0766] Step 1: Data collection

[0767] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating JPEG image data. The collected image data is sent from the camera to a server via a network. The collar, which is the terminal, measures the pet's body temperature, activity level, sleep level, and location information in real time, and temporarily stores each piece of data in its internal storage. The toilet, which is the terminal, measures the amount of toilet use, pH balance, and weight each time the pet uses the toilet, and generates the data.

[0768] Step 2: Send data

[0769] The devices (camera, collar, and toilet) periodically send the collected data to a server. JPEG image data generated by the camera, sensor data measured by the collar, and data measured by the toilet are transmitted via wireless communication such as Wi-Fi. The input here is the data generated by each device, and the output is the data sent to the server.

[0770] Step 3: Receiving and storing data

[0771] The server constantly receives data sent from the camera, collar, and toilet, and stores it in a database. The server manages the database using, for example, MySQL or PostgreSQL, and classifies and stores the data from each device along with a timestamp. The input is the data sent from each device, and the output is the data stored in the database.

[0772] Step 4: Data analysis

[0773] The server uses Python data analysis libraries such as "Pandas" and "NumPy" to analyze the stored data. It learns the pet's normal behavior patterns and standard values ​​based on past data, and calculates, for example, the normal body temperature range and average activity level. The input is the past data stored in the database, and the output is various standard values ​​as the analysis results.

[0774] Step 5: Anomaly detection

[0775] The server compares the analyzed data with real-time data to detect abnormalities. For example, if a pet's body temperature is outside the normal range, it will be recognized as an abnormality and generate an abnormality alert. The input is the analyzed reference value and real-time data, and the output is an abnormality alert.

[0776] Step 6: User Notification

[0777] If an abnormality is detected, the server sends an abnormality alert to the app and notifies the user. The abnormality alert includes specific details of the abnormality and countermeasures. For example, a notification may be sent saying, "Your pet's temperature is too high. Please consult a veterinarian." The input is the generated abnormality alert, and the output is the notification sent to the app.

[0778] Step 7: Data display and advice

[0779] The server generates advice on the amount of exercise and the quantity and quality of food for the pet's health based on the collected and analyzed data. This advice is provided to the user through the app. The input is the data stored in the database and the analysis results, and the output is the advice displayed on the app.

[0780] Step 8: Emotion recognition and notification adjustment

[0781] The server uses an "emotion recognition API" to recognize the user's emotions and analyzes the user's facial expressions and tone of voice obtained through the app. Based on the user's emotional state, the server adjusts the notification content. For example, if the user is feeling stressed, the notification wording will be changed to a more gentle one. The input is the user's facial expression and voice data, and the output is the adjusted notification content.

[0782] The above is the flow of processing by the system, and details of the specific operations performed at each step.

[0783] (Application example 2)

[0784] 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."

[0785] It is necessary to provide a system that can effectively and comprehensively monitor the health status of pets and respond quickly when abnormalities occur. It is also necessary to provide notifications and advice on pet health management taking into account the user's emotional state. Such a system can reduce pet health risks and ease the burden on users.

[0786] The specific processing by the specific 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 a photographing means that detects movement and generates image data, a sensor means that measures body temperature, activity level, sleep level, and location information, and a measuring means that measures usage, pH balance, and weight. This makes it possible to comprehensively monitor the pet's health and quickly notify the user when an abnormality is detected. In addition, by using an emotion engine that recognizes the user's emotional state and adjusts the content of notifications and advice, it is possible to reduce the user's stress and encourage appropriate responses.

[0787] The "photography means for detecting the operation and generating image data" is a device used to monitor the operation of a factory robot and generate data in the form of images.

[0788] The "sensor means for measuring body temperature, activity level, sleep level and location information" refers to a group of devices for measuring the body temperature, activity level during work, time spent stopped and current location of a factory robot.

[0789] "Measuring means for measuring usage amount, pH balance and weight" refers to a group of devices that measure a certain usage amount, pH balance and weight of a liquid related to a factory robot.

[0790] The "data management means" is a system including devices and software for receiving and storing data transmitted from the above-mentioned photographing means, sensor means, and measurement means.

[0791] The "abnormality detection means" refers to a device and software that analyzes the data stored in the data management means and detects abnormalities that deviate from the normal operating range.

[0792] The "notification means" is a device and system for notifying the user of an abnormality detected by the abnormality detection means.

[0793] "Display means" refers to a device for visually displaying data to a user, and includes, for example, a display or a monitor.

[0794] The "emotion engine" is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[0795] The system for implementing this invention is configured to monitor the operating status and maintenance status of factory robots and to respond quickly when an abnormality is detected. The system is mainly composed of the following hardware and software.

[0796] Hardware

[0797] 1. Photography Method:

[0798] It includes a camera for monitoring the operation of a factory robot, which detects the movement and generates image data.

[0799] 2. Sensor means:

[0800] Temperature sensors that measure the body temperature of factory robots

[0801] Vibration sensor that measures activity

[0802] A sensor that measures sleep amount (time inactive)

[0803] Location sensors that measure location information

[0804] 3. Measurement methods:

[0805] It includes sensors that measure usage, Ph balance, and weight.

[0806] software

[0807] 1. Data Management Measures:

[0808] The system includes a server that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[0809] 2. Anomaly detection methods:

[0810] It includes software that analyzes stored data and detects anomalies that deviate from normal operating ranges.

[0811] 3. Means of notification:

[0812] This is a system for notifying a user of an abnormality detected by an abnormality detection means.

[0813] 4. Display means:

[0814] It includes a display or monitor for visually displaying data to the user.

[0815] 5. Emotion Engine:

[0816] It is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[0817] Processing flow

[0818] The server constantly receives data sent from the imaging means, sensor means, and measurement means and stores it in a database. Based on the stored data, it learns the factory robot's normal operating patterns and reference values. For example, it calculates the normal body temperature range and average activity level based on past data. If an abnormality is detected, the server generates an abnormality alert and promptly notifies the user via the notification means.

[0819] Specific examples

[0820] For example, if a factory robot's temperature sensor detects a temperature of 80 degrees, the server will determine this and generate an abnormality alert. This alert will be sent to the user via the notification system as a message such as "The robot's temperature is abnormal. Please cool it down." Furthermore, if the emotion engine detects stress in the user when receiving the alert, it can change the content of the notification to a gentler one such as "Please do not panic. However, the robot's temperature is high, so please be careful."

[0821] Prompt Sentence Examples

[0822] To recognize the user's emotions and notify them with an appropriate message, we use an emotion engine. When notifying, we analyze the user's emotional state, and if a specific emotion (e.g., stress) is detected, we change the message to a calmer one. Can you please give us an example program for this implementation?

[0823] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0824] Step 1:

[0825] A camera (photographing means) detects the movement of the factory robot and generates image data.

[0826] Input: Camera detects factory robot movement.

[0827] Data processing: Analyzes the movements captured by the camera and generates image data.

[0828] Output: The generated image data is sent to the server.

[0829] Step 2:

[0830] The sensor means measures the temperature, activity level, sleep level, and location information of the factory robot.

[0831] Input: The temperature sensor measures the factory robot's temperature, the vibration sensor measures activity, the inactivity sensor measures sleep, and the location sensor obtains its current location.

[0832] Data processing: Organize the data collected by each sensor and convert it into a format that can be sent to the server.

[0833] Output: The measured body temperature, activity level, sleep level, and location information are sent to the server.

[0834] Step 3:

[0835] The measuring device measures the usage, pH balance and weight of the factory robot.

[0836] Inputs: Usage sensor measures the usage of the factory robot, pH balance sensor measures the pH balance of the liquid, weight sensor measures the weight of the robot.

[0837] Data Processing: Collected usage, pH balance and weight data is compiled and converted into a format to be sent to the server.

[0838] Output: Sends measured usage, pH balance, and weight data to the server.

[0839] Step 4:

[0840] The server stores the received data in a database.

[0841] Input: Various data sent from photography means, sensor means, and measurement means.

[0842] Data processing: The received data is converted into an appropriate data format and recorded in the database.

[0843] Output: The various saved data is stored in a database.

[0844] Step 5:

[0845] The server learns normal ranges and benchmark values ​​based on the stored data.

[0846] Input: Past and current factory robot data stored in a database.

[0847] Data processing: Analysis software performs statistical analysis to calculate the robot's normal operating range and reference values.

[0848] Output: Sets the calculated baseline or normal operating range.

[0849] Step 6:

[0850] The server detects an anomaly.

[0851] Input: Current data and learned baseline.

[0852] Data processing: Determine whether the current data deviates from the baseline and generate an alert if an anomaly is detected.

[0853] Output: Generated anomaly alerts.

[0854] Step 7:

[0855] The server notifies the user of the abnormality.

[0856] Input: The generated anomaly alert.

[0857] Data processing: Converts the alert content into a format that can be notified to the user.

[0858] Output: A notification message to the user.

[0859] Step 8:

[0860] The server recognizes the user's emotional state.

[0861] Input: User's facial and voice data.

[0862] Data processing: The emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[0863] Output: Adjust notification content based on the perceived emotional state.

[0864] Step 9:

[0865] The server displays the data to the user.

[0866] Input: Data and analysis results stored in a database.

[0867] Data processing: The data is formatted into an appropriate format and displayed on the user's display or monitor.

[0868] Output: Visually displayed data and analysis results.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] [Third embodiment]

[0873] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0874] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0875] 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).

[0876] 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.

[0877] 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.

[0878] 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).

[0879] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] The present invention relates to a system for monitoring the health condition of a pet, which includes: an imaging device for detecting the behavior of the pet and generating image data; a sensor device for measuring the pet's body temperature, activity level, sleep level, and location information; a measuring device for measuring the pet's litter box usage, pH balance, and weight; a data management device for receiving and storing data transmitted from the imaging device, sensor device, and measuring device; an anomaly detection device for analyzing the data stored in the data management device and detecting anomalies; a notification device for notifying a user of an anomaly detected by the anomaly detection device; and a display device for displaying the data to the user.

[0886] A specific example for implementing this system will now be described.

[0887] Data collection and transmission

[0888] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[0889] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[0890] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[0891] Receiving and analyzing data

[0892] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[0893] The server analyzes the stored data to learn your pet's normal behavior patterns and compares current data with past data to detect anomalies.

[0894] Alert generation and user notification

[0895] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[0896] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[0897] Data display and advice

[0898] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[0899] Location tracking when lost

[0900] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[0901] Specific examples

[0902] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[0903] In this way, the present invention allows for effective monitoring of the health status of pets and rapid response when abnormalities are detected.

[0904] The processing flow will be explained below.

[0905] Step 1:

[0906] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[0907] Step 2:

[0908] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[0909] Step 3:

[0910] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[0911] Step 4:

[0912] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[0913] Step 5:

[0914] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[0915] Step 6:

[0916] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[0917] Step 7:

[0918] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[0919] Step 8:

[0920] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[0921] Step 9:

[0922] Users can launch the app to view alert notifications and other health data, allowing them to quickly recognize any abnormalities in their pets and take appropriate measures.

[0923] Step 10:

[0924] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[0925] Step 11:

[0926] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[0927] Step 12:

[0928] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[0929] Step 13:

[0930] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[0931] Example 1

[0932] 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."

[0933] There is a demand for continuous, real-time monitoring of pet health conditions and prompt notification to users when abnormalities are detected. However, conventional systems are limited to collecting and notifying users of specific data items (such as body temperature and activity level), making comprehensive health management of pets difficult. Furthermore, delays in notification of abnormalities can make it difficult to respond quickly. Furthermore, conventional systems lacked features such as advice provision and location tracking, resulting in a lack of comprehensive support for pet health and safety management.

[0934] 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.

[0935] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measurement that measures usage, pH balance, and weight; a data management system that receives and stores data transmitted from the camera, sensor, and measurement systems; an anomaly detection system that analyzes the data stored in the data management system and detects abnormalities; a notification system that notifies the user of abnormalities detected by the anomaly detection system; a display system that displays the data to the user; a system that receives real-time location information and displays it to the user; a system that generates advice regarding exercise and diet based on the collected data and provides it to the user; and a system that generates anomaly alert messages using a generative AI model. This allows for comprehensive and real-time monitoring and management of pet health, and allows for prompt notification and appropriate advice when an abnormality is detected. Furthermore, tracking location information facilitates pet safety management.

[0936] The "photography means" is a device that detects the movements of the pet and generates image data.

[0937] The "sensor means" is a device that measures the pet's body temperature, activity level, sleep level, and location information.

[0938] The "measuring means" is a device that measures the pet's litter box usage, pH balance, and weight.

[0939] The "data management means" is a device that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[0940] The "abnormality detection means" is a device that analyzes the data stored in the data management means and detects abnormalities.

[0941] The "notification means" is a device that notifies the user of an abnormality detected by the abnormality detection means.

[0942] A "display means" is a device that displays the data to a user.

[0943] The "means for receiving real-time location information and displaying it to the user" is a device that receives real-time location information of a pet and displays that information to the user.

[0944] The "means for generating advice regarding the amount of exercise and diet based on collected data and providing it to the user" is a device that generates advice regarding the amount of exercise and diet based on collected data and provides that advice to the user.

[0945] "Means for generating an abnormality alert statement using a generative AI model" refers to a device that generates an alert statement regarding an abnormality using a generative AI model.

[0946] The present invention is a comprehensive system for monitoring the health of pets and quickly detecting and notifying abnormalities. The system is composed of the following elements:

[0947] 1. Filming Method

[0948] This system includes a camera that detects pet movements and generates image data. The camera is installed in a room, generates image data at regular intervals, and sends it to a server via a network. This camera can be a commercially available surveillance camera.

[0949] 2. Sensor means

[0950] The system includes a collar that measures your pet's body temperature, activity level, sleep level, and location. The collar is attached to your pet's neck and measures data in real time, periodically sending it to a server. The collar is equipped with a body temperature sensor, an acceleration sensor, and a GPS module.

[0951] 3. Measurement methods

[0952] This includes toilets that measure pet toilet usage, pH balance, and weight. The toilet measures this data every time the pet uses it and sends it to a server via the network. This toilet is equipped with a weight sensor and a pH sensor.

[0953] 4. Data Management Measures

[0954] The server receives the data sent from the camera, collar, and litter box and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[0955] 5. Anomaly Detection Methods

[0956] The server analyzes the stored data and detects anomalies using anomaly detection algorithms or machine learning models (such as the Python libraries Scikit-learn and TensorFlow).

[0957] 6. Means of notification

[0958] If an anomaly is detected, the server generates an anomaly alert and notifies the user through an application, such as a mobile app with notification functionality for Android or iOS.

[0959] 7. Display means

[0960] The server visualizes the collected data and displays it to the user through an app, which can be a web app or a mobile app's dashboard function.

[0961] 8. A means of receiving and displaying real-time location information to the user

[0962] The server receives real-time location information transmitted from the collar and displays it to the user, allowing them to quickly locate their pet if it gets lost.

[0963] 9. Means for generating and providing advice to users regarding exercise and diet based on collected data

[0964] The server generates advice on exercise and diet based on the collected data, and provides this advice to the user through the app.

[0965] 10. How to generate anomaly alerts using generative AI models

[0966] The server uses a generative AI model to generate an alert message based on the detected anomaly. For example, by inputting a prompt message such as "Please generate a notification message when my pet's temperature is too high" into a generative AI model (such as GPT-3), the server can generate an appropriate notification message.

[0967] Specific examples

[0968] For example, if the collar device measures a pet's temperature as 39.5°C and sends that data to the server, the server will determine that this is outside the normal body temperature range (e.g., 38.0°C to 39.0°C) and generate an abnormality alert. The generated alert is sent from the server to the app, and the user will receive a notification such as: "Your pet's temperature is too high. Please consult your veterinarian."

[0969] Prompt Sentence Examples

[0970] "Generate a notification message if your pet's temperature is too high"

[0971] "Please create an alert statement for when the toilet's pH balance is abnormal."

[0972] "Please provide an example of a location notification for when a pet gets lost."

[0973] In this way, the present invention allows for comprehensive monitoring of the health status of pets and allows for rapid response if an abnormality is detected.

[0974] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0975] Step 1:

[0976] The device collects data

[0977] Specific operation: A camera installed in the room captures the pet's movements at regular intervals and generates image data. The collar also measures the pet's temperature, activity level, sleep level, and location, while the toilet measures usage, pH balance, and weight.

[0978] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage

[0979] Output: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0980] Step 2:

[0981] The device sends the data to the server

[0982] Specific operation: All devices (camera, collar, and toilet) send the collected data to a server via a network. For example, the camera sends image data to the server, and the collar sends body temperature and activity data to the server.

[0983] Input: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[0984] Output: Various data sent to the server

[0985] Step 3:

[0986] The server receives and stores the data

[0987] Specific operation: The server receives data sent from the terminal via the network and stores it in a database. Specifically, it stores the data using MySQL or PostgreSQL.

[0988] Input: Various data sent from the terminal

[0989] Output: Various data stored in the database

[0990] Step 4:

[0991] The server analyzes the stored data

[0992] How it works: The server retrieves data stored in the database and analyzes it using anomaly detection algorithms and machine learning models, such as Scikit-learn and TensorFlow.

[0993] Input: Various data stored in the database

[0994] Output: Analysis results, anomaly detection results

[0995] Step 5:

[0996] If the server detects an anomaly, it generates an anomaly alert.

[0997] Specific operation: Based on the analysis results, if an abnormality is detected, such as if the pet's body temperature is outside the normal range, the server will generate an abnormality alert. For example, it will generate an alert message such as "Temperature is too high." A generative AI model (such as GPT-3) can be used in this process.

[0998] Input: Analysis results, anomaly detection results

[0999] Output: Abnormal alert statement

[1000] Step 6:

[1001] The server notifies the user of an abnormality alert.

[1002] Specific operation: The generated abnormality alert is notified to the user via the application from the server. The user receives a notification on their smartphone app and can check the situation.

[1003] Input: Abnormal alert statement

[1004] Output: User notification

[1005] Step 7:

[1006] The server generates advice based on the collected data and provides it to the user.

[1007] How it works: The server analyzes the collected data and generates advice on exercise and diet for pets. These advice are provided to users through an application. Generative AI models may also be used.

[1008] Input: Analysis results, collected data

[1009] Output: Advice on exercise and diet

[1010] Step 8:

[1011] The server displays the collected data to the user.

[1012] Specific operation: The server visualizes the collected data and displays it to the user through an application. For example, it can display the progress of activity levels and sleep patterns in graph form.

[1013] Input: Analysis results, collected data

[1014] Output: Data visualization information displayed to the user

[1015] Step 9:

[1016] Displaying real-time location information to users

[1017] Specific operation: The server receives the real-time GPS location information sent by the collar and displays it to the user through the application, allowing the user to know the current location of their pet at any time.

[1018] Input: Real-time location

[1019] Output: Location displayed to the user

[1020] In this way, the system of the present invention can comprehensively monitor the health status of pets and quickly notify users if any abnormalities are detected. Furthermore, by providing advice on exercise and diet, as well as real-time location information, the system supports the health and safety management of pets.

[1021] (Application example 1)

[1022] 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."

[1023] Conventional pet health management systems only collect data such as pet body temperature and activity levels, and lack the mechanisms for quickly responding when abnormalities are detected in real time. Furthermore, they lack sufficient functionality for tracking a pet's location if it gets lost, or for providing detailed health information based on the collected data. Particularly when managing pets in brick-and-mortar stores, store staff are required to quickly grasp the pet's health status and take appropriate action, but no such system existed.

[1024] 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.

[1025] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measuring device that measures usage, pH balance, and weight; a data management device that receives and stores data transmitted from the camera, sensor, and measuring device; an anomaly detection device that analyzes the data stored in the data management device and detects abnormalities; a notification device that notifies the user of abnormalities detected by the anomaly detection device; a display device that analyzes collected data and provides the user with detailed health information; and a tracking device that tracks the location of the pet if it gets lost. This allows for real-time monitoring of the pet's health and for prompt notification to a store staff member if an abnormality is detected. Furthermore, even if the pet gets lost, the location can be quickly identified and detailed health information can be provided to the customer.

[1026] The "photography means for detecting movements and generating image data" is a device for capturing the movements of a pet and generating image data based on the movements.

[1027] The "sensor means for measuring body temperature, activity level, sleep level and location information" is a sensor device for measuring a pet's body temperature, activity level, sleep level and location information in real time.

[1028] "Measuring means for measuring usage, pH balance and weight" refers to a device for measuring the amount of toilet used by a pet, the pH balance of urine and the weight of the pet.

[1029] The "data management means" is a system for receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[1030] The "abnormality detection means" is a function for analyzing data stored in the data management means and detecting values ​​or states that are outside the normal range.

[1031] The "notification means" is a communication system for notifying the user of an abnormality detected by the abnormality detection means, and is a device or application for sending alerts and messages.

[1032] The "display means" refers to a device or interface for visually displaying the collected data and analysis results to the user.

[1033] The "tracking means" is a system that tracks the location of a pet in real time if the pet gets lost and notifies the user of the location.

[1034] A "server" is a central processing unit that receives, stores, analyzes, notifies, and displays data, and is a device that manages the entire system in cooperation with various means.

[1035] The present invention is a system for monitoring the health of pets, detecting and notifying abnormalities, and is composed of various devices and programs, including a photographing means, a sensor means, a measuring means, a data management means, an abnormality detection means, a notification means, a display means, and a tracking means.

[1036] Specific system configuration

[1037] Data collection

[1038] The camera serving as the terminal is installed in the pet shop, and captures the movements of the pet at regular intervals to generate image data.

[1039] The sensor terminal is attached to the pet's collar and measures body temperature, activity level, sleep level, and location information in real time.

[1040] The terminal measuring device is installed in the pet's toilet and measures the amount used, the pH balance of the urine, and the pet's weight.

[1041] Data reception and analysis

[1042] The server continuously receives various data and stores it in a database. The server processes HTTP requests using Python and the requests module.

[1043] The stored data is analyzed and anomalies are detected using an anomaly detection algorithm, which is an analytical method based on a generative AI model.

[1044] User Notification and Data Display

[1045] If an abnormality is detected, the server generates an abnormality alert and sends the alert to the user via the notification means.

[1046] Users can receive real-time notifications of abnormalities using their smartphones.

[1047] The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[1048] Specific operation examples

[1049] For example, if a pet's temperature is measured at 39.5 degrees, this data is sent to the server. The server determines that this is outside the normal temperature range and generates an abnormality alert saying, "Your pet's temperature is too high. Please consult a veterinarian." This alert is then sent to the user via their smartphone.

[1050] Prompt Sentence Examples

[1051] "Latest data from your pet's collar: Temperature: 39.5°C Activity: 15 Please detect any abnormalities and generate appropriate notification messages."

[1052] This system allows real-time monitoring of the health status of pets in pet shops, allowing for swift action if an abnormality occurs. In addition, detailed health information can be provided to customers when they purchase a pet, allowing them to purchase with peace of mind.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] Data collection

[1056] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1057] Behavior: The camera captures your pet's movements and generates image data. Sensors also measure your pet's body temperature, activity level, sleep level, and location. Furthermore, a measuring device measures your pet's toilet usage, urine pH balance, and weight.

[1058] Output: Each piece of data collected is generated.

[1059] Step 2:

[1060] Data transmission

[1061] Input: collected image data, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1062] How it works: Cameras, sensors, and measuring devices send their data to a server.

[1063] Output: The data arrives at the server.

[1064] Step 3:

[1065] Data reception

[1066] Input: Data sent from cameras, sensors, and measurement devices

[1067] How it works: The server receives each piece of data and stores it in a database.

[1068] Output: Each data stored in the database is obtained.

[1069] Step 4:

[1070] Data analysis

[1071] Input: Image data stored in the database, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1072] How it works: The server analyzes the stored data and uses generative AI models to detect anomalies.

[1073] Output: A judgment result is obtained as to whether or not there is an abnormality.

[1074] Step 5:

[1075] Generate anomaly notifications

[1076] Input: Abnormality judgment result

[1077] How it works: If an anomaly is detected, the server generates an anomaly alert.

[1078] Output: An anomaly alert is generated.

[1079] Step 6:

[1080] User Notification

[1081] Input: Generated anomaly alert

[1082] Operation: The server sends an abnormality alert to the smartphone via the notification means.

[1083] Output: An abnormality alert is displayed on the user's smartphone.

[1084] Step 7:

[1085] Data display and advice generation

[1086] Input: Parsed data stored in a database

[1087] Operation: The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[1088] Output: Data and advice are displayed on the user's smartphone.

[1089] Step 8:

[1090] Location Tracking

[1091] Input: Real-time location information sent from the sensor

[1092] How it works: The server tracks the location of the pet and notifies the user if the pet gets lost.

[1093] Output: Real-time location information is displayed on the user's smartphone.

[1094] 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.

[1095] The present invention relates to a system for monitoring the health condition of a pet. The system includes an imaging means for detecting the behavior of the pet and generating image data, a sensor means for measuring the pet's body temperature, activity level, sleep level, and location information, a measurement means for measuring the pet's litter box usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the imaging means, sensor means, and measurement means, anomaly detection means for analyzing the data stored in the data management means and detecting anomalies, a notification means for notifying a user of an anomaly detected by the anomaly detection means, a display means for displaying the data to the user, and an emotion engine for recognizing the user's emotions.

[1096] Data collection and transmission

[1097] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[1098] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[1099] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[1100] Receiving and analyzing data

[1101] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[1102] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[1103] Alert generation and user notification

[1104] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[1105] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[1106] Data display and advice

[1107] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[1108] Location tracking when lost

[1109] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[1110] Emotion Engine Functions

[1111] The server is equipped with an emotion engine for recognizing the user's emotions, which analyzes the user's facial expressions and tone of voice to recognize the user's current emotional state.

[1112] Based on the recognized emotion, the server can tailor pet care suggestions, for example, calming notifications and advice about the pet's health if the user is feeling stressed.

[1113] Specific examples

[1114] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[1115] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[1116] In this way, the present invention allows for effective monitoring of the pet's health and, when an abnormality is detected, a response that takes into account the user's emotional state.

[1117] The processing flow will be explained below.

[1118] Step 1:

[1119] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[1120] Step 2:

[1121] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[1122] Step 3:

[1123] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[1124] Step 4:

[1125] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[1126] Step 5:

[1127] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[1128] Step 6:

[1129] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[1130] Step 7:

[1131] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[1132] Step 8:

[1133] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[1134] Step 9:

[1135] When a user launches the app, the app uses the camera and microphone to capture the user's facial expressions and tone of voice.

[1136] Step 10:

[1137] The server analyzes the user's facial expressions and tone of voice through an emotion engine to recognize the user's current emotional state, for example, determining whether the user is feeling stressed.

[1138] Step 11:

[1139] The server then adjusts the wording of notifications and advice about the pet's health based on the recognized emotion. For example, if the user is feeling stressed, the notification content will be changed to a stress-reducing message such as "Don't worry, but be careful as your pet has a high temperature."

[1140] Step 12:

[1141] The server sends tailored notifications and advice to the app, which the user can then view.

[1142] Step 13:

[1143] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[1144] Step 14:

[1145] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[1146] Step 15:

[1147] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[1148] Step 16:

[1149] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[1150] Example 2

[1151] 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."

[1152] Conventional pet health monitoring systems are capable of measuring various health indicators of pets and collecting data. However, these systems have difficulty in providing effective notifications to users. In particular, they can be stressful for users because they provide uniform notifications without considering the user's emotional state. Furthermore, in actual use, there is a need for a system that can comprehensively manage data from individual devices, detect abnormalities, and take appropriate action.

[1153] 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.

[1154] In this invention, the server includes a photographing means for detecting movements and generating image data, a sensor means for measuring body temperature, activity level, sleep level, and location information, a measuring means for measuring usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the photographing means, the sensor means, and the measuring means, an abnormality detection means for analyzing the data stored in the data management means and detecting abnormalities, a notification means for notifying the user of abnormalities detected by the abnormality detection means, a display means for displaying the data to the user, and an emotion recognition means for recognizing the user's emotions and adjusting the content of the notification. This makes it possible to effectively monitor the health condition of a pet and, when an abnormality is detected, to provide a notification that takes into account the user's emotional state.

[1155] The "photography means" is a device that has the function of detecting the movements of the pet and generating image data.

[1156] The "sensor means" is a device that has the function of measuring the pet's body temperature, activity level, sleep level, and location information.

[1157] "Measuring means" refers to a device that has the function of measuring the amount of pet use, pH balance, and weight.

[1158] The "data management means" is a device that has the function of receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[1159] The "abnormality detection means" is a device that has the function of analyzing the data stored in the data management means and detecting abnormalities.

[1160] The "notification means" is a device having a function of notifying the user of an abnormality detected by the abnormality detection means.

[1161] A "display means" is a device that has the function of displaying data to a user.

[1162] The "emotion recognition means" is a device that has the function of recognizing the user's emotions and adjusting the notification content.

[1163] This invention relates to a system that monitors the health condition of pets and notifies the user when an abnormality is detected. This system measures and collects pet behavior, health indicators, and location information, and analyzes this data to detect abnormalities in the pet and notify the user in a way that takes into account the user's emotional state.

[1164] This system consists of the following elements:

[1165] Filming method

[1166] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals to generate image data. For example, a "general web camera" is used for this camera, and image data in JPEG format is generated. The generated image data is then sent to the server via the network.

[1167] Sensor Means

[1168] The collar, which serves as the terminal, is attached to the pet's neck and measures body temperature, activity, sleep, and location information in real time. This collar uses a "pet wearable device," for example, and the measured data is periodically sent to a server.

[1169] Measurement methods

[1170] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and the measured data is sent to a server via a network.

[1171] Data Management Measures

[1172] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database, typically using MySQL or PostgreSQL.

[1173] Data analysis

[1174] The server analyzes the stored data and learns the pet's normal behavior patterns and baseline values. For analysis, it uses data analysis libraries such as "Pandas" and "NumPy." It can calculate the pet's normal body temperature range and average activity level from past data.

[1175] Anomaly detection

[1176] The server compares the analyzed data with real-time data to detect anomalies, for example, if a pet's temperature exceeds the normal range, it generates an anomaly alert.

[1177] User Notifications

[1178] The server sends the generated anomaly alert to the app, which notifies the user. The app then displays a push notification on the user's smartphone and suggests specific measures to address the anomaly.

[1179] Data Display and Advice

[1180] The server analyzes the collected data and generates advice on the pet's health, such as the amount of exercise and the quantity and quality of food, which is then provided to the user via the app.

[1181] emotion recognition

[1182] The server uses an "emotion recognition API" to recognize the user's emotions. The app analyzes the user's facial expressions and tone of voice to recognize their current emotional state. Based on the recognized emotion, the server adjusts the notification content.

[1183] Specific examples

[1184] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[1185] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[1186] Prompt statement

[1187] Prompt sentence for the AI ​​model to generate a specific example:

[1188] Describe a system that monitors the health of pets. Data is collected from cameras, collars, and litter boxes, and analyzed on a server to detect abnormalities. If an abnormality is detected, the system notifies the user and uses an emotion engine to provide notifications that take the user's emotions into account.

[1189] In this way, the present invention effectively monitors the health of a pet and allows for a response that takes into account the user's emotional state when an abnormality is detected.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1: Data collection

[1192] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating JPEG image data. The collected image data is sent from the camera to a server via a network. The collar, which is the terminal, measures the pet's body temperature, activity level, sleep level, and location information in real time, and temporarily stores each piece of data in its internal storage. The toilet, which is the terminal, measures the amount of toilet use, pH balance, and weight each time the pet uses the toilet, and generates the data.

[1193] Step 2: Send data

[1194] The devices (camera, collar, and toilet) periodically send the collected data to a server. JPEG image data generated by the camera, sensor data measured by the collar, and data measured by the toilet are transmitted via wireless communication such as Wi-Fi. The input here is the data generated by each device, and the output is the data sent to the server.

[1195] Step 3: Receiving and storing data

[1196] The server constantly receives data sent from the camera, collar, and toilet, and stores it in a database. The server manages the database using, for example, MySQL or PostgreSQL, and classifies and stores the data from each device along with a timestamp. The input is the data sent from each device, and the output is the data stored in the database.

[1197] Step 4: Data analysis

[1198] The server uses Python data analysis libraries such as "Pandas" and "NumPy" to analyze the stored data. It learns the pet's normal behavior patterns and standard values ​​based on past data, and calculates, for example, the normal body temperature range and average activity level. The input is the past data stored in the database, and the output is various standard values ​​as the analysis results.

[1199] Step 5: Anomaly detection

[1200] The server compares the analyzed data with real-time data to detect abnormalities. For example, if a pet's body temperature is outside the normal range, it will be recognized as an abnormality and generate an abnormality alert. The input is the analyzed reference value and real-time data, and the output is an abnormality alert.

[1201] Step 6: User Notification

[1202] If an abnormality is detected, the server sends an abnormality alert to the app and notifies the user. The abnormality alert includes specific details of the abnormality and countermeasures. For example, a notification may be sent saying, "Your pet's temperature is too high. Please consult a veterinarian." The input is the generated abnormality alert, and the output is the notification sent to the app.

[1203] Step 7: Data display and advice

[1204] The server generates advice on the amount of exercise and the quantity and quality of food for the pet's health based on the collected and analyzed data. This advice is provided to the user through the app. The input is the data stored in the database and the analysis results, and the output is the advice displayed on the app.

[1205] Step 8: Emotion recognition and notification adjustment

[1206] The server uses an "emotion recognition API" to recognize the user's emotions and analyzes the user's facial expressions and tone of voice obtained through the app. Based on the user's emotional state, the server adjusts the notification content. For example, if the user is feeling stressed, the notification wording will be changed to a more gentle one. The input is the user's facial expression and voice data, and the output is the adjusted notification content.

[1207] The above is the flow of processing by the system, and details of the specific operations performed at each step.

[1208] (Application example 2)

[1209] 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."

[1210] It is necessary to provide a system that can effectively and comprehensively monitor the health status of pets and respond quickly when abnormalities occur. It is also necessary to provide notifications and advice on pet health management taking into account the user's emotional state. Such a system can reduce pet health risks and ease the burden on users.

[1211] The specific processing by the specific 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 a photographing means that detects movement and generates image data, a sensor means that measures body temperature, activity level, sleep level, and location information, and a measuring means that measures usage, pH balance, and weight. This makes it possible to comprehensively monitor the pet's health and quickly notify the user when an abnormality is detected. In addition, by using an emotion engine that recognizes the user's emotional state and adjusts the content of notifications and advice, it is possible to reduce the user's stress and encourage appropriate responses.

[1212] The "photography means for detecting the operation and generating image data" is a device used to monitor the operation of a factory robot and generate data in the form of images.

[1213] The "sensor means for measuring body temperature, activity level, sleep level and location information" refers to a group of devices for measuring the body temperature, activity level during work, time spent stopped and current location of a factory robot.

[1214] "Measuring means for measuring usage amount, pH balance and weight" refers to a group of devices that measure a certain usage amount, pH balance and weight of a liquid related to a factory robot.

[1215] The "data management means" is a system including devices and software for receiving and storing data transmitted from the above-mentioned photographing means, sensor means, and measurement means.

[1216] The "abnormality detection means" refers to a device and software that analyzes the data stored in the data management means and detects abnormalities that deviate from the normal operating range.

[1217] The "notification means" is a device and system for notifying the user of an abnormality detected by the abnormality detection means.

[1218] "Display means" refers to a device for visually displaying data to a user, and includes, for example, a display or a monitor.

[1219] The "emotion engine" is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[1220] The system for implementing this invention is configured to monitor the operating status and maintenance status of factory robots and to respond quickly when an abnormality is detected. The system is mainly composed of the following hardware and software.

[1221] Hardware

[1222] 1. Photography Method:

[1223] It includes a camera for monitoring the operation of a factory robot, which detects the movement and generates image data.

[1224] 2. Sensor means:

[1225] Temperature sensors that measure the body temperature of factory robots

[1226] Vibration sensor that measures activity

[1227] A sensor that measures sleep amount (time inactive)

[1228] Location sensors that measure location information

[1229] 3. Measurement methods:

[1230] It includes sensors that measure usage, Ph balance, and weight.

[1231] software

[1232] 1. Data Management Measures:

[1233] The system includes a server that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[1234] 2. Anomaly detection methods:

[1235] It includes software that analyzes stored data and detects anomalies that deviate from normal operating ranges.

[1236] 3. Means of notification:

[1237] This is a system for notifying a user of an abnormality detected by an abnormality detection means.

[1238] 4. Display means:

[1239] It includes a display or monitor for visually displaying data to the user.

[1240] 5. Emotion Engine:

[1241] It is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[1242] Processing flow

[1243] The server constantly receives data sent from the imaging means, sensor means, and measurement means and stores it in a database. Based on the stored data, it learns the factory robot's normal operating patterns and reference values. For example, it calculates the normal body temperature range and average activity level based on past data. If an abnormality is detected, the server generates an abnormality alert and promptly notifies the user via the notification means.

[1244] Specific examples

[1245] For example, if a factory robot's temperature sensor detects a temperature of 80 degrees, the server will determine this and generate an abnormality alert. This alert will be sent to the user via the notification system as a message such as "The robot's temperature is abnormal. Please cool it down." Furthermore, if the emotion engine detects stress in the user when receiving the alert, it can change the content of the notification to a gentler one such as "Please do not panic. However, the robot's temperature is high, so please be careful."

[1246] Prompt Sentence Examples

[1247] To recognize the user's emotions and notify them with an appropriate message, we use an emotion engine. When notifying, we analyze the user's emotional state, and if a specific emotion (e.g., stress) is detected, we change the message to a calmer one. Can you please give us an example program for this implementation?

[1248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1249] Step 1:

[1250] A camera (photographing means) detects the movement of the factory robot and generates image data.

[1251] Input: Camera detects factory robot movement.

[1252] Data processing: Analyzes the movements captured by the camera and generates image data.

[1253] Output: The generated image data is sent to the server.

[1254] Step 2:

[1255] The sensor means measures the temperature, activity level, sleep level, and location information of the factory robot.

[1256] Input: The temperature sensor measures the factory robot's temperature, the vibration sensor measures activity, the inactivity sensor measures sleep, and the location sensor obtains its current location.

[1257] Data processing: Organize the data collected by each sensor and convert it into a format that can be sent to the server.

[1258] Output: The measured body temperature, activity level, sleep level, and location information are sent to the server.

[1259] Step 3:

[1260] The measuring device measures the usage, pH balance and weight of the factory robot.

[1261] Inputs: Usage sensor measures the usage of the factory robot, pH balance sensor measures the pH balance of the liquid, weight sensor measures the weight of the robot.

[1262] Data Processing: Collected usage, pH balance and weight data is compiled and converted into a format to be sent to the server.

[1263] Output: Sends measured usage, pH balance, and weight data to the server.

[1264] Step 4:

[1265] The server stores the received data in a database.

[1266] Input: Various data sent from photography means, sensor means, and measurement means.

[1267] Data processing: The received data is converted into an appropriate data format and recorded in the database.

[1268] Output: The various saved data is stored in a database.

[1269] Step 5:

[1270] The server learns normal ranges and benchmark values ​​based on the stored data.

[1271] Input: Past and current factory robot data stored in a database.

[1272] Data processing: Analysis software performs statistical analysis to calculate the robot's normal operating range and reference values.

[1273] Output: Sets the calculated baseline or normal operating range.

[1274] Step 6:

[1275] The server detects an anomaly.

[1276] Input: Current data and learned baseline.

[1277] Data processing: Determine whether the current data deviates from the baseline and generate an alert if an anomaly is detected.

[1278] Output: Generated anomaly alerts.

[1279] Step 7:

[1280] The server notifies the user of the abnormality.

[1281] Input: The generated anomaly alert.

[1282] Data processing: Converts the alert content into a format that can be notified to the user.

[1283] Output: A notification message to the user.

[1284] Step 8:

[1285] The server recognizes the user's emotional state.

[1286] Input: User's facial and voice data.

[1287] Data processing: The emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[1288] Output: Adjust notification content based on the perceived emotional state.

[1289] Step 9:

[1290] The server displays the data to the user.

[1291] Input: Data and analysis results stored in a database.

[1292] Data processing: The data is formatted into an appropriate format and displayed on the user's display or monitor.

[1293] Output: Visually displayed data and analysis results.

[1294] 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.

[1295] 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.

[1296] 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.

[1297] [Fourth embodiment]

[1298] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1299] 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.

[1300] 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).

[1301] 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.

[1302] 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.

[1303] 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).

[1304] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1305] 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.

[1306] 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.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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."

[1311] The present invention relates to a system for monitoring the health condition of a pet, which includes: an imaging device for detecting the behavior of the pet and generating image data; a sensor device for measuring the pet's body temperature, activity level, sleep level, and location information; a measuring device for measuring the pet's litter box usage, pH balance, and weight; a data management device for receiving and storing data transmitted from the imaging device, sensor device, and measuring device; an anomaly detection device for analyzing the data stored in the data management device and detecting anomalies; a notification device for notifying a user of an anomaly detected by the anomaly detection device; and a display device for displaying the data to the user.

[1312] A specific example for implementing this system will now be described.

[1313] Data collection and transmission

[1314] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[1315] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[1316] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[1317] Receiving and analyzing data

[1318] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[1319] The server analyzes the stored data to learn your pet's normal behavior patterns and compares current data with past data to detect anomalies.

[1320] Alert generation and user notification

[1321] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[1322] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[1323] Data display and advice

[1324] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[1325] Location tracking when lost

[1326] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[1327] Specific examples

[1328] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[1329] In this way, the present invention allows for effective monitoring of the health status of pets and rapid response when abnormalities are detected.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[1333] Step 2:

[1334] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[1335] Step 3:

[1336] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[1337] Step 4:

[1338] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[1339] Step 5:

[1340] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[1341] Step 6:

[1342] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[1343] Step 7:

[1344] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[1345] Step 8:

[1346] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[1347] Step 9:

[1348] Users can launch the app to view alert notifications and other health data, allowing them to quickly recognize any abnormalities in their pets and take appropriate measures.

[1349] Step 10:

[1350] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[1351] Step 11:

[1352] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[1353] Step 12:

[1354] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[1355] Step 13:

[1356] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[1357] Example 1

[1358] 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."

[1359] There is a demand for continuous, real-time monitoring of pet health conditions and prompt notification to users when abnormalities are detected. However, conventional systems are limited to collecting and notifying users of specific data items (such as body temperature and activity level), making comprehensive health management of pets difficult. Furthermore, delays in notification of abnormalities can make it difficult to respond quickly. Furthermore, conventional systems lacked features such as advice provision and location tracking, resulting in a lack of comprehensive support for pet health and safety management.

[1360] 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.

[1361] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measurement that measures usage, pH balance, and weight; a data management system that receives and stores data transmitted from the camera, sensor, and measurement systems; an anomaly detection system that analyzes the data stored in the data management system and detects abnormalities; a notification system that notifies the user of abnormalities detected by the anomaly detection system; a display system that displays the data to the user; a system that receives real-time location information and displays it to the user; a system that generates advice regarding exercise and diet based on the collected data and provides it to the user; and a system that generates anomaly alert messages using a generative AI model. This allows for comprehensive and real-time monitoring and management of pet health, and allows for prompt notification and appropriate advice when an abnormality is detected. Furthermore, tracking location information facilitates pet safety management.

[1362] The "photography means" is a device that detects the movements of the pet and generates image data.

[1363] The "sensor means" is a device that measures the pet's body temperature, activity level, sleep level, and location information.

[1364] The "measuring means" is a device that measures the pet's litter box usage, pH balance, and weight.

[1365] The "data management means" is a device that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[1366] The "abnormality detection means" is a device that analyzes the data stored in the data management means and detects abnormalities.

[1367] The "notification means" is a device that notifies the user of an abnormality detected by the abnormality detection means.

[1368] A "display means" is a device that displays the data to a user.

[1369] The "means for receiving real-time location information and displaying it to the user" is a device that receives real-time location information of a pet and displays that information to the user.

[1370] The "means for generating advice regarding the amount of exercise and diet based on collected data and providing it to the user" is a device that generates advice regarding the amount of exercise and diet based on collected data and provides that advice to the user.

[1371] "Means for generating an abnormality alert statement using a generative AI model" refers to a device that generates an alert statement regarding an abnormality using a generative AI model.

[1372] The present invention is a comprehensive system for monitoring the health of pets and quickly detecting and notifying abnormalities. The system is composed of the following elements:

[1373] 1. Filming Method

[1374] This system includes a camera that detects pet movements and generates image data. The camera is installed in a room, generates image data at regular intervals, and sends it to a server via a network. This camera can be a commercially available surveillance camera.

[1375] 2. Sensor means

[1376] The system includes a collar that measures your pet's body temperature, activity level, sleep level, and location. The collar is attached to your pet's neck and measures data in real time, periodically sending it to a server. The collar is equipped with a body temperature sensor, an acceleration sensor, and a GPS module.

[1377] 3. Measurement methods

[1378] This includes toilets that measure pet toilet usage, pH balance, and weight. The toilet measures this data every time the pet uses it and sends it to a server via the network. This toilet is equipped with a weight sensor and a pH sensor.

[1379] 4. Data Management Measures

[1380] The server receives the data sent from the camera, collar, and litter box and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[1381] 5. Anomaly Detection Methods

[1382] The server analyzes the stored data and detects anomalies using anomaly detection algorithms or machine learning models (such as the Python libraries Scikit-learn and TensorFlow).

[1383] 6. Means of notification

[1384] If an anomaly is detected, the server generates an anomaly alert and notifies the user through an application, such as a mobile app with notification functionality for Android or iOS.

[1385] 7. Display means

[1386] The server visualizes the collected data and displays it to the user through an app, which can be a web app or a mobile app's dashboard function.

[1387] 8. A means of receiving and displaying real-time location information to the user

[1388] The server receives real-time location information transmitted from the collar and displays it to the user, allowing them to quickly locate their pet if it gets lost.

[1389] 9. Means for generating and providing advice to users regarding exercise and diet based on collected data

[1390] The server generates advice on exercise and diet based on the collected data, and provides this advice to the user through the app.

[1391] 10. How to generate anomaly alerts using generative AI models

[1392] The server uses a generative AI model to generate an alert message based on the detected anomaly. For example, by inputting a prompt message such as "Please generate a notification message when my pet's temperature is too high" into a generative AI model (such as GPT-3), the server can generate an appropriate notification message.

[1393] Specific examples

[1394] For example, if the collar device measures a pet's temperature as 39.5°C and sends that data to the server, the server will determine that this is outside the normal body temperature range (e.g., 38.0°C to 39.0°C) and generate an abnormality alert. The generated alert is sent from the server to the app, and the user will receive a notification such as: "Your pet's temperature is too high. Please consult your veterinarian."

[1395] Prompt Sentence Examples

[1396] "Generate a notification message if your pet's temperature is too high"

[1397] "Please create an alert statement for when the toilet's pH balance is abnormal."

[1398] "Please provide an example of a location notification for when a pet gets lost."

[1399] In this way, the present invention allows for comprehensive monitoring of the health status of pets and allows for rapid response if an abnormality is detected.

[1400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] The device collects data

[1403] Specific operation: A camera installed in the room captures the pet's movements at regular intervals and generates image data. The collar also measures the pet's temperature, activity level, sleep level, and location, while the toilet measures usage, pH balance, and weight.

[1404] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage

[1405] Output: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[1406] Step 2:

[1407] The device sends the data to the server

[1408] Specific operation: All devices (camera, collar, and toilet) send the collected data to a server via a network. For example, the camera sends image data to the server, and the collar sends body temperature and activity data to the server.

[1409] Input: Image data, body temperature data, activity data, sleep data, location data, toilet usage data, pH data, weight data

[1410] Output: Various data sent to the server

[1411] Step 3:

[1412] The server receives and stores the data

[1413] Specific operation: The server receives data sent from the terminal via the network and stores it in a database. Specifically, it stores the data using MySQL or PostgreSQL.

[1414] Input: Various data sent from the terminal

[1415] Output: Various data stored in the database

[1416] Step 4:

[1417] The server analyzes the stored data

[1418] How it works: The server retrieves data stored in the database and analyzes it using anomaly detection algorithms and machine learning models, such as Scikit-learn and TensorFlow.

[1419] Input: Various data stored in the database

[1420] Output: Analysis results, anomaly detection results

[1421] Step 5:

[1422] If the server detects an anomaly, it generates an anomaly alert.

[1423] Specific operation: Based on the analysis results, if an abnormality is detected, such as if the pet's body temperature is outside the normal range, the server will generate an abnormality alert. For example, it will generate an alert message such as "Temperature is too high." A generative AI model (such as GPT-3) can be used in this process.

[1424] Input: Analysis results, anomaly detection results

[1425] Output: Abnormal alert statement

[1426] Step 6:

[1427] The server notifies the user of an abnormality alert.

[1428] Specific operation: The generated abnormality alert is notified to the user via the application from the server. The user receives a notification on their smartphone app and can check the situation.

[1429] Input: Abnormal alert statement

[1430] Output: User notification

[1431] Step 7:

[1432] The server generates advice based on the collected data and provides it to the user.

[1433] How it works: The server analyzes the collected data and generates advice on exercise and diet for pets. These advice are provided to users through an application. Generative AI models may also be used.

[1434] Input: Analysis results, collected data

[1435] Output: Advice on exercise and diet

[1436] Step 8:

[1437] The server displays the collected data to the user.

[1438] Specific operation: The server visualizes the collected data and displays it to the user through an application. For example, it can display the progress of activity levels and sleep patterns in graph form.

[1439] Input: Analysis results, collected data

[1440] Output: Data visualization information displayed to the user

[1441] Step 9:

[1442] Displaying real-time location information to users

[1443] Specific operation: The server receives the real-time GPS location information sent by the collar and displays it to the user through the application, allowing the user to know the current location of their pet at any time.

[1444] Input: Real-time location

[1445] Output: Location displayed to the user

[1446] In this way, the system of the present invention can comprehensively monitor the health status of pets and quickly notify users if any abnormalities are detected. Furthermore, by providing advice on exercise and diet, as well as real-time location information, the system supports the health and safety management of pets.

[1447] (Application example 1)

[1448] 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."

[1449] Conventional pet health management systems only collect data such as pet body temperature and activity levels, and lack the mechanisms for quickly responding when abnormalities are detected in real time. Furthermore, they lack sufficient functionality for tracking a pet's location if it gets lost, or for providing detailed health information based on the collected data. Particularly when managing pets in brick-and-mortar stores, store staff are required to quickly grasp the pet's health status and take appropriate action, but no such system existed.

[1450] 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.

[1451] In this invention, the server includes a camera that detects movement and generates image data; a sensor that measures body temperature, activity level, sleep level, and location information; a measuring device that measures usage, pH balance, and weight; a data management device that receives and stores data transmitted from the camera, sensor, and measuring device; an anomaly detection device that analyzes the data stored in the data management device and detects abnormalities; a notification device that notifies the user of abnormalities detected by the anomaly detection device; a display device that analyzes collected data and provides the user with detailed health information; and a tracking device that tracks the location of the pet if it gets lost. This allows for real-time monitoring of the pet's health and for prompt notification to a store staff member if an abnormality is detected. Furthermore, even if the pet gets lost, the location can be quickly identified and detailed health information can be provided to the customer.

[1452] The "photography means for detecting movements and generating image data" is a device for capturing the movements of a pet and generating image data based on the movements.

[1453] The "sensor means for measuring body temperature, activity level, sleep level and location information" is a sensor device for measuring a pet's body temperature, activity level, sleep level and location information in real time.

[1454] "Measuring means for measuring usage, pH balance and weight" refers to a device for measuring the amount of toilet used by a pet, the pH balance of urine and the weight of the pet.

[1455] The "data management means" is a system for receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[1456] The "abnormality detection means" is a function for analyzing data stored in the data management means and detecting values ​​or states that are outside the normal range.

[1457] The "notification means" is a communication system for notifying the user of an abnormality detected by the abnormality detection means, and is a device or application for sending alerts and messages.

[1458] The "display means" refers to a device or interface for visually displaying the collected data and analysis results to the user.

[1459] The "tracking means" is a system that tracks the location of a pet in real time if the pet gets lost and notifies the user of the location.

[1460] A "server" is a central processing unit that receives, stores, analyzes, notifies, and displays data, and is a device that manages the entire system in cooperation with various means.

[1461] The present invention is a system for monitoring the health of pets, detecting and notifying abnormalities, and is composed of various devices and programs, including a photographing means, a sensor means, a measuring means, a data management means, an abnormality detection means, a notification means, a display means, and a tracking means.

[1462] Specific system configuration

[1463] Data collection

[1464] The camera serving as the terminal is installed in the pet shop, and captures the movements of the pet at regular intervals to generate image data.

[1465] The sensor terminal is attached to the pet's collar and measures body temperature, activity level, sleep level, and location information in real time.

[1466] The terminal measuring device is installed in the pet's toilet and measures the amount used, the pH balance of the urine, and the pet's weight.

[1467] Data reception and analysis

[1468] The server continuously receives various data and stores it in a database. The server processes HTTP requests using Python and the requests module.

[1469] The stored data is analyzed and anomalies are detected using an anomaly detection algorithm, which is an analytical method based on a generative AI model.

[1470] User Notification and Data Display

[1471] If an abnormality is detected, the server generates an abnormality alert and sends the alert to the user via the notification means.

[1472] Users can receive real-time notifications of abnormalities using their smartphones.

[1473] The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[1474] Specific operation examples

[1475] For example, if a pet's temperature is measured at 39.5 degrees, this data is sent to the server. The server determines that this is outside the normal temperature range and generates an abnormality alert saying, "Your pet's temperature is too high. Please consult a veterinarian." This alert is then sent to the user via their smartphone.

[1476] Prompt Sentence Examples

[1477] "Latest data from your pet's collar: Temperature: 39.5°C Activity: 15 Please detect any abnormalities and generate appropriate notification messages."

[1478] This system allows real-time monitoring of the health status of pets in pet shops, allowing for swift action if an abnormality occurs. In addition, detailed health information can be provided to customers when they purchase a pet, allowing them to purchase with peace of mind.

[1479] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1480] Step 1:

[1481] Data collection

[1482] Input: Pet behavior, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1483] Behavior: The camera captures your pet's movements and generates image data. Sensors also measure your pet's body temperature, activity level, sleep level, and location. Furthermore, a measuring device measures your pet's toilet usage, urine pH balance, and weight.

[1484] Output: Each piece of data collected is generated.

[1485] Step 2:

[1486] Data transmission

[1487] Input: collected image data, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1488] How it works: Cameras, sensors, and measuring devices send their data to a server.

[1489] Output: The data arrives at the server.

[1490] Step 3:

[1491] Data reception

[1492] Input: Data sent from cameras, sensors, and measurement devices

[1493] How it works: The server receives each piece of data and stores it in a database.

[1494] Output: Each data stored in the database is obtained.

[1495] Step 4:

[1496] Data analysis

[1497] Input: Image data stored in the database, body temperature, activity level, sleep level, location information, toilet usage, urine pH balance, weight

[1498] How it works: The server analyzes the stored data and uses generative AI models to detect anomalies.

[1499] Output: A judgment result is obtained as to whether or not there is an abnormality.

[1500] Step 5:

[1501] Generate anomaly notifications

[1502] Input: Abnormality judgment result

[1503] How it works: If an anomaly is detected, the server generates an anomaly alert.

[1504] Output: An anomaly alert is generated.

[1505] Step 6:

[1506] User Notification

[1507] Input: Generated anomaly alert

[1508] Operation: The server sends an abnormality alert to the smartphone via the notification means.

[1509] Output: An abnormality alert is displayed on the user's smartphone.

[1510] Step 7:

[1511] Data display and advice generation

[1512] Input: Parsed data stored in a database

[1513] Operation: The server generates advice regarding the amount of exercise and the quantity and quality of food based on the collected data and provides it to the user through a display means.

[1514] Output: Data and advice are displayed on the user's smartphone.

[1515] Step 8:

[1516] Location Tracking

[1517] Input: Real-time location information sent from the sensor

[1518] How it works: The server tracks the location of the pet and notifies the user if the pet gets lost.

[1519] Output: Real-time location information is displayed on the user's smartphone.

[1520] 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.

[1521] The present invention relates to a system for monitoring the health condition of a pet. The system includes an imaging means for detecting the behavior of the pet and generating image data, a sensor means for measuring the pet's body temperature, activity level, sleep level, and location information, a measurement means for measuring the pet's litter box usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the imaging means, sensor means, and measurement means, anomaly detection means for analyzing the data stored in the data management means and detecting anomalies, a notification means for notifying a user of an anomaly detected by the anomaly detection means, a display means for displaying the data to the user, and an emotion engine for recognizing the user's emotions.

[1522] Data collection and transmission

[1523] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating image data that is then sent to a server via a network.

[1524] The collar is attached to the pet's neck and measures its temperature, activity, sleep, and location in real time, and this data is also periodically sent to a server.

[1525] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and this data is sent to a server via the network.

[1526] Receiving and analyzing data

[1527] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database.

[1528] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[1529] Alert generation and user notification

[1530] If an abnormality is detected, for example if a pet's temperature exceeds the normal range, the server generates an abnormality alert.

[1531] The generated alert is sent from the server to the app, which notifies the user. The user receives a detailed notification through the app and can take appropriate measures.

[1532] Data display and advice

[1533] The server analyzes the collected data and generates advice on the amount of exercise and the quantity and quality of food, which is then provided to the user through the app.

[1534] Location tracking when lost

[1535] The server receives real-time location information sent from the collar terminal and displays it to the user through the app, allowing the location of the pet to be quickly identified even if the pet gets lost.

[1536] Emotion Engine Functions

[1537] The server is equipped with an emotion engine for recognizing the user's emotions, which analyzes the user's facial expressions and tone of voice to recognize the user's current emotional state.

[1538] Based on the recognized emotion, the server can tailor pet care suggestions, for example, calming notifications and advice about the pet's health if the user is feeling stressed.

[1539] Specific examples

[1540] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[1541] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[1542] In this way, the present invention allows for effective monitoring of the pet's health and, when an abnormality is detected, a response that takes into account the user's emotional state.

[1543] The processing flow will be explained below.

[1544] Step 1:

[1545] The camera terminal takes pictures of the pet's movements at regular intervals and generates image data, which is then sent to a server via a network.

[1546] Step 2:

[1547] The collar, which acts as a terminal, measures the pet's temperature, activity level, and sleep level in real time, and transmits this measurement data to a server at regular intervals.

[1548] Step 3:

[1549] The toilet terminal measures the amount of toilet use, pH balance, and weight of your pet each time it uses the toilet, and this data is also sent to the server.

[1550] Step 4:

[1551] The server receives the data transmitted from the camera, collar, and litter box and stores it in a database.

[1552] Step 5:

[1553] The server analyzes the stored data and learns your pet's normal behavior patterns and baseline values, such as calculating your pet's normal body temperature range and average activity level based on past data.

[1554] Step 6:

[1555] The server compares new data received in real time with past data to check for any anomalies, such as whether body temperature is above normal or whether activity is too low.

[1556] Step 7:

[1557] The server generates an alert if it detects an abnormality, for example, if the body temperature exceeds normal, it generates an alert saying "Pet's temperature is too high."

[1558] Step 8:

[1559] The server sends the generated alerts to the app, through which users receive alert notifications in real time.

[1560] Step 9:

[1561] When a user launches the app, the app uses the camera and microphone to capture the user's facial expressions and tone of voice.

[1562] Step 10:

[1563] The server analyzes the user's facial expressions and tone of voice through an emotion engine to recognize the user's current emotional state, for example, determining whether the user is feeling stressed.

[1564] Step 11:

[1565] The server then adjusts the wording of notifications and advice about the pet's health based on the recognized emotion. For example, if the user is feeling stressed, the notification content will be changed to a stress-reducing message such as "Don't worry, but be careful as your pet has a high temperature."

[1566] Step 12:

[1567] The server sends tailored notifications and advice to the app, which the user can then view.

[1568] Step 13:

[1569] The server generates advice on the amount of exercise and the quantity and quality of food based on the collected and analyzed data. For example, if a pet is not getting the appropriate amount of exercise, it will generate advice such as "You need to exercise more."

[1570] Step 14:

[1571] The server sends the generated advice to the app, where users can view the advice and use it to help maintain their pet's health.

[1572] Step 15:

[1573] The collar terminal measures the pet's location in real time and sends it to a server, which can be used to locate the pet if it gets lost.

[1574] Step 16:

[1575] The server provides the location information sent from the collar to the app, which allows users to check their pet's current location in real time.

[1576] Example 2

[1577] 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."

[1578] Conventional pet health monitoring systems are capable of measuring various health indicators of pets and collecting data. However, these systems have difficulty in providing effective notifications to users. In particular, they can be stressful for users because they provide uniform notifications without considering the user's emotional state. Furthermore, in actual use, there is a need for a system that can comprehensively manage data from individual devices, detect abnormalities, and take appropriate action.

[1579] 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.

[1580] In this invention, the server includes a photographing means for detecting movements and generating image data, a sensor means for measuring body temperature, activity level, sleep level, and location information, a measuring means for measuring usage, pH balance, and weight, a data management means for receiving and storing data transmitted from the photographing means, the sensor means, and the measuring means, an abnormality detection means for analyzing the data stored in the data management means and detecting abnormalities, a notification means for notifying the user of abnormalities detected by the abnormality detection means, a display means for displaying the data to the user, and an emotion recognition means for recognizing the user's emotions and adjusting the content of the notification. This makes it possible to effectively monitor the health condition of a pet and, when an abnormality is detected, to provide a notification that takes into account the user's emotional state.

[1581] The "photography means" is a device that has the function of detecting the movements of the pet and generating image data.

[1582] The "sensor means" is a device that has the function of measuring the pet's body temperature, activity level, sleep level, and location information.

[1583] "Measuring means" refers to a device that has the function of measuring the amount of pet use, pH balance, and weight.

[1584] The "data management means" is a device that has the function of receiving and storing data transmitted from the imaging means, sensor means, and measurement means.

[1585] The "abnormality detection means" is a device that has the function of analyzing the data stored in the data management means and detecting abnormalities.

[1586] The "notification means" is a device having a function of notifying the user of an abnormality detected by the abnormality detection means.

[1587] A "display means" is a device that has the function of displaying data to a user.

[1588] The "emotion recognition means" is a device that has the function of recognizing the user's emotions and adjusting the notification content.

[1589] This invention relates to a system that monitors the health condition of pets and notifies the user when an abnormality is detected. This system measures and collects pet behavior, health indicators, and location information, and analyzes this data to detect abnormalities in the pet and notify the user in a way that takes into account the user's emotional state.

[1590] This system consists of the following elements:

[1591] Filming method

[1592] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals to generate image data. For example, a "general web camera" is used for this camera, and image data in JPEG format is generated. The generated image data is then sent to the server via the network.

[1593] Sensor Means

[1594] The collar, which serves as the terminal, is attached to the pet's neck and measures body temperature, activity, sleep, and location information in real time. This collar uses a "pet wearable device," for example, and the measured data is periodically sent to a server.

[1595] Measurement methods

[1596] The toilet terminal measures the amount of use, pH balance, and weight of the pet each time it uses it, and the measured data is sent to a server via a network.

[1597] Data Management Measures

[1598] The server constantly receives data sent from the camera, collar, and litter box and stores it in a database, typically using MySQL or PostgreSQL.

[1599] Data analysis

[1600] The server analyzes the stored data and learns the pet's normal behavior patterns and baseline values. For analysis, it uses data analysis libraries such as "Pandas" and "NumPy." It can calculate the pet's normal body temperature range and average activity level from past data.

[1601] Anomaly detection

[1602] The server compares the analyzed data with real-time data to detect anomalies, for example, if a pet's temperature exceeds the normal range, it generates an anomaly alert.

[1603] User Notifications

[1604] The server sends the generated anomaly alert to the app, which notifies the user. The app then displays a push notification on the user's smartphone and suggests specific measures to address the anomaly.

[1605] Data Display and Advice

[1606] The server analyzes the collected data and generates advice on the pet's health, such as the amount of exercise and the quantity and quality of food, which is then provided to the user via the app.

[1607] emotion recognition

[1608] The server uses an "emotion recognition API" to recognize the user's emotions. The app analyzes the user's facial expressions and tone of voice to recognize their current emotional state. Based on the recognized emotion, the server adjusts the notification content.

[1609] Specific examples

[1610] For example, if a collar device measures a pet's temperature as 39.5°C and sends that data to a server, the server will determine that the temperature is outside the normal range and generate an abnormality alert. The server will then send the alert to the app, and the user will receive a notification saying, "Your pet's temperature is too high. Please consult your veterinarian."

[1611] Furthermore, if the emotion engine recognizes that the user is stressed when they open the app, it can change the content of the notification to something that will reduce stress, such as "Don't worry, but be careful as your pet has a high temperature."

[1612] Prompt statement

[1613] Prompt sentence for the AI ​​model to generate a specific example:

[1614] Describe a system that monitors the health of pets. Data is collected from cameras, collars, and litter boxes, and analyzed on a server to detect abnormalities. If an abnormality is detected, the system notifies the user and uses an emotion engine to provide notifications that take the user's emotions into account.

[1615] In this way, the present invention effectively monitors the health of a pet and allows for a response that takes into account the user's emotional state when an abnormality is detected.

[1616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1617] Step 1: Data collection

[1618] The camera, which is the terminal, is installed in the room and takes pictures of the pet's movements at regular intervals, generating JPEG image data. The collected image data is sent from the camera to a server via a network. The collar, which is the terminal, measures the pet's body temperature, activity level, sleep level, and location information in real time, and temporarily stores each piece of data in its internal storage. The toilet, which is the terminal, measures the amount of toilet use, pH balance, and weight each time the pet uses the toilet, and generates the data.

[1619] Step 2: Send data

[1620] The devices (camera, collar, and toilet) periodically send the collected data to a server. JPEG image data generated by the camera, sensor data measured by the collar, and data measured by the toilet are transmitted via wireless communication such as Wi-Fi. The input here is the data generated by each device, and the output is the data sent to the server.

[1621] Step 3: Receiving and storing data

[1622] The server constantly receives data sent from the camera, collar, and toilet, and stores it in a database. The server manages the database using, for example, MySQL or PostgreSQL, and classifies and stores the data from each device along with a timestamp. The input is the data sent from each device, and the output is the data stored in the database.

[1623] Step 4: Data analysis

[1624] The server uses Python data analysis libraries such as "Pandas" and "NumPy" to analyze the stored data. It learns the pet's normal behavior patterns and standard values ​​based on past data, and calculates, for example, the normal body temperature range and average activity level. The input is the past data stored in the database, and the output is various standard values ​​as the analysis results.

[1625] Step 5: Anomaly detection

[1626] The server compares the analyzed data with real-time data to detect abnormalities. For example, if a pet's body temperature is outside the normal range, it will be recognized as an abnormality and generate an abnormality alert. The input is the analyzed reference value and real-time data, and the output is an abnormality alert.

[1627] Step 6: User Notification

[1628] If an abnormality is detected, the server sends an abnormality alert to the app and notifies the user. The abnormality alert includes specific details of the abnormality and countermeasures. For example, a notification may be sent saying, "Your pet's temperature is too high. Please consult a veterinarian." The input is the generated abnormality alert, and the output is the notification sent to the app.

[1629] Step 7: Data display and advice

[1630] The server generates advice on the amount of exercise and the quantity and quality of food for the pet's health based on the collected and analyzed data. This advice is provided to the user through the app. The input is the data stored in the database and the analysis results, and the output is the advice displayed on the app.

[1631] Step 8: Emotion recognition and notification adjustment

[1632] The server uses an "emotion recognition API" to recognize the user's emotions and analyzes the user's facial expressions and tone of voice obtained through the app. Based on the user's emotional state, the server adjusts the notification content. For example, if the user is feeling stressed, the notification wording will be changed to a more gentle one. The input is the user's facial expression and voice data, and the output is the adjusted notification content.

[1633] The above is the flow of processing by the system, and details of the specific operations performed at each step.

[1634] (Application example 2)

[1635] 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."

[1636] It is necessary to provide a system that can effectively and comprehensively monitor the health status of pets and respond quickly when abnormalities occur. It is also necessary to provide notifications and advice on pet health management taking into account the user's emotional state. Such a system can reduce pet health risks and ease the burden on users.

[1637] The specific processing by the specific 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 a photographing means that detects movement and generates image data, a sensor means that measures body temperature, activity level, sleep level, and location information, and a measuring means that measures usage, pH balance, and weight. This makes it possible to comprehensively monitor the pet's health and quickly notify the user when an abnormality is detected. In addition, by using an emotion engine that recognizes the user's emotional state and adjusts the content of notifications and advice, it is possible to reduce the user's stress and encourage appropriate responses.

[1638] The "photography means for detecting the operation and generating image data" is a device used to monitor the operation of a factory robot and generate data in the form of images.

[1639] The "sensor means for measuring body temperature, activity level, sleep level and location information" refers to a group of devices for measuring the body temperature, activity level during work, time spent stopped and current location of a factory robot.

[1640] "Measuring means for measuring usage amount, pH balance and weight" refers to a group of devices that measure a certain usage amount, pH balance and weight of a liquid related to a factory robot.

[1641] The "data management means" is a system including devices and software for receiving and storing data transmitted from the above-mentioned photographing means, sensor means, and measurement means.

[1642] The "abnormality detection means" refers to a device and software that analyzes the data stored in the data management means and detects abnormalities that deviate from the normal operating range.

[1643] The "notification means" is a device and system for notifying the user of an abnormality detected by the abnormality detection means.

[1644] "Display means" refers to a device for visually displaying data to a user, and includes, for example, a display or a monitor.

[1645] The "emotion engine" is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[1646] The system for implementing this invention is configured to monitor the operating status and maintenance status of factory robots and to respond quickly when an abnormality is detected. The system is mainly composed of the following hardware and software.

[1647] Hardware

[1648] 1. Photography Method:

[1649] It includes a camera for monitoring the operation of a factory robot, which detects the movement and generates image data.

[1650] 2. Sensor means:

[1651] Temperature sensors that measure the body temperature of factory robots

[1652] Vibration sensor that measures activity

[1653] A sensor that measures sleep amount (time inactive)

[1654] Location sensors that measure location information

[1655] 3. Measurement methods:

[1656] It includes sensors that measure usage, Ph balance, and weight.

[1657] software

[1658] 1. Data Management Measures:

[1659] The system includes a server that receives and stores data transmitted from the imaging means, sensor means, and measurement means.

[1660] 2. Anomaly detection methods:

[1661] It includes software that analyzes stored data and detects anomalies that deviate from normal operating ranges.

[1662] 3. Means of notification:

[1663] This is a system for notifying a user of an abnormality detected by an abnormality detection means.

[1664] 4. Display means:

[1665] It includes a display or monitor for visually displaying data to the user.

[1666] 5. Emotion Engine:

[1667] It is a software module and analysis engine that analyzes the user's facial expressions and voice to recognize their current emotional state.

[1668] Processing flow

[1669] The server constantly receives data sent from the imaging means, sensor means, and measurement means and stores it in a database. Based on the stored data, it learns the factory robot's normal operating patterns and reference values. For example, it calculates the normal body temperature range and average activity level based on past data. If an abnormality is detected, the server generates an abnormality alert and promptly notifies the user via the notification means.

[1670] Specific examples

[1671] For example, if a factory robot's temperature sensor detects a temperature of 80 degrees, the server will determine this and generate an abnormality alert. This alert will be sent to the user via the notification system as a message such as "The robot's temperature is abnormal. Please cool it down." Furthermore, if the emotion engine detects stress in the user when receiving the alert, it can change the content of the notification to a gentler one such as "Please do not panic. However, the robot's temperature is high, so please be careful."

[1672] Prompt Sentence Examples

[1673] To recognize the user's emotions and notify them with an appropriate message, we use an emotion engine. When notifying, we analyze the user's emotional state, and if a specific emotion (e.g., stress) is detected, we change the message to a calmer one. Can you please give us an example program for this implementation?

[1674] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1675] Step 1:

[1676] A camera (photographing means) detects the movement of the factory robot and generates image data.

[1677] Input: Camera detects factory robot movement.

[1678] Data processing: Analyzes the movements captured by the camera and generates image data.

[1679] Output: The generated image data is sent to the server.

[1680] Step 2:

[1681] The sensor means measures the temperature, activity level, sleep level, and location information of the factory robot.

[1682] Input: The temperature sensor measures the factory robot's temperature, the vibration sensor measures activity, the inactivity sensor measures sleep, and the location sensor obtains its current location.

[1683] Data processing: Organize the data collected by each sensor and convert it into a format that can be sent to the server.

[1684] Output: The measured body temperature, activity level, sleep level, and location information are sent to the server.

[1685] Step 3:

[1686] The measuring device measures the usage, pH balance and weight of the factory robot.

[1687] Inputs: Usage sensor measures the usage of the factory robot, pH balance sensor measures the pH balance of the liquid, weight sensor measures the weight of the robot.

[1688] Data Processing: Collected usage, pH balance and weight data is compiled and converted into a format to be sent to the server.

[1689] Output: Sends measured usage, pH balance, and weight data to the server.

[1690] Step 4:

[1691] The server stores the received data in a database.

[1692] Input: Various data sent from photography means, sensor means, and measurement means.

[1693] Data processing: The received data is converted into an appropriate data format and recorded in the database.

[1694] Output: The various saved data is stored in a database.

[1695] Step 5:

[1696] The server learns normal ranges and benchmark values ​​based on the stored data.

[1697] Input: Past and current factory robot data stored in a database.

[1698] Data processing: Analysis software performs statistical analysis to calculate the robot's normal operating range and reference values.

[1699] Output: Sets the calculated baseline or normal operating range.

[1700] Step 6:

[1701] The server detects an anomaly.

[1702] Input: Current data and learned baseline.

[1703] Data processing: Determine whether the current data deviates from the baseline and generate an alert if an anomaly is detected.

[1704] Output: Generated anomaly alerts.

[1705] Step 7:

[1706] The server notifies the user of the abnormality.

[1707] Input: The generated anomaly alert.

[1708] Data processing: Converts the alert content into a format that can be notified to the user.

[1709] Output: A notification message to the user.

[1710] Step 8:

[1711] The server recognizes the user's emotional state.

[1712] Input: User's facial and voice data.

[1713] Data processing: The emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[1714] Output: Adjust notification content based on the perceived emotional state.

[1715] Step 9:

[1716] The server displays the data to the user.

[1717] Input: Data and analysis results stored in a database.

[1718] Data processing: The data is formatted into an appropriate format and displayed on the user's display or monitor.

[1719] Output: Visually displayed data and analysis results.

[1720] 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.

[1721] 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.

[1722] 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.

[1723] 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.

[1724] 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.

[1725] 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.

[1726] 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).

[1727] 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.

[1728] 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."

[1729] 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.

[1730] 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).

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] The following is further disclosed regarding the above embodiment.

[1742] (Claim 1)

[1743] A system for monitoring the health of a pet, comprising:

[1744] an imaging means for detecting a motion and generating image data;

[1745] a sensor means for measuring body temperature, activity level, sleep level, and location information;

[1746] A measuring device to measure dosage, pH balance and weight;

[1747] a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means;

[1748] an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly;

[1749] a notification means for notifying a user of an abnormality detected by the abnormality detection means;

[1750] display means for displaying said data to a user;

[1751] A system including:

[1752] (Claim 2)

[1753] 10. The system of claim 1, further comprising means for generating an alert when the anomaly detection means detects that the pet's temperature is outside of a normal range.

[1754] (Claim 3)

[1755] 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food.

[1756] "Example 1"

[1757] (Claim 1)

[1758] an imaging means for detecting a motion and generating image data;

[1759] a sensor means for measuring body temperature, activity level, sleep level, and location information;

[1760] A measuring device to measure dosage, pH balance and weight;

[1761] a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means;

[1762] an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly;

[1763] a notification means for notifying a user of an abnormality detected by the abnormality detection means;

[1764] display means for displaying said data to a user;

[1765] means for receiving and displaying real-time location information to a user;

[1766] A means for generating advice on the amount of exercise and diet based on the collected data and providing the advice to the user;

[1767] A means for generating an anomaly alert statement using a generative AI model;

[1768] A system including:

[1769] (Claim 2)

[1770] 10. The system of claim 1, further comprising means for generating an alert when the anomaly detection means detects that the pet's temperature is outside of a normal range.

[1771] (Claim 3)

[1772] 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food.

[1773] "Application Example 1"

[1774] (Claim 1)

[1775] A system for monitoring the health of a pet, comprising:

[1776] an imaging means for detecting a motion and generating image data;

[1777] a sensor means for measuring body temperature, activity level, sleep level, and location information;

[1778] A measuring device to measure dosage, pH balance and weight;

[1779] a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means;

[1780] an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly;

[1781] a notification means for notifying a user of an abnormality detected by the abnormality detection means;

[1782] a display means for analyzing the collected data and providing detailed health information to the user;

[1783] A tracking method to track your pet's location if it gets lost;

[1784] A system including:

[1785] (Claim 2)

[1786] 10. The system of claim 1, further comprising means for generating an alert when the anomaly detection means detects that the pet's temperature is outside of a normal range.

[1787] (Claim 3)

[1788] 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food.

[1789] "Example 2: Combining Emotion Engines"

[1790] (Claim 1)

[1791] an imaging means for detecting a motion and generating image data;

[1792] a sensor means for measuring body temperature, activity level, sleep level, and location information;

[1793] A measuring device to measure dosage, pH balance and weight;

[1794] a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means;

[1795] an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly;

[1796] a notification means for notifying a user of an abnormality detected by the abnormality detection means;

[1797] display means for displaying said data to a user;

[1798] emotion recognition means for recognizing the emotion of a user and adjusting the notification content;

[1799] A system including:

[1800] (Claim 2)

[1801] 10. The system of claim 1, further comprising means for generating an alert when the anomaly detection means detects that the pet's temperature is outside of a normal range.

[1802] (Claim 3)

[1803] 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food.

[1804] "Application example 2 when combining emotion engines"

[1805] (Claim 1)

[1806] A system for monitoring the health of a pet, comprising:

[1807] an imaging means for detecting a motion and generating image data;

[1808] a sensor means for measuring body temperature, activity level, sleep level, and location information;

[1809] A measuring device to measure dosage, pH balance and weight;

[1810] a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means;

[1811] an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly;

[1812] a notification means for notifying a user of an abnormality detected by the abnormality detection means;

[1813] display means for displaying said data to a user;

[1814] a system including an emotion engine for recognizing the emotional state of a user and adjusting the content of notifications and advice;

[1815] A system including:

[1816] (Claim 2)

[1817] 10. The system of claim 1, further comprising means for generating an alert when the anomaly detection means detects that the pet's temperature is outside of a normal range.

[1818] (Claim 3)

[1819] 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food. [Explanation of symbols]

[1820] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for monitoring the health of a pet, comprising: an imaging means for detecting a motion and generating image data; a sensor means for measuring body temperature, activity level, sleep level, and location information; A measuring device to measure dosage, pH balance and weight; a data management means for receiving and storing data transmitted from the imaging means, the sensor means, and the measuring means; an anomaly detection means for analyzing the data stored in the data management means and detecting an anomaly; a notification means for notifying a user of an abnormality detected by the abnormality detection means; display means for displaying said data to a user; A system including:

2. 10. The system of claim 1, further comprising means for generating an alert if the anomaly detection means detects that the pet's temperature is outside of a normal range.

3. 2. The system according to claim 1, wherein the notification means further comprises means for providing the user with advice regarding the amount of exercise and the amount and quality of food.

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