system

Abstract

To provide a system that comprehensively manages the health condition of pets, detects abnormalities early, and enables prompt action. [Solution] A system including: a collar including means for measuring a pet's body temperature, means for measuring the pet's activity level, and means for measuring the pet's location information; a camera equipped with means for recognizing the pet's movements; a toilet having means for measuring the pet's toilet usage, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation; means for saving and analyzing data collected from each of the above means; means for detecting abnormalities and generating an alert based on the analysis results; means for notifying the user of the alert; means for generating and providing exercise and dietary advice to the user; and means for tracking the pet's current location and providing the user with location information.

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] Today's pet owners face challenges in checking their pets' health on a daily basis, especially since pets cannot verbally express their condition, making it difficult to detect abnormalities early. Furthermore, there are limited means to check a pet's location in real time and quickly find it if it gets lost. In addition, managing a pet's activity level and dietary balance can be difficult, making it difficult to receive appropriate advice on maintaining a pet's health. Therefore, there is a need for a system that can comprehensively manage a pet's health, detect abnormalities early, and enable prompt response. [Means for solving the problem]

[0005] The present invention provides a toilet equipped with a collar including means for measuring a pet's body temperature, activity level, and location information, a camera equipped with means for recognizing the pet's movements, and means for measuring the pet's toilet usage, weight, and pH balance of urination and defecation. The system also includes means for storing and analyzing data collected from these means, means for detecting abnormalities and generating alerts based on the analysis results, means for notifying the user of the alerts, means for generating and providing exercise and dietary advice to the user, and means for tracking the pet's current location and providing the user with location information. This system allows users to monitor their pet's health in real time, respond immediately when an abnormality occurs, and receive appropriate advice on daily health management.

[0006] "Means for measuring body temperature" refers to a sensor or device for measuring a pet's body temperature and recording that data.

[0007] "Means for measuring activity level" refers to sensors or devices that measure the degree of movement or activity of a pet and record that data.

[0008] "Means for measuring location information" refers to a GPS module or device for identifying the current location of a pet and collecting and recording that location data.

[0009] A "collar" is a device that is attached to a pet and has multiple sensor functions (measuring body temperature, activity, and location information).

[0010] The "means for recognizing movement" is a system that uses a camera or motion detection sensor to detect the movements and movements of a pet and record that data.

[0011] A "camera" is a device that records video of a pet's movements and recognizes their movements through motion detection.

[0012] "Means for measuring toilet usage" refers to sensors or devices that measure the amount of urination and defecation that pets produce when using the toilet and record that data.

[0013] "Means for measuring weight" refers to a sensor or device for measuring a pet's weight and recording that data.

[0014] "Means for measuring pH balance" refers to sensors or devices that measure the pH value of a pet's urination and defecation and record the data.

[0015] The "toilet" is a device for monitoring pets' excretory activities, and is equipped with functions to measure toilet usage, weight, pH balance, etc.

[0016] "Means for storing and analyzing data" refers to servers and software for storing data collected from each sensor in a database and analyzing it.

[0017] The "means of detecting anomalies and generating alerts" refers to a system that analyzes stored data and generates and sends alerts when data that deviates from normal values ​​is discovered.

[0018] "Means for notifying users" refers to the notification system used to deliver generated alerts and health advice to users.

[0019] The "means for generating and providing advice on exercise and diet" is a system that automatically generates appropriate advice on exercise and diet based on pet health data and provides it to users.

[0020] "Means for tracking current location and providing location information to users" refers to a system that uses the pet's GPS data to constantly monitor the current location and displays and notifies the user of real-time location information.

[0021] The "System" refers to a combination of hardware and software that integrates the above-mentioned means to comprehensively manage and monitor pet health and location information. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] The present invention provides a system for comprehensively managing the health of pets. This system is composed of multiple elements that cooperate to monitor the health of pets, and if an abnormality occurs, it promptly notifies the user and provides appropriate advice.

[0044] Program processing

[0045] The system operates as follows.

[0046] server:

[0047] 1. Data Receipt and Storage:

[0048] The server receives data from the pet's collar, camera, and litter box.

[0049] The received data is stored in the respective databases.

[0050] 2. Data analysis and anomaly detection:

[0051] The server periodically analyzes the stored data and assesses the pet's health.

[0052] It compares with normal values ​​and generates an alert if an abnormality is detected.

[0053] 3. Advice Generation:

[0054] Based on the results of data analysis, appropriate advice regarding your pet's exercise and diet is automatically generated.

[0055] 4. Visualizing the results:

[0056] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[0057] Device (user's smartphone or computer):

[0058] 1. Notification display:

[0059] Alerts and advice sent from the server are displayed to the user as push notifications.

[0060] Open the app to view detailed data and advice.

[0061] 2. Data browsing:

[0062] Users can check their past health data and current health status in real time within the app.

[0063] You can check your pet's location on a map.

[0064] User:

[0065] 1. Monitoring and Response:

[0066] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0067] Based on the advice provided, you can adjust your pet's exercise and diet.

[0068] Specific examples

[0069] Example 1: Your pet has a high body temperature

[0070] 1. Data Collection:

[0071] The collar measures the body temperature and records it as 38.5°C (normal is 37.5°C).

[0072] The data is sent to a server via Wi-Fi.

[0073] 2. Data Analysis:

[0074] The server receives the temperature data and stores it in a database.

[0075] Compared to normal values, 38.5℃ is considered to be a high temperature.

[0076] The server generates an alert and notifies the user.

[0077] 3. Notice and Response:

[0078] A notification will appear on your device saying, "Your pet's temperature is high. Please check immediately."

[0079] The user checks the notification and checks the status of the pet.

[0080] Example 2: If your pet is not very active

[0081] 1. Data Collection:

[0082] The collar measures daily activity and records that the dog is only moving about 30% of normal activity.

[0083] The data is sent to the server via Bluetooth.

[0084] 2. Data Analysis:

[0085] The server receives the activity data and stores it in a database.

[0086] Compared to normal values, it is determined that the amount of activity is low.

[0087] The server generates the advice and notifies the user.

[0088] 3. Notice and Response:

[0089] An advice notification will appear on your device saying, "Your pet is less active than usual. Try letting it spend more time playing with toys."

[0090] Users can check the advice and spend more time playing with their pets.

[0091] In this way, the system of the present invention can closely monitor the health condition of a pet and respond quickly and appropriately when an abnormality occurs, allowing pet owners to manage their pet's health with peace of mind, which greatly contributes to maintaining the pet's health.

[0092] The processing flow will be explained below.

[0093] Step 1: Data measurement and collection

[0094] Collar: Your pet's temperature sensor measures its temperature at regular intervals (e.g., every hour), the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[0095] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[0096] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[0097] Step 2: Send data

[0098] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[0099] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[0100] Step 3: Save Data

[0101] The server stores the received data in a database.

[0102] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[0103] Step 4: Data analysis

[0104] The server periodically analyzes the stored data to determine the pet's health status.

[0105] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[0106] Step 5: Alert Generation

[0107] The server generates an alert based on the analysis results.

[0108] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[0109] Step 6: Advice Generation

[0110] The server automatically generates advice on exercise and diet based on the results of data analysis.

[0111] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[0112] Step 7: Visualize the results

[0113] The server converts the analysis results and advice into graphs and dashboards that users can view in the app.

[0114] For example, body temperature data is displayed as a line graph and activity levels are displayed as a bar graph.

[0115] Step 8: Notifications

[0116] The device displays alerts and advice sent from the server to the user as push notifications.

[0117] For example, a notification could be sent to your smartphone saying, "Your pet's temperature is high. Please check immediately."

[0118] Step 9: View Data

[0119] Users can view detailed data and advice by opening the app.

[0120] Users can check their pet's past health data and current health status in real time, and can also view their pet's location on a map.

[0121] Step 10: Monitor and respond

[0122] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0123] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[0124] This series of processes allows you to comprehensively manage your pet's health and respond quickly if an abnormality occurs.

[0125] Example 1

[0126] 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."

[0127] Comprehensive, real-time management of pet health is an important issue for many pet owners. However, current systems collect and analyze various data separately, often resulting in inconsistent information and delays. Furthermore, when an abnormality occurs, users often have to think of a solution themselves, making it difficult to respond quickly and accurately. To solve these problems, integrated management and analysis of each data, rapid notification of abnormalities, and provision of specific advice are required.

[0128] 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.

[0129] In this invention, the server includes an imaging device equipped with means for measuring a pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements; a device having means for measuring the pet's excretion volume, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation; means for storing and analyzing data collected from the above means; means for detecting abnormalities and generating alerts based on the analysis results; means for notifying the user of the alerts; means for generating advice based on prompt sentences using a generative AI model to generate and provide exercise and dietary advice to the user; and means for tracking the pet's current location and providing the user with location information. This allows for comprehensive management of the pet's health condition in real time and enables prompt and appropriate response when an abnormality occurs. Furthermore, accurate advice is provided using a generative AI model, allowing the user to immediately know specific countermeasures.

[0130] A "means for measuring a pet's body temperature" is a device that is equipped with a sensor on a pet's collar or attached device and measures the pet's body temperature periodically or in real time.

[0131] A "means for measuring a pet's activity level" is a device that uses an acceleration sensor or gyro sensor to measure a pet's daily activity and exercise level.

[0132] A "means for measuring pet location information" is a device equipped with a GPS function that tracks a pet's current location in real time and obtains location information.

[0133] The "devices" are devices used to monitor the health of pets, such as collars that can be attached to pets, imaging devices, scales, and excrement detection sensors.

[0134] An "imaging device" is a device that uses a camera and image analysis technology to capture and analyze a pet's behavior in order to recognize the pet's movements.

[0135] The "means for measuring excretion volume" is a device that uses a sensor installed in the pet's toilet to measure the volume of urination and defecation.

[0136] The "means for measuring the weight of a pet" is a device that uses a scale to periodically measure the weight of a pet and obtains the data.

[0137] The "means for measuring pH balance" is a device equipped with a sensor for measuring the pH value of a pet's urine and feces.

[0138] The "means for storing and analyzing data" refers to a server system for storing various data in a database and analyzing the data using statistical analysis and machine learning techniques.

[0139] The "means for detecting abnormalities and generating alerts" refers to a system that generates and notifies an alert based on information when an abnormality is detected by comparing with normal data.

[0140] "Means of notifying users" refers to a system that sends alerts and analysis results to users' devices as push notifications or messages.

[0141] The "means for generating and providing advice" is a system that uses a generative AI model to automatically generate appropriate exercise and diet advice based on prompt text and provide it to users.

[0142] A "generative AI model" is a type of artificial intelligence model that generates natural language sentences based on input prompts.

[0143] A "prompt sentence" is a text sentence that is input into a generative AI model and serves as a reference sentence for the model to generate advice or text based on that prompt.

[0144] This invention is a system for comprehensively managing the health of pets. This system collects and stores data from a series of devices that measure pet body temperature, activity level, location information, excretion volume, weight, and pH balance of urination and feces, and based on the results, detects abnormalities, notifies the user, and provides appropriate advice.

[0145] First, to measure your pet's body temperature, a small temperature sensor is attached to the pet's collar. This temperature sensor monitors your pet's temperature in real time and transmits the data to a server via Wi-Fi. Similarly, to measure your pet's activity level, an accelerometer and gyro sensor are attached to the collar to measure your pet's daily activity. This data is then transmitted to the server at specific time intervals.

[0146] To measure the location of your pet, a device with GPS functionality is used, which tracks your pet's current location in real time and obtains its location information. The obtained data is sent to a server via Wi-Fi or Bluetooth.

[0147] In addition, a camera is installed as an imaging device to recognize the pet's movements. The camera captures video data and analyzes the pet's behavior using image analysis technology. This video data is also sent to the server for storage and analysis.

[0148] To measure the amount of excretion from pets, a sensor installed in the toilet is used. The sensor periodically records the amount of urination and defecation from the pet and sends the data to a server. In addition, a scale is used to measure the weight of the pet and obtain the data. To measure the pH balance of urination and defecation, a pH sensor is installed in the toilet and measures the pH value of urination and defecation.

[0149] The server uses a database management system such as MySQL (registered trademark) or MongoDB to store this data in a database. The data is then analyzed using a Python program and the Pandas library. If an anomaly is detected by comparing the analysis with normal data, the server runs an anomaly detection algorithm using a machine learning library such as Scikit-learn and generates an alert.

[0150] Furthermore, a generative AI model is used to generate exercise and diet advice based on prompts. Specifically, the following prompts are input into the generative AI model:

[0151] "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?"

[0152] The generated advice is provided to the user. Push notifications are sent to the user's device in real time using Firebase Cloud Messaging (FCM). Users can check their pet's detailed data and advice through an application developed in React Native or Swift. The service also provides a function to display the pet's location on a map using the Google® Maps API.

[0153] For example, if a pet's body temperature rises to 38.5°C, higher than the normal 37.5°C, the server analyzes this data and detects the abnormality. As a result, an alert is generated stating, "Your pet's temperature is high. Please check immediately." The generative AI model then generates advice such as, "We recommend using a wet towel to cool the pet down." These notifications and advice are sent to the user's device as push notifications.

[0154] This allows the system to comprehensively manage pet health conditions in real time, enabling prompt and appropriate responses when abnormalities occur. Furthermore, by using generative AI models to provide accurate advice, users can instantly learn specific countermeasures.

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] Step 1: Receiving data

[0157] The server receives data on pets' temperature, activity, location, movement, waste output, weight, and pH balance from various sensor devices. These devices transmit data to the server using Wi-Fi or Bluetooth. For example, a collar transmits temperature data via Wi-Fi, which is then received by the server.

[0158] Input: Sensor data from collar, camera, and litter box

[0159] Output: Various sensor data received by the server

[0160] Step 2: Save data

[0161] The server stores the received data in the respective databases. Body temperature data is stored in the "temperatures" table in MySQL, and activity data is stored in the "activities" collection in MongoDB. Video data and excrement data are also stored in appropriate storage.

[0162] Input: Various sensor data

[0163] Output: Various data stored in the database

[0164] Step 3: Data analysis

[0165] The server periodically analyzes the stored data using Python and Pandas. It compares body temperature and activity data with normal values ​​to detect abnormalities. For example, if body temperature rises from 37.5°C to 38.5°C, it is considered abnormal.

[0166] Input: Data stored in a database

[0167] Output: Presence or absence of abnormalities as analysis results

[0168] Step 4: Anomaly detection and alerting

[0169] The server runs an anomaly detection algorithm using Scikit-learn and generates an alert if an anomaly is detected. The generated alert is saved in a database and added to an alert queue. For example, if an abnormality in body temperature is detected, an alert such as "Temperature is abnormally high" is generated.

[0170] Input: Presence or absence of abnormalities as analysis results

[0171] Output: Generated alerts

[0172] Step 5: Advice Generation

[0173] The server uses a generative AI model to generate advice based on a prompt. For example, if the prompt is "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?", the server generates the advice "I recommend using a wet towel to cool him down."

[0174] Input: prompt statement

[0175] Output: Generated advice

[0176] Step 6: Notification of alerts and advice

[0177] The server notifies the user of the generated alert and advice. A push notification is sent using Firebase Cloud Messaging (FCM), and a notification saying "Your pet's temperature is high. Please check immediately" is displayed on the user's device. The generated advice is also displayed.

[0178] Input: Generated alerts and advice

[0179] Output: Push notification to the user's device

[0180] Step 7: Viewing Data

[0181] Users can access detailed data and advice by opening the app on their smartphone or computer. The app also allows users to check past health data and current health status in real time, and displays their pet's location on a map using Google Maps API.

[0182] Input: User actions

[0183] Output: Detailed data and advice displayed within the app

[0184] Step 8: User interaction

[0185] Users can quickly respond based on the advice provided, for example, if their pet has a high temperature, they can follow the advice and take measures to cool their pet down, allowing them to quickly and appropriately manage their pet's health.

[0186] Input: In-app advice

[0187] Output: Specific measures for pets

[0188] (Application example 1)

[0189] 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."

[0190] Currently, systems for effectively monitoring the health of pets exist on the market, but there is a lack of systems that specifically address the needs of pet shops. Because pet shops must manage a large number of animals at once and provide healthy pets to customers, it is important to monitor the health of individual pets in real time and respond quickly if an abnormality occurs. However, conventional systems have difficulty meeting these requirements. Therefore, an objective of the present invention is to provide a system that monitors the health of pets for sale in the store in real time and immediately notifies the customer if an abnormality occurs.

[0191] 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.

[0192] In this invention, the server includes: a collar including means for measuring the pet's body temperature, a means for measuring the pet's activity level, and a means for measuring the pet's location information; a camera equipped with means for recognizing the pet's movements; a toilet having means for measuring the pet's litter box usage, a means for measuring the pet's weight, and a means for measuring the pH balance of the pet's urination and defecation; a means for storing and analyzing data collected from the above means; a means for detecting abnormalities and generating alerts based on the analysis results; a means for notifying the user of the alerts; a means for generating and providing exercise and dietary advice to the user; a means for tracking the pet's current location and providing the user with location information; a means for monitoring the pet's health in real time using sensors and cameras installed in the store; and a means for immediately notifying a store staff member when an abnormality occurs. This enables the pet shop to efficiently manage the health of individual pets and quickly respond when an abnormality is detected.

[0193] The "means for measuring body temperature" is a device that has the function of continuously measuring the pet's body temperature and transmitting the data to a server.

[0194] A "means for measuring activity levels" is a device that uses sensors to capture a pet's movement and level of exercise and collects that activity data.

[0195] A "means for measuring location information" is a device that identifies a pet's current location using GPS or other location information technology and records it as data.

[0196] A "camera equipped with a means for recognizing motion" is a device that uses motion detection technology built into the camera to monitor and record the movements and behavior of pets.

[0197] A "means for measuring toilet usage" is a device that uses a sensor to measure the frequency and amount of toilet usage by pets and records that data.

[0198] "Means for measuring weight" refers to a device that accurately measures the weight of a pet, and is generally a weighing scale.

[0199] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet with a function that measures the pH level when a pet urinates or defecates.

[0200] "Means for storing and analyzing data" refers to a device that stores collected data in a database or the like and performs analytical processing based on this data.

[0201] The "means for detecting anomalies and generating alerts" is a device that detects abnormal conditions from the analyzed data and generates a notification to notify the user.

[0202] "Means of notifying users" refers to a function that notifies users of detected abnormalities or alerts via their smartphones or other devices.

[0203] The "means for generating and providing advice on exercise and diet" is a device that automatically generates advice on exercise and diet according to the health condition of a pet and provides it to the user.

[0204] The "means for tracking the current location and providing location information" is a device that continuously tracks the current location of a pet and provides that location information to the user.

[0205] "Means for monitoring health conditions in real time using sensors and cameras installed in the store" refers to a function that uses sensors and cameras installed in the pet shop to monitor the health conditions of pets in real time.

[0206] "Means for immediately notifying store staff when an abnormality occurs" refers to a function that immediately notifies store staff when an abnormality is detected based on the pet's health data.

[0207] This invention provides a system for monitoring the health of pets in pet shops in real time and immediately notifying them if an abnormality occurs. This system includes a collar that measures the pet's body temperature, activity level, and location information, a camera that recognizes the pet's movements, a toilet that measures the amount of toilet use, weight, and pH balance of urination and defecation, and a server that stores and analyzes this data.

[0208] The system's program is implemented in Python, and the server operates as follows: It receives data sent from pets' collars, cameras, and toilets, and stores the data in an SQLite database. The collected data is analyzed periodically to evaluate the pet's health. If an abnormality is detected based on the analysis results, an alert is generated and sent to the store clerk's terminal.

[0209] The server detects abnormalities based on body temperature, activity level, and location data, and immediately generates an alert if, for example, the body temperature exceeds 38.0°C or the activity level falls below 10. It also constantly tracks the pet's current location and provides the pet's location information within the pet shop to the store staff. Furthermore, the server uses sensors and cameras within the store to monitor the pet's health in real time, and immediately notifies the staff if an abnormality occurs.

[0210] Consider the following scenario as a concrete example: A pet's collar measures its temperature and records it as 38.5°C. The data is sent to a server, which receives the temperature data and detects that it is high compared to normal values. The server generates an alert, and a notification appears on the store clerk's device saying, "Your pet's temperature is high. Please check it immediately."

[0211] The server also has a means to generate and provide exercise and dietary advice to the user after detecting an abnormality. For example, if the pet's activity level is lower than normal, a notification will be displayed on the store clerk's device saying, "Your pet's activity level is lower than normal. Try letting it spend more time playing with toys." This advice encourages specific actions to maintain the pet's health.

[0212] Furthermore, it is possible to utilize a generative AI model. By inputting the following prompt sentence into the generative AI model, more advanced advice can be obtained.

[0213] Example prompt sentence:

[0214] “How do you monitor your pet’s health?

[0215] The data is as follows:

[0216] Pet ID: 1

[0217] Body temperature: 38.5℃

[0218] Activity amount: 10

[0219] Serving size: 50 grams

[0220] The threshold for detecting anomalies is:

[0221] Body temperature > 38.0°C

[0222] Activity amount < 10

[0223] Food intake < 100 grams

[0224] Use this data to generate alerts about your pet's health."

[0225] As described above, the system of the present invention can efficiently manage the health conditions of pets in pet shops and can respond quickly when an abnormality occurs.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1: Collect data

[0228] Sensors and collars that measure pets' body temperature, activity levels, and location information collect data. This data is sent to a server via Wi-Fi or Bluetooth. The input is data from each sensor, and the output is raw data sent to the server. Specifically, the collar measures body temperature at regular intervals and sends the data to the server.

[0229] Step 2: Save your data

[0230] The server stores the received data in an SQLite database. The input is the raw data sent from the sensor, and the output is the data stored in the database. Specifically, the server inserts the raw data into the appropriate tables.

[0231] Step 3: Analyze the data

[0232] The server periodically analyzes the stored data and evaluates the pet's health. The input is data obtained from the database, and the output is the analysis results. Specifically, the server compares the body temperature, activity level, and location data with normal values ​​to check for any abnormalities.

[0233] Step 4: Detect anomalies and generate alerts

[0234] The server generates an alert based on the analysis results if an abnormality is detected. The input is the analysis results, and the output is the generated alert. Specifically, if the body temperature exceeds a specified value, an "Abnormal Body Temperature" alert is generated.

[0235] Step 5: Alert Notification

[0236] The generated alert is sent to the store clerk's device. The input is the generated alert, and the output is the notification displayed on the device. Specifically, the server generates a push notification and sends it to the store clerk's smartphone or tablet.

[0237] Step 6: User (store clerk) response

[0238] The store clerk who receives the notification checks the pet's condition and takes appropriate action. The input is the alert notification displayed on the terminal, and the output is the clerk's response action. Specifically, the clerk checks the notification and takes action such as cooling the pet if its body temperature is high.

[0239] Step 7: Generating and Providing Advice

[0240] Based on the analysis results, the server automatically generates advice on exercise and diet and provides it to the store clerk's terminal. The input is the analysis results, and the output is the generated advice. Specifically, if the amount of activity is low, the server generates advice such as "Please increase the amount of time the child spends playing with toys."

[0241] Step 8: Tracking and Providing Location Information

[0242] The server constantly tracks the location of pets and provides them to store clerks as needed. The input is location data, and the output is location information provision. Specifically, the server displays the location information of a specific pet on a map and provides it to store clerks.

[0243] Step 9: Real-time monitoring

[0244] The health of pets is monitored in real time using sensors and cameras installed in the store. The input is data from the sensors and cameras, and the output is health status information updated in real time. Specifically, the camera captures the pet's movements and sends that information to a server for analysis.

[0245] Step 10: Emergency Notification

[0246] When an abnormality occurs, the store clerk is notified immediately. The input is the abnormality detection data obtained through real-time monitoring, and the output is the immediately sent notification. Specifically, when an abnormality is detected, a notification is sent stating that "immediate action is required."

[0247] 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.

[0248] The present invention provides a system that comprehensively manages the health status of a pet, recognizes the user's emotions, and personalizes notifications and advice based on those emotions. This system is configured as follows.

[0249] System configuration and program processing

[0250] The system includes the following elements:

[0251] 1. A collar that measures your pet's temperature, activity, and location

[0252] 2. Pet movement detection camera

[0253] 3. A toilet that measures your pet's toilet usage, weight, and pH balance of urination and defecation

[0254] 4. Server for storing and analyzing collected data

[0255] 5. A device that generates alerts based on data analysis and notifies users of them.

[0256] 6. A means of providing users with appropriate exercise and dietary advice

[0257] 7. A way to track your pet's current location and provide location information to you

[0258] 8. Emotion engine that recognizes user emotions and adjusts notification and advice content

[0259] Program processing

[0260] server

[0261] 1. Data Receipt and Storage:

[0262] The server receives data from the collar, camera, and toilet and stores each piece of data in a database.

[0263] 2. Data analysis and anomaly detection:

[0264] The stored data is analyzed periodically to determine the pet's health status, and an alert is generated if an abnormality is detected from the analysis results.

[0265] 3. Advice Generation:

[0266] Based on the analysis results, appropriate advice regarding your pet's exercise and diet is automatically generated.

[0267] 4. Visualizing the results:

[0268] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[0269] Terminal

[0270] 1. Emotion recognition:

[0271] The emotion engine analyzes the user's emotions from facial expressions and tone of voice, learning emotional patterns based on the user's interaction history.

[0272] 2. Notification display:

[0273] Alerts and advice sent from the server are displayed to the user as push notifications. The notification content is personalized based on the analysis results of the emotion engine.

[0274] 3. Data Viewing:

[0275] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[0276] User

[0277] 1. Monitoring and Response:

[0278] Users can monitor their pet's health in real time, respond quickly when an alert occurs, and manage their pet's exercise and diet appropriately based on the advice provided.

[0279] Specific examples

[0280] Example 1: Your pet has a high body temperature

[0281] 1. Data Collection:

[0282] The collar measures your pet's temperature and records it as 39.0°C. The data is then sent to a server via Wi-Fi.

[0283] 2. Data Analysis:

[0284] The server receives the temperature data and stores it in a database. It compares it with the normal temperature and determines that 39.0°C is a high temperature. It generates an alert and notifies the user.

[0285] 3. Notifications and Emotion Recognition:

[0286] The device will display a notification saying, "Your pet's temperature is high. Please check immediately." The emotion engine will analyze the user's facial expressions and, if they are feeling anxious, will display an additional notification saying, "Your pet's temperature is high. Please stay calm and check and contact your veterinarian."

[0287] 4. Support:

[0288] Users can view notifications, check in on their pet, and follow the provided calming strategies if they feel anxious.

[0289] Example 2: If your pet is not very active

[0290] 1. Data Collection:

[0291] The collar measures daily activity and records when the dog is only moving about 30% of normal activity, sending the data to a server via Bluetooth.

[0292] 2. Data Analysis:

[0293] The server receives the activity data and stores it in a database. It compares it with the normal value and determines that the activity level is low. It generates advice and notifies the user.

[0294] 3. Notifications and Emotion Recognition:

[0295] The device will display a notification with advice such as, "Your pet is less active than usual. Try letting them spend more time playing with toys." If the emotion engine determines that the user is tired from the tone of their voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy play."

[0296] 4. Support:

[0297] Users can check the advice and increase the amount of time they spend playing with their pets without any stress.

[0298] In this way, this system comprehensively manages the health condition of pets and takes into consideration the user's feelings, allowing them to care for their pets with peace of mind.

[0299] The processing flow will be explained below.

[0300] Step 1: Data measurement and collection

[0301] Collar: Your pet's temperature sensor measures its temperature every hour, the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[0302] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[0303] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[0304] Step 2: Send data

[0305] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[0306] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[0307] Step 3: Save Data

[0308] The server stores the received data in a database.

[0309] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[0310] Step 4: Data analysis

[0311] The server analyzes the stored data at regular intervals to assess the pet's health.

[0312] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[0313] Step 5: Emotion Recognition

[0314] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0315] For example, it uses a camera and microphone to analyze a user's facial expressions and voice to identify stress or anxiety.

[0316] Step 6: Alert Generation

[0317] The server generates an alert based on the analysis results.

[0318] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[0319] Step 7: Advice Generation

[0320] The server automatically generates advice on exercise and diet based on the results of data analysis.

[0321] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[0322] Step 8: Notifications and Personalization

[0323] The device displays alerts and advice sent from the server to the user as push notifications.

[0324] Personalize notifications based on the results of emotion engine analysis (e.g., "Your pet has a high temperature. Please stay calm and check and contact your veterinarian.").

[0325] Step 9: View Data

[0326] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[0327] Step 10: Monitor and respond

[0328] Users can monitor their pet's health in real time and respond quickly when an alert occurs.

[0329] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[0330] Specific examples

[0331] Example 1: Your pet has a high body temperature

[0332] Step 1: The collar measures your pet's temperature and records it as 39.0°C. The data is sent to a server via Wi-Fi.

[0333] Step 2: The server receives the temperature data and stores it in a database.

[0334] Step 3: The server compares the temperature to its normal value and determines that 39.0°C is a high temperature.

[0335] Step 4: The server generates an alert and notifies the user.

[0336] Step 5: The device's emotion engine analyzes the user's facial expression and if they are feeling anxious, it will send an additional notification saying, "Your pet has a high temperature. Please stay calm and check it and contact your veterinarian."

[0337] Step 6: The user checks the notification, checks in on their pet, and follows the provided calming strategies if they feel uneasy.

[0338] Example 2: If your pet is not very active

[0339] Step 1: The collar measures your daily activity and records that you're only moving about 30% of your normal activity. The data is then sent via Bluetooth to a server.

[0340] Step 2: The server receives the activity data and stores it in a database.

[0341] Step 3: The server determines that the activity is low compared to normal.

[0342] Step 4: The server generates the advice and notifies the user.

[0343] Step 5: Your device will display a notification with the advice, "Your pet's activity level is lower than usual. Try increasing the amount of time it spends playing with toys."

[0344] Step 6: If the emotion engine determines that the user is tired from their tone of voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy games."

[0345] Step 7: The user checks the advice and increases playtime with their pet without forcing themselves.

[0346] Example 2

[0347] 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."

[0348] Conventional pet health management systems simply collect and store data, making it difficult to comprehensively understand a pet's health condition. Furthermore, alerts and advice are provided uniformly without considering the user's emotions, meaning they are unable to provide the necessary information at the timing and with the content the user desires. Furthermore, there is a lack of methods for detecting anomalies and generating advice that are suited to specific conditions, making these systems less effective in managing pet health.

[0349] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera equipped with means for measuring the pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements, a toilet equipped with means for measuring the pet's toilet usage amount, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation, means for storing and analyzing data collected from the above means, means for detecting abnormalities and generating alerts based on the analysis results, means for notifying the user of the alerts, means for generating and providing exercise and dietary advice to the user, means for tracking the pet's current location and providing the user with location information, and means for recognizing the user's emotions and adjusting the content of notifications and advice. This makes it possible to comprehensively manage and analyze the pet's health condition and take appropriate measures based on the user's emotions.

[0350] A "means for measuring body temperature" is a device that has the function of measuring a pet's body temperature and recording and transmitting that data.

[0351] A "means for measuring activity levels" is a device that has the function of measuring a pet's movements and exercise, and recording and transmitting that data.

[0352] A "means for measuring location information" is a device that has the function of identifying the current location of a pet and recording and transmitting that information.

[0353] A "camera equipped with a means for recognizing movement" is a camera device that recognizes and records pet movements and behavior as video footage and analyzes the data.

[0354] A "means for measuring litter box usage" is a device that has the function of measuring the amount of litter box used by a pet and recording and transmitting that data.

[0355] A "means for measuring weight" is a device that has the function of measuring a pet's weight and recording and transmitting that data.

[0356] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet device that has the function of measuring the pH balance of a pet's urination and defecation, and recording and transmitting that data.

[0357] The "means for storing and analyzing data" refers to a server that stores data collected from each device and analyzes it to determine health status and abnormalities.

[0358] "Means for detecting anomalies and generating alerts" refers to a system function that detects anomalies from the analysis results and generates an alert to notify the user.

[0359] "Means of notifying users" refers to communication functions and devices used to notify users of alerts and advice.

[0360] The "means for generating and providing advice" is a function that automatically generates and provides advice on exercise and diet for pet health management based on the analysis results.

[0361] "Means for tracking current location and providing location information to the user" refers to a function that tracks the pet's location in real time and provides that information to the user.

[0362] "Means for recognizing user emotions and adjusting notification and advice content" refers to a system that has the ability to analyze and recognize user emotions and personalize the content of alerts and advice based on those emotions.

[0363] The present invention relates to a system for comprehensively managing pet health conditions, recognizing the user's emotions, and providing personalized notifications and advice based on those emotions. The system includes the following components:

[0364] server

[0365] The server receives data from the collar, camera, and litter box, such as the pet's body temperature, activity level, location, movement, litter box usage, weight, and pH balance of urination and defecation, and stores it in a database. The stored data is periodically analyzed using Python libraries (e.g., Pandas, NumPy). If an abnormal value is detected during the analysis, an alert is generated. In addition, appropriate advice regarding the pet's exercise and diet is automatically generated based on the analysis results. These analysis results and advice are converted into graphs and dashboards, and summarized in a format that can be viewed by the user in the app. The specific software used is an SQL database, Python, Matplotlib, and D3.js.

[0366] As a concrete example, consider the case where a pet's body temperature reaches 39.0°C. The collar measures the temperature and sends the data to a server via Wi-Fi. The server receives the data and stores it in a database. It analyzes the stored data and determines that the body temperature is abnormally high. It generates an abnormal alert indicating that the body temperature is high and notifies the user.

[0367] Terminal

[0368] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice. Using OpenCV and a voice analysis library, it learns the user's emotional patterns and personalizes the content of alerts and advice based on those patterns. Alerts and advice sent from the server are displayed to the user as push notifications. The content of the notifications is also adjusted according to the user's emotions. By opening the app, users can view detailed data and advice, and can also check their pet's current location on a map. The map display uses the Google Maps API.

[0369] As a concrete example, consider a case where a pet's activity level is only about 30% of normal. The collar measures the amount of activity throughout the day and sends the data via Bluetooth to a server. The server receives the data, stores it in a database, and detects a decrease in activity level. It generates advice to encourage activity and notifies the device. The device determines from the tone of the user's voice that the pet is tired, and displays additional advice such as, "When you are tired, it's a good idea to incorporate some easy games."

[0370] User

[0371] Users can monitor their pet's health in real time through their device and respond quickly when an alert occurs. They can check advice on the app and properly manage their pet's exercise and diet. Users can also check their pet's current location and respond quickly if their pet gets lost.

[0372] An example prompt is the text prompt "Your pet's temperature is higher than normal. Please provide advice to help the user stay calm."

[0373] As described above, the present invention is a system that can comprehensively manage the health condition of a pet and take appropriate action depending on the user's emotions.

[0374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0375] Step 1: Receiving and storing data

[0376] The server receives data packets sent from the collar, camera, and litter box. Specific input data includes the pet's body temperature, activity level, location, litter box usage, weight, and pH balance of urination and defecation. This data is received via Wi-Fi or Bluetooth through a communication module. The received data is stored in a database using a Python library (e.g., SQLAlchemy). Specific output data is various data stored in related tables in the database.

[0377] Step 2: Data analysis and anomaly detection

[0378] The server periodically analyzes the stored data. The input is various health data stored in a database. The data is processed using Python libraries (e.g., Pandas, NumPy) and each data point is compared with a normal value. If an abnormality is detected as a result of the analysis, an alert is generated. Specifically, a body temperature above 38°C is considered abnormal. The output is a flag indicating an abnormality and a trigger for generating an alert.

[0379] Step 3: Advice Generation

[0380] Based on the analysis results, the server generates appropriate advice regarding pet exercise and diet. The input is the data analysis results and anomaly flags. Specific advice sentences are created using generative AI models and rule-based systems. For example, if the pet has a high body temperature, the server generates advice such as "Give the pet plenty of water and use a cooling sheet." The output is advice sentences provided to the user.

[0381] Step 4: Visualize the results

[0382] The server converts the analysis results and advice into graphs and dashboards. The input is the analysis results and generated advice. Matplotlib and D3.js are used to visualize the data and integrate it into a web application. The output is graphs and dashboards that users can view in the app. For example, a graph showing the progression of body temperature over time.

[0383] Step 5: Emotion Recognition

[0384] The device's emotion engine analyzes the user's facial expressions and tone of voice. The input is audio and video data collected by the device's camera and microphone. Using OpenCV and voice analysis libraries, it analyzes the user's facial expressions and tone of voice to recognize emotional patterns. The output is a tag of the user's emotional state (e.g., anxiety, fatigue).

[0385] Step 6: Notifications and Personalization

[0386] The device displays alerts and advice sent from the server to the user as push notifications. The input is the alert notification from the server and the analysis results of the emotion engine. The notification content is personalized based on the results of the emotion engine. For example, if a pet's temperature is high, a notification saying "Your pet's temperature is high. Please stay calm and check and contact a veterinarian" is displayed. The output is a personalized notification message.

[0387] Step 7: View detailed data

[0388] Users can view detailed data and advice by opening the app on their device. The input is the analysis results and advice sent from the server. The app displays this data, allowing users to check past temperature and activity data over time. The output is detailed data and interactive graphs displayed in the user interface.

[0389] Step 8: Check your location

[0390] The app allows users to check their pet's current location on a map. The input is location information sent from the collar. The location information is plotted on a map using the Google Maps API. The output is the pet's current location displayed on the map.

[0391] Step 9: Monitor and respond

[0392] Users can monitor their pet's health in real time through the app and respond quickly when an alert occurs. The input is push notifications and detailed data from the device. The user follows the advice to manage their pet's health, for example, using a cooling sheet if the pet has a high temperature. The output is the user's appropriate action based on the pet's health condition.

[0393] (Application example 2)

[0394] 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."

[0395] In today's pet care industry, there are limited means for comprehensively managing pet health. Furthermore, the services and advice provided do not take into account the user's emotions, making it difficult for users to properly manage their pet's health. Furthermore, in physical stores, there is an insufficient system for suggesting optimal services and products based on the pet's health and the user's emotions.

[0396] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the notification content and advice content, and means for proposing pet-related services and products based on the user's emotions. This makes it possible to respond in consideration of the user's emotions, and to propose optimal pet care services and products even in physical stores.

[0397] A "means for measuring pet body temperature" is a device that measures a pet's body temperature in real time using a temperature sensor integrated into a pet's collar or other wearable device.

[0398] A "means for measuring pet activity" is a device that measures the distance traveled and the amount of exercise a pet performs through a pet's collar or other wearable device.

[0399] A "means for measuring pet location information" is a device that uses GPS or other location measurement technology to determine the current location of a pet.

[0400] A "camera equipped with a means for recognizing pet movements" is a camera device that captures pet movements and analyzes them using image analysis technology.

[0401] A "means for measuring pet litter box usage" is a device equipped with a sensor that records the number and frequency of a pet's litter box use.

[0402] "Means for measuring pet weight" refers to a scale or wearable device that measures the weight of a pet when it is on board.

[0403] A "toilet with a means for measuring the pH balance of a pet's urination and defecation" is a toilet device equipped with a sensor for detecting the pH balance when a pet urinates or defecates.

[0404] "Means for storing and analyzing data collected from the above means" refers to servers and software systems that accumulate data obtained from various sensors and devices and analyze that data.

[0405] "Means for detecting anomalies and generating alerts based on analysis results" refers to systems or software that detect anomalies from analyzed data and issue warnings to users.

[0406] The "means of notifying users of the above alerts" refers to a system for pushing warnings and notifications to users' devices such as smartphones and tablets.

[0407] "Means for recognizing the user's emotions and adjusting the content of notifications and advice" refers to a system that uses a camera and microphone to analyze the user's facial expressions and voice, and then appropriately adjusts notifications and advice based on those emotions.

[0408] The "means for generating and providing exercise and dietary advice to users" is a system that generates optimal advice regarding the amount of exercise and diet for pets based on the analyzed data and provides it to users.

[0409] "Means for tracking a pet's current location and providing location information to the user" refers to a system that uses location tracking technology such as GPS to identify a pet's current location and notify the user of that information.

[0410] The "means for suggesting pet-related services and products based on the user's emotions" is a system that analyzes the user's emotional state and recommends the most suitable pet care services and products based on the results.

[0411] To implement this invention, the following system configuration and process are required.

[0412] The system includes multiple measuring devices to collect information such as a pet's body temperature, activity level, and location, a server to analyze and notify this data, and an emotion engine that recognizes the user's emotions and personalizes the notification content.

[0413] System Configuration

[0414] Collar: Includes a means to measure your pet's temperature, activity, and location.

[0415] Hardware used: Body temperature sensor, accelerometer, GPS module

[0416] Software used: Data transmission protocols (e.g. Bluetooth, Wi-Fi)

[0417] Camera: Equipped with a means to recognize pet movements and record footage.

[0418] Hardware used: High resolution camera (e.g. Logitech C920)

[0419] Software used: Image analysis algorithms (e.g., OpenCV)

[0420] Toilet: Includes a means to measure your pet's toilet usage, weight, and pH balance of urination and defecation.

[0421] Hardware used: Weight scale, pH sensor

[0422] Software used: Sensor data analysis tools (e.g., SciPy)

[0423] Server: Stores and analyzes various data, detects abnormalities, and generates alerts.

[0424] Software used: databases (e.g., MySQL), data analysis tools (e.g., NumPy, SciPy)

[0425] Device: Notify users and adjust advice.

[0426] Hardware used: Smartphone, tablet

[0427] Software used: Notification system API (e.g. Firebase Cloud Messaging)

[0428] Emotion engine: Recognizes user emotions and personalizes notifications and advice.

[0429] Software used: Facial expression recognition algorithms (e.g. DLib), voice analysis engine

[0430] Program Processing Details

[0431] The server receives data such as body temperature, activity level, and location information sent from the pet's collar and stores it in a database. The server then analyzes the stored data and generates an alert if an abnormality is detected, sending a notification to the user's device. Data analysis tools such as NumPy and SciPy are used for the analysis.

[0432] The device checks the notification received by the user, and an emotion engine analyzes the user's facial expressions and voice to recognize their emotions. For example, if the user is feeling anxious, the notification content will be personalized and provide advice such as, "Your pet's temperature is high. Please stay calm and check it and contact your veterinarian." Notifications on the device use notification system APIs such as Firebase Cloud Messaging.

[0433] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes their emotions using DLib and a voice analysis engine. For example, if a user receives a notification that their pet's temperature is rising and looks anxious, the system will provide additional advice such as "stay calm."

[0434] Specific examples

[0435] Example 1: Your pet has a high body temperature

[0436] 1. Data collection: The collar measures the pet's temperature to be 39.0°C and sends the data to the server.

[0437] 2. Data analysis: The server analyzes the body temperature data, determines that 39.0°C is a high temperature, and generates an alert.

[0438] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet's temperature is high. Please check immediately." If the emotion engine analyzes the user's facial expression and they are feeling anxious, an additional notification will be displayed saying, "Please stay calm and check and contact your veterinarian."

[0439] Example 2: If your pet is not very active

[0440] 1. Data collection: The collar records activity at approximately 30% of normal activity and sends the data to a server.

[0441] 2. Data analysis: The server analyzes the activity data, determines that the activity level is low, and generates advice.

[0442] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet is less active than usual. Try spending more time playing with toys." If the emotion engine determines that the user is tired based on their tone of voice, additional advice will be given: "When you're tired, it's a good idea to incorporate some easy play."

[0443] Prompt Sentence Examples

[0444] "The pet's temperature is high and the user is feeling anxious. Please advise the user to remain calm and, as the pet's temperature is high, suggest that they seek appropriate medical attention."

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1: Data collection

[0447] The collar measures the pet's body temperature, activity level, and location information in real time. This data is sent to a server using Bluetooth or Wi-Fi. The input is body temperature, activity level, and location information data, and the output is the raw data sent to the server. This stage includes specific operations: the body temperature sensor measures body temperature, the accelerometer measures activity level, and the GPS measures location information.

[0448] Step 2: Save your data

[0449] The server stores the received data in a database. The input is the raw data sent from the collar, and the output is structured data stored in the database. This processing step uses a database management system such as MySQL or PostgreSQL to store the temperature, activity, and location data in tables.

[0450] Step 3: Data analysis

[0451] The server periodically launches a process to analyze the stored data. The input is the raw data stored in the database, and the output is the analysis results. Specifically, statistical analysis is performed using NumPy and SciPy to detect outliers. At this time, a threshold is set to detect anomalies by comparing with past data.

[0452] Step 4: Anomaly detection and alerting

[0453] The server detects anomalies based on the analysis results and generates an alert. The input is the analysis results and the output is the generated alert. If an anomaly is detected, an alert message is generated and sent to the user's device. This process uses an algorithm that triggers an alert when certain conditions are met.

[0454] Step 5: Alert Notification

[0455] An alert notification is displayed on the device. At this stage, the alert is pushed to the user in real time. The input is the alert message sent from the server, and the output is the notification displayed on the device. Specifically, the alert is sent using a service such as Firebase Cloud Messaging.

[0456] Step 6: User sentiment analysis

[0457] The device uses a camera and microphone to analyze the user's emotions. The input is the user's facial expression images and voice data, and the output is the emotion determination result. Facial expressions are analyzed using DLib, and tone of voice is analyzed using a voice analysis engine. Specifically, the system involves the user showing their facial expressions in front of the camera and speaking into the microphone.

[0458] Step 7: Personalize your notifications

[0459] The device personalizes the content of notifications and advice based on the results of emotion analysis. The input is the emotion determination result and the alert message, and the output is the adjusted notification content. If the emotion engine analyzes the user's emotions and detects anxiety or fatigue, it adds advice appropriate to that state. For example, it generates messages such as "Please stay calm" or "We recommend some easy games."

[0460] Step 8: Inform and advise users

[0461] The device displays personalized notification content and advice to the user. The input is the tailored notification content, and the output is the notification message displayed to the user. Specifically, this includes the behavior of displaying a personalized message on the screen of a smartphone or tablet.

[0462] Step 9: Propose your service or product

[0463] The device suggests services and products offered in physical stores based on the user's emotions and the pet's health condition. The input is the emotion assessment result and health condition data, and the output is a list of suggested services and products. For example, if the user is worried about their pet's high temperature, suggestions such as "This product will make it easy to manage their pet's temperature" or "We recommend using this service" will be displayed.

[0464] Through the above steps, the present invention provides a system that comprehensively manages the health condition of a pet and takes appropriate measures taking into account the user's emotions.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] In the smart glasses 214, 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.

[0480] 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."

[0481] The present invention provides a system for comprehensively managing the health of pets. This system is composed of multiple elements that cooperate to monitor the health of pets, and if an abnormality occurs, it promptly notifies the user and provides appropriate advice.

[0482] Program processing

[0483] The system operates as follows.

[0484] server:

[0485] 1. Data Receipt and Storage:

[0486] The server receives data from the pet's collar, camera, and litter box.

[0487] The received data is stored in the respective databases.

[0488] 2. Data analysis and anomaly detection:

[0489] The server periodically analyzes the stored data and assesses the pet's health.

[0490] It compares with normal values ​​and generates an alert if an abnormality is detected.

[0491] 3. Advice Generation:

[0492] Based on the results of data analysis, appropriate advice regarding your pet's exercise and diet is automatically generated.

[0493] 4. Visualizing the results:

[0494] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[0495] Device (user's smartphone or computer):

[0496] 1. Notification display:

[0497] Alerts and advice sent from the server are displayed to the user as push notifications.

[0498] Open the app to view detailed data and advice.

[0499] 2. Data browsing:

[0500] Users can check their past health data and current health status in real time within the app.

[0501] You can check your pet's location on a map.

[0502] User:

[0503] 1. Monitoring and Response:

[0504] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0505] Based on the advice provided, you can adjust your pet's exercise and diet.

[0506] Specific examples

[0507] Example 1: Your pet has a high body temperature

[0508] 1. Data Collection:

[0509] The collar measures the body temperature and records it as 38.5°C (normal is 37.5°C).

[0510] The data is sent to a server via Wi-Fi.

[0511] 2. Data Analysis:

[0512] The server receives the temperature data and stores it in a database.

[0513] Compared to normal values, 38.5℃ is considered to be a high temperature.

[0514] The server generates an alert and notifies the user.

[0515] 3. Notice and Response:

[0516] A notification will appear on your device saying, "Your pet's temperature is high. Please check immediately."

[0517] The user checks the notification and checks the status of the pet.

[0518] Example 2: If your pet is not very active

[0519] 1. Data Collection:

[0520] The collar measures daily activity and records that the dog is only moving about 30% of normal activity.

[0521] The data is sent to the server via Bluetooth.

[0522] 2. Data Analysis:

[0523] The server receives the activity data and stores it in a database.

[0524] Compared to normal values, it is determined that the amount of activity is low.

[0525] The server generates the advice and notifies the user.

[0526] 3. Notice and Response:

[0527] An advice notification will appear on your device saying, "Your pet is less active than usual. Try letting it spend more time playing with toys."

[0528] Users can check the advice and spend more time playing with their pets.

[0529] In this way, the system of the present invention can closely monitor the health condition of a pet and respond quickly and appropriately when an abnormality occurs, allowing pet owners to manage their pet's health with peace of mind, which greatly contributes to maintaining the pet's health.

[0530] The processing flow will be explained below.

[0531] Step 1: Data measurement and collection

[0532] Collar: Your pet's temperature sensor measures its temperature at regular intervals (e.g., every hour), the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[0533] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[0534] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[0535] Step 2: Send data

[0536] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[0537] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[0538] Step 3: Save Data

[0539] The server stores the received data in a database.

[0540] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[0541] Step 4: Data analysis

[0542] The server periodically analyzes the stored data to determine the pet's health status.

[0543] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[0544] Step 5: Alert Generation

[0545] The server generates an alert based on the analysis results.

[0546] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[0547] Step 6: Advice Generation

[0548] The server automatically generates advice on exercise and diet based on the results of data analysis.

[0549] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[0550] Step 7: Visualize the results

[0551] The server converts the analysis results and advice into graphs and dashboards that users can view in the app.

[0552] For example, body temperature data is displayed as a line graph and activity levels are displayed as a bar graph.

[0553] Step 8: Notifications

[0554] The device displays alerts and advice sent from the server to the user as push notifications.

[0555] For example, a notification could be sent to your smartphone saying, "Your pet's temperature is high. Please check immediately."

[0556] Step 9: View Data

[0557] Users can view detailed data and advice by opening the app.

[0558] Users can check their pet's past health data and current health status in real time, and can also view their pet's location on a map.

[0559] Step 10: Monitor and respond

[0560] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0561] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[0562] This series of processes allows you to comprehensively manage your pet's health and respond quickly if an abnormality occurs.

[0563] Example 1

[0564] 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."

[0565] Comprehensive, real-time management of pet health is an important issue for many pet owners. However, current systems collect and analyze various data separately, often resulting in inconsistent information and delays. Furthermore, when an abnormality occurs, users often have to think of a solution themselves, making it difficult to respond quickly and accurately. To solve these problems, integrated management and analysis of each data, rapid notification of abnormalities, and provision of specific advice are required.

[0566] 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.

[0567] In this invention, the server includes an imaging device equipped with means for measuring a pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements; a device having means for measuring the pet's excretion volume, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation; means for storing and analyzing data collected from the above means; means for detecting abnormalities and generating alerts based on the analysis results; means for notifying the user of the alerts; means for generating advice based on prompt sentences using a generative AI model to generate and provide exercise and dietary advice to the user; and means for tracking the pet's current location and providing the user with location information. This allows for comprehensive management of the pet's health condition in real time and enables prompt and appropriate response when an abnormality occurs. Furthermore, accurate advice is provided using a generative AI model, allowing the user to immediately know specific countermeasures.

[0568] A "means for measuring a pet's body temperature" is a device that is equipped with a sensor on a pet's collar or attached device and measures the pet's body temperature periodically or in real time.

[0569] A "means for measuring a pet's activity level" is a device that uses an acceleration sensor or gyro sensor to measure a pet's daily activity and exercise level.

[0570] A "means for measuring pet location information" is a device equipped with a GPS function that tracks a pet's current location in real time and obtains location information.

[0571] The "devices" are devices used to monitor the health of pets, such as collars that can be attached to pets, imaging devices, scales, and excrement detection sensors.

[0572] An "imaging device" is a device that uses a camera and image analysis technology to capture and analyze a pet's behavior in order to recognize the pet's movements.

[0573] The "means for measuring excretion volume" is a device that uses a sensor installed in the pet's toilet to measure the volume of urination and defecation.

[0574] The "means for measuring the weight of a pet" is a device that uses a scale to periodically measure the weight of a pet and obtains the data.

[0575] The "means for measuring pH balance" is a device equipped with a sensor for measuring the pH value of a pet's urine and feces.

[0576] The "means for storing and analyzing data" refers to a server system for storing various data in a database and analyzing the data using statistical analysis and machine learning techniques.

[0577] The "means for detecting abnormalities and generating alerts" refers to a system that generates and notifies an alert based on information when an abnormality is detected by comparing with normal data.

[0578] "Means of notifying users" refers to a system that sends alerts and analysis results to users' devices as push notifications or messages.

[0579] The "means for generating and providing advice" is a system that uses a generative AI model to automatically generate appropriate exercise and diet advice based on prompt text and provide it to users.

[0580] A "generative AI model" is a type of artificial intelligence model that generates natural language sentences based on input prompts.

[0581] A "prompt sentence" is a text sentence that is input into a generative AI model and serves as a reference sentence for the model to generate advice or text based on that prompt.

[0582] This invention is a system for comprehensively managing the health of pets. This system collects and stores data from a series of devices that measure pet body temperature, activity level, location information, excretion volume, weight, and pH balance of urination and feces, and based on the results, detects abnormalities, notifies the user, and provides appropriate advice.

[0583] First, to measure your pet's body temperature, a small temperature sensor is attached to the pet's collar. This temperature sensor monitors your pet's temperature in real time and transmits the data to a server via Wi-Fi. Similarly, to measure your pet's activity level, an accelerometer and gyro sensor are attached to the collar to measure your pet's daily activity. This data is then transmitted to the server at specific time intervals.

[0584] To measure the location of your pet, a device with GPS functionality is used, which tracks your pet's current location in real time and obtains its location information. The obtained data is sent to a server via Wi-Fi or Bluetooth.

[0585] In addition, a camera is installed as an imaging device to recognize the pet's movements. The camera captures video data and analyzes the pet's behavior using image analysis technology. This video data is also sent to the server for storage and analysis.

[0586] To measure the amount of excretion from pets, a sensor installed in the toilet is used. The sensor periodically records the amount of urination and defecation from the pet and sends the data to a server. In addition, a scale is used to measure the weight of the pet and obtain the data. To measure the pH balance of urination and defecation, a pH sensor is installed in the toilet and measures the pH value of urination and defecation.

[0587] The server uses a database management system such as MySQL or MongoDB to store this data in a database. It then analyzes the data using Python programs and the Pandas library. If an anomaly is detected by comparing the analysis with normal data, the server runs an anomaly detection algorithm using a machine learning library such as Scikit-learn to generate an alert.

[0588] Furthermore, a generative AI model is used to generate exercise and diet advice based on prompts. Specifically, the following prompts are input into the generative AI model:

[0589] "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?"

[0590] The generated advice is provided to the user. Push notifications are sent to the user's device in real time using Firebase Cloud Messaging (FCM). Users can check their pet's detailed data and advice through an application developed in React Native or Swift. The app also provides a function to display the pet's location on a map using the Google Maps API.

[0591] For example, if a pet's body temperature rises to 38.5°C, higher than the normal 37.5°C, the server analyzes this data and detects the abnormality. As a result, an alert is generated stating, "Your pet's temperature is high. Please check immediately." The generative AI model then generates advice such as, "We recommend using a wet towel to cool the pet down." These notifications and advice are sent to the user's device as push notifications.

[0592] This allows the system to comprehensively manage pet health conditions in real time, enabling prompt and appropriate responses when abnormalities occur. Furthermore, by using generative AI models to provide accurate advice, users can instantly learn specific countermeasures.

[0593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0594] Step 1: Receiving data

[0595] The server receives data on pets' temperature, activity, location, movement, waste output, weight, and pH balance from various sensor devices. These devices transmit data to the server using Wi-Fi or Bluetooth. For example, a collar transmits temperature data via Wi-Fi, which is then received by the server.

[0596] Input: Sensor data from collar, camera, and litter box

[0597] Output: Various sensor data received by the server

[0598] Step 2: Save data

[0599] The server stores the received data in the respective databases. Body temperature data is stored in the "temperatures" table in MySQL, and activity data is stored in the "activities" collection in MongoDB. Video data and excrement data are also stored in appropriate storage.

[0600] Input: Various sensor data

[0601] Output: Various data stored in the database

[0602] Step 3: Data analysis

[0603] The server periodically analyzes the stored data using Python and Pandas. It compares body temperature and activity data with normal values ​​to detect abnormalities. For example, if body temperature rises from 37.5°C to 38.5°C, it is considered abnormal.

[0604] Input: Data stored in a database

[0605] Output: Presence or absence of abnormalities as analysis results

[0606] Step 4: Anomaly detection and alerting

[0607] The server runs an anomaly detection algorithm using Scikit-learn and generates an alert if an anomaly is detected. The generated alert is saved in a database and added to an alert queue. For example, if an abnormality in body temperature is detected, an alert such as "Temperature is abnormally high" is generated.

[0608] Input: Presence or absence of abnormalities as analysis results

[0609] Output: Generated alerts

[0610] Step 5: Advice Generation

[0611] The server uses a generative AI model to generate advice based on a prompt. For example, if the prompt is "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?", the server generates the advice "I recommend using a wet towel to cool him down."

[0612] Input: prompt statement

[0613] Output: Generated advice

[0614] Step 6: Notification of alerts and advice

[0615] The server notifies the user of the generated alert and advice. A push notification is sent using Firebase Cloud Messaging (FCM), and a notification saying "Your pet's temperature is high. Please check immediately" is displayed on the user's device. The generated advice is also displayed.

[0616] Input: Generated alerts and advice

[0617] Output: Push notification to the user's device

[0618] Step 7: Viewing Data

[0619] Users can access detailed data and advice by opening the app on their smartphone or computer. The app also allows users to check past health data and current health status in real time, and displays their pet's location on a map using Google Maps API.

[0620] Input: User actions

[0621] Output: Detailed data and advice displayed within the app

[0622] Step 8: User interaction

[0623] Users can quickly respond based on the advice provided, for example, if their pet has a high temperature, they can follow the advice and take measures to cool their pet down, allowing them to quickly and appropriately manage their pet's health.

[0624] Input: In-app advice

[0625] Output: Specific measures for pets

[0626] (Application example 1)

[0627] 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."

[0628] Currently, systems for effectively monitoring the health of pets exist on the market, but there is a lack of systems that specifically address the needs of pet shops. Because pet shops must manage a large number of animals at once and provide healthy pets to customers, it is important to monitor the health of individual pets in real time and respond quickly if an abnormality occurs. However, conventional systems have difficulty meeting these requirements. Therefore, an objective of the present invention is to provide a system that monitors the health of pets for sale in the store in real time and immediately notifies the customer if an abnormality occurs.

[0629] 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.

[0630] In this invention, the server includes: a collar including means for measuring the pet's body temperature, a means for measuring the pet's activity level, and a means for measuring the pet's location information; a camera equipped with means for recognizing the pet's movements; a toilet having means for measuring the pet's litter box usage, a means for measuring the pet's weight, and a means for measuring the pH balance of the pet's urination and defecation; a means for storing and analyzing data collected from the above means; a means for detecting abnormalities and generating alerts based on the analysis results; a means for notifying the user of the alerts; a means for generating and providing exercise and dietary advice to the user; a means for tracking the pet's current location and providing the user with location information; a means for monitoring the pet's health in real time using sensors and cameras installed in the store; and a means for immediately notifying a store staff member when an abnormality occurs. This enables the pet shop to efficiently manage the health of individual pets and quickly respond when an abnormality is detected.

[0631] The "means for measuring body temperature" is a device that has the function of continuously measuring the pet's body temperature and transmitting the data to a server.

[0632] A "means for measuring activity levels" is a device that uses sensors to capture a pet's movement and level of exercise and collects that activity data.

[0633] A "means for measuring location information" is a device that identifies a pet's current location using GPS or other location information technology and records it as data.

[0634] A "camera equipped with a means for recognizing motion" is a device that uses motion detection technology built into the camera to monitor and record the movements and behavior of pets.

[0635] A "means for measuring toilet usage" is a device that uses a sensor to measure the frequency and amount of toilet usage by pets and records that data.

[0636] "Means for measuring weight" refers to a device that accurately measures the weight of a pet, and is generally a weighing scale.

[0637] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet with a function that measures the pH level when a pet urinates or defecates.

[0638] "Means for storing and analyzing data" refers to a device that stores collected data in a database or the like and performs analytical processing based on this data.

[0639] The "means for detecting anomalies and generating alerts" is a device that detects abnormal conditions from the analyzed data and generates a notification to notify the user.

[0640] "Means of notifying users" refers to a function that notifies users of detected abnormalities or alerts via their smartphones or other devices.

[0641] The "means for generating and providing advice on exercise and diet" is a device that automatically generates advice on exercise and diet according to the health condition of a pet and provides it to the user.

[0642] The "means for tracking the current location and providing location information" is a device that continuously tracks the current location of a pet and provides that location information to the user.

[0643] "Means for monitoring health conditions in real time using sensors and cameras installed in the store" refers to a function that uses sensors and cameras installed in the pet shop to monitor the health conditions of pets in real time.

[0644] "Means for immediately notifying store staff when an abnormality occurs" refers to a function that immediately notifies store staff when an abnormality is detected based on the pet's health data.

[0645] This invention provides a system for monitoring the health of pets in pet shops in real time and immediately notifying them if an abnormality occurs. This system includes a collar that measures the pet's body temperature, activity level, and location information, a camera that recognizes the pet's movements, a toilet that measures the amount of toilet use, weight, and pH balance of urination and defecation, and a server that stores and analyzes this data.

[0646] The system's program is implemented in Python, and the server operates as follows: It receives data sent from pets' collars, cameras, and toilets, and stores the data in an SQLite database. The collected data is analyzed periodically to evaluate the pet's health. If an abnormality is detected based on the analysis results, an alert is generated and sent to the store clerk's terminal.

[0647] The server detects abnormalities based on body temperature, activity level, and location data, and immediately generates an alert if, for example, the body temperature exceeds 38.0°C or the activity level falls below 10. It also constantly tracks the pet's current location and provides the pet's location information within the pet shop to the store staff. Furthermore, the server uses sensors and cameras within the store to monitor the pet's health in real time, and immediately notifies the staff if an abnormality occurs.

[0648] Consider the following scenario as a concrete example: A pet's collar measures its temperature and records it as 38.5°C. The data is sent to a server, which receives the temperature data and detects that it is high compared to normal values. The server generates an alert, and a notification appears on the store clerk's device saying, "Your pet's temperature is high. Please check it immediately."

[0649] The server also has a means to generate and provide exercise and dietary advice to the user after detecting an abnormality. For example, if the pet's activity level is lower than normal, a notification will be displayed on the store clerk's device saying, "Your pet's activity level is lower than normal. Try letting it spend more time playing with toys." This advice encourages specific actions to maintain the pet's health.

[0650] Furthermore, it is possible to utilize a generative AI model. By inputting the following prompt sentence into the generative AI model, more advanced advice can be obtained.

[0651] Example prompt sentence:

[0652] “How do you monitor your pet’s health?

[0653] The data is as follows:

[0654] Pet ID: 1

[0655] Body temperature: 38.5℃

[0656] Activity amount: 10

[0657] Serving size: 50 grams

[0658] The threshold for detecting anomalies is:

[0659] Body temperature > 38.0°C

[0660] Activity amount < 10

[0661] Food intake < 100 grams

[0662] Use this data to generate alerts about your pet's health."

[0663] As described above, the system of the present invention can efficiently manage the health conditions of pets in pet shops and can respond quickly when an abnormality occurs.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1: Collect data

[0666] Sensors and collars that measure pets' body temperature, activity levels, and location information collect data. This data is sent to a server via Wi-Fi or Bluetooth. The input is data from each sensor, and the output is raw data sent to the server. Specifically, the collar measures body temperature at regular intervals and sends the data to the server.

[0667] Step 2: Save your data

[0668] The server stores the received data in an SQLite database. The input is the raw data sent from the sensor, and the output is the data stored in the database. Specifically, the server inserts the raw data into the appropriate tables.

[0669] Step 3: Analyze the data

[0670] The server periodically analyzes the stored data and evaluates the pet's health. The input is data obtained from the database, and the output is the analysis results. Specifically, the server compares the body temperature, activity level, and location data with normal values ​​to check for any abnormalities.

[0671] Step 4: Detect anomalies and generate alerts

[0672] The server generates an alert based on the analysis results if an abnormality is detected. The input is the analysis results, and the output is the generated alert. Specifically, if the body temperature exceeds a specified value, an "Abnormal Body Temperature" alert is generated.

[0673] Step 5: Alert Notification

[0674] The generated alert is sent to the store clerk's device. The input is the generated alert, and the output is the notification displayed on the device. Specifically, the server generates a push notification and sends it to the store clerk's smartphone or tablet.

[0675] Step 6: User (store clerk) response

[0676] The store clerk who receives the notification checks the pet's condition and takes appropriate action. The input is the alert notification displayed on the terminal, and the output is the clerk's response action. Specifically, the clerk checks the notification and takes action such as cooling the pet if its body temperature is high.

[0677] Step 7: Generating and Providing Advice

[0678] Based on the analysis results, the server automatically generates advice on exercise and diet and provides it to the store clerk's terminal. The input is the analysis results, and the output is the generated advice. Specifically, if the amount of activity is low, the server generates advice such as "Please increase the amount of time the child spends playing with toys."

[0679] Step 8: Tracking and Providing Location Information

[0680] The server constantly tracks the location of pets and provides them to store clerks as needed. The input is location data, and the output is location information provision. Specifically, the server displays the location information of a specific pet on a map and provides it to store clerks.

[0681] Step 9: Real-time monitoring

[0682] The health of pets is monitored in real time using sensors and cameras installed in the store. The input is data from the sensors and cameras, and the output is health status information updated in real time. Specifically, the camera captures the pet's movements and sends that information to a server for analysis.

[0683] Step 10: Emergency Notification

[0684] When an abnormality occurs, the store clerk is notified immediately. The input is the abnormality detection data obtained through real-time monitoring, and the output is the immediately sent notification. Specifically, when an abnormality is detected, a notification is sent stating that "immediate action is required."

[0685] 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.

[0686] The present invention provides a system that comprehensively manages the health status of a pet, recognizes the user's emotions, and personalizes notifications and advice based on those emotions. This system is configured as follows.

[0687] System configuration and program processing

[0688] The system includes the following elements:

[0689] 1. A collar that measures your pet's temperature, activity, and location

[0690] 2. Pet movement detection camera

[0691] 3. A toilet that measures your pet's toilet usage, weight, and pH balance of urination and defecation

[0692] 4. Server for storing and analyzing collected data

[0693] 5. A device that generates alerts based on data analysis and notifies users of them.

[0694] 6. A means of providing users with appropriate exercise and dietary advice

[0695] 7. A way to track your pet's current location and provide location information to you

[0696] 8. Emotion engine that recognizes user emotions and adjusts notification and advice content

[0697] Program processing

[0698] server

[0699] 1. Data Receipt and Storage:

[0700] The server receives data from the collar, camera, and toilet and stores each piece of data in a database.

[0701] 2. Data analysis and anomaly detection:

[0702] The stored data is analyzed periodically to determine the pet's health status, and an alert is generated if an abnormality is detected from the analysis results.

[0703] 3. Advice Generation:

[0704] Based on the analysis results, appropriate advice regarding your pet's exercise and diet is automatically generated.

[0705] 4. Visualizing the results:

[0706] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[0707] Terminal

[0708] 1. Emotion recognition:

[0709] The emotion engine analyzes the user's emotions from facial expressions and tone of voice, learning emotional patterns based on the user's interaction history.

[0710] 2. Notification display:

[0711] Alerts and advice sent from the server are displayed to the user as push notifications. The notification content is personalized based on the analysis results of the emotion engine.

[0712] 3. Data Viewing:

[0713] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[0714] User

[0715] 1. Monitoring and Response:

[0716] Users can monitor their pet's health in real time, respond quickly when an alert occurs, and manage their pet's exercise and diet appropriately based on the advice provided.

[0717] Specific examples

[0718] Example 1: Your pet has a high body temperature

[0719] 1. Data Collection:

[0720] The collar measures your pet's temperature and records it as 39.0°C. The data is then sent to a server via Wi-Fi.

[0721] 2. Data Analysis:

[0722] The server receives the temperature data and stores it in a database. It compares it with the normal temperature and determines that 39.0°C is a high temperature. It generates an alert and notifies the user.

[0723] 3. Notifications and Emotion Recognition:

[0724] The device will display a notification saying, "Your pet's temperature is high. Please check immediately." The emotion engine will analyze the user's facial expressions and, if they are feeling anxious, will display an additional notification saying, "Your pet's temperature is high. Please stay calm and check and contact your veterinarian."

[0725] 4. Support:

[0726] Users can view notifications, check in on their pet, and follow the provided calming strategies if they feel anxious.

[0727] Example 2: If your pet is not very active

[0728] 1. Data Collection:

[0729] The collar measures daily activity and records when the dog is only moving about 30% of normal activity, sending the data to a server via Bluetooth.

[0730] 2. Data Analysis:

[0731] The server receives the activity data and stores it in a database. It compares it with the normal value and determines that the activity level is low. It generates advice and notifies the user.

[0732] 3. Notifications and Emotion Recognition:

[0733] The device will display a notification with advice such as, "Your pet is less active than usual. Try letting them spend more time playing with toys." If the emotion engine determines that the user is tired from the tone of their voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy play."

[0734] 4. Support:

[0735] Users can check the advice and increase the amount of time they spend playing with their pets without any stress.

[0736] In this way, this system comprehensively manages the health condition of pets and takes into consideration the user's feelings, allowing them to care for their pets with peace of mind.

[0737] The processing flow will be explained below.

[0738] Step 1: Data measurement and collection

[0739] Collar: Your pet's temperature sensor measures its temperature every hour, the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[0740] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[0741] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[0742] Step 2: Send data

[0743] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[0744] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[0745] Step 3: Save Data

[0746] The server stores the received data in a database.

[0747] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[0748] Step 4: Data analysis

[0749] The server analyzes the stored data at regular intervals to assess the pet's health.

[0750] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[0751] Step 5: Emotion Recognition

[0752] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0753] For example, it uses a camera and microphone to analyze a user's facial expressions and voice to identify stress or anxiety.

[0754] Step 6: Alert Generation

[0755] The server generates an alert based on the analysis results.

[0756] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[0757] Step 7: Advice Generation

[0758] The server automatically generates advice on exercise and diet based on the results of data analysis.

[0759] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[0760] Step 8: Notifications and Personalization

[0761] The device displays alerts and advice sent from the server to the user as push notifications.

[0762] Personalize notifications based on the results of emotion engine analysis (e.g., "Your pet has a high temperature. Please stay calm and check and contact your veterinarian.").

[0763] Step 9: View Data

[0764] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[0765] Step 10: Monitor and respond

[0766] Users can monitor their pet's health in real time and respond quickly when an alert occurs.

[0767] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[0768] Specific examples

[0769] Example 1: Your pet has a high body temperature

[0770] Step 1: The collar measures your pet's temperature and records it as 39.0°C. The data is sent to a server via Wi-Fi.

[0771] Step 2: The server receives the temperature data and stores it in a database.

[0772] Step 3: The server compares the temperature to its normal value and determines that 39.0°C is a high temperature.

[0773] Step 4: The server generates an alert and notifies the user.

[0774] Step 5: The device's emotion engine analyzes the user's facial expression and if they are feeling anxious, it will send an additional notification saying, "Your pet has a high temperature. Please stay calm and check it and contact your veterinarian."

[0775] Step 6: The user checks the notification, checks in on their pet, and follows the provided calming strategies if they feel uneasy.

[0776] Example 2: If your pet is not very active

[0777] Step 1: The collar measures your daily activity and records that you're only moving about 30% of your normal activity. The data is then sent via Bluetooth to a server.

[0778] Step 2: The server receives the activity data and stores it in a database.

[0779] Step 3: The server determines that the activity is low compared to normal.

[0780] Step 4: The server generates the advice and notifies the user.

[0781] Step 5: Your device will display a notification with the advice, "Your pet's activity level is lower than usual. Try increasing the amount of time it spends playing with toys."

[0782] Step 6: If the emotion engine determines that the user is tired from their tone of voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy games."

[0783] Step 7: The user checks the advice and increases playtime with their pet without forcing themselves.

[0784] Example 2

[0785] 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."

[0786] Conventional pet health management systems simply collect and store data, making it difficult to comprehensively understand a pet's health condition. Furthermore, alerts and advice are provided uniformly without considering the user's emotions, meaning they are unable to provide the necessary information at the timing and with the content the user desires. Furthermore, there is a lack of methods for detecting anomalies and generating advice that are suited to specific conditions, making these systems less effective in managing pet health.

[0787] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera equipped with means for measuring the pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements, a toilet equipped with means for measuring the pet's toilet usage amount, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation, means for storing and analyzing data collected from the above means, means for detecting abnormalities and generating alerts based on the analysis results, means for notifying the user of the alerts, means for generating and providing exercise and dietary advice to the user, means for tracking the pet's current location and providing the user with location information, and means for recognizing the user's emotions and adjusting the content of notifications and advice. This makes it possible to comprehensively manage and analyze the pet's health condition and take appropriate measures based on the user's emotions.

[0788] A "means for measuring body temperature" is a device that has the function of measuring a pet's body temperature and recording and transmitting that data.

[0789] A "means for measuring activity levels" is a device that has the function of measuring a pet's movements and exercise, and recording and transmitting that data.

[0790] A "means for measuring location information" is a device that has the function of identifying the current location of a pet and recording and transmitting that information.

[0791] A "camera equipped with a means for recognizing movement" is a camera device that recognizes and records pet movements and behavior as video footage and analyzes the data.

[0792] A "means for measuring litter box usage" is a device that has the function of measuring the amount of litter box used by a pet and recording and transmitting that data.

[0793] A "means for measuring weight" is a device that has the function of measuring a pet's weight and recording and transmitting that data.

[0794] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet device that has the function of measuring the pH balance of a pet's urination and defecation, and recording and transmitting that data.

[0795] The "means for storing and analyzing data" refers to a server that stores data collected from each device and analyzes it to determine health status and abnormalities.

[0796] "Means for detecting anomalies and generating alerts" refers to a system function that detects anomalies from the analysis results and generates an alert to notify the user.

[0797] "Means of notifying users" refers to communication functions and devices used to notify users of alerts and advice.

[0798] The "means for generating and providing advice" is a function that automatically generates and provides advice on exercise and diet for pet health management based on the analysis results.

[0799] "Means for tracking current location and providing location information to the user" refers to a function that tracks the pet's location in real time and provides that information to the user.

[0800] "Means for recognizing user emotions and adjusting notification and advice content" refers to a system that has the ability to analyze and recognize user emotions and personalize the content of alerts and advice based on those emotions.

[0801] The present invention relates to a system for comprehensively managing pet health conditions, recognizing the user's emotions, and providing personalized notifications and advice based on those emotions. The system includes the following components:

[0802] server

[0803] The server receives data from the collar, camera, and litter box, such as the pet's body temperature, activity level, location, movement, litter box usage, weight, and pH balance of urination and defecation, and stores it in a database. The stored data is periodically analyzed using Python libraries (e.g., Pandas, NumPy). If an abnormal value is detected during the analysis, an alert is generated. In addition, appropriate advice regarding the pet's exercise and diet is automatically generated based on the analysis results. These analysis results and advice are converted into graphs and dashboards, and summarized in a format that can be viewed by the user in the app. The specific software used is an SQL database, Python, Matplotlib, and D3.js.

[0804] As a concrete example, consider the case where a pet's body temperature reaches 39.0°C. The collar measures the temperature and sends the data to a server via Wi-Fi. The server receives the data and stores it in a database. It analyzes the stored data and determines that the body temperature is abnormally high. It generates an abnormal alert indicating that the body temperature is high and notifies the user.

[0805] Terminal

[0806] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice. Using OpenCV and a voice analysis library, it learns the user's emotional patterns and personalizes the content of alerts and advice based on those patterns. Alerts and advice sent from the server are displayed to the user as push notifications. The content of the notifications is also adjusted according to the user's emotions. By opening the app, users can view detailed data and advice, and can also check their pet's current location on a map. The map display uses the Google Maps API.

[0807] As a concrete example, consider a case where a pet's activity level is only about 30% of normal. The collar measures the amount of activity throughout the day and sends the data via Bluetooth to a server. The server receives the data, stores it in a database, and detects a decrease in activity level. It generates advice to encourage activity and notifies the device. The device determines from the tone of the user's voice that the pet is tired, and displays additional advice such as, "When you are tired, it's a good idea to incorporate some easy games."

[0808] User

[0809] Users can monitor their pet's health in real time through their device and respond quickly when an alert occurs. They can check advice on the app and properly manage their pet's exercise and diet. Users can also check their pet's current location and respond quickly if their pet gets lost.

[0810] An example prompt is the text prompt "Your pet's temperature is higher than normal. Please provide advice to help the user stay calm."

[0811] As described above, the present invention is a system that can comprehensively manage the health condition of a pet and take appropriate action depending on the user's emotions.

[0812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0813] Step 1: Receiving and storing data

[0814] The server receives data packets sent from the collar, camera, and litter box. Specific input data includes the pet's body temperature, activity level, location, litter box usage, weight, and pH balance of urination and defecation. This data is received via Wi-Fi or Bluetooth through a communication module. The received data is stored in a database using a Python library (e.g., SQLAlchemy). Specific output data is various data stored in related tables in the database.

[0815] Step 2: Data analysis and anomaly detection

[0816] The server periodically analyzes the stored data. The input is various health data stored in a database. The data is processed using Python libraries (e.g., Pandas, NumPy) and each data point is compared with a normal value. If an abnormality is detected as a result of the analysis, an alert is generated. Specifically, a body temperature above 38°C is considered abnormal. The output is a flag indicating an abnormality and a trigger for generating an alert.

[0817] Step 3: Advice Generation

[0818] Based on the analysis results, the server generates appropriate advice regarding pet exercise and diet. The input is the data analysis results and anomaly flags. Specific advice sentences are created using generative AI models and rule-based systems. For example, if the pet has a high body temperature, the server generates advice such as "Give the pet plenty of water and use a cooling sheet." The output is advice sentences provided to the user.

[0819] Step 4: Visualize the results

[0820] The server converts the analysis results and advice into graphs and dashboards. The input is the analysis results and generated advice. Matplotlib and D3.js are used to visualize the data and integrate it into a web application. The output is graphs and dashboards that users can view in the app. For example, a graph showing the progression of body temperature over time.

[0821] Step 5: Emotion Recognition

[0822] The device's emotion engine analyzes the user's facial expressions and tone of voice. The input is audio and video data collected by the device's camera and microphone. Using OpenCV and voice analysis libraries, it analyzes the user's facial expressions and tone of voice to recognize emotional patterns. The output is a tag of the user's emotional state (e.g., anxiety, fatigue).

[0823] Step 6: Notifications and Personalization

[0824] The device displays alerts and advice sent from the server to the user as push notifications. The input is the alert notification from the server and the analysis results of the emotion engine. The notification content is personalized based on the results of the emotion engine. For example, if a pet's temperature is high, a notification saying "Your pet's temperature is high. Please stay calm and check and contact a veterinarian" is displayed. The output is a personalized notification message.

[0825] Step 7: View detailed data

[0826] Users can view detailed data and advice by opening the app on their device. The input is the analysis results and advice sent from the server. The app displays this data, allowing users to check past temperature and activity data over time. The output is detailed data and interactive graphs displayed in the user interface.

[0827] Step 8: Check your location

[0828] The app allows users to check their pet's current location on a map. The input is location information sent from the collar. The location information is plotted on a map using the Google Maps API. The output is the pet's current location displayed on the map.

[0829] Step 9: Monitor and respond

[0830] Users can monitor their pet's health in real time through the app and respond quickly when an alert occurs. The input is push notifications and detailed data from the device. The user follows the advice to manage their pet's health, for example, using a cooling sheet if the pet has a high temperature. The output is the user's appropriate action based on the pet's health condition.

[0831] (Application example 2)

[0832] 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."

[0833] In today's pet care industry, there are limited means for comprehensively managing pet health. Furthermore, the services and advice provided do not take into account the user's emotions, making it difficult for users to properly manage their pet's health. Furthermore, in physical stores, there is an insufficient system for suggesting optimal services and products based on the pet's health and the user's emotions.

[0834] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the notification content and advice content, and means for proposing pet-related services and products based on the user's emotions. This makes it possible to respond in consideration of the user's emotions, and to propose optimal pet care services and products even in physical stores.

[0835] A "means for measuring pet body temperature" is a device that measures a pet's body temperature in real time using a temperature sensor integrated into a pet's collar or other wearable device.

[0836] A "means for measuring pet activity" is a device that measures the distance traveled and the amount of exercise a pet performs through a pet's collar or other wearable device.

[0837] A "means for measuring pet location information" is a device that uses GPS or other location measurement technology to determine the current location of a pet.

[0838] A "camera equipped with a means for recognizing pet movements" is a camera device that captures pet movements and analyzes them using image analysis technology.

[0839] A "means for measuring pet litter box usage" is a device equipped with a sensor that records the number and frequency of a pet's litter box use.

[0840] "Means for measuring pet weight" refers to a scale or wearable device that measures the weight of a pet when it is on board.

[0841] A "toilet with a means for measuring the pH balance of a pet's urination and defecation" is a toilet device equipped with a sensor for detecting the pH balance when a pet urinates or defecates.

[0842] "Means for storing and analyzing data collected from the above means" refers to servers and software systems that accumulate data obtained from various sensors and devices and analyze that data.

[0843] "Means for detecting anomalies and generating alerts based on analysis results" refers to systems or software that detect anomalies from analyzed data and issue warnings to users.

[0844] The "means of notifying users of the above alerts" refers to a system for pushing warnings and notifications to users' devices such as smartphones and tablets.

[0845] "Means for recognizing the user's emotions and adjusting the content of notifications and advice" refers to a system that uses a camera and microphone to analyze the user's facial expressions and voice, and then appropriately adjusts notifications and advice based on those emotions.

[0846] The "means for generating and providing exercise and dietary advice to users" is a system that generates optimal advice regarding the amount of exercise and diet for pets based on the analyzed data and provides it to users.

[0847] "Means for tracking a pet's current location and providing location information to the user" refers to a system that uses location tracking technology such as GPS to identify a pet's current location and notify the user of that information.

[0848] The "means for suggesting pet-related services and products based on the user's emotions" is a system that analyzes the user's emotional state and recommends the most suitable pet care services and products based on the results.

[0849] To implement this invention, the following system configuration and process are required.

[0850] The system includes multiple measuring devices to collect information such as a pet's body temperature, activity level, and location, a server to analyze and notify this data, and an emotion engine that recognizes the user's emotions and personalizes the notification content.

[0851] System Configuration

[0852] Collar: Includes a means to measure your pet's temperature, activity, and location.

[0853] Hardware used: Body temperature sensor, accelerometer, GPS module

[0854] Software used: Data transmission protocols (e.g. Bluetooth, Wi-Fi)

[0855] Camera: Equipped with a means to recognize pet movements and record footage.

[0856] Hardware used: High resolution camera (e.g. Logitech C920)

[0857] Software used: Image analysis algorithms (e.g., OpenCV)

[0858] Toilet: Includes a means to measure your pet's toilet usage, weight, and pH balance of urination and defecation.

[0859] Hardware used: Weight scale, pH sensor

[0860] Software used: Sensor data analysis tools (e.g., SciPy)

[0861] Server: Stores and analyzes various data, detects abnormalities, and generates alerts.

[0862] Software used: databases (e.g., MySQL), data analysis tools (e.g., NumPy, SciPy)

[0863] Device: Notify users and adjust advice.

[0864] Hardware used: Smartphone, tablet

[0865] Software used: Notification system API (e.g. Firebase Cloud Messaging)

[0866] Emotion engine: Recognizes user emotions and personalizes notifications and advice.

[0867] Software used: Facial expression recognition algorithms (e.g. DLib), voice analysis engine

[0868] Program Processing Details

[0869] The server receives data such as body temperature, activity level, and location information sent from the pet's collar and stores it in a database. The server then analyzes the stored data and generates an alert if an abnormality is detected, sending a notification to the user's device. Data analysis tools such as NumPy and SciPy are used for the analysis.

[0870] The device checks the notification received by the user, and an emotion engine analyzes the user's facial expressions and voice to recognize their emotions. For example, if the user is feeling anxious, the notification content will be personalized and provide advice such as, "Your pet's temperature is high. Please stay calm and check it and contact your veterinarian." Notifications on the device use notification system APIs such as Firebase Cloud Messaging.

[0871] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes their emotions using DLib and a voice analysis engine. For example, if a user receives a notification that their pet's temperature is rising and looks anxious, the system will provide additional advice such as "stay calm."

[0872] Specific examples

[0873] Example 1: Your pet has a high body temperature

[0874] 1. Data collection: The collar measures the pet's temperature to be 39.0°C and sends the data to the server.

[0875] 2. Data analysis: The server analyzes the body temperature data, determines that 39.0°C is a high temperature, and generates an alert.

[0876] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet's temperature is high. Please check immediately." If the emotion engine analyzes the user's facial expression and they are feeling anxious, an additional notification will be displayed saying, "Please stay calm and check and contact your veterinarian."

[0877] Example 2: If your pet is not very active

[0878] 1. Data collection: The collar records activity at approximately 30% of normal activity and sends the data to a server.

[0879] 2. Data analysis: The server analyzes the activity data, determines that the activity level is low, and generates advice.

[0880] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet is less active than usual. Try spending more time playing with toys." If the emotion engine determines that the user is tired based on their tone of voice, additional advice will be given: "When you're tired, it's a good idea to incorporate some easy play."

[0881] Prompt Sentence Examples

[0882] "The pet's temperature is high and the user is feeling anxious. Please advise the user to remain calm and, as the pet's temperature is high, suggest that they seek appropriate medical attention."

[0883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0884] Step 1: Data collection

[0885] The collar measures the pet's body temperature, activity level, and location information in real time. This data is sent to a server using Bluetooth or Wi-Fi. The input is body temperature, activity level, and location information data, and the output is the raw data sent to the server. This stage includes specific operations: the body temperature sensor measures body temperature, the accelerometer measures activity level, and the GPS measures location information.

[0886] Step 2: Save your data

[0887] The server stores the received data in a database. The input is the raw data sent from the collar, and the output is structured data stored in the database. This processing step uses a database management system such as MySQL or PostgreSQL to store the temperature, activity, and location data in tables.

[0888] Step 3: Data analysis

[0889] The server periodically launches a process to analyze the stored data. The input is the raw data stored in the database, and the output is the analysis results. Specifically, statistical analysis is performed using NumPy and SciPy to detect outliers. At this time, a threshold is set to detect anomalies by comparing with past data.

[0890] Step 4: Anomaly detection and alerting

[0891] The server detects anomalies based on the analysis results and generates an alert. The input is the analysis results and the output is the generated alert. If an anomaly is detected, an alert message is generated and sent to the user's device. This process uses an algorithm that triggers an alert when certain conditions are met.

[0892] Step 5: Alert Notification

[0893] An alert notification is displayed on the device. At this stage, the alert is pushed to the user in real time. The input is the alert message sent from the server, and the output is the notification displayed on the device. Specifically, the alert is sent using a service such as Firebase Cloud Messaging.

[0894] Step 6: User sentiment analysis

[0895] The device uses a camera and microphone to analyze the user's emotions. The input is the user's facial expression images and voice data, and the output is the emotion determination result. Facial expressions are analyzed using DLib, and tone of voice is analyzed using a voice analysis engine. Specifically, the system involves the user showing their facial expressions in front of the camera and speaking into the microphone.

[0896] Step 7: Personalize your notifications

[0897] The device personalizes the content of notifications and advice based on the results of emotion analysis. The input is the emotion determination result and the alert message, and the output is the adjusted notification content. If the emotion engine analyzes the user's emotions and detects anxiety or fatigue, it adds advice appropriate to that state. For example, it generates messages such as "Please stay calm" or "We recommend some easy games."

[0898] Step 8: Inform and advise users

[0899] The device displays personalized notification content and advice to the user. The input is the tailored notification content, and the output is the notification message displayed to the user. Specifically, this includes the behavior of displaying a personalized message on the screen of a smartphone or tablet.

[0900] Step 9: Propose your service or product

[0901] The device suggests services and products offered in physical stores based on the user's emotions and the pet's health condition. The input is the emotion assessment result and health condition data, and the output is a list of suggested services and products. For example, if the user is worried about their pet's high temperature, suggestions such as "This product will make it easy to manage their pet's temperature" or "We recommend using this service" will be displayed.

[0902] Through the above steps, the present invention provides a system that comprehensively manages the health condition of a pet and takes appropriate measures taking into account the user's emotions.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] [Third embodiment]

[0907] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0908] 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.

[0909] 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).

[0910] 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.

[0911] 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.

[0912] 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).

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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."

[0919] The present invention provides a system for comprehensively managing the health of pets. This system is composed of multiple elements that cooperate to monitor the health of pets, and if an abnormality occurs, it promptly notifies the user and provides appropriate advice.

[0920] Program processing

[0921] The system operates as follows.

[0922] server:

[0923] 1. Data Receipt and Storage:

[0924] The server receives data from the pet's collar, camera, and litter box.

[0925] The received data is stored in the respective databases.

[0926] 2. Data analysis and anomaly detection:

[0927] The server periodically analyzes the stored data and assesses the pet's health.

[0928] It compares with normal values ​​and generates an alert if an abnormality is detected.

[0929] 3. Advice Generation:

[0930] Based on the results of data analysis, appropriate advice regarding your pet's exercise and diet is automatically generated.

[0931] 4. Visualizing the results:

[0932] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[0933] Device (user's smartphone or computer):

[0934] 1. Notification display:

[0935] Alerts and advice sent from the server are displayed to the user as push notifications.

[0936] Open the app to view detailed data and advice.

[0937] 2. Data browsing:

[0938] Users can check their past health data and current health status in real time within the app.

[0939] You can check your pet's location on a map.

[0940] User:

[0941] 1. Monitoring and Response:

[0942] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0943] Based on the advice provided, you can adjust your pet's exercise and diet.

[0944] Specific examples

[0945] Example 1: Your pet has a high body temperature

[0946] 1. Data Collection:

[0947] The collar measures the body temperature and records it as 38.5°C (normal is 37.5°C).

[0948] The data is sent to a server via Wi-Fi.

[0949] 2. Data Analysis:

[0950] The server receives the temperature data and stores it in a database.

[0951] Compared to normal values, 38.5℃ is considered to be a high temperature.

[0952] The server generates an alert and notifies the user.

[0953] 3. Notice and Response:

[0954] A notification will appear on your device saying, "Your pet's temperature is high. Please check immediately."

[0955] The user checks the notification and checks the status of the pet.

[0956] Example 2: If your pet is not very active

[0957] 1. Data Collection:

[0958] The collar measures daily activity and records that the dog is only moving about 30% of normal activity.

[0959] The data is sent to the server via Bluetooth.

[0960] 2. Data Analysis:

[0961] The server receives the activity data and stores it in a database.

[0962] Compared to normal values, it is determined that the amount of activity is low.

[0963] The server generates the advice and notifies the user.

[0964] 3. Notice and Response:

[0965] An advice notification will appear on your device saying, "Your pet is less active than usual. Try letting it spend more time playing with toys."

[0966] Users can check the advice and spend more time playing with their pets.

[0967] In this way, the system of the present invention can closely monitor the health condition of a pet and respond quickly and appropriately when an abnormality occurs, allowing pet owners to manage their pet's health with peace of mind, which greatly contributes to maintaining the pet's health.

[0968] The processing flow will be explained below.

[0969] Step 1: Data measurement and collection

[0970] Collar: Your pet's temperature sensor measures its temperature at regular intervals (e.g., every hour), the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[0971] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[0972] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[0973] Step 2: Send data

[0974] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[0975] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[0976] Step 3: Save Data

[0977] The server stores the received data in a database.

[0978] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[0979] Step 4: Data analysis

[0980] The server periodically analyzes the stored data to determine the pet's health status.

[0981] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[0982] Step 5: Alert Generation

[0983] The server generates an alert based on the analysis results.

[0984] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[0985] Step 6: Advice Generation

[0986] The server automatically generates advice on exercise and diet based on the results of data analysis.

[0987] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[0988] Step 7: Visualize the results

[0989] The server converts the analysis results and advice into graphs and dashboards that users can view in the app.

[0990] For example, body temperature data is displayed as a line graph and activity levels are displayed as a bar graph.

[0991] Step 8: Notifications

[0992] The device displays alerts and advice sent from the server to the user as push notifications.

[0993] For example, a notification could be sent to your smartphone saying, "Your pet's temperature is high. Please check immediately."

[0994] Step 9: View Data

[0995] Users can view detailed data and advice by opening the app.

[0996] Users can check their pet's past health data and current health status in real time, and can also view their pet's location on a map.

[0997] Step 10: Monitor and respond

[0998] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[0999] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[1000] This series of processes allows you to comprehensively manage your pet's health and respond quickly if an abnormality occurs.

[1001] Example 1

[1002] 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."

[1003] Comprehensive, real-time management of pet health is an important issue for many pet owners. However, current systems collect and analyze various data separately, often resulting in inconsistent information and delays. Furthermore, when an abnormality occurs, users often have to think of a solution themselves, making it difficult to respond quickly and accurately. To solve these problems, integrated management and analysis of each data, rapid notification of abnormalities, and provision of specific advice are required.

[1004] 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.

[1005] In this invention, the server includes an imaging device equipped with means for measuring a pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements; a device having means for measuring the pet's excretion volume, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation; means for storing and analyzing data collected from the above means; means for detecting abnormalities and generating alerts based on the analysis results; means for notifying the user of the alerts; means for generating advice based on prompt sentences using a generative AI model to generate and provide exercise and dietary advice to the user; and means for tracking the pet's current location and providing the user with location information. This allows for comprehensive management of the pet's health condition in real time and enables prompt and appropriate response when an abnormality occurs. Furthermore, accurate advice is provided using a generative AI model, allowing the user to immediately know specific countermeasures.

[1006] A "means for measuring a pet's body temperature" is a device that is equipped with a sensor on a pet's collar or attached device and measures the pet's body temperature periodically or in real time.

[1007] A "means for measuring a pet's activity level" is a device that uses an acceleration sensor or gyro sensor to measure a pet's daily activity and exercise level.

[1008] A "means for measuring pet location information" is a device equipped with a GPS function that tracks a pet's current location in real time and obtains location information.

[1009] The "devices" are devices used to monitor the health of pets, such as collars that can be attached to pets, imaging devices, scales, and excrement detection sensors.

[1010] An "imaging device" is a device that uses a camera and image analysis technology to capture and analyze a pet's behavior in order to recognize the pet's movements.

[1011] The "means for measuring excretion volume" is a device that uses a sensor installed in the pet's toilet to measure the volume of urination and defecation.

[1012] The "means for measuring the weight of a pet" is a device that uses a scale to periodically measure the weight of a pet and obtains the data.

[1013] The "means for measuring pH balance" is a device equipped with a sensor for measuring the pH value of a pet's urine and feces.

[1014] The "means for storing and analyzing data" refers to a server system for storing various data in a database and analyzing the data using statistical analysis and machine learning techniques.

[1015] The "means for detecting abnormalities and generating alerts" refers to a system that generates and notifies an alert based on information when an abnormality is detected by comparing with normal data.

[1016] "Means of notifying users" refers to a system that sends alerts and analysis results to users' devices as push notifications or messages.

[1017] The "means for generating and providing advice" is a system that uses a generative AI model to automatically generate appropriate exercise and diet advice based on prompt text and provide it to users.

[1018] A "generative AI model" is a type of artificial intelligence model that generates natural language sentences based on input prompts.

[1019] A "prompt sentence" is a text sentence that is input into a generative AI model and serves as a reference sentence for the model to generate advice or text based on that prompt.

[1020] This invention is a system for comprehensively managing the health of pets. This system collects and stores data from a series of devices that measure pet body temperature, activity level, location information, excretion volume, weight, and pH balance of urination and feces, and based on the results, detects abnormalities, notifies the user, and provides appropriate advice.

[1021] First, to measure your pet's body temperature, a small temperature sensor is attached to the pet's collar. This temperature sensor monitors your pet's temperature in real time and transmits the data to a server via Wi-Fi. Similarly, to measure your pet's activity level, an accelerometer and gyro sensor are attached to the collar to measure your pet's daily activity. This data is then transmitted to the server at specific time intervals.

[1022] To measure the location of your pet, a device with GPS functionality is used, which tracks your pet's current location in real time and obtains its location information. The obtained data is sent to a server via Wi-Fi or Bluetooth.

[1023] In addition, a camera is installed as an imaging device to recognize the pet's movements. The camera captures video data and analyzes the pet's behavior using image analysis technology. This video data is also sent to the server for storage and analysis.

[1024] To measure the amount of excretion from pets, a sensor installed in the toilet is used. The sensor periodically records the amount of urination and defecation from the pet and sends the data to a server. In addition, a scale is used to measure the weight of the pet and obtain the data. To measure the pH balance of urination and defecation, a pH sensor is installed in the toilet and measures the pH value of urination and defecation.

[1025] The server uses a database management system such as MySQL or MongoDB to store this data in a database. It then analyzes the data using Python programs and the Pandas library. If an anomaly is detected by comparing the analysis with normal data, the server runs an anomaly detection algorithm using a machine learning library such as Scikit-learn to generate an alert.

[1026] Furthermore, a generative AI model is used to generate exercise and diet advice based on prompts. Specifically, the following prompts are input into the generative AI model:

[1027] "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?"

[1028] The generated advice is provided to the user. Push notifications are sent to the user's device in real time using Firebase Cloud Messaging (FCM). Users can check their pet's detailed data and advice through an application developed in React Native or Swift. The app also provides a function to display the pet's location on a map using the Google Maps API.

[1029] For example, if a pet's body temperature rises to 38.5°C, higher than the normal 37.5°C, the server analyzes this data and detects the abnormality. As a result, an alert is generated stating, "Your pet's temperature is high. Please check immediately." The generative AI model then generates advice such as, "We recommend using a wet towel to cool the pet down." These notifications and advice are sent to the user's device as push notifications.

[1030] This allows the system to comprehensively manage pet health conditions in real time, enabling prompt and appropriate responses when abnormalities occur. Furthermore, by using generative AI models to provide accurate advice, users can instantly learn specific countermeasures.

[1031] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1032] Step 1: Receiving data

[1033] The server receives data on pets' temperature, activity, location, movement, waste output, weight, and pH balance from various sensor devices. These devices transmit data to the server using Wi-Fi or Bluetooth. For example, a collar transmits temperature data via Wi-Fi, which is then received by the server.

[1034] Input: Sensor data from collar, camera, and litter box

[1035] Output: Various sensor data received by the server

[1036] Step 2: Save data

[1037] The server stores the received data in the respective databases. Body temperature data is stored in the "temperatures" table in MySQL, and activity data is stored in the "activities" collection in MongoDB. Video data and excrement data are also stored in appropriate storage.

[1038] Input: Various sensor data

[1039] Output: Various data stored in the database

[1040] Step 3: Data analysis

[1041] The server periodically analyzes the stored data using Python and Pandas. It compares body temperature and activity data with normal values ​​to detect abnormalities. For example, if body temperature rises from 37.5°C to 38.5°C, it is considered abnormal.

[1042] Input: Data stored in a database

[1043] Output: Presence or absence of abnormalities as analysis results

[1044] Step 4: Anomaly detection and alerting

[1045] The server runs an anomaly detection algorithm using Scikit-learn and generates an alert if an anomaly is detected. The generated alert is saved in a database and added to an alert queue. For example, if an abnormality in body temperature is detected, an alert such as "Temperature is abnormally high" is generated.

[1046] Input: Presence or absence of abnormalities as analysis results

[1047] Output: Generated alerts

[1048] Step 5: Advice Generation

[1049] The server uses a generative AI model to generate advice based on a prompt. For example, if the prompt is "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?", the server generates the advice "I recommend using a wet towel to cool him down."

[1050] Input: prompt statement

[1051] Output: Generated advice

[1052] Step 6: Notification of alerts and advice

[1053] The server notifies the user of the generated alert and advice. A push notification is sent using Firebase Cloud Messaging (FCM), and a notification saying "Your pet's temperature is high. Please check immediately" is displayed on the user's device. The generated advice is also displayed.

[1054] Input: Generated alerts and advice

[1055] Output: Push notification to the user's device

[1056] Step 7: Viewing Data

[1057] Users can access detailed data and advice by opening the app on their smartphone or computer. The app also allows users to check past health data and current health status in real time, and displays their pet's location on a map using Google Maps API.

[1058] Input: User actions

[1059] Output: Detailed data and advice displayed within the app

[1060] Step 8: User interaction

[1061] Users can quickly respond based on the advice provided, for example, if their pet has a high temperature, they can follow the advice and take measures to cool their pet down, allowing them to quickly and appropriately manage their pet's health.

[1062] Input: In-app advice

[1063] Output: Specific measures for pets

[1064] (Application example 1)

[1065] 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."

[1066] Currently, systems for effectively monitoring the health of pets exist on the market, but there is a lack of systems that specifically address the needs of pet shops. Because pet shops must manage a large number of animals at once and provide healthy pets to customers, it is important to monitor the health of individual pets in real time and respond quickly if an abnormality occurs. However, conventional systems have difficulty meeting these requirements. Therefore, an objective of the present invention is to provide a system that monitors the health of pets for sale in the store in real time and immediately notifies the customer if an abnormality occurs.

[1067] 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.

[1068] In this invention, the server includes: a collar including means for measuring the pet's body temperature, a means for measuring the pet's activity level, and a means for measuring the pet's location information; a camera equipped with means for recognizing the pet's movements; a toilet having means for measuring the pet's litter box usage, a means for measuring the pet's weight, and a means for measuring the pH balance of the pet's urination and defecation; a means for storing and analyzing data collected from the above means; a means for detecting abnormalities and generating alerts based on the analysis results; a means for notifying the user of the alerts; a means for generating and providing exercise and dietary advice to the user; a means for tracking the pet's current location and providing the user with location information; a means for monitoring the pet's health in real time using sensors and cameras installed in the store; and a means for immediately notifying a store staff member when an abnormality occurs. This enables the pet shop to efficiently manage the health of individual pets and quickly respond when an abnormality is detected.

[1069] The "means for measuring body temperature" is a device that has the function of continuously measuring the pet's body temperature and transmitting the data to a server.

[1070] A "means for measuring activity levels" is a device that uses sensors to capture a pet's movement and level of exercise and collects that activity data.

[1071] A "means for measuring location information" is a device that identifies a pet's current location using GPS or other location information technology and records it as data.

[1072] A "camera equipped with a means for recognizing motion" is a device that uses motion detection technology built into the camera to monitor and record the movements and behavior of pets.

[1073] A "means for measuring toilet usage" is a device that uses a sensor to measure the frequency and amount of toilet usage by pets and records that data.

[1074] "Means for measuring weight" refers to a device that accurately measures the weight of a pet, and is generally a weighing scale.

[1075] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet with a function that measures the pH level when a pet urinates or defecates.

[1076] "Means for storing and analyzing data" refers to a device that stores collected data in a database or the like and performs analytical processing based on this data.

[1077] The "means for detecting anomalies and generating alerts" is a device that detects abnormal conditions from the analyzed data and generates a notification to notify the user.

[1078] "Means of notifying users" refers to a function that notifies users of detected abnormalities or alerts via their smartphones or other devices.

[1079] The "means for generating and providing advice on exercise and diet" is a device that automatically generates advice on exercise and diet according to the health condition of a pet and provides it to the user.

[1080] The "means for tracking the current location and providing location information" is a device that continuously tracks the current location of a pet and provides that location information to the user.

[1081] "Means for monitoring health conditions in real time using sensors and cameras installed in the store" refers to a function that uses sensors and cameras installed in the pet shop to monitor the health conditions of pets in real time.

[1082] "Means for immediately notifying store staff when an abnormality occurs" refers to a function that immediately notifies store staff when an abnormality is detected based on the pet's health data.

[1083] This invention provides a system for monitoring the health of pets in pet shops in real time and immediately notifying them if an abnormality occurs. This system includes a collar that measures the pet's body temperature, activity level, and location information, a camera that recognizes the pet's movements, a toilet that measures the amount of toilet use, weight, and pH balance of urination and defecation, and a server that stores and analyzes this data.

[1084] The system's program is implemented in Python, and the server operates as follows: It receives data sent from pets' collars, cameras, and toilets, and stores the data in an SQLite database. The collected data is analyzed periodically to evaluate the pet's health. If an abnormality is detected based on the analysis results, an alert is generated and sent to the store clerk's terminal.

[1085] The server detects abnormalities based on body temperature, activity level, and location data, and immediately generates an alert if, for example, the body temperature exceeds 38.0°C or the activity level falls below 10. It also constantly tracks the pet's current location and provides the pet's location information within the pet shop to the store staff. Furthermore, the server uses sensors and cameras within the store to monitor the pet's health in real time, and immediately notifies the staff if an abnormality occurs.

[1086] Consider the following scenario as a concrete example: A pet's collar measures its temperature and records it as 38.5°C. The data is sent to a server, which receives the temperature data and detects that it is high compared to normal values. The server generates an alert, and a notification appears on the store clerk's device saying, "Your pet's temperature is high. Please check it immediately."

[1087] The server also has a means to generate and provide exercise and dietary advice to the user after detecting an abnormality. For example, if the pet's activity level is lower than normal, a notification will be displayed on the store clerk's device saying, "Your pet's activity level is lower than normal. Try letting it spend more time playing with toys." This advice encourages specific actions to maintain the pet's health.

[1088] Furthermore, it is possible to utilize a generative AI model. By inputting the following prompt sentence into the generative AI model, more advanced advice can be obtained.

[1089] Example prompt sentence:

[1090] “How do you monitor your pet’s health?

[1091] The data is as follows:

[1092] Pet ID: 1

[1093] Body temperature: 38.5℃

[1094] Activity amount: 10

[1095] Serving size: 50 grams

[1096] The threshold for detecting anomalies is:

[1097] Body temperature > 38.0°C

[1098] Activity amount < 10

[1099] Food intake < 100 grams

[1100] Use this data to generate alerts about your pet's health."

[1101] As described above, the system of the present invention can efficiently manage the health conditions of pets in pet shops and can respond quickly when an abnormality occurs.

[1102] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1103] Step 1: Collect data

[1104] Sensors and collars that measure pets' body temperature, activity levels, and location information collect data. This data is sent to a server via Wi-Fi or Bluetooth. The input is data from each sensor, and the output is raw data sent to the server. Specifically, the collar measures body temperature at regular intervals and sends the data to the server.

[1105] Step 2: Save your data

[1106] The server stores the received data in an SQLite database. The input is the raw data sent from the sensor, and the output is the data stored in the database. Specifically, the server inserts the raw data into the appropriate tables.

[1107] Step 3: Analyze the data

[1108] The server periodically analyzes the stored data and evaluates the pet's health. The input is data obtained from the database, and the output is the analysis results. Specifically, the server compares the body temperature, activity level, and location data with normal values ​​to check for any abnormalities.

[1109] Step 4: Detect anomalies and generate alerts

[1110] The server generates an alert based on the analysis results if an abnormality is detected. The input is the analysis results, and the output is the generated alert. Specifically, if the body temperature exceeds a specified value, an "Abnormal Body Temperature" alert is generated.

[1111] Step 5: Alert Notification

[1112] The generated alert is sent to the store clerk's device. The input is the generated alert, and the output is the notification displayed on the device. Specifically, the server generates a push notification and sends it to the store clerk's smartphone or tablet.

[1113] Step 6: User (store clerk) response

[1114] The store clerk who receives the notification checks the pet's condition and takes appropriate action. The input is the alert notification displayed on the terminal, and the output is the clerk's response action. Specifically, the clerk checks the notification and takes action such as cooling the pet if its body temperature is high.

[1115] Step 7: Generating and Providing Advice

[1116] Based on the analysis results, the server automatically generates advice on exercise and diet and provides it to the store clerk's terminal. The input is the analysis results, and the output is the generated advice. Specifically, if the amount of activity is low, the server generates advice such as "Please increase the amount of time the child spends playing with toys."

[1117] Step 8: Tracking and Providing Location Information

[1118] The server constantly tracks the location of pets and provides them to store clerks as needed. The input is location data, and the output is location information provision. Specifically, the server displays the location information of a specific pet on a map and provides it to store clerks.

[1119] Step 9: Real-time monitoring

[1120] The health of pets is monitored in real time using sensors and cameras installed in the store. The input is data from the sensors and cameras, and the output is health status information updated in real time. Specifically, the camera captures the pet's movements and sends that information to a server for analysis.

[1121] Step 10: Emergency Notification

[1122] When an abnormality occurs, the store clerk is notified immediately. The input is the abnormality detection data obtained through real-time monitoring, and the output is the immediately sent notification. Specifically, when an abnormality is detected, a notification is sent stating that "immediate action is required."

[1123] 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.

[1124] The present invention provides a system that comprehensively manages the health status of a pet, recognizes the user's emotions, and personalizes notifications and advice based on those emotions. This system is configured as follows.

[1125] System configuration and program processing

[1126] The system includes the following elements:

[1127] 1. A collar that measures your pet's temperature, activity, and location

[1128] 2. Pet movement detection camera

[1129] 3. A toilet that measures your pet's toilet usage, weight, and pH balance of urination and defecation

[1130] 4. Server for storing and analyzing collected data

[1131] 5. A device that generates alerts based on data analysis and notifies users of them.

[1132] 6. A means of providing users with appropriate exercise and dietary advice

[1133] 7. A way to track your pet's current location and provide location information to you

[1134] 8. Emotion engine that recognizes user emotions and adjusts notification and advice content

[1135] Program processing

[1136] server

[1137] 1. Data Receipt and Storage:

[1138] The server receives data from the collar, camera, and toilet and stores each piece of data in a database.

[1139] 2. Data analysis and anomaly detection:

[1140] The stored data is analyzed periodically to determine the pet's health status, and an alert is generated if an abnormality is detected from the analysis results.

[1141] 3. Advice Generation:

[1142] Based on the analysis results, appropriate advice regarding your pet's exercise and diet is automatically generated.

[1143] 4. Visualizing the results:

[1144] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[1145] Terminal

[1146] 1. Emotion recognition:

[1147] The emotion engine analyzes the user's emotions from facial expressions and tone of voice, learning emotional patterns based on the user's interaction history.

[1148] 2. Notification display:

[1149] Alerts and advice sent from the server are displayed to the user as push notifications. The notification content is personalized based on the analysis results of the emotion engine.

[1150] 3. Data Viewing:

[1151] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[1152] User

[1153] 1. Monitoring and Response:

[1154] Users can monitor their pet's health in real time, respond quickly when an alert occurs, and manage their pet's exercise and diet appropriately based on the advice provided.

[1155] Specific examples

[1156] Example 1: Your pet has a high body temperature

[1157] 1. Data Collection:

[1158] The collar measures your pet's temperature and records it as 39.0°C. The data is then sent to a server via Wi-Fi.

[1159] 2. Data Analysis:

[1160] The server receives the temperature data and stores it in a database. It compares it with the normal temperature and determines that 39.0°C is a high temperature. It generates an alert and notifies the user.

[1161] 3. Notifications and Emotion Recognition:

[1162] The device will display a notification saying, "Your pet's temperature is high. Please check immediately." The emotion engine will analyze the user's facial expressions and, if they are feeling anxious, will display an additional notification saying, "Your pet's temperature is high. Please stay calm and check and contact your veterinarian."

[1163] 4. Support:

[1164] Users can view notifications, check in on their pet, and follow the provided calming strategies if they feel anxious.

[1165] Example 2: If your pet is not very active

[1166] 1. Data Collection:

[1167] The collar measures daily activity and records when the dog is only moving about 30% of normal activity, sending the data to a server via Bluetooth.

[1168] 2. Data Analysis:

[1169] The server receives the activity data and stores it in a database. It compares it with the normal value and determines that the activity level is low. It generates advice and notifies the user.

[1170] 3. Notifications and Emotion Recognition:

[1171] The device will display a notification with advice such as, "Your pet is less active than usual. Try letting them spend more time playing with toys." If the emotion engine determines that the user is tired from the tone of their voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy play."

[1172] 4. Support:

[1173] Users can check the advice and increase the amount of time they spend playing with their pets without any stress.

[1174] In this way, this system comprehensively manages the health condition of pets and takes into consideration the user's feelings, allowing them to care for their pets with peace of mind.

[1175] The processing flow will be explained below.

[1176] Step 1: Data measurement and collection

[1177] Collar: Your pet's temperature sensor measures its temperature every hour, the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[1178] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[1179] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[1180] Step 2: Send data

[1181] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[1182] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[1183] Step 3: Save Data

[1184] The server stores the received data in a database.

[1185] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[1186] Step 4: Data analysis

[1187] The server analyzes the stored data at regular intervals to assess the pet's health.

[1188] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[1189] Step 5: Emotion Recognition

[1190] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[1191] For example, it uses a camera and microphone to analyze a user's facial expressions and voice to identify stress or anxiety.

[1192] Step 6: Alert Generation

[1193] The server generates an alert based on the analysis results.

[1194] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[1195] Step 7: Advice Generation

[1196] The server automatically generates advice on exercise and diet based on the results of data analysis.

[1197] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[1198] Step 8: Notifications and Personalization

[1199] The device displays alerts and advice sent from the server to the user as push notifications.

[1200] Personalize notifications based on the results of emotion engine analysis (e.g., "Your pet has a high temperature. Please stay calm and check and contact your veterinarian.").

[1201] Step 9: View Data

[1202] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[1203] Step 10: Monitor and respond

[1204] Users can monitor their pet's health in real time and respond quickly when an alert occurs.

[1205] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[1206] Specific examples

[1207] Example 1: Your pet has a high body temperature

[1208] Step 1: The collar measures your pet's temperature and records it as 39.0°C. The data is sent to a server via Wi-Fi.

[1209] Step 2: The server receives the temperature data and stores it in a database.

[1210] Step 3: The server compares the temperature to its normal value and determines that 39.0°C is a high temperature.

[1211] Step 4: The server generates an alert and notifies the user.

[1212] Step 5: The device's emotion engine analyzes the user's facial expression and if they are feeling anxious, it will send an additional notification saying, "Your pet has a high temperature. Please stay calm and check it and contact your veterinarian."

[1213] Step 6: The user checks the notification, checks in on their pet, and follows the provided calming strategies if they feel uneasy.

[1214] Example 2: If your pet is not very active

[1215] Step 1: The collar measures your daily activity and records that you're only moving about 30% of your normal activity. The data is then sent via Bluetooth to a server.

[1216] Step 2: The server receives the activity data and stores it in a database.

[1217] Step 3: The server determines that the activity is low compared to normal.

[1218] Step 4: The server generates the advice and notifies the user.

[1219] Step 5: Your device will display a notification with the advice, "Your pet's activity level is lower than usual. Try increasing the amount of time it spends playing with toys."

[1220] Step 6: If the emotion engine determines that the user is tired from their tone of voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy games."

[1221] Step 7: The user checks the advice and increases playtime with their pet without forcing themselves.

[1222] Example 2

[1223] 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."

[1224] Conventional pet health management systems simply collect and store data, making it difficult to comprehensively understand a pet's health condition. Furthermore, alerts and advice are provided uniformly without considering the user's emotions, meaning they are unable to provide the necessary information at the timing and with the content the user desires. Furthermore, there is a lack of methods for detecting anomalies and generating advice that are suited to specific conditions, making these systems less effective in managing pet health.

[1225] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera equipped with means for measuring the pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements, a toilet equipped with means for measuring the pet's toilet usage amount, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation, means for storing and analyzing data collected from the above means, means for detecting abnormalities and generating alerts based on the analysis results, means for notifying the user of the alerts, means for generating and providing exercise and dietary advice to the user, means for tracking the pet's current location and providing the user with location information, and means for recognizing the user's emotions and adjusting the content of notifications and advice. This makes it possible to comprehensively manage and analyze the pet's health condition and take appropriate measures based on the user's emotions.

[1226] A "means for measuring body temperature" is a device that has the function of measuring a pet's body temperature and recording and transmitting that data.

[1227] A "means for measuring activity levels" is a device that has the function of measuring a pet's movements and exercise, and recording and transmitting that data.

[1228] A "means for measuring location information" is a device that has the function of identifying the current location of a pet and recording and transmitting that information.

[1229] A "camera equipped with a means for recognizing movement" is a camera device that recognizes and records pet movements and behavior as video footage and analyzes the data.

[1230] A "means for measuring litter box usage" is a device that has the function of measuring the amount of litter box used by a pet and recording and transmitting that data.

[1231] A "means for measuring weight" is a device that has the function of measuring a pet's weight and recording and transmitting that data.

[1232] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet device that has the function of measuring the pH balance of a pet's urination and defecation, and recording and transmitting that data.

[1233] The "means for storing and analyzing data" refers to a server that stores data collected from each device and analyzes it to determine health status and abnormalities.

[1234] "Means for detecting anomalies and generating alerts" refers to a system function that detects anomalies from the analysis results and generates an alert to notify the user.

[1235] "Means of notifying users" refers to communication functions and devices used to notify users of alerts and advice.

[1236] The "means for generating and providing advice" is a function that automatically generates and provides advice on exercise and diet for pet health management based on the analysis results.

[1237] "Means for tracking current location and providing location information to the user" refers to a function that tracks the pet's location in real time and provides that information to the user.

[1238] "Means for recognizing user emotions and adjusting notification and advice content" refers to a system that has the ability to analyze and recognize user emotions and personalize the content of alerts and advice based on those emotions.

[1239] The present invention relates to a system for comprehensively managing pet health conditions, recognizing the user's emotions, and providing personalized notifications and advice based on those emotions. The system includes the following components:

[1240] server

[1241] The server receives data from the collar, camera, and litter box, such as the pet's body temperature, activity level, location, movement, litter box usage, weight, and pH balance of urination and defecation, and stores it in a database. The stored data is periodically analyzed using Python libraries (e.g., Pandas, NumPy). If an abnormal value is detected during the analysis, an alert is generated. In addition, appropriate advice regarding the pet's exercise and diet is automatically generated based on the analysis results. These analysis results and advice are converted into graphs and dashboards, and summarized in a format that can be viewed by the user in the app. The specific software used is an SQL database, Python, Matplotlib, and D3.js.

[1242] As a concrete example, consider the case where a pet's body temperature reaches 39.0°C. The collar measures the temperature and sends the data to a server via Wi-Fi. The server receives the data and stores it in a database. It analyzes the stored data and determines that the body temperature is abnormally high. It generates an abnormal alert indicating that the body temperature is high and notifies the user.

[1243] Terminal

[1244] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice. Using OpenCV and a voice analysis library, it learns the user's emotional patterns and personalizes the content of alerts and advice based on those patterns. Alerts and advice sent from the server are displayed to the user as push notifications. The content of the notifications is also adjusted according to the user's emotions. By opening the app, users can view detailed data and advice, and can also check their pet's current location on a map. The map display uses the Google Maps API.

[1245] As a concrete example, consider a case where a pet's activity level is only about 30% of normal. The collar measures the amount of activity throughout the day and sends the data via Bluetooth to a server. The server receives the data, stores it in a database, and detects a decrease in activity level. It generates advice to encourage activity and notifies the device. The device determines from the tone of the user's voice that the pet is tired, and displays additional advice such as, "When you are tired, it's a good idea to incorporate some easy games."

[1246] User

[1247] Users can monitor their pet's health in real time through their device and respond quickly when an alert occurs. They can check advice on the app and properly manage their pet's exercise and diet. Users can also check their pet's current location and respond quickly if their pet gets lost.

[1248] An example prompt is the text prompt "Your pet's temperature is higher than normal. Please provide advice to help the user stay calm."

[1249] As described above, the present invention is a system that can comprehensively manage the health condition of a pet and take appropriate action depending on the user's emotions.

[1250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1251] Step 1: Receiving and storing data

[1252] The server receives data packets sent from the collar, camera, and litter box. Specific input data includes the pet's body temperature, activity level, location, litter box usage, weight, and pH balance of urination and defecation. This data is received via Wi-Fi or Bluetooth through a communication module. The received data is stored in a database using a Python library (e.g., SQLAlchemy). Specific output data is various data stored in related tables in the database.

[1253] Step 2: Data analysis and anomaly detection

[1254] The server periodically analyzes the stored data. The input is various health data stored in a database. The data is processed using Python libraries (e.g., Pandas, NumPy) and each data point is compared with a normal value. If an abnormality is detected as a result of the analysis, an alert is generated. Specifically, a body temperature above 38°C is considered abnormal. The output is a flag indicating an abnormality and a trigger for generating an alert.

[1255] Step 3: Advice Generation

[1256] Based on the analysis results, the server generates appropriate advice regarding pet exercise and diet. The input is the data analysis results and anomaly flags. Specific advice sentences are created using generative AI models and rule-based systems. For example, if the pet has a high body temperature, the server generates advice such as "Give the pet plenty of water and use a cooling sheet." The output is advice sentences provided to the user.

[1257] Step 4: Visualize the results

[1258] The server converts the analysis results and advice into graphs and dashboards. The input is the analysis results and generated advice. Matplotlib and D3.js are used to visualize the data and integrate it into a web application. The output is graphs and dashboards that users can view in the app. For example, a graph showing the progression of body temperature over time.

[1259] Step 5: Emotion Recognition

[1260] The device's emotion engine analyzes the user's facial expressions and tone of voice. The input is audio and video data collected by the device's camera and microphone. Using OpenCV and voice analysis libraries, it analyzes the user's facial expressions and tone of voice to recognize emotional patterns. The output is a tag of the user's emotional state (e.g., anxiety, fatigue).

[1261] Step 6: Notifications and Personalization

[1262] The device displays alerts and advice sent from the server to the user as push notifications. The input is the alert notification from the server and the analysis results of the emotion engine. The notification content is personalized based on the results of the emotion engine. For example, if a pet's temperature is high, a notification saying "Your pet's temperature is high. Please stay calm and check and contact a veterinarian" is displayed. The output is a personalized notification message.

[1263] Step 7: View detailed data

[1264] Users can view detailed data and advice by opening the app on their device. The input is the analysis results and advice sent from the server. The app displays this data, allowing users to check past temperature and activity data over time. The output is detailed data and interactive graphs displayed in the user interface.

[1265] Step 8: Check your location

[1266] The app allows users to check their pet's current location on a map. The input is location information sent from the collar. The location information is plotted on a map using the Google Maps API. The output is the pet's current location displayed on the map.

[1267] Step 9: Monitor and respond

[1268] Users can monitor their pet's health in real time through the app and respond quickly when an alert occurs. The input is push notifications and detailed data from the device. The user follows the advice to manage their pet's health, for example, using a cooling sheet if the pet has a high temperature. The output is the user's appropriate action based on the pet's health condition.

[1269] (Application example 2)

[1270] 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."

[1271] In today's pet care industry, there are limited means for comprehensively managing pet health. Furthermore, the services and advice provided do not take into account the user's emotions, making it difficult for users to properly manage their pet's health. Furthermore, in physical stores, there is an insufficient system for suggesting optimal services and products based on the pet's health and the user's emotions.

[1272] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the notification content and advice content, and means for proposing pet-related services and products based on the user's emotions. This makes it possible to respond in consideration of the user's emotions, and to propose optimal pet care services and products even in physical stores.

[1273] A "means for measuring pet body temperature" is a device that measures a pet's body temperature in real time using a temperature sensor integrated into a pet's collar or other wearable device.

[1274] A "means for measuring pet activity" is a device that measures the distance traveled and the amount of exercise a pet performs through a pet's collar or other wearable device.

[1275] A "means for measuring pet location information" is a device that uses GPS or other location measurement technology to determine the current location of a pet.

[1276] A "camera equipped with a means for recognizing pet movements" is a camera device that captures pet movements and analyzes them using image analysis technology.

[1277] A "means for measuring pet litter box usage" is a device equipped with a sensor that records the number and frequency of a pet's litter box use.

[1278] "Means for measuring pet weight" refers to a scale or wearable device that measures the weight of a pet when it is on board.

[1279] A "toilet with a means for measuring the pH balance of a pet's urination and defecation" is a toilet device equipped with a sensor for detecting the pH balance when a pet urinates or defecates.

[1280] "Means for storing and analyzing data collected from the above means" refers to servers and software systems that accumulate data obtained from various sensors and devices and analyze that data.

[1281] "Means for detecting anomalies and generating alerts based on analysis results" refers to systems or software that detect anomalies from analyzed data and issue warnings to users.

[1282] The "means of notifying users of the above alerts" refers to a system for pushing warnings and notifications to users' devices such as smartphones and tablets.

[1283] "Means for recognizing the user's emotions and adjusting the content of notifications and advice" refers to a system that uses a camera and microphone to analyze the user's facial expressions and voice, and then appropriately adjusts notifications and advice based on those emotions.

[1284] The "means for generating and providing exercise and dietary advice to users" is a system that generates optimal advice regarding the amount of exercise and diet for pets based on the analyzed data and provides it to users.

[1285] "Means for tracking a pet's current location and providing location information to the user" refers to a system that uses location tracking technology such as GPS to identify a pet's current location and notify the user of that information.

[1286] The "means for suggesting pet-related services and products based on the user's emotions" is a system that analyzes the user's emotional state and recommends the most suitable pet care services and products based on the results.

[1287] To implement this invention, the following system configuration and process are required.

[1288] The system includes multiple measuring devices to collect information such as a pet's body temperature, activity level, and location, a server to analyze and notify this data, and an emotion engine that recognizes the user's emotions and personalizes the notification content.

[1289] System Configuration

[1290] Collar: Includes a means to measure your pet's temperature, activity, and location.

[1291] Hardware used: Body temperature sensor, accelerometer, GPS module

[1292] Software used: Data transmission protocols (e.g. Bluetooth, Wi-Fi)

[1293] Camera: Equipped with a means to recognize pet movements and record footage.

[1294] Hardware used: High resolution camera (e.g. Logitech C920)

[1295] Software used: Image analysis algorithms (e.g., OpenCV)

[1296] Toilet: Includes a means to measure your pet's toilet usage, weight, and pH balance of urination and defecation.

[1297] Hardware used: Weight scale, pH sensor

[1298] Software used: Sensor data analysis tools (e.g., SciPy)

[1299] Server: Stores and analyzes various data, detects abnormalities, and generates alerts.

[1300] Software used: databases (e.g., MySQL), data analysis tools (e.g., NumPy, SciPy)

[1301] Device: Notify users and adjust advice.

[1302] Hardware used: Smartphone, tablet

[1303] Software used: Notification system API (e.g. Firebase Cloud Messaging)

[1304] Emotion engine: Recognizes user emotions and personalizes notifications and advice.

[1305] Software used: Facial expression recognition algorithms (e.g. DLib), voice analysis engine

[1306] Program Processing Details

[1307] The server receives data such as body temperature, activity level, and location information sent from the pet's collar and stores it in a database. The server then analyzes the stored data and generates an alert if an abnormality is detected, sending a notification to the user's device. Data analysis tools such as NumPy and SciPy are used for the analysis.

[1308] The device checks the notification received by the user, and an emotion engine analyzes the user's facial expressions and voice to recognize their emotions. For example, if the user is feeling anxious, the notification content will be personalized and provide advice such as, "Your pet's temperature is high. Please stay calm and check it and contact your veterinarian." Notifications on the device use notification system APIs such as Firebase Cloud Messaging.

[1309] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes their emotions using DLib and a voice analysis engine. For example, if a user receives a notification that their pet's temperature is rising and looks anxious, the system will provide additional advice such as "stay calm."

[1310] Specific examples

[1311] Example 1: Your pet has a high body temperature

[1312] 1. Data collection: The collar measures the pet's temperature to be 39.0°C and sends the data to the server.

[1313] 2. Data analysis: The server analyzes the body temperature data, determines that 39.0°C is a high temperature, and generates an alert.

[1314] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet's temperature is high. Please check immediately." If the emotion engine analyzes the user's facial expression and they are feeling anxious, an additional notification will be displayed saying, "Please stay calm and check and contact your veterinarian."

[1315] Example 2: If your pet is not very active

[1316] 1. Data collection: The collar records activity at approximately 30% of normal activity and sends the data to a server.

[1317] 2. Data analysis: The server analyzes the activity data, determines that the activity level is low, and generates advice.

[1318] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet is less active than usual. Try spending more time playing with toys." If the emotion engine determines that the user is tired based on their tone of voice, additional advice will be given: "When you're tired, it's a good idea to incorporate some easy play."

[1319] Prompt Sentence Examples

[1320] "The pet's temperature is high and the user is feeling anxious. Please advise the user to remain calm and, as the pet's temperature is high, suggest that they seek appropriate medical attention."

[1321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1322] Step 1: Data collection

[1323] The collar measures the pet's body temperature, activity level, and location information in real time. This data is sent to a server using Bluetooth or Wi-Fi. The input is body temperature, activity level, and location information data, and the output is the raw data sent to the server. This stage includes specific operations: the body temperature sensor measures body temperature, the accelerometer measures activity level, and the GPS measures location information.

[1324] Step 2: Save your data

[1325] The server stores the received data in a database. The input is the raw data sent from the collar, and the output is structured data stored in the database. This processing step uses a database management system such as MySQL or PostgreSQL to store the temperature, activity, and location data in tables.

[1326] Step 3: Data analysis

[1327] The server periodically launches a process to analyze the stored data. The input is the raw data stored in the database, and the output is the analysis results. Specifically, statistical analysis is performed using NumPy and SciPy to detect outliers. At this time, a threshold is set to detect anomalies by comparing with past data.

[1328] Step 4: Anomaly detection and alerting

[1329] The server detects anomalies based on the analysis results and generates an alert. The input is the analysis results and the output is the generated alert. If an anomaly is detected, an alert message is generated and sent to the user's device. This process uses an algorithm that triggers an alert when certain conditions are met.

[1330] Step 5: Alert Notification

[1331] An alert notification is displayed on the device. At this stage, the alert is pushed to the user in real time. The input is the alert message sent from the server, and the output is the notification displayed on the device. Specifically, the alert is sent using a service such as Firebase Cloud Messaging.

[1332] Step 6: User sentiment analysis

[1333] The device uses a camera and microphone to analyze the user's emotions. The input is the user's facial expression images and voice data, and the output is the emotion determination result. Facial expressions are analyzed using DLib, and tone of voice is analyzed using a voice analysis engine. Specifically, the system involves the user showing their facial expressions in front of the camera and speaking into the microphone.

[1334] Step 7: Personalize your notifications

[1335] The device personalizes the content of notifications and advice based on the results of emotion analysis. The input is the emotion determination result and the alert message, and the output is the adjusted notification content. If the emotion engine analyzes the user's emotions and detects anxiety or fatigue, it adds advice appropriate to that state. For example, it generates messages such as "Please stay calm" or "We recommend some easy games."

[1336] Step 8: Inform and advise users

[1337] The device displays personalized notification content and advice to the user. The input is the tailored notification content, and the output is the notification message displayed to the user. Specifically, this includes the behavior of displaying a personalized message on the screen of a smartphone or tablet.

[1338] Step 9: Propose your service or product

[1339] The device suggests services and products offered in physical stores based on the user's emotions and the pet's health condition. The input is the emotion assessment result and health condition data, and the output is a list of suggested services and products. For example, if the user is worried about their pet's high temperature, suggestions such as "This product will make it easy to manage their pet's temperature" or "We recommend using this service" will be displayed.

[1340] Through the above steps, the present invention provides a system that comprehensively manages the health condition of a pet and takes appropriate measures taking into account the user's emotions.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] [Fourth embodiment]

[1345] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1346] 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.

[1347] 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).

[1348] 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.

[1349] 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.

[1350] 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).

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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."

[1358] The present invention provides a system for comprehensively managing the health of pets. This system is composed of multiple elements that cooperate to monitor the health of pets, and if an abnormality occurs, it promptly notifies the user and provides appropriate advice.

[1359] Program processing

[1360] The system operates as follows.

[1361] server:

[1362] 1. Data Receipt and Storage:

[1363] The server receives data from the pet's collar, camera, and litter box.

[1364] The received data is stored in the respective databases.

[1365] 2. Data analysis and anomaly detection:

[1366] The server periodically analyzes the stored data and assesses the pet's health.

[1367] It compares with normal values ​​and generates an alert if an abnormality is detected.

[1368] 3. Advice Generation:

[1369] Based on the results of data analysis, appropriate advice regarding your pet's exercise and diet is automatically generated.

[1370] 4. Visualizing the results:

[1371] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[1372] Device (user's smartphone or computer):

[1373] 1. Notification display:

[1374] Alerts and advice sent from the server are displayed to the user as push notifications.

[1375] Open the app to view detailed data and advice.

[1376] 2. Data browsing:

[1377] Users can check their past health data and current health status in real time within the app.

[1378] You can check your pet's location on a map.

[1379] User:

[1380] 1. Monitoring and Response:

[1381] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[1382] Based on the advice provided, you can adjust your pet's exercise and diet.

[1383] Specific examples

[1384] Example 1: Your pet has a high body temperature

[1385] 1. Data Collection:

[1386] The collar measures the body temperature and records it as 38.5°C (normal is 37.5°C).

[1387] The data is sent to a server via Wi-Fi.

[1388] 2. Data Analysis:

[1389] The server receives the temperature data and stores it in a database.

[1390] Compared to normal values, 38.5℃ is considered to be a high temperature.

[1391] The server generates an alert and notifies the user.

[1392] 3. Notice and Response:

[1393] A notification will appear on your device saying, "Your pet's temperature is high. Please check immediately."

[1394] The user checks the notification and checks the status of the pet.

[1395] Example 2: If your pet is not very active

[1396] 1. Data Collection:

[1397] The collar measures daily activity and records that the dog is only moving about 30% of normal activity.

[1398] The data is sent to the server via Bluetooth.

[1399] 2. Data Analysis:

[1400] The server receives the activity data and stores it in a database.

[1401] Compared to normal values, it is determined that the amount of activity is low.

[1402] The server generates the advice and notifies the user.

[1403] 3. Notice and Response:

[1404] An advice notification will appear on your device saying, "Your pet is less active than usual. Try letting it spend more time playing with toys."

[1405] Users can check the advice and spend more time playing with their pets.

[1406] In this way, the system of the present invention can closely monitor the health condition of a pet and respond quickly and appropriately when an abnormality occurs, allowing pet owners to manage their pet's health with peace of mind, which greatly contributes to maintaining the pet's health.

[1407] The processing flow will be explained below.

[1408] Step 1: Data measurement and collection

[1409] Collar: Your pet's temperature sensor measures its temperature at regular intervals (e.g., every hour), the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[1410] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[1411] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[1412] Step 2: Send data

[1413] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[1414] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[1415] Step 3: Save Data

[1416] The server stores the received data in a database.

[1417] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[1418] Step 4: Data analysis

[1419] The server periodically analyzes the stored data to determine the pet's health status.

[1420] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[1421] Step 5: Alert Generation

[1422] The server generates an alert based on the analysis results.

[1423] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[1424] Step 6: Advice Generation

[1425] The server automatically generates advice on exercise and diet based on the results of data analysis.

[1426] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[1427] Step 7: Visualize the results

[1428] The server converts the analysis results and advice into graphs and dashboards that users can view in the app.

[1429] For example, body temperature data is displayed as a line graph and activity levels are displayed as a bar graph.

[1430] Step 8: Notifications

[1431] The device displays alerts and advice sent from the server to the user as push notifications.

[1432] For example, a notification could be sent to your smartphone saying, "Your pet's temperature is high. Please check immediately."

[1433] Step 9: View Data

[1434] Users can view detailed data and advice by opening the app.

[1435] Users can check their pet's past health data and current health status in real time, and can also view their pet's location on a map.

[1436] Step 10: Monitor and respond

[1437] Users can monitor their pet's health in real time and respond quickly if any abnormalities occur.

[1438] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[1439] This series of processes allows you to comprehensively manage your pet's health and respond quickly if an abnormality occurs.

[1440] Example 1

[1441] 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."

[1442] Comprehensive, real-time management of pet health is an important issue for many pet owners. However, current systems collect and analyze various data separately, often resulting in inconsistent information and delays. Furthermore, when an abnormality occurs, users often have to think of a solution themselves, making it difficult to respond quickly and accurately. To solve these problems, integrated management and analysis of each data, rapid notification of abnormalities, and provision of specific advice are required.

[1443] 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.

[1444] In this invention, the server includes an imaging device equipped with means for measuring a pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements; a device having means for measuring the pet's excretion volume, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation; means for storing and analyzing data collected from the above means; means for detecting abnormalities and generating alerts based on the analysis results; means for notifying the user of the alerts; means for generating advice based on prompt sentences using a generative AI model to generate and provide exercise and dietary advice to the user; and means for tracking the pet's current location and providing the user with location information. This allows for comprehensive management of the pet's health condition in real time and enables prompt and appropriate response when an abnormality occurs. Furthermore, accurate advice is provided using a generative AI model, allowing the user to immediately know specific countermeasures.

[1445] A "means for measuring a pet's body temperature" is a device that is equipped with a sensor on a pet's collar or attached device and measures the pet's body temperature periodically or in real time.

[1446] A "means for measuring a pet's activity level" is a device that uses an acceleration sensor or gyro sensor to measure a pet's daily activity and exercise level.

[1447] A "means for measuring pet location information" is a device equipped with a GPS function that tracks a pet's current location in real time and obtains location information.

[1448] The "devices" are devices used to monitor the health of pets, such as collars that can be attached to pets, imaging devices, scales, and excrement detection sensors.

[1449] An "imaging device" is a device that uses a camera and image analysis technology to capture and analyze a pet's behavior in order to recognize the pet's movements.

[1450] The "means for measuring excretion volume" is a device that uses a sensor installed in the pet's toilet to measure the volume of urination and defecation.

[1451] The "means for measuring the weight of a pet" is a device that uses a scale to periodically measure the weight of a pet and obtains the data.

[1452] The "means for measuring pH balance" is a device equipped with a sensor for measuring the pH value of a pet's urine and feces.

[1453] The "means for storing and analyzing data" refers to a server system for storing various data in a database and analyzing the data using statistical analysis and machine learning techniques.

[1454] The "means for detecting abnormalities and generating alerts" refers to a system that generates and notifies an alert based on information when an abnormality is detected by comparing with normal data.

[1455] "Means of notifying users" refers to a system that sends alerts and analysis results to users' devices as push notifications or messages.

[1456] The "means for generating and providing advice" is a system that uses a generative AI model to automatically generate appropriate exercise and diet advice based on prompt text and provide it to users.

[1457] A "generative AI model" is a type of artificial intelligence model that generates natural language sentences based on input prompts.

[1458] A "prompt sentence" is a text sentence that is input into a generative AI model and serves as a reference sentence for the model to generate advice or text based on that prompt.

[1459] This invention is a system for comprehensively managing the health of pets. This system collects and stores data from a series of devices that measure pet body temperature, activity level, location information, excretion volume, weight, and pH balance of urination and feces, and based on the results, detects abnormalities, notifies the user, and provides appropriate advice.

[1460] First, to measure your pet's body temperature, a small temperature sensor is attached to the pet's collar. This temperature sensor monitors your pet's temperature in real time and transmits the data to a server via Wi-Fi. Similarly, to measure your pet's activity level, an accelerometer and gyro sensor are attached to the collar to measure your pet's daily activity. This data is then transmitted to the server at specific time intervals.

[1461] To measure the location of your pet, a device with GPS functionality is used, which tracks your pet's current location in real time and obtains its location information. The obtained data is sent to a server via Wi-Fi or Bluetooth.

[1462] In addition, a camera is installed as an imaging device to recognize the pet's movements. The camera captures video data and analyzes the pet's behavior using image analysis technology. This video data is also sent to the server for storage and analysis.

[1463] To measure the amount of excretion from pets, a sensor installed in the toilet is used. The sensor periodically records the amount of urination and defecation from the pet and sends the data to a server. In addition, a scale is used to measure the weight of the pet and obtain the data. To measure the pH balance of urination and defecation, a pH sensor is installed in the toilet and measures the pH value of urination and defecation.

[1464] The server uses a database management system such as MySQL or MongoDB to store this data in a database. It then analyzes the data using Python programs and the Pandas library. If an anomaly is detected by comparing the analysis with normal data, the server runs an anomaly detection algorithm using a machine learning library such as Scikit-learn to generate an alert.

[1465] Furthermore, a generative AI model is used to generate exercise and diet advice based on prompts. Specifically, the following prompts are input into the generative AI model:

[1466] "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?"

[1467] The generated advice is provided to the user. Push notifications are sent to the user's device in real time using Firebase Cloud Messaging (FCM). Users can check their pet's detailed data and advice through an application developed in React Native or Swift. The app also provides a function to display the pet's location on a map using the Google Maps API.

[1468] For example, if a pet's body temperature rises to 38.5°C, higher than the normal 37.5°C, the server analyzes this data and detects the abnormality. As a result, an alert is generated stating, "Your pet's temperature is high. Please check immediately." The generative AI model then generates advice such as, "We recommend using a wet towel to cool the pet down." These notifications and advice are sent to the user's device as push notifications.

[1469] This allows the system to comprehensively manage pet health conditions in real time, enabling prompt and appropriate responses when abnormalities occur. Furthermore, by using generative AI models to provide accurate advice, users can instantly learn specific countermeasures.

[1470] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1471] Step 1: Receiving data

[1472] The server receives data on pets' temperature, activity, location, movement, waste output, weight, and pH balance from various sensor devices. These devices transmit data to the server using Wi-Fi or Bluetooth. For example, a collar transmits temperature data via Wi-Fi, which is then received by the server.

[1473] Input: Sensor data from collar, camera, and litter box

[1474] Output: Various sensor data received by the server

[1475] Step 2: Save data

[1476] The server stores the received data in the respective databases. Body temperature data is stored in the "temperatures" table in MySQL, and activity data is stored in the "activities" collection in MongoDB. Video data and excrement data are also stored in appropriate storage.

[1477] Input: Various sensor data

[1478] Output: Various data stored in the database

[1479] Step 3: Data analysis

[1480] The server periodically analyzes the stored data using Python and Pandas. It compares body temperature and activity data with normal values ​​to detect abnormalities. For example, if body temperature rises from 37.5°C to 38.5°C, it is considered abnormal.

[1481] Input: Data stored in a database

[1482] Output: Presence or absence of abnormalities as analysis results

[1483] Step 4: Anomaly detection and alerting

[1484] The server runs an anomaly detection algorithm using Scikit-learn and generates an alert if an anomaly is detected. The generated alert is saved in a database and added to an alert queue. For example, if an abnormality in body temperature is detected, an alert such as "Temperature is abnormally high" is generated.

[1485] Input: Presence or absence of abnormalities as analysis results

[1486] Output: Generated alerts

[1487] Step 5: Advice Generation

[1488] The server uses a generative AI model to generate advice based on a prompt. For example, if the prompt is "My pet's temperature has risen from 37.5°C to 38.5°C. What is the appropriate way to treat my pet?", the server generates the advice "I recommend using a wet towel to cool him down."

[1489] Input: prompt statement

[1490] Output: Generated advice

[1491] Step 6: Notification of alerts and advice

[1492] The server notifies the user of the generated alert and advice. A push notification is sent using Firebase Cloud Messaging (FCM), and a notification saying "Your pet's temperature is high. Please check immediately" is displayed on the user's device. The generated advice is also displayed.

[1493] Input: Generated alerts and advice

[1494] Output: Push notification to the user's device

[1495] Step 7: Viewing Data

[1496] Users can access detailed data and advice by opening the app on their smartphone or computer. The app also allows users to check past health data and current health status in real time, and displays their pet's location on a map using Google Maps API.

[1497] Input: User actions

[1498] Output: Detailed data and advice displayed within the app

[1499] Step 8: User interaction

[1500] Users can quickly respond based on the advice provided, for example, if their pet has a high temperature, they can follow the advice and take measures to cool their pet down, allowing them to quickly and appropriately manage their pet's health.

[1501] Input: In-app advice

[1502] Output: Specific measures for pets

[1503] (Application example 1)

[1504] 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."

[1505] Currently, systems for effectively monitoring the health of pets exist on the market, but there is a lack of systems that specifically address the needs of pet shops. Because pet shops must manage a large number of animals at once and provide healthy pets to customers, it is important to monitor the health of individual pets in real time and respond quickly if an abnormality occurs. However, conventional systems have difficulty meeting these requirements. Therefore, an objective of the present invention is to provide a system that monitors the health of pets for sale in the store in real time and immediately notifies the customer if an abnormality occurs.

[1506] 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.

[1507] In this invention, the server includes: a collar including means for measuring the pet's body temperature, a means for measuring the pet's activity level, and a means for measuring the pet's location information; a camera equipped with means for recognizing the pet's movements; a toilet having means for measuring the pet's litter box usage, a means for measuring the pet's weight, and a means for measuring the pH balance of the pet's urination and defecation; a means for storing and analyzing data collected from the above means; a means for detecting abnormalities and generating alerts based on the analysis results; a means for notifying the user of the alerts; a means for generating and providing exercise and dietary advice to the user; a means for tracking the pet's current location and providing the user with location information; a means for monitoring the pet's health in real time using sensors and cameras installed in the store; and a means for immediately notifying a store staff member when an abnormality occurs. This enables the pet shop to efficiently manage the health of individual pets and quickly respond when an abnormality is detected.

[1508] The "means for measuring body temperature" is a device that has the function of continuously measuring the pet's body temperature and transmitting the data to a server.

[1509] A "means for measuring activity levels" is a device that uses sensors to capture a pet's movement and level of exercise and collects that activity data.

[1510] A "means for measuring location information" is a device that identifies a pet's current location using GPS or other location information technology and records it as data.

[1511] A "camera equipped with a means for recognizing motion" is a device that uses motion detection technology built into the camera to monitor and record the movements and behavior of pets.

[1512] A "means for measuring toilet usage" is a device that uses a sensor to measure the frequency and amount of toilet usage by pets and records that data.

[1513] "Means for measuring weight" refers to a device that accurately measures the weight of a pet, and is generally a weighing scale.

[1514] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet with a function that measures the pH level when a pet urinates or defecates.

[1515] "Means for storing and analyzing data" refers to a device that stores collected data in a database or the like and performs analytical processing based on this data.

[1516] The "means for detecting anomalies and generating alerts" is a device that detects abnormal conditions from the analyzed data and generates a notification to notify the user.

[1517] "Means of notifying users" refers to a function that notifies users of detected abnormalities or alerts via their smartphones or other devices.

[1518] The "means for generating and providing advice on exercise and diet" is a device that automatically generates advice on exercise and diet according to the health condition of a pet and provides it to the user.

[1519] The "means for tracking the current location and providing location information" is a device that continuously tracks the current location of a pet and provides that location information to the user.

[1520] "Means for monitoring health conditions in real time using sensors and cameras installed in the store" refers to a function that uses sensors and cameras installed in the pet shop to monitor the health conditions of pets in real time.

[1521] "Means for immediately notifying store staff when an abnormality occurs" refers to a function that immediately notifies store staff when an abnormality is detected based on the pet's health data.

[1522] This invention provides a system for monitoring the health of pets in pet shops in real time and immediately notifying them if an abnormality occurs. This system includes a collar that measures the pet's body temperature, activity level, and location information, a camera that recognizes the pet's movements, a toilet that measures the amount of toilet use, weight, and pH balance of urination and defecation, and a server that stores and analyzes this data.

[1523] The system's program is implemented in Python, and the server operates as follows: It receives data sent from pets' collars, cameras, and toilets, and stores the data in an SQLite database. The collected data is analyzed periodically to evaluate the pet's health. If an abnormality is detected based on the analysis results, an alert is generated and sent to the store clerk's terminal.

[1524] The server detects abnormalities based on body temperature, activity level, and location data, and immediately generates an alert if, for example, the body temperature exceeds 38.0°C or the activity level falls below 10. It also constantly tracks the pet's current location and provides the pet's location information within the pet shop to the store staff. Furthermore, the server uses sensors and cameras within the store to monitor the pet's health in real time, and immediately notifies the staff if an abnormality occurs.

[1525] Consider the following scenario as a concrete example: A pet's collar measures its temperature and records it as 38.5°C. The data is sent to a server, which receives the temperature data and detects that it is high compared to normal values. The server generates an alert, and a notification appears on the store clerk's device saying, "Your pet's temperature is high. Please check it immediately."

[1526] The server also has a means to generate and provide exercise and dietary advice to the user after detecting an abnormality. For example, if the pet's activity level is lower than normal, a notification will be displayed on the store clerk's device saying, "Your pet's activity level is lower than normal. Try letting it spend more time playing with toys." This advice encourages specific actions to maintain the pet's health.

[1527] Furthermore, it is possible to utilize a generative AI model. By inputting the following prompt sentence into the generative AI model, more advanced advice can be obtained.

[1528] Example prompt sentence:

[1529] “How do you monitor your pet’s health?

[1530] The data is as follows:

[1531] Pet ID: 1

[1532] Body temperature: 38.5℃

[1533] Activity amount: 10

[1534] Serving size: 50 grams

[1535] The threshold for detecting anomalies is:

[1536] Body temperature > 38.0°C

[1537] Activity amount < 10

[1538] Food intake < 100 grams

[1539] Use this data to generate alerts about your pet's health."

[1540] As described above, the system of the present invention can efficiently manage the health conditions of pets in pet shops and can respond quickly when an abnormality occurs.

[1541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1542] Step 1: Collect data

[1543] Sensors and collars that measure pets' body temperature, activity levels, and location information collect data. This data is sent to a server via Wi-Fi or Bluetooth. The input is data from each sensor, and the output is raw data sent to the server. Specifically, the collar measures body temperature at regular intervals and sends the data to the server.

[1544] Step 2: Save your data

[1545] The server stores the received data in an SQLite database. The input is the raw data sent from the sensor, and the output is the data stored in the database. Specifically, the server inserts the raw data into the appropriate tables.

[1546] Step 3: Analyze the data

[1547] The server periodically analyzes the stored data and evaluates the pet's health. The input is data obtained from the database, and the output is the analysis results. Specifically, the server compares the body temperature, activity level, and location data with normal values ​​to check for any abnormalities.

[1548] Step 4: Detect anomalies and generate alerts

[1549] The server generates an alert based on the analysis results if an abnormality is detected. The input is the analysis results, and the output is the generated alert. Specifically, if the body temperature exceeds a specified value, an "Abnormal Body Temperature" alert is generated.

[1550] Step 5: Alert Notification

[1551] The generated alert is sent to the store clerk's device. The input is the generated alert, and the output is the notification displayed on the device. Specifically, the server generates a push notification and sends it to the store clerk's smartphone or tablet.

[1552] Step 6: User (store clerk) response

[1553] The store clerk who receives the notification checks the pet's condition and takes appropriate action. The input is the alert notification displayed on the terminal, and the output is the clerk's response action. Specifically, the clerk checks the notification and takes action such as cooling the pet if its body temperature is high.

[1554] Step 7: Generating and Providing Advice

[1555] Based on the analysis results, the server automatically generates advice on exercise and diet and provides it to the store clerk's terminal. The input is the analysis results, and the output is the generated advice. Specifically, if the amount of activity is low, the server generates advice such as "Please increase the amount of time the child spends playing with toys."

[1556] Step 8: Tracking and Providing Location Information

[1557] The server constantly tracks the location of pets and provides them to store clerks as needed. The input is location data, and the output is location information provision. Specifically, the server displays the location information of a specific pet on a map and provides it to store clerks.

[1558] Step 9: Real-time monitoring

[1559] The health of pets is monitored in real time using sensors and cameras installed in the store. The input is data from the sensors and cameras, and the output is health status information updated in real time. Specifically, the camera captures the pet's movements and sends that information to a server for analysis.

[1560] Step 10: Emergency Notification

[1561] When an abnormality occurs, the store clerk is notified immediately. The input is the abnormality detection data obtained through real-time monitoring, and the output is the immediately sent notification. Specifically, when an abnormality is detected, a notification is sent stating that "immediate action is required."

[1562] 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.

[1563] The present invention provides a system that comprehensively manages the health status of a pet, recognizes the user's emotions, and personalizes notifications and advice based on those emotions. This system is configured as follows.

[1564] System configuration and program processing

[1565] The system includes the following elements:

[1566] 1. A collar that measures your pet's temperature, activity, and location

[1567] 2. Pet movement detection camera

[1568] 3. A toilet that measures your pet's toilet usage, weight, and pH balance of urination and defecation

[1569] 4. Server for storing and analyzing collected data

[1570] 5. A device that generates alerts based on data analysis and notifies users of them.

[1571] 6. A means of providing users with appropriate exercise and dietary advice

[1572] 7. A way to track your pet's current location and provide location information to you

[1573] 8. Emotion engine that recognizes user emotions and adjusts notification and advice content

[1574] Program processing

[1575] server

[1576] 1. Data Receipt and Storage:

[1577] The server receives data from the collar, camera, and toilet and stores each piece of data in a database.

[1578] 2. Data analysis and anomaly detection:

[1579] The stored data is analyzed periodically to determine the pet's health status, and an alert is generated if an abnormality is detected from the analysis results.

[1580] 3. Advice Generation:

[1581] Based on the analysis results, appropriate advice regarding your pet's exercise and diet is automatically generated.

[1582] 4. Visualizing the results:

[1583] The analysis results and advice are converted into graphs and dashboards that users can view in the app.

[1584] Terminal

[1585] 1. Emotion recognition:

[1586] The emotion engine analyzes the user's emotions from facial expressions and tone of voice, learning emotional patterns based on the user's interaction history.

[1587] 2. Notification display:

[1588] Alerts and advice sent from the server are displayed to the user as push notifications. The notification content is personalized based on the analysis results of the emotion engine.

[1589] 3. Data Viewing:

[1590] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[1591] User

[1592] 1. Monitoring and Response:

[1593] Users can monitor their pet's health in real time, respond quickly when an alert occurs, and manage their pet's exercise and diet appropriately based on the advice provided.

[1594] Specific examples

[1595] Example 1: Your pet has a high body temperature

[1596] 1. Data Collection:

[1597] The collar measures your pet's temperature and records it as 39.0°C. The data is then sent to a server via Wi-Fi.

[1598] 2. Data Analysis:

[1599] The server receives the temperature data and stores it in a database. It compares it with the normal temperature and determines that 39.0°C is a high temperature. It generates an alert and notifies the user.

[1600] 3. Notifications and Emotion Recognition:

[1601] The device will display a notification saying, "Your pet's temperature is high. Please check immediately." The emotion engine will analyze the user's facial expressions and, if they are feeling anxious, will display an additional notification saying, "Your pet's temperature is high. Please stay calm and check and contact your veterinarian."

[1602] 4. Support:

[1603] Users can view notifications, check in on their pet, and follow the provided calming strategies if they feel anxious.

[1604] Example 2: If your pet is not very active

[1605] 1. Data Collection:

[1606] The collar measures daily activity and records when the dog is only moving about 30% of normal activity, sending the data to a server via Bluetooth.

[1607] 2. Data Analysis:

[1608] The server receives the activity data and stores it in a database. It compares it with the normal value and determines that the activity level is low. It generates advice and notifies the user.

[1609] 3. Notifications and Emotion Recognition:

[1610] The device will display a notification with advice such as, "Your pet is less active than usual. Try letting them spend more time playing with toys." If the emotion engine determines that the user is tired from the tone of their voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy play."

[1611] 4. Support:

[1612] Users can check the advice and increase the amount of time they spend playing with their pets without any stress.

[1613] In this way, this system comprehensively manages the health condition of pets and takes into consideration the user's feelings, allowing them to care for their pets with peace of mind.

[1614] The processing flow will be explained below.

[1615] Step 1: Data measurement and collection

[1616] Collar: Your pet's temperature sensor measures its temperature every hour, the activity sensor collects acceleration data every minute, and the GPS module obtains its location every 10 minutes.

[1617] Cameras: Motion detection sensors and cameras capture real-time footage and record data whenever movement is detected.

[1618] Toilet: A weight sensor measures weight, a pH sensor records the pH value of urine, and a sensor measures toilet usage. All measurement data is recorded.

[1619] Step 2: Send data

[1620] The collar, camera, and litter box each transmit the data they collect to a server via Wi-Fi or Bluetooth.

[1621] For example, collar data is automatically sent to the server every hour, and camera and litter box data is also sent to the server periodically.

[1622] Step 3: Save Data

[1623] The server stores the received data in a database.

[1624] For example, body temperature data is stored in a "body temperature" table, activity amount data is stored in an "activity amount" table, and location information is stored in a "location" table.

[1625] Step 4: Data analysis

[1626] The server analyzes the stored data at regular intervals to assess the pet's health.

[1627] For example, if the body temperature data exceeds 39°C, it will be judged as abnormal and an alert will be generated. The same analysis will be performed if the activity level is outside the normal range.

[1628] Step 5: Emotion Recognition

[1629] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[1630] For example, it uses a camera and microphone to analyze a user's facial expressions and voice to identify stress or anxiety.

[1631] Step 6: Alert Generation

[1632] The server generates an alert based on the analysis results.

[1633] For example, if the body temperature exceeds 39°C, a message will be generated stating "Your pet's temperature is high" to notify the user.

[1634] Step 7: Advice Generation

[1635] The server automatically generates advice on exercise and diet based on the results of data analysis.

[1636] For example, if the activity level is low, the system generates advice such as "You need more exercise. Let me play with my toys."

[1637] Step 8: Notifications and Personalization

[1638] The device displays alerts and advice sent from the server to the user as push notifications.

[1639] Personalize notifications based on the results of emotion engine analysis (e.g., "Your pet has a high temperature. Please stay calm and check and contact your veterinarian.").

[1640] Step 9: View Data

[1641] Users can open the app to view detailed data and advice, and can also check their pet's location on a map.

[1642] Step 10: Monitor and respond

[1643] Users can monitor their pet's health in real time and respond quickly when an alert occurs.

[1644] For example, when an alert is displayed, the system will check the status of your pet and take action such as contacting a veterinarian if necessary.

[1645] Specific examples

[1646] Example 1: Your pet has a high body temperature

[1647] Step 1: The collar measures your pet's temperature and records it as 39.0°C. The data is sent to a server via Wi-Fi.

[1648] Step 2: The server receives the temperature data and stores it in a database.

[1649] Step 3: The server compares the temperature to its normal value and determines that 39.0°C is a high temperature.

[1650] Step 4: The server generates an alert and notifies the user.

[1651] Step 5: The device's emotion engine analyzes the user's facial expression and if they are feeling anxious, it will send an additional notification saying, "Your pet has a high temperature. Please stay calm and check it and contact your veterinarian."

[1652] Step 6: The user checks the notification, checks in on their pet, and follows the provided calming strategies if they feel uneasy.

[1653] Example 2: If your pet is not very active

[1654] Step 1: The collar measures your daily activity and records that you're only moving about 30% of your normal activity. The data is then sent via Bluetooth to a server.

[1655] Step 2: The server receives the activity data and stores it in a database.

[1656] Step 3: The server determines that the activity is low compared to normal.

[1657] Step 4: The server generates the advice and notifies the user.

[1658] Step 5: Your device will display a notification with the advice, "Your pet's activity level is lower than usual. Try increasing the amount of time it spends playing with toys."

[1659] Step 6: If the emotion engine determines that the user is tired from their tone of voice, it will provide additional advice such as, "When you're tired, it's a good idea to incorporate some easy games."

[1660] Step 7: The user checks the advice and increases playtime with their pet without forcing themselves.

[1661] Example 2

[1662] 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."

[1663] Conventional pet health management systems simply collect and store data, making it difficult to comprehensively understand a pet's health condition. Furthermore, alerts and advice are provided uniformly without considering the user's emotions, meaning they are unable to provide the necessary information at the timing and with the content the user desires. Furthermore, there is a lack of methods for detecting anomalies and generating advice that are suited to specific conditions, making these systems less effective in managing pet health.

[1664] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera equipped with means for measuring the pet's body temperature, means for measuring the pet's activity level, means for measuring the pet's location information, and means for recognizing the pet's movements, a toilet equipped with means for measuring the pet's toilet usage amount, means for measuring the pet's weight, and means for measuring the pH balance of the pet's urination and defecation, means for storing and analyzing data collected from the above means, means for detecting abnormalities and generating alerts based on the analysis results, means for notifying the user of the alerts, means for generating and providing exercise and dietary advice to the user, means for tracking the pet's current location and providing the user with location information, and means for recognizing the user's emotions and adjusting the content of notifications and advice. This makes it possible to comprehensively manage and analyze the pet's health condition and take appropriate measures based on the user's emotions.

[1665] A "means for measuring body temperature" is a device that has the function of measuring a pet's body temperature and recording and transmitting that data.

[1666] A "means for measuring activity levels" is a device that has the function of measuring a pet's movements and exercise, and recording and transmitting that data.

[1667] A "means for measuring location information" is a device that has the function of identifying the current location of a pet and recording and transmitting that information.

[1668] A "camera equipped with a means for recognizing movement" is a camera device that recognizes and records pet movements and behavior as video footage and analyzes the data.

[1669] A "means for measuring litter box usage" is a device that has the function of measuring the amount of litter box used by a pet and recording and transmitting that data.

[1670] A "means for measuring weight" is a device that has the function of measuring a pet's weight and recording and transmitting that data.

[1671] A "toilet with a means for measuring the pH balance of urination and defecation" is a toilet device that has the function of measuring the pH balance of a pet's urination and defecation, and recording and transmitting that data.

[1672] The "means for storing and analyzing data" refers to a server that stores data collected from each device and analyzes it to determine health status and abnormalities.

[1673] "Means for detecting anomalies and generating alerts" refers to a system function that detects anomalies from the analysis results and generates an alert to notify the user.

[1674] "Means of notifying users" refers to communication functions and devices used to notify users of alerts and advice.

[1675] The "means for generating and providing advice" is a function that automatically generates and provides advice on exercise and diet for pet health management based on the analysis results.

[1676] "Means for tracking current location and providing location information to the user" refers to a function that tracks the pet's location in real time and provides that information to the user.

[1677] "Means for recognizing user emotions and adjusting notification and advice content" refers to a system that has the ability to analyze and recognize user emotions and personalize the content of alerts and advice based on those emotions.

[1678] The present invention relates to a system for comprehensively managing pet health conditions, recognizing the user's emotions, and providing personalized notifications and advice based on those emotions. The system includes the following components:

[1679] server

[1680] The server receives data from the collar, camera, and litter box, such as the pet's body temperature, activity level, location, movement, litter box usage, weight, and pH balance of urination and defecation, and stores it in a database. The stored data is periodically analyzed using Python libraries (e.g., Pandas, NumPy). If an abnormal value is detected during the analysis, an alert is generated. In addition, appropriate advice regarding the pet's exercise and diet is automatically generated based on the analysis results. These analysis results and advice are converted into graphs and dashboards, and summarized in a format that can be viewed by the user in the app. The specific software used is an SQL database, Python, Matplotlib, and D3.js.

[1681] As a concrete example, consider the case where a pet's body temperature reaches 39.0°C. The collar measures the temperature and sends the data to a server via Wi-Fi. The server receives the data and stores it in a database. It analyzes the stored data and determines that the body temperature is abnormally high. It generates an abnormal alert indicating that the body temperature is high and notifies the user.

[1682] Terminal

[1683] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice. Using OpenCV and a voice analysis library, it learns the user's emotional patterns and personalizes the content of alerts and advice based on those patterns. Alerts and advice sent from the server are displayed to the user as push notifications. The content of the notifications is also adjusted according to the user's emotions. By opening the app, users can view detailed data and advice, and can also check their pet's current location on a map. The map display uses the Google Maps API.

[1684] As a concrete example, consider a case where a pet's activity level is only about 30% of normal. The collar measures the amount of activity throughout the day and sends the data via Bluetooth to a server. The server receives the data, stores it in a database, and detects a decrease in activity level. It generates advice to encourage activity and notifies the device. The device determines from the tone of the user's voice that the pet is tired, and displays additional advice such as, "When you are tired, it's a good idea to incorporate some easy games."

[1685] User

[1686] Users can monitor their pet's health in real time through their device and respond quickly when an alert occurs. They can check advice on the app and properly manage their pet's exercise and diet. Users can also check their pet's current location and respond quickly if their pet gets lost.

[1687] An example prompt is the text prompt "Your pet's temperature is higher than normal. Please provide advice to help the user stay calm."

[1688] As described above, the present invention is a system that can comprehensively manage the health condition of a pet and take appropriate action depending on the user's emotions.

[1689] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1690] Step 1: Receiving and storing data

[1691] The server receives data packets sent from the collar, camera, and litter box. Specific input data includes the pet's body temperature, activity level, location, litter box usage, weight, and pH balance of urination and defecation. This data is received via Wi-Fi or Bluetooth through a communication module. The received data is stored in a database using a Python library (e.g., SQLAlchemy). Specific output data is various data stored in related tables in the database.

[1692] Step 2: Data analysis and anomaly detection

[1693] The server periodically analyzes the stored data. The input is various health data stored in a database. The data is processed using Python libraries (e.g., Pandas, NumPy) and each data point is compared with a normal value. If an abnormality is detected as a result of the analysis, an alert is generated. Specifically, a body temperature above 38°C is considered abnormal. The output is a flag indicating an abnormality and a trigger for generating an alert.

[1694] Step 3: Advice Generation

[1695] Based on the analysis results, the server generates appropriate advice regarding pet exercise and diet. The input is the data analysis results and anomaly flags. Specific advice sentences are created using generative AI models and rule-based systems. For example, if the pet has a high body temperature, the server generates advice such as "Give the pet plenty of water and use a cooling sheet." The output is advice sentences provided to the user.

[1696] Step 4: Visualize the results

[1697] The server converts the analysis results and advice into graphs and dashboards. The input is the analysis results and generated advice. Matplotlib and D3.js are used to visualize the data and integrate it into a web application. The output is graphs and dashboards that users can view in the app. For example, a graph showing the progression of body temperature over time.

[1698] Step 5: Emotion Recognition

[1699] The device's emotion engine analyzes the user's facial expressions and tone of voice. The input is audio and video data collected by the device's camera and microphone. Using OpenCV and voice analysis libraries, it analyzes the user's facial expressions and tone of voice to recognize emotional patterns. The output is a tag of the user's emotional state (e.g., anxiety, fatigue).

[1700] Step 6: Notifications and Personalization

[1701] The device displays alerts and advice sent from the server to the user as push notifications. The input is the alert notification from the server and the analysis results of the emotion engine. The notification content is personalized based on the results of the emotion engine. For example, if a pet's temperature is high, a notification saying "Your pet's temperature is high. Please stay calm and check and contact a veterinarian" is displayed. The output is a personalized notification message.

[1702] Step 7: View detailed data

[1703] Users can view detailed data and advice by opening the app on their device. The input is the analysis results and advice sent from the server. The app displays this data, allowing users to check past temperature and activity data over time. The output is detailed data and interactive graphs displayed in the user interface.

[1704] Step 8: Check your location

[1705] The app allows users to check their pet's current location on a map. The input is location information sent from the collar. The location information is plotted on a map using the Google Maps API. The output is the pet's current location displayed on the map.

[1706] Step 9: Monitor and respond

[1707] Users can monitor their pet's health in real time through the app and respond quickly when an alert occurs. The input is push notifications and detailed data from the device. The user follows the advice to manage their pet's health, for example, using a cooling sheet if the pet has a high temperature. The output is the user's appropriate action based on the pet's health condition.

[1708] (Application example 2)

[1709] 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."

[1710] In today's pet care industry, there are limited means for comprehensively managing pet health. Furthermore, the services and advice provided do not take into account the user's emotions, making it difficult for users to properly manage their pet's health. Furthermore, in physical stores, there is an insufficient system for suggesting optimal services and products based on the pet's health and the user's emotions.

[1711] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the notification content and advice content, and means for proposing pet-related services and products based on the user's emotions. This makes it possible to respond in consideration of the user's emotions, and to propose optimal pet care services and products even in physical stores.

[1712] A "means for measuring pet body temperature" is a device that measures a pet's body temperature in real time using a temperature sensor integrated into a pet's collar or other wearable device.

[1713] A "means for measuring pet activity" is a device that measures the distance traveled and the amount of exercise a pet performs through a pet's collar or other wearable device.

[1714] A "means for measuring pet location information" is a device that uses GPS or other location measurement technology to determine the current location of a pet.

[1715] A "camera equipped with a means for recognizing pet movements" is a camera device that captures pet movements and analyzes them using image analysis technology.

[1716] A "means for measuring pet litter box usage" is a device equipped with a sensor that records the number and frequency of a pet's litter box use.

[1717] "Means for measuring pet weight" refers to a scale or wearable device that measures the weight of a pet when it is on board.

[1718] A "toilet with a means for measuring the pH balance of a pet's urination and defecation" is a toilet device equipped with a sensor for detecting the pH balance when a pet urinates or defecates.

[1719] "Means for storing and analyzing data collected from the above means" refers to servers and software systems that accumulate data obtained from various sensors and devices and analyze that data.

[1720] "Means for detecting anomalies and generating alerts based on analysis results" refers to systems or software that detect anomalies from analyzed data and issue warnings to users.

[1721] The "means of notifying users of the above alerts" refers to a system for pushing warnings and notifications to users' devices such as smartphones and tablets.

[1722] "Means for recognizing the user's emotions and adjusting the content of notifications and advice" refers to a system that uses a camera and microphone to analyze the user's facial expressions and voice, and then appropriately adjusts notifications and advice based on those emotions.

[1723] The "means for generating and providing exercise and dietary advice to users" is a system that generates optimal advice regarding the amount of exercise and diet for pets based on the analyzed data and provides it to users.

[1724] "Means for tracking a pet's current location and providing location information to the user" refers to a system that uses location tracking technology such as GPS to identify a pet's current location and notify the user of that information.

[1725] The "means for suggesting pet-related services and products based on the user's emotions" is a system that analyzes the user's emotional state and recommends the most suitable pet care services and products based on the results.

[1726] To implement this invention, the following system configuration and process are required.

[1727] The system includes multiple measuring devices to collect information such as a pet's body temperature, activity level, and location, a server to analyze and notify this data, and an emotion engine that recognizes the user's emotions and personalizes the notification content.

[1728] System Configuration

[1729] Collar: Includes a means to measure your pet's temperature, activity, and location.

[1730] Hardware used: Body temperature sensor, accelerometer, GPS module

[1731] Software used: Data transmission protocols (e.g. Bluetooth, Wi-Fi)

[1732] Camera: Equipped with a means to recognize pet movements and record footage.

[1733] Hardware used: High resolution camera (e.g. Logitech C920)

[1734] Software used: Image analysis algorithms (e.g., OpenCV)

[1735] Toilet: Includes a means to measure your pet's toilet usage, weight, and pH balance of urination and defecation.

[1736] Hardware used: Weight scale, pH sensor

[1737] Software used: Sensor data analysis tools (e.g., SciPy)

[1738] Server: Stores and analyzes various data, detects abnormalities, and generates alerts.

[1739] Software used: databases (e.g., MySQL), data analysis tools (e.g., NumPy, SciPy)

[1740] Device: Notify users and adjust advice.

[1741] Hardware used: Smartphone, tablet

[1742] Software used: Notification system API (e.g. Firebase Cloud Messaging)

[1743] Emotion engine: Recognizes user emotions and personalizes notifications and advice.

[1744] Software used: Facial expression recognition algorithms (e.g. DLib), voice analysis engine

[1745] Program Processing Details

[1746] The server receives data such as body temperature, activity level, and location information sent from the pet's collar and stores it in a database. The server then analyzes the stored data and generates an alert if an abnormality is detected, sending a notification to the user's device. Data analysis tools such as NumPy and SciPy are used for the analysis.

[1747] The device checks the notification received by the user, and an emotion engine analyzes the user's facial expressions and voice to recognize their emotions. For example, if the user is feeling anxious, the notification content will be personalized and provide advice such as, "Your pet's temperature is high. Please stay calm and check it and contact your veterinarian." Notifications on the device use notification system APIs such as Firebase Cloud Messaging.

[1748] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes their emotions using DLib and a voice analysis engine. For example, if a user receives a notification that their pet's temperature is rising and looks anxious, the system will provide additional advice such as "stay calm."

[1749] Specific examples

[1750] Example 1: Your pet has a high body temperature

[1751] 1. Data collection: The collar measures the pet's temperature to be 39.0°C and sends the data to the server.

[1752] 2. Data analysis: The server analyzes the body temperature data, determines that 39.0°C is a high temperature, and generates an alert.

[1753] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet's temperature is high. Please check immediately." If the emotion engine analyzes the user's facial expression and they are feeling anxious, an additional notification will be displayed saying, "Please stay calm and check and contact your veterinarian."

[1754] Example 2: If your pet is not very active

[1755] 1. Data collection: The collar records activity at approximately 30% of normal activity and sends the data to a server.

[1756] 2. Data analysis: The server analyzes the activity data, determines that the activity level is low, and generates advice.

[1757] 3. Notifications and Emotion Recognition: A notification will appear on the device saying, "Your pet is less active than usual. Try spending more time playing with toys." If the emotion engine determines that the user is tired based on their tone of voice, additional advice will be given: "When you're tired, it's a good idea to incorporate some easy play."

[1758] Prompt Sentence Examples

[1759] "The pet's temperature is high and the user is feeling anxious. Please advise the user to remain calm and, as the pet's temperature is high, suggest that they seek appropriate medical attention."

[1760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1761] Step 1: Data collection

[1762] The collar measures the pet's body temperature, activity level, and location information in real time. This data is sent to a server using Bluetooth or Wi-Fi. The input is body temperature, activity level, and location information data, and the output is the raw data sent to the server. This stage includes specific operations: the body temperature sensor measures body temperature, the accelerometer measures activity level, and the GPS measures location information.

[1763] Step 2: Save your data

[1764] The server stores the received data in a database. The input is the raw data sent from the collar, and the output is structured data stored in the database. This processing step uses a database management system such as MySQL or PostgreSQL to store the temperature, activity, and location data in tables.

[1765] Step 3: Data analysis

[1766] The server periodically launches a process to analyze the stored data. The input is the raw data stored in the database, and the output is the analysis results. Specifically, statistical analysis is performed using NumPy and SciPy to detect outliers. At this time, a threshold is set to detect anomalies by comparing with past data.

[1767] Step 4: Anomaly detection and alerting

[1768] The server detects anomalies based on the analysis results and generates an alert. The input is the analysis results and the output is the generated alert. If an anomaly is detected, an alert message is generated and sent to the user's device. This process uses an algorithm that triggers an alert when certain conditions are met.

[1769] Step 5: Alert Notification

[1770] An alert notification is displayed on the device. At this stage, the alert is pushed to the user in real time. The input is the alert message sent from the server, and the output is the notification displayed on the device. Specifically, the alert is sent using a service such as Firebase Cloud Messaging.

[1771] Step 6: User sentiment analysis

[1772] The device uses a camera and microphone to analyze the user's emotions. The input is the user's facial expression images and voice data, and the output is the emotion determination result. Facial expressions are analyzed using DLib, and tone of voice is analyzed using a voice analysis engine. Specifically, the system involves the user showing their facial expressions in front of the camera and speaking into the microphone.

[1773] Step 7: Personalize your notifications

[1774] The device personalizes the content of notifications and advice based on the results of emotion analysis. The input is the emotion determination result and the alert message, and the output is the adjusted notification content. If the emotion engine analyzes the user's emotions and detects anxiety or fatigue, it adds advice appropriate to that state. For example, it generates messages such as "Please stay calm" or "We recommend some easy games."

[1775] Step 8: Inform and advise users

[1776] The device displays personalized notification content and advice to the user. The input is the tailored notification content, and the output is the notification message displayed to the user. Specifically, this includes the behavior of displaying a personalized message on the screen of a smartphone or tablet.

[1777] Step 9: Propose your service or product

[1778] The device suggests services and products offered in physical stores based on the user's emotions and the pet's h...

Claims

1. A means for measuring the temperature of a pet; A means for measuring the activity of a pet; a collar including means for measuring the location information of a pet; a camera equipped with a means for recognizing the movement of a pet; A means for measuring pet litter box usage; a means for measuring the weight of the pet; a toilet having a means for measuring the pH balance of a pet's urination and defecation; A means for storing and analyzing data collected from each of the above means; A means for detecting anomalies and generating alerts based on the analysis results; a means for notifying a user of said alert; A means for generating and providing exercise and dietary advice to users; a means for tracking the current location of the pet and providing the location information to the user; A system including:

2. The system according to claim 1, further comprising a means for visualizing the data collected from each of said means.

3. The system described in claim 1, characterized in that the means for measuring the activity level of the pet measures data at regular intervals, the means for measuring the body temperature measures data at regular intervals, and the means for measuring the location information measures data at regular intervals.

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