system

Abstract

Provide a system. 【Solution means】 Shooting means for shooting the behavior of a pet, Receiving means for receiving the shot data, Analysis means for analyzing the received data and judging the health state of the pet, Notification means for performing notification when an abnormality is detected based on the analysis result, Reception means for receiving consultations regarding the training from the user, Generation means for generating appropriate advice according to the received consultation, Transmission means for transmitting the generated advice to the user, Module for transmitting the analyzed health state information to a communication device, Display device for displaying the health information transmitted by the module, Device for monitoring the behavior of the pet and recording the operation data, A system including

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the expansion of the pet market, the need for pet owners to appropriately manage the health status and training methods of their pets has been increasing. However, many pet owners do not have time to receive advice from experts. Furthermore, it is difficult to grasp the health status of pets in real time, and prompt responses are required when abnormalities occur. Therefore, there is a need to provide a system that allows pet owners to efficiently and effectively manage the health and training of their pets.

Means for Solving the Problems

[0005] This invention uses a recording means to capture the pet's movements, recording the pet's real-time state, and transmitting this data to a server via a receiving means. The server analyzes the data to determine the pet's health status and, if necessary, notifies the user of any abnormalities via a notification means. Furthermore, consultations regarding training from the user are received via a reception means, and based on the consultation content, a generation means generates appropriate advice, which is then provided to the user via a transmission means. This enables the user to respond quickly and appropriately to their pet, supporting improved quality of life through pet health management and training.

[0006] "Means of recording" refers to a device or function that records the actions and condition of a pet as photographs or videos.

[0007] "Receiving means" refers to the function that receives captured data in order to transmit it to a server.

[0008] "Analysis means" refers to algorithms and processes for processing received data and evaluating the health status of pets.

[0009] "Notification means" refers to a function that informs the user of anomalies based on the analysis results.

[0010] "Reception method" refers to an interface or function that accepts user inquiries regarding discipline.

[0011] "Generation method" refers to the process or algorithm that generates appropriate advice based on the user's inquiry.

[0012] "Transmission means" refers to the function for sending and communicating the generated advice to the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for managing the health of pets and supporting their training. It aims to analyze the behavior and condition of pets using AI and provide appropriate advice to pet owners. A specific embodiment of this system is described below.

[0035] Users can use their smartphones or dedicated devices to take photos and videos of their pets' daily activities and conditions. The device can add supplementary information such as the date, time, and location of the capture to this media data and send the data to a server.

[0036] The server analyzes the received data in detail using AI-based image recognition technology and motion analysis algorithms. Based on the information extracted by the analysis, it assesses the pet's health and detects signs of abnormalities if necessary. For example, if it detects a skin rash or abnormal behavior, the server immediately notifies the user.

[0037] Furthermore, users can send specific questions about pet training to the server via their device. The server analyzes the received information, and an AI-based advice generation system provides optimal training methods and strategies. This advice is then sent to the user as easy-to-follow steps.

[0038] For example, if a user consults the server about a problem where "the dog won't stop barking," the server can suggest an appropriate approach, such as "training methods to suppress excessive barking," and provide step-by-step guidance.

[0039] The introduction of this system will allow users to monitor their pets' health in real time from the comfort of their homes and receive expert training advice. This is expected to improve pet health management and overall pet care.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users capture their pet's actions and condition using their smartphone or a dedicated device. The captured data, along with metadata including the date and time of capture and location information, is saved within the app.

[0043] Step 2:

[0044] The device sends the stored data to the server via the internet. The transmission is automatic, and a backup is maintained on the device after the data has been sent.

[0045] Step 3:

[0046] The server converts the received photo and video data into an appropriate format. During this process, pre-processing such as resolution adjustment and noise reduction is performed.

[0047] Step 4:

[0048] The server uses AI technology to analyze the data. It uses image recognition technology to diagnose abnormalities and movements on the pet's body surface and records the results in a database.

[0049] Step 5:

[0050] The server determines whether there is an anomaly based on the analysis results. If an anomaly is detected, it generates a notification message according to the type and severity of the anomaly.

[0051] Step 6:

[0052] Notification messages are sent from the server to the device as push notifications. This notification allows users to instantly receive important information about their pet's condition.

[0053] Step 7:

[0054] Users can submit questions about pet training via text or voice through the app.

[0055] Step 8:

[0056] The terminal sends the user's inquiry details to the server. All data is transmitted using a secure protocol.

[0057] Step 9:

[0058] The server analyzes the received consultation content using natural language processing technology. It extracts important keywords and phrases to identify the problem.

[0059] Step 10:

[0060] The server uses AI to generate advice tailored to the consultation content. This advice includes effective training procedures and improvement strategies.

[0061] Step 11:

[0062] Advice is sent from the server to the device and made available for viewing within the app. Users can then implement the suggested methods and use them to help train their pets.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Modern livestock owners are required to monitor their pets' health and behavioral changes in real time, enabling them to train and manage their pets quickly and effectively. However, it is difficult for owners to conduct these observations on a daily basis, and specialized knowledge and diagnoses are often required. Therefore, there is a need for a system that allows for easy access to early detection of health problems in pets and guidance on appropriate training.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for recording the pet's movements and state, means for adding time information and location information to the recorded information, and means for analyzing the received information by image and motion analysis using artificial intelligence. This enables detailed analysis of the pet's health condition, early detection of abnormalities, and appropriate training guidance.

[0068] A "pet" is an animal kept as a household pet, and usually refers to companion animals such as dogs and cats.

[0069] "Actions" refer to the physical movements and behavioral patterns that pets perform.

[0070] "Condition" refers to the physiological and emotional aspects related to a pet's health, and usually includes its physical and emotional state.

[0071] "Means of recording" refers to devices or methods that have the function of saving the actions and condition of a pet in the form of photographs, videos, etc.

[0072] "Time information" refers to information indicating the exact date and time the data was acquired, which is useful for later analysis and tracking.

[0073] "Location information" refers to information that indicates the specific geographical location where the data was acquired, and provides context for the analysis.

[0074] A "data processing device" refers to a computer-based mechanism for receiving, storing, and analyzing information.

[0075] "Artificial intelligence" is a technology that allows machines to imitate human intellectual behavior, and is particularly used for data analysis and processing.

[0076] "Analysis" refers to the act of investigating and evaluating data in detail in order to draw some kind of conclusion.

[0077] "Health status" refers to the overall assessment results regarding the pet's physical and psychological health.

[0078] "Signs of abnormality" refer to indicators of unusual changes in a pet's health or behavior that warrant attention.

[0079] A "warning" refers to an informational notification intended to alert the user to detected anomalies or concerning situations.

[0080] "Discipline" refers to the process of teaching pets desirable behaviors through guidance and instruction.

[0081] A "generative model" refers to an algorithm or technique used to create new data or content from given information.

[0082] "Guidance content" refers to systematically organized information that includes advice and specific behavioral guidelines related to pet training.

[0083] "Means of transmission" refers to the function of communicating to deliver analysis results and advice to the user.

[0084] This invention is a system aimed at managing the health of pets and supporting their training. Users record their pets' daily actions and conditions as images or videos using a smartphone or a dedicated device. In this process, the device automatically supplements the recorded media data by adding time and location information.

[0085] The device sends the supplemented data to the server via the internet. The server applies image recognition technology and motion analysis algorithms to analyze the received data. This analysis uses frameworks such as TENSORFLOW® and PyTorch, and leverages generative AI models to obtain detailed information about the pet's movements and condition. In particular, it performs health assessments and detects signs of abnormalities. For example, it can detect skin abnormalities from image data and provide information to alert the user.

[0086] Furthermore, users can send questions about pet training to the server via their device. The server uses a generative AI model to analyze these questions and generate optimal training procedures and approaches. For example, if a user inputs the problem "My dog ​​barks too often and it's causing problems" as a prompt, the server will generate and provide "Specific training methods to reduce excessive barking" as a set of procedures.

[0087] This will allow users to manage their pets' health in real time and receive expert advice to effectively train their pets. The introduction of this system is expected to improve the efficiency of pet health management and training.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] Users capture their pet's daily actions and condition using a smartphone or dedicated device. The input is image or video data, and users capture the footage for the purpose of understanding specific behaviors or health conditions.

[0091] Step 2:

[0092] The device automatically adds time and location information to the captured media data. The input is the image or video data acquired in step 1, and the output is the media data with time and location information added. This contextualizes the data.

[0093] Step 3:

[0094] The terminal sends the supplemented media data to the server via a secure network. The input is media data with additional information, which is transferred to the server. This communication is performed using a security protocol.

[0095] Step 4:

[0096] The server analyzes the received data. The input is media data sent from the terminal. The server analyzes the data using AI-based image recognition technology and motion analysis algorithms to extract health indicators and behavioral patterns of the pet. The output is the analyzed insights and health assessment.

[0097] Step 5:

[0098] The server notifies the user of a warning message if an anomaly is detected in the analysis results. The input is the analysis results obtained in step 4, and the output is a notification message containing details of the anomaly and recommended countermeasures. The user receives this and can take immediate action.

[0099] Step 6:

[0100] Users input their pet training problems in text format into their terminal and send the inquiry to the server. The input consists of prompts representing questions or problems related to training, and the output is an inquiry request sent to the server.

[0101] Step 7:

[0102] The server uses a generative AI model to generate advice based on the user's inquiry. The input is a prompt from the user, and the output is a suggestion of specific training procedures and countermeasures. The server returns this output to the terminal.

[0103] Step 8:

[0104] The server sends the generated advice to the user, providing it as a concrete action plan. The input is the advice generated in step 7, and the output is the training guidance and action plan supplied to the user. The user can use this to improve their pet's training.

[0105] (Application Example 1)

[0106] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0107] In pet health management and training, there is a challenge in that pet owners often have difficulty receiving information and advice at the right time. In particular, early detection of abnormalities in pets in daily life and taking necessary measures is a crucial challenge for pet owners. Furthermore, it is difficult to consistently monitor pet behavior, and the lack of immediate insights based on that behavior is also a problem.

[0108] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0109] In this invention, the server includes: a shooting means for capturing images of the pet's movements; a receiving means for receiving the captured data; an analysis means for analyzing the received data and determining the pet's health condition; a notification means for notifying the user if an abnormality is detected based on the analysis results; a receiving means for receiving consultations from the user regarding training; a generating means for generating appropriate advice in response to the received consultations; a transmitting means for sending the generated advice to the user; a module for transmitting the analyzed health condition information to a communication device; a display device for displaying the health information transmitted by the module; and a device for monitoring the pet's behavior and recording movement data. This enables the user to manage the pet's health condition in real time and receive expert advice.

[0110] "Filming equipment" refers to a device used to film the actions of a pet, making the captured video and images available for subsequent processing.

[0111] A "receiving device" is a device that acquires data obtained by a shooting device and provides the function of transmitting it to a server for analysis and evaluation.

[0112] An "analysis device" is a device that evaluates the health status of a pet based on received data and performs processing to detect abnormalities or changes.

[0113] A "notification mechanism" is a system for informing the user of information when an anomaly is detected by the analysis mechanism.

[0114] A "reception system" is a device that has the function of receiving questions and inquiries from users and managing them appropriately.

[0115] A "generation means" is a device that creates advice based on the content of the consultation received through the reception means and optimizes that advice.

[0116] A "transmission device" is a device that has the function of delivering the generated advice to the user.

[0117] A "module" is a component that transmits health information to a communication device based on the analysis results.

[0118] A "display device" is a device that displays received health information so that it can be visually confirmed.

[0119] A "device for recording behavioral data" is a device that monitors a pet's behavior and accumulates data on its behavioral patterns.

[0120] This invention aims to realize a system that manages a pet's health status in real time and provides appropriate training advice. Specific embodiments of this system are described below.

[0121] The server uses a camera as a means of capturing the pet's movements. The captured data is first acquired by a receiving means and then processed by an analysis means using image recognition technology. As an analysis means, the server analyzes the image data using TensorFlow and evaluates the pet's health status using an AI model. If an abnormality is detected through this analysis, an alert is immediately sent to the user by a notification means.

[0122] The terminal accepts questions from users regarding dog training. These questions are sent to a generation system, where AI generates appropriate training methods. For example, in response to the problem of "a dog that won't stop barking," the generation AI model suggests "a step-by-step training method to suppress excessive barking." The generated advice is delivered to the user's smartphone via a transmission system. Real-time data communication is enabled by using Firebase.

[0123] Furthermore, the analyzed health status information is transmitted to a communication device and displayed on a display device. This allows users to visually check their pet's health status. The pet's behavior is constantly monitored by a device that records behavioral data, and this information is accumulated and used for long-term health management.

[0124] For example, if a user inputs the problem "My cat has lost its appetite recently," the AI ​​will use past behavioral data and health assessments to suggest potential causes of the loss of appetite and advise whether a veterinary examination should be sought immediately. Furthermore, if the user inputs a prompt such as "Please tell me about my pet's recent health condition," the system will respond with the latest health assessment.

[0125] This allows users to monitor their pets' detailed health status and take appropriate action at home, without requiring specialized knowledge or expensive equipment.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The user films the pet's movements with a camera.

[0129] The input is image and video data acquired by the camera, and the output is this data being saved on a smartphone or dedicated device. Specifically, the user takes pictures of their pet in motion and registers the files in the system.

[0130] Step 2:

[0131] The device receives the captured data along with additional information and sends it to the server.

[0132] The input consists of captured image and video data, along with the date, time, and location information of the capture. The output is this data transferred to the server. The terminal adds time and location information to the data and transmits it to the server via the network.

[0133] Step 3:

[0134] The server uses image recognition to analyze the data and assess the pet's health.

[0135] The input is received image and video data, and the output is health assessment data as a result of the analysis. The server uses TensorFlow to analyze the image data and detect abnormalities in behavior and physical condition.

[0136] Step 4:

[0137] If the server detects an anomaly, it will notify the user through a notification system.

[0138] The input is the result of the analysis, and the output is an alert sent to the user's device. The server evaluates the analysis results and, if an anomaly is detected, sends a warning in real time using Firebase or similar tools.

[0139] Step 5:

[0140] The user sends questions about discipline through their device.

[0141] The input is the question entered by the user, and the output is the question data sent to the server. The user sends a question about pet training to the system from the input screen on their terminal.

[0142] Step 6:

[0143] The server uses a generated AI model to produce advice in response to the user's question.

[0144] The input is the user's inquiry, and the output is the generated specific advice. The server analyzes the received question and generates corresponding advice based on past training data.

[0145] Step 7:

[0146] The server sends the generated advice to the user's terminal.

[0147] The input is the generated advice, and the output is the advice information displayed on the user's smartphone. The server sends the completed advice to the user's smartphone via a notification system.

[0148] Step 8:

[0149] Based on the advice received by the user, the system helps with pet training and health management.

[0150] The input is advice displayed on the user's device, and the output is the specific training and management methods the user implements. The user follows the advice and takes actions to correct the pet's behavior and maintain its health.

[0151] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0152] This invention combines a system for supporting pet health management and training with an emotion engine that recognizes the user's emotions. This system not only evaluates the pet's condition but also provides flexible advice tailored to the user's emotions, enabling more effective user support.

[0153] Users first film their pet's movements with their smartphone or a dedicated device, and the data is sent from the device to a server. The server uses AI technology to analyze the received data and evaluate the pet's health. If signs of stress or abnormal behavioral patterns are detected, the server automatically notifies the user, enabling early detection of problems.

[0154] Furthermore, if a user wants to discuss pet training issues, they can send questions via voice or text through their device. In this case, the emotion engine analyzes the user's emotions from their voice tone and selected words. For example, if the server determines that the user is stressed, it will generate advice in a gentle tone appropriate to that emotion.

[0155] If a user reports a problem such as "I can't stop my dog ​​from barking excessively," the emotion engine recognizes the user's frustration, and the server can provide empathetic advice such as, "First, take a deep breath, and then try training slowly." This advice is generated in real time and sent immediately to the user's device.

[0156] In this way, this system supports pet management while taking the user's emotions into consideration, helping users care for their pets in a better mental state and promoting improved health and behavior of the pets.

[0157] The following describes the processing flow.

[0158] Step 1:

[0159] Users use their smartphones or dedicated devices to photograph their pets' actions and condition. The captured data is temporarily stored on the device.

[0160] Step 2:

[0161] The device sends the captured data to the server. During this process, time and location information is added as metadata to maintain data consistency and accuracy.

[0162] Step 3:

[0163] The server uses AI technology to analyze the received data and assess the pet's health. Image recognition technology is used to detect skin rashes, abnormal movements, and other issues.

[0164] Step 4:

[0165] The server determines whether an anomaly has been detected based on the analysis results. If an anomaly is found, the server generates a notification with content appropriate to its type and severity.

[0166] Step 5:

[0167] The server pushes the generated notification to the device, allowing the user to immediately see the anomaly. The notification includes a detailed description of the anomaly and recommended actions.

[0168] Step 6:

[0169] Users submit questions about pet training via voice or text input on their device. The content of the question, including the user's emotions, is sent to the server.

[0170] Step 7:

[0171] The server uses an emotion engine to recognize the user's emotions from the received audio or text. Emotion analysis identifies the user's state of mind and stress level.

[0172] Step 8:

[0173] The server generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will offer gentle suggestions to help them relax.

[0174] Step 9:

[0175] The generated advice is promptly sent from the server to the user's terminal. Based on this advice, the user can take specific actions to improve their pet's training.

[0176] (Example 2)

[0177] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0178] Balancing pet health management and training in modern times is a significant burden for pet owners. Furthermore, there are few ways to receive appropriate advice that takes the owner's feelings into consideration, highlighting the need for methods that reduce stress while providing effective pet care.

[0179] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0180] In this invention, the server includes recording means for recording the pet's movements, analysis means for analyzing the transmitted information and evaluating the animal's health condition, and generation means for analyzing the user's emotions in response to training-related questions and generating appropriate advice. This enables effective pet health management and training through advice that takes the owner's emotions into consideration.

[0181] "Recording means" refers to a device or method that has the function of capturing and saving the movements and behaviors of a pet in the form of video and audio.

[0182] "Transmission means" refers to methods or technologies that have the function of transferring recorded data to other devices or systems via a communication line.

[0183] The "analysis means" refers to a function that uses algorithms and AI technology to evaluate the health status and behavioral patterns of pets based on the transmitted data.

[0184] A "warning mechanism" is a notification function that immediately informs the user of any anomalies detected by the analysis results, and may be provided via email or push notification.

[0185] The "reception mechanism" is a function that receives questions and feedback from users regarding training and allows for the next steps in processing them.

[0186] "Generation method" refers to a function that uses generation AI technology to create emotionally conscious responses and advice in response to user inquiries.

[0187] A "notification mechanism" is a communication function that promptly conveys generated advice and warnings to the user and prompts them to take necessary actions.

[0188] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to interpret the meaning of text data and audio data and to respond appropriately.

[0189] A "generative AI model" is an artificial intelligence technology that uses large amounts of data and machine learning algorithms to generate natural language text that can be used for human interaction.

[0190] A "prompt" is a sentence of instruction or question input to a generative AI model to obtain output based on a specific context or condition.

[0191] This invention combines a system that supports pet health management and training with a function that analyzes the user's emotions. This system performs a series of processes to observe the pet's behavior, evaluate its health condition, and provide appropriate advice.

[0192] Users first record their pet's movements using a smartphone or dedicated device. This step utilizes the device's camera function and, if necessary, a dedicated application. The application includes a user-friendly interface and an automatic data saving function.

[0193] The terminal transmits the recorded data to the server using a secure communication protocol. This communication uses common protocols such as HTTP or HTTPS. The transmitted data is compressed in preparation for the analysis process on the server.

[0194] The server analyzes the transmitted data using a machine learning framework. Specifically, it utilizes libraries such as TensorFlow and PyTorch to analyze the pet's behavior patterns, facial expressions, and voice, and to evaluate its health in detail. If an anomaly is detected in the analysis results, the server immediately notifies the user. This notification is provided via email or push notification through a dedicated app.

[0195] Furthermore, users can send questions about pet training to the server via voice or text through their device. The server then uses an emotion engine to analyze the user's emotions based on their voice tone and selected words. Natural language processing techniques are used for this emotion analysis. IBM Watson® and other similar tools may be helpful in this process.

[0196] After the emotions are analyzed, the server uses a generative AI model to generate advice that is appropriate to the user's emotions. OpenAI's GPT-3® is one example of a generative AI model used. If a user consults the server about a problem such as "I can't stop my dog ​​from barking excessively," the server can recognize that frustration and provide advice that is empathetic to the user's feelings.

[0197] Examples of prompts include, "If the user is feeling stressed, please tell me how to improve their pet's behavior in a gentle tone." Based on this prompt, the AI ​​generates a response that matches the user's emotions and immediately notifies the device. This allows the user to smoothly manage and train their pet's health.

[0198] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0199] Step 1:

[0200] Users record their pet's movements using a smartphone or dedicated device. The input is video data obtained using a camera, and the output is video data stored on the device. This process utilizes the device's built-in camera or a dedicated recording app, and begins when the user presses a record button.

[0201] Step 2:

[0202] The terminal transmits recorded video data to the server using a secure communication protocol. Recorded video data is used as input, and the output is compressed data uploaded to the server. Here, a data compression algorithm is executed to improve communication efficiency, and the data is transmitted via a secure protocol such as HTTPS.

[0203] Step 3:

[0204] The server analyzes the transmitted data and assesses the pet's health. Compressed video data is used as input, and the output is the health assessment result. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis, performing behavioral pattern analysis and anomaly detection frame by frame.

[0205] Step 4:

[0206] The server notifies the user if an anomaly is detected based on the analysis results. The analysis results are used as input, and the output is a notification message indicating the anomaly. Notifications are sent via email or push notifications through a dedicated app.

[0207] Step 5:

[0208] Users consult a server via their device regarding pet training issues. Input can be in the form of voice or text. Voice questions are recorded via a microphone, while text questions are entered within the app.

[0209] Step 6:

[0210] The server uses an emotion engine to analyze the user's emotions in response to received questions. Input can be either audio or text data, and output is the analyzed emotion information. Natural language processing techniques are applied to the emotion analysis; in the case of audio, the analysis is based on intonation and tone, and in the case of text, it is based on vocabulary selection.

[0211] Step 7:

[0212] The server uses a generative AI model based on emotional information to generate user-appropriate advice. Emotional information and prompt text are used as input, and the output is user-appropriate advice. A generative AI model like OpenAI is utilized to generate appropriate content that is sensitive to the user's emotions, based on the prompt.

[0213] Step 8:

[0214] The server sends the generated advice to the user's terminal. The generated advice is used as input, and the output is the received message displayed on the user's terminal. This notification is in real time, allowing the user to immediately utilize the received advice.

[0215] (Application Example 2)

[0216] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0217] Traditional pet management systems have limited functions for monitoring pets' health and supporting training, and they cannot provide advice that is tailored to the owner's feelings. Therefore, there is a need to reduce owner stress while more accurately understanding the pet's health and taking appropriate action quickly.

[0218] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0219] In this invention, the server includes recording means for recording the pet's movements, processing means for processing the acquired information and evaluating the pet's health condition, and emotion analysis means for analyzing the user's emotions and generating advice corresponding to those emotions. This makes it possible to provide advice that is sensitive to the owner's emotions in real time, and to more effectively support the health management and training of pets.

[0220] A "recording device" is a system for observing a pet's behavior and accumulating that information.

[0221] "Acquisition means" refers to the process of transmitting recorded data to a server or other device to make it available for use.

[0222] "Processing means" refers to algorithms and mechanisms for analyzing acquired data and evaluating the health status and abnormal behavior of pets.

[0223] A "reporting mechanism" is a function that communicates appropriate information to the owner when an abnormality is detected based on the pet's health condition.

[0224] A "reception method" refers to an interface or system for receiving consultations and questions from users regarding dog training.

[0225] A "generation method" is a system for creating appropriate advice and suggestions based on the content of inquiries received through the reception method.

[0226] "Means of provision" refers to the process of presenting advice and information created by the means of generation to pet owners and enabling them to utilize it.

[0227] "Emotional analysis tools" refer to algorithms and technologies used to analyze a user's emotional state and provide appropriate feedback or responses.

[0228] This invention is a system for supporting pet health management and training, and is implemented using home robots and smart devices. The main components of the system include recording means, acquisition means, processing means, reporting means, receiving means, generation means, provision means, and emotion analysis means.

[0229] The server uses a home robot to capture images of pets' movements with a camera and stores the video data using a recording device. The recording device has the function of adding time data and location data to the acquired information. This information is transmitted to the server in real time through an acquisition device. The server uses a processing device to analyze this data and evaluate the pet's health condition and any abnormalities in its behavior.

[0230] If an abnormality is detected, the reporting system automatically informs the owner of the situation. When the owner seeks advice on training, they can send their consultation details to the reception system via the terminal. The server uses an emotion analysis system to analyze the user's emotions. Based on this analysis, the generation system generates appropriate advice, which is then presented to the owner through the delivery system.

[0231] As a concrete example, if a pet owner encounters a problem with excessive barking, they might input into the device, "I want to stop my pet from barking unnecessarily, but I'm feeling stressed. Please give me some advice on what to do." In this case, the emotion analysis system would detect the stress, and the generated advice would be something like, "First, take a deep breath, and then slowly begin training." In this way, it is possible to provide flexible support that responds to the user's emotional state.

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The device records the pet's movements using a camera. The input is live video data from the camera, and the output is a recorded video file. Time and location data are added and stored as part of the video file.

[0235] Step 2:

[0236] The terminal sends the recorded video file to the server. The input is the video file, and the output is the completion of the data transfer to the server. An internet connection is used for data transmission.

[0237] Step 3:

[0238] The server analyzes the received video data using processing tools. The input is the received video file, and the output is the evaluation result of the pet's health condition. An AI model analyzes the pet's movements in the video and detects signs of stress or abnormal behavior.

[0239] Step 4:

[0240] If the server detects an abnormality in the system's health status, it will notify the user using a reporting mechanism. The input is the analyzed abnormal data, and the output is a notification message to the user. The notification is delivered via a smartphone application.

[0241] Step 5:

[0242] The user submits a consultation request regarding training using their device. The input is the consultation content entered by the user in text or voice, and the output is confirmation that the consultation data has been successfully sent to the server.

[0243] Step 6:

[0244] The server analyzes the user's emotions using emotion analysis tools based on the consultation data. The input is the user's consultation content, and the output is a determination of the user's emotional state. The AI ​​model infers emotions from the tone and content of the words.

[0245] Step 7:

[0246] The server generates advice based on the results of emotion analysis. The input is the emotional state and the content of the consultation, and the output is the generated advice message. The content incorporates advice to alleviate stress.

[0247] Step 8:

[0248] The server notifies the user of the generated advice through a delivery mechanism. The input is the generated advice message, and the output is the presentation of the advice to the user. It is displayed on the terminal or read aloud.

[0249] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0250] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0251] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0255] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0256] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0257] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0258] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0259] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0260] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0261] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0262] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0263] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0264] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0265] This invention is a system for managing the health of pets and supporting their training. It aims to analyze the behavior and condition of pets using AI and provide appropriate advice to pet owners. A specific embodiment of this system is described below.

[0266] Users can use their smartphones or dedicated devices to take photos and videos of their pets' daily activities and conditions. The device can add supplementary information such as the date, time, and location of the capture to this media data and send the data to a server.

[0267] The server analyzes the received data in detail using AI-based image recognition technology and motion analysis algorithms. Based on the information extracted by the analysis, it assesses the pet's health and detects signs of abnormalities if necessary. For example, if it detects a skin rash or abnormal behavior, the server immediately notifies the user.

[0268] Furthermore, users can send specific questions about pet training to the server via their device. The server analyzes the received information, and an AI-based advice generation system provides optimal training methods and strategies. This advice is then sent to the user as easy-to-follow steps.

[0269] For example, if a user consults the server about a problem where "the dog won't stop barking," the server can suggest an appropriate approach, such as "training methods to suppress excessive barking," and provide step-by-step guidance.

[0270] The introduction of this system will allow users to monitor their pets' health in real time from the comfort of their homes and receive expert training advice. This is expected to improve pet health management and overall pet care.

[0271] The following describes the processing flow.

[0272] Step 1:

[0273] Users capture their pet's actions and condition using their smartphone or a dedicated device. The captured data, along with metadata including the date and time of capture and location information, is saved within the app.

[0274] Step 2:

[0275] The device sends the stored data to the server via the internet. The transmission is automatic, and a backup is maintained on the device after the data has been sent.

[0276] Step 3:

[0277] The server converts the received photo and video data into an appropriate format. At this time, preprocessing such as resolution adjustment and noise removal of the data is performed.

[0278] Step 4:

[0279] The server analyzes the data by making full use of AI technology. Using image recognition technology, it diagnoses abnormalities and behaviors on the body surface of the pet and records the results in the database.

[0280] Step 5:

[0281] The server determines whether there is an abnormality based on the analysis results. If an abnormality is detected, a notification message corresponding to the type and severity of the abnormality is generated.

[0282] Step 6:

[0283] The notification message is sent from the server to the terminal as a push notification. Through this notification, the user can immediately grasp important information regarding the pet's condition.

[0284] Step 7:

[0285] The user inputs consultations regarding pet training in text or voice through the app.

[0286] Step 8:

[0287] The terminal sends the consultation content from the user to the server. All data is sent using a secure protocol.

[0288] Step 9:

[0289] The server analyzes the received consultation content using natural language processing technology. It extracts important keywords and phrases to identify the problem.

[0290] Step 10:

[0291] The server uses AI to generate advice tailored to the consultation content. This advice includes effective training procedures and improvement strategies.

[0292] Step 11:

[0293] Advice is sent from the server to the device and made available for viewing within the app. Users can then implement the suggested methods and use them to help train their pets.

[0294] (Example 1)

[0295] Next, we will describe Example 1. 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".

[0296] Modern livestock owners are required to monitor their pets' health and behavioral changes in real time, enabling them to train and manage their pets quickly and effectively. However, it is difficult for owners to conduct these observations on a daily basis, and specialized knowledge and diagnoses are often required. Therefore, there is a need for a system that allows for easy access to early detection of health problems in pets and guidance on appropriate training.

[0297] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0298] In this invention, the server includes means for recording the pet's movements and state, means for adding time information and location information to the recorded information, and means for analyzing the received information by image and motion analysis using artificial intelligence. This enables detailed analysis of the pet's health condition, early detection of abnormalities, and appropriate training guidance.

[0299] A "pet" is an animal kept as a household pet, and usually refers to companion animals such as dogs and cats.

[0300] "Action" refers to the physical movements and behavioral patterns performed by a pet.

[0301] "State" refers to the physiological and emotional aspects related to the health of a pet, usually including the physical condition and mood.

[0302] "Recording means" refers to a device or method having a function to save the actions and states of a pet in forms such as photos and videos.

[0303] "Time information" refers to the information indicating the exact date and time when data is acquired, which is useful for later analysis and tracking.

[0304] "Location information" refers to the information indicating a specific geographical location where data is acquired, which provides the context for analysis.

[0305] "Data processing device" refers to a computer-based mechanism for receiving, storing, and analyzing information.

[0306] "Artificial intelligence" refers to the technology by which machines imitate human intellectual behavior, especially used for data analysis and processing.

[0307] "Analysis" refers to the act of investigating and evaluating in detail to draw some conclusions using data.

[0308] "Health status" refers to the comprehensive evaluation result regarding the physical and psychological health of a pet.

[0309] "Signs of abnormality" refer to the indicators showing changes in the health or behavior of a pet different from normal, indicating situations that require attention.

[0310] "Warning" refers to the information notification to prompt the user's attention regarding detected abnormalities or situations of concern.

[0311] "Training" refers to the process of enabling a pet to learn desirable behaviors through behavioral guidelines and instructions.

[0312] A "generative model" refers to an algorithm or technique used to create new data or content from given information.

[0313] "Guidance content" refers to systematically organized information that includes advice and specific behavioral guidelines related to pet training.

[0314] "Means of transmission" refers to the function of communicating to deliver analysis results and advice to the user.

[0315] This invention is a system aimed at managing the health of pets and supporting their training. Users record their pets' daily actions and conditions as images or videos using a smartphone or a dedicated device. In this process, the device automatically supplements the recorded media data by adding time and location information.

[0316] The device sends the supplemented data to the server via the internet. The server applies image recognition technology and motion analysis algorithms to analyze the received data. This analysis uses frameworks such as TensorFlow and PyTorch, and leverages generative AI models to obtain detailed information about the pet's movements and condition. In particular, it performs health assessments and detects signs of abnormalities. For example, it can detect skin abnormalities from image data and provide information to alert the user.

[0317] Furthermore, users can send questions about pet training to the server via their device. The server uses a generative AI model to analyze these questions and generate optimal training procedures and approaches. For example, if a user inputs the problem "My dog ​​barks too often and it's causing problems" as a prompt, the server will generate and provide "Specific training methods to reduce excessive barking" as a set of procedures.

[0318] This will allow users to manage their pets' health in real time and receive expert advice to effectively train their pets. The introduction of this system is expected to improve the efficiency of pet health management and training.

[0319] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0320] Step 1:

[0321] Users capture their pet's daily actions and condition using a smartphone or dedicated device. The input is image or video data, and users capture the footage for the purpose of understanding specific behaviors or health conditions.

[0322] Step 2:

[0323] The device automatically adds time and location information to the captured media data. The input is the image or video data acquired in step 1, and the output is the media data with time and location information added. This contextualizes the data.

[0324] Step 3:

[0325] The terminal sends the supplemented media data to the server via a secure network. The input is media data with additional information, which is transferred to the server. This communication is performed using a security protocol.

[0326] Step 4:

[0327] The server analyzes the received data. The input is media data sent from the terminal. The server analyzes the data using AI-based image recognition technology and motion analysis algorithms to extract health indicators and behavioral patterns of the pet. The output is the analyzed insights and health assessment.

[0328] Step 5:

[0329] The server notifies the user of a warning message if an anomaly is detected in the analysis results. The input is the analysis results obtained in step 4, and the output is a notification message containing details of the anomaly and recommended countermeasures. The user receives this and can take immediate action.

[0330] Step 6:

[0331] Users input their pet training problems in text format into their terminal and send the inquiry to the server. The input consists of prompts representing questions or problems related to training, and the output is an inquiry request sent to the server.

[0332] Step 7:

[0333] The server uses a generative AI model to generate advice based on the user's inquiry. The input is a prompt from the user, and the output is a suggestion of specific training procedures and countermeasures. The server returns this output to the terminal.

[0334] Step 8:

[0335] The server sends the generated advice to the user, providing it as a concrete action plan. The input is the advice generated in step 7, and the output is the training guidance and action plan supplied to the user. The user can use this to improve their pet's training.

[0336] (Application Example 1)

[0337] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0338] In pet health management and training, there is a challenge in that pet owners often have difficulty receiving information and advice at the right time. In particular, early detection of abnormalities in pets in daily life and taking necessary measures is a crucial challenge for pet owners. Furthermore, it is difficult to consistently monitor pet behavior, and the lack of immediate insights based on that behavior is also a problem.

[0339] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0340] In this invention, the server includes: a shooting means for capturing images of the pet's movements; a receiving means for receiving the captured data; an analysis means for analyzing the received data and determining the pet's health condition; a notification means for notifying the user if an abnormality is detected based on the analysis results; a receiving means for receiving consultations from the user regarding training; a generating means for generating appropriate advice in response to the received consultations; a transmitting means for sending the generated advice to the user; a module for transmitting the analyzed health condition information to a communication device; a display device for displaying the health information transmitted by the module; and a device for monitoring the pet's behavior and recording movement data. This enables the user to manage the pet's health condition in real time and receive expert advice.

[0341] "Filming equipment" refers to a device used to film the actions of a pet, making the captured video and images available for subsequent processing.

[0342] A "receiving device" is a device that acquires data obtained by a shooting device and provides the function of transmitting it to a server for analysis and evaluation.

[0343] An "analysis device" is a device that evaluates the health status of a pet based on received data and performs processing to detect abnormalities or changes.

[0344] A "notification mechanism" is a system for informing the user of information when an anomaly is detected by the analysis mechanism.

[0345] A "reception system" is a device that has the function of receiving questions and inquiries from users and managing them appropriately.

[0346] A "generation means" is a device that creates advice based on the content of the consultation received through the reception means and optimizes that advice.

[0347] A "transmission device" is a device that has the function of delivering the generated advice to the user.

[0348] A "module" is a component that transmits health information to a communication device based on the analysis results.

[0349] A "display device" is a device that displays received health information so that it can be visually confirmed.

[0350] A "device for recording behavioral data" is a device that monitors a pet's behavior and accumulates data on its behavioral patterns.

[0351] This invention aims to realize a system that manages a pet's health status in real time and provides appropriate training advice. Specific embodiments of this system are described below.

[0352] The server uses a camera as a means of capturing the pet's movements. The captured data is first acquired by a receiving means and then processed by an analysis means using image recognition technology. As an analysis means, the server analyzes the image data using TensorFlow and evaluates the pet's health status using an AI model. If an abnormality is detected through this analysis, an alert is immediately sent to the user by a notification means.

[0353] The terminal accepts questions from users regarding dog training. These questions are sent to a generation system, where AI generates appropriate training methods. For example, in response to the problem of "a dog that won't stop barking," the generation AI model suggests "a step-by-step training method to suppress excessive barking." The generated advice is delivered to the user's smartphone via a transmission system. Real-time data communication is enabled by using Firebase.

[0354] Furthermore, the analyzed health status information is transmitted to a communication device and displayed on a display device. This allows users to visually check their pet's health status. The pet's behavior is constantly monitored by a device that records behavioral data, and this information is accumulated and used for long-term health management.

[0355] For example, if a user inputs the problem "My cat has lost its appetite recently," the AI ​​will use past behavioral data and health assessments to suggest potential causes of the loss of appetite and advise whether a veterinary examination should be sought immediately. Furthermore, if the user inputs a prompt such as "Please tell me about my pet's recent health condition," the system will respond with the latest health assessment.

[0356] This allows users to monitor their pets' detailed health status and take appropriate action at home, without requiring specialized knowledge or expensive equipment.

[0357] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0358] Step 1:

[0359] The user films the pet's movements with a camera.

[0360] The input is image and video data acquired by the camera, and the output is this data being saved on a smartphone or dedicated device. Specifically, the user takes pictures of their pet in motion and registers the files in the system.

[0361] Step 2:

[0362] The device receives the captured data along with additional information and sends it to the server.

[0363] The input consists of captured image and video data, along with the date, time, and location information of the capture. The output is this data transferred to the server. The terminal adds time and location information to the data and transmits it to the server via the network.

[0364] Step 3:

[0365] The server uses image recognition to analyze the data and assess the pet's health.

[0366] The input is received image and video data, and the output is health assessment data as a result of the analysis. The server uses TensorFlow to analyze the image data and detect abnormalities in behavior and physical condition.

[0367] Step 4:

[0368] If the server detects an anomaly, it will notify the user through a notification system.

[0369] The input is the result of the analysis, and the output is an alert sent to the user's device. The server evaluates the analysis results and, if an anomaly is detected, sends a warning in real time using Firebase or similar tools.

[0370] Step 5:

[0371] The user sends questions about discipline through their device.

[0372] The input is the question entered by the user, and the output is the question data sent to the server. The user sends a question about pet training to the system from the input screen on their terminal.

[0373] Step 6:

[0374] The server uses a generated AI model to produce advice in response to the user's question.

[0375] The input is the user's inquiry, and the output is the generated specific advice. The server analyzes the received question and generates corresponding advice based on past training data.

[0376] Step 7:

[0377] The server sends the generated advice to the user's terminal.

[0378] The input is the generated advice, and the output is the advice information displayed on the user's smartphone. The server sends the completed advice to the user's smartphone via a notification system.

[0379] Step 8:

[0380] Based on the advice received by the user, the system helps with pet training and health management.

[0381] The input is advice displayed on the user's device, and the output is the specific training and management methods the user implements. The user follows the advice and takes actions to correct the pet's behavior and maintain its health.

[0382] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0383] This invention combines a system for supporting pet health management and training with an emotion engine that recognizes the user's emotions. This system not only evaluates the pet's condition but also provides flexible advice tailored to the user's emotions, enabling more effective user support.

[0384] Users first film their pet's movements with their smartphone or a dedicated device, and the data is sent from the device to a server. The server uses AI technology to analyze the received data and evaluate the pet's health. If signs of stress or abnormal behavioral patterns are detected, the server automatically notifies the user, enabling early detection of problems.

[0385] Furthermore, if a user wants to discuss pet training issues, they can send questions via voice or text through their device. In this case, the emotion engine analyzes the user's emotions from their voice tone and selected words. For example, if the server determines that the user is stressed, it will generate advice in a gentle tone appropriate to that emotion.

[0386] If a user reports a problem such as "I can't stop my dog ​​from barking excessively," the emotion engine recognizes the user's frustration, and the server can provide empathetic advice such as, "First, take a deep breath, and then try training slowly." This advice is generated in real time and sent immediately to the user's device.

[0387] In this way, this system supports pet management while taking the user's emotions into consideration, helping users care for their pets in a better mental state and promoting improved health and behavior of the pets.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] Users use their smartphones or dedicated devices to photograph their pets' actions and condition. The captured data is temporarily stored on the device.

[0391] Step 2:

[0392] The device sends the captured data to the server. During this process, time and location information is added as metadata to maintain data consistency and accuracy.

[0393] Step 3:

[0394] The server uses AI technology to analyze the received data and assess the pet's health. Image recognition technology is used to detect skin rashes, abnormal movements, and other issues.

[0395] Step 4:

[0396] The server determines whether an anomaly has been detected based on the analysis results. If an anomaly is found, the server generates a notification with content appropriate to its type and severity.

[0397] Step 5:

[0398] The server pushes the generated notification to the device, allowing the user to immediately see the anomaly. The notification includes a detailed description of the anomaly and recommended actions.

[0399] Step 6:

[0400] Users submit questions about pet training via voice or text input on their device. The content of the question, including the user's emotions, is sent to the server.

[0401] Step 7:

[0402] The server uses an emotion engine to recognize the user's emotions from the received audio or text. Emotion analysis identifies the user's state of mind and stress level.

[0403] Step 8:

[0404] The server generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will offer gentle suggestions to help them relax.

[0405] Step 9:

[0406] The generated advice is promptly sent from the server to the user's terminal. Based on this advice, the user can take specific actions to improve their pet's training.

[0407] (Example 2)

[0408] Next, we will describe Example 2. 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".

[0409] Balancing pet health management and training in modern times is a significant burden for pet owners. Furthermore, there are few ways to receive appropriate advice that takes the owner's feelings into consideration, highlighting the need for methods that reduce stress while providing effective pet care.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0411] In this invention, the server includes recording means for recording the pet's movements, analysis means for analyzing the transmitted information and evaluating the animal's health condition, and generation means for analyzing the user's emotions in response to training-related questions and generating appropriate advice. This enables effective pet health management and training through advice that takes the owner's emotions into consideration.

[0412] "Recording means" refers to a device or method that has the function of capturing and saving the movements and behaviors of a pet in the form of video and audio.

[0413] "Transmission means" refers to methods or technologies that have the function of transferring recorded data to other devices or systems via a communication line.

[0414] The "analysis means" refers to a function that uses algorithms and AI technology to evaluate the health status and behavioral patterns of pets based on the transmitted data.

[0415] A "warning mechanism" is a notification function that immediately informs the user of any anomalies detected by the analysis results, and may be provided via email or push notification.

[0416] The "reception mechanism" is a function that receives questions and feedback from users regarding training and allows for the next steps in processing them.

[0417] "Generation method" refers to a function that uses generation AI technology to create emotionally conscious responses and advice in response to user inquiries.

[0418] A "notification mechanism" is a communication function that promptly conveys generated advice and warnings to the user and prompts them to take necessary actions.

[0419] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to interpret the meaning of text data and audio data and to respond appropriately.

[0420] A "generative AI model" is an artificial intelligence technology that uses large amounts of data and machine learning algorithms to generate natural language text that can be used for human interaction.

[0421] A "prompt" is a sentence of instruction or question input to a generative AI model to obtain output based on a specific context or condition.

[0422] This invention combines a system that supports pet health management and training with a function that analyzes the user's emotions. This system performs a series of processes to observe the pet's behavior, evaluate its health condition, and provide appropriate advice.

[0423] Users first record their pet's movements using a smartphone or dedicated device. This step utilizes the device's camera function and, if necessary, a dedicated application. The application includes a user-friendly interface and an automatic data saving function.

[0424] The terminal transmits the recorded data to the server using a secure communication protocol. This communication uses common protocols such as HTTP or HTTPS. The transmitted data is compressed in preparation for the analysis process on the server.

[0425] The server analyzes the transmitted data using a machine learning framework. Specifically, it utilizes libraries such as TensorFlow and PyTorch to analyze the pet's behavior patterns, facial expressions, and voice, and to evaluate its health in detail. If an anomaly is detected in the analysis results, the server immediately notifies the user. This notification is provided via email or push notification through a dedicated app.

[0426] Furthermore, users can send questions about pet training to the server via voice or text through their device. The server then uses an emotion engine to analyze the user's emotions from their voice tone and selected words. Natural language processing techniques are used for this emotion analysis. IBM Watson and other similar tools may be helpful in this process.

[0427] After the emotions are analyzed, the server uses a generative AI model to generate advice that is appropriate to the user's emotions. OpenAI's GPT-3 is one example of a generative AI model used. If a user consults the server about the problem of "not being able to stop their dog from barking excessively," the server can recognize that frustration and provide advice that is empathetic to their feelings.

[0428] Examples of prompts include, "If the user is feeling stressed, please tell me how to improve their pet's behavior in a gentle tone." Based on this prompt, the AI ​​generates a response that matches the user's emotions and immediately notifies the device. This allows the user to smoothly manage and train their pet's health.

[0429] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0430] Step 1:

[0431] Users record their pet's movements using a smartphone or dedicated device. The input is video data obtained using a camera, and the output is video data stored on the device. This process utilizes the device's built-in camera or a dedicated recording app, and begins when the user presses a record button.

[0432] Step 2:

[0433] The terminal transmits recorded video data to the server using a secure communication protocol. Recorded video data is used as input, and the output is compressed data uploaded to the server. Here, a data compression algorithm is executed to improve communication efficiency, and the data is transmitted via a secure protocol such as HTTPS.

[0434] Step 3:

[0435] The server analyzes the transmitted data and assesses the pet's health. Compressed video data is used as input, and the output is the health assessment result. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis, performing behavioral pattern analysis and anomaly detection frame by frame.

[0436] Step 4:

[0437] The server notifies the user if an anomaly is detected based on the analysis results. The analysis results are used as input, and the output is a notification message indicating the anomaly. Notifications are sent via email or push notifications through a dedicated app.

[0438] Step 5:

[0439] Users consult a server via their device regarding pet training issues. Input can be in the form of voice or text questions. Voice questions are recorded via a microphone, while text questions are entered within the app.

[0440] Step 6:

[0441] The server uses an emotion engine to analyze the user's emotions in response to received questions. Input can be either audio or text data, and output is the analyzed emotion information. Natural language processing techniques are applied to the emotion analysis; in the case of audio, the analysis is based on intonation and tone, and in the case of text, it is based on vocabulary selection.

[0442] Step 7:

[0443] The server uses a generative AI model based on emotional information to generate user-appropriate advice. Emotional information and prompt text are used as input, and the output is user-appropriate advice. A generative AI model like OpenAI is utilized to generate appropriate content that is sensitive to the user's emotions, based on the prompt.

[0444] Step 8:

[0445] The server sends the generated advice to the user's terminal. The generated advice is used as input, and the output is the received message displayed on the user's terminal. This notification is in real time, allowing the user to immediately utilize the received advice.

[0446] (Application Example 2)

[0447] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0448] Traditional pet management systems have limited functions for monitoring pets' health and supporting training, and they cannot provide advice that is tailored to the owner's feelings. Therefore, there is a need to reduce owner stress while more accurately understanding the pet's health and taking appropriate action quickly.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0450] In this invention, the server includes recording means for recording the pet's movements, processing means for processing the acquired information and evaluating the pet's health condition, and emotion analysis means for analyzing the user's emotions and generating advice corresponding to those emotions. This makes it possible to provide advice that is sensitive to the owner's emotions in real time, and to more effectively support the health management and training of pets.

[0451] A "recording device" is a system for observing a pet's behavior and accumulating that information.

[0452] "Acquisition means" refers to the process of transmitting recorded data to a server or other device to make it available for use.

[0453] "Processing means" refers to algorithms and mechanisms for analyzing acquired data and evaluating the health status and abnormal behavior of pets.

[0454] A "reporting mechanism" is a function that communicates appropriate information to the owner when an abnormality is detected based on the pet's health condition.

[0455] A "reception method" refers to an interface or system for receiving consultations and questions from users regarding dog training.

[0456] A "generation method" is a system for creating appropriate advice and suggestions based on the content of inquiries received through the reception method.

[0457] "Means of provision" refers to the process of presenting advice and information created by the means of generation to pet owners and enabling them to utilize it.

[0458] "Emotional analysis tools" refer to algorithms and technologies used to analyze a user's emotional state and provide appropriate feedback or responses.

[0459] This invention is a system for supporting pet health management and training, and is implemented using home robots and smart devices. The main components of the system include recording means, acquisition means, processing means, reporting means, receiving means, generation means, provision means, and emotion analysis means.

[0460] The server uses a home robot to capture images of pets' movements with a camera and stores the video data using a recording device. The recording device has the function of adding time data and location data to the acquired information. This information is transmitted to the server in real time through an acquisition device. The server uses a processing device to analyze this data and evaluate the pet's health condition and any abnormalities in its behavior.

[0461] If an abnormality is detected, the reporting system automatically informs the owner of the situation. When the owner seeks advice on training, they can send their consultation details to the reception system via the terminal. The server uses an emotion analysis system to analyze the user's emotions. Based on this analysis, the generation system generates appropriate advice, which is then presented to the owner through the delivery system.

[0462] As a concrete example, if a pet owner encounters a problem with excessive barking, they might input into the device, "I want to stop my pet from barking unnecessarily, but I'm feeling stressed. Please give me some advice on what to do." In this case, the emotion analysis system would detect the stress, and the generated advice would be something like, "First, take a deep breath, and then slowly begin training." In this way, it is possible to provide flexible support that responds to the user's emotional state.

[0463] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0464] Step 1:

[0465] The device records the pet's movements using a camera. The input is live video data from the camera, and the output is a recorded video file. Time and location data are added and stored as part of the video file.

[0466] Step 2:

[0467] The terminal sends the recorded video file to the server. The input is the video file, and the output is the completion of the data transfer to the server. An internet connection is used for data transmission.

[0468] Step 3:

[0469] The server analyzes the received video data using processing tools. The input is the received video file, and the output is the evaluation result of the pet's health condition. An AI model analyzes the pet's movements in the video and detects signs of stress or abnormal behavior.

[0470] Step 4:

[0471] If the server detects an abnormality in the system's health status, it will notify the user using a reporting mechanism. The input is the analyzed abnormal data, and the output is a notification message to the user. The notification is delivered via a smartphone application.

[0472] Step 5:

[0473] The user submits a consultation request regarding training using their device. The input is the consultation content entered by the user in text or voice, and the output is confirmation that the consultation data has been successfully sent to the server.

[0474] Step 6:

[0475] The server analyzes the user's emotions using emotion analysis tools based on the consultation data. The input is the user's consultation content, and the output is a determination of the user's emotional state. The AI ​​model infers emotions from the tone and content of the words.

[0476] Step 7:

[0477] The server generates advice based on the results of emotion analysis. The input is the emotional state and the content of the consultation, and the output is the generated advice message. The content incorporates advice to alleviate stress.

[0478] Step 8:

[0479] The server notifies the user of the generated advice through a delivery mechanism. The input is the generated advice message, and the output is the presentation of the advice to the user. It is displayed on the terminal or read aloud.

[0480] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0481] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0482] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0483] [Third Embodiment]

[0484] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0485] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0486] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0487] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0488] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0489] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0490] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0491] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0492] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0493] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0494] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0495] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0496] This invention is a system for managing the health of pets and supporting their training. It aims to analyze the behavior and condition of pets using AI and provide appropriate advice to pet owners. A specific embodiment of this system is described below.

[0497] Users can use their smartphones or dedicated devices to take photos and videos of their pets' daily activities and conditions. The device can add supplementary information such as the date, time, and location of the capture to this media data and send the data to a server.

[0498] The server analyzes the received data in detail using AI-based image recognition technology and motion analysis algorithms. Based on the information extracted by the analysis, it assesses the pet's health and detects signs of abnormalities if necessary. For example, if it detects a skin rash or abnormal behavior, the server immediately notifies the user.

[0499] Furthermore, users can send specific questions about pet training to the server via their device. The server analyzes the received information, and an AI-based advice generation system provides optimal training methods and strategies. This advice is then sent to the user as easy-to-follow steps.

[0500] For example, if a user consults the server about a problem where "the dog won't stop barking," the server can suggest an appropriate approach, such as "training methods to suppress excessive barking," and provide step-by-step guidance.

[0501] The introduction of this system will allow users to monitor their pets' health in real time from the comfort of their homes and receive expert training advice. This is expected to improve pet health management and overall pet care.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] Users capture their pet's actions and condition using their smartphone or a dedicated device. The captured data, along with metadata including the date and time of capture and location information, is saved within the app.

[0505] Step 2:

[0506] The device sends the stored data to the server via the internet. The transmission is automatic, and a backup is maintained on the device after the data has been sent.

[0507] Step 3:

[0508] The server converts the received photo and video data into an appropriate format. During this process, pre-processing such as adjusting the data resolution and removing noise is performed.

[0509] Step 4:

[0510] The server uses AI technology to analyze the data. It uses image recognition technology to diagnose abnormalities and movements on the pet's body surface and records the results in a database.

[0511] Step 5:

[0512] The server determines whether there is an anomaly based on the analysis results. If an anomaly is detected, it generates a notification message according to the type and severity of the anomaly.

[0513] Step 6:

[0514] Notification messages are sent from the server to the device as push notifications. This notification allows users to instantly receive important information about their pet's condition.

[0515] Step 7:

[0516] Users can submit questions about pet training via text or voice through the app.

[0517] Step 8:

[0518] The terminal sends the user's inquiry details to the server. All data is transmitted using a secure protocol.

[0519] Step 9:

[0520] The server analyzes the received consultation content using natural language processing technology. It extracts important keywords and phrases to identify the problem.

[0521] Step 10:

[0522] The server uses AI to generate advice tailored to the consultation content. This advice includes effective training procedures and improvement strategies.

[0523] Step 11:

[0524] Advice is sent from the server to the device and made available for viewing within the app. Users can then implement the suggested methods and use them to help train their pets.

[0525] (Example 1)

[0526] Next, we will describe Example 1. 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."

[0527] Modern livestock owners are required to monitor their pets' health and behavioral changes in real time, enabling them to train and manage their pets quickly and effectively. However, it is difficult for owners to conduct these observations on a daily basis, and specialized knowledge and diagnoses are often required. Therefore, there is a need for a system that allows for easy access to early detection of health problems in pets and guidance on appropriate training.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0529] In this invention, the server includes means for recording the pet's movements and state, means for adding time information and location information to the recorded information, and means for analyzing the received information by image and motion analysis using artificial intelligence. This enables detailed analysis of the pet's health condition, early detection of abnormalities, and appropriate training guidance.

[0530] A "pet" is an animal kept as a household pet, and usually refers to companion animals such as dogs and cats.

[0531] "Actions" refer to the physical movements and behavioral patterns that pets perform.

[0532] "Condition" refers to the physiological and emotional aspects related to a pet's health, and usually includes its physical and emotional state.

[0533] "Means of recording" refers to devices or methods that have the function of saving the actions and condition of a pet in the form of photographs, videos, etc.

[0534] "Time information" refers to information indicating the exact date and time the data was acquired, which is useful for later analysis and tracking.

[0535] "Location information" refers to information that indicates the specific geographical location where the data was acquired, and provides context for the analysis.

[0536] A "data processing device" refers to a computer-based mechanism for receiving, storing, and analyzing information.

[0537] "Artificial intelligence" is a technology that allows machines to imitate human intellectual behavior, and is particularly used for data analysis and processing.

[0538] "Analysis" refers to the act of investigating and evaluating data in detail in order to draw some kind of conclusion.

[0539] "Health status" refers to the overall assessment results regarding the pet's physical and psychological health.

[0540] "Signs of abnormality" refer to indicators of unusual changes in a pet's health or behavior that warrant attention.

[0541] A "warning" refers to an informational notification intended to alert the user to detected anomalies or concerning situations.

[0542] "Discipline" refers to the process of teaching pets desirable behaviors through guidance and instruction.

[0543] A "generative model" refers to an algorithm or technique used to create new data or content from given information.

[0544] "Guidance content" refers to systematically organized information that includes advice and specific behavioral guidelines related to pet training.

[0545] "Means of transmission" refers to the function of communicating to deliver analysis results and advice to the user.

[0546] This invention is a system aimed at managing the health of pets and supporting their training. Users record their pets' daily actions and conditions as images or videos using a smartphone or a dedicated device. In this process, the device automatically supplements the recorded media data by adding time and location information.

[0547] The device sends the supplemented data to the server via the internet. The server applies image recognition technology and motion analysis algorithms to analyze the received data. This analysis uses frameworks such as TensorFlow and PyTorch, and leverages generative AI models to obtain detailed information about the pet's movements and condition. In particular, it performs health assessments and detects signs of abnormalities. For example, it can detect skin abnormalities from image data and provide information to alert the user.

[0548] Furthermore, users can send questions about pet training to the server via their device. The server uses a generative AI model to analyze these questions and generate optimal training procedures and approaches. For example, if a user inputs the problem "My dog ​​barks too often and it's causing problems" as a prompt, the server will generate and provide "Specific training methods to reduce excessive barking" as a set of procedures.

[0549] This will allow users to manage their pets' health in real time and receive expert advice to effectively train their pets. The introduction of this system is expected to improve the efficiency of pet health management and training.

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1:

[0552] Users capture their pet's daily actions and condition using a smartphone or dedicated device. The input is image or video data, and users capture the footage for the purpose of understanding specific behaviors or health conditions.

[0553] Step 2:

[0554] The device automatically adds time and location information to the captured media data. The input is the image or video data acquired in step 1, and the output is the media data with time and location information added. This contextualizes the data.

[0555] Step 3:

[0556] The terminal sends the supplemented media data to the server via a secure network. The input is media data with additional information, which is transferred to the server. This communication is performed using a security protocol.

[0557] Step 4:

[0558] The server analyzes the received data. The input is media data sent from the terminal. The server analyzes the data using AI-based image recognition technology and motion analysis algorithms to extract health indicators and behavioral patterns of the pet. The output is the analyzed insights and health assessment.

[0559] Step 5:

[0560] The server notifies the user of a warning message if an anomaly is detected in the analysis results. The input is the analysis results obtained in step 4, and the output is a notification message containing details of the anomaly and recommended countermeasures. The user receives this and can take immediate action.

[0561] Step 6:

[0562] Users input their pet training problems in text format into their terminal and send the inquiry to the server. The input consists of prompts representing questions or problems related to training, and the output is an inquiry request sent to the server.

[0563] Step 7:

[0564] The server uses a generative AI model to generate advice based on the user's inquiry. The input is a prompt from the user, and the output is a suggestion of specific training procedures and countermeasures. The server returns this output to the terminal.

[0565] Step 8:

[0566] The server sends the generated advice to the user, providing it as a concrete action plan. The input is the advice generated in step 7, and the output is the training guidance and action plan supplied to the user. The user can use this to improve their pet's training.

[0567] (Application Example 1)

[0568] Next, we will explain Application Example 1. In the following explanation, 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."

[0569] In pet health management and training, there is a challenge in that pet owners often have difficulty receiving information and advice at the right time. In particular, early detection of abnormalities in pets in daily life and taking necessary measures is a crucial challenge for pet owners. Furthermore, it is difficult to consistently monitor pet behavior, and the lack of immediate insights based on that behavior is also a problem.

[0570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0571] In this invention, the server includes: a shooting means for capturing images of the pet's movements; a receiving means for receiving the captured data; an analysis means for analyzing the received data and determining the pet's health condition; a notification means for notifying the user if an abnormality is detected based on the analysis results; a receiving means for receiving consultations from the user regarding training; a generating means for generating appropriate advice in response to the received consultations; a transmitting means for sending the generated advice to the user; a module for transmitting the analyzed health condition information to a communication device; a display device for displaying the health information transmitted by the module; and a device for monitoring the pet's behavior and recording movement data. This enables the user to manage the pet's health condition in real time and receive expert advice.

[0572] "Filming equipment" refers to a device used to film the actions of a pet, making the captured video and images available for subsequent processing.

[0573] A "receiving device" is a device that acquires data obtained by a shooting device and provides the function of transmitting it to a server for analysis and evaluation.

[0574] An "analysis device" is a device that evaluates the health status of a pet based on received data and performs processing to detect abnormalities or changes.

[0575] A "notification mechanism" is a system for informing the user of information when an anomaly is detected by the analysis mechanism.

[0576] A "reception system" is a device that has the function of receiving questions and inquiries from users and managing them appropriately.

[0577] A "generation means" is a device that creates advice based on the content of the consultation received through the reception means and optimizes that advice.

[0578] A "transmission device" is a device that has the function of delivering the generated advice to the user.

[0579] A "module" is a component that transmits health information to a communication device based on the analysis results.

[0580] A "display device" is a device that displays received health information so that it can be visually confirmed.

[0581] A "device for recording behavioral data" is a device that monitors a pet's behavior and accumulates data on its behavioral patterns.

[0582] This invention aims to realize a system that manages a pet's health status in real time and provides appropriate training advice. Specific embodiments of this system are described below.

[0583] The server uses a camera as a means of capturing the pet's movements. The captured data is first acquired by a receiving means and then processed by an analysis means using image recognition technology. As an analysis means, the server analyzes the image data using TensorFlow and evaluates the pet's health status using an AI model. If an abnormality is detected through this analysis, an alert is immediately sent to the user by a notification means.

[0584] The terminal accepts questions from users regarding dog training. These questions are sent to a generation system, where AI generates appropriate training methods. For example, in response to the problem of "a dog that won't stop barking," the generation AI model suggests "a step-by-step training method to suppress excessive barking." The generated advice is delivered to the user's smartphone via a transmission system. Real-time data communication is enabled by using Firebase.

[0585] Furthermore, the analyzed health status information is transmitted to a communication device and displayed on a display device. This allows users to visually check their pet's health status. The pet's behavior is constantly monitored by a device that records behavioral data, and this information is accumulated and used for long-term health management.

[0586] For example, if a user inputs the problem "My cat has lost its appetite recently," the AI ​​will use past behavioral data and health assessments to suggest potential causes of the loss of appetite and advise whether a veterinary examination should be sought immediately. Furthermore, if the user inputs a prompt such as "Please tell me about my pet's recent health condition," the system will respond with the latest health assessment.

[0587] This allows users to monitor their pets' detailed health status and take appropriate action at home, without requiring specialized knowledge or expensive equipment.

[0588] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0589] Step 1:

[0590] The user films the pet's movements with a camera.

[0591] The input is image and video data acquired by the camera, and the output is this data being saved on a smartphone or dedicated device. Specifically, the user takes pictures of their pet in motion and registers the files in the system.

[0592] Step 2:

[0593] The device receives the captured data along with additional information and sends it to the server.

[0594] The input consists of captured image and video data, along with the date, time, and location information of the capture. The output is this data transferred to the server. The terminal adds time and location information to the data and transmits it to the server via the network.

[0595] Step 3:

[0596] The server uses image recognition to analyze the data and assess the pet's health.

[0597] The input is received image and video data, and the output is health assessment data as a result of the analysis. The server uses TensorFlow to analyze the image data and detect abnormalities in behavior and physical condition.

[0598] Step 4:

[0599] If the server detects an anomaly, it will notify the user through a notification system.

[0600] The input is the result of the analysis, and the output is an alert sent to the user's device. The server evaluates the analysis results and, if an anomaly is detected, sends a warning in real time using Firebase or similar tools.

[0601] Step 5:

[0602] The user sends questions about discipline through their device.

[0603] The input is the question entered by the user, and the output is the question data sent to the server. The user sends a question about pet training to the system from the input screen on their terminal.

[0604] Step 6:

[0605] The server uses a generated AI model to produce advice in response to the user's question.

[0606] The input is the user's inquiry, and the output is the generated specific advice. The server analyzes the received question and generates corresponding advice based on past training data.

[0607] Step 7:

[0608] The server sends the generated advice to the user's terminal.

[0609] The input is the generated advice, and the output is the advice information displayed on the user's smartphone. The server sends the completed advice to the user's smartphone via a notification system.

[0610] Step 8:

[0611] Based on the advice received by the user, the system helps with pet training and health management.

[0612] The input is advice displayed on the user's device, and the output is the specific training and management methods the user implements. The user follows the advice and takes actions to correct the pet's behavior and maintain its health.

[0613] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0614] This invention combines a system for supporting pet health management and training with an emotion engine that recognizes the user's emotions. This system not only evaluates the pet's condition but also provides flexible advice tailored to the user's emotions, enabling more effective user support.

[0615] Users first film their pet's movements with their smartphone or a dedicated device, and the data is sent from the device to a server. The server uses AI technology to analyze the received data and evaluate the pet's health. If signs of stress or abnormal behavioral patterns are detected, the server automatically notifies the user, enabling early detection of problems.

[0616] Furthermore, if a user wants to discuss pet training issues, they can send questions via voice or text through their device. In this case, the emotion engine analyzes the user's emotions from their voice tone and selected words. For example, if the server determines that the user is stressed, it will generate advice in a gentle tone appropriate to that emotion.

[0617] If a user reports a problem such as "I can't stop my dog ​​from barking excessively," the emotion engine recognizes the user's frustration, and the server can provide empathetic advice such as, "First, take a deep breath, and then try training slowly." This advice is generated in real time and sent immediately to the user's device.

[0618] In this way, this system supports pet management while taking the user's emotions into consideration, helping users care for their pets in a better mental state and promoting improved health and behavior of the pets.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users use their smartphones or dedicated devices to photograph their pets' actions and condition. The captured data is temporarily stored on the device.

[0622] Step 2:

[0623] The device sends the captured data to the server. During this process, time and location information is added as metadata to maintain data consistency and accuracy.

[0624] Step 3:

[0625] The server uses AI technology to analyze the received data and assess the pet's health. Image recognition technology is used to detect skin rashes, abnormal movements, and other issues.

[0626] Step 4:

[0627] The server determines whether an anomaly has been detected based on the analysis results. If an anomaly is found, the server generates a notification with content appropriate to its type and severity.

[0628] Step 5:

[0629] The server pushes the generated notification to the device, allowing the user to immediately see the anomaly. The notification includes a detailed description of the anomaly and recommended actions.

[0630] Step 6:

[0631] Users submit questions about pet training via voice or text input on their device. The content of the question, including the user's emotions, is sent to the server.

[0632] Step 7:

[0633] The server uses an emotion engine to recognize the user's emotions from the received audio or text. Emotion analysis identifies the user's state of mind and stress level.

[0634] Step 8:

[0635] The server generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will offer gentle suggestions to help them relax.

[0636] Step 9:

[0637] The generated advice is promptly sent from the server to the user's terminal. Based on this advice, the user can take specific actions to improve their pet's training.

[0638] (Example 2)

[0639] Next, we will describe Example 2. 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."

[0640] Balancing pet health management and training in modern times is a significant burden for pet owners. Furthermore, there are few ways to receive appropriate advice that takes the owner's feelings into consideration, highlighting the need for methods that reduce stress while providing effective pet care.

[0641] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0642] In this invention, the server includes recording means for recording the pet's movements, analysis means for analyzing the transmitted information and evaluating the animal's health condition, and generation means for analyzing the user's emotions in response to training-related questions and generating appropriate advice. This enables effective pet health management and training through advice that takes the owner's emotions into consideration.

[0643] "Recording means" refers to a device or method that has the function of capturing and saving the movements and behaviors of a pet in the form of video and audio.

[0644] "Transmission means" refers to methods or technologies that have the function of transferring recorded data to other devices or systems via a communication line.

[0645] The "analysis means" refers to a function that uses algorithms and AI technology to evaluate the health status and behavioral patterns of pets based on the transmitted data.

[0646] A "warning mechanism" is a notification function that immediately informs the user of any anomalies detected by the analysis results, and may be provided via email or push notification.

[0647] The "reception mechanism" is a function that receives questions and feedback from users regarding training and allows for the next steps in processing them.

[0648] "Generation method" refers to a function that uses generation AI technology to create emotionally conscious responses and advice in response to user inquiries.

[0649] A "notification mechanism" is a communication function that promptly conveys generated advice and warnings to the user and prompts them to take necessary actions.

[0650] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to interpret the meaning of text data and audio data and to respond appropriately.

[0651] A "generative AI model" is an artificial intelligence technology that uses large amounts of data and machine learning algorithms to generate natural language text that can be used for human interaction.

[0652] A "prompt" is a sentence of instruction or question input to a generative AI model to obtain output based on a specific context or condition.

[0653] This invention combines a system that supports pet health management and training with a function that analyzes the user's emotions. This system performs a series of processes to observe the pet's behavior, evaluate its health condition, and provide appropriate advice.

[0654] Users first record their pet's movements using a smartphone or dedicated device. This step utilizes the device's camera function and, if necessary, a dedicated application. The application includes a user-friendly interface and an automatic data saving function.

[0655] The terminal transmits the recorded data to the server using a secure communication protocol. This communication uses common protocols such as HTTP or HTTPS. The transmitted data is compressed in preparation for the analysis process on the server.

[0656] The server analyzes the transmitted data using a machine learning framework. Specifically, it utilizes libraries such as TensorFlow and PyTorch to analyze the pet's behavior patterns, facial expressions, and voice, and to evaluate its health in detail. If an anomaly is detected in the analysis results, the server immediately notifies the user. This notification is provided via email or push notification through a dedicated app.

[0657] Furthermore, users can send questions about pet training to the server via voice or text through their device. The server then uses an emotion engine to analyze the user's emotions from their voice tone and selected words. Natural language processing techniques are used for this emotion analysis. IBM Watson and other similar tools may be helpful in this process.

[0658] After the emotions are analyzed, the server uses a generative AI model to generate advice that is appropriate to the user's emotions. OpenAI's GPT-3 is one example of a generative AI model used. If a user consults the server about the problem of "not being able to stop their dog from barking excessively," the server can recognize that frustration and provide advice that is empathetic to their feelings.

[0659] Examples of prompts include, "If the user is feeling stressed, please tell me how to improve their pet's behavior in a gentle tone." Based on this prompt, the AI ​​generates a response that matches the user's emotions and immediately notifies the device. This allows the user to smoothly manage and train their pet's health.

[0660] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0661] Step 1:

[0662] Users record their pet's movements using a smartphone or dedicated device. The input is video data obtained using a camera, and the output is video data stored on the device. This process utilizes the device's built-in camera or a dedicated recording app, and begins when the user presses a record button.

[0663] Step 2:

[0664] The terminal transmits recorded video data to the server using a secure communication protocol. Recorded video data is used as input, and the output is compressed data uploaded to the server. Here, a data compression algorithm is executed to improve communication efficiency, and the data is transmitted via a secure protocol such as HTTPS.

[0665] Step 3:

[0666] The server analyzes the transmitted data and assesses the pet's health. Compressed video data is used as input, and the output is the health assessment result. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis, performing behavioral pattern analysis and anomaly detection frame by frame.

[0667] Step 4:

[0668] The server notifies the user if an anomaly is detected based on the analysis results. The analysis results are used as input, and the output is a notification message indicating the anomaly. Notifications are sent via email or push notifications through a dedicated app.

[0669] Step 5:

[0670] Users consult a server via their device regarding pet training issues. Input can be in the form of voice or text questions. Voice questions are recorded via a microphone, while text questions are entered within the app.

[0671] Step 6:

[0672] The server uses an emotion engine to analyze the user's emotions in response to received questions. Input can be either audio or text data, and output is the analyzed emotion information. Natural language processing techniques are applied to the emotion analysis; in the case of audio, the analysis is based on intonation and tone, and in the case of text, it is based on vocabulary selection.

[0673] Step 7:

[0674] The server uses a generative AI model based on emotional information to generate user-appropriate advice. Emotional information and prompt text are used as input, and the output is user-appropriate advice. A generative AI model like OpenAI is utilized to generate appropriate content that is sensitive to the user's emotions, based on the prompt.

[0675] Step 8:

[0676] The server sends the generated advice to the user's terminal. The generated advice is used as input, and the output is the received message displayed on the user's terminal. This notification is in real time, allowing the user to immediately utilize the received advice.

[0677] (Application Example 2)

[0678] Next, we will explain Application Example 2. In the following explanation, 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."

[0679] Traditional pet management systems have limited functions for monitoring pets' health and supporting training, and they cannot provide advice that is tailored to the owner's feelings. Therefore, there is a need to reduce owner stress while more accurately understanding the pet's health and taking appropriate action quickly.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0681] In this invention, the server includes recording means for recording the pet's movements, processing means for processing the acquired information and evaluating the pet's health condition, and emotion analysis means for analyzing the user's emotions and generating advice corresponding to those emotions. This makes it possible to provide advice that is sensitive to the owner's emotions in real time, and to more effectively support the health management and training of pets.

[0682] A "recording device" is a system for observing a pet's behavior and accumulating that information.

[0683] "Acquisition means" refers to the process of transmitting recorded data to a server or other device to make it available for use.

[0684] "Processing means" refers to algorithms and mechanisms for analyzing acquired data and evaluating the health status and abnormal behavior of pets.

[0685] A "reporting mechanism" is a function that communicates appropriate information to the owner when an abnormality is detected based on the pet's health condition.

[0686] A "reception method" refers to an interface or system for receiving consultations and questions from users regarding dog training.

[0687] A "generation method" is a system for creating appropriate advice and suggestions based on the content of inquiries received through the reception method.

[0688] "Means of provision" refers to the process of presenting advice and information created by the means of generation to pet owners and enabling them to utilize it.

[0689] "Emotional analysis tools" refer to algorithms and technologies used to analyze a user's emotional state and provide appropriate feedback or responses.

[0690] This invention is a system for supporting pet health management and training, and is implemented using home robots and smart devices. The main components of the system include recording means, acquisition means, processing means, reporting means, receiving means, generation means, provision means, and emotion analysis means.

[0691] The server uses a home robot to capture images of pets' movements with a camera and stores the video data using a recording device. The recording device has the function of adding time data and location data to the acquired information. This information is transmitted to the server in real time through an acquisition device. The server uses a processing device to analyze this data and evaluate the pet's health condition and any abnormalities in its behavior.

[0692] If an abnormality is detected, the reporting system automatically informs the owner of the situation. When the owner seeks advice on training, they can send their consultation details to the reception system via the terminal. The server uses an emotion analysis system to analyze the user's emotions. Based on this analysis, the generation system generates appropriate advice, which is then presented to the owner through the delivery system.

[0693] As a concrete example, if a pet owner encounters a problem with excessive barking, they might input into the device, "I want to stop my pet from barking unnecessarily, but I'm feeling stressed. Please give me some advice on what to do." In this case, the emotion analysis system would detect the stress, and the generated advice would be something like, "First, take a deep breath, and then slowly begin training." In this way, it is possible to provide flexible support that responds to the user's emotional state.

[0694] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0695] Step 1:

[0696] The device records the pet's movements using a camera. The input is live video data from the camera, and the output is a recorded video file. Time and location data are added and stored as part of the video file.

[0697] Step 2:

[0698] The terminal sends the recorded video file to the server. The input is the video file, and the output is the completion of the data transfer to the server. An internet connection is used for data transmission.

[0699] Step 3:

[0700] The server analyzes the received video data using processing tools. The input is the received video file, and the output is the evaluation result of the pet's health condition. An AI model analyzes the pet's movements in the video and detects signs of stress or abnormal behavior.

[0701] Step 4:

[0702] If the server detects an abnormality in the system's health status, it will notify the user using a reporting mechanism. The input is the analyzed abnormal data, and the output is a notification message to the user. The notification is delivered via a smartphone application.

[0703] Step 5:

[0704] The user submits a consultation request regarding training using their device. The input is the consultation content entered by the user in text or voice, and the output is confirmation that the consultation data has been successfully sent to the server.

[0705] Step 6:

[0706] The server analyzes the user's emotions using emotion analysis tools based on the consultation data. The input is the user's consultation content, and the output is a determination of the user's emotional state. The AI ​​model infers emotions from the tone and content of the words.

[0707] Step 7:

[0708] The server generates advice based on the results of emotion analysis. The input is the emotional state and the content of the consultation, and the output is the generated advice message. The content incorporates advice to alleviate stress.

[0709] Step 8:

[0710] The server notifies the user of the generated advice through a delivery mechanism. The input is the generated advice message, and the output is the presentation of the advice to the user. It is displayed on the terminal or read aloud.

[0711] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0712] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0713] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0714] [Fourth Embodiment]

[0715] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0716] As shown in Figure 7, the 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.

[0717] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0718] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0719] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0720] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0721] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0722] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0723] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0724] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0725] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0726] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0727] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] This invention is a system for managing the health of pets and supporting their training. It aims to analyze the behavior and condition of pets using AI and provide appropriate advice to pet owners. A specific embodiment of this system is described below.

[0729] Users can use their smartphones or dedicated devices to take photos and videos of their pets' daily activities and conditions. The device can add supplementary information such as the date, time, and location of the capture to this media data and send the data to a server.

[0730] The server analyzes the received data in detail using AI-based image recognition technology and motion analysis algorithms. Based on the information extracted by the analysis, it assesses the pet's health and detects signs of abnormalities if necessary. For example, if it detects a skin rash or abnormal behavior, the server immediately notifies the user.

[0731] Furthermore, users can send specific questions about pet training to the server via their device. The server analyzes the received information, and an AI-based advice generation system provides optimal training methods and strategies. This advice is then sent to the user as easy-to-follow steps.

[0732] For example, if a user consults the server about a problem where "the dog won't stop barking," the server can suggest an appropriate approach, such as "training methods to suppress excessive barking," and provide step-by-step guidance.

[0733] The introduction of this system will allow users to monitor their pets' health in real time from the comfort of their homes and receive expert training advice. This is expected to improve pet health management and overall pet care.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] Users capture their pet's actions and condition using their smartphone or a dedicated device. The captured data, along with metadata including the date and time of capture and location information, is saved within the app.

[0737] Step 2:

[0738] The device sends the stored data to the server via the internet. The transmission is automatic, and a backup is maintained on the device after the data has been sent.

[0739] Step 3:

[0740] The server converts the received photo and video data into an appropriate format. During this process, pre-processing such as adjusting the data resolution and removing noise is performed.

[0741] Step 4:

[0742] The server uses AI technology to analyze the data. It uses image recognition technology to diagnose abnormalities and movements on the pet's body surface and records the results in a database.

[0743] Step 5:

[0744] The server determines whether there is an anomaly based on the analysis results. If an anomaly is detected, it generates a notification message according to the type and severity of the anomaly.

[0745] Step 6:

[0746] Notification messages are sent from the server to the device as push notifications. This notification allows users to instantly receive important information about their pet's condition.

[0747] Step 7:

[0748] Users can submit questions about pet training via text or voice through the app.

[0749] Step 8:

[0750] The terminal sends the user's inquiry details to the server. All data is transmitted using a secure protocol.

[0751] Step 9:

[0752] The server analyzes the received consultation content using natural language processing technology. It extracts important keywords and phrases to identify the problem.

[0753] Step 10:

[0754] The server uses AI to generate advice tailored to the consultation content. This advice includes effective training procedures and improvement strategies.

[0755] Step 11:

[0756] Advice is sent from the server to the device and made available for viewing within the app. Users can then implement the suggested methods and use them to help train their pets.

[0757] (Example 1)

[0758] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] Modern livestock owners are required to monitor their pets' health and behavioral changes in real time, enabling them to train and manage their pets quickly and effectively. However, it is difficult for owners to conduct these observations on a daily basis, and specialized knowledge and diagnoses are often required. Therefore, there is a need for a system that allows for easy access to early detection of health problems in pets and guidance on appropriate training.

[0760] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0761] In this invention, the server includes means for recording the pet's movements and state, means for adding time information and location information to the recorded information, and means for analyzing the received information by image and motion analysis using artificial intelligence. This enables detailed analysis of the pet's health condition, early detection of abnormalities, and appropriate training guidance.

[0762] A "pet" is an animal kept as a household pet, and usually refers to companion animals such as dogs and cats.

[0763] "Actions" refer to the physical movements and behavioral patterns that pets perform.

[0764] "Condition" refers to the physiological and emotional aspects related to a pet's health, and usually includes its physical and emotional state.

[0765] "Means of recording" refers to devices or methods that have the function of saving the actions and condition of a pet in the form of photographs, videos, etc.

[0766] "Time information" refers to information indicating the exact date and time the data was acquired, which is useful for later analysis and tracking.

[0767] "Location information" refers to information that indicates the specific geographical location where the data was acquired, and provides context for the analysis.

[0768] A "data processing device" refers to a computer-based mechanism for receiving, storing, and analyzing information.

[0769] "Artificial intelligence" is a technology that allows machines to imitate human intellectual behavior, and is particularly used for data analysis and processing.

[0770] "Analysis" refers to the act of investigating and evaluating data in detail in order to draw some kind of conclusion.

[0771] "Health status" refers to the overall assessment results regarding the pet's physical and psychological health.

[0772] "Signs of abnormality" refer to indicators of unusual changes in a pet's health or behavior that warrant attention.

[0773] A "warning" refers to an informational notification intended to alert the user to detected anomalies or concerning situations.

[0774] "Discipline" refers to the process of teaching pets desirable behaviors through guidance and instruction.

[0775] A "generative model" refers to an algorithm or technique used to create new data or content from given information.

[0776] "Guidance content" refers to systematically organized information that includes advice and specific behavioral guidelines related to pet training.

[0777] "Means of transmission" refers to the function of communicating to deliver analysis results and advice to the user.

[0778] This invention is a system aimed at managing the health of pets and supporting their training. Users record their pets' daily actions and conditions as images or videos using a smartphone or a dedicated device. In this process, the device automatically supplements the recorded media data by adding time and location information.

[0779] The device sends the supplemented data to the server via the internet. The server applies image recognition technology and motion analysis algorithms to analyze the received data. This analysis uses frameworks such as TensorFlow and PyTorch, and leverages generative AI models to obtain detailed information about the pet's movements and condition. In particular, it performs health assessments and detects signs of abnormalities. For example, it can detect skin abnormalities from image data and provide information to alert the user.

[0780] Furthermore, users can send questions about pet training to the server via their device. The server uses a generative AI model to analyze these questions and generate optimal training procedures and approaches. For example, if a user inputs the problem "My dog ​​barks too often and it's causing problems" as a prompt, the server will generate and provide "Specific training methods to reduce excessive barking" as a set of procedures.

[0781] This will allow users to manage their pets' health in real time and receive expert advice to effectively train their pets. The introduction of this system is expected to improve the efficiency of pet health management and training.

[0782] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0783] Step 1:

[0784] Users capture their pet's daily actions and condition using a smartphone or dedicated device. The input is image or video data, and users capture the footage for the purpose of understanding specific behaviors or health conditions.

[0785] Step 2:

[0786] The device automatically adds time and location information to the captured media data. The input is the image or video data acquired in step 1, and the output is the media data with time and location information added. This contextualizes the data.

[0787] Step 3:

[0788] The terminal sends the supplemented media data to the server via a secure network. The input is media data with additional information, which is transferred to the server. This communication is performed using a security protocol.

[0789] Step 4:

[0790] The server analyzes the received data. The input is media data sent from the terminal. The server analyzes the data using AI-based image recognition technology and motion analysis algorithms to extract health indicators and behavioral patterns of the pet. The output is the analyzed insights and health assessment.

[0791] Step 5:

[0792] The server notifies the user of a warning message if an anomaly is detected in the analysis results. The input is the analysis results obtained in step 4, and the output is a notification message containing details of the anomaly and recommended countermeasures. The user receives this and can take immediate action.

[0793] Step 6:

[0794] Users input their pet training problems in text format into their terminal and send the inquiry to the server. The input consists of prompts representing questions or problems related to training, and the output is an inquiry request sent to the server.

[0795] Step 7:

[0796] The server uses a generative AI model to generate advice based on the user's inquiry. The input is a prompt from the user, and the output is a suggestion of specific training procedures and countermeasures. The server returns this output to the terminal.

[0797] Step 8:

[0798] The server sends the generated advice to the user, providing it as a concrete action plan. The input is the advice generated in step 7, and the output is the training guidance and action plan supplied to the user. The user can use this to improve their pet's training.

[0799] (Application Example 1)

[0800] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] In pet health management and training, there is a challenge in that pet owners often have difficulty receiving information and advice at the right time. In particular, early detection of abnormalities in pets in daily life and taking necessary measures is a crucial challenge for pet owners. Furthermore, it is difficult to consistently monitor pet behavior, and the lack of immediate insights based on that behavior is also a problem.

[0802] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0803] In this invention, the server includes: a shooting means for capturing images of the pet's movements; a receiving means for receiving the captured data; an analysis means for analyzing the received data and determining the pet's health condition; a notification means for notifying the user if an abnormality is detected based on the analysis results; a receiving means for receiving consultations from the user regarding training; a generating means for generating appropriate advice in response to the received consultations; a transmitting means for sending the generated advice to the user; a module for transmitting the analyzed health condition information to a communication device; a display device for displaying the health information transmitted by the module; and a device for monitoring the pet's behavior and recording movement data. This enables the user to manage the pet's health condition in real time and receive expert advice.

[0804] "Filming equipment" refers to a device used to film the actions of a pet, making the captured video and images available for subsequent processing.

[0805] A "receiving device" is a device that acquires data obtained by a shooting device and provides the function of transmitting it to a server for analysis and evaluation.

[0806] An "analysis device" is a device that evaluates the health status of a pet based on received data and performs processing to detect abnormalities or changes.

[0807] A "notification mechanism" is a system for informing the user of information when an anomaly is detected by the analysis mechanism.

[0808] A "reception system" is a device that has the function of receiving questions and inquiries from users and managing them appropriately.

[0809] A "generation means" is a device that creates advice based on the content of the consultation received through the reception means and optimizes that advice.

[0810] A "transmission device" is a device that has the function of delivering the generated advice to the user.

[0811] A "module" is a component that transmits health information to a communication device based on the analysis results.

[0812] A "display device" is a device that displays received health information so that it can be visually confirmed.

[0813] A "device for recording behavioral data" is a device that monitors a pet's behavior and accumulates data on its behavioral patterns.

[0814] This invention aims to realize a system that manages a pet's health status in real time and provides appropriate training advice. Specific embodiments of this system are described below.

[0815] The server uses a camera as a means of capturing the pet's movements. The captured data is first acquired by a receiving means and then processed by an analysis means using image recognition technology. As an analysis means, the server analyzes the image data using TensorFlow and evaluates the pet's health status using an AI model. If an abnormality is detected through this analysis, an alert is immediately sent to the user by a notification means.

[0816] The terminal accepts questions from users regarding dog training. These questions are sent to a generation system, where AI generates appropriate training methods. For example, in response to the problem of "a dog that won't stop barking," the generation AI model suggests "a step-by-step training method to suppress excessive barking." The generated advice is delivered to the user's smartphone via a transmission system. Real-time data communication is enabled by using Firebase.

[0817] Furthermore, the analyzed health status information is transmitted to a communication device and displayed on a display device. This allows users to visually check their pet's health status. The pet's behavior is constantly monitored by a device that records behavioral data, and this information is accumulated and used for long-term health management.

[0818] For example, if a user inputs the problem "My cat has lost its appetite recently," the AI ​​will use past behavioral data and health assessments to suggest potential causes of the loss of appetite and advise whether a veterinary examination should be sought immediately. Furthermore, if the user inputs a prompt such as "Please tell me about my pet's recent health condition," the system will respond with the latest health assessment.

[0819] This allows users to monitor their pets' detailed health status and take appropriate action at home, without requiring specialized knowledge or expensive equipment.

[0820] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0821] Step 1:

[0822] The user films the pet's movements with a camera.

[0823] The input is image and video data acquired by the camera, and the output is this data being saved on a smartphone or dedicated device. Specifically, the user takes pictures of their pet in motion and registers the files in the system.

[0824] Step 2:

[0825] The device receives the captured data along with additional information and sends it to the server.

[0826] The input consists of captured image and video data, along with the date, time, and location information of the capture. The output is this data transferred to the server. The terminal adds time and location information to the data and transmits it to the server via the network.

[0827] Step 3:

[0828] The server uses image recognition to analyze the data and assess the pet's health.

[0829] The input is received image and video data, and the output is health assessment data as a result of the analysis. The server uses TensorFlow to analyze the image data and detect abnormalities in behavior and physical condition.

[0830] Step 4:

[0831] If the server detects an anomaly, it will notify the user through a notification system.

[0832] The input is the result of the analysis, and the output is an alert sent to the user's device. The server evaluates the analysis results and, if an anomaly is detected, sends a warning in real time using Firebase or similar tools.

[0833] Step 5:

[0834] The user sends questions about discipline through their device.

[0835] The input is the question entered by the user, and the output is the question data sent to the server. The user sends a question about pet training to the system from the input screen on their terminal.

[0836] Step 6:

[0837] The server uses a generated AI model to produce advice in response to the user's question.

[0838] The input is the user's inquiry, and the output is the generated specific advice. The server analyzes the received question and generates corresponding advice based on past training data.

[0839] Step 7:

[0840] The server sends the generated advice to the user's terminal.

[0841] The input is the generated advice, and the output is the advice information displayed on the user's smartphone. The server sends the completed advice to the user's smartphone via a notification system.

[0842] Step 8:

[0843] Based on the advice received by the user, the system helps with pet training and health management.

[0844] The input is advice displayed on the user's device, and the output is the specific training and management methods the user implements. The user follows the advice and takes actions to correct the pet's behavior and maintain its health.

[0845] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0846] This invention combines a system for supporting pet health management and training with an emotion engine that recognizes the user's emotions. This system not only evaluates the pet's condition but also provides flexible advice tailored to the user's emotions, enabling more effective user support.

[0847] Users first film their pet's movements with their smartphone or a dedicated device, and the data is sent from the device to a server. The server uses AI technology to analyze the received data and evaluate the pet's health. If signs of stress or abnormal behavioral patterns are detected, the server automatically notifies the user, enabling early detection of problems.

[0848] Furthermore, if a user wants to discuss pet training issues, they can send questions via voice or text through their device. In this case, the emotion engine analyzes the user's emotions from their voice tone and selected words. For example, if the server determines that the user is stressed, it will generate advice in a gentle tone appropriate to that emotion.

[0849] If a user reports a problem such as "I can't stop my dog ​​from barking excessively," the emotion engine recognizes the user's frustration, and the server can provide empathetic advice such as, "First, take a deep breath, and then try training slowly." This advice is generated in real time and sent immediately to the user's device.

[0850] In this way, this system supports pet management while taking the user's emotions into consideration, helping users care for their pets in a better mental state and promoting improved health and behavior of the pets.

[0851] The following describes the processing flow.

[0852] Step 1:

[0853] Users use their smartphones or dedicated devices to photograph their pets' actions and condition. The captured data is temporarily stored on the device.

[0854] Step 2:

[0855] The device sends the captured data to the server. During this process, time and location information is added as metadata to maintain data consistency and accuracy.

[0856] Step 3:

[0857] The server uses AI technology to analyze the received data and assess the pet's health. Image recognition technology is used to detect skin rashes, abnormal movements, and other issues.

[0858] Step 4:

[0859] The server determines whether an anomaly has been detected based on the analysis results. If an anomaly is found, the server generates a notification with content appropriate to its type and severity.

[0860] Step 5:

[0861] The server pushes the generated notification to the device, allowing the user to immediately see the anomaly. The notification includes a detailed description of the anomaly and recommended actions.

[0862] Step 6:

[0863] Users submit questions about pet training via voice or text input on their device. The content of the question, including the user's emotions, is sent to the server.

[0864] Step 7:

[0865] The server uses an emotion engine to recognize the user's emotions from the received audio or text. Emotion analysis identifies the user's state of mind and stress level.

[0866] Step 8:

[0867] The server generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will offer gentle suggestions to help them relax.

[0868] Step 9:

[0869] The generated advice is promptly sent from the server to the user's terminal. Based on this advice, the user can take specific actions to improve their pet's training.

[0870] (Example 2)

[0871] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0872] Balancing pet health management and training in modern times is a significant burden for pet owners. Furthermore, there are few ways to receive appropriate advice that takes the owner's feelings into consideration, highlighting the need for methods that reduce stress while providing effective pet care.

[0873] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0874] In this invention, the server includes recording means for recording the pet's movements, analysis means for analyzing the transmitted information and evaluating the animal's health condition, and generation means for analyzing the user's emotions in response to training-related questions and generating appropriate advice. This enables effective pet health management and training through advice that takes the owner's emotions into consideration.

[0875] "Recording means" refers to a device or method that has the function of capturing and saving the movements and behaviors of a pet in the form of video and audio.

[0876] "Transmission means" refers to methods or technologies that have the function of transferring recorded data to other devices or systems via a communication line.

[0877] The "analysis means" refers to a function that uses algorithms and AI technology to evaluate the health status and behavioral patterns of pets based on the transmitted data.

[0878] A "warning mechanism" is a notification function that immediately informs the user of any anomalies detected by the analysis results, and may be provided via email or push notification.

[0879] The "reception mechanism" is a function that receives questions and feedback from users regarding training and allows for the next steps in processing them.

[0880] "Generation method" refers to a function that uses generation AI technology to create emotionally conscious responses and advice in response to user inquiries.

[0881] A "notification mechanism" is a communication function that promptly conveys generated advice and warnings to the user and prompts them to take necessary actions.

[0882] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used to interpret the meaning of text data and audio data and to respond appropriately.

[0883] A "generative AI model" is an artificial intelligence technology that uses large amounts of data and machine learning algorithms to generate natural language text that can be used for human interaction.

[0884] A "prompt" is a sentence of instruction or question input to a generative AI model to obtain output based on a specific context or condition.

[0885] This invention combines a system that supports pet health management and training with a function that analyzes the user's emotions. This system performs a series of processes to observe the pet's behavior, evaluate its health condition, and provide appropriate advice.

[0886] Users first record their pet's movements using a smartphone or dedicated device. This step utilizes the device's camera function and, if necessary, a dedicated application. The application includes a user-friendly interface and an automatic data saving function.

[0887] The terminal transmits the recorded data to the server using a secure communication protocol. This communication uses common protocols such as HTTP or HTTPS. The transmitted data is compressed in preparation for the analysis process on the server.

[0888] The server analyzes the transmitted data using a machine learning framework. Specifically, it utilizes libraries such as TensorFlow and PyTorch to analyze the pet's behavior patterns, facial expressions, and voice, and to evaluate its health in detail. If an anomaly is detected in the analysis results, the server immediately notifies the user. This notification is provided via email or push notification through a dedicated app.

[0889] Furthermore, users can send questions about pet training to the server via voice or text through their device. The server then uses an emotion engine to analyze the user's emotions from their voice tone and selected words. Natural language processing techniques are used for this emotion analysis. IBM Watson and other similar tools may be helpful in this process.

[0890] After the emotions are analyzed, the server uses a generative AI model to generate advice that is appropriate to the user's emotions. OpenAI's GPT-3 is one example of a generative AI model used. If a user consults the server about the problem of "not being able to stop their dog from barking excessively," the server can recognize that frustration and provide advice that is empathetic to their feelings.

[0891] Examples of prompts include, "If the user is feeling stressed, please tell me how to improve their pet's behavior in a gentle tone." Based on this prompt, the AI ​​generates a response that matches the user's emotions and immediately notifies the device. This allows the user to smoothly manage and train their pet's health.

[0892] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0893] Step 1:

[0894] Users record their pet's movements using a smartphone or dedicated device. The input is video data obtained using a camera, and the output is video data stored on the device. This process utilizes the device's built-in camera or a dedicated recording app, and begins when the user presses a record button.

[0895] Step 2:

[0896] The terminal transmits recorded video data to the server using a secure communication protocol. Recorded video data is used as input, and the output is compressed data uploaded to the server. Here, a data compression algorithm is executed to improve communication efficiency, and the data is transmitted via a secure protocol such as HTTPS.

[0897] Step 3:

[0898] The server analyzes the transmitted data and assesses the pet's health. Compressed video data is used as input, and the output is the health assessment result. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis, performing behavioral pattern analysis and anomaly detection frame by frame.

[0899] Step 4:

[0900] The server notifies the user if an anomaly is detected based on the analysis results. The analysis results are used as input, and the output is a notification message indicating the anomaly. Notifications are sent via email or push notifications through a dedicated app.

[0901] Step 5:

[0902] Users consult a server via their device regarding pet training issues. Input can be in the form of voice or text questions. Voice questions are recorded via a microphone, while text questions are entered within the app.

[0903] Step 6:

[0904] The server uses an emotion engine to analyze the user's emotions in response to received questions. Input can be either audio or text data, and output is the analyzed emotion information. Natural language processing techniques are applied to the emotion analysis; in the case of audio, the analysis is based on intonation and tone, and in the case of text, it is based on vocabulary selection.

[0905] Step 7:

[0906] The server uses a generative AI model based on emotional information to generate user-appropriate advice. Emotional information and prompt text are used as input, and the output is user-appropriate advice. A generative AI model like OpenAI is utilized to generate appropriate content that is sensitive to the user's emotions, based on the prompt.

[0907] Step 8:

[0908] The server sends the generated advice to the user's terminal. The generated advice is used as input, and the output is the received message displayed on the user's terminal. This notification is in real time, allowing the user to immediately utilize the received advice.

[0909] (Application Example 2)

[0910] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0911] Traditional pet management systems have limited functions for monitoring pets' health and supporting training, and they cannot provide advice that is tailored to the owner's feelings. Therefore, there is a need to reduce owner stress while more accurately understanding the pet's health and taking appropriate action quickly.

[0912] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0913] In this invention, the server includes recording means for recording the pet's movements, processing means for processing the acquired information and evaluating the pet's health condition, and emotion analysis means for analyzing the user's emotions and generating advice corresponding to those emotions. This makes it possible to provide advice that is sensitive to the owner's emotions in real time, and to more effectively support the health management and training of pets.

[0914] A "recording device" is a system for observing a pet's behavior and accumulating that information.

[0915] "Acquisition means" refers to the process of transmitting recorded data to a server or other device to make it available for use.

[0916] "Processing means" refers to algorithms and mechanisms for analyzing acquired data and evaluating the health status and abnormal behavior of pets.

[0917] A "reporting mechanism" is a function that communicates appropriate information to the owner when an abnormality is detected based on the pet's health condition.

[0918] A "reception method" refers to an interface or system for receiving consultations and questions from users regarding dog training.

[0919] A "generation method" is a system for creating appropriate advice and suggestions based on the content of inquiries received through the reception method.

[0920] "Means of provision" refers to the process of presenting advice and information created by the means of generation to pet owners and enabling them to utilize it.

[0921] "Emotional analysis tools" refer to algorithms and technologies used to analyze a user's emotional state and provide appropriate feedback or responses.

[0922] This invention is a system for supporting pet health management and training, and is implemented using home robots and smart devices. The main components of the system include recording means, acquisition means, processing means, reporting means, receiving means, generation means, provision means, and emotion analysis means.

[0923] The server uses a home robot to capture images of pets' movements with a camera and stores the video data using a recording device. The recording device has the function of adding time data and location data to the acquired information. This information is transmitted to the server in real time through an acquisition device. The server uses a processing device to analyze this data and evaluate the pet's health condition and any abnormalities in its behavior.

[0924] If an abnormality is detected, the reporting system automatically informs the owner of the situation. When the owner seeks advice on training, they can send their consultation details to the reception system via the terminal. The server uses an emotion analysis system to analyze the user's emotions. Based on this analysis, the generation system generates appropriate advice, which is then presented to the owner through the delivery system.

[0925] As a concrete example, if a pet owner encounters a problem with excessive barking, they might input into the device, "I want to stop my pet from barking unnecessarily, but I'm feeling stressed. Please give me some advice on what to do." In this case, the emotion analysis system would detect the stress, and the generated advice would be something like, "First, take a deep breath, and then slowly begin training." In this way, it is possible to provide flexible support that responds to the user's emotional state.

[0926] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0927] Step 1:

[0928] The device records the pet's movements using a camera. The input is live video data from the camera, and the output is a recorded video file. Time and location data are added and stored as part of the video file.

[0929] Step 2:

[0930] The terminal sends the recorded video file to the server. The input is the video file, and the output is the completion of the data transfer to the server. An internet connection is used for data transmission.

[0931] Step 3:

[0932] The server analyzes the received video data using processing tools. The input is the received video file, and the output is the evaluation result of the pet's health condition. An AI model analyzes the pet's movements in the video and detects signs of stress or abnormal behavior.

[0933] Step 4:

[0934] If the server detects an abnormality in the system's health status, it will notify the user using a reporting mechanism. The input is the analyzed abnormal data, and the output is a notification message to the user. The notification is delivered via a smartphone application.

[0935] Step 5:

[0936] The user submits a consultation request regarding training using their device. The input is the consultation content entered by the user in text or voice, and the output is confirmation that the consultation data has been successfully sent to the server.

[0937] Step 6:

[0938] The server analyzes the user's emotions using emotion analysis tools based on the consultation data. The input is the user's consultation content, and the output is a determination of the user's emotional state. The AI ​​model infers emotions from the tone and content of the words.

[0939] Step 7:

[0940] The server generates advice based on the results of emotion analysis. The input is the emotional state and the content of the consultation, and the output is the generated advice message. The content incorporates advice to alleviate stress.

[0941] Step 8:

[0942] The server notifies the user of the generated advice through a delivery mechanism. The input is the generated advice message, and the output is the presentation of the advice to the user. It is displayed on the terminal or read aloud.

[0943] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0944] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0945] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0946] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0947] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0948] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0949] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0950] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0951] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0952] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0953] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0954] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0955] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0956] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0957] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0958] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0959] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0960] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0961] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0962] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0963] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0964] The following is further disclosed regarding the embodiments described above.

[0965] (Claim 1)

[0966] A means of filming pet movements,

[0967] A receiving means for receiving captured data,

[0968] An analytical means that analyzes received data to determine the health status of the pet,

[0969] A notification means that notifies when an anomaly is detected based on the analysis results,

[0970] A means of receiving inquiries from users regarding discipline,

[0971] A means for generating appropriate advice in response to inquiries received,

[0972] A means for sending the generated advice to the user,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, wherein the shooting means includes means for adding time information and location information to the shooting data.

[0976] (Claim 3)

[0977] The system according to claim 1, wherein the analysis means includes means for generating notifications of different content according to the severity of the anomaly.

[0978] "Example 1"

[0979] (Claim 1)

[0980] A means of recording the behavior and condition of a pet,

[0981] A means for adding time information and location information to recorded information,

[0982] Means for transmitting the added information to a data processing device,

[0983] A means for analyzing received information through image and motion analysis using artificial intelligence,

[0984] A means for evaluating health status based on analysis results and providing warnings when signs of abnormality are detected,

[0985] A means of receiving information from users regarding training,

[0986] A means of generating instructional content using a generative model based on the information received,

[0987] A means of transmitting the generated instructional content,

[0988] A system that includes this.

[0989] (Claim 2)

[0990] The system according to claim 1, wherein the analysis means uses specific evaluation criteria to evaluate the health status based on the analysis results.

[0991] (Claim 3)

[0992] The system according to claim 1, wherein the generation means includes means for generating instructional content in natural language using a generative model.

[0993] "Application Example 1"

[0994] (Claim 1)

[0995] A means of filming pet movements,

[0996] A receiving means for receiving captured data,

[0997] An analytical means that analyzes received data to determine the health status of the pet,

[0998] A notification means that notifies when an anomaly is detected based on the analysis results,

[0999] A means of receiving inquiries from users regarding discipline,

[1000] A means for generating appropriate advice in response to inquiries received,

[1001] A means for sending the generated advice to the user,

[1002] A module that transmits the analyzed health status information to a communication device,

[1003] A display device that displays health information transmitted by the module,

[1004] A device that monitors pet behavior and records behavioral data,

[1005] A system that includes this.

[1006] (Claim 2)

[1007] The system according to claim 1, wherein the shooting means includes means for adding time information and location information to the shooting data.

[1008] (Claim 3)

[1009] The system according to claim 1, wherein the analysis means includes means for generating notifications of different content according to the severity of the anomaly.

[1010] "Example 2 of combining an emotion engine"

[1011] (Claim 1)

[1012] A recording device for recording the movements of a pet,

[1013] A means for transmitting recorded information,

[1014] An analytical means for analyzing transmitted information and evaluating the health status of animals,

[1015] A warning system that issues a warning when an anomaly is detected based on the analysis results,

[1016] A means of receiving questions from users regarding discipline,

[1017] A generation method that analyzes emotions in response to received questions and generates appropriate advice,

[1018] A notification means for sending the generated advice to the user,

[1019] A system that includes this.

[1020] (Claim 2)

[1021] The system according to claim 1, comprising natural language processing technology for analyzing emotions.

[1022] (Claim 3)

[1023] The system according to claim 1, wherein the generation means includes means for utilizing emotion-based prompt sentences using a generation AI model.

[1024] "Application example 2 when combining with an emotional engine"

[1025] (Claim 1)

[1026] A recording device for recording the movements of a pet,

[1027] A means of acquiring recorded information,

[1028] A processing means for processing acquired information and evaluating the health status of the pet,

[1029] A reporting means that reports when an anomaly is detected based on the processing results,

[1030] A means of receiving inquiries from users regarding training,

[1031] A means for generating appropriate advice in response to a consultation received,

[1032] A means of providing the generated advice to the user,

[1033] An emotion analysis means for analyzing the user's emotions and generating advice corresponding to those emotions,

[1034] A system that includes this.

[1035] (Claim 2)

[1036] The system according to claim 1, wherein the recording means includes means for adding time data and location data to the recorded information.

[1037] (Claim 3)

[1038] The system according to claim 1, wherein the processing means includes means for generating reports of different content according to the severity of the anomaly. [Explanation of Symbols]

[1039] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of filming pet movements, A receiving means for receiving captured data, An analytical means that analyzes received data to determine the health status of the pet, A notification means that notifies when an anomaly is detected based on the analysis results, A means of receiving inquiries from users regarding discipline, A means for generating appropriate advice in response to inquiries received, A means for sending the generated advice to the user, A module that transmits the analyzed health status information to a communication device, A display device that displays health information transmitted by the module, A device that monitors pet behavior and records behavioral data, A system that includes this.

2. The system according to claim 1, wherein the shooting means includes means for adding time information and location information to the shooting data.

3. The system according to claim 1, wherein the analysis means includes means for generating notifications of different content according to the severity of the anomaly.

Images