system

Abstract

We provide the system. [Solution] A means of obtaining animal image data from a terminal, A means of determining the health status of an animal by analyzing acquired image data, A means of sending an abnormality notification to the terminal based on the determined health status, A means of receiving inquiries about animal training and care via a terminal, A means of generating appropriate advice based on the consultation content and sending it to the terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including 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 as a 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] In recent years, coexistence with pets has become widespread, and pet health management and training have become important issues. However, it is often difficult for busy pet owners to secure time to directly consult veterinarians or experts. As a result, there is a possibility of overlooking the health condition of pets or inappropriate training. In such a situation, there is a demand for a system that enables pet owners to quickly and accurately receive support regarding pet health management and training while staying at home.

Means for Solving the Problems

[0005] This invention provides a system that transmits image data of animals captured by a terminal to a server and analyzes this image data to determine the health status of the animals. Furthermore, this system has a function to send a notification to the terminal when an abnormality in the animal's health status is detected. In addition, it receives consultations regarding animal training and care, generates appropriate advice using a machine learning model, and provides it to the terminal, thereby reducing the burden on pet owners and improving the quality of life for pets.

[0006] A "terminal" is an electronic device operated by the user to take pictures of pets and transmit the data.

[0007] "Image data" refers to digital information obtained from photographs and videos of animals taken with a device.

[0008] A "server" is a computer system that receives and analyzes image data, processes the information, and sends notifications to terminals.

[0009] "Health status" refers to the physical and mental health condition of an animal, and includes an assessment of whether it is normal or abnormal.

[0010] An "abnormality notification" is an informational message provided to the user when an abnormality is detected in the animal's health condition.

[0011] "Consultation" refers to the act of a user sending questions about training and care via their device.

[0012] "Advice" refers to suggestions and proposals provided in response to user inquiries using a generative AI model.

[0013] A "machine learning model" is a type of artificial intelligence technology that learns patterns from data to perform predictions and classifications. [Brief explanation of the drawing]

[0014] [Figure 1]It 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

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

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] 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), etc.

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for supporting pet health management and training. This system consists of a terminal used by the user on a daily basis and a server that processes and analyzes data.

[0036] The user first takes photos and videos of their pet using their device. This data is then sent from the device to a server. The server converts the received image data into a specific format and performs AI-based analysis. This analysis evaluates the pet's health and detects any abnormalities.

[0037] The server generates a notification indicating whether or not there is an abnormality based on the analysis results and sends it back to the terminal. The user can check this notification on the terminal and understand the health status of their pet. For example, if image analysis reveals an abnormal discoloration on the pet's skin, the server sends a message such as "An abnormality has been observed on the skin" to the terminal to inform the user.

[0038] Furthermore, users can input questions about training and care in text or voice format into their device. These questions are sent to a server, where they are analyzed by a machine learning model. The server then generates and sends optimal advice to the user's device, taking into account factors such as the type and age of the user's pet and their past consultation history. For example, if a user asks, "I want to stop my dog ​​from barking excessively," the AI ​​model will suggest specific training methods tailored to that dog breed.

[0039] This format allows users to constantly monitor their pets' health and receive expert advice on training from the comfort of their homes. This system improves the quality of life for pets and provides support to reduce the burden on pet owners.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users take pictures and videos of their pets using the device's camera. When taking pictures, they adjust the lighting and focus to ensure that the pet's condition is clearly visible.

[0043] Step 2:

[0044] The device saves the captured image data to local storage. Next, it encodes and compresses the saved data in order to send it to the server.

[0045] Step 3:

[0046] The server receives image data transmitted from the terminal. The received data is formatted to a format suitable for analysis by the AI ​​model.

[0047] Step 4:

[0048] The server inputs formatted image data into an AI model. The AI ​​model extracts features from the images and analyzes the pet's health condition. For example, it can detect eye redness or skin abnormalities.

[0049] Step 5:

[0050] The server evaluates the pet's health based on the results of the AI ​​analysis. If an abnormality is detected, it generates a notification that includes the nature of the abnormality and its severity.

[0051] Step 6:

[0052] The server sends the generated notification to the device. The device displays this notification to the user, providing information about the pet's health status. For example, it might display a message such as, "No abnormalities in the skin; please continue to check regularly."

[0053] Step 7:

[0054] Users input questions about training and care into the device. Input can be via text or voice, and the device sends this information to the server as text.

[0055] Step 8:

[0056] The server receives user questions and analyzes them using machine learning models. The analysis also takes into account information based on the pet's past data and breed.

[0057] Step 9:

[0058] The server generates the most appropriate advice based on the question and sends it to the terminal. The advice includes specific training methods and care instructions.

[0059] Step 10:

[0060] The device displays advice received from the server, allowing the user to review and implement it. The user can then provide care and discipline according to the advice.

[0061] (Example 1)

[0062] 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."

[0063] In modern times, many pet owners lack sufficient knowledge regarding animal health management and behavioral modification. As a result, the quality of life for their pets declines, and it becomes difficult for owners to take appropriate action. Furthermore, obtaining appropriate information often requires consulting with experts, which can be burdensome in terms of time and expense.

[0064] 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.

[0065] In this invention, the server includes means for acquiring visual data of an organism from an information terminal, means for analyzing the acquired visual data to evaluate the health status of the organism, and means for transmitting an abnormality warning to the information terminal based on the evaluated health status. This makes it possible to remotely and easily and quickly grasp the health status of an organism and take immediate action as needed.

[0066] An "information terminal" is a computer device used by a user for capturing, transmitting, and receiving data.

[0067] "Visual data of living organisms" refers to image or video data that records the state of living organisms and is used for analyzing their health status.

[0068] A "generative AI model" is an artificial intelligence inference model that learns from large amounts of data and performs data analysis and pattern recognition.

[0069] An "abnormality warning" is information indicating an unusual condition in the health of an organism, and is communicated to the user.

[0070] "Behavior modification" refers to methods and guidance aimed at improving or correcting undesirable behaviors in living organisms.

[0071] This invention is a system that uses digital devices to analyze the health status and behavior of living organisms and provides necessary notifications and guidance. It primarily consists of a configuration in which information terminals and servers work in conjunction.

[0072] Users first use information devices such as smartphones or tablets to capture visual data of living organisms. This includes images and videos of pets. The device then sends the captured data to a cloud server. Internet connectivity is used for transmission, and the data is transmitted securely via the HTTPS protocol.

[0073] The server is responsible for analyzing the received visual data. During this process, it uses generative AI models based on machine learning frameworks such as TENSORFLOW® and PyTorch to process the data. The AI ​​model on the server evaluates the health status of the organism and analyzes any abnormalities detected. Based on the analysis results, it generates an anomaly warning and sends a notification to the information terminal.

[0074] Furthermore, users can submit questions about modifying or caring for their pets through the information terminal. For example, if a user enters a prompt such as "I want to stop my dog ​​from barking unnecessarily," the server analyzes this information and generates appropriate guidance using a generative AI model. The server creates the guidance considering the species, age, and past data of the pet, and then sends it back to the information terminal.

[0075] This system provides a timely and convenient way for pet owners to manage the health and behavior of their animals. Examples of specific prompts include "I want to know how to cure my cat's constipation" and "I would like some advice on potty training my dog."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user captures visual data of the organism using an information terminal. The user uses a smartphone camera app to record the pet's activities as photos and videos. The input is image data of the organism, and this data is necessary for the next processing step.

[0079] Step 2:

[0080] The device sends the captured data to the server. The device uploads the image data to the cloud server using the HTTPS protocol via an internet connection. The input is the image data captured by the device, and the output is the data on the server to which it was sent.

[0081] Step 3:

[0082] The server converts the received data into a specific format. The server uses an image processing library to convert data from JPEG to a more easily analyzable format such as PNG. The input is compressed image data, and the output is data formatted for analysis.

[0083] Step 4:

[0084] The server analyzes data using a generated AI model. The server uses TensorFlow or PyTorch to evaluate the health status of organisms using the AI ​​model. Visual data provided by the user serves as input, and the output is a health status evaluation value as a result of the analysis.

[0085] Step 5:

[0086] The server generates an anomaly warning based on the analysis results and sends it to the terminal. The server then determines whether a warning is necessary based on the obtained evaluation values ​​and generates a warning message such as "An anomaly has been detected." This message is then sent back to the information terminal. The input is the analysis results, and the output is the warning message.

[0087] Step 6:

[0088] The user inputs questions about behavioral modification or care into an information terminal. The user sends the question to the terminal via text input or voice input. For example, the input might be a prompt such as, "Please tell me how to trim my cat's claws."

[0089] Step 7:

[0090] The server analyzes the question using a generating AI model, generates guidance, and sends it to the terminal. The server inputs the received question into the AI ​​model and constructs guidance that takes into account the type of organism and past data. The input is the user's question, and the output is specific advice as guidance content.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's busy lifestyle, support for pet health management and training is extremely important. However, it is a difficult task for pet owners to constantly monitor their pets' health and provide appropriate training. In particular, early detection of health problems in pets and mastering effective training methods require specialized knowledge. As a result, pet owners end up with unnecessary worries and burdens. Furthermore, many pet owners do not have the time to closely monitor their pets' condition due to work and family circumstances, which can lead to a decline in the pets' quality of life.

[0094] 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.

[0095] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, means for sending an abnormality notification to the terminal based on the determined health status, means for receiving consultations regarding animal training and care from the terminal, means for generating appropriate advice based on the consultation content and sending it to the terminal, means for using an autonomous mobile device that tracks the animal's condition in real time and notifies of abnormalities by analyzing the obtained data, and means for using speech synthesis technology to communicate the notification content to the user by voice. As a result, pet owners can constantly monitor their pet's health status and easily obtain expert advice on training at home.

[0096] A "terminal" is a communication device used to capture image data of animals and transmit that data to a server.

[0097] "Image data" refers to digital information that visually captures the appearance of an animal and is used to assess its health condition.

[0098] "Health status" refers to the overall physical condition of an animal, including any physical abnormalities or signs of disease.

[0099] "Notification" refers to a method of informing the user of the animal's condition by sending information determined by the server to the terminal.

[0100] An "autonomous mobile device" is a robotic device that tracks the movements of animals while collecting necessary data.

[0101] "Speech synthesis technology" is a technology that converts text information into speech and conveys it to the user.

[0102] A "machine learning model" is a type of artificial intelligence used for data analysis and decision-making, enabling predictions and classifications based on past data.

[0103] This invention is a system for supporting pet health management and training. The system consists of a user terminal, an autonomous mobile unit, and a server.

[0104] First, the user uses their device to acquire image data of their pet. The image data is sent to a server, which analyzes it using a machine learning model to determine the pet's health status. Based on the health status, an abnormality notification is generated and sent to the device. This allows the user to constantly monitor their pet's health.

[0105] Furthermore, users can input questions about pet training and care through their devices. The input questions are sent to a server, where a machine learning model generates optimal advice based on past data and the pet's characteristics, and then sends this advice back to the device.

[0106] The autonomous mobile device tracks the pet's movements in real time and transmits the data captured by its camera to a server, enabling early detection of any abnormalities in the pet. If an abnormality is detected, this data can be used to directly inform the user via speech synthesis technology through a notification from the server.

[0107] For example, if a pet's loss of appetite is detected, entering a specific prompt such as "My 5-year-old Labrador has recently lost its appetite. Please provide advice on managing its health and training suggestions" will immediately provide expert advice.

[0108] Thus, the system of the present invention utilizes hardware such as a consumer robot, a user terminal, and AI technology to monitor a pet's health in real time and support appropriate training. This configuration reduces the burden on pet owners and improves the quality of life for pets.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a device to acquire image data of their pet. The device uses its camera to take still images and videos of the pet and sends the data to the server. The input is the pet's image data, and the output is the data sent to the server.

[0112] Step 2:

[0113] The server analyzes the received image data using a machine learning model. The input is image data sent from the terminal, and the model extracts data features to determine the pet's health status. The output is a diagnostic result regarding the pet's health status.

[0114] Step 3:

[0115] The server determines whether there are any abnormalities based on the health status and sends the result as a notification to the terminal. The input is the diagnostic result obtained through analysis, and the output is an abnormality notification message.

[0116] Step 4:

[0117] Users input questions about pet training and care via text or voice through their device. The input is the user's question, and the output is data transferred to the server.

[0118] Step 5:

[0119] The server analyzes the input question using a machine learning model and generates optimal advice based on the pet's characteristics and past data. The input is the question submitted by the user, and the output is the generated advice message.

[0120] Step 6:

[0121] The server sends the generated advice to the terminal, which then displays or audibly communicates the content to the user for confirmation. The input is the advice message from the server, and the output is the notification to the user.

[0122] Step 7:

[0123] An autonomous mobile device tracks the pet's movements and transmits the data captured by its camera to a server in real time. The input is real-time video of the pet, and the output is the data sent to the server.

[0124] Step 8:

[0125] The server analyzes data from the autonomous mobile device, and if an anomaly is detected, it generates a notification using speech synthesis technology and informs the user via voice. The input is the analyzed data, and the output is the voice notification.

[0126] 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.

[0127] This invention provides a system that offers support for pet health management and training, and in particular, enables more personalized information delivery by incorporating an emotion engine that recognizes the user's emotions. This system consists of a terminal used by the user and a server that analyzes the data.

[0128] The user takes photos and videos of their pet using the camera on their device. The captured image data is sent from the device to the server. On the server, the image data is analyzed using an AI model to evaluate the pet's health. Depending on the information obtained from the health check, if a normal or abnormal result is detected, the result is notified to the device. For example, if the image analysis detects an abnormality in the pet's skin, the server generates a message such as "There is an abnormality in the skin; a veterinary examination is recommended" and sends it to the device.

[0129] The emotion engine, a key feature of this invention, uses voice and text data received by the terminal from the user to infer the user's emotions. When the user inputs a question about training or care, the emotion engine analyzes the emotion and sends it to the server. For example, if the emotion engine determines that the user is stressed about a problem with their pet, the server generates advice in a tone appropriate to that emotion. This might be something like, "Let's stay calm. Pay attention to specific behavioral patterns and try the following methods."

[0130] The server sends emotionally optimized advice to the device, allowing the user to review it and try appropriate training methods. For example, if a user emotionally inputs "My pet just won't stop barking," the system detects that emotion and provides a response that combines comfort and specific actions.

[0131] In this way, support that takes into account the user's own emotional state is possible, resulting in a comprehensive pet care system that not only monitors the pet's condition but also enriches the user experience.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user uses the device to take photos and videos of their pet. Once the shooting is complete, the device saves the data and prepares to send it to the server.

[0135] Step 2:

[0136] The device sends the stored image data to the server. During this process, compression is performed to optimize the data size.

[0137] Step 3:

[0138] The server receives image data sent from the terminal and converts the data into a format that the AI ​​model can analyze.

[0139] Step 4:

[0140] The server inputs the converted image data into an AI model to perform a health analysis. The AI ​​model meticulously analyzes the condition of the pet's eyes and skin to detect signs of health abnormalities.

[0141] Step 5:

[0142] The server evaluates the pet's health based on the analysis results and generates a notification message with details of any abnormalities.

[0143] Step 6:

[0144] The server sends the generated notification to the device. The device displays the notification to the user, allowing the user to understand the pet's current health status.

[0145] Step 7:

[0146] When a user enters a question about pet training or care into the device, the device sends that input data (text or voice) to the emotion engine.

[0147] Step 8:

[0148] The emotion engine analyzes received user data and estimates the user's emotional state. It identifies emotions such as stress and anxiety and transmits the results to the server.

[0149] Step 9:

[0150] The server generates optimal advice in response to the user's question, taking into account the analysis results of the emotion engine. The tone of this advice is adjusted to match the user's emotional state.

[0151] Step 10:

[0152] The server sends the generated advice to the terminal, which then displays the advice to the user. The user can then follow the advice to train and care for their pet.

[0153] (Example 2)

[0154] 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".

[0155] Monitoring and caring for pet health is a crucial issue for many pet owners. However, conventional technology struggles to accurately assess a pet's health and provide training and care advice that takes the owner's feelings into consideration. Without appropriate support that considers both the owner's feelings and the pet's condition, stress and misunderstandings can arise.

[0156] 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.

[0157] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, and means for analyzing emotional data received from the terminal to infer the user's emotions. This enables monitoring of the pet's health while simultaneously providing personalized advice that takes emotions into account, allowing for appropriate management and support of both.

[0158] A "terminal" refers to an input / output device that the user directly operates, such as a device used to acquire pet image data or send consultation details.

[0159] "Image data" refers to a digital file format that records visual information about animals and is used for analyzing their health status.

[0160] A "server" is a central processing unit that receives data from terminals via a network, performs analysis and processing, and transmits the results back to the terminals.

[0161] "Means for determining health status" refers to technical components used to evaluate the physical condition and any abnormalities of an animal by analyzing acquired image data.

[0162] "Emotional data" refers to information about emotions obtained from a user's language expressions and voice, and is used to analyze the user's psychological state.

[0163] "Means of inferring emotions" refer to technical components that analyze user emotional data, understand emotions, and reflect them in advice.

[0164] "Means of generating advice" refers to a function that creates personalized advice based on analysis results and sends it to the device.

[0165] This invention is a system for providing advice that takes into account the health management of pets and the feelings of their owners. The system mainly consists of terminals and a server.

[0166] The terminal is a user-operated device equipped with a camera for taking photos and videos of pets. Image data captured by this camera is sent to a server via a data transmission function. The terminal also includes an interface for users to input questions and emotions related to their pets. It supports both voice and text input.

[0167] The server is responsible for analyzing image data sent from the device. The AI ​​model running on the server is based on machine learning frameworks such as TensorFlow and PyTorch, and analyzes the image data to determine the pet's health status. Based on this analysis, if an abnormality in the health status is detected, it generates an appropriate notification and sends it to the device.

[0168] Furthermore, the server analyzes the user's emotional data received from the terminal. This uses natural language processing technology to infer the user's emotions and reflect them in appropriate advice. The advice is personalized by a generative AI model and delivered in an emotionally sensitive manner. For example, if a user inputs "My pet just won't stop barking," the system analyzes the stress the user is feeling from this statement and generates specific advice such as, "If your pet continues to bark unnecessarily, you can train it to stop by ignoring it. Stay calm and keep trying."

[0169] This system not only monitors the health of pets but also provides support that takes into account the user's feelings, thereby supporting a richer life for both pets and their owners.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user takes photos and videos of their pet using the device's camera. When taking a picture, the device temporarily saves the image data to its internal memory. The image data moves to the next step when the user performs a send operation through the application.

[0173] Step 2:

[0174] The terminal transmits image data stored in its internal memory to a server via the internet. The input is image data, and the output is a data stream sent to the server. In this process, the terminal uses a data transfer protocol to ensure reliable data transmission.

[0175] Step 3:

[0176] The server receives the incoming image data and analyzes it using an AI model. It takes image data as input and performs data calculations to evaluate the pet's health using image analysis technology. The output is a judgment regarding the pet's health. Specifically, if an abnormality is found, it identifies the affected area and type.

[0177] Step 4:

[0178] Based on the analysis results, the server generates an abnormality report message if an abnormality is detected in the health status. The input is the result of the image analysis, and the output is the message text. The server then prepares to send the generated abnormality report message to the terminal.

[0179] Step 5:

[0180] The terminal receives an anomaly report message sent from the server and notifies the user. The terminal uses its notification function to display the message to the user and issue a warning alarm if necessary. The output is a visual and auditory presentation of the message to the user.

[0181] Step 6:

[0182] The user uses the device to input questions and feelings about pet training and care via voice or text. The input data is recorded on the device and passed to the next processing step. An example of input is a prompt phrase such as "My pet just won't stop barking."

[0183] Step 7:

[0184] The terminal sends the user's questions and sentiment data to the server. The data is collected by the terminal as input and becomes a data stream sent to the server as output. The terminal processes the data and sends it to the server in the correct format.

[0185] Step 8:

[0186] The server receives user emotion data and performs emotion analysis using natural language processing technology. The input is text data containing emotions, and the output is an estimated result of the user's emotional state. Through emotion analysis, data is generated to determine appropriate countermeasures based on the user's psychological state.

[0187] Step 9:

[0188] The server generates appropriate advice based on the sentiment analysis results. The advice is formulated by referencing the sentiment estimation results as input, and a text message is created to send to the user as output. The generating AI model optimizes this prompt and adjusts the advice to match the user's emotional tone.

[0189] Step 10:

[0190] The device receives advice messages sent from the server and notifies the user of their content. Along with displaying the message, the device sets a notification sound if necessary, providing the user with visual and auditory feedback as output. Based on this, the user can attempt training or care.

[0191] (Application Example 2)

[0192] 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".

[0193] In pet health management, owners are required to have a detailed understanding of their pet's condition and to provide appropriate training and care. However, it is not easy to constantly monitor a pet's health in daily life and to receive personalized advice that takes into account the owner's own emotional state. Conventional systems have the function to analyze the pet's condition, but they lack the function to generate advice that takes the owner's emotions into account, and therefore have the problem of not being able to provide comprehensive support that is easy for owners to use.

[0194] 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.

[0195] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the image data to determine the animal's health status, and emotion analysis means for analyzing the user's emotional state. This makes it possible to monitor the pet's health status and provide specific and helpful advice optimized according to the user's emotions.

[0196] A "terminal" refers to an electronic device used to manipulate and send / receive digital data.

[0197] "Animal image data" refers to visual information represented in the form of photographs or videos of animals, such as pets.

[0198] "Health status" refers to information that describes an animal's physical condition or signs of illness.

[0199] An "abnormality notification" refers to a message that alerts the user when a problem is detected in an animal's health.

[0200] "Consultation" refers to communication that pet owners engage in to seek advice or address questions regarding their pet's training or health.

[0201] "Advice" refers to instructions that include suggestions and recommendations regarding training and care.

[0202] "Emotional analysis tools" refer to functions that perform a process to analyze and understand a user's emotional state based on data.

[0203] A "machine learning model" refers to an algorithm that learns specific patterns based on large amounts of data and uses them for prediction and classification.

[0204] "Optimization" refers to the process of adjusting the output and operation of a system according to its purpose and conditions in order to obtain better results.

[0205] The system implementing this invention is intended for animal health management and support for pet owners. It is primarily realized using terminals, servers, and emotion analysis means and generative AI models that function between them.

[0206] The server first receives image data of the animal sent from the terminal. The image data is analyzed by a machine learning model implemented on the server to determine the animal's health status. If, for example, a skin abnormality or signs of poor health are detected, an abnormality notification is issued. Specifically, a message such as "A skin abnormality has been observed, so a veterinary examination is recommended" is sent to the terminal.

[0207] In addition, when users input questions about pet training and care through their devices, that data is also sent to the server. The server uses emotion analysis to infer the user's emotional state and generates advice based on that information. In this process, a generative AI model is used to generate specific and optimized advice that is tailored to the user's emotions. For example, if a user inputs "My pet just won't stop barking" along with their stress, they will be given advice in a tone such as, "Let's deal with this calmly. Pay attention to specific behavioral patterns and try the following methods."

[0208] The hardware used will include a device equipped with a camera and a Wi-Fi module for network communication. The software may utilize machine learning frameworks such as TensorFlow and natural language processing algorithms for sentiment analysis.

[0209] As a result, users can stay informed about their pet's health in a timely manner and receive advice based on their own feelings. This enables comprehensive pet care that goes beyond simple monitoring.

[0210] An example of a prompt message for a generative AI model is, "Generate a message that will comfort and offer helpful advice when the user is agitated." In this way, the system can provide a service that takes into account both the animal's health and the owner's feelings.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The device acquires image data of animals. The user takes photos and videos of their pet using the device's camera and saves the data to the device. The input is the camera device and the captured image data, and the output is the data saved on the device.

[0214] Step 2:

[0215] The device sends image data to the server. The device sends captured image data to the server over the network via Wi-Fi. The input is the image data stored on the device, and the output is the image data received by the server.

[0216] Step 3:

[0217] The server receives image data and performs analysis. The server inputs the image data into a machine learning model to analyze the characteristics of the animal's health. The input is the image data received by the server, and the output is the analysis results indicating the health status. If an abnormality is detected, the specific abnormality is identified.

[0218] Step 4:

[0219] The server generates notifications based on the animal's health status when abnormalities occur. Based on the analysis results, the server creates messages such as, "Skin abnormalities detected; veterinary consultation is recommended." The input is the analyzed health status data, and the output is the generated notification message.

[0220] Step 5:

[0221] The user inputs their consultation details from their device and sends them to the server. The user inputs their questions about pet training and care as text into their device and sends them to the server. The input is the user's text data, and the output is the consultation content sent to the server.

[0222] Step 6:

[0223] The server analyzes the content of the consultation and the user's emotions. The server processes the text data using emotion analysis tools to infer the user's emotional state. The input is the received consultation content, and the output is the inferred emotional state of the user.

[0224] Step 7:

[0225] The server generates advice based on the user's emotional state. Using a generative AI model, it creates specific advice tailored to the user's emotional state. For example, a user experiencing stress might be advised to "calm down and handle the situation calmly." The input is the user's emotional state and the content of their inquiry, while the output is optimized advice.

[0226] Step 8:

[0227] The server sends the generated advice to the terminal. The server sends the advice it has created to the terminal via the network and displays it to the user. The input is the generated advice, and the output is the advice displayed on the terminal.

[0228] These processing steps enable the management of pet health and the provision of advice tailored to the user's emotions.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention provides a system for supporting pet health management and training. This system consists of a terminal used by the user on a daily basis and a server that processes and analyzes data.

[0246] The user first takes photos and videos of their pet using their device. This data is then sent from the device to a server. The server converts the received image data into a specific format and performs AI-based analysis. This analysis evaluates the pet's health and detects any abnormalities.

[0247] The server generates a notification indicating whether or not there is an abnormality based on the analysis results and sends it back to the terminal. The user can check this notification on the terminal and understand the health status of their pet. For example, if image analysis reveals an abnormal discoloration on the pet's skin, the server sends a message such as "An abnormality has been observed on the skin" to the terminal to inform the user.

[0248] Furthermore, users can input questions about training and care in text or voice format into their device. These questions are sent to a server, where they are analyzed by a machine learning model. The server then generates and sends optimal advice to the user's device, taking into account factors such as the type and age of the user's pet and their past consultation history. For example, if a user asks, "I want to stop my dog ​​from barking excessively," the AI ​​model will suggest specific training methods tailored to that dog breed.

[0249] This format allows users to constantly monitor their pets' health and receive expert advice on training from the comfort of their homes. This system improves the quality of life for pets and provides support to reduce the burden on pet owners.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users take pictures and videos of their pets using the device's camera. When taking pictures, they adjust the lighting and focus to ensure that the pet's condition is clearly visible.

[0253] Step 2:

[0254] The device saves the captured image data to local storage. Next, it encodes and compresses the saved data in order to send it to the server.

[0255] Step 3:

[0256] The server receives image data transmitted from the terminal. The received data is formatted to a format suitable for analysis by the AI ​​model.

[0257] Step 4:

[0258] The server inputs formatted image data into an AI model. The AI ​​model extracts features from the images and analyzes the pet's health condition. For example, it can detect eye redness or skin abnormalities.

[0259] Step 5:

[0260] The server evaluates the pet's health based on the results of the AI ​​analysis. If an abnormality is detected, it generates a notification that includes the nature of the abnormality and its severity.

[0261] Step 6:

[0262] The server sends the generated notification to the device. The device displays this notification to the user, providing information about the pet's health status. For example, it might display a message such as, "No abnormalities in the skin; please continue to check regularly."

[0263] Step 7:

[0264] Users input questions about training and care into the device. Input can be via text or voice, and the device sends this information to the server as text.

[0265] Step 8:

[0266] The server receives user questions and analyzes them using machine learning models. The analysis also takes into account information based on the pet's past data and breed.

[0267] Step 9:

[0268] The server generates the most appropriate advice based on the question and sends it to the terminal. The advice includes specific training methods and care instructions.

[0269] Step 10:

[0270] The device displays advice received from the server, allowing the user to review and implement it. The user can then provide care and discipline according to the advice.

[0271] (Example 1)

[0272] 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."

[0273] In modern times, many pet owners lack sufficient knowledge regarding animal health management and behavioral modification. As a result, the quality of life for their pets declines, and it becomes difficult for owners to take appropriate action. Furthermore, obtaining appropriate information often requires consulting with experts, which can be burdensome in terms of time and expense.

[0274] 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.

[0275] In this invention, the server includes means for acquiring visual data of an organism from an information terminal, means for analyzing the acquired visual data to evaluate the health status of the organism, and means for transmitting an abnormality warning to the information terminal based on the evaluated health status. This makes it possible to remotely and easily and quickly grasp the health status of an organism and take immediate action as needed.

[0276] An "information terminal" is a computer device used by a user for capturing, transmitting, and receiving data.

[0277] "Visual data of living organisms" refers to image or video data that records the state of living organisms and is used for analyzing their health status.

[0278] A "generative AI model" is an artificial intelligence inference model that learns from large amounts of data and performs data analysis and pattern recognition.

[0279] An "abnormality warning" is information indicating an unusual condition in the health of an organism, and is communicated to the user.

[0280] "Behavior modification" refers to methods and guidance aimed at improving or correcting undesirable behaviors in living organisms.

[0281] This invention is a system that uses digital devices to analyze the health status and behavior of living organisms and provides necessary notifications and guidance. It primarily consists of a configuration in which information terminals and servers work in conjunction.

[0282] Users first use information devices such as smartphones or tablets to capture visual data of living organisms. This includes images and videos of pets. The device then sends the captured data to a cloud server. Internet connectivity is used for transmission, and the data is transmitted securely via the HTTPS protocol.

[0283] The server is responsible for analyzing the received visual data. It processes the data using generative AI models based on machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model on the server evaluates the health status of the organism and analyzes any abnormalities detected. Based on the analysis results, it generates an anomaly warning and sends a notification to the information terminal.

[0284] Furthermore, through the information terminal, the user can send questions regarding the behavior modification and care of organisms. For example, when the user inputs a prompt sentence such as "I want to stop my dog from barking uselessly", the server analyzes this information and generates appropriate guidance using the generated AI model. The server creates the guidance while considering the type and age of the organism and past data, and sends it back to the information terminal again.

[0285] This system timely manages the health and behavior of organisms and provides a comfortable and efficient method for the owner. Examples of specific prompt sentences include "I want to know how to cure my cat's constipation" and "I would like advice on my dog's potty training".

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The user takes visual data of the organism with the information terminal. The user uses the camera app of the smartphone to record the activities of the pet as photos or videos. The input is the image data of the organism, and this data is required for the following processing.

[0289] Step 2:

[0290] The terminal sends the captured data to the server. The terminal uploads the image data to the cloud server using the HTTPS protocol via the Internet connection. The input is the image data captured by the terminal, and the output is the data on the server that has been sent.

[0291] Step 3:

[0292] The server converts the received data into a specific format. The server uses an image processing library to convert from JPEG to a format such as PNG that is easy to analyze. The input is the compressed image data, and the output is the data formatted for analysis.

[0293] Step 4:

[0294] The server analyzes data using a generated AI model. The server uses TensorFlow or PyTorch to evaluate the health status of organisms using the AI ​​model. Visual data provided by the user serves as input, and the output is a health status evaluation value as a result of the analysis.

[0295] Step 5:

[0296] The server generates an anomaly warning based on the analysis results and sends it to the terminal. The server then determines whether a warning is necessary based on the obtained evaluation values ​​and generates a warning message such as "An anomaly has been detected." This message is then sent back to the information terminal. The input is the analysis results, and the output is the warning message.

[0297] Step 6:

[0298] The user inputs questions about behavioral modification or care into an information terminal. The user sends the question to the terminal via text input or voice input. For example, the input might be a prompt such as, "Please tell me how to trim my cat's claws."

[0299] Step 7:

[0300] The server analyzes the question using a generating AI model, generates guidance, and sends it to the terminal. The server inputs the received question into the AI ​​model and constructs guidance that takes into account the type of organism and past data. The input is the user's question, and the output is specific advice as guidance content.

[0301] (Application Example 1)

[0302] 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."

[0303] In a modern busy living environment, support regarding the health management and training of pets is extremely important. However, it is a difficult task for pet owners to constantly monitor the health condition of their pets and conduct appropriate training. Especially when it comes to early detection of health abnormalities in pets and acquisition of accurate training methods, specialized knowledge is required. As a result, pet owners will bear unnecessary worries and burdens. Also, since many pet owners do not have the time to carefully grasp the condition of their pets due to work or family circumstances, there is a risk that the quality of life of the pets themselves will decline.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for acquiring image data of an animal from a terminal, means for analyzing the acquired image data to judge the health condition of the animal, means for transmitting a notification of an abnormality to the terminal based on the judged health condition, means for receiving consultations regarding the training and care of the animal from the terminal, means for generating appropriate advice based on the consultation content and transmitting it to the terminal, means for using an autonomous mobile body that tracks the situation of the animal in real time and notifies an abnormality by analyzing the obtained data, and means for using a voice synthesis technology for communicating the notification content to the user by voice. Thereby, pet owners can constantly monitor the health condition of their pets and easily obtain specialized advice regarding training at home.

[0306] The "terminal" is a communication device for taking image data of an animal and transmitting the data to the server.

[0307] The "image data" refers to digital information that visually captures the appearance of an animal and is used for judging the health condition.

[0308] The "health condition" refers to the general physical condition of an animal, including signs of physical abnormalities and diseases.

[0309] "Notification" refers to a method of informing the user of the animal's condition by sending information determined by the server to the terminal.

[0310] An "autonomous mobile device" is a robotic device that tracks the movements of animals while collecting necessary data.

[0311] "Speech synthesis technology" is a technology that converts text information into speech and conveys it to the user.

[0312] A "machine learning model" is a type of artificial intelligence used for data analysis and decision-making, enabling predictions and classifications based on past data.

[0313] This invention is a system for supporting pet health management and training. The system consists of a user terminal, an autonomous mobile unit, and a server.

[0314] First, the user uses their device to acquire image data of their pet. The image data is sent to a server, which analyzes it using a machine learning model to determine the pet's health status. Based on the health status, an abnormality notification is generated and sent to the device. This allows the user to constantly monitor their pet's health.

[0315] Furthermore, users can input questions about pet training and care through their devices. The input questions are sent to a server, where a machine learning model generates optimal advice based on past data and the pet's characteristics, and then sends this advice back to the device.

[0316] The autonomous mobile device tracks the pet's movements in real time and transmits the data captured by its camera to a server, enabling early detection of any abnormalities in the pet. If an abnormality is detected, this data can be used to directly inform the user via speech synthesis technology through a notification from the server.

[0317] For example, if a pet's loss of appetite is detected, entering a specific prompt such as "My 5-year-old Labrador has recently lost its appetite. Please provide advice on managing its health and training suggestions" will immediately provide expert advice.

[0318] Thus, the system of the present invention utilizes hardware such as a consumer robot, a user terminal, and AI technology to monitor a pet's health in real time and support appropriate training. This configuration reduces the burden on pet owners and improves the quality of life for pets.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user uses a device to acquire image data of their pet. The device uses its camera to take still images and videos of the pet and sends the data to the server. The input is the pet's image data, and the output is the data sent to the server.

[0322] Step 2:

[0323] The server analyzes the received image data using a machine learning model. The input is image data sent from the terminal, and the model extracts data features to determine the pet's health status. The output is a diagnostic result regarding the pet's health status.

[0324] Step 3:

[0325] The server determines whether there are any abnormalities based on the health status and sends the result as a notification to the terminal. The input is the diagnostic result obtained through analysis, and the output is an abnormality notification message.

[0326] Step 4:

[0327] Users input questions about pet training and care via text or voice through their device. The input is the user's question, and the output is data transferred to the server.

[0328] Step 5:

[0329] The server analyzes the input question using a machine learning model and generates optimal advice based on the pet's characteristics and past data. The input is the question submitted by the user, and the output is the generated advice message.

[0330] Step 6:

[0331] The server sends the generated advice to the terminal, which then displays or audibly communicates the content to the user for confirmation. The input is the advice message from the server, and the output is the notification to the user.

[0332] Step 7:

[0333] An autonomous mobile device tracks the pet's movements and transmits the data captured by its camera to a server in real time. The input is real-time video of the pet, and the output is the data sent to the server.

[0334] Step 8:

[0335] The server analyzes data from the autonomous mobile device, and if an anomaly is detected, it generates a notification using speech synthesis technology and informs the user via voice. The input is the analyzed data, and the output is the voice notification.

[0336] 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.

[0337] This invention provides a system that offers support for pet health management and training, and in particular, enables more personalized information delivery by incorporating an emotion engine that recognizes the user's emotions. This system consists of a terminal used by the user and a server that analyzes the data.

[0338] The user takes photos and videos of their pet using the camera on their device. The captured image data is sent from the device to the server. On the server, the image data is analyzed using an AI model to evaluate the pet's health. Depending on the information obtained from the health check, if a normal or abnormal result is detected, the result is notified to the device. For example, if the image analysis detects an abnormality in the pet's skin, the server generates a message such as "There is an abnormality in the skin; a veterinary examination is recommended" and sends it to the device.

[0339] The emotion engine, a key feature of this invention, uses voice and text data received by the terminal from the user to infer the user's emotions. When the user inputs a question about training or care, the emotion engine analyzes the emotion and sends it to the server. For example, if the emotion engine determines that the user is stressed about a problem with their pet, the server generates advice in a tone appropriate to that emotion. This might be something like, "Let's stay calm. Pay attention to specific behavioral patterns and try the following methods."

[0340] The server sends emotionally optimized advice to the device, allowing the user to review it and try appropriate training methods. For example, if a user emotionally inputs "My pet just won't stop barking," the system detects that emotion and provides a response that combines comfort and specific actions.

[0341] In this way, support that takes into account the user's own emotional state is possible, resulting in a comprehensive pet care system that not only monitors the pet's condition but also enriches the user experience.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user uses the device to take photos and videos of their pet. Once the shooting is complete, the device saves the data and prepares to send it to the server.

[0345] Step 2:

[0346] The device sends the stored image data to the server. During this process, compression is performed to optimize the data size.

[0347] Step 3:

[0348] The server receives image data sent from the terminal and converts the data into a format that the AI ​​model can analyze.

[0349] Step 4:

[0350] The server inputs the converted image data into an AI model to perform a health analysis. The AI ​​model meticulously analyzes the condition of the pet's eyes and skin to detect signs of health abnormalities.

[0351] Step 5:

[0352] The server evaluates the pet's health based on the analysis results and generates a notification message with details of any abnormalities.

[0353] Step 6:

[0354] The server sends the generated notification to the device. The device displays the notification to the user, allowing the user to understand the pet's current health status.

[0355] Step 7:

[0356] When a user enters a question about pet training or care into the device, the device sends that input data (text or voice) to the emotion engine.

[0357] Step 8:

[0358] The emotion engine analyzes received user data and estimates the user's emotional state. It identifies emotions such as stress and anxiety and transmits the results to the server.

[0359] Step 9:

[0360] The server generates optimal advice in response to the user's question, taking into account the analysis results of the emotion engine. The tone of this advice is adjusted to match the user's emotional state.

[0361] Step 10:

[0362] The server sends the generated advice to the terminal, which then displays the advice to the user. The user can then follow the advice to train and care for their pet.

[0363] (Example 2)

[0364] 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".

[0365] Monitoring and caring for pet health is a crucial issue for many pet owners. However, conventional technology struggles to accurately assess a pet's health and provide training and care advice that takes the owner's feelings into consideration. Without appropriate support that considers both the owner's feelings and the pet's condition, stress and misunderstandings can arise.

[0366] 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.

[0367] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, and means for analyzing emotional data received from the terminal to infer the user's emotions. This enables monitoring of the pet's health while simultaneously providing personalized advice that takes emotions into account, allowing for appropriate management and support of both.

[0368] A "terminal" refers to an input / output device that the user directly operates, such as a device used to acquire pet image data or send consultation details.

[0369] "Image data" refers to a digital file format that records visual information about animals and is used for analyzing their health status.

[0370] A "server" is a central processing unit that receives data from terminals via a network, performs analysis and processing, and transmits the results back to the terminals.

[0371] "Means for determining health status" refers to technical components used to evaluate the physical condition and any abnormalities of an animal by analyzing acquired image data.

[0372] "Emotional data" refers to information about emotions obtained from a user's language expressions and voice, and is used to analyze the user's psychological state.

[0373] "Means of inferring emotions" refer to technical components that analyze user emotional data, understand emotions, and reflect them in advice.

[0374] "Means of generating advice" refers to a function that creates personalized advice based on analysis results and sends it to the device.

[0375] This invention is a system for providing advice that takes into account the health management of pets and the feelings of their owners. The system mainly consists of terminals and a server.

[0376] The terminal is a user-operated device equipped with a camera for taking photos and videos of pets. Image data captured by this camera is sent to a server via a data transmission function. The terminal also includes an interface for users to input questions and emotions related to their pets. It supports both voice and text input.

[0377] The server is responsible for analyzing image data sent from the device. The AI ​​model running on the server is based on machine learning frameworks such as TensorFlow and PyTorch, and analyzes the image data to determine the pet's health status. Based on this analysis, if an abnormality in the health status is detected, it generates an appropriate notification and sends it to the device.

[0378] Furthermore, the server analyzes the user's emotional data received from the terminal. This uses natural language processing technology to infer the user's emotions and reflect them in appropriate advice. The advice is personalized by a generative AI model and delivered in an emotionally sensitive manner. For example, if a user inputs "My pet just won't stop barking," the system analyzes the stress the user is feeling from this statement and generates specific advice such as, "If your pet continues to bark unnecessarily, you can train it to stop by ignoring it. Stay calm and keep trying."

[0379] This system not only monitors the health of pets but also provides support that takes into account the user's feelings, thereby supporting a richer life for both pets and their owners.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user takes photos and videos of their pet using the device's camera. When taking a picture, the device temporarily saves the image data to its internal memory. The image data moves to the next step when the user performs a send operation through the application.

[0383] Step 2:

[0384] The terminal transmits image data stored in its internal memory to a server via the internet. The input is image data, and the output is a data stream sent to the server. In this process, the terminal uses a data transfer protocol to ensure reliable data transmission.

[0385] Step 3:

[0386] The server receives the incoming image data and analyzes it using an AI model. It takes image data as input and performs data calculations to evaluate the pet's health using image analysis technology. The output is a judgment regarding the pet's health. Specifically, if an abnormality is found, it identifies the affected area and type.

[0387] Step 4:

[0388] Based on the analysis results, the server generates an abnormality report message if an abnormality is detected in the health status. The input is the result of the image analysis, and the output is the message text. The server then prepares to send the generated abnormality report message to the terminal.

[0389] Step 5:

[0390] The terminal receives an anomaly report message sent from the server and notifies the user. The terminal uses its notification function to display the message to the user and issue a warning alarm if necessary. The output is a visual and auditory presentation of the message to the user.

[0391] Step 6:

[0392] The user uses the device to input questions and feelings about pet training and care via voice or text. The input data is recorded on the device and passed to the next processing step. An example of input is a prompt phrase such as "My pet just won't stop barking."

[0393] Step 7:

[0394] The terminal sends the user's questions and sentiment data to the server. The data is collected by the terminal as input and becomes a data stream sent to the server as output. The terminal processes the data and sends it to the server in the correct format.

[0395] Step 8:

[0396] The server receives user emotion data and performs emotion analysis using natural language processing technology. The input is text data containing emotions, and the output is an estimated result of the user's emotional state. Through emotion analysis, data is generated to determine appropriate countermeasures based on the user's psychological state.

[0397] Step 9:

[0398] The server generates appropriate advice based on the sentiment analysis results. The advice is formulated by referencing the sentiment estimation results as input, and a text message is created to send to the user as output. The generating AI model optimizes this prompt and adjusts the advice to match the user's emotional tone.

[0399] Step 10:

[0400] The device receives advice messages sent from the server and notifies the user of their content. Along with displaying the message, the device sets a notification sound if necessary, providing the user with visual and auditory feedback as output. Based on this, the user can attempt training or care.

[0401] (Application Example 2)

[0402] 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 will be referred to as the "terminal."

[0403] In pet health management, owners are required to have a detailed understanding of their pet's condition and to provide appropriate training and care. However, it is not easy to constantly monitor a pet's health in daily life and to receive personalized advice that takes into account the owner's own emotional state. Conventional systems have the function to analyze the pet's condition, but they lack the function to generate advice that takes the owner's emotions into account, and therefore have the problem of not being able to provide comprehensive support that is easy for owners to use.

[0404] 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.

[0405] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the image data to determine the animal's health status, and emotion analysis means for analyzing the user's emotional state. This makes it possible to monitor the pet's health status and provide specific and helpful advice optimized according to the user's emotions.

[0406] A "terminal" refers to an electronic device used to manipulate and send / receive digital data.

[0407] "Animal image data" refers to visual information represented in the form of photographs or videos of animals, such as pets.

[0408] "Health status" refers to information that describes an animal's physical condition or signs of illness.

[0409] An "abnormality notification" refers to a message that alerts the user when a problem is detected in an animal's health.

[0410] "Consultation" refers to communication that pet owners engage in to seek advice or address questions regarding their pet's training or health.

[0411] "Advice" refers to instructions that include suggestions and recommendations regarding training and care.

[0412] "Emotional analysis tools" refer to functions that perform a process to analyze and understand a user's emotional state based on data.

[0413] A "machine learning model" refers to an algorithm that learns specific patterns based on large amounts of data and uses them for prediction and classification.

[0414] "Optimization" refers to the process of adjusting the output and operation of a system according to its purpose and conditions in order to obtain better results.

[0415] The system implementing this invention is intended for animal health management and support for pet owners. It is primarily realized using terminals, servers, and emotion analysis means and generative AI models that function between them.

[0416] The server first receives image data of the animal sent from the terminal. The image data is analyzed by a machine learning model implemented on the server to determine the animal's health status. If, for example, a skin abnormality or signs of poor health are detected, an abnormality notification is issued. Specifically, a message such as "A skin abnormality has been observed, so a veterinary examination is recommended" is sent to the terminal.

[0417] In addition, when users input questions about pet training and care through their devices, that data is also sent to the server. The server uses emotion analysis to infer the user's emotional state and generates advice based on that information. In this process, a generative AI model is used to generate specific and optimized advice that is tailored to the user's emotions. For example, if a user inputs "My pet just won't stop barking" along with their stress, they will be given advice in a tone such as, "Let's deal with this calmly. Pay attention to specific behavioral patterns and try the following methods."

[0418] The hardware used will include a device equipped with a camera and a Wi-Fi module for network communication. The software may utilize machine learning frameworks such as TensorFlow and natural language processing algorithms for sentiment analysis.

[0419] As a result, users can stay informed about their pet's health in a timely manner and receive advice based on their own feelings. This enables comprehensive pet care that goes beyond simple monitoring.

[0420] An example of a prompt message for a generative AI model is, "Generate a message that will comfort and offer helpful advice when the user is agitated." In this way, the system can provide a service that takes into account both the animal's health and the owner's feelings.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The device acquires image data of animals. The user takes photos and videos of their pet using the device's camera and saves the data to the device. The input is the camera device and the captured image data, and the output is the data saved on the device.

[0424] Step 2:

[0425] The device sends image data to the server. The device sends captured image data to the server over the network via Wi-Fi. The input is the image data stored on the device, and the output is the image data received by the server.

[0426] Step 3:

[0427] The server receives image data and performs analysis. The server inputs the image data into a machine learning model to analyze the characteristics of the animal's health. The input is the image data received by the server, and the output is the analysis results indicating the health status. If an abnormality is detected, the specific abnormality is identified.

[0428] Step 4:

[0429] The server generates notifications based on the animal's health status when abnormalities occur. Based on the analysis results, the server creates messages such as, "Skin abnormalities detected; veterinary consultation is recommended." The input is the analyzed health status data, and the output is the generated notification message.

[0430] Step 5:

[0431] The user inputs their consultation details from their device and sends them to the server. The user inputs their questions about pet training and care as text into their device and sends them to the server. The input is the user's text data, and the output is the consultation content sent to the server.

[0432] Step 6:

[0433] The server analyzes the content of the consultation and the user's emotions. The server processes the text data using emotion analysis tools to infer the user's emotional state. The input is the received consultation content, and the output is the inferred emotional state of the user.

[0434] Step 7:

[0435] The server generates advice based on the user's emotional state. Using a generative AI model, it creates specific advice tailored to the user's emotional state. For example, a user experiencing stress might be advised to "calm down and handle the situation calmly." The input is the user's emotional state and the content of their inquiry, while the output is optimized advice.

[0436] Step 8:

[0437] The server sends the generated advice to the terminal. The server sends the advice it has created to the terminal via the network and displays it to the user. The input is the generated advice, and the output is the advice displayed on the terminal.

[0438] These processing steps enable the management of pet health and the provision of advice tailored to the user's emotions.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Third Embodiment]

[0443] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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".

[0455] This invention provides a system for supporting pet health management and training. This system consists of a terminal used by the user on a daily basis and a server that processes and analyzes data.

[0456] The user first takes photos and videos of their pet using their device. This data is then sent from the device to a server. The server converts the received image data into a specific format and performs AI-based analysis. This analysis evaluates the pet's health and detects any abnormalities.

[0457] The server generates a notification indicating whether or not there is an abnormality based on the analysis results and sends it back to the terminal. The user can check this notification on the terminal and understand the health status of their pet. For example, if image analysis reveals an abnormal discoloration on the pet's skin, the server sends a message such as "An abnormality has been observed on the skin" to the terminal to inform the user.

[0458] Furthermore, users can input questions about training and care in text or voice format into their device. These questions are sent to a server, where they are analyzed by a machine learning model. The server then generates and sends optimal advice to the user's device, taking into account factors such as the type and age of the user's pet and their past consultation history. For example, if a user asks, "I want to stop my dog ​​from barking excessively," the AI ​​model will suggest specific training methods tailored to that dog breed.

[0459] This format allows users to constantly monitor their pets' health and receive expert advice on training from the comfort of their homes. This system improves the quality of life for pets and provides support to reduce the burden on pet owners.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] Users take pictures and videos of their pets using the device's camera. When taking pictures, they adjust the lighting and focus to ensure that the pet's condition is clearly visible.

[0463] Step 2:

[0464] The device saves the captured image data to local storage. Next, it encodes and compresses the saved data in order to send it to the server.

[0465] Step 3:

[0466] The server receives image data transmitted from the terminal. The received data is formatted to a format suitable for analysis by the AI ​​model.

[0467] Step 4:

[0468] The server inputs formatted image data into an AI model. The AI ​​model extracts features from the images and analyzes the pet's health condition. For example, it can detect eye redness or skin abnormalities.

[0469] Step 5:

[0470] The server evaluates the pet's health based on the results of the AI ​​analysis. If an abnormality is detected, it generates a notification that includes the nature of the abnormality and its severity.

[0471] Step 6:

[0472] The server sends the generated notification to the device. The device displays this notification to the user, providing information about the pet's health status. For example, it might display a message such as, "No abnormalities in the skin; please continue to check regularly."

[0473] Step 7:

[0474] Users input questions about training and care into the device. Input can be via text or voice, and the device sends this information to the server as text.

[0475] Step 8:

[0476] The server receives user questions and analyzes them using machine learning models. The analysis also takes into account information based on the pet's past data and breed.

[0477] Step 9:

[0478] The server generates the most appropriate advice based on the question and sends it to the terminal. The advice includes specific training methods and care instructions.

[0479] Step 10:

[0480] The device displays advice received from the server, allowing the user to review and implement it. The user can then provide care and discipline according to the advice.

[0481] (Example 1)

[0482] 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."

[0483] In modern times, many pet owners lack sufficient knowledge regarding animal health management and behavioral modification. As a result, the quality of life for their pets declines, and it becomes difficult for owners to take appropriate action. Furthermore, obtaining appropriate information often requires consulting with experts, which can be burdensome in terms of time and expense.

[0484] 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.

[0485] In this invention, the server includes means for acquiring visual data of an organism from an information terminal, means for analyzing the acquired visual data to evaluate the health status of the organism, and means for transmitting an abnormality warning to the information terminal based on the evaluated health status. This makes it possible to remotely and easily and quickly grasp the health status of an organism and take immediate action as needed.

[0486] An "information terminal" is a computer device used by a user for capturing, transmitting, and receiving data.

[0487] "Visual data of living organisms" refers to image or video data that records the state of living organisms and is used for analyzing their health status.

[0488] A "generative AI model" is an artificial intelligence inference model that learns from large amounts of data and performs data analysis and pattern recognition.

[0489] An "abnormality warning" is information indicating an unusual condition in the health of an organism, and is communicated to the user.

[0490] "Behavior modification" refers to methods and guidance aimed at improving or correcting undesirable behaviors in living organisms.

[0491] This invention is a system that uses digital devices to analyze the health status and behavior of living organisms and provides necessary notifications and guidance. It primarily consists of a configuration in which information terminals and servers work in conjunction.

[0492] Users first use information devices such as smartphones or tablets to capture visual data of living organisms. This includes images and videos of pets. The device then sends the captured data to a cloud server. Internet connectivity is used for transmission, and the data is transmitted securely via the HTTPS protocol.

[0493] The server is responsible for analyzing the received visual data. It processes the data using generative AI models based on machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model on the server evaluates the health status of the organism and analyzes any abnormalities detected. Based on the analysis results, it generates an anomaly warning and sends a notification to the information terminal.

[0494] Furthermore, users can submit questions about modifying or caring for their pets through the information terminal. For example, if a user enters a prompt such as "I want to stop my dog ​​from barking unnecessarily," the server analyzes this information and generates appropriate guidance using a generative AI model. The server creates the guidance considering the species, age, and past data of the pet, and then sends it back to the information terminal.

[0495] This system provides a timely and convenient way for pet owners to manage the health and behavior of their animals. Examples of specific prompts include "I want to know how to cure my cat's constipation" and "I would like some advice on potty training my dog."

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The user captures visual data of the organism using an information terminal. The user uses a smartphone camera app to record the pet's activities as photos and videos. The input is image data of the organism, and this data is necessary for the next processing step.

[0499] Step 2:

[0500] The device sends the captured data to the server. The device uploads the image data to the cloud server using the HTTPS protocol via an internet connection. The input is the image data captured by the device, and the output is the data on the server to which it was sent.

[0501] Step 3:

[0502] The server converts the received data into a specific format. The server uses an image processing library to convert data from JPEG to a more easily analyzable format such as PNG. The input is compressed image data, and the output is data formatted for analysis.

[0503] Step 4:

[0504] The server analyzes data using a generated AI model. The server uses TensorFlow or PyTorch to evaluate the health status of organisms using the AI ​​model. Visual data provided by the user serves as input, and the output is a health status evaluation value as a result of the analysis.

[0505] Step 5:

[0506] The server generates an anomaly warning based on the analysis results and sends it to the terminal. The server then determines whether a warning is necessary based on the obtained evaluation values ​​and generates a warning message such as "An anomaly has been detected." This message is then sent back to the information terminal. The input is the analysis results, and the output is the warning message.

[0507] Step 6:

[0508] The user inputs questions about behavioral modification or care into an information terminal. The user sends the question to the terminal via text input or voice input. For example, the input might be a prompt such as, "Please tell me how to trim my cat's claws."

[0509] Step 7:

[0510] The server analyzes the question using a generating AI model, generates guidance, and sends it to the terminal. The server inputs the received question into the AI ​​model and constructs guidance that takes into account the type of organism and past data. The input is the user's question, and the output is specific advice as guidance content.

[0511] (Application Example 1)

[0512] 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."

[0513] In today's busy lifestyle, support for pet health management and training is extremely important. However, it is a difficult task for pet owners to constantly monitor their pets' health and provide appropriate training. In particular, early detection of health problems in pets and mastering effective training methods require specialized knowledge. As a result, pet owners end up with unnecessary worries and burdens. Furthermore, many pet owners do not have the time to closely monitor their pets' condition due to work and family circumstances, which can lead to a decline in the pets' quality of life.

[0514] 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.

[0515] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, means for sending an abnormality notification to the terminal based on the determined health status, means for receiving consultations regarding animal training and care from the terminal, means for generating appropriate advice based on the consultation content and sending it to the terminal, means for using an autonomous mobile device that tracks the animal's condition in real time and notifies of abnormalities by analyzing the obtained data, and means for using speech synthesis technology to communicate the notification content to the user by voice. As a result, pet owners can constantly monitor their pet's health status and easily obtain expert advice on training at home.

[0516] A "terminal" is a communication device used to capture image data of animals and transmit that data to a server.

[0517] "Image data" refers to digital information that visually captures the appearance of an animal and is used to assess its health condition.

[0518] "Health status" refers to the overall physical condition of an animal, including any physical abnormalities or signs of disease.

[0519] "Notification" refers to a method of informing the user of the animal's condition by sending information determined by the server to the terminal.

[0520] An "autonomous mobile device" is a robotic device that tracks the movements of animals while collecting necessary data.

[0521] "Speech synthesis technology" is a technology that converts text information into speech and conveys it to the user.

[0522] A "machine learning model" is a type of artificial intelligence used for data analysis and decision-making, enabling predictions and classifications based on past data.

[0523] This invention is a system for supporting pet health management and training. The system consists of a user terminal, an autonomous mobile unit, and a server.

[0524] First, the user uses their device to acquire image data of their pet. The image data is sent to a server, which analyzes it using a machine learning model to determine the pet's health status. Based on the health status, an abnormality notification is generated and sent to the device. This allows the user to constantly monitor their pet's health.

[0525] Furthermore, users can input questions about pet training and care through their devices. The input questions are sent to a server, where a machine learning model generates optimal advice based on past data and the pet's characteristics, and then sends this advice back to the device.

[0526] The autonomous mobile device tracks the pet's movements in real time and transmits the data captured by its camera to a server, enabling early detection of any abnormalities in the pet. If an abnormality is detected, this data can be used to directly inform the user via speech synthesis technology through a notification from the server.

[0527] For example, if a pet's loss of appetite is detected, entering a specific prompt such as "My 5-year-old Labrador has recently lost its appetite. Please provide advice on managing its health and training suggestions" will immediately provide expert advice.

[0528] Thus, the system of the present invention utilizes hardware such as a consumer robot, a user terminal, and AI technology to monitor a pet's health in real time and support appropriate training. This configuration reduces the burden on pet owners and improves the quality of life for pets.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The user uses a device to acquire image data of their pet. The device uses its camera to take still images and videos of the pet and sends the data to the server. The input is the pet's image data, and the output is the data sent to the server.

[0532] Step 2:

[0533] The server analyzes the received image data using a machine learning model. The input is image data sent from the terminal, and the model extracts data features to determine the pet's health status. The output is a diagnostic result regarding the pet's health status.

[0534] Step 3:

[0535] The server determines whether there are any abnormalities based on the health status and sends the result as a notification to the terminal. The input is the diagnostic result obtained through analysis, and the output is an abnormality notification message.

[0536] Step 4:

[0537] Users input questions about pet training and care via text or voice through their device. The input is the user's question, and the output is data transferred to the server.

[0538] Step 5:

[0539] The server analyzes the input question using a machine learning model and generates optimal advice based on the pet's characteristics and past data. The input is the question submitted by the user, and the output is the generated advice message.

[0540] Step 6:

[0541] The server sends the generated advice to the terminal, which then displays or audibly communicates the content to the user for confirmation. The input is the advice message from the server, and the output is the notification to the user.

[0542] Step 7:

[0543] An autonomous mobile device tracks the pet's movements and transmits the data captured by its camera to a server in real time. The input is real-time video of the pet, and the output is the data sent to the server.

[0544] Step 8:

[0545] The server analyzes data from the autonomous mobile device, and if an anomaly is detected, it generates a notification using speech synthesis technology and informs the user via voice. The input is the analyzed data, and the output is the voice notification.

[0546] 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.

[0547] This invention provides a system that offers support for pet health management and training, and in particular, enables more personalized information delivery by incorporating an emotion engine that recognizes the user's emotions. This system consists of a terminal used by the user and a server that analyzes the data.

[0548] The user takes photos and videos of their pet using the camera on their device. The captured image data is sent from the device to the server. On the server, the image data is analyzed using an AI model to evaluate the pet's health. Depending on the information obtained from the health check, if a normal or abnormal result is detected, the result is notified to the device. For example, if the image analysis detects an abnormality in the pet's skin, the server generates a message such as "There is an abnormality in the skin; a veterinary examination is recommended" and sends it to the device.

[0549] The emotion engine, a key feature of this invention, uses voice and text data received by the terminal from the user to infer the user's emotions. When the user inputs a question about training or care, the emotion engine analyzes the emotion and sends it to the server. For example, if the emotion engine determines that the user is stressed about a problem with their pet, the server generates advice in a tone appropriate to that emotion. This might be something like, "Let's stay calm. Pay attention to specific behavioral patterns and try the following methods."

[0550] The server sends emotionally optimized advice to the device, allowing the user to review it and try appropriate training methods. For example, if a user emotionally inputs "My pet just won't stop barking," the system detects that emotion and provides a response that combines comfort and specific actions.

[0551] In this way, support that takes into account the user's own emotional state is possible, resulting in a comprehensive pet care system that not only monitors the pet's condition but also enriches the user experience.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The user uses the device to take photos and videos of their pet. Once the shooting is complete, the device saves the data and prepares to send it to the server.

[0555] Step 2:

[0556] The device sends the stored image data to the server. During this process, compression is performed to optimize the data size.

[0557] Step 3:

[0558] The server receives image data sent from the terminal and converts the data into a format that the AI ​​model can analyze.

[0559] Step 4:

[0560] The server inputs the converted image data into an AI model to perform a health analysis. The AI ​​model meticulously analyzes the condition of the pet's eyes and skin to detect signs of health abnormalities.

[0561] Step 5:

[0562] The server evaluates the pet's health based on the analysis results and generates a notification message with details of any abnormalities.

[0563] Step 6:

[0564] The server sends the generated notification to the device. The device displays the notification to the user, allowing the user to understand the pet's current health status.

[0565] Step 7:

[0566] When a user enters a question about pet training or care into the device, the device sends that input data (text or voice) to the emotion engine.

[0567] Step 8:

[0568] The emotion engine analyzes received user data and estimates the user's emotional state. It identifies emotions such as stress and anxiety and transmits the results to the server.

[0569] Step 9:

[0570] The server generates optimal advice in response to the user's question, taking into account the analysis results of the emotion engine. The tone of this advice is adjusted to match the user's emotional state.

[0571] Step 10:

[0572] The server sends the generated advice to the terminal, which then displays the advice to the user. The user can then follow the advice to train and care for their pet.

[0573] (Example 2)

[0574] 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."

[0575] Monitoring and caring for pet health is a crucial issue for many pet owners. However, conventional technology struggles to accurately assess a pet's health and provide training and care advice that takes the owner's feelings into consideration. Without appropriate support that considers both the owner's feelings and the pet's condition, stress and misunderstandings can arise.

[0576] 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.

[0577] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, and means for analyzing emotional data received from the terminal to infer the user's emotions. This enables monitoring of the pet's health while simultaneously providing personalized advice that takes emotions into account, allowing for appropriate management and support of both.

[0578] A "terminal" refers to an input / output device that the user directly operates, such as a device used to acquire pet image data or send consultation details.

[0579] "Image data" refers to a digital file format that records visual information about animals and is used for analyzing their health status.

[0580] A "server" is a central processing unit that receives data from terminals via a network, performs analysis and processing, and transmits the results back to the terminals.

[0581] "Means for determining health status" refers to technical components used to evaluate the physical condition and any abnormalities of an animal by analyzing acquired image data.

[0582] "Emotional data" refers to information about emotions obtained from a user's language expressions and voice, and is used to analyze the user's psychological state.

[0583] "Means of inferring emotions" refer to technical components that analyze user emotional data, understand emotions, and reflect them in advice.

[0584] "Means of generating advice" refers to a function that creates personalized advice based on analysis results and sends it to the device.

[0585] This invention is a system for providing advice that takes into account the health management of pets and the feelings of their owners. The system mainly consists of terminals and a server.

[0586] The terminal is a user-operated device equipped with a camera for taking photos and videos of pets. Image data captured by this camera is sent to a server via a data transmission function. The terminal also includes an interface for users to input questions and emotions related to their pets. It supports both voice and text input.

[0587] The server is responsible for analyzing image data sent from the device. The AI ​​model running on the server is based on machine learning frameworks such as TensorFlow and PyTorch, and analyzes the image data to determine the pet's health status. Based on this analysis, if an abnormality in the health status is detected, it generates an appropriate notification and sends it to the device.

[0588] Furthermore, the server analyzes the user's emotional data received from the terminal. This uses natural language processing technology to infer the user's emotions and reflect them in appropriate advice. The advice is personalized by a generative AI model and delivered in an emotionally sensitive manner. For example, if a user inputs "My pet just won't stop barking," the system analyzes the stress the user is feeling from this statement and generates specific advice such as, "If your pet continues to bark unnecessarily, you can train it to stop by ignoring it. Stay calm and keep trying."

[0589] This system not only monitors the health of pets but also provides support that takes into account the user's feelings, thereby supporting a richer life for both pets and their owners.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The user takes photos and videos of their pet using the device's camera. When taking a picture, the device temporarily saves the image data to its internal memory. The image data moves to the next step when the user performs a send operation through the application.

[0593] Step 2:

[0594] The terminal transmits image data stored in its internal memory to a server via the internet. The input is image data, and the output is a data stream sent to the server. In this process, the terminal uses a data transfer protocol to ensure reliable data transmission.

[0595] Step 3:

[0596] The server receives the incoming image data and analyzes it using an AI model. It takes image data as input and performs data calculations to evaluate the pet's health using image analysis technology. The output is a judgment regarding the pet's health. Specifically, if an abnormality is found, it identifies the affected area and type.

[0597] Step 4:

[0598] Based on the analysis results, the server generates an abnormality report message if an abnormality is detected in the health status. The input is the result of the image analysis, and the output is the message text. The server then prepares to send the generated abnormality report message to the terminal.

[0599] Step 5:

[0600] The terminal receives an anomaly report message sent from the server and notifies the user. The terminal uses its notification function to display the message to the user and issue a warning alarm if necessary. The output is a visual and auditory presentation of the message to the user.

[0601] Step 6:

[0602] The user uses the device to input questions and feelings about pet training and care via voice or text. The input data is recorded on the device and passed to the next processing step. An example of input is a prompt phrase such as "My pet just won't stop barking."

[0603] Step 7:

[0604] The terminal sends the user's questions and sentiment data to the server. The data is collected by the terminal as input and becomes a data stream sent to the server as output. The terminal processes the data and sends it to the server in the correct format.

[0605] Step 8:

[0606] The server receives user emotion data and performs emotion analysis using natural language processing technology. The input is text data containing emotions, and the output is an estimated result of the user's emotional state. Through emotion analysis, data is generated to determine appropriate countermeasures based on the user's psychological state.

[0607] Step 9:

[0608] The server generates appropriate advice based on the sentiment analysis results. The advice is formulated by referencing the sentiment estimation results as input, and a text message is created to send to the user as output. The generating AI model optimizes this prompt and adjusts the advice to match the user's emotional tone.

[0609] Step 10:

[0610] The device receives advice messages sent from the server and notifies the user of their content. Along with displaying the message, the device sets a notification sound if necessary, providing the user with visual and auditory feedback as output. Based on this, the user can attempt training or care.

[0611] (Application Example 2)

[0612] 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."

[0613] In pet health management, owners are required to have a detailed understanding of their pet's condition and to provide appropriate training and care. However, it is not easy to constantly monitor a pet's health in daily life and to receive personalized advice that takes into account the owner's own emotional state. Conventional systems have the function to analyze the pet's condition, but they lack the function to generate advice that takes the owner's emotions into account, and therefore have the problem of not being able to provide comprehensive support that is easy for owners to use.

[0614] 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.

[0615] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the image data to determine the animal's health status, and emotion analysis means for analyzing the user's emotional state. This makes it possible to monitor the pet's health status and provide specific and helpful advice optimized according to the user's emotions.

[0616] A "terminal" refers to an electronic device used to manipulate and send / receive digital data.

[0617] "Animal image data" refers to visual information represented in the form of photographs or videos of animals, such as pets.

[0618] "Health status" refers to information that describes an animal's physical condition or signs of illness.

[0619] An "abnormality notification" refers to a message that alerts the user when a problem is detected in an animal's health.

[0620] "Consultation" refers to communication that pet owners engage in to seek advice or address questions regarding their pet's training or health.

[0621] "Advice" refers to instructions that include suggestions and recommendations regarding training and care.

[0622] "Emotional analysis tools" refer to functions that perform a process to analyze and understand a user's emotional state based on data.

[0623] A "machine learning model" refers to an algorithm that learns specific patterns based on large amounts of data and uses them for prediction and classification.

[0624] "Optimization" refers to the process of adjusting the output and operation of a system according to its purpose and conditions in order to obtain better results.

[0625] The system implementing this invention is intended for animal health management and support for pet owners. It is primarily realized using terminals, servers, and emotion analysis means and generative AI models that function between them.

[0626] The server first receives image data of the animal sent from the terminal. The image data is analyzed by a machine learning model implemented on the server to determine the animal's health status. If, for example, a skin abnormality or signs of poor health are detected, an abnormality notification is issued. Specifically, a message such as "A skin abnormality has been observed, so a veterinary examination is recommended" is sent to the terminal.

[0627] In addition, when users input questions about pet training and care through their devices, that data is also sent to the server. The server uses emotion analysis to infer the user's emotional state and generates advice based on that information. In this process, a generative AI model is used to generate specific and optimized advice that is tailored to the user's emotions. For example, if a user inputs "My pet just won't stop barking" along with their stress, they will be given advice in a tone such as, "Let's deal with this calmly. Pay attention to specific behavioral patterns and try the following methods."

[0628] The hardware used will include a device equipped with a camera and a Wi-Fi module for network communication. The software may utilize machine learning frameworks such as TensorFlow and natural language processing algorithms for sentiment analysis.

[0629] As a result, users can stay informed about their pet's health in a timely manner and receive advice based on their own feelings. This enables comprehensive pet care that goes beyond simple monitoring.

[0630] An example of a prompt message for a generative AI model is, "Generate a message that will comfort and offer helpful advice when the user is agitated." In this way, the system can provide a service that takes into account both the animal's health and the owner's feelings.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The device acquires image data of animals. The user takes photos and videos of their pet using the device's camera and saves the data to the device. The input is the camera device and the captured image data, and the output is the data saved on the device.

[0634] Step 2:

[0635] The device sends image data to the server. The device sends captured image data to the server over the network via Wi-Fi. The input is the image data stored on the device, and the output is the image data received by the server.

[0636] Step 3:

[0637] The server receives image data and performs analysis. The server inputs the image data into a machine learning model to analyze the characteristics of the animal's health. The input is the image data received by the server, and the output is the analysis results indicating the health status. If an abnormality is detected, the specific abnormality is identified.

[0638] Step 4:

[0639] The server generates notifications based on the animal's health status when abnormalities occur. Based on the analysis results, the server creates messages such as, "Skin abnormalities detected; veterinary consultation is recommended." The input is the analyzed health status data, and the output is the generated notification message.

[0640] Step 5:

[0641] The user inputs their consultation details from their device and sends them to the server. The user inputs their questions about pet training and care as text into their device and sends them to the server. The input is the user's text data, and the output is the consultation content sent to the server.

[0642] Step 6:

[0643] The server analyzes the content of the consultation and the user's emotions. The server processes the text data using emotion analysis tools to infer the user's emotional state. The input is the received consultation content, and the output is the inferred emotional state of the user.

[0644] Step 7:

[0645] The server generates advice based on the user's emotional state. Using a generative AI model, it creates specific advice tailored to the user's emotional state. For example, a user experiencing stress might be advised to "calm down and handle the situation calmly." The input is the user's emotional state and the content of their inquiry, while the output is optimized advice.

[0646] Step 8:

[0647] The server sends the generated advice to the terminal. The server sends the advice it has created to the terminal via the network and displays it to the user. The input is the generated advice, and the output is the advice displayed on the terminal.

[0648] These processing steps enable the management of pet health and the provision of advice tailored to the user's emotions.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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).

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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".

[0666] This invention provides a system for supporting pet health management and training. This system consists of a terminal used by the user on a daily basis and a server that processes and analyzes data.

[0667] The user first takes photos and videos of their pet using their device. This data is then sent from the device to a server. The server converts the received image data into a specific format and performs AI-based analysis. This analysis evaluates the pet's health and detects any abnormalities.

[0668] The server generates a notification indicating whether or not there is an abnormality based on the analysis results and sends it back to the terminal. The user can check this notification on the terminal and understand the health status of their pet. For example, if image analysis reveals an abnormal discoloration on the pet's skin, the server sends a message such as "An abnormality has been observed on the skin" to the terminal to inform the user.

[0669] Furthermore, users can input questions about training and care in text or voice format into their device. These questions are sent to a server, where they are analyzed by a machine learning model. The server then generates and sends optimal advice to the user's device, taking into account factors such as the type and age of the user's pet and their past consultation history. For example, if a user asks, "I want to stop my dog ​​from barking excessively," the AI ​​model will suggest specific training methods tailored to that dog breed.

[0670] This format allows users to constantly monitor their pets' health and receive expert advice on training from the comfort of their homes. This system improves the quality of life for pets and provides support to reduce the burden on pet owners.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] Users take pictures and videos of their pets using the device's camera. When taking pictures, they adjust the lighting and focus to ensure that the pet's condition is clearly visible.

[0674] Step 2:

[0675] The device saves the captured image data to local storage. Next, it encodes and compresses the saved data in order to send it to the server.

[0676] Step 3:

[0677] The server receives image data transmitted from the terminal. The received data is formatted to a format suitable for analysis by the AI ​​model.

[0678] Step 4:

[0679] The server inputs formatted image data into an AI model. The AI ​​model extracts features from the images and analyzes the pet's health condition. For example, it can detect eye redness or skin abnormalities.

[0680] Step 5:

[0681] The server evaluates the pet's health based on the results of the AI ​​analysis. If an abnormality is detected, it generates a notification that includes the nature of the abnormality and its severity.

[0682] Step 6:

[0683] The server sends the generated notification to the device. The device displays this notification to the user, providing information about the pet's health status. For example, it might display a message such as, "No abnormalities in the skin; please continue to check regularly."

[0684] Step 7:

[0685] Users input questions about training and care into the device. Input can be via text or voice, and the device sends this information to the server as text.

[0686] Step 8:

[0687] The server receives user questions and analyzes them using machine learning models. The analysis also takes into account information based on the pet's past data and breed.

[0688] Step 9:

[0689] The server generates the most appropriate advice based on the question and sends it to the terminal. The advice includes specific training methods and care instructions.

[0690] Step 10:

[0691] The device displays advice received from the server, allowing the user to review and implement it. The user can then provide care and discipline according to the advice.

[0692] (Example 1)

[0693] 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".

[0694] In modern times, many pet owners lack sufficient knowledge regarding animal health management and behavioral modification. As a result, the quality of life for their pets declines, and it becomes difficult for owners to take appropriate action. Furthermore, obtaining appropriate information often requires consulting with experts, which can be burdensome in terms of time and expense.

[0695] 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.

[0696] In this invention, the server includes means for acquiring visual data of an organism from an information terminal, means for analyzing the acquired visual data to evaluate the health status of the organism, and means for transmitting an abnormality warning to the information terminal based on the evaluated health status. This makes it possible to remotely and easily and quickly grasp the health status of an organism and take immediate action as needed.

[0697] An "information terminal" is a computer device used by a user for capturing, transmitting, and receiving data.

[0698] "Visual data of living organisms" refers to image or video data that records the state of living organisms and is used for analyzing their health status.

[0699] A "generative AI model" is an artificial intelligence inference model that learns from large amounts of data and performs data analysis and pattern recognition.

[0700] An "abnormality warning" is information indicating an unusual condition in the health of an organism, and is communicated to the user.

[0701] "Behavior modification" refers to methods and guidance aimed at improving or correcting undesirable behaviors in living organisms.

[0702] This invention is a system that uses digital devices to analyze the health status and behavior of living organisms and provides necessary notifications and guidance. It primarily consists of a configuration in which information terminals and servers work in conjunction.

[0703] Users first use information devices such as smartphones or tablets to capture visual data of living organisms. This includes images and videos of pets. The device then sends the captured data to a cloud server. Internet connectivity is used for transmission, and the data is transmitted securely via the HTTPS protocol.

[0704] The server is responsible for analyzing the received visual data. It processes the data using generative AI models based on machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model on the server evaluates the health status of the organism and analyzes any abnormalities detected. Based on the analysis results, it generates an anomaly warning and sends a notification to the information terminal.

[0705] Furthermore, users can submit questions about modifying or caring for their pets through the information terminal. For example, if a user enters a prompt such as "I want to stop my dog ​​from barking unnecessarily," the server analyzes this information and generates appropriate guidance using a generative AI model. The server creates the guidance considering the species, age, and past data of the pet, and then sends it back to the information terminal.

[0706] This system provides a timely and convenient way for pet owners to manage the health and behavior of their animals. Examples of specific prompts include "I want to know how to cure my cat's constipation" and "I would like some advice on potty training my dog."

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The user captures visual data of the organism using an information terminal. The user uses a smartphone camera app to record the pet's activities as photos and videos. The input is image data of the organism, and this data is necessary for the next processing step.

[0710] Step 2:

[0711] The device sends the captured data to the server. The device uploads the image data to the cloud server using the HTTPS protocol via an internet connection. The input is the image data captured by the device, and the output is the data on the server to which it was sent.

[0712] Step 3:

[0713] The server converts the received data into a specific format. The server uses an image processing library to convert data from JPEG to a more easily analyzable format such as PNG. The input is compressed image data, and the output is data formatted for analysis.

[0714] Step 4:

[0715] The server analyzes data using a generated AI model. The server uses TensorFlow or PyTorch to evaluate the health status of organisms using the AI ​​model. Visual data provided by the user serves as input, and the output is a health status evaluation value as a result of the analysis.

[0716] Step 5:

[0717] The server generates an anomaly warning based on the analysis results and sends it to the terminal. The server then determines whether a warning is necessary based on the obtained evaluation values ​​and generates a warning message such as "An anomaly has been detected." This message is then sent back to the information terminal. The input is the analysis results, and the output is the warning message.

[0718] Step 6:

[0719] The user inputs questions about behavioral modification or care into an information terminal. The user sends the question to the terminal via text input or voice input. For example, the input might be a prompt such as, "Please tell me how to trim my cat's claws."

[0720] Step 7:

[0721] The server analyzes the question using a generating AI model, generates guidance, and sends it to the terminal. The server inputs the received question into the AI ​​model and constructs guidance that takes into account the type of organism and past data. The input is the user's question, and the output is specific advice as guidance content.

[0722] (Application Example 1)

[0723] 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".

[0724] In today's busy lifestyle, support for pet health management and training is extremely important. However, it is a difficult task for pet owners to constantly monitor their pets' health and provide appropriate training. In particular, early detection of health problems in pets and mastering effective training methods require specialized knowledge. As a result, pet owners end up with unnecessary worries and burdens. Furthermore, many pet owners do not have the time to closely monitor their pets' condition due to work and family circumstances, which can lead to a decline in the pets' quality of life.

[0725] 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.

[0726] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, means for sending an abnormality notification to the terminal based on the determined health status, means for receiving consultations regarding animal training and care from the terminal, means for generating appropriate advice based on the consultation content and sending it to the terminal, means for using an autonomous mobile device that tracks the animal's condition in real time and notifies of abnormalities by analyzing the obtained data, and means for using speech synthesis technology to communicate the notification content to the user by voice. As a result, pet owners can constantly monitor their pet's health status and easily obtain expert advice on training at home.

[0727] A "terminal" is a communication device used to capture image data of animals and transmit that data to a server.

[0728] "Image data" refers to digital information that visually captures the appearance of an animal and is used to assess its health condition.

[0729] "Health status" refers to the overall physical condition of an animal, including any physical abnormalities or signs of disease.

[0730] "Notification" refers to a method of informing the user of the animal's condition by sending information determined by the server to the terminal.

[0731] An "autonomous mobile device" is a robotic device that tracks the movements of animals while collecting necessary data.

[0732] "Speech synthesis technology" is a technology that converts text information into speech and conveys it to the user.

[0733] A "machine learning model" is a type of artificial intelligence used for data analysis and decision-making, enabling predictions and classifications based on past data.

[0734] This invention is a system for supporting pet health management and training. The system consists of a user terminal, an autonomous mobile unit, and a server.

[0735] First, the user uses their device to acquire image data of their pet. The image data is sent to a server, which analyzes it using a machine learning model to determine the pet's health status. Based on the health status, an abnormality notification is generated and sent to the device. This allows the user to constantly monitor their pet's health.

[0736] Furthermore, users can input questions about pet training and care through their devices. The input questions are sent to a server, where a machine learning model generates optimal advice based on past data and the pet's characteristics, and then sends this advice back to the device.

[0737] The autonomous mobile device tracks the pet's movements in real time and transmits the data captured by its camera to a server, enabling early detection of any abnormalities in the pet. If an abnormality is detected, this data can be used to directly inform the user via speech synthesis technology through a notification from the server.

[0738] For example, if a pet's loss of appetite is detected, entering a specific prompt such as "My 5-year-old Labrador has recently lost its appetite. Please provide advice on managing its health and training suggestions" will immediately provide expert advice.

[0739] Thus, the system of the present invention utilizes hardware such as a consumer robot, a user terminal, and AI technology to monitor a pet's health in real time and support appropriate training. This configuration reduces the burden on pet owners and improves the quality of life for pets.

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The user uses a device to acquire image data of their pet. The device uses its camera to take still images and videos of the pet and sends the data to the server. The input is the pet's image data, and the output is the data sent to the server.

[0743] Step 2:

[0744] The server analyzes the received image data using a machine learning model. The input is image data sent from the terminal, and the model extracts data features to determine the pet's health status. The output is a diagnostic result regarding the pet's health status.

[0745] Step 3:

[0746] The server determines whether there are any abnormalities based on the health status and sends the result as a notification to the terminal. The input is the diagnostic result obtained through analysis, and the output is an abnormality notification message.

[0747] Step 4:

[0748] Users input questions about pet training and care via text or voice through their device. The input is the user's question, and the output is data transferred to the server.

[0749] Step 5:

[0750] The server analyzes the input question using a machine learning model and generates optimal advice based on the pet's characteristics and past data. The input is the question submitted by the user, and the output is the generated advice message.

[0751] Step 6:

[0752] The server sends the generated advice to the terminal, which then displays or audibly communicates the content to the user for confirmation. The input is the advice message from the server, and the output is the notification to the user.

[0753] Step 7:

[0754] An autonomous mobile device tracks the pet's movements and transmits the data captured by its camera to a server in real time. The input is real-time video of the pet, and the output is the data sent to the server.

[0755] Step 8:

[0756] The server analyzes data from the autonomous mobile device, and if an anomaly is detected, it generates a notification using speech synthesis technology and informs the user via voice. The input is the analyzed data, and the output is the voice notification.

[0757] 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.

[0758] This invention provides a system that offers support for pet health management and training, and in particular, enables more personalized information delivery by incorporating an emotion engine that recognizes the user's emotions. This system consists of a terminal used by the user and a server that analyzes the data.

[0759] The user takes photos and videos of their pet using the camera on their device. The captured image data is sent from the device to the server. On the server, the image data is analyzed using an AI model to evaluate the pet's health. Depending on the information obtained from the health check, if a normal or abnormal result is detected, the result is notified to the device. For example, if the image analysis detects an abnormality in the pet's skin, the server generates a message such as "There is an abnormality in the skin; a veterinary examination is recommended" and sends it to the device.

[0760] The emotion engine, a key feature of this invention, uses voice and text data received by the terminal from the user to infer the user's emotions. When the user inputs a question about training or care, the emotion engine analyzes the emotion and sends it to the server. For example, if the emotion engine determines that the user is stressed about a problem with their pet, the server generates advice in a tone appropriate to that emotion. This might be something like, "Let's stay calm. Pay attention to specific behavioral patterns and try the following methods."

[0761] The server sends emotionally optimized advice to the device, allowing the user to review it and try appropriate training methods. For example, if a user emotionally inputs "My pet just won't stop barking," the system detects that emotion and provides a response that combines comfort and specific actions.

[0762] In this way, support that takes into account the user's own emotional state is possible, resulting in a comprehensive pet care system that not only monitors the pet's condition but also enriches the user experience.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The user uses the device to take photos and videos of their pet. Once the shooting is complete, the device saves the data and prepares to send it to the server.

[0766] Step 2:

[0767] The device sends the stored image data to the server. During this process, compression is performed to optimize the data size.

[0768] Step 3:

[0769] The server receives image data sent from the terminal and converts the data into a format that the AI ​​model can analyze.

[0770] Step 4:

[0771] The server inputs the converted image data into an AI model to perform a health analysis. The AI ​​model meticulously analyzes the condition of the pet's eyes and skin to detect signs of health abnormalities.

[0772] Step 5:

[0773] The server evaluates the pet's health based on the analysis results and generates a notification message with details of any abnormalities.

[0774] Step 6:

[0775] The server sends the generated notification to the device. The device displays the notification to the user, allowing the user to understand the pet's current health status.

[0776] Step 7:

[0777] When a user enters a question about pet training or care into the device, the device sends that input data (text or voice) to the emotion engine.

[0778] Step 8:

[0779] The emotion engine analyzes received user data and estimates the user's emotional state. It identifies emotions such as stress and anxiety and transmits the results to the server.

[0780] Step 9:

[0781] The server generates optimal advice in response to the user's question, taking into account the analysis results of the emotion engine. The tone of this advice is adjusted to match the user's emotional state.

[0782] Step 10:

[0783] The server sends the generated advice to the terminal, which then displays the advice to the user. The user can then follow the advice to train and care for their pet.

[0784] (Example 2)

[0785] 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".

[0786] Monitoring and caring for pet health is a crucial issue for many pet owners. However, conventional technology struggles to accurately assess a pet's health and provide training and care advice that takes the owner's feelings into consideration. Without appropriate support that considers both the owner's feelings and the pet's condition, stress and misunderstandings can arise.

[0787] 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.

[0788] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the acquired image data to determine the animal's health status, and means for analyzing emotional data received from the terminal to infer the user's emotions. This enables monitoring of the pet's health while simultaneously providing personalized advice that takes emotions into account, allowing for appropriate management and support of both.

[0789] A "terminal" refers to an input / output device that the user directly operates, such as a device used to acquire pet image data or send consultation details.

[0790] "Image data" refers to a digital file format that records visual information about animals and is used for analyzing their health status.

[0791] A "server" is a central processing unit that receives data from terminals via a network, performs analysis and processing, and transmits the results back to the terminals.

[0792] "Means for determining health status" refers to technical components used to evaluate the physical condition and any abnormalities of an animal by analyzing acquired image data.

[0793] "Emotional data" refers to information about emotions obtained from a user's language expressions and voice, and is used to analyze the user's psychological state.

[0794] "Means of inferring emotions" refer to technical components that analyze user emotional data, understand emotions, and reflect them in advice.

[0795] "Means of generating advice" refers to a function that creates personalized advice based on analysis results and sends it to the device.

[0796] This invention is a system for providing advice that takes into account the health management of pets and the feelings of their owners. The system mainly consists of terminals and a server.

[0797] The terminal is a user-operated device equipped with a camera for taking photos and videos of pets. Image data captured by this camera is sent to a server via a data transmission function. The terminal also includes an interface for users to input questions and emotions related to their pets. It supports both voice and text input.

[0798] The server is responsible for analyzing image data sent from the device. The AI ​​model running on the server is based on machine learning frameworks such as TensorFlow and PyTorch, and analyzes the image data to determine the pet's health status. Based on this analysis, if an abnormality in the health status is detected, it generates an appropriate notification and sends it to the device.

[0799] Furthermore, the server analyzes the user's emotional data received from the terminal. This uses natural language processing technology to infer the user's emotions and reflect them in appropriate advice. The advice is personalized by a generative AI model and delivered in an emotionally sensitive manner. For example, if a user inputs "My pet just won't stop barking," the system analyzes the stress the user is feeling from this statement and generates specific advice such as, "If your pet continues to bark unnecessarily, you can train it to stop by ignoring it. Stay calm and keep trying."

[0800] This system not only monitors the health of pets but also provides support that takes into account the user's feelings, thereby supporting a richer life for both pets and their owners.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] The user takes photos and videos of their pet using the device's camera. When taking a picture, the device temporarily saves the image data to its internal memory. The image data moves to the next step when the user performs a send operation through the application.

[0804] Step 2:

[0805] The terminal transmits image data stored in its internal memory to a server via the internet. The input is image data, and the output is a data stream sent to the server. In this process, the terminal uses a data transfer protocol to ensure reliable data transmission.

[0806] Step 3:

[0807] The server receives the incoming image data and analyzes it using an AI model. It takes image data as input and performs data calculations to evaluate the pet's health using image analysis technology. The output is a judgment regarding the pet's health. Specifically, if an abnormality is found, it identifies the affected area and type.

[0808] Step 4:

[0809] Based on the analysis results, the server generates an abnormality report message if an abnormality is detected in the health status. The input is the result of the image analysis, and the output is the message text. The server then prepares to send the generated abnormality report message to the terminal.

[0810] Step 5:

[0811] The terminal receives an anomaly report message sent from the server and notifies the user. The terminal uses its notification function to display the message to the user and issue a warning alarm if necessary. The output is a visual and auditory presentation of the message to the user.

[0812] Step 6:

[0813] The user uses the device to input questions and feelings about pet training and care via voice or text. The input data is recorded on the device and passed to the next processing step. An example of input is a prompt phrase such as "My pet just won't stop barking."

[0814] Step 7:

[0815] The terminal sends the user's questions and sentiment data to the server. The data is collected by the terminal as input and becomes a data stream sent to the server as output. The terminal processes the data and sends it to the server in the correct format.

[0816] Step 8:

[0817] The server receives user emotion data and performs emotion analysis using natural language processing technology. The input is text data containing emotions, and the output is an estimated result of the user's emotional state. Through emotion analysis, data is generated to determine appropriate countermeasures based on the user's psychological state.

[0818] Step 9:

[0819] The server generates appropriate advice based on the sentiment analysis results. The advice is formulated by referencing the sentiment estimation results as input, and a text message is created to send to the user as output. The generating AI model optimizes this prompt and adjusts the advice to match the user's emotional tone.

[0820] Step 10:

[0821] The device receives advice messages sent from the server and notifies the user of their content. Along with displaying the message, the device sets a notification sound if necessary, providing the user with visual and auditory feedback as output. Based on this, the user can attempt training or care.

[0822] (Application Example 2)

[0823] 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".

[0824] In pet health management, owners are required to have a detailed understanding of their pet's condition and to provide appropriate training and care. However, it is not easy to constantly monitor a pet's health in daily life and to receive personalized advice that takes into account the owner's own emotional state. Conventional systems have the function to analyze the pet's condition, but they lack the function to generate advice that takes the owner's emotions into account, and therefore have the problem of not being able to provide comprehensive support that is easy for owners to use.

[0825] 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.

[0826] In this invention, the server includes means for acquiring animal image data from a terminal, means for analyzing the image data to determine the animal's health status, and emotion analysis means for analyzing the user's emotional state. This makes it possible to monitor the pet's health status and provide specific and helpful advice optimized according to the user's emotions.

[0827] A "terminal" refers to an electronic device used to manipulate and send / receive digital data.

[0828] "Animal image data" refers to visual information represented in the form of photographs or videos of animals, such as pets.

[0829] "Health status" refers to information that describes an animal's physical condition or signs of illness.

[0830] An "abnormality notification" refers to a message that alerts the user when a problem is detected in an animal's health.

[0831] "Consultation" refers to communication that pet owners engage in to seek advice or address questions regarding their pet's training or health.

[0832] "Advice" refers to instructions that include suggestions and recommendations regarding training and care.

[0833] "Emotional analysis tools" refer to functions that perform a process to analyze and understand a user's emotional state based on data.

[0834] A "machine learning model" refers to an algorithm that learns specific patterns based on large amounts of data and uses them for prediction and classification.

[0835] "Optimization" refers to the process of adjusting the output and operation of a system according to its purpose and conditions in order to obtain better results.

[0836] The system implementing this invention is intended for animal health management and support for pet owners. It is primarily realized using terminals, servers, and emotion analysis means and generative AI models that function between them.

[0837] The server first receives image data of the animal sent from the terminal. The image data is analyzed by a machine learning model implemented on the server to determine the animal's health status. If, for example, a skin abnormality or signs of poor health are detected, an abnormality notification is issued. Specifically, a message such as "A skin abnormality has been observed, so a veterinary examination is recommended" is sent to the terminal.

[0838] In addition, when users input questions about pet training and care through their devices, that data is also sent to the server. The server uses emotion analysis to infer the user's emotional state and generates advice based on that information. In this process, a generative AI model is used to generate specific and optimized advice that is tailored to the user's emotions. For example, if a user inputs "My pet just won't stop barking" along with their stress, they will be given advice in a tone such as, "Let's deal with this calmly. Pay attention to specific behavioral patterns and try the following methods."

[0839] The hardware used will include a device equipped with a camera and a Wi-Fi module for network communication. The software may utilize machine learning frameworks such as TensorFlow and natural language processing algorithms for sentiment analysis.

[0840] As a result, users can stay informed about their pet's health in a timely manner and receive advice based on their own feelings. This enables comprehensive pet care that goes beyond simple monitoring.

[0841] An example of a prompt message for a generative AI model is, "Generate a message that will comfort and offer helpful advice when the user is agitated." In this way, the system can provide a service that takes into account both the animal's health and the owner's feelings.

[0842] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0843] Step 1:

[0844] The device acquires image data of animals. The user takes photos and videos of their pet using the device's camera and saves the data to the device. The input is the camera device and the captured image data, and the output is the data saved on the device.

[0845] Step 2:

[0846] The device sends image data to the server. The device sends captured image data to the server over the network via Wi-Fi. The input is the image data stored on the device, and the output is the image data received by the server.

[0847] Step 3:

[0848] The server receives image data and performs analysis. The server inputs the image data into a machine learning model to analyze the characteristics of the animal's health. The input is the image data received by the server, and the output is the analysis results indicating the health status. If an abnormality is detected, the specific abnormality is identified.

[0849] Step 4:

[0850] The server generates notifications based on the animal's health status when abnormalities occur. Based on the analysis results, the server creates messages such as, "Skin abnormalities detected; veterinary consultation is recommended." The input is the analyzed health status data, and the output is the generated notification message.

[0851] Step 5:

[0852] The user inputs their consultation details from their device and sends them to the server. The user inputs their questions about pet training and care as text into their device and sends them to the server. The input is the user's text data, and the output is the consultation content sent to the server.

[0853] Step 6:

[0854] The server analyzes the content of the consultation and the user's emotions. The server processes the text data using emotion analysis tools to infer the user's emotional state. The input is the received consultation content, and the output is the inferred emotional state of the user.

[0855] Step 7:

[0856] The server generates advice based on the user's emotional state. Using a generative AI model, it creates specific advice tailored to the user's emotional state. For example, a user experiencing stress might be advised to "calm down and handle the situation calmly." The input is the user's emotional state and the content of their inquiry, while the output is optimized advice.

[0857] Step 8:

[0858] The server sends the generated advice to the terminal. The server sends the advice it has created to the terminal via the network and displays it to the user. The input is the generated advice, and the output is the advice displayed on the terminal.

[0859] These processing steps enable the management of pet health and the provision of advice tailored to the user's emotions.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] The following is further disclosed regarding the embodiments described above.

[0882] (Claim 1)

[0883] A means of obtaining animal image data from a terminal,

[0884] A means of determining the health status of an animal by analyzing acquired image data,

[0885] A means of sending an abnormality notification to the terminal based on the determined health status,

[0886] A means of receiving inquiries about animal training and care via a terminal,

[0887] A means of generating appropriate advice based on the consultation content and sending it to the terminal,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, characterized in that it uses a machine learning model in the analysis of image data.

[0891] (Claim 3)

[0892] The system according to claim 1, characterized in that it generates advice using a machine learning model in response to consultation content received from a terminal.

[0893] "Example 1"

[0894] (Claim 1)

[0895] A means of acquiring visual data of living organisms from an information terminal,

[0896] A means of analyzing acquired visual data to evaluate the health status of an organism,

[0897] A means for sending an abnormality warning to an information terminal based on the assessed health status,

[0898] A means of receiving inquiries regarding the modification and care of living organisms from an information terminal,

[0899] A means of generating appropriate guidance based on the content of the inquiry and transmitting it to an information terminal,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, characterized in that it uses a generative AI model in the analysis of visual data.

[0903] (Claim 3)

[0904] The system according to claim 1, characterized in that it generates guidance using a generation AI model in response to inquiries received from an information terminal.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] A means of obtaining animal image data from a terminal,

[0908] A means of determining the health status of an animal by analyzing acquired image data,

[0909] A means of sending an abnormality notification to the terminal based on the determined health status,

[0910] A means of receiving inquiries about animal training and care via a terminal,

[0911] A means of generating appropriate advice based on the consultation content and sending it to the terminal,

[0912] A method using an autonomous mobile device that tracks the animal's status in real time and notifies of anomalies by analyzing the obtained data,

[0913] A means of using speech synthesis technology to convey notification content to the user by voice,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, characterized in that it uses a machine learning model in the analysis of image data.

[0917] (Claim 3)

[0918] The system according to claim 1, characterized in that it generates advice using a machine learning model in response to consultation content received from a terminal.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] A means of obtaining animal image data from a terminal,

[0922] A means of determining the health status of an animal by analyzing acquired image data,

[0923] A means of sending an abnormality notification to the terminal based on the determined health status,

[0924] A means of receiving inquiries about animal training and care via a terminal,

[0925] A method for analyzing emotional data received from a device to infer the user's emotions,

[0926] A means for generating appropriate advice based on the user's emotions and sending it to the terminal,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, characterized in that it uses a machine learning model in the analysis of image data.

[0930] (Claim 3)

[0931] The system according to claim 1, characterized in that it generates advice using a machine learning model based on consultation content and emotion data received from a terminal.

[0932] "Application example 2 when combining with an emotional engine"

[0933] (Claim 1)

[0934] A means of obtaining animal image data from a terminal,

[0935] A means of determining the health status of an animal by analyzing acquired image data,

[0936] A means of sending an abnormality notification to the terminal based on the determined health status,

[0937] A means of receiving inquiries about animal training and care via a terminal,

[0938] A means of generating appropriate advice based on the consultation content and sending it to the terminal,

[0939] A means of analyzing the emotional state of a user,

[0940] A means for optimizing the content of advice according to the user's emotions obtained by emotion analysis means,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, characterized in that it uses a machine learning model in the analysis of image data.

[0944] (Claim 3)

[0945] The system according to claim 1, characterized in that it generates advice using a machine learning model based on the consultation content and the user's emotional state received from the terminal. [Explanation of symbols]

[0946] 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 obtaining animal image data from a terminal, A means of determining the health status of an animal by analyzing acquired image data, A means of sending an abnormality notification to the terminal based on the determined health status, A means of receiving inquiries about animal training and care via a terminal, A means of generating appropriate advice based on the consultation content and sending it to the terminal, A system that includes this.

2. The system according to claim 1, characterized in that a machine learning model is used in the analysis of image data.

3. The system according to claim 1, characterized in that it generates advice using a machine learning model in response to consultation content received from a terminal.

Images