system

Abstract

Provide a system. [Solution] A means to photograph the daily lives of rescued animals, means for transmitting the captured video data to a server; a means for analyzing the video data on a server and assessing the behavior and health status of the animals; A means of notifying the user of the analysis results on their device; A means for users to receive notifications on their devices and display appropriate countermeasures; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Rescued animals, especially dogs and cats, can exhibit problematic behaviors due to stress and anxiety caused by past experiences of abandonment or abuse. As a result, they are often deemed difficult to care for and are abandoned again or euthanized. The challenge is to reduce the number of animals euthanized and ensure stable care by quickly understanding the behavior and condition of these rescued animals and taking appropriate measures. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] The system includes a means for filming the daily lives of rescued animals, a means for transmitting the filmed video data to a server, a means for analyzing the video data on the server and evaluating the behavior and health of the animals, a means for notifying the user of the analysis results, and a means for receiving the notification on the user's terminal and displaying appropriate countermeasures. This makes it possible to detect stress and anxiety in rescued animals early and take appropriate measures, thereby easing the difficulties of raising them and reducing the number of animals that are euthanized.

[0007] "Rescued animals" are animals such as dogs and cats that have been separated from their owners due to abandonment or abuse, and are now being cared for in shelters or by new owners.

[0008] "Filming means" refers to a device or system for recording the behavior and status of protected animals as video data using a video camera, smartphone, etc.

[0009] "Video data" refers to digital data that captures the daily activities of protected animals as a series of image frames.

[0010] A "server" is a computer system that sends, receives, and processes data over a network, and plays a central role in performing calculations such as analysis.

[0011] "Video data transmission means" refers to a technology or protocol for uploading video data acquired by the image capture means to a server via a network.

[0012] "Analysis methods" refer to technologies that use generative AI models and algorithms on a server to assess animal behavior and health status from video data.

[0013] "Animal behavioral assessment" is the process of analyzing various behavioral patterns and behaviors of rescued animals to assess their stress, anxiety, and health status, and to evaluate specific conditions and signs.

[0014] "Result notification means" refers to the process or system by which the server sends a notification to the user's device based on the analysis results.

[0015] "User Device" means a device used by a User, such as a computer, smartphone, or tablet, that receives notifications and instructions on how to respond.

[0016] "Countermeasure display means" refers to a technology or interface that shows the user specific guidelines for action or countermeasures based on the notification received on the user's device. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system for supporting the care of rescued animals, particularly dogs and cats. Specifically, it uses video analysis technology to continuously monitor the daily lives of rescued animals, early detect signs of stress or anxiety and changes in their health, and suggest appropriate countermeasures.

[0039] First, the devices record the daily lives of the rescued animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then periodically sent to a server.

[0040] The server receives the transmitted video data and performs analysis using a generative AI model. The generative AI model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions, such as signs of stress, abnormal behavior, or changes in health status. The analysis results are stored on the server, and appropriate countermeasures are generated as needed.

[0041] The server then sends the analysis results to the user's device. The notification includes details of the detected issues and recommended solutions. The notification is sent via email or app push notification. When the user receives the notification, detailed information and specific solutions are displayed on the user's device.

[0042] For example, if a rescue dog is stressed, the user may receive a notification like this: "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian." Specific solutions like this allow users to take appropriate action early on.

[0043] This allows users to reduce the stress and anxiety of rescued animals and take early action to maintain their health. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0047] Step 2:

[0048] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0049] Step 3:

[0050] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0051] Step 4:

[0052] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0053] Step 5:

[0054] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0055] Step 6:

[0056] The results are sent to the user's device. The server generates a notification message that combines the analysis results and countermeasures, and sends it to the user's device. Notifications are sent via email, app push notifications, and other methods.

[0057] Step 7:

[0058] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures, so that the user can take immediate action.

[0059] Step 8:

[0060] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0061] Example 1

[0062] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0063] Health management and behavior monitoring of rescued animals is extremely important, especially because it is necessary to detect stress and changes in their health status early. However, conventional methods make it difficult to monitor animals on a daily basis, making it difficult to respond quickly and appropriately. Furthermore, there are challenges in understanding the animals' condition in real time and taking appropriate countermeasures early.

[0064] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0065] In this invention, the server includes a means for periodically recording the daily lives of the protected animals and saving them as video data, a means for transmitting the saved video data to the server via the Internet, and a means for analyzing the video data received by the server using a generative model to evaluate the behavioral patterns and health status of the animals. This makes it possible to monitor the status of the animals in real time and propose appropriate countermeasures at an early stage.

[0066] "Rescued animals" refers to animals that are rescued and cared for by animal shelters or animal welfare organizations.

[0067] "Filming means" refers to a device for recording the daily life of an animal as video footage, and includes communication devices such as video cameras and smartphones.

[0068] "Video data" refers to digital data containing captured video information, and is used to analyze animal behavior and health conditions.

[0069] "Server" refers to a computer system that receives video data via the Internet and is responsible for analyzing and storing the data.

[0070] "Generative models" refer to deep learning models trained using AI techniques to analyze animal behavior patterns and health conditions.

[0071] "Analysis means" refers to the process of inputting received video data into a generative model to evaluate the animal's behavioral patterns and health status.

[0072] "Database" refers to a digital database system for storing analysis results and generated countermeasures.

[0073] "Notification means" refers to the communication method used to communicate the analysis results and appropriate countermeasures to the user's device, and includes email and push notifications.

[0074] "User terminal" refers to a communication device used by a user, such as a computer or smartphone, that has the ability to receive and display notifications.

[0075] "Response measures" refer to specific steps to be taken to address animal health or behavioral issues based on the analysis results.

[0076] MODE FOR CARRYING OUT THE INVENTION

[0077] This invention is a care support system for rescued animals, particularly dogs and cats. Specifically, it is a system that uses video analysis technology to continuously monitor the daily lives of rescued animals, detects signs of stress or anxiety and changes in their health at an early stage, and suggests appropriate countermeasures.

[0078] Hardware and Software

[0079] The terminals used are communication devices such as video cameras and smartphones. These terminals film the animals' daily lives and capture video at regular intervals. The video data is stored in local storage and periodically sent to a server via the Internet. The secure HTTP protocol (HTTPS) is used for transmission.

[0080] The server stores the received video data and analyzes it using a generative AI model built using Python and deep learning libraries such as TENSORFLOW®. The model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions.

[0081] The analysis results are stored in a database (e.g., MySQL or MongoDB). The server then generates appropriate countermeasures based on the analysis results. These countermeasures are determined by pre-configured rule-based systems or AI models.

[0082] The generated countermeasures are sent from the server to the user's device via email or smartphone push notification. The user receives the notification and checks the recommended countermeasures. Detailed information and specific countermeasures are displayed on the user's device.

[0083] Examples of concrete examples and prompts

[0084] For example, if a behavior analysis system for rescue dogs were introduced, the following process would be performed: The user installs a video camera in their living room to monitor the area where their pet is active. The camera captures video every 10 minutes and sends it to a cloud server. The server analyzes the video using a generative AI model using TensorFlow and detects that the pet is experiencing stress. The server generates countermeasures, such as providing a quiet environment where the pet can relax, and notifies the user.

[0085] Below are some example prompts for a generative AI model:

[0086] Generate an AI model that detects signs of health and stress from video footage of animals. Analyze the video data below to assess your pet's behavior and suggest appropriate actions when specific issues arise.

[0087] Video data: "sample_video.mp4"

[0088] Examples of workarounds:

[0089] Stress: "Provide a quiet and calming environment."

[0090] Health issues: "Consider consulting a veterinarian."

[0091] Please return the detected issues and recommended solutions in JSON format.

[0092] This system allows users to monitor and manage the status of rescued animals in real time and take appropriate action in a timely manner, which in turn makes it easier to find new homes for rescued animals and contributes to reducing the number of animals that are euthanized.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1: Recording a video

[0095] The device captures the daily life of the rescued animals. The specific operation of this processing step is to use a video camera or smartphone and place it in the living room or animal's activity area. The device captures 30 seconds of video every 10 minutes.

[0096] Input: Animal activity footage

[0097] Output: Video data (e.g. MP4 format file)

[0098] Step 2: Save the video data locally

[0099] The device saves the captured video data to local storage, using MP4 format video files.

[0100] Input: Recorded video data

[0101] Output: Video data stored in local storage

[0102] Step 3: Send video data to the server

[0103] The device then sends the saved video data to a server via the Internet. This transmission is done automatically at regular intervals and is securely transmitted using the HTTP protocol.

[0104] Input: Video data stored in local storage

[0105] Output: Video data uploaded to the server

[0106] Step 4: Video Analysis

[0107] The server receives the video data sent from the device and analyzes it using a generative AI model, which uses a deep learning model powered by TensorFlow to evaluate the animal's behavioral patterns and health status.

[0108] Input: Video data stored on the server

[0109] Output: Behavioral and health status analysis results

[0110] Step 5: Save the analysis results and generate countermeasures

[0111] The server stores the analysis results in a database, and then generates appropriate countermeasures based on the stored analysis results, which are determined by pre-defined rule-based systems and AI models.

[0112] Input: Behavioral and health status analysis results

[0113] Output: Analysis results stored in a database and generated countermeasures

[0114] Step 6: Notification of diagnosis results

[0115] The server notifies the user of the analysis results and the generated countermeasures via email or smartphone push notifications, providing the user with information in real time.

[0116] Input: Generated countermeasures and analysis results

[0117] Output: Notification sent to user device

[0118] Step 7: Receiving and Viewing Notifications

[0119] The user will receive a notification on their device and check the recommended course of action. Specifically, detailed information and specific actions will be displayed on the user's device. For example, the message may read, "Your pet is stressed. Try the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0120] Input: Notification to user device

[0121] Output: Solution displayed on the terminal

[0122] (Application example 1)

[0123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] When caring for rescued animals, especially dogs and cats, it is difficult for owners to quickly identify their animals' stress and health conditions and take appropriate measures. Furthermore, in situations where real-time response is required when an abnormality occurs, owners often lack the information necessary to take prompt action. These issues can make it difficult to properly care for rescued animals.

[0125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0126] In this invention, the server includes a means for analyzing the captured video data and evaluating the behavior and health condition of the animal, a means for notifying the user of the analysis results to the user's terminal, and a means for notifying the user in real time if the analysis results detect an abnormality. This enables owners to quickly and accurately grasp the stress and health condition of the rescued animal and take appropriate measures early on.

[0127] "Rescued animals" are mammals, such as dogs and cats, that are kept in shelters or temporary captive environments.

[0128] "Daily life" refers to the general activities and behaviors that rescued animals engage in on a daily basis.

[0129] "Filming means" refers to the means of recording animal activity as video data using a camera, smartphone, etc.

[0130] A "server" is a computer system for receiving, storing, and analyzing video data.

[0131] "Video data" refers to video data that records the daily activities of protected animals.

[0132] "Analysis methods" are methods that use AI technology to analyze video data and evaluate the behavior and health of animals.

[0133] A "generative AI model" is an artificial intelligence model trained to predict and assess animal behavior and health status from input data.

[0134] A "prompt sentence" is an input sentence that requests a specific analysis from a generative AI model.

[0135] The "notification means" is a means for transmitting the analysis results to the user's terminal.

[0136] "Real-time notification means" refers to a means for immediately notifying the user when an abnormality is detected.

[0137] "User Device" means a device used by a User to check the status of a rescued animal, including a smartphone or computer.

[0138] "Means for displaying appropriate countermeasures" refers to a means for presenting users with recommended countermeasures based on the results of AI analysis.

[0139] This invention is a system that monitors the daily lives of protected animals and detects abnormalities in their health and behavior at an early stage. The system mainly consists of the following components:

[0140] System configuration

[0141] The system includes a "filming means" for filming the daily lives of animals, a means for transmitting the filmed video data to a "server," an "analysis means" for analyzing the video data on the server, a "notification means" for notifying the user of the analysis results, and a "countermeasure display means" for presenting appropriate countermeasures on the user's terminal.

[0142] Filming method

[0143] The video recording device and communication terminal are used to record the daily behavior and behavior of the protected animals as video data, which is then sent to a server at regular intervals.

[0144] Server and analysis method

[0145] The server stores the received video data and then analyzes it using a generative AI model trained to assess animal behavior and health. The analysis method uses software including OpenCV and other image processing libraries, for example. This detects abnormal animal behavior and signs of stress.

[0146] Notification means

[0147] The analysis results are evaluated in real time, and if an anomaly is detected, the user is immediately notified via a notification method. The notification is sent to the user's device, such as a smartphone or computer. The notification includes details of the detected problem and recommended countermeasures. The notification method uses, for example, the HTTP communication library "requests."

[0148] Countermeasure display method

[0149] When a user receives a notification, specific countermeasures are displayed on the user's device, including recommended countermeasures based on the analysis results of the generative AI model, allowing the user to take appropriate action quickly.

[0150] Specific examples

[0151] For example, if a rescued dog is exhibiting abnormal behavior and appears stressed, the analytics tool will send a notification to the user saying, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." These solutions allow owners to take appropriate measures early on.

[0152] Prompt Sentence Examples

[0153] An example of a prompt to be input into the generative AI model is, "Analyze the video data below and detect stress or abnormal behavior in the pet. If an abnormality is detected, please provide specific countermeasures."

[0154] This system will enable real-time monitoring of the health and behavioral abnormalities of rescued animals, enabling prompt and appropriate responses, which will improve the quality of life of rescued animals and promote proper care.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The device uses a video camera or smartphone to record the daily behavior and behavior of the protected animals. The input is continuous video data, which is saved as a video file. The output is a video file.

[0158] Step 2:

[0159] The device uploads video data captured at regular intervals to the server. The input is a saved video file, which is sent using the HTTP communication library "requests." The output is the video data saved on the server.

[0160] Step 3:

[0161] The server temporarily stores the received video data in storage. The input is the uploaded video data, which is saved in an analyzable format. The output is the saved video data file.

[0162] Step 4:

[0163] The server analyzes the stored video data using a generative AI model. This analysis uses a prompt to evaluate the animal's behavior and health condition: "Analyze the following video data and detect stress or abnormal behavior in your pet. If abnormalities are detected, please provide specific countermeasures." The input is the stored video data, and the output is the analysis results regarding the animal's behavior and health condition.

[0164] Step 5:

[0165] The server evaluates the analysis results and generates data to notify users if an anomaly is detected. The input is the analysis result from the generative AI model, and the output is the anomaly information to be notified and recommended countermeasures.

[0166] Step 6:

[0167] The server notifies the user's device, such as a smartphone or computer, of the analysis results and recommended actions. The input is the data on the recommended actions, which is sent in the form of a push notification or email. The output is a notification message that is displayed on the user's device.

[0168] Step 7:

[0169] The user device displays the content of the received notification message to the user. The input is the notification data sent from the server, and the output is a notification message that the user can check. Specifically, the application displays a message such as "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian."

[0170] In this way, the health and behavioral abnormalities of protected animals can be monitored in real time, enabling prompt and appropriate responses.

[0171] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0172] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0173] First, the devices record the daily lives of the protected animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then sent to a server via a network.

[0174] The server receives and analyzes the transmitted video data. A pre-trained generative AI model is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress or anxiety. The analysis results are stored on the server, and appropriate countermeasures are generated.

[0175] The server then uses an emotion engine to assess the user's emotional state, which analyzes the user's voice and facial expressions to determine their emotional state, for example, whether they are stressed or calm.

[0176] The server then sends a notification to the user's device based on the analysis results and the evaluation results of the emotion engine. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. Notifications can be sent via email, app push notifications, etc.

[0177] The user's device will display the received notification and offer specific suggestions for dealing with the situation. For example, if a rescue dog is feeling stressed, the notification will include suggestions such as: "Your pet is feeling stressed. Try the following: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." If the user's emotions are heightened, the notification will be milder and presented in a more user-friendly format.

[0178] This allows users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health. In addition, a notification method based on the user's emotional state further promotes the reduction of stress and anxiety. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0179] The processing flow will be explained below.

[0180] Step 1:

[0181] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0182] Step 2:

[0183] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0184] Step 3:

[0185] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0186] Step 4:

[0187] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0188] Step 5:

[0189] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0190] Step 6:

[0191] Evaluate the user's emotional state. Using an emotion engine, the system analyzes the user's voice and facial expressions to determine their emotional state. It also uses a camera and microphone to collect data to assess whether the user is stressed or calm.

[0192] Step 7:

[0193] The results are then sent to the user's device. The server combines the analysis results with the emotion engine's evaluation results to generate a notification message, which is then sent to the user's device. The notification is customized based on the user's emotional state and sent via an appropriate method, such as email or app push notification.

[0194] Step 8:

[0195] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures. If the user is feeling stressed, the content and wording of the notification will be adjusted and presented in a gentler tone.

[0196] Step 9:

[0197] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0198] Example 2

[0199] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0200] When caring for rescued animals, especially dogs and cats, there is a need to detect stress and changes in their health at an early stage and take appropriate countermeasures. However, conventional systems lack sufficient means for comprehensively monitoring and evaluating the behavior and health of animals, and do not present appropriate countermeasures based on the user's emotional state. This increases the risk of compromising the welfare of animals, and the present invention aims to solve these problems.

[0201] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0202] In this invention, the server includes a means for filming the daily life of the animals, a means for transmitting the video data to the server, a means for analyzing the video data in the server and evaluating the behavior and health state of the animals, a means for analyzing the emotional state of the user, a means for notifying the user terminal of the analysis results, and a means for receiving the notification in the user terminal and displaying appropriate countermeasures. This makes it possible to detect stress and health conditions of protected animals early and provide appropriate countermeasures according to the emotional state of the user.

[0203] "Protected animals" refers to animals kept by humans, particularly mammals such as dogs and cats, that are protected.

[0204] "Means" refers to a method, apparatus, software, or combination thereof for accomplishing a particular purpose.

[0205] "Video data" refers to digital video data that records the daily lives of animals.

[0206] "Server" refers to a computer system that stores, analyzes, and manages data over a network.

[0207] "Analysis" refers to the process of evaluating the behavior and health of animals based on collected data.

[0208] "Behavior and health" refers to an animal's behavior, such as movement, posture, eating and sleeping patterns, as well as health-related indicators such as weight, body temperature and respiratory rate.

[0209] "Evaluating" refers to judging and reporting an animal's behavior and health status numerically or verbally through data analysis.

[0210] "User" refers to a person who uses the system, typically an animal owner.

[0211] "Emotional state" refers to the user's psychological state, as analyzed from voice, facial expressions, etc.

[0212] "Notification" refers to a message or alert sent from the server to a user device.

[0213] "Countermeasures" refers to specific instructions or suggestions for action provided based on the analysis results.

[0214] "User device" refers to the device used by a User to receive and view Notifications, including smartphones, tablets, and computers.

[0215] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and automatically performs a specific task (in this case, video analysis).

[0216] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0217] First, the device records the daily life of the protected animals. The device can be a video camera or a smartphone. These devices capture video at regular intervals and save it as video data. For example, the device's video camera can be used to record 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured data is saved in the device's local storage. The saved data is then sent to a server via the Internet.

[0218] The server receives and analyzes the transmitted video data. A pre-trained generative AI model, such as "YOLOv4," is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status and detects signs of stress and anxiety. The analysis results are stored on the server. The server also uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, "IBM Watson (registered trademark) Tone Analyzer" is used.

[0219] The server then sends a notification to the user's device based on the analysis results and the emotion engine's evaluation. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. The notification can be sent via email or app push notification. Specific examples include, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0220] The user's device will display the received notification and provide specific countermeasures. The user can check the notification message on their smartphone or tablet and take the suggested countermeasures.

[0221] Prompt Sentence Examples

[0222] Sample prompts for detecting stress in animals:

[0223] Prompt: "Analyze the following video data to detect whether the animal is experiencing stress."

[0224] Analysis results: Signs of stress (frequent yawning, sitting down, etc.) were detected from footage of rescue dogs, and the dogs reported feeling stressed.

[0225] Example prompts for user sentiment analysis:

[0226] Prompt: "Analyze the user's voice data and assess their current emotional state."

[0227] Analysis results: If the user is determined to be stressed, the response will be "I am stressed."

[0228] Example prompt for generating notifications:

[0229] Prompt: "Please suggest some measures to reduce stress for rescue dogs. The user is currently experiencing stress."

[0230] Analysis: "The user is stressed. Consider these gentle solutions: 1. Allow the user to rest in a quiet place. 2. Increase walks. 3. Consider consulting a veterinarian."

[0231] This will enable users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health, thereby improving the welfare of the animals.

[0232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0233] Step 1:

[0234] The device is used to capture the daily lives of rescued animals.

[0235] Input: Camera device and set capture time.

[0236] Specific operation: For example, the device's video camera captures 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured video data is stored in the device's local storage.

[0237] Output: Saved video data.

[0238] Step 2:

[0239] The terminal uses a means for transmitting the stored video data to the server.

[0240] Input: Stored video data and network connection.

[0241] How it works: The device uses Wi-Fi or mobile data to send the video data to the specified URL on the server immediately after shooting. The data is encrypted during transmission.

[0242] Output: Video data sent to the server.

[0243] Step 3:

[0244] The server uses a means for analyzing the received video data.

[0245] Input: Received video data.

[0246] How it works: The server analyzes video data using a pre-trained generative AI model (e.g., YOLOv4). The model evaluates the animals' behavioral patterns and health status, and detects signs of stress or anxiety.

[0247] Output: Analysis results (assessment of animal behavior patterns and health status).

[0248] Step 4:

[0249] The server employs a means for analyzing the user's emotional state.

[0250] Input: User's voice data and facial expression data.

[0251] How it works: The server receives voice data sent by the user in real time and analyzes it using an "emotion engine." The emotion engine uses, for example, the "IBM Watson Tone Analyzer" to determine the user's emotional state, such as whether they are tense or relaxed.

[0252] Output: Evaluation result of the user's emotional state.

[0253] Step 5:

[0254] The server uses a means to send a notification to the user's device based on the analysis results and the emotion engine's evaluation results.

[0255] Input: Analysis results of video data and evaluation results of user's emotional state.

[0256] Specific operation: The server visualizes the detected problems and solutions, creates notifications in expressions that correspond to the user's emotional state, and sends them as emails or push notifications to smartphone apps.

[0257] Output: Notification message to the user's terminal.

[0258] Step 6:

[0259] The user's device receives the notification and uses a means to display appropriate countermeasures.

[0260] Input: The notification message sent by the server.

[0261] Specific Action: The user checks the notification on their smartphone or tablet and takes specific action according to the instructions provided, such as "Your pet is stressed. Consider the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0262] Output: User action taken.

[0263] (Application example 2)

[0264] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] Conventional home security systems lack the means to detect suspicious behavior or changes in user emotions in real time and respond quickly and appropriately. Furthermore, there is no effective system for monitoring pet behavior and health to ensure home safety. Therefore, there is a need for technology to comprehensively improve home safety.

[0266] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing video footage from within the home and monitoring for suspicious behavior, means for analyzing the user's voice and facial expressions to determine the user's emotional state, and means for notifying the user's terminal of the analysis results. This makes it possible to detect abnormal behavior and emotional changes within the home early and provide the user with prompt and appropriate countermeasures.

[0267] "Rescued animals" refers to animals that are under specific management and care.

[0268] "Filming means" refers to a device or group of devices used to record images of animals or household activities.

[0269] "Video data" refers to data that includes a series of video frames captured by a camera.

[0270] "Server" refers to a computer system for receiving, storing, and analyzing data over a network.

[0271] "Analysis means" refers to algorithms or models used to analyze received video data and detect specific patterns or anomalies.

[0272] "Notification means" refers to a method or device for notifying the user of the analysis results.

[0273] "User Device" refers to the device (e.g., smartphone or tablet) used by a User to receive information.

[0274] "Appropriate countermeasures" refer to the actions or measures that users should take based on the analysis results.

[0275] "Home video" refers to video data captured by a camera installed in the home.

[0276] "Suspicious behavior" refers to irregular movements or actions that deviate from normal patterns of behavior.

[0277] "Voice and facial expression analysis" refers to the process of determining a user's emotional state from collected audio and video data.

[0278] "Emotional state" refers to the psychological state or mood a user is feeling at that moment.

[0279] The present invention is a security system for improving safety within the home, monitoring the daily lives of protected animals, detecting suspicious behavior, and analyzing the user's emotional state to provide appropriate notifications.

[0280] 1. Photography methods and data collection:

[0281] Video cameras and smartphones installed in homes periodically capture footage of animals and the home and save it as video data. The data collected by these devices is then sent to a server via a network.

[0282] 2. Data analysis by the server:

[0283] The server is equipped with a generative AI model using deep learning frameworks such as TensorFlow and PyTorch to analyze the received video data. Using this model, the system evaluates the behavioral patterns of animals and household members to detect signs of stress, anxiety, or suspicious behavior. Furthermore, it uses an emotion engine (e.g., Microsoft® Azure® Emotion API or Google® Cloud Speech-to-Text API) to analyze the user's voice and facial expressions to determine their emotional state.

[0284] 3. Notification Methods and User Devices:

[0285] The analysis results on the server are sent to the user's smartphone, tablet, or other device via a cloud service (e.g., AWS (registered trademark) Lambda). Notifications are sent via email or app push notifications. The notification content includes details of the detected problem and countermeasures based on the user's emotional state.

[0286] 4. Example:

[0287] For example, if a home camera detects suspicious activity in the living room in the middle of the night and a child's crying or startled scream is recorded by the microphone, the server will analyze this data. The AI ​​will detect the abnormality and send a notification to the parent's smartphone saying, "Abnormal activity has been detected in the living room late at night. Your child appears to be startled. Please take immediate action."

[0288] 5. Example prompt:

[0289] "A loud noise has been detected that may startle a child in the middle of the night. Check your smart home cameras for suspicious activity. Consider contacting the police if necessary."

[0290] This invention makes it possible to detect abnormal behavior and emotional changes in the home at an early stage and provide users with prompt and appropriate countermeasures, thereby improving safety in the home overall and providing a safe and secure living environment.

[0291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0292] Step 1:

[0293] The device periodically captures what is happening in the home using a video camera or smartphone and records it as video data. The input is real-time video and audio data, and the output is the captured video data.

[0294] Step 2:

[0295] The device transmits the captured video data to the server via the network. The input is the video data stored on the device, and the output is the video data uploaded to the server.

[0296] Step 3:

[0297] The video data received by the server is analyzed using a generative AI model (a model using TensorFlow or PyTorch). The input is the transmitted video data, and the output is the analysis results of animal behavior patterns and abnormal behavior within the home. Specifically, the AI ​​model analyzes the movements of animals and people in the video and determines whether there are any patterns or abnormalities.

[0298] Step 4:

[0299] The server uses an emotion engine (such as the Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's voice and facial expression data, and the output is the user's emotional evaluation result. Specifically, the emotion engine analyzes voice tone and facial expression changes to identify the emotional state (e.g., surprise or fear).

[0300] Step 5:

[0301] Based on the behavioral pattern analysis results and emotional state evaluation results acquired by the server, the server generates appropriate notification content and sends it to the user's device. The input is the analysis results and emotional evaluation results, and the output is notification data for the user's device. Specifically, the server formats the analysis results into a prompt sentence and generates the notification content.

[0302] Step 6:

[0303] The user device receives the notification from the server and displays the appropriate countermeasures. The input is the notification data sent from the server, and the output is the countermeasures displayed on the device. The user can receive this notification and act according to the countermeasures. Specifically, the user device may display a notification such as, "Abnormal activity has been detected in the living room late at night. It appears that a child is startled. Please take immediate action."

[0304] This series of steps makes it possible to detect abnormal behavior and emotional changes within the home in real time and notify the user of prompt and appropriate countermeasures.

[0305] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0306] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0307] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0308] [Second embodiment]

[0309] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0310] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0311] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0312] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0313] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0314] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0315] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0316] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0317] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0318] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0319] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0320] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0321] This invention is a system for supporting the care of rescued animals, particularly dogs and cats. Specifically, it uses video analysis technology to continuously monitor the daily lives of rescued animals, early detect signs of stress or anxiety and changes in their health, and suggest appropriate countermeasures.

[0322] First, the devices record the daily lives of the rescued animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then periodically sent to a server.

[0323] The server receives the transmitted video data and performs analysis using a generative AI model. The generative AI model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions, such as signs of stress, abnormal behavior, or changes in health status. The analysis results are stored on the server, and appropriate countermeasures are generated as needed.

[0324] The server then sends the analysis results to the user's device. The notification includes details of the detected issues and recommended solutions. The notification is sent via email or app push notification. When the user receives the notification, detailed information and specific solutions are displayed on the user's device.

[0325] For example, if a rescue dog is stressed, the user may receive a notification like this: "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian." Specific solutions like this allow users to take appropriate action early on.

[0326] This allows users to reduce the stress and anxiety of rescued animals and take early action to maintain their health. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0330] Step 2:

[0331] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0332] Step 3:

[0333] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0334] Step 4:

[0335] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0336] Step 5:

[0337] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0338] Step 6:

[0339] The results are sent to the user's device. The server generates a notification message that combines the analysis results and countermeasures, and sends it to the user's device. Notifications are sent via email, app push notifications, and other methods.

[0340] Step 7:

[0341] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures, so that the user can take immediate action.

[0342] Step 8:

[0343] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0344] Example 1

[0345] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0346] Health management and behavior monitoring of rescued animals is extremely important, especially because it is necessary to detect stress and changes in their health status early. However, conventional methods make it difficult to monitor animals on a daily basis, making it difficult to respond quickly and appropriately. Furthermore, there are challenges in understanding the animals' condition in real time and taking appropriate countermeasures early.

[0347] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0348] In this invention, the server includes a means for periodically recording the daily lives of the protected animals and saving them as video data, a means for transmitting the saved video data to the server via the Internet, and a means for analyzing the video data received by the server using a generative model to evaluate the behavioral patterns and health status of the animals. This makes it possible to monitor the status of the animals in real time and propose appropriate countermeasures at an early stage.

[0349] "Rescued animals" refers to animals that are rescued and cared for by animal shelters or animal welfare organizations.

[0350] "Filming means" refers to a device for recording the daily life of an animal as video footage, and includes communication devices such as video cameras and smartphones.

[0351] "Video data" refers to digital data containing captured video information, and is used to analyze animal behavior and health conditions.

[0352] "Server" refers to a computer system that receives video data via the Internet and is responsible for analyzing and storing the data.

[0353] "Generative models" refer to deep learning models trained using AI techniques to analyze animal behavior patterns and health conditions.

[0354] "Analysis means" refers to the process of inputting received video data into a generative model to evaluate the animal's behavioral patterns and health status.

[0355] "Database" refers to a digital database system for storing analysis results and generated countermeasures.

[0356] "Notification means" refers to the communication method used to communicate the analysis results and appropriate countermeasures to the user's device, and includes email and push notifications.

[0357] "User terminal" refers to a communication device used by a user, such as a computer or smartphone, that has the ability to receive and display notifications.

[0358] "Response measures" refer to specific steps to be taken to address animal health or behavioral issues based on the analysis results.

[0359] MODE FOR CARRYING OUT THE INVENTION

[0360] This invention is a care support system for rescued animals, particularly dogs and cats. Specifically, it is a system that uses video analysis technology to continuously monitor the daily lives of rescued animals, detects signs of stress or anxiety and changes in their health at an early stage, and suggests appropriate countermeasures.

[0361] Hardware and Software

[0362] The terminals used are communication devices such as video cameras and smartphones. These terminals film the animals' daily lives and capture video at regular intervals. The video data is stored in local storage and periodically sent to a server via the Internet. The secure HTTP protocol (HTTPS) is used for transmission.

[0363] The server stores the received video data and analyzes it using a generative AI model built using Python and deep learning libraries such as TensorFlow. The model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions.

[0364] The analysis results are stored in a database (e.g., MySQL or MongoDB). The server then generates appropriate countermeasures based on the analysis results. These countermeasures are determined by pre-configured rule-based systems or AI models.

[0365] The generated countermeasures are sent from the server to the user's device via email or smartphone push notification. The user receives the notification and checks the recommended countermeasures. Detailed information and specific countermeasures are displayed on the user's device.

[0366] Examples of concrete examples and prompts

[0367] For example, if a behavior analysis system for rescue dogs were introduced, the following process would be performed: The user installs a video camera in their living room to monitor the area where their pet is active. The camera captures video every 10 minutes and sends it to a cloud server. The server analyzes the video using a generative AI model using TensorFlow and detects that the pet is experiencing stress. The server generates countermeasures, such as providing a quiet environment where the pet can relax, and notifies the user.

[0368] Below are some example prompts for a generative AI model:

[0369] Generate an AI model that detects signs of health and stress from video footage of animals. Analyze the video data below to assess your pet's behavior and suggest appropriate actions when specific issues arise.

[0370] Video data: "sample_video.mp4"

[0371] Examples of workarounds:

[0372] Stress: "Provide a quiet and calming environment."

[0373] Health issues: "Consider consulting a veterinarian."

[0374] Please return the detected issues and recommended solutions in JSON format.

[0375] This system allows users to monitor and manage the status of rescued animals in real time and take appropriate action in a timely manner, which in turn makes it easier to find new homes for rescued animals and contributes to reducing the number of animals that are euthanized.

[0376] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0377] Step 1: Recording a video

[0378] The device captures the daily life of the rescued animals. The specific operation of this processing step is to use a video camera or smartphone and place it in the living room or animal's activity area. The device captures 30 seconds of video every 10 minutes.

[0379] Input: Animal activity footage

[0380] Output: Video data (e.g. MP4 format file)

[0381] Step 2: Save the video data locally

[0382] The device saves the captured video data to local storage, using MP4 format video files.

[0383] Input: Recorded video data

[0384] Output: Video data stored in local storage

[0385] Step 3: Send video data to the server

[0386] The device then sends the saved video data to a server via the Internet. This transmission is done automatically at regular intervals and is securely transmitted using the HTTP protocol.

[0387] Input: Video data stored in local storage

[0388] Output: Video data uploaded to the server

[0389] Step 4: Video Analysis

[0390] The server receives the video data sent from the device and analyzes it using a generative AI model, which uses a deep learning model powered by TensorFlow to evaluate the animal's behavioral patterns and health status.

[0391] Input: Video data stored on the server

[0392] Output: Behavioral and health status analysis results

[0393] Step 5: Save the analysis results and generate countermeasures

[0394] The server stores the analysis results in a database, and then generates appropriate countermeasures based on the stored analysis results, which are determined by pre-defined rule-based systems and AI models.

[0395] Input: Behavioral and health status analysis results

[0396] Output: Analysis results stored in a database and generated countermeasures

[0397] Step 6: Notification of diagnosis results

[0398] The server notifies the user of the analysis results and the generated countermeasures via email or smartphone push notifications, providing the user with information in real time.

[0399] Input: Generated countermeasures and analysis results

[0400] Output: Notification sent to user device

[0401] Step 7: Receiving and Viewing Notifications

[0402] The user will receive a notification on their device and check the recommended course of action. Specifically, detailed information and specific actions will be displayed on the user's device. For example, the message may read, "Your pet is stressed. Try the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0403] Input: Notification to user device

[0404] Output: Solution displayed on the terminal

[0405] (Application example 1)

[0406] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0407] When caring for rescued animals, especially dogs and cats, it is difficult for owners to quickly identify their animals' stress and health conditions and take appropriate measures. Furthermore, in situations where real-time response is required when an abnormality occurs, owners often lack the information necessary to take prompt action. These issues can make it difficult to properly care for rescued animals.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0409] In this invention, the server includes a means for analyzing the captured video data and evaluating the behavior and health condition of the animal, a means for notifying the user of the analysis results to the user's terminal, and a means for notifying the user in real time if the analysis results detect an abnormality. This enables owners to quickly and accurately grasp the stress and health condition of the rescued animal and take appropriate measures early on.

[0410] "Rescued animals" are mammals, such as dogs and cats, that are kept in shelters or temporary captive environments.

[0411] "Daily life" refers to the general activities and behaviors that rescued animals engage in on a daily basis.

[0412] "Filming means" refers to the means of recording animal activity as video data using a camera, smartphone, etc.

[0413] A "server" is a computer system for receiving, storing, and analyzing video data.

[0414] "Video data" refers to video data that records the daily activities of protected animals.

[0415] "Analysis methods" are methods that use AI technology to analyze video data and evaluate the behavior and health of animals.

[0416] A "generative AI model" is an artificial intelligence model trained to predict and assess animal behavior and health status from input data.

[0417] A "prompt sentence" is an input sentence that requests a specific analysis from a generative AI model.

[0418] The "notification means" is a means for transmitting the analysis results to the user's terminal.

[0419] "Real-time notification means" refers to a means for immediately notifying the user when an abnormality is detected.

[0420] "User Device" means a device used by a User to check the status of a rescued animal, including a smartphone or computer.

[0421] "Means for displaying appropriate countermeasures" refers to a means for presenting users with recommended countermeasures based on the results of AI analysis.

[0422] This invention is a system that monitors the daily lives of protected animals and detects abnormalities in their health and behavior at an early stage. The system mainly consists of the following components:

[0423] System configuration

[0424] The system includes a "filming means" for filming the daily lives of animals, a means for transmitting the filmed video data to a "server," an "analysis means" for analyzing the video data on the server, a "notification means" for notifying the user of the analysis results, and a "countermeasure display means" for presenting appropriate countermeasures on the user's terminal.

[0425] Filming method

[0426] The video recording device and communication terminal are used to record the daily behavior and behavior of the protected animals as video data, which is then sent to a server at regular intervals.

[0427] Server and analysis method

[0428] The server stores the received video data and then analyzes it using a generative AI model trained to assess animal behavior and health. The analysis method uses software including OpenCV and other image processing libraries, for example. This detects abnormal animal behavior and signs of stress.

[0429] Notification means

[0430] The analysis results are evaluated in real time, and if an anomaly is detected, the user is immediately notified via a notification method. The notification is sent to the user's device, such as a smartphone or computer. The notification includes details of the detected problem and recommended countermeasures. The notification method uses, for example, the HTTP communication library "requests."

[0431] Countermeasure display method

[0432] When a user receives a notification, specific countermeasures are displayed on the user's device, including recommended countermeasures based on the analysis results of the generative AI model, allowing the user to take appropriate action quickly.

[0433] Specific examples

[0434] For example, if a rescued dog is exhibiting abnormal behavior and appears stressed, the analytics tool will send a notification to the user saying, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." These solutions allow owners to take appropriate measures early on.

[0435] Prompt Sentence Examples

[0436] An example of a prompt to be input into the generative AI model is, "Analyze the video data below and detect stress or abnormal behavior in the pet. If an abnormality is detected, please provide specific countermeasures."

[0437] This system will enable real-time monitoring of the health and behavioral abnormalities of rescued animals, enabling prompt and appropriate responses, which will improve the quality of life of rescued animals and promote proper care.

[0438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0439] Step 1:

[0440] The device uses a video camera or smartphone to record the daily behavior and behavior of the protected animals. The input is continuous video data, which is saved as a video file. The output is a video file.

[0441] Step 2:

[0442] The device uploads video data captured at regular intervals to the server. The input is a saved video file, which is sent using the HTTP communication library "requests." The output is the video data saved on the server.

[0443] Step 3:

[0444] The server temporarily stores the received video data in storage. The input is the uploaded video data, which is saved in an analyzable format. The output is the saved video data file.

[0445] Step 4:

[0446] The server analyzes the stored video data using a generative AI model. This analysis uses a prompt to evaluate the animal's behavior and health condition: "Analyze the following video data and detect stress or abnormal behavior in your pet. If abnormalities are detected, please provide specific countermeasures." The input is the stored video data, and the output is the analysis results regarding the animal's behavior and health condition.

[0447] Step 5:

[0448] The server evaluates the analysis results and generates data to notify users if an anomaly is detected. The input is the analysis result from the generative AI model, and the output is the anomaly information to be notified and recommended countermeasures.

[0449] Step 6:

[0450] The server notifies the user's device, such as a smartphone or computer, of the analysis results and recommended actions. The input is the data on the recommended actions, which is sent in the form of a push notification or email. The output is a notification message that is displayed on the user's device.

[0451] Step 7:

[0452] The user device displays the content of the received notification message to the user. The input is the notification data sent from the server, and the output is a notification message that the user can check. Specifically, the application displays a message such as "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian."

[0453] In this way, the health and behavioral abnormalities of protected animals can be monitored in real time, enabling prompt and appropriate responses.

[0454] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0455] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0456] First, the devices record the daily lives of the protected animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then sent to a server via a network.

[0457] The server receives and analyzes the transmitted video data. A pre-trained generative AI model is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress or anxiety. The analysis results are stored on the server, and appropriate countermeasures are generated.

[0458] The server then uses an emotion engine to assess the user's emotional state, which analyzes the user's voice and facial expressions to determine their emotional state, for example, whether they are stressed or calm.

[0459] The server then sends a notification to the user's device based on the analysis results and the evaluation results of the emotion engine. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. Notifications can be sent via email, app push notifications, etc.

[0460] The user's device will display the received notification and offer specific suggestions for dealing with the situation. For example, if a rescue dog is feeling stressed, the notification will include suggestions such as: "Your pet is feeling stressed. Try the following: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." If the user's emotions are heightened, the notification will be milder and presented in a more user-friendly format.

[0461] This allows users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health. In addition, a notification method based on the user's emotional state further promotes the reduction of stress and anxiety. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0462] The processing flow will be explained below.

[0463] Step 1:

[0464] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0465] Step 2:

[0466] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0467] Step 3:

[0468] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0469] Step 4:

[0470] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0471] Step 5:

[0472] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0473] Step 6:

[0474] Evaluate the user's emotional state. Using an emotion engine, the system analyzes the user's voice and facial expressions to determine their emotional state. It also uses a camera and microphone to collect data to assess whether the user is stressed or calm.

[0475] Step 7:

[0476] The results are then sent to the user's device. The server combines the analysis results with the emotion engine's evaluation results to generate a notification message, which is then sent to the user's device. The notification is customized based on the user's emotional state and sent via an appropriate method, such as email or app push notification.

[0477] Step 8:

[0478] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures. If the user is feeling stressed, the content and wording of the notification will be adjusted and presented in a gentler tone.

[0479] Step 9:

[0480] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0481] Example 2

[0482] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0483] When caring for rescued animals, especially dogs and cats, there is a need to detect stress and changes in their health at an early stage and take appropriate countermeasures. However, conventional systems lack sufficient means for comprehensively monitoring and evaluating the behavior and health of animals, and do not present appropriate countermeasures based on the user's emotional state. This increases the risk of compromising the welfare of animals, and the present invention aims to solve these problems.

[0484] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0485] In this invention, the server includes a means for filming the daily life of the animals, a means for transmitting the video data to the server, a means for analyzing the video data in the server and evaluating the behavior and health state of the animals, a means for analyzing the emotional state of the user, a means for notifying the user terminal of the analysis results, and a means for receiving the notification in the user terminal and displaying appropriate countermeasures. This makes it possible to detect stress and health conditions of protected animals early and provide appropriate countermeasures according to the emotional state of the user.

[0486] "Protected animals" refers to animals kept by humans, particularly mammals such as dogs and cats, that are protected.

[0487] "Means" refers to a method, apparatus, software, or combination thereof for accomplishing a particular purpose.

[0488] "Video data" refers to digital video data that records the daily lives of animals.

[0489] "Server" refers to a computer system that stores, analyzes, and manages data over a network.

[0490] "Analysis" refers to the process of evaluating the behavior and health of animals based on collected data.

[0491] "Behavior and health" refers to an animal's behavior, such as movement, posture, eating and sleeping patterns, as well as health-related indicators such as weight, body temperature and respiratory rate.

[0492] "Evaluating" refers to judging and reporting an animal's behavior and health status numerically or verbally through data analysis.

[0493] "User" refers to a person who uses the system, typically an animal owner.

[0494] "Emotional state" refers to the user's psychological state, as analyzed from voice, facial expressions, etc.

[0495] "Notification" refers to a message or alert sent from the server to a user device.

[0496] "Countermeasures" refers to specific instructions or suggestions for action provided based on the analysis results.

[0497] "User device" refers to the device used by a User to receive and view Notifications, including smartphones, tablets, and computers.

[0498] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and automatically performs a specific task (in this case, video analysis).

[0499] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0500] First, the device records the daily life of the protected animals. The device can be a video camera or a smartphone. These devices capture video at regular intervals and save it as video data. For example, the device's video camera can be used to record 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured data is saved in the device's local storage. The saved data is then sent to a server via the Internet.

[0501] The server receives the transmitted video data and analyzes it. A pre-trained generative AI model, such as "YOLOv4," is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress and anxiety. The analysis results are stored on the server. The server also uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, "IBM Watson Tone Analyzer" is used.

[0502] The server then sends a notification to the user's device based on the analysis results and the emotion engine's evaluation. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. The notification can be sent via email or app push notification. Specific examples include, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0503] The user's device will display the received notification and provide specific countermeasures. The user can check the notification message on their smartphone or tablet and take the suggested countermeasures.

[0504] Prompt Sentence Examples

[0505] Sample prompts for detecting stress in animals:

[0506] Prompt: "Analyze the following video data to detect whether the animal is experiencing stress."

[0507] Analysis results: Signs of stress (frequent yawning, sitting down, etc.) were detected from footage of rescue dogs, and the dogs reported feeling stressed.

[0508] Example prompts for user sentiment analysis:

[0509] Prompt: "Analyze the user's voice data and assess their current emotional state."

[0510] Analysis results: If the user is determined to be stressed, the response will be "I am stressed."

[0511] Example prompt for generating notifications:

[0512] Prompt: "Please suggest some measures to reduce stress for rescue dogs. The user is currently experiencing stress."

[0513] Analysis: "The user is stressed. Consider these gentle solutions: 1. Allow the user to rest in a quiet place. 2. Increase walks. 3. Consider consulting a veterinarian."

[0514] This will enable users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health, thereby improving the welfare of the animals.

[0515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0516] Step 1:

[0517] The device is used to capture the daily lives of rescued animals.

[0518] Input: Camera device and set capture time.

[0519] Specific operation: For example, the device's video camera captures 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured video data is stored in the device's local storage.

[0520] Output: Saved video data.

[0521] Step 2:

[0522] The terminal uses a means for transmitting the stored video data to the server.

[0523] Input: Stored video data and network connection.

[0524] How it works: The device uses Wi-Fi or mobile data to send the video data to the specified URL on the server immediately after shooting. The data is encrypted during transmission.

[0525] Output: Video data sent to the server.

[0526] Step 3:

[0527] The server uses a means for analyzing the received video data.

[0528] Input: Received video data.

[0529] How it works: The server analyzes video data using a pre-trained generative AI model (e.g., YOLOv4). The model evaluates the animals' behavioral patterns and health status, and detects signs of stress or anxiety.

[0530] Output: Analysis results (assessment of animal behavior patterns and health status).

[0531] Step 4:

[0532] The server employs a means for analyzing the user's emotional state.

[0533] Input: User's voice data and facial expression data.

[0534] How it works: The server receives voice data sent by the user in real time and analyzes it using an "emotion engine." The emotion engine uses, for example, the "IBM Watson Tone Analyzer" to determine the user's emotional state, such as whether they are tense or relaxed.

[0535] Output: Evaluation result of the user's emotional state.

[0536] Step 5:

[0537] The server uses a means to send a notification to the user's device based on the analysis results and the emotion engine's evaluation results.

[0538] Input: Analysis results of video data and evaluation results of user's emotional state.

[0539] Specific operation: The server visualizes the detected problems and solutions, creates notifications in expressions that correspond to the user's emotional state, and sends them as emails or push notifications to smartphone apps.

[0540] Output: Notification message to the user's terminal.

[0541] Step 6:

[0542] The user's device receives the notification and uses a means to display appropriate countermeasures.

[0543] Input: The notification message sent by the server.

[0544] Specific Action: The user checks the notification on their smartphone or tablet and takes specific action according to the instructions provided, such as "Your pet is stressed. Consider the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0545] Output: User action taken.

[0546] (Application example 2)

[0547] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0548] Conventional home security systems lack the means to detect suspicious behavior or changes in user emotions in real time and respond quickly and appropriately. Furthermore, there is no effective system for monitoring pet behavior and health to ensure home safety. Therefore, there is a need for technology to comprehensively improve home safety.

[0549] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing video footage from within the home and monitoring for suspicious behavior, means for analyzing the user's voice and facial expressions to determine the user's emotional state, and means for notifying the user's terminal of the analysis results. This makes it possible to detect abnormal behavior and emotional changes within the home early and provide the user with prompt and appropriate countermeasures.

[0550] "Rescued animals" refers to animals that are under specific management and care.

[0551] "Filming means" refers to a device or group of devices used to record images of animals or household activities.

[0552] "Video data" refers to data that includes a series of video frames captured by a camera.

[0553] "Server" refers to a computer system for receiving, storing, and analyzing data over a network.

[0554] "Analysis means" refers to algorithms or models used to analyze received video data and detect specific patterns or anomalies.

[0555] "Notification means" refers to a method or device for notifying the user of the analysis results.

[0556] "User Device" refers to the device (e.g., smartphone or tablet) used by a User to receive information.

[0557] "Appropriate countermeasures" refer to the actions or measures that users should take based on the analysis results.

[0558] "Home video" refers to video data captured by a camera installed in the home.

[0559] "Suspicious behavior" refers to irregular movements or actions that deviate from normal patterns of behavior.

[0560] "Voice and facial expression analysis" refers to the process of determining a user's emotional state from collected audio and video data.

[0561] "Emotional state" refers to the psychological state or mood a user is feeling at that moment.

[0562] The present invention is a security system for improving safety within the home, monitoring the daily lives of protected animals, detecting suspicious behavior, and analyzing the user's emotional state to provide appropriate notifications.

[0563] 1. Photography methods and data collection:

[0564] Video cameras and smartphones installed in homes periodically capture footage of animals and the home and save it as video data. The data collected by these devices is then sent to a server via a network.

[0565] 2. Data analysis by the server:

[0566] The server is equipped with a generative AI model using deep learning frameworks such as TensorFlow and PyTorch to analyze the received video data. This model evaluates the behavioral patterns of animals and household members to detect signs of stress, anxiety, or suspicious behavior. Furthermore, an emotion engine (e.g., Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) is used to analyze the user's voice and facial expressions to determine their emotional state.

[0567] 3. Notification Methods and User Devices:

[0568] The analysis results on the server are sent to the user's smartphone, tablet, or other device via a cloud service (e.g., AWS Lambda). Notifications are sent via email or app push notifications. The notification content includes details of the detected problem and countermeasures based on the user's emotional state.

[0569] 4. Example:

[0570] For example, if a home camera detects suspicious activity in the living room in the middle of the night and a child's crying or startled scream is recorded by the microphone, the server will analyze this data. The AI ​​will detect the abnormality and send a notification to the parent's smartphone saying, "Abnormal activity has been detected in the living room late at night. Your child appears to be startled. Please take immediate action."

[0571] 5. Example prompt:

[0572] "A loud noise has been detected that may startle a child in the middle of the night. Check your smart home cameras for suspicious activity. Consider contacting the police if necessary."

[0573] This invention makes it possible to detect abnormal behavior and emotional changes in the home at an early stage and provide users with prompt and appropriate countermeasures, thereby improving safety in the home overall and providing a safe and secure living environment.

[0574] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0575] Step 1:

[0576] The device periodically captures what is happening in the home using a video camera or smartphone and records it as video data. The input is real-time video and audio data, and the output is the captured video data.

[0577] Step 2:

[0578] The device transmits the captured video data to the server via the network. The input is the video data stored on the device, and the output is the video data uploaded to the server.

[0579] Step 3:

[0580] The video data received by the server is analyzed using a generative AI model (a model using TensorFlow or PyTorch). The input is the transmitted video data, and the output is the analysis results of animal behavior patterns and abnormal behavior within the home. Specifically, the AI ​​model analyzes the movements of animals and people in the video and determines whether there are any patterns or abnormalities.

[0581] Step 4:

[0582] The server uses an emotion engine (such as the Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's voice and facial expression data, and the output is the user's emotional evaluation result. Specifically, the emotion engine analyzes voice tone and facial expression changes to identify the emotional state (e.g., surprise or fear).

[0583] Step 5:

[0584] Based on the behavioral pattern analysis results and emotional state evaluation results acquired by the server, the server generates appropriate notification content and sends it to the user's device. The input is the analysis results and emotional evaluation results, and the output is notification data for the user's device. Specifically, the server formats the analysis results into a prompt sentence and generates the notification content.

[0585] Step 6:

[0586] The user device receives the notification from the server and displays the appropriate countermeasures. The input is the notification data sent from the server, and the output is the countermeasures displayed on the device. The user can receive this notification and act according to the countermeasures. Specifically, the user device may display a notification such as, "Abnormal activity has been detected in the living room late at night. It appears that a child is startled. Please take immediate action."

[0587] This series of steps makes it possible to detect abnormal behavior and emotional changes within the home in real time and notify the user of prompt and appropriate countermeasures.

[0588] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0589] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0590] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0591] [Third embodiment]

[0592] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0593] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0594] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0595] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0596] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0598] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0599] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0600] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0601] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0602] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0603] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0604] This invention is a system for supporting the care of rescued animals, particularly dogs and cats. Specifically, it uses video analysis technology to continuously monitor the daily lives of rescued animals, early detect signs of stress or anxiety and changes in their health, and suggest appropriate countermeasures.

[0605] First, the devices record the daily lives of the rescued animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then periodically sent to a server.

[0606] The server receives the transmitted video data and performs analysis using a generative AI model. The generative AI model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions, such as signs of stress, abnormal behavior, or changes in health status. The analysis results are stored on the server, and appropriate countermeasures are generated as needed.

[0607] The server then sends the analysis results to the user's device. The notification includes details of the detected issues and recommended solutions. The notification is sent via email or app push notification. When the user receives the notification, detailed information and specific solutions are displayed on the user's device.

[0608] For example, if a rescue dog is stressed, the user may receive a notification like this: "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian." Specific solutions like this allow users to take appropriate action early on.

[0609] This allows users to reduce the stress and anxiety of rescued animals and take early action to maintain their health. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0610] The processing flow will be explained below.

[0611] Step 1:

[0612] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0613] Step 2:

[0614] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0615] Step 3:

[0616] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0617] Step 4:

[0618] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0619] Step 5:

[0620] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0621] Step 6:

[0622] The results are sent to the user's device. The server generates a notification message that combines the analysis results and countermeasures, and sends it to the user's device. Notifications are sent via email, app push notifications, and other methods.

[0623] Step 7:

[0624] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures, so that the user can take immediate action.

[0625] Step 8:

[0626] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0627] Example 1

[0628] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0629] Health management and behavior monitoring of rescued animals is extremely important, especially because it is necessary to detect stress and changes in their health status early. However, conventional methods make it difficult to monitor animals on a daily basis, making it difficult to respond quickly and appropriately. Furthermore, there are challenges in understanding the animals' condition in real time and taking appropriate countermeasures early.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0631] In this invention, the server includes a means for periodically recording the daily lives of the protected animals and saving them as video data, a means for transmitting the saved video data to the server via the Internet, and a means for analyzing the video data received by the server using a generative model to evaluate the behavioral patterns and health status of the animals. This makes it possible to monitor the status of the animals in real time and propose appropriate countermeasures at an early stage.

[0632] "Rescued animals" refers to animals that are rescued and cared for by animal shelters or animal welfare organizations.

[0633] "Filming means" refers to a device for recording the daily life of an animal as video footage, and includes communication devices such as video cameras and smartphones.

[0634] "Video data" refers to digital data containing captured video information, and is used to analyze animal behavior and health conditions.

[0635] "Server" refers to a computer system that receives video data via the Internet and is responsible for analyzing and storing the data.

[0636] "Generative models" refer to deep learning models trained using AI techniques to analyze animal behavior patterns and health conditions.

[0637] "Analysis means" refers to the process of inputting received video data into a generative model to evaluate the animal's behavioral patterns and health status.

[0638] "Database" refers to a digital database system for storing analysis results and generated countermeasures.

[0639] "Notification means" refers to the communication method used to communicate the analysis results and appropriate countermeasures to the user's device, and includes email and push notifications.

[0640] "User terminal" refers to a communication device used by a user, such as a computer or smartphone, that has the ability to receive and display notifications.

[0641] "Response measures" refer to specific steps to be taken to address animal health or behavioral issues based on the analysis results.

[0642] MODE FOR CARRYING OUT THE INVENTION

[0643] This invention is a care support system for rescued animals, particularly dogs and cats. Specifically, it is a system that uses video analysis technology to continuously monitor the daily lives of rescued animals, detects signs of stress or anxiety and changes in their health at an early stage, and suggests appropriate countermeasures.

[0644] Hardware and Software

[0645] The terminals used are communication devices such as video cameras and smartphones. These terminals film the animals' daily lives and capture video at regular intervals. The video data is stored in local storage and periodically sent to a server via the Internet. The secure HTTP protocol (HTTPS) is used for transmission.

[0646] The server stores the received video data and analyzes it using a generative AI model built using Python and deep learning libraries such as TensorFlow. The model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions.

[0647] The analysis results are stored in a database (e.g., MySQL or MongoDB). The server then generates appropriate countermeasures based on the analysis results. These countermeasures are determined by pre-configured rule-based systems or AI models.

[0648] The generated countermeasures are sent from the server to the user's device via email or smartphone push notification. The user receives the notification and checks the recommended countermeasures. Detailed information and specific countermeasures are displayed on the user's device.

[0649] Examples of concrete examples and prompts

[0650] For example, if a behavior analysis system for rescue dogs were introduced, the following process would be performed: The user installs a video camera in their living room to monitor the area where their pet is active. The camera captures video every 10 minutes and sends it to a cloud server. The server analyzes the video using a generative AI model using TensorFlow and detects that the pet is experiencing stress. The server generates countermeasures, such as providing a quiet environment where the pet can relax, and notifies the user.

[0651] Below are some example prompts for a generative AI model:

[0652] Generate an AI model that detects signs of health and stress from video footage of animals. Analyze the video data below to assess your pet's behavior and suggest appropriate actions when specific issues arise.

[0653] Video data: "sample_video.mp4"

[0654] Examples of workarounds:

[0655] Stress: "Provide a quiet and calming environment."

[0656] Health issues: "Consider consulting a veterinarian."

[0657] Please return the detected issues and recommended solutions in JSON format.

[0658] This system allows users to monitor and manage the status of rescued animals in real time and take appropriate action in a timely manner, which in turn makes it easier to find new homes for rescued animals and contributes to reducing the number of animals that are euthanized.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1: Recording a video

[0661] The device captures the daily life of the rescued animals. The specific operation of this processing step is to use a video camera or smartphone and place it in the living room or animal's activity area. The device captures 30 seconds of video every 10 minutes.

[0662] Input: Animal activity footage

[0663] Output: Video data (e.g. MP4 format file)

[0664] Step 2: Save the video data locally

[0665] The device saves the captured video data to local storage, using MP4 format video files.

[0666] Input: Recorded video data

[0667] Output: Video data stored in local storage

[0668] Step 3: Send video data to the server

[0669] The device then sends the saved video data to a server via the Internet. This transmission is done automatically at regular intervals and is securely transmitted using the HTTP protocol.

[0670] Input: Video data stored in local storage

[0671] Output: Video data uploaded to the server

[0672] Step 4: Video Analysis

[0673] The server receives the video data sent from the device and analyzes it using a generative AI model, which uses a deep learning model powered by TensorFlow to evaluate the animal's behavioral patterns and health status.

[0674] Input: Video data stored on the server

[0675] Output: Behavioral and health status analysis results

[0676] Step 5: Save the analysis results and generate countermeasures

[0677] The server stores the analysis results in a database, and then generates appropriate countermeasures based on the stored analysis results, which are determined by pre-defined rule-based systems and AI models.

[0678] Input: Behavioral and health status analysis results

[0679] Output: Analysis results stored in a database and generated countermeasures

[0680] Step 6: Notification of diagnosis results

[0681] The server notifies the user of the analysis results and the generated countermeasures via email or smartphone push notifications, providing the user with information in real time.

[0682] Input: Generated countermeasures and analysis results

[0683] Output: Notification sent to user device

[0684] Step 7: Receiving and Viewing Notifications

[0685] The user will receive a notification on their device and check the recommended course of action. Specifically, detailed information and specific actions will be displayed on the user's device. For example, the message may read, "Your pet is stressed. Try the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0686] Input: Notification to user device

[0687] Output: Solution displayed on the terminal

[0688] (Application example 1)

[0689] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0690] When caring for rescued animals, especially dogs and cats, it is difficult for owners to quickly identify their animals' stress and health conditions and take appropriate measures. Furthermore, in situations where real-time response is required when an abnormality occurs, owners often lack the information necessary to take prompt action. These issues can make it difficult to properly care for rescued animals.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0692] In this invention, the server includes a means for analyzing the captured video data and evaluating the behavior and health condition of the animal, a means for notifying the user of the analysis results to the user's terminal, and a means for notifying the user in real time if the analysis results detect an abnormality. This enables owners to quickly and accurately grasp the stress and health condition of the rescued animal and take appropriate measures early on.

[0693] "Rescued animals" are mammals, such as dogs and cats, that are kept in shelters or temporary captive environments.

[0694] "Daily life" refers to the general activities and behaviors that rescued animals engage in on a daily basis.

[0695] "Filming means" refers to the means of recording animal activity as video data using a camera, smartphone, etc.

[0696] A "server" is a computer system for receiving, storing, and analyzing video data.

[0697] "Video data" refers to video data that records the daily activities of protected animals.

[0698] "Analysis methods" are methods that use AI technology to analyze video data and evaluate the behavior and health of animals.

[0699] A "generative AI model" is an artificial intelligence model trained to predict and assess animal behavior and health status from input data.

[0700] A "prompt sentence" is an input sentence that requests a specific analysis from a generative AI model.

[0701] The "notification means" is a means for transmitting the analysis results to the user's terminal.

[0702] "Real-time notification means" refers to a means for immediately notifying the user when an abnormality is detected.

[0703] "User Device" means a device used by a User to check the status of a rescued animal, including a smartphone or computer.

[0704] "Means for displaying appropriate countermeasures" refers to a means for presenting users with recommended countermeasures based on the results of AI analysis.

[0705] This invention is a system that monitors the daily lives of protected animals and detects abnormalities in their health and behavior at an early stage. The system mainly consists of the following components:

[0706] System configuration

[0707] The system includes a "filming means" for filming the daily lives of animals, a means for transmitting the filmed video data to a "server," an "analysis means" for analyzing the video data on the server, a "notification means" for notifying the user of the analysis results, and a "countermeasure display means" for presenting appropriate countermeasures on the user's terminal.

[0708] Filming method

[0709] The video recording device and communication terminal are used to record the daily behavior and behavior of the protected animals as video data, which is then sent to a server at regular intervals.

[0710] Server and analysis method

[0711] The server stores the received video data and then analyzes it using a generative AI model trained to assess animal behavior and health. The analysis method uses software including OpenCV and other image processing libraries, for example. This detects abnormal animal behavior and signs of stress.

[0712] Notification means

[0713] The analysis results are evaluated in real time, and if an anomaly is detected, the user is immediately notified via a notification method. The notification is sent to the user's device, such as a smartphone or computer. The notification includes details of the detected problem and recommended countermeasures. The notification method uses, for example, the HTTP communication library "requests."

[0714] Countermeasure display method

[0715] When a user receives a notification, specific countermeasures are displayed on the user's device, including recommended countermeasures based on the analysis results of the generative AI model, allowing the user to take appropriate action quickly.

[0716] Specific examples

[0717] For example, if a rescued dog is exhibiting abnormal behavior and appears stressed, the analytics tool will send a notification to the user saying, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." These solutions allow owners to take appropriate measures early on.

[0718] Prompt Sentence Examples

[0719] An example of a prompt to be input into the generative AI model is, "Analyze the video data below and detect stress or abnormal behavior in the pet. If an abnormality is detected, please provide specific countermeasures."

[0720] This system will enable real-time monitoring of the health and behavioral abnormalities of rescued animals, enabling prompt and appropriate responses, which will improve the quality of life of rescued animals and promote proper care.

[0721] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0722] Step 1:

[0723] The device uses a video camera or smartphone to record the daily behavior and behavior of the protected animals. The input is continuous video data, which is saved as a video file. The output is a video file.

[0724] Step 2:

[0725] The device uploads video data captured at regular intervals to the server. The input is a saved video file, which is sent using the HTTP communication library "requests." The output is the video data saved on the server.

[0726] Step 3:

[0727] The server temporarily stores the received video data in storage. The input is the uploaded video data, which is saved in an analyzable format. The output is the saved video data file.

[0728] Step 4:

[0729] The server analyzes the stored video data using a generative AI model. This analysis uses a prompt to evaluate the animal's behavior and health condition: "Analyze the following video data and detect stress or abnormal behavior in your pet. If abnormalities are detected, please provide specific countermeasures." The input is the stored video data, and the output is the analysis results regarding the animal's behavior and health condition.

[0730] Step 5:

[0731] The server evaluates the analysis results and generates data to notify users if an anomaly is detected. The input is the analysis result from the generative AI model, and the output is the anomaly information to be notified and recommended countermeasures.

[0732] Step 6:

[0733] The server notifies the user's device, such as a smartphone or computer, of the analysis results and recommended actions. The input is the data on the recommended actions, which is sent in the form of a push notification or email. The output is a notification message that is displayed on the user's device.

[0734] Step 7:

[0735] The user device displays the content of the received notification message to the user. The input is the notification data sent from the server, and the output is a notification message that the user can check. Specifically, the application displays a message such as "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian."

[0736] In this way, the health and behavioral abnormalities of protected animals can be monitored in real time, enabling prompt and appropriate responses.

[0737] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0738] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0739] First, the devices record the daily lives of the protected animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then sent to a server via a network.

[0740] The server receives and analyzes the transmitted video data. A pre-trained generative AI model is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress or anxiety. The analysis results are stored on the server, and appropriate countermeasures are generated.

[0741] The server then uses an emotion engine to assess the user's emotional state, which analyzes the user's voice and facial expressions to determine their emotional state, for example, whether they are stressed or calm.

[0742] The server then sends a notification to the user's device based on the analysis results and the evaluation results of the emotion engine. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. Notifications can be sent via email, app push notifications, etc.

[0743] The user's device will display the received notification and offer specific suggestions for dealing with the situation. For example, if a rescue dog is feeling stressed, the notification will include suggestions such as: "Your pet is feeling stressed. Try the following: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." If the user's emotions are heightened, the notification will be milder and presented in a more user-friendly format.

[0744] This allows users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health. In addition, a notification method based on the user's emotional state further promotes the reduction of stress and anxiety. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0745] The processing flow will be explained below.

[0746] Step 1:

[0747] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0748] Step 2:

[0749] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0750] Step 3:

[0751] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0752] Step 4:

[0753] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0754] Step 5:

[0755] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0756] Step 6:

[0757] Evaluate the user's emotional state. Using an emotion engine, the system analyzes the user's voice and facial expressions to determine their emotional state. It also uses a camera and microphone to collect data to assess whether the user is stressed or calm.

[0758] Step 7:

[0759] The results are then sent to the user's device. The server combines the analysis results with the emotion engine's evaluation results to generate a notification message, which is then sent to the user's device. The notification is customized based on the user's emotional state and sent via an appropriate method, such as email or app push notification.

[0760] Step 8:

[0761] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures. If the user is feeling stressed, the content and wording of the notification will be adjusted and presented in a gentler tone.

[0762] Step 9:

[0763] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0764] Example 2

[0765] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0766] When caring for rescued animals, especially dogs and cats, there is a need to detect stress and changes in their health at an early stage and take appropriate countermeasures. However, conventional systems lack sufficient means for comprehensively monitoring and evaluating the behavior and health of animals, and do not present appropriate countermeasures based on the user's emotional state. This increases the risk of compromising the welfare of animals, and the present invention aims to solve these problems.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0768] In this invention, the server includes a means for filming the daily life of the animals, a means for transmitting the video data to the server, a means for analyzing the video data in the server and evaluating the behavior and health state of the animals, a means for analyzing the emotional state of the user, a means for notifying the user terminal of the analysis results, and a means for receiving the notification in the user terminal and displaying appropriate countermeasures. This makes it possible to detect stress and health conditions of protected animals early and provide appropriate countermeasures according to the emotional state of the user.

[0769] "Protected animals" refers to animals kept by humans, particularly mammals such as dogs and cats, that are protected.

[0770] "Means" refers to a method, apparatus, software, or combination thereof for accomplishing a particular purpose.

[0771] "Video data" refers to digital video data that records the daily lives of animals.

[0772] "Server" refers to a computer system that stores, analyzes, and manages data over a network.

[0773] "Analysis" refers to the process of evaluating the behavior and health of animals based on collected data.

[0774] "Behavior and health" refers to an animal's behavior, such as movement, posture, eating and sleeping patterns, as well as health-related indicators such as weight, body temperature and respiratory rate.

[0775] "Evaluating" refers to judging and reporting an animal's behavior and health status numerically or verbally through data analysis.

[0776] "User" refers to a person who uses the system, typically an animal owner.

[0777] "Emotional state" refers to the user's psychological state, as analyzed from voice, facial expressions, etc.

[0778] "Notification" refers to a message or alert sent from the server to a user device.

[0779] "Countermeasures" refers to specific instructions or suggestions for action provided based on the analysis results.

[0780] "User device" refers to the device used by a User to receive and view Notifications, including smartphones, tablets, and computers.

[0781] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and automatically performs a specific task (in this case, video analysis).

[0782] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[0783] First, the device records the daily life of the protected animals. The device can be a video camera or a smartphone. These devices capture video at regular intervals and save it as video data. For example, the device's video camera can be used to record 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured data is saved in the device's local storage. The saved data is then sent to a server via the Internet.

[0784] The server receives the transmitted video data and analyzes it. A pre-trained generative AI model, such as "YOLOv4," is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress and anxiety. The analysis results are stored on the server. The server also uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, "IBM Watson Tone Analyzer" is used.

[0785] The server then sends a notification to the user's device based on the analysis results and the emotion engine's evaluation. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. The notification can be sent via email or app push notification. Specific examples include, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0786] The user's device will display the received notification and provide specific countermeasures. The user can check the notification message on their smartphone or tablet and take the suggested countermeasures.

[0787] Prompt Sentence Examples

[0788] Sample prompts for detecting stress in animals:

[0789] Prompt: "Analyze the following video data to detect whether the animal is experiencing stress."

[0790] Analysis results: Signs of stress (frequent yawning, sitting down, etc.) were detected from footage of rescue dogs, and the dogs reported feeling stressed.

[0791] Example prompts for user sentiment analysis:

[0792] Prompt: "Analyze the user's voice data and assess their current emotional state."

[0793] Analysis results: If the user is determined to be stressed, the response will be "I am stressed."

[0794] Example prompt for generating notifications:

[0795] Prompt: "Please suggest some measures to reduce stress for rescue dogs. The user is currently experiencing stress."

[0796] Analysis: "The user is stressed. Consider these gentle solutions: 1. Allow the user to rest in a quiet place. 2. Increase walks. 3. Consider consulting a veterinarian."

[0797] This will enable users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health, thereby improving the welfare of the animals.

[0798] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0799] Step 1:

[0800] The device is used to capture the daily lives of rescued animals.

[0801] Input: Camera device and set capture time.

[0802] Specific operation: For example, the device's video camera captures 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured video data is stored in the device's local storage.

[0803] Output: Saved video data.

[0804] Step 2:

[0805] The terminal uses a means for transmitting the stored video data to the server.

[0806] Input: Stored video data and network connection.

[0807] How it works: The device uses Wi-Fi or mobile data to send the video data to the specified URL on the server immediately after shooting. The data is encrypted during transmission.

[0808] Output: Video data sent to the server.

[0809] Step 3:

[0810] The server uses a means for analyzing the received video data.

[0811] Input: Received video data.

[0812] How it works: The server analyzes video data using a pre-trained generative AI model (e.g., YOLOv4). The model evaluates the animals' behavioral patterns and health status, and detects signs of stress or anxiety.

[0813] Output: Analysis results (assessment of animal behavior patterns and health status).

[0814] Step 4:

[0815] The server employs a means for analyzing the user's emotional state.

[0816] Input: User's voice data and facial expression data.

[0817] How it works: The server receives voice data sent by the user in real time and analyzes it using an "emotion engine." The emotion engine uses, for example, the "IBM Watson Tone Analyzer" to determine the user's emotional state, such as whether they are tense or relaxed.

[0818] Output: Evaluation result of the user's emotional state.

[0819] Step 5:

[0820] The server uses a means to send a notification to the user's device based on the analysis results and the emotion engine's evaluation results.

[0821] Input: Analysis results of video data and evaluation results of user's emotional state.

[0822] Specific operation: The server visualizes the detected problems and solutions, creates notifications in expressions that correspond to the user's emotional state, and sends them as emails or push notifications to smartphone apps.

[0823] Output: Notification message to the user's terminal.

[0824] Step 6:

[0825] The user's device receives the notification and uses a means to display appropriate countermeasures.

[0826] Input: The notification message sent by the server.

[0827] Specific Action: The user checks the notification on their smartphone or tablet and takes specific action according to the instructions provided, such as "Your pet is stressed. Consider the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0828] Output: User action taken.

[0829] (Application example 2)

[0830] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0831] Conventional home security systems lack the means to detect suspicious behavior or changes in user emotions in real time and respond quickly and appropriately. Furthermore, there is no effective system for monitoring pet behavior and health to ensure home safety. Therefore, there is a need for technology to comprehensively improve home safety.

[0832] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing video footage from within the home and monitoring for suspicious behavior, means for analyzing the user's voice and facial expressions to determine the user's emotional state, and means for notifying the user's terminal of the analysis results. This makes it possible to detect abnormal behavior and emotional changes within the home early and provide the user with prompt and appropriate countermeasures.

[0833] "Rescued animals" refers to animals that are under specific management and care.

[0834] "Filming means" refers to a device or group of devices used to record images of animals or household activities.

[0835] "Video data" refers to data that includes a series of video frames captured by a camera.

[0836] "Server" refers to a computer system for receiving, storing, and analyzing data over a network.

[0837] "Analysis means" refers to algorithms or models used to analyze received video data and detect specific patterns or anomalies.

[0838] "Notification means" refers to a method or device for notifying the user of the analysis results.

[0839] "User Device" refers to the device (e.g., smartphone or tablet) used by a User to receive information.

[0840] "Appropriate countermeasures" refer to the actions or measures that users should take based on the analysis results.

[0841] "Home video" refers to video data captured by a camera installed in the home.

[0842] "Suspicious behavior" refers to irregular movements or actions that deviate from normal patterns of behavior.

[0843] "Voice and facial expression analysis" refers to the process of determining a user's emotional state from collected audio and video data.

[0844] "Emotional state" refers to the psychological state or mood a user is feeling at that moment.

[0845] The present invention is a security system for improving safety within the home, monitoring the daily lives of protected animals, detecting suspicious behavior, and analyzing the user's emotional state to provide appropriate notifications.

[0846] 1. Photography methods and data collection:

[0847] Video cameras and smartphones installed in homes periodically capture footage of animals and the home and save it as video data. The data collected by these devices is then sent to a server via a network.

[0848] 2. Data analysis by the server:

[0849] The server is equipped with a generative AI model using deep learning frameworks such as TensorFlow and PyTorch to analyze the received video data. This model evaluates the behavioral patterns of animals and household members to detect signs of stress, anxiety, or suspicious behavior. Furthermore, an emotion engine (e.g., Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) is used to analyze the user's voice and facial expressions to determine their emotional state.

[0850] 3. Notification Methods and User Devices:

[0851] The analysis results on the server are sent to the user's smartphone, tablet, or other device via a cloud service (e.g., AWS Lambda). Notifications are sent via email or app push notifications. The notification content includes details of the detected problem and countermeasures based on the user's emotional state.

[0852] 4. Example:

[0853] For example, if a home camera detects suspicious activity in the living room in the middle of the night and a child's crying or startled scream is recorded by the microphone, the server will analyze this data. The AI ​​will detect the abnormality and send a notification to the parent's smartphone saying, "Abnormal activity has been detected in the living room late at night. Your child appears to be startled. Please take immediate action."

[0854] 5. Example prompt:

[0855] "A loud noise has been detected that may startle a child in the middle of the night. Check your smart home cameras for suspicious activity. Consider contacting the police if necessary."

[0856] This invention makes it possible to detect abnormal behavior and emotional changes in the home at an early stage and provide users with prompt and appropriate countermeasures, thereby improving safety in the home overall and providing a safe and secure living environment.

[0857] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0858] Step 1:

[0859] The device periodically captures what is happening in the home using a video camera or smartphone and records it as video data. The input is real-time video and audio data, and the output is the captured video data.

[0860] Step 2:

[0861] The device transmits the captured video data to the server via the network. The input is the video data stored on the device, and the output is the video data uploaded to the server.

[0862] Step 3:

[0863] The video data received by the server is analyzed using a generative AI model (a model using TensorFlow or PyTorch). The input is the transmitted video data, and the output is the analysis results of animal behavior patterns and abnormal behavior within the home. Specifically, the AI ​​model analyzes the movements of animals and people in the video and determines whether there are any patterns or abnormalities.

[0864] Step 4:

[0865] The server uses an emotion engine (such as the Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's voice and facial expression data, and the output is the user's emotional evaluation result. Specifically, the emotion engine analyzes voice tone and facial expression changes to identify the emotional state (e.g., surprise or fear).

[0866] Step 5:

[0867] Based on the behavioral pattern analysis results and emotional state evaluation results acquired by the server, the server generates appropriate notification content and sends it to the user's device. The input is the analysis results and emotional evaluation results, and the output is notification data for the user's device. Specifically, the server formats the analysis results into a prompt sentence and generates the notification content.

[0868] Step 6:

[0869] The user device receives the notification from the server and displays the appropriate countermeasures. The input is the notification data sent from the server, and the output is the countermeasures displayed on the device. The user can receive this notification and act according to the countermeasures. Specifically, the user device may display a notification such as, "Abnormal activity has been detected in the living room late at night. It appears that a child is startled. Please take immediate action."

[0870] This series of steps makes it possible to detect abnormal behavior and emotional changes within the home in real time and notify the user of prompt and appropriate countermeasures.

[0871] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0872] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0874] [Fourth embodiment]

[0875] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0876] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0877] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0878] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0879] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0880] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0881] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0882] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0883] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0884] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0885] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0886] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0887] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0888] This invention is a system for supporting the care of rescued animals, particularly dogs and cats. Specifically, it uses video analysis technology to continuously monitor the daily lives of rescued animals, early detect signs of stress or anxiety and changes in their health, and suggest appropriate countermeasures.

[0889] First, the devices record the daily lives of the rescued animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then periodically sent to a server.

[0890] The server receives the transmitted video data and performs analysis using a generative AI model. The generative AI model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions, such as signs of stress, abnormal behavior, or changes in health status. The analysis results are stored on the server, and appropriate countermeasures are generated as needed.

[0891] The server then sends the analysis results to the user's device. The notification includes details of the detected issues and recommended solutions. The notification is sent via email or app push notification. When the user receives the notification, detailed information and specific solutions are displayed on the user's device.

[0892] For example, if a rescue dog is stressed, the user may receive a notification like this: "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian." Specific solutions like this allow users to take appropriate action early on.

[0893] This allows users to reduce the stress and anxiety of rescued animals and take early action to maintain their health. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[0897] Step 2:

[0898] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[0899] Step 3:

[0900] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[0901] Step 4:

[0902] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[0903] Step 5:

[0904] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[0905] Step 6:

[0906] The results are sent to the user's device. The server generates a notification message that combines the analysis results and countermeasures, and sends it to the user's device. Notifications are sent via email, app push notifications, and other methods.

[0907] Step 7:

[0908] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures, so that the user can take immediate action.

[0909] Step 8:

[0910] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[0911] Example 1

[0912] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0913] Health management and behavior monitoring of rescued animals is extremely important, especially because it is necessary to detect stress and changes in their health status early. However, conventional methods make it difficult to monitor animals on a daily basis, making it difficult to respond quickly and appropriately. Furthermore, there are challenges in understanding the animals' condition in real time and taking appropriate countermeasures early.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0915] In this invention, the server includes a means for periodically recording the daily lives of the protected animals and saving them as video data, a means for transmitting the saved video data to the server via the Internet, and a means for analyzing the video data received by the server using a generative model to evaluate the behavioral patterns and health status of the animals. This makes it possible to monitor the status of the animals in real time and propose appropriate countermeasures at an early stage.

[0916] "Rescued animals" refers to animals that are rescued and cared for by animal shelters or animal welfare organizations.

[0917] "Filming means" refers to a device for recording the daily life of an animal as video footage, and includes communication devices such as video cameras and smartphones.

[0918] "Video data" refers to digital data containing captured video information, and is used to analyze animal behavior and health conditions.

[0919] "Server" refers to a computer system that receives video data via the Internet and is responsible for analyzing and storing the data.

[0920] "Generative models" refer to deep learning models trained using AI techniques to analyze animal behavior patterns and health conditions.

[0921] "Analysis means" refers to the process of inputting received video data into a generative model to evaluate the animal's behavioral patterns and health status.

[0922] "Database" refers to a digital database system for storing analysis results and generated countermeasures.

[0923] "Notification means" refers to the communication method used to communicate the analysis results and appropriate countermeasures to the user's device, and includes email and push notifications.

[0924] "User terminal" refers to a communication device used by a user, such as a computer or smartphone, that has the ability to receive and display notifications.

[0925] "Response measures" refer to specific steps to be taken to address animal health or behavioral issues based on the analysis results.

[0926] MODE FOR CARRYING OUT THE INVENTION

[0927] This invention is a care support system for rescued animals, particularly dogs and cats. Specifically, it is a system that uses video analysis technology to continuously monitor the daily lives of rescued animals, detects signs of stress or anxiety and changes in their health at an early stage, and suggests appropriate countermeasures.

[0928] Hardware and Software

[0929] The terminals used are communication devices such as video cameras and smartphones. These terminals film the animals' daily lives and capture video at regular intervals. The video data is stored in local storage and periodically sent to a server via the Internet. The secure HTTP protocol (HTTPS) is used for transmission.

[0930] The server stores the received video data and analyzes it using a generative AI model built using Python and deep learning libraries such as TensorFlow. The model evaluates the animal's behavioral patterns and health status and detects signs of specific behaviors or health conditions.

[0931] The analysis results are stored in a database (e.g., MySQL or MongoDB). The server then generates appropriate countermeasures based on the analysis results. These countermeasures are determined by pre-configured rule-based systems or AI models.

[0932] The generated countermeasures are sent from the server to the user's device via email or smartphone push notification. The user receives the notification and checks the recommended countermeasures. Detailed information and specific countermeasures are displayed on the user's device.

[0933] Examples of concrete examples and prompts

[0934] For example, if a behavior analysis system for rescue dogs were introduced, the following process would be performed: The user installs a video camera in their living room to monitor the area where their pet is active. The camera captures video every 10 minutes and sends it to a cloud server. The server analyzes the video using a generative AI model using TensorFlow and detects that the pet is experiencing stress. The server generates countermeasures, such as providing a quiet environment where the pet can relax, and notifies the user.

[0935] Below are some example prompts for a generative AI model:

[0936] Generate an AI model that detects signs of health and stress from video footage of animals. Analyze the video data below to assess your pet's behavior and suggest appropriate actions when specific issues arise.

[0937] Video data: "sample_video.mp4"

[0938] Examples of workarounds:

[0939] Stress: "Provide a quiet and calming environment."

[0940] Health issues: "Consider consulting a veterinarian."

[0941] Please return the detected issues and recommended solutions in JSON format.

[0942] This system allows users to monitor and manage the status of rescued animals in real time and take appropriate action in a timely manner, which in turn makes it easier to find new homes for rescued animals and contributes to reducing the number of animals that are euthanized.

[0943] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0944] Step 1: Recording a video

[0945] The device captures the daily life of the rescued animals. The specific operation of this processing step is to use a video camera or smartphone and place it in the living room or animal's activity area. The device captures 30 seconds of video every 10 minutes.

[0946] Input: Animal activity footage

[0947] Output: Video data (e.g. MP4 format file)

[0948] Step 2: Save the video data locally

[0949] The device saves the captured video data to local storage, using MP4 format video files.

[0950] Input: Recorded video data

[0951] Output: Video data stored in local storage

[0952] Step 3: Send video data to the server

[0953] The device then sends the saved video data to a server via the Internet. This transmission is done automatically at regular intervals and is securely transmitted using the HTTP protocol.

[0954] Input: Video data stored in local storage

[0955] Output: Video data uploaded to the server

[0956] Step 4: Video Analysis

[0957] The server receives the video data sent from the device and analyzes it using a generative AI model, which uses a deep learning model powered by TensorFlow to evaluate the animal's behavioral patterns and health status.

[0958] Input: Video data stored on the server

[0959] Output: Behavioral and health status analysis results

[0960] Step 5: Save the analysis results and generate countermeasures

[0961] The server stores the analysis results in a database, and then generates appropriate countermeasures based on the stored analysis results, which are determined by pre-defined rule-based systems and AI models.

[0962] Input: Behavioral and health status analysis results

[0963] Output: Analysis results stored in a database and generated countermeasures

[0964] Step 6: Notification of diagnosis results

[0965] The server notifies the user of the analysis results and the generated countermeasures via email or smartphone push notifications, providing the user with information in real time.

[0966] Input: Generated countermeasures and analysis results

[0967] Output: Notification sent to user device

[0968] Step 7: Receiving and Viewing Notifications

[0969] The user will receive a notification on their device and check the recommended course of action. Specifically, detailed information and specific actions will be displayed on the user's device. For example, the message may read, "Your pet is stressed. Try the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[0970] Input: Notification to user device

[0971] Output: Solution displayed on the terminal

[0972] (Application example 1)

[0973] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0974] When caring for rescued animals, especially dogs and cats, it is difficult for owners to quickly identify their animals' stress and health conditions and take appropriate measures. Furthermore, in situations where real-time response is required when an abnormality occurs, owners often lack the information necessary to take prompt action. These issues can make it difficult to properly care for rescued animals.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0976] In this invention, the server includes a means for analyzing the captured video data and evaluating the behavior and health condition of the animal, a means for notifying the user of the analysis results to the user's terminal, and a means for notifying the user in real time if the analysis results detect an abnormality. This enables owners to quickly and accurately grasp the stress and health condition of the rescued animal and take appropriate measures early on.

[0977] "Rescued animals" are mammals, such as dogs and cats, that are kept in shelters or temporary captive environments.

[0978] "Daily life" refers to the general activities and behaviors that rescued animals engage in on a daily basis.

[0979] "Filming means" refers to the means of recording animal activity as video data using a camera, smartphone, etc.

[0980] A "server" is a computer system for receiving, storing, and analyzing video data.

[0981] "Video data" refers to video data that records the daily activities of protected animals.

[0982] "Analysis methods" are methods that use AI technology to analyze video data and evaluate the behavior and health of animals.

[0983] A "generative AI model" is an artificial intelligence model trained to predict and assess animal behavior and health status from input data.

[0984] A "prompt sentence" is an input sentence that requests a specific analysis from a generative AI model.

[0985] The "notification means" is a means for transmitting the analysis results to the user's terminal.

[0986] "Real-time notification means" refers to a means for immediately notifying the user when an abnormality is detected.

[0987] "User Device" means a device used by a User to check the status of a rescued animal, including a smartphone or computer.

[0988] "Means for displaying appropriate countermeasures" refers to a means for presenting users with recommended countermeasures based on the results of AI analysis.

[0989] This invention is a system that monitors the daily lives of protected animals and detects abnormalities in their health and behavior at an early stage. The system mainly consists of the following components:

[0990] System configuration

[0991] The system includes a "filming means" for filming the daily lives of animals, a means for transmitting the filmed video data to a "server," an "analysis means" for analyzing the video data on the server, a "notification means" for notifying the user of the analysis results, and a "countermeasure display means" for presenting appropriate countermeasures on the user's terminal.

[0992] Filming method

[0993] The video recording device and communication terminal are used to record the daily behavior and behavior of the protected animals as video data, which is then sent to a server at regular intervals.

[0994] Server and analysis method

[0995] The server stores the received video data and then analyzes it using a generative AI model trained to assess animal behavior and health. The analysis method uses software including OpenCV and other image processing libraries, for example. This detects abnormal animal behavior and signs of stress.

[0996] Notification means

[0997] The analysis results are evaluated in real time, and if an anomaly is detected, the user is immediately notified via a notification method. The notification is sent to the user's device, such as a smartphone or computer. The notification includes details of the detected problem and recommended countermeasures. The notification method uses, for example, the HTTP communication library "requests."

[0998] Countermeasure display method

[0999] When a user receives a notification, specific countermeasures are displayed on the user's device, including recommended countermeasures based on the analysis results of the generative AI model, allowing the user to take appropriate action quickly.

[1000] Specific examples

[1001] For example, if a rescued dog is exhibiting abnormal behavior and appears stressed, the analytics tool will send a notification to the user saying, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." These solutions allow owners to take appropriate measures early on.

[1002] Prompt Sentence Examples

[1003] An example of a prompt to be input into the generative AI model is, "Analyze the video data below and detect stress or abnormal behavior in the pet. If an abnormality is detected, please provide specific countermeasures."

[1004] This system will enable real-time monitoring of the health and behavioral abnormalities of rescued animals, enabling prompt and appropriate responses, which will improve the quality of life of rescued animals and promote proper care.

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] The device uses a video camera or smartphone to record the daily behavior and behavior of the protected animals. The input is continuous video data, which is saved as a video file. The output is a video file.

[1008] Step 2:

[1009] The device uploads video data captured at regular intervals to the server. The input is a saved video file, which is sent using the HTTP communication library "requests." The output is the video data saved on the server.

[1010] Step 3:

[1011] The server temporarily stores the received video data in storage. The input is the uploaded video data, which is saved in an analyzable format. The output is the saved video data file.

[1012] Step 4:

[1013] The server analyzes the stored video data using a generative AI model. This analysis uses a prompt to evaluate the animal's behavior and health condition: "Analyze the following video data and detect stress or abnormal behavior in your pet. If abnormalities are detected, please provide specific countermeasures." The input is the stored video data, and the output is the analysis results regarding the animal's behavior and health condition.

[1014] Step 5:

[1015] The server evaluates the analysis results and generates data to notify users if an anomaly is detected. The input is the analysis result from the generative AI model, and the output is the anomaly information to be notified and recommended countermeasures.

[1016] Step 6:

[1017] The server notifies the user's device, such as a smartphone or computer, of the analysis results and recommended actions. The input is the data on the recommended actions, which is sent in the form of a push notification or email. The output is a notification message that is displayed on the user's device.

[1018] Step 7:

[1019] The user device displays the content of the received notification message to the user. The input is the notification data sent from the server, and the output is a notification message that the user can check. Specifically, the application displays a message such as "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walk and playtime. 3. Consider consulting a veterinarian."

[1020] In this way, the health and behavioral abnormalities of protected animals can be monitored in real time, enabling prompt and appropriate responses.

[1021] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1022] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[1023] First, the devices record the daily lives of the protected animals. These devices are video cameras or smartphones. These devices capture video at regular intervals and save it as video data. The saved data is then sent to a server via a network.

[1024] The server receives and analyzes the transmitted video data. A pre-trained generative AI model is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress or anxiety. The analysis results are stored on the server, and appropriate countermeasures are generated.

[1025] The server then uses an emotion engine to assess the user's emotional state, which analyzes the user's voice and facial expressions to determine their emotional state, for example, whether they are stressed or calm.

[1026] The server then sends a notification to the user's device based on the analysis results and the evaluation results of the emotion engine. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. Notifications can be sent via email, app push notifications, etc.

[1027] The user's device will display the received notification and offer specific suggestions for dealing with the situation. For example, if a rescue dog is feeling stressed, the notification will include suggestions such as: "Your pet is feeling stressed. Try the following: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian." If the user's emotions are heightened, the notification will be milder and presented in a more user-friendly format.

[1028] This allows users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health. In addition, a notification method based on the user's emotional state further promotes the reduction of stress and anxiety. As a result, it becomes easier to care for rescued animals, making it easier to find adopters, and ultimately reducing the number of animals that are euthanized. This system contributes to animal welfare and conservation activities and helps solve social problems.

[1029] The processing flow will be explained below.

[1030] Step 1:

[1031] The daily lives of protected animals are filmed. A device (video camera or smartphone) captures the daily lives of protected animals as video at regular intervals. The captured video data is saved on the device as a video file. The video files are saved in regular intervals, making subsequent processing easy.

[1032] Step 2:

[1033] The captured video data is sent to the server. The device uploads the saved video file to the server via the network. Specifically, the video data is sent using a protocol such as an HTTP POST request or FTP. At this time, the success or failure of the transmission is checked.

[1034] Step 3:

[1035] The server receives the uploaded video data, verifies the integrity of the received video data, and prepares it for the next analysis step: inspects the video data for any missing or errors, and stores it in the appropriate format.

[1036] Step 4:

[1037] Analyze video data. The server uses pre-trained generative AI models to assess the behavior and health of rescued animals. It analyzes frame-by-frame video data to identify specific behavioral or health changes, such as excessive barking, abnormal movements, or decreased appetite.

[1038] Step 5:

[1039] The server stores the analysis results in a database and generates appropriate countermeasures if signs of stress or anxiety are detected. The generated countermeasures are prepared for delivery to the user.

[1040] Step 6:

[1041] Evaluate the user's emotional state. Using an emotion engine, the system analyzes the user's voice and facial expressions to determine their emotional state. It also uses a camera and microphone to collect data to assess whether the user is stressed or calm.

[1042] Step 7:

[1043] The results are then sent to the user's device. The server combines the analysis results with the emotion engine's evaluation results to generate a notification message, which is then sent to the user's device. The notification is customized based on the user's emotional state and sent via an appropriate method, such as email or app push notification.

[1044] Step 8:

[1045] The analysis results and countermeasures are displayed on the user's device that receives the notification. The user's device displays the received notification message to the user and provides specific countermeasures. If the user is feeling stressed, the content and wording of the notification will be adjusted and presented in a gentler tone.

[1046] Step 9:

[1047] Users implement the provided measures. By following the measures provided, such as moving the animal to a quieter area, increasing play and exercise time, or contacting a veterinarian, users can reduce the animal's stress and anxiety, thereby maintaining the animal's health and making it easier to care for.

[1048] Example 2

[1049] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1050] When caring for rescued animals, especially dogs and cats, there is a need to detect stress and changes in their health at an early stage and take appropriate countermeasures. However, conventional systems lack sufficient means for comprehensively monitoring and evaluating the behavior and health of animals, and do not present appropriate countermeasures based on the user's emotional state. This increases the risk of compromising the welfare of animals, and the present invention aims to solve these problems.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1052] In this invention, the server includes a means for filming the daily life of the animals, a means for transmitting the video data to the server, a means for analyzing the video data in the server and evaluating the behavior and health state of the animals, a means for analyzing the emotional state of the user, a means for notifying the user terminal of the analysis results, and a means for receiving the notification in the user terminal and displaying appropriate countermeasures. This makes it possible to detect stress and health conditions of protected animals early and provide appropriate countermeasures according to the emotional state of the user.

[1053] "Protected animals" refers to animals kept by humans, particularly mammals such as dogs and cats, that are protected.

[1054] "Means" refers to a method, apparatus, software, or combination thereof for accomplishing a particular purpose.

[1055] "Video data" refers to digital video data that records the daily lives of animals.

[1056] "Server" refers to a computer system that stores, analyzes, and manages data over a network.

[1057] "Analysis" refers to the process of evaluating the behavior and health of animals based on collected data.

[1058] "Behavior and health" refers to an animal's behavior, such as movement, posture, eating and sleeping patterns, as well as health-related indicators such as weight, body temperature and respiratory rate.

[1059] "Evaluating" refers to judging and reporting an animal's behavior and health status numerically or verbally through data analysis.

[1060] "User" refers to a person who uses the system, typically an animal owner.

[1061] "Emotional state" refers to the user's psychological state, as analyzed from voice, facial expressions, etc.

[1062] "Notification" refers to a message or alert sent from the server to a user device.

[1063] "Countermeasures" refers to specific instructions or suggestions for action provided based on the analysis results.

[1064] "User device" refers to the device used by a User to receive and view Notifications, including smartphones, tablets, and computers.

[1065] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and automatically performs a specific task (in this case, video analysis).

[1066] This invention is a system that supports the care of rescued animals, especially dogs and cats, and also combines it with a function to recognize the user's emotions. Specifically, the system uses video analysis technology and an emotion engine to monitor the daily lives of rescued animals, detect signs of stress or anxiety and changes in their health at an early stage, and present appropriate countermeasures according to the user's emotional state.

[1067] First, the device records the daily life of the protected animals. The device can be a video camera or a smartphone. These devices capture video at regular intervals and save it as video data. For example, the device's video camera can be used to record 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured data is saved in the device's local storage. The saved data is then sent to a server via the Internet.

[1068] The server receives the transmitted video data and analyzes it. A pre-trained generative AI model, such as "YOLOv4," is used to analyze the video data. This model evaluates the animal's behavioral patterns and health status, and detects signs of stress and anxiety. The analysis results are stored on the server. The server also uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, "IBM Watson Tone Analyzer" is used.

[1069] The server then sends a notification to the user's device based on the analysis results and the emotion engine's evaluation. The notification includes details of the problem detected and a solution appropriate to the user's emotional state. The notification can be sent via email or app push notification. Specific examples include, "Your pet is stressed. Try the following solutions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[1070] The user's device will display the received notification and provide specific countermeasures. The user can check the notification message on their smartphone or tablet and take the suggested countermeasures.

[1071] Prompt Sentence Examples

[1072] Sample prompts for detecting stress in animals:

[1073] Prompt: "Analyze the following video data to detect whether the animal is experiencing stress."

[1074] Analysis results: Signs of stress (frequent yawning, sitting down, etc.) were detected from footage of rescue dogs, and the dogs reported feeling stressed.

[1075] Example prompts for user sentiment analysis:

[1076] Prompt: "Analyze the user's voice data and assess their current emotional state."

[1077] Analysis results: If the user is determined to be stressed, the response will be "I am stressed."

[1078] Example prompt for generating notifications:

[1079] Prompt: "Please suggest some measures to reduce stress for rescue dogs. The user is currently experiencing stress."

[1080] Analysis: "The user is stressed. Consider these gentle solutions: 1. Allow the user to rest in a quiet place. 2. Increase walks. 3. Consider consulting a veterinarian."

[1081] This will enable users to reduce the stress and anxiety of rescued animals and take appropriate measures early on to maintain their health, thereby improving the welfare of the animals.

[1082] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1083] Step 1:

[1084] The device is used to capture the daily lives of rescued animals.

[1085] Input: Camera device and set capture time.

[1086] Specific operation: For example, the device's video camera captures 10 seconds of video every minute between 9:00 AM and 10:00 AM every day. The captured video data is stored in the device's local storage.

[1087] Output: Saved video data.

[1088] Step 2:

[1089] The terminal uses a means for transmitting the stored video data to the server.

[1090] Input: Stored video data and network connection.

[1091] How it works: The device uses Wi-Fi or mobile data to send the video data to the specified URL on the server immediately after shooting. The data is encrypted during transmission.

[1092] Output: Video data sent to the server.

[1093] Step 3:

[1094] The server uses a means for analyzing the received video data.

[1095] Input: Received video data.

[1096] How it works: The server analyzes video data using a pre-trained generative AI model (e.g., YOLOv4). The model evaluates the animals' behavioral patterns and health status, and detects signs of stress or anxiety.

[1097] Output: Analysis results (assessment of animal behavior patterns and health status).

[1098] Step 4:

[1099] The server employs a means for analyzing the user's emotional state.

[1100] Input: User's voice data and facial expression data.

[1101] How it works: The server receives voice data sent by the user in real time and analyzes it using an "emotion engine." The emotion engine uses, for example, the "IBM Watson Tone Analyzer" to determine the user's emotional state, such as whether they are tense or relaxed.

[1102] Output: Evaluation result of the user's emotional state.

[1103] Step 5:

[1104] The server uses a means to send a notification to the user's device based on the analysis results and the emotion engine's evaluation results.

[1105] Input: Analysis results of video data and evaluation results of user's emotional state.

[1106] Specific operation: The server visualizes the detected problems and solutions, creates notifications in expressions that correspond to the user's emotional state, and sends them as emails or push notifications to smartphone apps.

[1107] Output: Notification message to the user's terminal.

[1108] Step 6:

[1109] The user's device receives the notification and uses a means to display appropriate countermeasures.

[1110] Input: The notification message sent by the server.

[1111] Specific Action: The user checks the notification on their smartphone or tablet and takes specific action according to the instructions provided, such as "Your pet is stressed. Consider the following actions: 1. Provide a quiet, calming environment. 2. Increase walks and playtime. 3. Consider consulting a veterinarian."

[1112] Output: User action taken.

[1113] (Application example 2)

[1114] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1115] Conventional home security systems lack the means to detect suspicious behavior or changes in user emotions in real time and respond quickly and appropriately. Furthermore, there is no effective system for monitoring pet behavior and health to ensure home safety. Therefore, there is a need for technology to comprehensively improve home safety.

[1116] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing video footage from within the home and monitoring for suspicious behavior, means for analyzing the user's voice and facial expressions to determine the user's emotional state, and means for notifying the user's terminal of the analysis results. This makes it possible to detect abnormal behavior and emotional changes within the home early and provide the user with prompt and appropriate countermeasures.

[1117] "Rescued animals" refers to animals that are under specific management and care.

[1118] "Filming means" refers to a device or group of devices used to record images of animals or household activities.

[1119] "Video data" refers to data that includes a series of video frames captured by a camera.

[1120] "Server" refers to a computer system for receiving, storing, and analyzing data over a network.

[1121] "Analysis means" refers to algorithms or models used to analyze received video data and detect specific patterns or anomalies.

[1122] "Notification means" refers to a method or device for notifying the user of the analysis results.

[1123] "User Device" refers to the device (e.g., smartphone or tablet) used by a User to receive information.

[1124] "Appropriate countermeasures" refer to the actions or measures that users should take based on the analysis results.

[1125] "Home video" refers to video data captured by a camera installed in the home.

[1126] "Suspicious behavior" refers to irregular movements or actions that deviate from normal patterns of behavior.

[1127] "Voice and facial expression analysis" refers to the process of determining a user's emotional state from collected audio and video data.

[1128] "Emotional state" refers to the psychological state or mood a user is feeling at that moment.

[1129] The present invention is a security system for improving safety within the home, monitoring the daily lives of protected animals, detecting suspicious behavior, and analyzing the user's emotional state to provide appropriate notifications.

[1130] 1. Photography methods and data collection:

[1131] Video cameras and smartphones installed in homes periodically capture footage of animals and the home and save it as video data. The data collected by these devices is then sent to a server via a network.

[1132] 2. Data analysis by the server:

[1133] The server is equipped with a generative AI model using deep learning frameworks such as TensorFlow and PyTorch to analyze the received video data. This model evaluates the behavioral patterns of animals and household members to detect signs of stress, anxiety, or suspicious behavior. Furthermore, an emotion engine (e.g., Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) is used to analyze the user's voice and facial expressions to determine their emotional state.

[1134] 3. Notification Methods and User Devices:

[1135] The analysis results on the server are sent to the user's smartphone, tablet, or other device via a cloud service (e.g., AWS Lambda). Notifications are sent via email or app push notifications. The notification content includes details of the detected problem and countermeasures based on the user's emotional state.

[1136] 4. Example:

[1137] For example, if a home camera detects suspicious activity in the living room in the middle of the night and a child's crying or startled scream is recorded by the microphone, the server will analyze this data. The AI ​​will detect the abnormality and send a notification to the parent's smartphone saying, "Abnormal activity has been detected in the living room late at night. Your child appears to be startled. Please take immediate action."

[1138] 5. Example prompt:

[1139] "A loud noise has been detected that may startle a child in the middle of the night. Check your smart home cameras for suspicious activity. Consider contacting the police if necessary."

[1140] This invention makes it possible to detect abnormal behavior and emotional changes in the home at an early stage and provide users with prompt and appropriate countermeasures, thereby improving safety in the home overall and providing a safe and secure living environment.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] The device periodically captures what is happening in the home using a video camera or smartphone and records it as video data. The input is real-time video and audio data, and the output is the captured video data.

[1144] Step 2:

[1145] The device transmits the captured video data to the server via the network. The input is the video data stored on the device, and the output is the video data uploaded to the server.

[1146] Step 3:

[1147] The video data received by the server is analyzed using a generative AI model (a model using TensorFlow or PyTorch). The input is the transmitted video data, and the output is the analysis results of animal behavior patterns and abnormal behavior within the home. Specifically, the AI ​​model analyzes the movements of animals and people in the video and determines whether there are any patterns or abnormalities.

[1148] Step 4:

[1149] The server uses an emotion engine (such as the Microsoft Azure Emotion API or Google Cloud Speech-to-Text API) to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's voice and facial expression data, and the output is the user's emotional evaluation result. Specifically, the emotion engine analyzes voice tone and facial expression changes to identify the emotional state (e.g., surprise or fear).

[1150] Step 5:

[1151] Based on the behavioral pattern analysis results and emotional state evaluation results acquired by the server, the server generates appropriate notification content and sends it to the user's device. The input is the analysis results and emotional evaluation results, and the output is notification data for the user's device. Specifically, the server formats the analysis results into a prompt sentence and generates the notification content.

[1152] Step 6:

[1153] The user device receives the notification from the server and displays the appropriate countermeasures. The input is the notification data sent from the server, and the output is the countermeasures displayed on the device. The user can receive this notification and act according to the countermeasures. Specifically, the user device may display a notification such as, "Abnormal activity has been detected in the living room late at night. It appears that a child is startled. Please take immediate action."

[1154] This series of steps makes it possible to detect abnormal behavior and emotional changes within the home in real time and notify the user of prompt and appropriate countermeasures.

[1155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1157] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1159] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1165] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1166] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1168] 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.

[1169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1170] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1171] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1176] The following is further disclosed regarding the above embodiment.

[1177] (Claim 1)

[1178] A means to photograph the daily lives of rescued animals,

[1179] means for transmitting the captured video data to a server;

[1180] a means for analyzing the video data on a server and assessing the behavior and health status of the animals;

[1181] A means of notifying the user of the analysis results on their device;

[1182] A means for users to receive notifications on their devices and display appropriate countermeasures;

[1183] A system including:

[1184] (Claim 2)

[1185] 2. The system according to claim 1, wherein the photographing means is a video camera or a smartphone.

[1186] (Claim 3)

[1187] The system of claim 1, wherein the server performs analysis using the generative AI model.

[1188] "Example 1"

[1189] (Claim 1)

[1190] A method for recording the daily lives of protected animals at regular intervals and saving them as video data,

[1191] means for transmitting the stored video data to a server via the Internet;

[1192] A means to analyze the video data received by the server using a generative model to evaluate the behavioral patterns and health status of animals;

[1193] A means for storing the analysis results in a database and generating appropriate countermeasures as needed;

[1194] A means for notifying the user of the analysis results and generated countermeasures;

[1195] A means for users to receive notifications and recommended actions on their devices;

[1196] A system including:

[1197] (Claim 2)

[1198] 2. The system according to claim 1, wherein the photographing means is a communication device.

[1199] (Claim 3)

[1200] The system of claim 1, wherein the server performs the analysis using deep learning technology.

[1201] "Application Example 1"

[1202] (Claim 1)

[1203] A means to photograph the daily lives of rescued animals,

[1204] means for transmitting the captured video data to a server;

[1205] a means for analyzing the video data on a server and assessing the behavior and health status of the animals;

[1206] A means of notifying the user of the analysis results on their device;

[1207] A means for users to receive notifications on their devices and display appropriate countermeasures;

[1208] If the analysis results detect an abnormality, a means to notify the user in real time,

[1209] A system including:

[1210] (Claim 2)

[1211] 2. The system according to claim 1, wherein the photographing means is an image photographing device or a communication terminal.

[1212] (Claim 3)

[1213] The system of claim 1, wherein the server performs analysis using a generative AI model and presents appropriate countermeasures based on the prompt sentence.

[1214] "Example 2: Combining Emotion Engines"

[1215] (Claim 1)

[1216] A means to photograph the daily lives of rescued animals,

[1217] means for transmitting the captured video data to a server;

[1218] a means for analyzing the video data on a server and assessing the behavior and health status of the animals;

[1219] a means for analyzing the emotional state of a user;

[1220] A means of notifying the user of the analysis results on their device;

[1221] A means for users to receive notifications on their devices and display appropriate countermeasures;

[1222] A system including:

[1223] (Claim 2)

[1224] 2. The system according to claim 1, wherein the photographing means is a video camera or a smartphone.

[1225] (Claim 3)

[1226] The system of claim 1, wherein the server performs analysis using the generative AI model.

[1227] "Application example 2 when combining emotion engines"

[1228] (Claim 1)

[1229] A means to photograph the daily lives of rescued animals,

[1230] means for transmitting the captured video data to a server;

[1231] a means for analyzing the video data on a server and assessing the behavior and health status of the animals;

[1232] A means of notifying the user of the analysis results on their device;

[1233] A means for users to receive notifications on their devices and display appropriate countermeasures;

[1234] A means of capturing footage from within the home and monitoring suspicious behavior;

[1235] A means of analyzing the user's voice and facial expressions to determine their emotional state;

[1236] A system including:

[1237] (Claim 2)

[1238] 2. The system according to claim 1, wherein the photographing means is a video camera or a smartphone.

[1239] (Claim 3)

[1240] The system of claim 1, wherein the server performs analysis using the generative AI model. [Explanation of symbols]

[1241] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to photograph the daily lives of rescued animals, means for transmitting the captured video data to a server; a means for analyzing the video data on a server and assessing the behavior and health status of the animals; A means of notifying the user of the analysis results on their device; A means for users to receive notifications on their devices and display appropriate countermeasures; A system including:

2. The system according to claim 1, wherein the photographing means is a video camera or a smartphone.

3. The system according to claim 1, wherein the server performs the analysis using the generative AI model.

Images