system

Abstract

We provide the system. [Solution] An observation means for detecting animal activity and generating data indicating said activity, Information processing means for acquiring data generated by the observation means and analyzing animal behavior based on the data, A guidance means that generates a training method for animals based on the analysis and provides the training method to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, it has been difficult for people with particularly busy lives to appropriately understand the behavior of animals and grasp their health status. For this reason, appropriate training methods and health management for animal behavior are not carried out, which may have an adverse effect on the well-being and health of animals. The present invention aims to solve these problems by monitoring the behavior and health status of animals in detail and providing appropriate training methods and health management instructions.

Means for Solving the Problems

[0005] The present invention provides a system that includes observation means for detecting animal activity and generating data indicating said activity, information processing means for acquiring the generated data and analyzing the animal's behavior, and guidance means for generating a training method for the animal based on the analysis and providing it to the user. This allows the user to understand the animal's behavior and health status in real time and perform appropriate training and health management.

[0006] "Animals" refers to living organisms that are capable of moving of their own free will, and in this invention, it particularly includes dogs and cats kept as pets.

[0007] "Activity" refers to the actions and movements of animals, including everyday movements such as walking, sleeping, eating, and playing.

[0008] "Observation means" refers to devices and mechanisms for detecting animal activity and collecting data indicating that activity, including, for example, cameras and sensors.

[0009] "Information processing means" refers to computer systems and programs that analyze data acquired by observation means and identify and evaluate animal behavior.

[0010] "Guidance means" refers to display devices and notification systems that provide users with instructions on training methods and health management based on the analyzed behavior of animals.

[0011] "Training methods" refer to specific procedures and techniques for promoting appropriate behavior in animals and suppressing inappropriate behavior.

[0012] "Health management instructions" refer to specific recommendations regarding diet, exercise, and medical care, aimed at maintaining and improving the health of animals.

[0013] "User" refers to a person who manages the behavior and health status of an animal using the system of the present invention, and is usually a pet owner. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The present invention provides a system for monitoring animal behavior and health status, and for offering appropriate training methods and health management instructions based on the analysis results. This system utilizes smart cameras and sensors as observation means, a server system as information processing means, and a user's smartphone application as guidance means.

[0036] First, the device uses smart cameras and sensors installed in the environment where the pet is present to collect data in real time, including animal movement, temperature, humidity, and even sounds. This allows the device to generate animal activity data.

[0037] Next, the device sends the acquired data to the server. The server is equipped with generative AI to analyze this data and identify animal behavior patterns. Specifically, the server recognizes specific actions from video data and uses sensor data to analyze environmental changes and activity levels.

[0038] After analysis, the server uses this information to generate training advice regarding the animal's behavior and management instructions regarding its health. For example, if an animal frequently wanders around the same area, it may be determined that excessive stress is the cause. Based on this, the server recommends playing relaxing ambient sounds or increasing playtime.

[0039] Users can receive information from the server via their smartphone app and view the animal's behavior history and analysis results. Furthermore, users can not only adjust the animal's behavior based on training advice from the server, but also improve the animal's living environment according to health management instructions.

[0040] This allows users to understand the animal's condition in real time and implement quick and appropriate countermeasures, thereby promoting the animal's health and well-being. In this way, the present invention can create a better living environment for both animals and users.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device collects real-time video and environmental data from smart cameras and sensors installed in the environment where animals are present. This includes data on animal movement, sounds, temperature, and humidity.

[0044] Step 2:

[0045] The terminal organizes the collected data chronologically and sends it to the server as data packets. Transmission takes place in real time using an internet connection.

[0046] Step 3:

[0047] The server stores the received data in a database. The stored data is organized along a timeline and used to evaluate the animals' past behavioral patterns.

[0048] Step 4:

[0049] The server analyzes data using generative AI. It identifies animal behavior in video data using image recognition and analyzes sensor data to evaluate the state of the animal's surrounding environment.

[0050] Step 5:

[0051] The server generates training advice and health management instructions based on the analysis results. This includes suggesting ways to improve behavioral abnormalities and recommending health-related preventative measures.

[0052] Step 6:

[0053] The server sends the generated advice and instructions to the user's smartphone app. Through notifications, the user can instantly receive the information.

[0054] Step 7:

[0055] Users review the advice and instructions received through the app and use them to adjust training and the environment as feedback to their animals. This leads to continuous improvements in the animals' behavior and health.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] When raising animals, it is necessary to understand their behavior and health status in real time and to provide appropriate training methods and health management instructions. However, doing this manually is laborious and time-consuming, and it is difficult to make accurate judgments. Furthermore, it is necessary to detect stress and abnormal behavior in animals early and to address them appropriately. To solve these problems, a system is needed that can automatically monitor animal behavior and health status and take appropriate action based on the analysis results.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes observation means for detecting animal activity and generating data, information processing means for analyzing animal behavior using a generated AI model, and guidance means for generating and providing training methods and health management instructions for the animal to the user. This enables accurate, real-time monitoring of animal behavior and health status, as well as appropriate training and management.

[0061] "Observation methods" refer to devices or functions that detect animal behavior and environmental information in real time and generate it as data.

[0062] "Information processing means" refers to devices and systems that acquire data obtained from observation means and analyze animal behavior using generative AI models, etc.

[0063] A "generative AI model" is an artificial intelligence technology used to analyze animal behavior, and refers to a technology that includes algorithms for identifying behavioral patterns based on large amounts of data.

[0064] "Guidance means" refers to a function or device that creates instructions for training methods and health management for animals based on analyzed information and provides them to users.

[0065] "User means" refers to the actions and tools used to adjust the animal's daily life in response to training methods and health management instructions provided by guidance means.

[0066] A "prompt message" is a text format used by users to input specific questions or requests to a system, and is used to receive instructions or advice based on the analysis results.

[0067] The system of this invention is configured as follows to monitor the behavior and health status of animals and provide appropriate training methods and health management instructions based on the analysis results.

[0068] First, the device uses a smart camera and various sensors as observation tools. This allows it to collect data in real time on animal movements, environmental temperature, humidity, and sounds. For example, the device records how pets move and how environmental changes affect animal behavior. This data is transmitted to a server using Wi-Fi or Bluetooth.

[0069] The server uses a generation AI model to analyze received data as an information processing tool. This AI model utilizes deep learning technology to identify animal behavior patterns and evaluate the relationship between environmental conditions and animal activity levels. For example, the server has the ability to recognize specific behaviors from video data and analyze continuous behavior patterns to determine the animal's stress level and health status.

[0070] Next, based on the analysis results, the server generates instructions for training the animal's behavior and managing its health. For example, if an animal frequently wanders around a specific area during the day, the server might recommend playing ambient sounds to reduce stress and increasing playtime.

[0071] Information is transmitted through the user's smartphone app. The app allows users to check the animal's behavioral history and health status, and improve the animal's living environment as needed. Users can also use prompts to ask specific questions to the server and receive answers. For example, a user might send a prompt such as, "My pet seems to be lacking exercise lately, what should I do?" and utilize the advice based on the analysis received.

[0072] By implementing this system, users can monitor the animals' condition in real time and provide prompt and appropriate care, thereby promoting the animals' health and well-being.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The device uses smart cameras and sensors to collect animal behavior and environmental data in real time. Input for this step includes multiple data points such as animal movement, environmental temperature, humidity, and sound. This data is converted into an analyzable format and output as animal activity data. Specifically, it captures video data, records audio, and compiles temperature and humidity measurements into digital information.

[0076] Step 2:

[0077] The terminal transmits the collected activity data to the server via wireless communication. The input for this step is the various data collected by the terminal. After transmission, the server outputs it in a unified data format that can be analyzed. Specifically, the data is compressed and encrypted, and a secure communication protocol is used to transfer the data to the server.

[0078] Step 3:

[0079] The server analyzes the received data using a generative AI model. The input for this step is behavioral and environmental data sent from the terminal. The server analyzes this data using AI and generates output such as animal behavioral patterns and causal relationships with the environment. Specifically, it inputs data into a deep learning model to recognize specific behaviors (such as meal times and rest times) and analyze the environmental factors associated with them.

[0080] Step 4:

[0081] The server generates training methods and health management instructions for the animals based on the analysis results. The input for this step is the analyzed behavioral patterns and environmental data. The output generates specific advice for improving the animals' stress management and activity levels. For example, if frequent behavior in the same location is determined to be caused by stress, the server will suggest playing environmental sounds or scheduling playtime.

[0082] Step 5:

[0083] The server notifies the user's smartphone app of the generated information. The input for this step is the generated training and health instruction data. The output is a notification displayed in the smartphone app in a format that is easy for the user to understand. Specifically, the information is sent using the push notification function, and the instructions are displayed within the app.

[0084] Step 6:

[0085] The user adjusts the animal's daily routine based on instructions received through the app. The input for this step is training advice and health management instructions received from the server. The output is the implementation of specific actions to maintain and improve the animal's health and well-being. Specific examples include purchasing new toys or adjusting the room temperature.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] Traditional methods of managing and operating equipment and animals made it difficult to monitor their status in real time or provide optimal operating plans, often leading to excessive burden and unnecessary downtime. This resulted in problems such as insufficient efficiency improvements and inadequate problem-solving.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes observation means for detecting the activity of an animal or device and generating data indicating said activity; information processing means for acquiring the data generated by the observation means and analyzing the patterns of operation based on said data; and guidance means for generating an optimal operation plan or maintenance instruction based on said analysis and providing said plan or instruction to the user. This enables efficient operation and maintenance of the device or animal in real time.

[0091] "Activity" refers to actions or changes in state performed by animals or devices.

[0092] "Observation means" refers to devices or mechanisms that detect the activity of animals or other objects and generate data from that activity.

[0093] "Information processing means" refers to a system for analyzing data acquired by observation means and identifying behavioral patterns.

[0094] "Guidance means" refers to devices or applications that have the function of providing users with action plans and instructions generated based on analysis results.

[0095] An "operation plan" refers to a set of guidelines and methods that specify how a device or animal should function optimally.

[0096] "Maintenance instructions" refer to instructions that guide the work and operation necessary for the maintenance and upkeep of equipment.

[0097] "Assessing the condition" refers to determining the current state or condition of an animal or device.

[0098] "Communication terminal" refers to digital devices such as smartphones and tablets that users use to receive information.

[0099] The system that implements this application monitors the activity of animals or devices in real time and provides optimal operation plans and maintenance instructions based on that activity. The system includes observation means, information processing means, and guidance means.

[0100] The system's observation methods involve collecting operational data on animals or devices using smart cameras and various sensors. This data includes operational patterns, vibrations, temperature, and sounds. Embedded systems such as Raspberry Pi or Arduino could be used to acquire this data.

[0101] Next, the server receives the data as an information processing tool and analyzes the behavioral patterns using a generated AI model. Apache® Kafka or Amazon Kinesis can be used for data processing, and Tensorflow® or PyTorch can be used for AI analysis. The analysis results are used to determine the current state of the device or animal and the efficiency of its operation.

[0102] Subsequently, as a guidance mechanism, the server generates an optimal operation plan and maintenance instructions based on the analysis results and notifies the user's communication terminal. The user can receive this information via a smartphone app and adjust the operation of the equipment and animals. Flutter® or React Native could be used for developing the mobile app.

[0103] For example, if abnormal vibrations are detected in a robot within a factory, the server quickly analyzes the issue and sends a notification to the user indicating that maintenance is required. This notification allows the user to implement preventative maintenance.

[0104] An example of a prompt for a generated AI model is, "Please suggest countermeasures if the factory robot exhibits an unusual operating pattern." This allows the AI ​​to generate specific countermeasures and provide them to the user as a notification.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The device uses smart cameras and sensors to collect real-time activity data of animals or devices. This data includes movement patterns, vibrations, temperature, and sounds. The input is raw data acquired from the environment, and the output is digitized activity data. The device generates this activity data and prepares it for transmission to the next step.

[0108] Step 2:

[0109] The terminal transmits collected activity data to the server. The input is activity data generated from smart cameras and sensors, and the output is a notification that transmission to the server is complete. The terminal uses a data communication protocol to ensure reliable data transmission.

[0110] Step 3:

[0111] The server stores the received data and begins analyzing the behavioral patterns using a generated AI model. The input is activity data sent from the terminal, and the output is the analyzed behavioral patterns and state evaluations. The server first preprocesses the data, then applies it to the AI ​​model to classify and evaluate the behavior of pets and devices.

[0112] Step 4:

[0113] The server generates an optimal operation plan or maintenance instructions based on the analysis results, using the generated AI model. The input is the operation pattern and state evaluation obtained in step 3, and the output is a specific operation plan or maintenance instructions. The server generates these and prepares them for communication to the user.

[0114] Step 5:

[0115] The server notifies the user's communication terminal of the operation plan or maintenance instructions. The input is the operation plan or instructions generated by the server, and the output is the notification message received by the user. The server ensures that the information is reliably delivered to the user through backend services and displayed in the application.

[0116] Step 6:

[0117] The user reviews the received operation plan and maintenance instructions, and then performs adjustments and maintenance activities for the equipment and animals. The input is the notification content displayed on the communication terminal, and the output is the user's specific actions based on that notification. This allows the user to monitor the actual status of the equipment and pets and take necessary actions.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a training and health management system that takes into account not only the behavior and health status of animals, but also the emotions of the user. This system includes observation means, information processing means, guidance means, and an emotion engine.

[0120] The device uses smart cameras and sensors placed in the environment to detect animal activity. This allows it to collect data such as the animal's location, movement, sound, and temperature in real time. This data indicates the animal's behavior and the environmental conditions.

[0121] The device transfers the collected data to the server. The server uses this data to analyze the animal's behavior and employs generative AI to identify abnormal behaviors and signs of health problems. Based on this analysis, appropriate training methods and health management instructions for the animal are generated.

[0122] Furthermore, a key feature of this system is that the server incorporates an emotion engine. This emotion engine recognizes the user's emotional state by analyzing their voice data. For example, it can determine whether a user is experiencing stress based on the tone and intensity of their voice.

[0123] The emotional state of the user, as recognized by the emotion engine, is used to adjust the animal's training methods. For example, if the user is stressed, the system may recommend giving the animal calmer instructions and, if necessary, play relaxation music.

[0124] Users can receive these analysis results and tailored advice through a smartphone app. For example, if a user is frustrated with an animal, the app can notify them that "it is recommended to give instructions in a calm tone" and suggest appropriate training methods for the animal. In this way, implementing the present invention enables comprehensive behavior and health management that takes into account the emotional states of both the animal and the user.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The device collects real-time data on animals using smart cameras and sensors installed in the environment. This includes data such as movement, sound, temperature, and humidity.

[0128] Step 2:

[0129] The device transfers the collected data to the server. This data includes information about animal behavior and is updated in real time.

[0130] Step 3:

[0131] The server stores the received data and uses generative AI to analyze animal behavior. Here, it detects specific behavioral patterns and evaluates abnormal behavior or health status.

[0132] Step 4:

[0133] The server generates training methods and health management instructions for the animals based on the analysis results. This includes specific suggestions for behavioral improvement.

[0134] Step 5:

[0135] The device transmits the user's voice data to the emotion engine. The emotion engine performs voice analysis and recognizes the user's emotional state.

[0136] Step 6:

[0137] The server's emotion engine takes the user's emotional state into account and adjusts the training methods for the animals accordingly. For example, if the user is feeling stressed, it recommends training methods that reduce the stimulation to the animals.

[0138] Step 7:

[0139] The server sends tailored training advice and health management instructions to the user's smartphone app. This allows the user to stay informed in real time and take appropriate action.

[0140] Step 8:

[0141] Users can train their animals and adjust their environments based on the information presented in the app. For example, they can change how they play with their pets according to the app's suggestions.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Conventional animal behavior management systems focus solely on animal behavior and fail to consider the user's emotional state, making it difficult to propose appropriate care and training for both the animal and the user. Furthermore, the lack of automatic adjustments to the user's emotions, which directly influence the animal's training methods, is also a problem.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes detection means for detecting animal activity and generating information indicating said activity, analysis means for analyzing animal behavior based on said information, and emotion analysis means for analyzing user voice data and recognizing emotional state. This enables comprehensive behavioral management and training guidance that takes into account both animal behavior and user emotions.

[0147] "Detection means" refers to devices and technologies for detecting animal activity and generating information about that activity.

[0148] "Analysis means" refers to devices and technologies used to evaluate and analyze the behavior and health status of animals using information generated by detection means.

[0149] "Guidance means" refers to devices and technologies that provide users with training methods and health management instructions generated based on analysis means.

[0150] "Emotional analysis means" refers to devices and technologies that analyze a user's voice data and recognize the user's emotional state.

[0151] "Adjustment means" refers to devices or technologies used to adjust animal training methods based on the emotional state of the user.

[0152] A "portable device" refers to a device that a user can carry with them and use to receive instructions on training methods and health management from guidance systems.

[0153] This invention is a system that comprehensively analyzes animal behavior and user emotions to propose appropriate training methods and health management. The following describes embodiments for carrying out the invention.

[0154] The device uses smart cameras and sensors to detect animal activity. These devices acquire data such as the animal's location, movement, sound, and temperature in real time. For example, if an animal moves beyond a certain range or makes a sound, that information is stored digitally.

[0155] This data is transferred from the terminal to the server. The server utilizes generative AI models to analyze the collected data. Specifically, the server uses TensorFlow to identify abnormal behavior and unusual health conditions in animals. The server also incorporates speech recognition software to recognize emotional states from the user's voice data. In this process, it analyzes the tone and intensity of the voice to identify emotions such as stress and calmness.

[0156] Based on the analysis results, the server generates training methods for the animal. The generated training methods are displayed on the user's mobile device. The user uses a dedicated application to receive feedback from the server. For example, if the user is feeling stressed, a notification such as "We recommend giving instructions in a calm tone" will be sent. If the animal is more agitated than usual, instructions such as playing calming music may also be included.

[0157] For example, this system can be used to solve the problem of dogs barking frequently. A smart camera captures the dog barking, and the generated AI analyzes the behavior. Furthermore, it determines the user's emotional state from their voice and suggests the most appropriate course of action based on the results.

[0158] An example of a prompt message would be, "Identify the reason why the dog barks frequently and suggest appropriate training methods for the user. Please also consider the user's emotional state." This allows the user to communicate with the animal more effectively with the help of the system.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The device activates smart cameras and sensors to detect animal activity. These devices collect environmental data in real time, including the animal's location, movement, sound, and temperature. The input for this step is raw data from the environment, and the output is the collected dataset. Specifically, the camera generates a video stream, and the sensors record numerical data such as sound and temperature.

[0162] Step 2:

[0163] The terminal transfers the collected data to the server. The data is encrypted using the TLS protocol and securely transmitted to the server. The input is the data collected by the terminal, and the output is the dataset stored on the server. Specifically, the terminal sends data packets using wireless communication.

[0164] Step 3:

[0165] The server analyzes the received data using a generative AI model. The input is the dataset received by the server, and the output is the identification of abnormal animal behavior and health status. Specifically, the server uses TensorFlow to input data into a machine learning model, detect anomalies, and report them.

[0166] Step 4:

[0167] The server incorporates an emotion analysis system that analyzes voice data from the user. The input is the user's voice data, and the output is information about the user's emotional state. Specifically, the server evaluates the tone and intensity of the voice and calculates stress and calmness levels.

[0168] Step 5:

[0169] The server generates a training method based on the results of analyzing animal behavior and the user's emotional state. The input is the analysis results from the previous step, and the output is the generated training method and instructions. Specifically, the server executes an algorithm written in Python to calculate the optimal response.

[0170] Step 6:

[0171] The user receives feedback and advice from the server through an app on their mobile device. The input is the training method and instructions sent from the server, and the output is specific guidance that is viewed on the user's device. In terms of specific actions, the app displays notifications on the UI and prompts the user to take the next action.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In recent years, there has been a growing demand for animal health management and behavioral training. However, conventional systems have a problem in that they make it difficult to provide comprehensive care that takes into account both the animal's activity monitoring and the user's emotional state. As a result, it is difficult to provide training and health management that is optimal for both the animal and the user.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes observation means for detecting animal activity and generating data, data processing means for analyzing animal behavior based on the data, and emotion analysis means for analyzing user voice data and recognizing emotional state. This makes it possible to closely understand the animal's health and behavior, as well as provide an optimal training method tailored to the user's emotional state.

[0177] "Observation tools" are devices and sensors used to detect animal activity and generate data based on that activity.

[0178] A "data processing system" is a computer system that analyzes data acquired by observation systems to evaluate the behavior and health status of animals.

[0179] A "guidance tool" is an interface that provides users with instructions on animal training methods and health management based on the analysis results.

[0180] An "emotion analysis system" is a system that analyzes a user's voice data to recognize the user's emotional state.

[0181] A "training method" is a technique designed to elicit specific behaviors or responses from animals, with the aim of modifying their behavior or improving their skills.

[0182] This invention is a system that supports animal health management and training in pet shops and pet care centers. The server acquires animal activity data using smart cameras installed in the store. This data is used to analyze animal behavior patterns using OpenCV, and then TensorFlow is used to evaluate the animal's health status using an AI model. The system also generates optimal training methods for the animals and provides them to the user. Users can receive these training methods and health management instructions via their smartphones.

[0183] Furthermore, the device collects user voice data and analyzes it using a voice analysis tool (e.g., Google® Cloud Speech-to-Text API) to recognize the user's emotional state. The emotional state recognized by the emotion engine is used to adjust the training methods for the animals, recommending the playback of relaxation music or changes to the training content.

[0184] As a concrete example, to ensure that users and their pets have a comfortable experience at a pet cafe, the system recommends ambient sounds that match the pet's level of calmness. For instance, if the system detects that the pet is restless, it sends a message to the user's smartphone saying, "We recommend playing calming music."

[0185] An example of a prompt message is, "Analyze this dog's behavior log and generate suggestions to help it relax." In this way, it becomes possible to provide a service that is harmonious for both the animal and the user.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The device operates a smart camera to collect real-time data on animal movements within the store. This data includes the animals' location and movement. The input is video data from the camera, and the output is initial data indicating the animals' movements.

[0189] Step 2:

[0190] The device uses the animal's motion data to perform image analysis with the OpenCV library and extract the animal's motion patterns. The input is the animal's motion data collected in step 1, and the output is the analysis data related to the motion patterns. This data processing makes it possible to capture specific animal behaviors.

[0191] Step 3:

[0192] The server uses TensorFlow to perform analysis with a generative AI model and evaluate the animal's health status. The input is the analysis data of the behavioral patterns obtained in step 2, and the output is an evaluation of the animal's health status. This allows for the identification of abnormalities and the detection of signs of health.

[0193] Step 4:

[0194] The server generates appropriate training methods based on the health status assessment results and provides them to the user through guidance. The input is the health status data assessed in step 3, and the output is a proposal for specific training methods. Based on this, the user can provide appropriate guidance to the animal.

[0195] Step 5:

[0196] The device uses the Google Cloud Speech-to-Text API to analyze the user's voice data, generating text data from the speech and recognizing their emotional state. The input is the user's voice data, and the output is data indicating their emotional state. This makes it possible to understand the user's stress and emotional state.

[0197] Step 6:

[0198] The server receives user emotional state data and adjusts the training content for the animal accordingly. The input is the emotional state data obtained in step 5, and the output is the adjusted training instructions. This enables an approach that provides the optimal environment for both the user and the animal.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] The present invention provides a system for monitoring animal behavior and health status, and for offering appropriate training methods and health management instructions based on the analysis results. This system utilizes smart cameras and sensors as observation means, a server system as information processing means, and a user's smartphone application as guidance means.

[0216] First, the device uses smart cameras and sensors installed in the environment where the pet is present to collect data in real time, including animal movement, temperature, humidity, and even sounds. This allows the device to generate animal activity data.

[0217] Next, the device sends the acquired data to the server. The server is equipped with generative AI to analyze this data and identify animal behavior patterns. Specifically, the server recognizes specific actions from video data and uses sensor data to analyze environmental changes and activity levels.

[0218] After analysis, the server uses this information to generate training advice regarding the animal's behavior and management instructions regarding its health. For example, if an animal frequently wanders around the same area, it may be determined that excessive stress is the cause. Based on this, the server recommends playing relaxing ambient sounds or increasing playtime.

[0219] Users can receive information from the server via their smartphone app and view the animal's behavior history and analysis results. Furthermore, users can not only adjust the animal's behavior based on training advice from the server, but also improve the animal's living environment according to health management instructions.

[0220] This allows users to understand the animal's condition in real time and implement quick and appropriate countermeasures, thereby promoting the animal's health and well-being. In this way, the present invention can create a better living environment for both animals and users.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The device collects real-time video and environmental data from smart cameras and sensors installed in the environment where animals are present. This includes data on animal movement, sounds, temperature, and humidity.

[0224] Step 2:

[0225] The terminal organizes the collected data chronologically and sends it to the server as data packets. Transmission takes place in real time using an internet connection.

[0226] Step 3:

[0227] The server stores the received data in a database. The stored data is organized along a timeline and used to evaluate the animals' past behavioral patterns.

[0228] Step 4:

[0229] The server analyzes data using generative AI. It identifies animal behavior in video data using image recognition and analyzes sensor data to evaluate the state of the animal's surrounding environment.

[0230] Step 5:

[0231] The server generates training advice and health management instructions based on the analysis results. This includes suggesting ways to improve behavioral abnormalities and recommending health-related preventative measures.

[0232] Step 6:

[0233] The server sends the generated advice and instructions to the user's smartphone app. Through notifications, the user can instantly receive the information.

[0234] Step 7:

[0235] Users review the advice and instructions received through the app and use them to adjust training and the environment as feedback to their animals. This leads to continuous improvements in the animals' behavior and health.

[0236] (Example 1)

[0237] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0238] When raising animals, it is necessary to understand their behavior and health status in real time and to provide appropriate training methods and health management instructions. However, doing this manually is laborious and time-consuming, and it is difficult to make accurate judgments. Furthermore, it is necessary to detect stress and abnormal behavior in animals early and to address them appropriately. To solve these problems, a system is needed that can automatically monitor animal behavior and health status and take appropriate action based on the analysis results.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes observation means for detecting animal activity and generating data, information processing means for analyzing animal behavior using a generated AI model, and guidance means for generating and providing training methods and health management instructions for the animal to the user. This enables accurate, real-time monitoring of animal behavior and health status, as well as appropriate training and management.

[0241] "Observation methods" refer to devices or functions that detect animal behavior and environmental information in real time and generate it as data.

[0242] "Information processing means" refers to devices and systems that acquire data obtained from observation means and analyze animal behavior using generative AI models, etc.

[0243] A "generative AI model" is an artificial intelligence technology used to analyze animal behavior, and refers to a technology that includes algorithms for identifying behavioral patterns based on large amounts of data.

[0244] "Guidance means" refers to a function or device that creates instructions for training methods and health management for animals based on analyzed information and provides them to users.

[0245] "User means" refers to the actions and tools used to adjust the animal's daily life in response to training methods and health management instructions provided by guidance means.

[0246] A "prompt message" is a text format used by users to input specific questions or requests to a system, and is used to receive instructions or advice based on the analysis results.

[0247] The system of this invention is configured as follows to monitor the behavior and health status of animals and provide appropriate training methods and health management instructions based on the analysis results.

[0248] First, the device uses a smart camera and various sensors as observation tools. This allows it to collect data in real time on animal movements, environmental temperature, humidity, and sounds. For example, the device records how pets move and how environmental changes affect animal behavior. This data is transmitted to a server using Wi-Fi or Bluetooth.

[0249] The server uses a generation AI model to analyze received data as an information processing tool. This AI model utilizes deep learning technology to identify animal behavior patterns and evaluate the relationship between environmental conditions and animal activity levels. For example, the server has the ability to recognize specific behaviors from video data and analyze continuous behavior patterns to determine the animal's stress level and health status.

[0250] Next, based on the analysis results, the server generates instructions for training the animal's behavior and managing its health. For example, if an animal frequently wanders around a specific area during the day, the server might recommend playing ambient sounds to reduce stress and increasing playtime.

[0251] Information is transmitted through the user's smartphone app. The app allows users to check the animal's behavioral history and health status, and improve the animal's living environment as needed. Users can also use prompts to ask specific questions to the server and receive answers. For example, a user might send a prompt such as, "My pet seems to be lacking exercise lately, what should I do?" and utilize the advice based on the analysis received.

[0252] By implementing this system, users can monitor the animals' condition in real time and provide prompt and appropriate care, thereby promoting the animals' health and well-being.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The device uses smart cameras and sensors to collect animal behavior and environmental data in real time. Input for this step includes multiple data points such as animal movement, environmental temperature, humidity, and sound. This data is converted into an analyzable format and output as animal activity data. Specifically, it captures video data, records audio, and compiles temperature and humidity measurements into digital information.

[0256] Step 2:

[0257] The terminal transmits the collected activity data to the server via wireless communication. The input for this step is the various data collected by the terminal. After transmission, the server outputs it in a unified data format that can be analyzed. Specifically, the data is compressed and encrypted, and a secure communication protocol is used to transfer the data to the server.

[0258] Step 3:

[0259] The server analyzes the received data using a generative AI model. The input for this step is behavioral and environmental data sent from the terminal. The server analyzes this data using AI and generates output such as animal behavioral patterns and causal relationships with the environment. Specifically, it inputs data into a deep learning model to recognize specific behaviors (such as meal times and rest times) and analyze the environmental factors associated with them.

[0260] Step 4:

[0261] The server generates training methods and health management instructions for the animals based on the analysis results. The input for this step is the analyzed behavioral patterns and environmental data. The output generates specific advice for improving the animals' stress management and activity levels. For example, if frequent behavior in the same location is determined to be caused by stress, the server will suggest playing environmental sounds or scheduling playtime.

[0262] Step 5:

[0263] The server notifies the user's smartphone app of the generated information. The input for this step is the generated training and health instruction data. The output is a notification displayed in the smartphone app in a format that is easy for the user to understand. Specifically, the information is sent using the push notification function, and the instructions are displayed within the app.

[0264] Step 6:

[0265] The user adjusts the animal's daily routine based on instructions received through the app. The input for this step is training advice and health management instructions received from the server. The output is the implementation of specific actions to maintain and improve the animal's health and well-being. Specific examples include purchasing new toys or adjusting the room temperature.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] Traditional methods of managing and operating equipment and animals made it difficult to monitor their status in real time or provide optimal operating plans, often leading to excessive burden and unnecessary downtime. This resulted in problems such as insufficient efficiency improvements and inadequate problem-solving.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes observation means for detecting the activity of an animal or device and generating data indicating said activity; information processing means for acquiring the data generated by the observation means and analyzing the patterns of operation based on said data; and guidance means for generating an optimal operation plan or maintenance instruction based on said analysis and providing said plan or instruction to the user. This enables efficient operation and maintenance of the device or animal in real time.

[0271] "Activity" refers to actions or changes in state performed by animals or devices.

[0272] "Observation means" refers to devices or mechanisms that detect the activity of animals or other objects and generate data from that activity.

[0273] "Information processing means" refers to a system for analyzing data acquired by observation means and identifying behavioral patterns.

[0274] "Guidance means" refers to devices or applications that have the function of providing users with action plans and instructions generated based on analysis results.

[0275] An "operation plan" refers to a set of guidelines and methods that specify how a device or animal should function optimally.

[0276] "Maintenance instructions" refer to instructions that guide the work and operation necessary for the maintenance and upkeep of equipment.

[0277] "Assessing the condition" refers to determining the current state or condition of an animal or device.

[0278] "Communication terminal" refers to digital devices such as smartphones and tablets that users use to receive information.

[0279] The system that implements this application monitors the activity of animals or devices in real time and provides optimal operation plans and maintenance instructions based on that activity. The system includes observation means, information processing means, and guidance means.

[0280] The system's observation methods involve collecting operational data on animals or devices using smart cameras and various sensors. This data includes operational patterns, vibrations, temperature, and sounds. Embedded systems such as Raspberry Pi or Arduino could be used to acquire this data.

[0281] Next, the server receives the data as an information processing tool and analyzes the behavioral patterns using a generated AI model. Apache Kafka or Amazon Kinesis can be used for data processing, and TensorFlow or PyTorch can be used for AI analysis. The analysis results are used to determine the current state of the device or animal and the efficiency of its operation.

[0282] Subsequently, as a guidance mechanism, the server generates an optimal operation plan and maintenance instructions based on the analysis results and notifies the user's communication terminal. The user can receive this information via a smartphone app and adjust the operation of the equipment and animals. Flutter or React Native could be used for developing the mobile app.

[0283] As a specific example, when a robot in a factory detects abnormal vibrations, the server quickly analyzes it and sends a notice to the user indicating that maintenance is required. With this notice, the user can perform preventive maintenance.

[0284] An example of a prompt sentence for the generative AI model is "Please propose countermeasures when the factory robot shows an operation pattern different from normal." With this, the AI can generate specific countermeasure methods and provide them to the user as a notice.

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The terminal uses a smart camera or sensor to collect the activity data of animals or devices in real time. This data includes operation patterns, vibrations, temperatures, and sounds. The input is raw data obtained from the environment, and the output is the digitized activity data. The terminal generates this activity data and prepares to send the data to the next step.

[0288] Step 2:

[0289] The terminal sends the collected activity data to the server. The input is the activity data generated from the smart camera or sensor, and the output is a notice of successful transmission to the server. The terminal uses a data communication protocol to ensure reliable data transmission.

[0290] Step 3:

[0291] The server saves the received data and starts analyzing the operation pattern using the generative AI model. The input is the activity data sent from the terminal, and the output is the analyzed operation pattern and state evaluation. The server first preprocesses the data and then applies it to the AI model to classify and evaluate the operations of pets or devices.

[0292] Step 4:

[0293] The server generates an optimal operation plan or maintenance instructions based on the analysis results, using the generated AI model. The input is the operation pattern and state evaluation obtained in step 3, and the output is a specific operation plan or maintenance instructions. The server generates these and prepares them for communication to the user.

[0294] Step 5:

[0295] The server notifies the user's communication terminal of the operation plan or maintenance instructions. The input is the operation plan or instructions generated by the server, and the output is the notification message received by the user. The server ensures that the information is reliably delivered to the user through backend services and displayed in the application.

[0296] Step 6:

[0297] The user reviews the received operation plan and maintenance instructions, and then performs adjustments and maintenance activities for the equipment and animals. The input is the notification content displayed on the communication terminal, and the output is the user's specific actions based on that notification. This allows the user to monitor the actual status of the equipment and pets and take necessary actions.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention is a training and health management system that takes into account not only the behavior and health status of animals, but also the emotions of the user. This system includes observation means, information processing means, guidance means, and an emotion engine.

[0300] The device uses smart cameras and sensors placed in the environment to detect animal activity. This allows it to collect data such as the animal's location, movement, sound, and temperature in real time. This data indicates the animal's behavior and the environmental conditions.

[0301] The device transfers the collected data to the server. The server uses this data to analyze the animal's behavior and employs generative AI to identify abnormal behaviors and signs of health problems. Based on this analysis, appropriate training methods and health management instructions for the animal are generated.

[0302] Furthermore, a key feature of this system is that the server incorporates an emotion engine. This emotion engine recognizes the user's emotional state by analyzing their voice data. For example, it can determine whether a user is experiencing stress based on the tone and intensity of their voice.

[0303] The emotional state of the user, as recognized by the emotion engine, is used to adjust the animal's training methods. For example, if the user is stressed, the system may recommend giving the animal calmer instructions and, if necessary, play relaxation music.

[0304] Users can receive these analysis results and tailored advice through a smartphone app. For example, if a user is frustrated with an animal, the app can notify them that "it is recommended to give instructions in a calm tone" and suggest appropriate training methods for the animal. In this way, implementing the present invention enables comprehensive behavior and health management that takes into account the emotional states of both the animal and the user.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The terminal collects real-time data of animals using smart cameras and sensors installed in the environment. This includes data such as movement, voice, temperature, and humidity.

[0308] Step 2:

[0309] The terminal transfers the collected data to the server. This data includes information indicating the behavior of the animals and is updated in real-time.

[0310] Step 3:

[0311] The server accumulates the received data and analyzes the behavior of the animals using generative AI. Here, specific behavior patterns are detected and an assessment of abnormal behavior or health status is performed.

[0312] Step 4:

[0313] The server generates instructions for training methods and health management for the animals based on the analysis results. This includes specific proposals for behavior improvement.

[0314] Step 5:

[0315] The terminal sends the user's voice data to the emotion engine. The emotion engine performs voice analysis and recognizes the user's emotional state.

[0316] Step 6:

[0317] The emotion engine of the server takes into account the user's emotional state and adjusts the training method for the animals. For example, when the user is feeling stressed, it recommends a training method that reduces the stimulation to the animals.

[0318] Step 7:

[0319] The server sends tailored training advice and health management instructions to the user's smartphone app. This allows the user to stay informed in real time and take appropriate action.

[0320] Step 8:

[0321] Users can train their animals and adjust their environments based on the information presented in the app. For example, they can change how they play with their pets according to the app's suggestions.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0324] Conventional animal behavior management systems focus solely on animal behavior and fail to consider the user's emotional state, making it difficult to propose appropriate care and training for both the animal and the user. Furthermore, the lack of automatic adjustments to the user's emotions, which directly influence the animal's training methods, is also a problem.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes detection means for detecting animal activity and generating information indicating said activity, analysis means for analyzing animal behavior based on said information, and emotion analysis means for analyzing user voice data and recognizing emotional state. This enables comprehensive behavioral management and training guidance that takes into account both animal behavior and user emotions.

[0327] "Detection means" refers to devices and technologies for detecting animal activity and generating information about that activity.

[0328] "Analysis means" refers to devices and technologies used to evaluate and analyze the behavior and health status of animals using information generated by detection means.

[0329] "Guidance means" refers to devices and technologies that provide users with training methods and health management instructions generated based on analysis means.

[0330] "Emotional analysis means" refers to devices and technologies that analyze a user's voice data and recognize the user's emotional state.

[0331] "Adjustment means" refers to devices or technologies used to adjust animal training methods based on the emotional state of the user.

[0332] A "portable device" refers to a device that a user can carry with them and use to receive instructions on training methods and health management from guidance systems.

[0333] This invention is a system that comprehensively analyzes animal behavior and user emotions to propose appropriate training methods and health management. The following describes embodiments for carrying out the invention.

[0334] The device uses smart cameras and sensors to detect animal activity. These devices acquire data such as the animal's location, movement, sound, and temperature in real time. For example, if an animal moves beyond a certain range or makes a sound, that information is stored digitally.

[0335] This data is transferred from the terminal to the server. The server utilizes generative AI models to analyze the collected data. Specifically, the server uses TensorFlow to identify abnormal behavior and unusual health conditions in animals. The server also incorporates speech recognition software to recognize emotional states from the user's voice data. In this process, it analyzes the tone and intensity of the voice to identify emotions such as stress and calmness.

[0336] Based on the analysis results, the server generates training methods for the animal. The generated training methods are displayed on the user's mobile device. The user uses a dedicated application to receive feedback from the server. For example, if the user is feeling stressed, a notification such as "We recommend giving instructions in a calm tone" will be sent. If the animal is more agitated than usual, instructions such as playing calming music may also be included.

[0337] For example, this system can be used to solve the problem of dogs barking frequently. A smart camera captures the dog barking, and the generated AI analyzes the behavior. Furthermore, it determines the user's emotional state from their voice and suggests the most appropriate course of action based on the results.

[0338] An example of a prompt message would be, "Identify the reason why the dog barks frequently and suggest appropriate training methods for the user. Please also consider the user's emotional state." This allows the user to communicate with the animal more effectively with the help of the system.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The device activates smart cameras and sensors to detect animal activity. These devices collect environmental data in real time, including the animal's location, movement, sound, and temperature. The input for this step is raw data from the environment, and the output is the collected dataset. Specifically, the camera generates a video stream, and the sensors record numerical data such as sound and temperature.

[0342] Step 2:

[0343] The terminal transfers the collected data to the server. The data is encrypted using the TLS protocol and securely transmitted to the server. The input is the data collected by the terminal, and the output is the dataset stored on the server. Specifically, the terminal sends data packets using wireless communication.

[0344] Step 3:

[0345] The server analyzes the received data using a generative AI model. The input is the dataset received by the server, and the output is the identification of abnormal animal behavior and health status. Specifically, the server uses TensorFlow to input data into a machine learning model, detect anomalies, and report them.

[0346] Step 4:

[0347] The server incorporates an emotion analysis system that analyzes voice data from the user. The input is the user's voice data, and the output is information about the user's emotional state. Specifically, the server evaluates the tone and intensity of the voice and calculates stress and calmness levels.

[0348] Step 5:

[0349] The server generates a training method based on the results of analyzing animal behavior and the user's emotional state. The input is the analysis results from the previous step, and the output is the generated training method and instructions. Specifically, the server executes an algorithm written in Python to calculate the optimal response.

[0350] Step 6:

[0351] The user receives feedback and advice from the server through an app on their mobile device. The input is the training method and instructions sent from the server, and the output is specific guidance that is viewed on the user's device. In terms of specific actions, the app displays notifications on the UI and prompts the user to take the next action.

[0352] (Application Example 2)

[0353] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0354] In recent years, there has been a growing demand for animal health management and behavioral training. However, conventional systems have a problem in that they make it difficult to provide comprehensive care that takes into account both the animal's activity monitoring and the user's emotional state. As a result, it is difficult to provide training and health management that is optimal for both the animal and the user.

[0355] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0356] In this invention, the server includes observation means for detecting animal activity and generating data, data processing means for analyzing animal behavior based on the data, and emotion analysis means for analyzing user voice data and recognizing emotional state. This makes it possible to closely understand the animal's health and behavior, as well as provide an optimal training method tailored to the user's emotional state.

[0357] "Observation tools" are devices and sensors used to detect animal activity and generate data based on that activity.

[0358] A "data processing system" is a computer system that analyzes data acquired by observation systems to evaluate the behavior and health status of animals.

[0359] A "guidance tool" is an interface that provides users with instructions on animal training methods and health management based on the analysis results.

[0360] An "emotion analysis system" is a system that analyzes a user's voice data to recognize the user's emotional state.

[0361] A "training method" is a technique designed to elicit specific behaviors or responses from animals, with the aim of modifying their behavior or improving their skills.

[0362] This invention is a system that supports animal health management and training in pet shops and pet care centers. The server acquires animal activity data using smart cameras installed in the store. This data is used to analyze animal behavior patterns using OpenCV, and then TensorFlow is used to evaluate the animal's health status using an AI model. The system also generates optimal training methods for the animals and provides them to the user. Users can receive these training methods and health management instructions via their smartphones.

[0363] Furthermore, the device collects user voice data and analyzes it using a voice analysis tool (e.g., Google Cloud Speech-to-Text API) to recognize the user's emotional state. The emotional state recognized by the emotion engine is used to adjust the training methods for the animals, recommending the playback of relaxation music or changes to the training content.

[0364] As a concrete example, to ensure that users and their pets have a comfortable experience at a pet cafe, the system recommends ambient sounds that match the pet's level of calmness. For instance, if the system detects that the pet is restless, it sends a message to the user's smartphone saying, "We recommend playing calming music."

[0365] An example of a prompt message is, "Analyze this dog's behavior log and generate suggestions to help it relax." In this way, it becomes possible to provide a service that is harmonious for both the animal and the user.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device operates a smart camera to collect real-time data on animal movements within the store. This data includes the animals' location and movement. The input is video data from the camera, and the output is initial data indicating the animals' movements.

[0369] Step 2:

[0370] The device uses the animal's motion data to perform image analysis with the OpenCV library and extract the animal's motion patterns. The input is the animal's motion data collected in step 1, and the output is the analysis data related to the motion patterns. This data processing makes it possible to capture specific animal behaviors.

[0371] Step 3:

[0372] The server uses TensorFlow to perform analysis with a generative AI model and evaluate the animal's health status. The input is the analysis data of the behavioral patterns obtained in step 2, and the output is an evaluation of the animal's health status. This allows for the identification of abnormalities and the detection of signs of health.

[0373] Step 4:

[0374] The server generates appropriate training methods based on the health status assessment results and provides them to the user through guidance. The input is the health status data assessed in step 3, and the output is a proposal for specific training methods. Based on this, the user can provide appropriate guidance to the animal.

[0375] Step 5:

[0376] The device uses the Google Cloud Speech-to-Text API to analyze the user's voice data, generating text data from the speech and recognizing their emotional state. The input is the user's voice data, and the output is data indicating their emotional state. This makes it possible to understand the user's stress and emotional state.

[0377] Step 6:

[0378] The server receives user emotional state data and adjusts the training content for the animal accordingly. The input is the emotional state data obtained in step 5, and the output is the adjusted training instructions. This enables an approach that provides the optimal environment for both the user and the animal.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] The present invention provides a system for monitoring animal behavior and health status, and for offering appropriate training methods and health management instructions based on the analysis results. This system utilizes smart cameras and sensors as observation means, a server system as information processing means, and a user's smartphone application as guidance means.

[0396] First, the device uses smart cameras and sensors installed in the environment where the pet is present to collect data in real time, including animal movement, temperature, humidity, and even sounds. This allows the device to generate animal activity data.

[0397] Next, the device sends the acquired data to the server. The server is equipped with generative AI to analyze this data and identify animal behavior patterns. Specifically, the server recognizes specific actions from video data and uses sensor data to analyze environmental changes and activity levels.

[0398] After analysis, the server uses this information to generate training advice regarding the animal's behavior and management instructions regarding its health. For example, if an animal frequently wanders around the same area, it may be determined that excessive stress is the cause. Based on this, the server recommends playing relaxing ambient sounds or increasing playtime.

[0399] Users can receive information from the server via their smartphone app and view the animal's behavior history and analysis results. Furthermore, users can not only adjust the animal's behavior based on training advice from the server, but also improve the animal's living environment according to health management instructions.

[0400] This allows users to understand the animal's condition in real time and implement quick and appropriate countermeasures, thereby promoting the animal's health and well-being. In this way, the present invention can create a better living environment for both animals and users.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The device collects real-time video and environmental data from smart cameras and sensors installed in the environment where animals are present. This includes data on animal movement, sounds, temperature, and humidity.

[0404] Step 2:

[0405] The terminal organizes the collected data chronologically and sends it to the server as data packets. Transmission takes place in real time using an internet connection.

[0406] Step 3:

[0407] The server stores the received data in a database. The stored data is organized along a timeline and used to evaluate the animals' past behavioral patterns.

[0408] Step 4:

[0409] The server analyzes data using generative AI. It identifies animal behavior in video data using image recognition and analyzes sensor data to evaluate the state of the animal's surrounding environment.

[0410] Step 5:

[0411] The server generates training advice and health management instructions based on the analysis results. This includes suggesting ways to improve behavioral abnormalities and recommending health-related preventative measures.

[0412] Step 6:

[0413] The server sends the generated advice and instructions to the user's smartphone app. Through notifications, the user can instantly receive the information.

[0414] Step 7:

[0415] Users review the advice and instructions received through the app and use them to adjust training and the environment as feedback to their animals. This leads to continuous improvements in the animals' behavior and health.

[0416] (Example 1)

[0417] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0418] When raising animals, it is necessary to understand their behavior and health status in real time and to provide appropriate training methods and health management instructions. However, doing this manually is laborious and time-consuming, and it is difficult to make accurate judgments. Furthermore, it is necessary to detect stress and abnormal behavior in animals early and to address them appropriately. To solve these problems, a system is needed that can automatically monitor animal behavior and health status and take appropriate action based on the analysis results.

[0419] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0420] In this invention, the server includes observation means for detecting animal activity and generating data, information processing means for analyzing animal behavior using a generated AI model, and guidance means for generating and providing training methods and health management instructions for the animal to the user. This enables accurate, real-time monitoring of animal behavior and health status, as well as appropriate training and management.

[0421] "Observation methods" refer to devices or functions that detect animal behavior and environmental information in real time and generate it as data.

[0422] "Information processing means" refers to devices and systems that acquire data obtained from observation means and analyze animal behavior using generative AI models, etc.

[0423] A "generative AI model" is an artificial intelligence technology used to analyze animal behavior, and refers to a technology that includes algorithms for identifying behavioral patterns based on large amounts of data.

[0424] "Guidance means" refers to a function or device that creates instructions for training methods and health management for animals based on analyzed information and provides them to users.

[0425] "User means" refers to the actions and tools used to adjust the animal's daily life in response to training methods and health management instructions provided by guidance means.

[0426] A "prompt message" is a text format used by users to input specific questions or requests to a system, and is used to receive instructions or advice based on the analysis results.

[0427] The system of this invention is configured as follows to monitor the behavior and health status of animals and provide appropriate training methods and health management instructions based on the analysis results.

[0428] First, the device uses a smart camera and various sensors as observation tools. This allows it to collect data in real time on animal movements, environmental temperature, humidity, and sounds. For example, the device records how pets move and how environmental changes affect animal behavior. This data is transmitted to a server using Wi-Fi or Bluetooth.

[0429] The server uses a generation AI model to analyze received data as an information processing tool. This AI model utilizes deep learning technology to identify animal behavior patterns and evaluate the relationship between environmental conditions and animal activity levels. For example, the server has the ability to recognize specific behaviors from video data and analyze continuous behavior patterns to determine the animal's stress level and health status.

[0430] Next, based on the analysis results, the server generates instructions for training the animal's behavior and managing its health. For example, if an animal frequently wanders around a specific area during the day, the server might recommend playing ambient sounds to reduce stress and increasing playtime.

[0431] Information is transmitted through the user's smartphone app. The app allows users to check the animal's behavioral history and health status, and improve the animal's living environment as needed. Users can also use prompts to ask specific questions to the server and receive answers. For example, a user might send a prompt such as, "My pet seems to be lacking exercise lately, what should I do?" and utilize the advice based on the analysis received.

[0432] By implementing this system, users can monitor the animals' condition in real time and provide prompt and appropriate care, thereby promoting the animals' health and well-being.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The device uses smart cameras and sensors to collect animal behavior and environmental data in real time. Input for this step includes multiple data points such as animal movement, environmental temperature, humidity, and sound. This data is converted into an analyzable format and output as animal activity data. Specifically, it captures video data, records audio, and compiles temperature and humidity measurements into digital information.

[0436] Step 2:

[0437] The terminal transmits the collected activity data to the server via wireless communication. The input for this step is the various data collected by the terminal. After transmission, the server outputs it in a unified data format that can be analyzed. Specifically, the data is compressed and encrypted, and a secure communication protocol is used to transfer the data to the server.

[0438] Step 3:

[0439] The server analyzes the received data using a generative AI model. The input for this step is behavioral and environmental data sent from the terminal. The server analyzes this data using AI and generates output such as animal behavioral patterns and causal relationships with the environment. Specifically, it inputs data into a deep learning model to recognize specific behaviors (such as meal times and rest times) and analyze the environmental factors associated with them.

[0440] Step 4:

[0441] The server generates training methods and health management instructions for the animals based on the analysis results. The input for this step is the analyzed behavioral patterns and environmental data. The output generates specific advice for improving the animals' stress management and activity levels. For example, if frequent behavior in the same location is determined to be caused by stress, the server will suggest playing environmental sounds or scheduling playtime.

[0442] Step 5:

[0443] The server notifies the user's smartphone app of the generated information. The input for this step is the generated training and health instruction data. The output is a notification displayed in the smartphone app in a format that is easy for the user to understand. Specifically, the information is sent using the push notification function, and the instructions are displayed within the app.

[0444] Step 6:

[0445] The user adjusts the animal's daily routine based on instructions received through the app. The input for this step is training advice and health management instructions received from the server. The output is the implementation of specific actions to maintain and improve the animal's health and well-being. Specific examples include purchasing new toys or adjusting the room temperature.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] Traditional methods of managing and operating equipment and animals made it difficult to monitor their status in real time or provide optimal operating plans, often leading to excessive burden and unnecessary downtime. This resulted in problems such as insufficient efficiency improvements and inadequate problem-solving.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes observation means for detecting the activity of an animal or device and generating data indicating said activity; information processing means for acquiring the data generated by the observation means and analyzing the patterns of operation based on said data; and guidance means for generating an optimal operation plan or maintenance instruction based on said analysis and providing said plan or instruction to the user. This enables efficient operation and maintenance of the device or animal in real time.

[0451] "Activity" refers to actions or changes in state performed by animals or devices.

[0452] "Observation means" refers to devices or mechanisms that detect the activity of animals or other objects and generate data from that activity.

[0453] "Information processing means" refers to a system for analyzing data acquired by observation means and identifying behavioral patterns.

[0454] "Guidance means" refers to devices or applications that have the function of providing users with action plans and instructions generated based on analysis results.

[0455] An "operation plan" refers to a set of guidelines and methods that specify how a device or animal should function optimally.

[0456] "Maintenance instructions" refer to instructions that guide the work and operation necessary for the maintenance and upkeep of equipment.

[0457] "Assessing the condition" refers to determining the current state or condition of an animal or device.

[0458] "Communication terminal" refers to digital devices such as smartphones and tablets that users use to receive information.

[0459] The system that implements this application monitors the activity of animals or devices in real time and provides optimal operation plans and maintenance instructions based on that activity. The system includes observation means, information processing means, and guidance means.

[0460] The system's observation methods involve collecting operational data on animals or devices using smart cameras and various sensors. This data includes operational patterns, vibrations, temperature, and sounds. Embedded systems such as Raspberry Pi or Arduino could be used to acquire this data.

[0461] Next, the server receives the data as an information processing tool and analyzes the behavioral patterns using a generated AI model. Apache Kafka or Amazon Kinesis can be used for data processing, and TensorFlow or PyTorch can be used for AI analysis. The analysis results are used to determine the current state of the device or animal and the efficiency of its operation.

[0462] Subsequently, as a guidance mechanism, the server generates an optimal operation plan and maintenance instructions based on the analysis results and notifies the user's communication terminal. The user can receive this information via a smartphone app and adjust the operation of the equipment and animals. Flutter or React Native could be used for developing the mobile app.

[0463] For example, if abnormal vibrations are detected in a robot within a factory, the server quickly analyzes the issue and sends a notification to the user indicating that maintenance is required. This notification allows the user to implement preventative maintenance.

[0464] An example of a prompt for a generated AI model is, "Please suggest countermeasures if the factory robot exhibits an unusual operating pattern." This allows the AI ​​to generate specific countermeasures and provide them to the user as a notification.

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The device uses smart cameras and sensors to collect real-time activity data of animals or devices. This data includes movement patterns, vibrations, temperature, and sounds. The input is raw data acquired from the environment, and the output is digitized activity data. The device generates this activity data and prepares it for transmission to the next step.

[0468] Step 2:

[0469] The terminal transmits collected activity data to the server. The input is activity data generated from smart cameras and sensors, and the output is a notification that transmission to the server is complete. The terminal uses a data communication protocol to ensure reliable data transmission.

[0470] Step 3:

[0471] The server stores the received data and begins analyzing the behavioral patterns using a generated AI model. The input is activity data sent from the terminal, and the output is the analyzed behavioral patterns and state evaluations. The server first preprocesses the data, then applies it to the AI ​​model to classify and evaluate the behavior of pets and devices.

[0472] Step 4:

[0473] The server generates an optimal operation plan or maintenance instructions based on the analysis results, using the generated AI model. The input is the operation pattern and state evaluation obtained in step 3, and the output is a specific operation plan or maintenance instructions. The server generates these and prepares them for communication to the user.

[0474] Step 5:

[0475] The server notifies the user's communication terminal of the operation plan or maintenance instructions. The input is the operation plan or instructions generated by the server, and the output is the notification message received by the user. The server ensures that the information is reliably delivered to the user through backend services and displayed in the application.

[0476] Step 6:

[0477] The user reviews the received operation plan and maintenance instructions, and then performs adjustments and maintenance activities for the equipment and animals. The input is the notification content displayed on the communication terminal, and the output is the user's specific actions based on that notification. This allows the user to monitor the actual status of the equipment and pets and take necessary actions.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] This invention is a training and health management system that takes into account not only the behavior and health status of animals, but also the emotions of the user. This system includes observation means, information processing means, guidance means, and an emotion engine.

[0480] The device uses smart cameras and sensors placed in the environment to detect animal activity. This allows it to collect data such as the animal's location, movement, sound, and temperature in real time. This data indicates the animal's behavior and the environmental conditions.

[0481] The device transfers the collected data to the server. The server uses this data to analyze the animal's behavior and employs generative AI to identify abnormal behaviors and signs of health problems. Based on this analysis, appropriate training methods and health management instructions for the animal are generated.

[0482] Furthermore, a key feature of this system is that the server incorporates an emotion engine. This emotion engine recognizes the user's emotional state by analyzing their voice data. For example, it can determine whether a user is experiencing stress based on the tone and intensity of their voice.

[0483] The emotional state of the user, as recognized by the emotion engine, is used to adjust the animal's training methods. For example, if the user is stressed, the system may recommend giving the animal calmer instructions and, if necessary, play relaxation music.

[0484] Users can receive these analysis results and tailored advice through a smartphone app. For example, if a user is frustrated with an animal, the app can notify them that "it is recommended to give instructions in a calm tone" and suggest appropriate training methods for the animal. In this way, implementing the present invention enables comprehensive behavior and health management that takes into account the emotional states of both the animal and the user.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The device collects real-time data on animals using smart cameras and sensors installed in the environment. This includes data such as movement, sound, temperature, and humidity.

[0488] Step 2:

[0489] The device transfers the collected data to the server. This data includes information about animal behavior and is updated in real time.

[0490] Step 3:

[0491] The server stores the received data and uses generative AI to analyze animal behavior. Here, it detects specific behavioral patterns and evaluates abnormal behavior or health status.

[0492] Step 4:

[0493] The server generates training methods and health management instructions for the animals based on the analysis results. This includes specific suggestions for behavioral improvement.

[0494] Step 5:

[0495] The device transmits the user's voice data to the emotion engine. The emotion engine performs voice analysis and recognizes the user's emotional state.

[0496] Step 6:

[0497] The server's emotion engine takes the user's emotional state into account and adjusts the training methods for the animals accordingly. For example, if the user is feeling stressed, it recommends training methods that reduce the stimulation to the animals.

[0498] Step 7:

[0499] The server sends tailored training advice and health management instructions to the user's smartphone app. This allows the user to stay informed in real time and take appropriate action.

[0500] Step 8:

[0501] Users can train their animals and adjust their environments based on the information presented in the app. For example, they can change how they play with their pets according to the app's suggestions.

[0502] (Example 2)

[0503] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] Conventional animal behavior management systems focus solely on animal behavior and fail to consider the user's emotional state, making it difficult to propose appropriate care and training for both the animal and the user. Furthermore, the lack of automatic adjustments to the user's emotions, which directly influence the animal's training methods, is also a problem.

[0505] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0506] In this invention, the server includes detection means for detecting animal activity and generating information indicating said activity, analysis means for analyzing animal behavior based on said information, and emotion analysis means for analyzing user voice data and recognizing emotional state. This enables comprehensive behavioral management and training guidance that takes into account both animal behavior and user emotions.

[0507] "Detection means" refers to devices and technologies for detecting animal activity and generating information about that activity.

[0508] "Analysis means" refers to devices and technologies used to evaluate and analyze the behavior and health status of animals using information generated by detection means.

[0509] "Guidance means" refers to devices and technologies that provide users with training methods and health management instructions generated based on analysis means.

[0510] "Emotional analysis means" refers to devices and technologies that analyze a user's voice data and recognize the user's emotional state.

[0511] "Adjustment means" refers to devices or technologies used to adjust animal training methods based on the emotional state of the user.

[0512] A "portable device" refers to a device that a user can carry with them and use to receive instructions on training methods and health management from guidance systems.

[0513] This invention is a system that comprehensively analyzes animal behavior and user emotions to propose appropriate training methods and health management. The following describes embodiments for carrying out the invention.

[0514] The device uses smart cameras and sensors to detect animal activity. These devices acquire data such as the animal's location, movement, sound, and temperature in real time. For example, if an animal moves beyond a certain range or makes a sound, that information is stored digitally.

[0515] This data is transferred from the terminal to the server. The server utilizes generative AI models to analyze the collected data. Specifically, the server uses TensorFlow to identify abnormal behavior and unusual health conditions in animals. The server also incorporates speech recognition software to recognize emotional states from the user's voice data. In this process, it analyzes the tone and intensity of the voice to identify emotions such as stress and calmness.

[0516] Based on the analysis results, the server generates training methods for the animal. The generated training methods are displayed on the user's mobile device. The user uses a dedicated application to receive feedback from the server. For example, if the user is feeling stressed, a notification such as "We recommend giving instructions in a calm tone" will be sent. If the animal is more agitated than usual, instructions such as playing calming music may also be included.

[0517] For example, this system can be used to solve the problem of dogs barking frequently. A smart camera captures the dog barking, and the generated AI analyzes the behavior. Furthermore, it determines the user's emotional state from their voice and suggests the most appropriate course of action based on the results.

[0518] An example of a prompt message would be, "Identify the reason why the dog barks frequently and suggest appropriate training methods for the user. Please also consider the user's emotional state." This allows the user to communicate with the animal more effectively with the help of the system.

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1:

[0521] The device activates smart cameras and sensors to detect animal activity. These devices collect environmental data in real time, including the animal's location, movement, sound, and temperature. The input for this step is raw data from the environment, and the output is the collected dataset. Specifically, the camera generates a video stream, and the sensors record numerical data such as sound and temperature.

[0522] Step 2:

[0523] The terminal transfers the collected data to the server. The data is encrypted using the TLS protocol and securely transmitted to the server. The input is the data collected by the terminal, and the output is the dataset stored on the server. Specifically, the terminal sends data packets using wireless communication.

[0524] Step 3:

[0525] The server analyzes the received data using a generative AI model. The input is the dataset received by the server, and the output is the identification of abnormal animal behavior and health status. Specifically, the server uses TensorFlow to input data into a machine learning model, detect anomalies, and report them.

[0526] Step 4:

[0527] The server incorporates an emotion analysis system that analyzes voice data from the user. The input is the user's voice data, and the output is information about the user's emotional state. Specifically, the server evaluates the tone and intensity of the voice and calculates stress and calmness levels.

[0528] Step 5:

[0529] The server generates a training method based on the results of analyzing animal behavior and the user's emotional state. The input is the analysis results from the previous step, and the output is the generated training method and instructions. Specifically, the server executes an algorithm written in Python to calculate the optimal response.

[0530] Step 6:

[0531] The user receives feedback and advice from the server through an app on their mobile device. The input is the training method and instructions sent from the server, and the output is specific guidance that is viewed on the user's device. In terms of specific actions, the app displays notifications on the UI and prompts the user to take the next action.

[0532] (Application Example 2)

[0533] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0534] In recent years, there has been a growing demand for animal health management and behavioral training. However, conventional systems have a problem in that they make it difficult to provide comprehensive care that takes into account both the animal's activity monitoring and the user's emotional state. As a result, it is difficult to provide training and health management that is optimal for both the animal and the user.

[0535] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0536] In this invention, the server includes observation means for detecting animal activity and generating data, data processing means for analyzing animal behavior based on the data, and emotion analysis means for analyzing user voice data and recognizing emotional state. This makes it possible to closely understand the animal's health and behavior, as well as provide an optimal training method tailored to the user's emotional state.

[0537] "Observation tools" are devices and sensors used to detect animal activity and generate data based on that activity.

[0538] A "data processing system" is a computer system that analyzes data acquired by observation systems to evaluate the behavior and health status of animals.

[0539] A "guidance tool" is an interface that provides users with instructions on animal training methods and health management based on the analysis results.

[0540] An "emotion analysis system" is a system that analyzes a user's voice data to recognize the user's emotional state.

[0541] A "training method" is a technique designed to elicit specific behaviors or responses from animals, with the aim of modifying their behavior or improving their skills.

[0542] This invention is a system that supports animal health management and training in pet shops and pet care centers. The server acquires animal activity data using smart cameras installed in the store. This data is used to analyze animal behavior patterns using OpenCV, and then TensorFlow is used to evaluate the animal's health status using an AI model. The system also generates optimal training methods for the animals and provides them to the user. Users can receive these training methods and health management instructions via their smartphones.

[0543] Furthermore, the device collects user voice data and analyzes it using a voice analysis tool (e.g., Google Cloud Speech-to-Text API) to recognize the user's emotional state. The emotional state recognized by the emotion engine is used to adjust the training methods for the animals, recommending the playback of relaxation music or changes to the training content.

[0544] As a concrete example, to ensure that users and their pets have a comfortable experience at a pet cafe, the system recommends ambient sounds that match the pet's level of calmness. For instance, if the system detects that the pet is restless, it sends a message to the user's smartphone saying, "We recommend playing calming music."

[0545] An example of a prompt message is, "Analyze this dog's behavior log and generate suggestions to help it relax." In this way, it becomes possible to provide a service that is harmonious for both the animal and the user.

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The device operates a smart camera to collect real-time data on animal movements within the store. This data includes the animals' location and movement. The input is video data from the camera, and the output is initial data indicating the animals' movements.

[0549] Step 2:

[0550] The device uses the animal's motion data to perform image analysis with the OpenCV library and extract the animal's motion patterns. The input is the animal's motion data collected in step 1, and the output is the analysis data related to the motion patterns. This data processing makes it possible to capture specific animal behaviors.

[0551] Step 3:

[0552] The server uses TensorFlow to perform analysis with a generative AI model and evaluate the animal's health status. The input is the analysis data of the behavioral patterns obtained in step 2, and the output is an evaluation of the animal's health status. This allows for the identification of abnormalities and the detection of signs of health.

[0553] Step 4:

[0554] The server generates appropriate training methods based on the health status assessment results and provides them to the user through guidance. The input is the health status data assessed in step 3, and the output is a proposal for specific training methods. Based on this, the user can provide appropriate guidance to the animal.

[0555] Step 5:

[0556] The device uses the Google Cloud Speech-to-Text API to analyze the user's voice data, generating text data from the speech and recognizing their emotional state. The input is the user's voice data, and the output is data indicating their emotional state. This makes it possible to understand the user's stress and emotional state.

[0557] Step 6:

[0558] The server receives user emotional state data and adjusts the training content for the animal accordingly. The input is the emotional state data obtained in step 5, and the output is the adjusted training instructions. This enables an approach that provides the optimal environment for both the user and the animal.

[0559] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0565] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0566] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0567] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0568] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0569] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0570] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0571] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0572] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0573] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0574] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0575] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] The present invention provides a system for monitoring animal behavior and health status, and for offering appropriate training methods and health management instructions based on the analysis results. This system utilizes smart cameras and sensors as observation means, a server system as information processing means, and a user's smartphone application as guidance means.

[0577] First, the device uses smart cameras and sensors installed in the environment where the pet is present to collect data in real time, including animal movement, temperature, humidity, and even sounds. This allows the device to generate animal activity data.

[0578] Next, the device sends the acquired data to the server. The server is equipped with generative AI to analyze this data and identify animal behavior patterns. Specifically, the server recognizes specific actions from video data and uses sensor data to analyze environmental changes and activity levels.

[0579] After analysis, the server uses this information to generate training advice regarding the animal's behavior and management instructions regarding its health. For example, if an animal frequently wanders around the same area, it may be determined that excessive stress is the cause. Based on this, the server recommends playing relaxing ambient sounds or increasing playtime.

[0580] Users can receive information from the server via their smartphone app and view the animal's behavior history and analysis results. Furthermore, users can not only adjust the animal's behavior based on training advice from the server, but also improve the animal's living environment according to health management instructions.

[0581] This allows users to understand the animal's condition in real time and implement quick and appropriate countermeasures, thereby promoting the animal's health and well-being. In this way, the present invention can create a better living environment for both animals and users.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The device collects real-time video and environmental data from smart cameras and sensors installed in the environment where animals are present. This includes data on animal movement, sounds, temperature, and humidity.

[0585] Step 2:

[0586] The terminal organizes the collected data chronologically and sends it to the server as data packets. Transmission takes place in real time using an internet connection.

[0587] Step 3:

[0588] The server stores the received data in a database. The stored data is organized along a timeline and used to evaluate the animals' past behavioral patterns.

[0589] Step 4:

[0590] The server analyzes data using generative AI. It identifies animal behavior in video data using image recognition and analyzes sensor data to evaluate the state of the animal's surrounding environment.

[0591] Step 5:

[0592] The server generates training advice and health management instructions based on the analysis results. This includes suggesting ways to improve behavioral abnormalities and recommending health-related preventative measures.

[0593] Step 6:

[0594] The server sends the generated advice and instructions to the user's smartphone app. Through notifications, the user can instantly receive the information.

[0595] Step 7:

[0596] Users review the advice and instructions received through the app and use them to adjust training and the environment as feedback to their animals. This leads to continuous improvements in the animals' behavior and health.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] When raising animals, it is necessary to understand their behavior and health status in real time and to provide appropriate training methods and health management instructions. However, doing this manually is laborious and time-consuming, and it is difficult to make accurate judgments. Furthermore, it is necessary to detect stress and abnormal behavior in animals early and to address them appropriately. To solve these problems, a system is needed that can automatically monitor animal behavior and health status and take appropriate action based on the analysis results.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes observation means for detecting animal activity and generating data, information processing means for analyzing animal behavior using a generated AI model, and guidance means for generating and providing training methods and health management instructions for the animal to the user. This enables accurate, real-time monitoring of animal behavior and health status, as well as appropriate training and management.

[0602] "Observation methods" refer to devices or functions that detect animal behavior and environmental information in real time and generate it as data.

[0603] "Information processing means" refers to devices and systems that acquire data obtained from observation means and analyze animal behavior using generative AI models, etc.

[0604] A "generative AI model" is an artificial intelligence technology used to analyze animal behavior, and refers to a technology that includes algorithms for identifying behavioral patterns based on large amounts of data.

[0605] "Guidance means" refers to a function or device that creates instructions for training methods and health management for animals based on analyzed information and provides them to users.

[0606] "User means" refers to the actions and tools used to adjust the animal's daily life in response to training methods and health management instructions provided by guidance means.

[0607] A "prompt message" is a text format used by users to input specific questions or requests to a system, and is used to receive instructions or advice based on the analysis results.

[0608] The system of this invention is configured as follows to monitor the behavior and health status of animals and provide appropriate training methods and health management instructions based on the analysis results.

[0609] First, the device uses a smart camera and various sensors as observation tools. This allows it to collect data in real time on animal movements, environmental temperature, humidity, and sounds. For example, the device records how pets move and how environmental changes affect animal behavior. This data is transmitted to a server using Wi-Fi or Bluetooth.

[0610] The server uses a generation AI model to analyze received data as an information processing tool. This AI model utilizes deep learning technology to identify animal behavior patterns and evaluate the relationship between environmental conditions and animal activity levels. For example, the server has the ability to recognize specific behaviors from video data and analyze continuous behavior patterns to determine the animal's stress level and health status.

[0611] Next, based on the analysis results, the server generates instructions for training the animal's behavior and managing its health. For example, if an animal frequently wanders around a specific area during the day, the server might recommend playing ambient sounds to reduce stress and increasing playtime.

[0612] Information is transmitted through the user's smartphone app. The app allows users to check the animal's behavioral history and health status, and improve the animal's living environment as needed. Users can also use prompts to ask specific questions to the server and receive answers. For example, a user might send a prompt such as, "My pet seems to be lacking exercise lately, what should I do?" and utilize the advice based on the analysis received.

[0613] By implementing this system, users can monitor the animals' condition in real time and provide prompt and appropriate care, thereby promoting the animals' health and well-being.

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The device uses smart cameras and sensors to collect animal behavior and environmental data in real time. Input for this step includes multiple data points such as animal movement, environmental temperature, humidity, and sound. This data is converted into an analyzable format and output as animal activity data. Specifically, it captures video data, records audio, and compiles temperature and humidity measurements into digital information.

[0617] Step 2:

[0618] The terminal transmits the collected activity data to the server via wireless communication. The input for this step is the various data collected by the terminal. After transmission, the server outputs it in a unified data format that can be analyzed. Specifically, the data is compressed and encrypted, and a secure communication protocol is used to transfer the data to the server.

[0619] Step 3:

[0620] The server analyzes the received data using a generative AI model. The input for this step is behavioral and environmental data sent from the terminal. The server analyzes this data using AI and generates output such as animal behavioral patterns and causal relationships with the environment. Specifically, it inputs data into a deep learning model to recognize specific behaviors (such as meal times and rest times) and analyze the environmental factors associated with them.

[0621] Step 4:

[0622] The server generates training methods and health management instructions for the animals based on the analysis results. The input for this step is the analyzed behavioral patterns and environmental data. The output generates specific advice for improving the animals' stress management and activity levels. For example, if frequent behavior in the same location is determined to be caused by stress, the server will suggest playing environmental sounds or scheduling playtime.

[0623] Step 5:

[0624] The server notifies the user's smartphone app of the generated information. The input for this step is the generated training and health instruction data. The output is a notification displayed in the smartphone app in a format that is easy for the user to understand. Specifically, the information is sent using the push notification function, and the instructions are displayed within the app.

[0625] Step 6:

[0626] The user adjusts the animal's daily routine based on instructions received through the app. The input for this step is training advice and health management instructions received from the server. The output is the implementation of specific actions to maintain and improve the animal's health and well-being. Specific examples include purchasing new toys or adjusting the room temperature.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] Traditional methods of managing and operating equipment and animals made it difficult to monitor their status in real time or provide optimal operating plans, often leading to excessive burden and unnecessary downtime. This resulted in problems such as insufficient efficiency improvements and inadequate problem-solving.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes observation means for detecting the activity of an animal or device and generating data indicating said activity; information processing means for acquiring the data generated by the observation means and analyzing the patterns of operation based on said data; and guidance means for generating an optimal operation plan or maintenance instruction based on said analysis and providing said plan or instruction to the user. This enables efficient operation and maintenance of the device or animal in real time.

[0632] "Activity" refers to actions or changes in state performed by animals or devices.

[0633] "Observation means" refers to devices or mechanisms that detect the activity of animals or other objects and generate data from that activity.

[0634] "Information processing means" refers to a system for analyzing data acquired by observation means and identifying behavioral patterns.

[0635] "Guidance means" refers to devices or applications that have the function of providing users with action plans and instructions generated based on analysis results.

[0636] An "operation plan" refers to a set of guidelines and methods that specify how a device or animal should function optimally.

[0637] "Maintenance instructions" refer to instructions that guide the work and operation necessary for the maintenance and upkeep of equipment.

[0638] "Assessing the condition" refers to determining the current state or condition of an animal or device.

[0639] "Communication terminal" refers to digital devices such as smartphones and tablets that users use to receive information.

[0640] The system that implements this application monitors the activity of animals or devices in real time and provides optimal operation plans and maintenance instructions based on that activity. The system includes observation means, information processing means, and guidance means.

[0641] The system's observation methods involve collecting operational data on animals or devices using smart cameras and various sensors. This data includes operational patterns, vibrations, temperature, and sounds. Embedded systems such as Raspberry Pi or Arduino could be used to acquire this data.

[0642] Next, the server receives the data as an information processing tool and analyzes the behavioral patterns using a generated AI model. Apache Kafka or Amazon Kinesis can be used for data processing, and TensorFlow or PyTorch can be used for AI analysis. The analysis results are used to determine the current state of the device or animal and the efficiency of its operation.

[0643] Subsequently, as a guidance mechanism, the server generates an optimal operation plan and maintenance instructions based on the analysis results and notifies the user's communication terminal. The user can receive this information via a smartphone app and adjust the operation of the equipment and animals. Flutter or React Native could be used for developing the mobile app.

[0644] For example, if abnormal vibrations are detected in a robot within a factory, the server quickly analyzes the issue and sends a notification to the user indicating that maintenance is required. This notification allows the user to implement preventative maintenance.

[0645] An example of a prompt for a generated AI model is, "Please suggest countermeasures if the factory robot exhibits an unusual operating pattern." This allows the AI ​​to generate specific countermeasures and provide them to the user as a notification.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The device uses smart cameras and sensors to collect real-time activity data of animals or devices. This data includes movement patterns, vibrations, temperature, and sounds. The input is raw data acquired from the environment, and the output is digitized activity data. The device generates this activity data and prepares it for transmission to the next step.

[0649] Step 2:

[0650] The terminal transmits collected activity data to the server. The input is activity data generated from smart cameras and sensors, and the output is a notification that transmission to the server is complete. The terminal uses a data communication protocol to ensure reliable data transmission.

[0651] Step 3:

[0652] The server stores the received data and begins analyzing the behavioral patterns using a generated AI model. The input is activity data sent from the terminal, and the output is the analyzed behavioral patterns and state evaluations. The server first preprocesses the data, then applies it to the AI ​​model to classify and evaluate the behavior of pets and devices.

[0653] Step 4:

[0654] The server generates an optimal operation plan or maintenance instructions based on the analysis results, using the generated AI model. The input is the operation pattern and state evaluation obtained in step 3, and the output is a specific operation plan or maintenance instructions. The server generates these and prepares them for communication to the user.

[0655] Step 5:

[0656] The server notifies the user's communication terminal of the operation plan or maintenance instructions. The input is the operation plan or instructions generated by the server, and the output is the notification message received by the user. The server ensures that the information is reliably delivered to the user through backend services and displayed in the application.

[0657] Step 6:

[0658] The user reviews the received operation plan and maintenance instructions, and then performs adjustments and maintenance activities for the equipment and animals. The input is the notification content displayed on the communication terminal, and the output is the user's specific actions based on that notification. This allows the user to monitor the actual status of the equipment and pets and take necessary actions.

[0659] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0660] This invention is a training and health management system that takes into account not only the behavior and health status of animals, but also the emotions of the user. This system includes observation means, information processing means, guidance means, and an emotion engine.

[0661] The device uses smart cameras and sensors placed in the environment to detect animal activity. This allows it to collect data such as the animal's location, movement, sound, and temperature in real time. This data indicates the animal's behavior and the environmental conditions.

[0662] The device transfers the collected data to the server. The server uses this data to analyze the animal's behavior and employs generative AI to identify abnormal behaviors and signs of health problems. Based on this analysis, appropriate training methods and health management instructions for the animal are generated.

[0663] Furthermore, a key feature of this system is that the server incorporates an emotion engine. This emotion engine recognizes the user's emotional state by analyzing their voice data. For example, it can determine whether a user is experiencing stress based on the tone and intensity of their voice.

[0664] The emotional state of the user, as recognized by the emotion engine, is used to adjust the animal's training methods. For example, if the user is stressed, the system may recommend giving the animal calmer instructions and, if necessary, play relaxation music.

[0665] Users can receive these analysis results and tailored advice through a smartphone app. For example, if a user is frustrated with an animal, the app can notify them that "it is recommended to give instructions in a calm tone" and suggest appropriate training methods for the animal. In this way, implementing the present invention enables comprehensive behavior and health management that takes into account the emotional states of both the animal and the user.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The device collects real-time data on animals using smart cameras and sensors installed in the environment. This includes data such as movement, sound, temperature, and humidity.

[0669] Step 2:

[0670] The device transfers the collected data to the server. This data includes information about animal behavior and is updated in real time.

[0671] Step 3:

[0672] The server stores the received data and uses generative AI to analyze animal behavior. Here, it detects specific behavioral patterns and evaluates abnormal behavior or health status.

[0673] Step 4:

[0674] The server generates training methods and health management instructions for the animals based on the analysis results. This includes specific suggestions for behavioral improvement.

[0675] Step 5:

[0676] The device transmits the user's voice data to the emotion engine. The emotion engine performs voice analysis and recognizes the user's emotional state.

[0677] Step 6:

[0678] The server's emotion engine takes the user's emotional state into account and adjusts the training methods for the animals accordingly. For example, if the user is feeling stressed, it recommends training methods that reduce the stimulation to the animals.

[0679] Step 7:

[0680] The server sends tailored training advice and health management instructions to the user's smartphone app. This allows the user to stay informed in real time and take appropriate action.

[0681] Step 8:

[0682] Users can train their animals and adjust their environments based on the information presented in the app. For example, they can change how they play with their pets according to the app's suggestions.

[0683] (Example 2)

[0684] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Conventional animal behavior management systems focus solely on animal behavior and fail to consider the user's emotional state, making it difficult to propose appropriate care and training for both the animal and the user. Furthermore, the lack of automatic adjustments to the user's emotions, which directly influence the animal's training methods, is also a problem.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0687] In this invention, the server includes detection means for detecting animal activity and generating information indicating said activity, analysis means for analyzing animal behavior based on said information, and emotion analysis means for analyzing user voice data and recognizing emotional state. This enables comprehensive behavioral management and training guidance that takes into account both animal behavior and user emotions.

[0688] "Detection means" refers to devices and technologies for detecting animal activity and generating information about that activity.

[0689] "Analysis means" refers to devices and technologies used to evaluate and analyze the behavior and health status of animals using information generated by detection means.

[0690] "Guidance means" refers to devices and technologies that provide users with training methods and health management instructions generated based on analysis means.

[0691] "Emotional analysis means" refers to devices and technologies that analyze a user's voice data and recognize the user's emotional state.

[0692] "Adjustment means" refers to devices or technologies used to adjust animal training methods based on the emotional state of the user.

[0693] A "portable device" refers to a device that a user can carry with them and use to receive instructions on training methods and health management from guidance systems.

[0694] This invention is a system that comprehensively analyzes animal behavior and user emotions to propose appropriate training methods and health management. The following describes embodiments for carrying out the invention.

[0695] The device uses smart cameras and sensors to detect animal activity. These devices acquire data such as the animal's location, movement, sound, and temperature in real time. For example, if an animal moves beyond a certain range or makes a sound, that information is stored digitally.

[0696] This data is transferred from the terminal to the server. The server utilizes generative AI models to analyze the collected data. Specifically, the server uses TensorFlow to identify abnormal behavior and unusual health conditions in animals. The server also incorporates speech recognition software to recognize emotional states from the user's voice data. In this process, it analyzes the tone and intensity of the voice to identify emotions such as stress and calmness.

[0697] Based on the analysis results, the server generates training methods for the animal. The generated training methods are displayed on the user's mobile device. The user uses a dedicated application to receive feedback from the server. For example, if the user is feeling stressed, a notification such as "We recommend giving instructions in a calm tone" will be sent. If the animal is more agitated than usual, instructions such as playing calming music may also be included.

[0698] For example, this system can be used to solve the problem of dogs barking frequently. A smart camera captures the dog barking, and the generated AI analyzes the behavior. Furthermore, it determines the user's emotional state from their voice and suggests the most appropriate course of action based on the results.

[0699] An example of a prompt message would be, "Identify the reason why the dog barks frequently and suggest appropriate training methods for the user. Please also consider the user's emotional state." This allows the user to communicate with the animal more effectively with the help of the system.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The device activates smart cameras and sensors to detect animal activity. These devices collect environmental data in real time, including the animal's location, movement, sound, and temperature. The input for this step is raw data from the environment, and the output is the collected dataset. Specifically, the camera generates a video stream, and the sensors record numerical data such as sound and temperature.

[0703] Step 2:

[0704] The terminal transfers the collected data to the server. The data is encrypted using the TLS protocol and securely transmitted to the server. The input is the data collected by the terminal, and the output is the dataset stored on the server. Specifically, the terminal sends data packets using wireless communication.

[0705] Step 3:

[0706] The server analyzes the received data using a generative AI model. The input is the dataset received by the server, and the output is the identification of abnormal animal behavior and health status. Specifically, the server uses TensorFlow to input data into a machine learning model, detect anomalies, and report them.

[0707] Step 4:

[0708] The server incorporates an emotion analysis system that analyzes voice data from the user. The input is the user's voice data, and the output is information about the user's emotional state. Specifically, the server evaluates the tone and intensity of the voice and calculates stress and calmness levels.

[0709] Step 5:

[0710] The server generates a training method based on the results of analyzing animal behavior and the user's emotional state. The input is the analysis results from the previous step, and the output is the generated training method and instructions. Specifically, the server executes an algorithm written in Python to calculate the optimal response.

[0711] Step 6:

[0712] The user receives feedback and advice from the server through an app on their mobile device. The input is the training method and instructions sent from the server, and the output is specific guidance that is viewed on the user's device. In terms of specific actions, the app displays notifications on the UI and prompts the user to take the next action.

[0713] (Application Example 2)

[0714] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] In recent years, there has been a growing demand for animal health management and behavioral training. However, conventional systems have a problem in that they make it difficult to provide comprehensive care that takes into account both the animal's activity monitoring and the user's emotional state. As a result, it is difficult to provide training and health management that is optimal for both the animal and the user.

[0716] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0717] In this invention, the server includes observation means for detecting animal activity and generating data, data processing means for analyzing animal behavior based on the data, and emotion analysis means for analyzing user voice data and recognizing emotional state. This makes it possible to closely understand the animal's health and behavior, as well as provide an optimal training method tailored to the user's emotional state.

[0718] "Observation tools" are devices and sensors used to detect animal activity and generate data based on that activity.

[0719] A "data processing system" is a computer system that analyzes data acquired by observation systems to evaluate the behavior and health status of animals.

[0720] A "guidance tool" is an interface that provides users with instructions on animal training methods and health management based on the analysis results.

[0721] An "emotion analysis system" is a system that analyzes a user's voice data to recognize the user's emotional state.

[0722] A "training method" is a technique designed to elicit specific behaviors or responses from animals, with the aim of modifying their behavior or improving their skills.

[0723] This invention is a system that supports animal health management and training in pet shops and pet care centers. The server acquires animal activity data using smart cameras installed in the store. This data is used to analyze animal behavior patterns using OpenCV, and then TensorFlow is used to evaluate the animal's health status using an AI model. The system also generates optimal training methods for the animals and provides them to the user. Users can receive these training methods and health management instructions via their smartphones.

[0724] Furthermore, the device collects user voice data and analyzes it using a voice analysis tool (e.g., Google Cloud Speech-to-Text API) to recognize the user's emotional state. The emotional state recognized by the emotion engine is used to adjust the training methods for the animals, recommending the playback of relaxation music or changes to the training content.

[0725] As a concrete example, to ensure that users and their pets have a comfortable experience at a pet cafe, the system recommends ambient sounds that match the pet's level of calmness. For instance, if the system detects that the pet is restless, it sends a message to the user's smartphone saying, "We recommend playing calming music."

[0726] An example of a prompt message is, "Analyze this dog's behavior log and generate suggestions to help it relax." In this way, it becomes possible to provide a service that is harmonious for both the animal and the user.

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The device operates a smart camera to collect real-time data on animal movements within the store. This data includes the animals' location and movement. The input is video data from the camera, and the output is initial data indicating the animals' movements.

[0730] Step 2:

[0731] The device uses the animal's motion data to perform image analysis with the OpenCV library and extract the animal's motion patterns. The input is the animal's motion data collected in step 1, and the output is the analysis data related to the motion patterns. This data processing makes it possible to capture specific animal behaviors.

[0732] Step 3:

[0733] The server uses TensorFlow to perform analysis with a generative AI model and evaluate the animal's health status. The input is the analysis data of the behavioral patterns obtained in step 2, and the output is an evaluation of the animal's health status. This allows for the identification of abnormalities and the detection of signs of health.

[0734] Step 4:

[0735] The server generates appropriate training methods based on the health status assessment results and provides them to the user through guidance. The input is the health status data assessed in step 3, and the output is a proposal for specific training methods. Based on this, the user can provide appropriate guidance to the animal.

[0736] Step 5:

[0737] The device uses the Google Cloud Speech-to-Text API to analyze the user's voice data, generating text data from the speech and recognizing their emotional state. The input is the user's voice data, and the output is data indicating their emotional state. This makes it possible to understand the user's stress and emotional state.

[0738] Step 6:

[0739] The server receives user emotional state data and adjusts the training content for the animal accordingly. The input is the emotional state data obtained in step 5, and the output is the adjusted training instructions. This enables an approach that provides the optimal environment for both the user and the animal.

[0740] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0741] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0742] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0743] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0744] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0745] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0746] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0747] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0748] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0749] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0750] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0751] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0752] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0753] 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.

[0754] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0755] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0756] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0757] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0758] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0759] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0760] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0761] The following is further disclosed regarding the embodiments described above.

[0762] (Claim 1)

[0763] An observation means for detecting animal activity and generating data indicating said activity,

[0764] Information processing means for acquiring data generated by the observation means and analyzing animal behavior based on the data,

[0765] A guidance means that generates a training method for animals based on the analysis and provides the training method to the user,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein the information processing means evaluates the health status of the animal and generates instructions for health management based on the evaluation.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the guidance means includes a function to notify the user's mobile terminal of the training method and instructions for health management.

[0771] "Example 1"

[0772] (Claim 1)

[0773] An observation means for detecting animal activity and generating data indicating said activity,

[0774] Information processing means that acquires data generated by the observation means and analyzes animal behavior based on the data using a generated AI model,

[0775] A guidance means that generates training methods and health management instructions for animals based on the analysis, and provides the training methods and instructions to the user,

[0776] A user means for adjusting the daily life of an animal in accordance with the training methods and instructions provided by the guidance means,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, wherein the information processing means evaluates the health status of the animal and generates instructions for health management based on specific questions from the user using prompt sentences.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the guidance means includes a function to notify the user's mobile terminal in real time of the training method and instructions for health management.

[0782] "Application Example 1"

[0783] (Claim 1)

[0784] Observation means for detecting the activity of an animal or device and generating data indicating said activity,

[0785] An information processing means that acquires data generated by the observation means and analyzes the patterns of operation based on the data,

[0786] A guidance means that generates an optimal operation plan or maintenance instruction based on the analysis and provides the user with the plan or instruction,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the information processing means evaluates the state of the animal or device and generates instructions for operation or maintenance based on the evaluation.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the guidance means includes a function to notify the user's communication terminal of the operation plan and instructions for operation or maintenance.

[0792] "Example 2 of combining an emotion engine"

[0793] (Claim 1)

[0794] A detection means for detecting animal activity and generating information indicating said activity,

[0795] An analysis means that acquires information generated by the detection means and analyzes the behavior of animals based on the information,

[0796] A guidance means that generates a training method for animals based on the analysis and provides the training method to the user,

[0797] An emotion analysis method that analyzes the user's voice data and recognizes their emotional state,

[0798] An adjustment means for adjusting the animal's training method based on the emotional state,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the analysis means evaluates the health status of the animal and generates instructions for health management based on the evaluation.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the guidance means includes a function to notify the user's portable device of the training method and instructions for health management.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] An observation means for detecting animal activity and generating data indicating said activity,

[0807] A data processing means that acquires data generated by the observation means and analyzes animal behavior based on the data,

[0808] A guidance means that generates a modified training method for animals based on the analysis and provides the training method to the user,

[0809] An emotion analysis method that analyzes the user's voice data to recognize the user's emotional state,

[0810] A means for adjusting the training method for an animal according to the emotional state recognized by the emotion analysis means,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the data processing means evaluates the health status of the animal and generates instructions for health management based on the evaluation.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the guidance means includes a function to notify the user's communication terminal of the training method and instructions for health management. [Explanation of Symbols]

[0816] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An observation means for detecting animal activity and generating data indicating said activity, Information processing means for acquiring data generated by the observation means and analyzing animal behavior based on the data, A guidance means that generates a training method for animals based on the analysis and provides the training method to the user, A system that includes this.

2. The system according to claim 1, wherein the information processing means evaluates the health status of the animal and generates instructions for health management based on the evaluation.

3. The system according to claim 1, wherein the guidance means includes a function to notify the user's mobile terminal of the training method and instructions for health management.

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