system

Abstract

We provide the system. [Solution] A device for acquiring animal biometric information, A data processing device that includes an algorithm for estimating the emotional state of an animal by analyzing acquired biometric information, A terminal device for displaying estimated emotional states and providing feedback to the user, A system including a control device for adjusting the environment based on an estimated emotional state.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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] There are problems such as insufficient communication between humans and animals and difficulty in grasping the emotional state of animals. In particular, the inability to detect animal stress and anxiety early may lead to overlooking health problems and a decline in animal welfare. Therefore, there is a need for technology to accurately judge animal emotions and provide appropriate care.

Means for Solving the Problems

[0005] This invention provides a data processing device that acquires biological information using a device that can be attached to an animal and analyzes the animal's emotional state using an algorithm based on that information. Furthermore, it provides a means to accurately understand an animal's emotions and respond appropriately through a system that includes a control device that adjusts the environment according to the emotional state via a terminal device that feeds the analysis results back to the user.

[0006] A "device for acquiring animal biometric information" is a device that uses sensors to collect biometric data such as heart rate, body temperature, and movement of animals.

[0007] A "data processing device" is a computing device that processes acquired biological information and estimates the emotional state of an animal based on an algorithm.

[0008] An "algorithm" is a set of computational methods and program instructions used to analyze the emotional state of an animal from its biological information.

[0009] A "terminal device" is a device that displays emotional state information transmitted from a data processing device and provides that information to the user.

[0010] A "control device" is a device or system used to physically adjust an animal's environment based on its estimated emotional state.

[0011] "Emotional state" is a term that refers to the psychological and emotional state of an animal, estimated based on its biological information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This 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

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that has a series of processes for acquiring biological information from an animal, analyzing that information, and providing feedback on the animal's emotional state to the user. Specifically, a wearable device attached to the animal acquires data such as heart rate, body temperature, and movement. This data is transmitted to a server via wireless communication.

[0034] The server receives this biometric information and uses advanced algorithms to analyze the animal's emotional state in real time. These algorithms are based on machine learning techniques and improve accuracy by comparing current data with past data. The analyzed emotional state is categorized into states such as joy, excitement, stress, and anxiety.

[0035] The analysis results are sent to a terminal. The terminal displays this information intuitively to the user via a dedicated application. For example, if an animal is stressed, the terminal will inform the user of the situation and suggest appropriate measures. These measures may include actions to improve the animal's comfort, such as changing the room temperature or playing calming music. The user can take the necessary actions by selecting options on the terminal.

[0036] Furthermore, the server has a mechanism that automatically notifies veterinary medical facilities when it detects abnormalities in an animal's health using the collected data. This system enables multi-sensory emotional feedback from animals, making it easier for users to maintain the animal's psychological and physical health.

[0037] As a concrete example, consider a scenario where a user attaches a device to their pet dog to collect behavioral data during walks. If the dog's heart rate becomes abnormally high, the server immediately analyzes the data and sends a notification to the device stating, "Your dog may be feeling anxious. We recommend taking a short break." The user can then adjust their behavior based on this advice, allowing them to spend more time with their dog with greater peace of mind.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user attaches a wearable device to the animal and connects the device to the system. Since the device is configured to automatically begin pairing when powered on, the user can complete the connection without any special action.

[0041] Step 2:

[0042] The server receives animal biometric information in real time from connected devices. Specifically, it acquires heart rate, body temperature, and motion sensor data as data streams using a specific protocol. This information is temporarily stored within the system and immediately ready for analysis.

[0043] Step 3:

[0044] The server uses a machine learning model to analyze the received biometric information. This model is trained on historical datasets and estimates the animal's emotional state based on patterns in the biometric data. For example, a sudden increase in heart rate or specific behavioral patterns may be associated with anxiety or excitement.

[0045] Step 4:

[0046] The server organizes the analysis results and categorizes the animals' emotional states. This categorized information is then sent to the terminal in an easy-to-understand format. Examples of emotions include joy, surprise, anxiety, and relaxation.

[0047] Step 5:

[0048] The device provides feedback to the user via push notifications and a dedicated application, based on emotional data received from the server. The user interface is designed for quick understanding by displaying icons and messages that indicate emotions.

[0049] Step 6:

[0050] The user reviews the feedback presented by the device and selects an action. For example, if the data indicates that the pet is stressed, the user can select "Play music to help the pet relax" on the device.

[0051] Step 7:

[0052] The device controls smart home devices and robots based on user instructions. The system uses APIs to adjust the environment to be comfortable for animals, such as changing lighting or playing music from speakers.

[0053] Step 8:

[0054] The server stores long-term data, tracking the animals' health status and emotional tendencies. This information can be regularly shared with veterinarians to assist in health management.

[0055] (Example 1)

[0056] 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."

[0057] Traditional animal care systems have made it difficult to accurately understand and respond quickly to the emotional state of animals, and methods for detecting sudden changes in health or stressors in real time have been limited. As a result, there have been cases where animal health management has been inadequate, and there is a need for methods that provide accurate and immediate feedback on the physiological and psychological state of animals and effectively adjust the environment based on that feedback.

[0058] 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.

[0059] In this invention, the server includes a sensor device for acquiring biological information of animals, a data processing device for analyzing the acquired biological information using machine learning technology and classifying the animal's emotional state in real time, and an information display means for displaying the classified emotional state and providing visual feedback to the user. This enables real-time understanding of the animal's emotions and health status, and allows for rapid response and effective environmental adjustments based on that understanding.

[0060] "Animal biometric information" refers to data that indicates the physiological state of an animal, such as its heart rate, body temperature, and movements.

[0061] A "sensor device" refers to a device attached to an animal to acquire biological information.

[0062] "Machine learning technology" refers to techniques that find patterns and rules from large amounts of data and use them to make predictions and classifications about future data.

[0063] A "data processing device" refers to a computing device that analyzes collected biological information and determines the emotional state of an animal.

[0064] "Information display means" refers to devices or interfaces that visually show the emotional state of an analyzed animal to the user.

[0065] "Environmental adjustment" refers to appropriately changing the physical environment in which an animal interacts, based on the animal's emotional state.

[0066] "Communication means" refers to a system for sharing information with veterinary medical facilities when an anomaly is detected.

[0067] This invention is a system that provides a series of processes for acquiring and analyzing biological information of animals and providing feedback on the animal's emotional state to the user. Specific embodiments are shown below.

[0068] The user first attaches a sensor device to the animal. This sensor device is capable of continuously acquiring biometric information such as the animal's heart rate, body temperature, and movement. The acquired biometric information is transmitted to a server via wireless communication such as Bluetooth.

[0069] The server uses a data processing unit equipped with machine learning technology to process the received biometric information. This data processing unit utilizes a pre-trained generative AI model to analyze the animal's emotional state in real time. By comparing it with past data, the accuracy of emotion classification is improved. Through this process, the animal's emotional state is classified into categories such as "joy," "excitement," "stress," and "anxiety."

[0070] The analyzed data is sent to the terminal and displayed to the user as visual feedback via a dedicated application. For example, if the analysis indicates that the animal is experiencing stress, the terminal will suggest, "The current environment is causing stress. Please consider lowering the room temperature." Furthermore, it has a function to automatically notify veterinary medical facilities, enabling a quick response when an abnormality is detected.

[0071] As a concrete example, consider a scenario where a user attaches a sensor device to their pet dog and sends data to a server during walks. If the dog's heart rate becomes higher than normal, the server immediately analyzes this data and sends a notification to the device saying, "Your dog may be excited. Take a short break and observe its condition."

[0072] For example, a prompt message could be something like, "If my pet cat seems to be feeling anxious, what measures would you suggest?" This would allow the system to provide specific suggestions for action.

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

[0074] Step 1:

[0075] The user attaches a sensor device to the animal. This device acquires biometric information such as heart rate, body temperature, and movement in real time. The acquired data is transmitted to a server using Bluetooth. The input is biometric data from the sensor, and the output is data transmission to the server. After attachment, the user can continue living with their pet as usual.

[0076] Step 2:

[0077] The server preprocesses the received biometric data, including imputing missing data and detecting and removing outliers. The input is raw data transmitted from the sensor, and the output is data formatted into a clean format. Specifically, the server uses a program to check data consistency and filters out inappropriate data.

[0078] Step 3:

[0079] The server inputs pre-processed data into a machine learning algorithm to analyze the animals' emotional states. This algorithm utilizes a generative AI model to classify emotional states such as "joy," "excitement," "stress," and "anxiety" in real time based on the data. The output is the classification result of the emotional states. In this process, the server refers to past data to improve the accuracy of the model.

[0080] Step 4:

[0081] The server sends the analysis results to the terminal. The input is the classification result of the emotional state, and the output is data presented as visual feedback on the terminal. The server processes the analysis results immediately and notifies the terminal at the appropriate time.

[0082] Step 5:

[0083] The terminal displays the analysis results using a dedicated application. The application displays the animal's emotional state in an easy-to-understand graphical interface and shows the user specific actions to reduce stress. The input is the analysis results sent from the server, and the output is specific advice presented to the user.

[0084] Step 6:

[0085] The user adjusts the animal's environment based on information displayed on the device. For example, they might lower the room temperature or play quiet music. The input is the suggestions from the device, and the output is the animal's behavior and reaction after the adjustments. The user selects an option from the presented options and takes action to improve the animal's comfort.

[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] In commercial facilities such as pet shops and animal cafes, there is a need to reduce stress and anxiety in animals and create safer and more comfortable interactions. However, it is difficult to understand an animal's emotional state in real time, and there is a risk that customers may misinterpret the animal's condition and cause excessive stress. Improving this situation and optimizing the interaction between animals and people is a challenge.

[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 a device for acquiring biological information of an animal, means for analyzing the acquired biological information and estimating the animal's emotional state, and equipment for visually displaying the estimated emotional state and providing information to the user. This allows the user to understand the animal's emotional state in real time and make appropriate contact.

[0091] A "device for acquiring animal biological information" is a device designed to continuously collect physiological data such as heart rate, body temperature, and movement patterns of animals.

[0092] "Means for estimating emotional states" refer to algorithms and data processing systems that analyze acquired biological information to infer what emotions (joy, excitement, stress, anxiety, etc.) an animal is experiencing.

[0093] A "device for visually presenting and providing information to users" is a device that displays the analyzed emotional state of an animal on a display or mobile device, allowing users to understand it intuitively.

[0094] A "control device for managing animal interactions in commercial facilities" is a device or system that has management functions to optimize the environment settings within the facility and interactions with animals according to the emotional state of the animals.

[0095] "A means of communication for recording acquired biological information and transmitting the information to a designated specialized facility" refers to a communication device that has the function of securely storing the collected data and transferring the data to a specific veterinary medical facility or research institution when necessary.

[0096] This invention is a system for commercial facilities where customers can interact with animals, which helps them understand the emotional state of animals and facilitates appropriate interactions between customers and animals.

[0097] The server receives biometric information such as heart rate, body temperature, and movement data from wearable devices attached to animals within the facility. These wearable devices have the capability to transmit data in real time using Bluetooth or Wi-Fi. The server stores the received biometric information and analyzes the animals' emotional states using machine learning models based on TENSORFLOW® and PyTorch. This analysis makes it possible to classify the animals' emotional states into categories such as joy, excitement, stress, and anxiety.

[0098] The terminal visualizes the analyzed results and provides an interface for facility visitors and staff to view. For example, by installing a dedicated application on a tablet or smartphone, visitors can easily check the current emotional state of the animals in the facility. This information is presented as specific messages such as "The cat is currently relaxed" or "The dog is excited, so caution is needed."

[0099] Based on this information, users can adjust their interactions with animals. For example, if they are notified that an animal is stressed, visitors can temporarily refrain from contacting that animal. Furthermore, if an abnormality is detected in an animal's health, the system automatically sends information to a designated veterinary facility, enabling a quick response.

[0100] As a concrete example, consider the case where a cat named Sakura at an animal cafe exhibits a higher-than-normal heart rate. In this case, based on the analysis results, the server notifies the store manager, "Sakura is a little agitated. Please provide a quiet environment." Based on this notification, the manager can reduce the cat's stress by adjusting the environment, such as changing the background music.

[0101] Possible prompts for using the generative AI model include requests such as, "Please suggest appropriate actions to take if the cat's heart rate exceeds the normal range."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server receives biometric information from wearable devices. Specifically, it acquires heart rate, body temperature, and activity information using Bluetooth or Wi-Fi. The input for this step is biometric data from the wearable device, and the output is a database on the server where that data is stored.

[0105] Step 2:

[0106] The server uses TensorFlow and PyTorch to analyze collected biometric data and estimate the emotional state of animals. The analysis involves cross-referencing with historical data and employing advanced algorithms. The input is the biometric data stored in step 1, and the output is the estimated emotional state. Specifically, the server supplies data to the analysis model and calculates results smoothly, taking response time into consideration.

[0107] Step 3:

[0108] The terminal visualizes the estimated emotional state results sent from the server and notifies the user. Here, the analysis results are converted into a visual message and displayed on the user's terminal display. The input is the estimated emotional state results from the server, and the output is the information provided on the user interface. The terminal appropriately lays out the message and displays it in an intuitively easy-to-understand format.

[0109] Step 4:

[0110] The user adjusts their interaction with the animal based on emotional state information provided by the device. For example, if the animal shows signs of stress, the user may reduce contact with the animal or adjust the environment. The input for this step is the emotional state information confirmed by the device, and the output is the user's physical actions. Specific actions include changing the music or making the environment around the animal quieter.

[0111] Step 5:

[0112] The server automatically transmits information to a designated veterinary facility if an abnormality in the animal's health is detected. The input is data on health abnormalities based on estimated emotional state and biometric information, and the output is a notification to the veterinary facility. Specifically, it sends abnormal data to a communication system to ensure prompt medical response.

[0113] 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.

[0114] This invention is a system that enables deeper interaction by acquiring biometric information from both animals and users and analyzing their respective emotional states. Animals are fitted with wearable devices that acquire heart rate, body temperature, and movement data. Similarly, users collect biometric information such as heart rate and voice tone using smartwatches or smartphones.

[0115] The server receives this biometric information and uses an emotion engine to estimate the emotional states of the animals and users. The emotion engine analyzes the data using machine learning techniques based on an animal emotion analysis algorithm. The potential for mutual influence between the emotional states of the animals and users is considered, and this relationship is examined, especially in interaction scenarios.

[0116] The analyzed emotional states are transmitted to the terminal as integrated data, combining the emotions of both the animal and the user. The terminal processes this information and provides clear feedback to the user. The information is customized so that the user's emotions help to deepen the understanding of the animal's emotions. For example, if the animal is feeling anxious but the user is calm, the terminal can offer advice on how to improve the situation.

[0117] Users can review this feedback and have the option to improve the environment by operating the control device through their terminal. For example, they can play music to help animals relax or adjust the lighting based on the user's emotions.

[0118] For example, when a user is playing with their pet, if the device detects that the pet is excited and the user is feeling stressed at that time, it will suggest appropriate actions to help both of them relax. These suggestions may include moving to a favorite spot or playing specific music. In this way, mutual understanding between the animal and the user can be promoted, making it possible to build a better relationship.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user attaches a wearable device to the animal and prepares a smartwatch or smartphone. This allows for the collection of biometric information from both the animal and the user. The connection process is automated via Bluetooth.

[0122] Step 2:

[0123] The server receives biometric information from the animals' wearable devices and biometric information from the users' devices. Animal data includes heart rate, body temperature, and movement information, while user data includes heart rate and voice tone.

[0124] Step 3:

[0125] The server activates an emotion engine to analyze both sets of biometric information individually. The emotion engine uses a machine learning model to estimate the emotional states of both the animal and the user. This process improves accuracy by comparing past and current data.

[0126] Step 4:

[0127] The server analyzes the relationship between the emotional states of the animals and the users and generates integrated emotional data. This data indicates whether the emotions of the animals and users are in harmony or whether improvement is needed.

[0128] Step 5:

[0129] The device receives integrated emotional data and displays feedback through the user interface. This feedback includes a visual representation of the emotional state and action suggestions. For example, a suggestion might be, "Your pet is a little agitated. Let's play some music to help it relax."

[0130] Step 6:

[0131] The user operates the control system based on feedback from their device to make appropriate environmental adjustments. This is achieved through integration with smart devices such as music and lighting. The selected action is executed immediately.

[0132] Step 7:

[0133] The server records all interactions and emotion analysis results in a database to help improve the accuracy of emotion estimation in the future. It also periodically generates reports on the animals' health status and shares them with veterinary facilities as needed.

[0134] (Example 2)

[0135] 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".

[0136] There is a challenge in simultaneously understanding the emotional states of both animals and users and providing feedback based on their interaction. Conventional technologies primarily analyze biometric information of either the animal or the user alone, making it difficult to realize interactions that take into account the mutual influence of their emotions.

[0137] 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.

[0138] In this invention, the server includes a device for acquiring biometric information of animals and users, information processing means including an algorithm for analyzing emotional states based on the acquired biometric information of animals and users, and means for transmitting the analyzed emotional states as integrated data to a terminal and providing feedback to the user. This enables better mutual understanding by providing feedback that takes into account the emotional states of both the animal and the user.

[0139] "Biometric information" refers to data obtained from the body, such as the heart rate, body temperature, movements, and voice tone of animals or users.

[0140] "Information processing means" refers to an algorithm for analyzing collected biometric information and estimating emotional states, and the computer system that executes it.

[0141] "Integrated data" refers to data that summarizes the results of analyzing the emotional states of animals and users, showing how their emotions interact with each other.

[0142] "Means of providing feedback" refers to devices that have the function of presenting users with specific actions or information based on analyzed integrated data.

[0143] "Control means for adjusting the environment" refers to a system that has the function of changing the physical environment according to the feedback received, such as adjusting lighting or music playback.

[0144] The embodiments for carrying out this invention are shown below.

[0145] First, the user attaches a wearable device to the animal and collects biometric information using a smartwatch or smartphone. The animal's wearable device measures data such as heart rate, body temperature, and movement in real time, while the user's smart device also collects data such as heart rate and voice tone. This group of devices uses standard biosensor technology available on the market.

[0146] Next, the server receives this biometric information and analyzes the data using advanced information processing tools. Specific software examples include machine learning libraries and data analysis tools. The emotion analysis algorithm installed on the server analyzes the biometric information and infers the emotional state of the animal and the user. This analysis uses a generative AI model and compares it with past datasets.

[0147] The analyzed emotional state is sent to the device as integrated data, and the device uses this data to provide feedback to the user. The feedback suggests specific actions and is customized, for example, "Your pet is excited. Play their favorite music to help them relax." This feedback is provided to the user through visual or audio output.

[0148] For example, if the system detects that a user is excited while playing with their pet, and the user is also feeling stressed, the device could suggest, "Let's move to another room and take a short break." This would lead to better mutual understanding and improved relationships.

[0149] An example of a prompt message would be, "Please explain the procedure for sentiment analysis based on animal and user biometric data."

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The user attaches a wearable device to the animal and prepares their own smartwatch or smartphone. The animal's heart rate, body temperature, and activity data are acquired as input. This data is collected digitally through multiple biosensors and then initially processed.

[0153] Step 2:

[0154] The terminal transmits the collected biometric information to the server. This step includes biometric data of both the animal and the user as input. The data is transmitted securely, and its integrity is verified on the server. The output is the result that the data has successfully reached the server.

[0155] Step 3:

[0156] The server performs emotion analysis via a generative AI model based on the received biometric information. Inputs include the animal's and user's heart rate, body temperature, and movement data. Based on this, the data is fed into the algorithm for analysis. The output is an analysis result indicating the emotional state of the animal and user.

[0157] Step 4:

[0158] The server integrates the analysis results and prepares them for transmission to the terminal. In this step, data on emotional states is input, and an integrated emotional dataset is generated as output. This data is organized to take into account the effects of interactions.

[0159] Step 5:

[0160] The terminal receives integrated data from the server and generates feedback for the user. The input is an integrated dataset, and specific action suggestions are created based on the analysis results. The output is a feedback message presented to the user.

[0161] Step 6:

[0162] The user takes specific actions based on feedback from the device. The input is the feedback message, and the output is the actual action taken. For example, it is possible to play music to relax an animal.

[0163] (Application Example 2)

[0164] 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".

[0165] In environments where animals and humans live together, understanding each other's emotional states is crucial for reducing stress and promoting comfortable interaction. However, conventional technologies have limited means of simultaneously analyzing the emotional states of both animals and humans and providing easily understandable feedback. To address this problem, there is a need for a system that can comprehensively analyze the emotions of both animals and humans and promote mutual understanding.

[0166] 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.

[0167] In this invention, the server includes means for acquiring animal and human biometric information, means including an algorithm that analyzes the acquired biometric information to estimate the emotional states of animals and humans and generate integrated data, and means for displaying the estimated emotional states of animals and humans and providing feedback to the user. This makes it possible to understand the emotional states of animals and humans from a broad perspective and propose appropriate actions, thereby creating a comfortable interaction environment.

[0168] "Animal biometric information" refers to biological or behavioral data such as the animal's heart rate, body temperature, and movement data.

[0169] "Human biometric information" refers to biological or behavioral data such as a person's heart rate, voice tone, and body temperature.

[0170] A "device" refers to an electronic device attached to animals or humans to acquire biometric information.

[0171] A "data processing device" refers to a computing device that analyzes acquired biological information and estimates the emotional states of animals and humans.

[0172] An "algorithm" refers to the procedures and calculation methods that a data processing device uses to estimate the emotional states of animals and humans.

[0173] "Integrated data" refers to the results of an analysis that combines the emotional states of animals and humans.

[0174] "Terminal device" refers to an electronic device that provides users with visual or auditory feedback on the emotional states of animals and humans.

[0175] A "control device" refers to a device that adjusts the environment based on an estimated emotional state.

[0176] "Feedback" refers to information and suggestions provided to users based on their analyzed emotional state.

[0177] The system for realizing this invention collects and analyzes biometric information from animals and humans, and provides feedback based on that data. Animals are fitted with wearable devices that acquire biometric information such as heart rate, body temperature, and movement data. Human biometric information is collected via smartwatches and smartphones. This information is transmitted to a server where data processing takes place.

[0178] The server collects biometric information and analyzes the data using algorithms to estimate the emotional states of animals and humans. The analysis utilizes animal emotion analysis algorithms and leverages machine learning techniques. In this process, the server also considers the interrelationships between animal and human emotional states. The integrated data resulting from the analysis is transmitted to a terminal device, providing users with visual or auditory feedback.

[0179] Users can receive feedback through their devices and adjust the environment by operating the control system based on the estimated emotional state. For example, if a pet is stressed, specific music can be played or the lighting adjusted to encourage relaxation.

[0180] As a concrete example, when a pet and its owner visit a pet supply store, after a few minutes inside, biometric data transmitted from the pet's wearable device and data obtained from the owner's smartwatch reveal that the pet is stressed. The server analyzes this information and suggests via the terminal, "Please use the pet relaxation area." An example of a prompt message used is, "Please consider a method to analyze emotions based on animal and human biometric data and provide optimal feedback in a physical store."

[0181] In this way, the system promotes mutual understanding between animals and humans and helps build better relationships.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server receives biometric data from animal wearable devices and human smartwatches and smartphones. The input data includes animal heart rate, body temperature, and movement data, as well as human heart rate and voice tone. This data is stored as biometric information necessary for subsequent processing and analysis.

[0185] Step 2:

[0186] The server estimates the emotional states of animals and humans based on the received biometric information. The server uses emotion analysis algorithms and machine learning techniques to process and analyze the data. The output of the analysis converts the emotional states of animals and humans into numerical values ​​and categories.

[0187] Step 3:

[0188] The server integrates estimated animal and human emotional states and generates integrated data that considers the relevance in interactions. It uses the previously generated emotional state data as input and combines it to perform new emotional assessments. The output is a list or matrix of integrated emotional states.

[0189] Step 4:

[0190] The server sends this integrated data to the terminal, which then prepares to provide appropriate feedback to the user. The terminal receives the integrated data and generates visual or auditory feedback for the user. The output may be a feedback message or action suggestion.

[0191] Step 5:

[0192] The user reviews the feedback provided through the device and operates the control unit as needed. Based on the feedback, they perform operations such as adjusting music playback or lighting to optimize the environment. As a result of this series of operations, the environment for both the pet and the owner is optimized.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Second Embodiment]

[0197] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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".

[0209] This invention is a system that has a series of processes for acquiring biological information from an animal, analyzing that information, and providing feedback on the animal's emotional state to the user. Specifically, a wearable device attached to the animal acquires data such as heart rate, body temperature, and movement. This data is transmitted to a server via wireless communication.

[0210] The server receives this biometric information and uses advanced algorithms to analyze the animal's emotional state in real time. These algorithms are based on machine learning techniques and improve accuracy by comparing current data with past data. The analyzed emotional state is categorized into states such as joy, excitement, stress, and anxiety.

[0211] The analysis results are sent to a terminal. The terminal displays this information intuitively to the user via a dedicated application. For example, if an animal is stressed, the terminal will inform the user of the situation and suggest appropriate measures. These measures may include actions to improve the animal's comfort, such as changing the room temperature or playing calming music. The user can take the necessary actions by selecting options on the terminal.

[0212] Furthermore, the server has a mechanism that automatically notifies veterinary medical facilities when it detects abnormalities in an animal's health using the collected data. This system enables multi-sensory emotional feedback from animals, making it easier for users to maintain the animal's psychological and physical health.

[0213] As a concrete example, consider a scenario where a user attaches a device to their pet dog to collect behavioral data during walks. If the dog's heart rate becomes abnormally high, the server immediately analyzes the data and sends a notification to the device stating, "Your dog may be feeling anxious. We recommend taking a short break." The user can then adjust their behavior based on this advice, allowing them to spend more time with their dog with greater peace of mind.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The user attaches a wearable device to the animal and connects the device to the system. Since the device is configured to automatically begin pairing when powered on, the user can complete the connection without any special action.

[0217] Step 2:

[0218] The server receives animal biometric information in real time from connected devices. Specifically, it acquires heart rate, body temperature, and motion sensor data as data streams using a specific protocol. This information is temporarily stored within the system and immediately ready for analysis.

[0219] Step 3:

[0220] The server uses a machine learning model to analyze the received biometric information. This model is trained on historical datasets and estimates the animal's emotional state based on patterns in the biometric data. For example, a sudden increase in heart rate or specific behavioral patterns may be associated with anxiety or excitement.

[0221] Step 4:

[0222] The server organizes the analysis results and categorizes the animals' emotional states. This categorized information is then sent to the terminal in an easy-to-understand format. Examples of emotions include joy, surprise, anxiety, and relaxation.

[0223] Step 5:

[0224] The device provides feedback to the user via push notifications and a dedicated application, based on emotional data received from the server. The user interface is designed for quick understanding by displaying icons and messages that indicate emotions.

[0225] Step 6:

[0226] The user reviews the feedback presented by the device and selects an action. For example, if the data indicates that the pet is stressed, the user can select "Play music to help the pet relax" on the device.

[0227] Step 7:

[0228] The device controls smart home devices and robots based on user instructions. The system uses APIs to adjust the environment to be comfortable for animals, such as changing lighting or playing music from speakers.

[0229] Step 8:

[0230] The server stores long-term data, tracking the animals' health status and emotional tendencies. This information can be regularly shared with veterinarians to assist in health management.

[0231] (Example 1)

[0232] 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."

[0233] Traditional animal care systems have made it difficult to accurately understand and respond quickly to the emotional state of animals, and methods for detecting sudden changes in health or stressors in real time have been limited. As a result, there have been cases where animal health management has been inadequate, and there is a need for methods that provide accurate and immediate feedback on the physiological and psychological state of animals and effectively adjust the environment based on that feedback.

[0234] 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.

[0235] In this invention, the server includes a sensor device for acquiring biological information of animals, a data processing device for analyzing the acquired biological information using machine learning technology and classifying the animal's emotional state in real time, and an information display means for displaying the classified emotional state and providing visual feedback to the user. This enables real-time understanding of the animal's emotions and health status, and allows for rapid response and effective environmental adjustments based on that understanding.

[0236] "Animal biometric information" refers to data that indicates the physiological state of an animal, such as its heart rate, body temperature, and movements.

[0237] A "sensor device" refers to a device attached to an animal to acquire biological information.

[0238] "Machine learning technology" refers to techniques that find patterns and rules from large amounts of data and use them to make predictions and classifications about future data.

[0239] A "data processing device" refers to a computing device that analyzes collected biological information and determines the emotional state of an animal.

[0240] "Information display means" refers to devices or interfaces that visually show the emotional state of an analyzed animal to the user.

[0241] "Environmental adjustment" refers to appropriately changing the physical environment in which an animal interacts, based on the animal's emotional state.

[0242] "Communication means" refers to a system for sharing information with veterinary medical facilities when an anomaly is detected.

[0243] This invention is a system that provides a series of processes for acquiring and analyzing biological information of animals and providing feedback on the animal's emotional state to the user. Specific embodiments are shown below.

[0244] The user first attaches a sensor device to the animal. This sensor device is capable of continuously acquiring biometric information such as the animal's heart rate, body temperature, and movement. The acquired biometric information is transmitted to a server via wireless communication such as Bluetooth.

[0245] The server uses a data processing unit equipped with machine learning technology to process the received biometric information. This data processing unit utilizes a pre-trained generative AI model to analyze the animal's emotional state in real time. By comparing it with past data, the accuracy of emotion classification is improved. Through this process, the animal's emotional state is classified into categories such as "joy," "excitement," "stress," and "anxiety."

[0246] The analyzed data is sent to the terminal and displayed to the user as visual feedback via a dedicated application. For example, if the analysis indicates that the animal is experiencing stress, the terminal will suggest, "The current environment is causing stress. Please consider lowering the room temperature." Furthermore, it has a function to automatically notify veterinary medical facilities, enabling a quick response when an abnormality is detected.

[0247] As a concrete example, consider a scenario where a user attaches a sensor device to their pet dog and sends data to a server during walks. If the dog's heart rate becomes higher than normal, the server immediately analyzes this data and sends a notification to the device saying, "Your dog may be excited. Take a short break and observe its condition."

[0248] For example, a prompt message could be something like, "If my pet cat seems to be feeling anxious, what measures would you suggest?" This would allow the system to provide specific suggestions for action.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The user attaches a sensor device to the animal. This device acquires biometric information such as heart rate, body temperature, and movement in real time. The acquired data is transmitted to a server using Bluetooth. The input is biometric data from the sensor, and the output is data transmission to the server. After attachment, the user can continue living with their pet as usual.

[0252] Step 2:

[0253] The server preprocesses the received biometric data, including imputing missing data and detecting and removing outliers. The input is raw data transmitted from the sensor, and the output is data formatted into a clean format. Specifically, the server uses a program to check data consistency and filters out inappropriate data.

[0254] Step 3:

[0255] The server inputs pre-processed data into a machine learning algorithm to analyze the animals' emotional states. This algorithm utilizes a generative AI model to classify emotional states such as "joy," "excitement," "stress," and "anxiety" in real time based on the data. The output is the classification result of the emotional states. In this process, the server refers to past data to improve the accuracy of the model.

[0256] Step 4:

[0257] The server sends the analysis results to the terminal. The input is the classification result of the emotional state, and the output is data presented as visual feedback on the terminal. The server processes the analysis results immediately and notifies the terminal at the appropriate time.

[0258] Step 5:

[0259] The terminal displays the analysis results using a dedicated application. The application displays the animal's emotional state in an easy-to-understand graphical interface and shows the user specific actions to reduce stress. The input is the analysis results sent from the server, and the output is specific advice presented to the user.

[0260] Step 6:

[0261] The user adjusts the animal's environment based on information displayed on the device. For example, they might lower the room temperature or play quiet music. The input is the suggestions from the device, and the output is the animal's behavior and reaction after the adjustments. The user selects an option from the presented options and takes action to improve the animal's comfort.

[0262] (Application Example 1)

[0263] 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."

[0264] In commercial facilities such as pet shops and animal cafes, there is a need to reduce stress and anxiety in animals and create safer and more comfortable interactions. However, it is difficult to understand an animal's emotional state in real time, and there is a risk that customers may misinterpret the animal's condition and cause excessive stress. Improving this situation and optimizing the interaction between animals and people is a challenge.

[0265] 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.

[0266] In this invention, the server includes a device for acquiring biological information of an animal, means for analyzing the acquired biological information and estimating the animal's emotional state, and equipment for visually displaying the estimated emotional state and providing information to the user. This allows the user to understand the animal's emotional state in real time and make appropriate contact.

[0267] A "device for acquiring animal biological information" is a device designed to continuously collect physiological data such as heart rate, body temperature, and movement patterns of animals.

[0268] "Means for estimating emotional states" refer to algorithms and data processing systems that analyze acquired biological information to infer what emotions (joy, excitement, stress, anxiety, etc.) an animal is experiencing.

[0269] A "device for visually presenting and providing information to users" is a device that displays the analyzed emotional state of an animal on a display or mobile device, allowing users to understand it intuitively.

[0270] A "control device for managing animal interactions in commercial facilities" is a device or system that has management functions to optimize the environment settings within the facility and interactions with animals according to the emotional state of the animals.

[0271] "A means of communication for recording acquired biological information and transmitting the information to a designated specialized facility" refers to a communication device that has the function of securely storing the collected data and transferring the data to a specific veterinary medical facility or research institution when necessary.

[0272] This invention is a system for commercial facilities where customers can interact with animals, which helps them understand the emotional state of animals and facilitates appropriate interactions between customers and animals.

[0273] The server receives biometric data such as heart rate, body temperature, and movement information from wearable devices attached to animals within the facility. These wearable devices have the capability to transmit data in real time using Bluetooth or Wi-Fi. The server stores the received biometric data and analyzes the animals' emotional states using machine learning models based on TensorFlow and PyTorch. This analysis makes it possible to classify the animals' emotional states into categories such as joy, excitement, stress, and anxiety.

[0274] The terminal visualizes the analyzed results and provides an interface for facility visitors and staff to view. For example, by installing a dedicated application on a tablet or smartphone, visitors can easily check the current emotional state of the animals in the facility. This information is presented as specific messages such as "The cat is currently relaxed" or "The dog is excited, so caution is needed."

[0275] Based on this information, users can adjust their interactions with animals. For example, if they are notified that an animal is stressed, visitors can temporarily refrain from contacting that animal. Furthermore, if an abnormality is detected in an animal's health, the system automatically sends information to a designated veterinary facility, enabling a quick response.

[0276] As a concrete example, consider the case where a cat named Sakura at an animal cafe exhibits a higher-than-normal heart rate. In this case, based on the analysis results, the server notifies the store manager, "Sakura is a little agitated. Please provide a quiet environment." Based on this notification, the manager can reduce the cat's stress by adjusting the environment, such as changing the background music.

[0277] Possible prompts for using the generative AI model include requests such as, "Please suggest appropriate actions to take if the cat's heart rate exceeds the normal range."

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The server receives biometric information from the animal's wearable device. Specifically, it acquires heart rate, body temperature, and activity information using Bluetooth or Wi-Fi. The input for this step is biometric data from the wearable device, and the output is a database on the server where that data is stored.

[0281] Step 2:

[0282] The server uses TensorFlow or PyTorch to estimate the emotional state of an animal in order to analyze the collected biological information. In the analysis, it is compared with past data and advanced algorithms are used. The input is the biological information data stored in Step 1, and the output is the estimated result of the emotional state. As a specific operation, data is supplied to the analysis model, and the result is calculated smoothly considering the response time.

[0283] Step 3:

[0284] The terminal visualizes the estimated result of the emotional state sent from the server and notifies the user. Here, the analysis result is converted into a visual message and displayed on the user's terminal display. The input is the estimated result of the emotional state from the server, and the output is the information provided on the user interface. The terminal performs the operation of appropriately laying out the message and displaying it in a format that is intuitively easy to understand.

[0285] Step 4:

[0286] The user adjusts the interaction with the animal based on the emotional state information provided by the terminal. For example, if the animal shows stress, the user refrains from contacting the animal or adjusts the environment. The input for this step is the emotional state information confirmed by the terminal, and the output is the user's physical behavior. Specific operations include changing the music and calming the situation around the animal.

[0287] Step 5:

[0288] When an abnormality is detected in the health state, the server automatically transmits information to the designated veterinary facility. The input is the data of the health abnormality based on the estimated emotional state and biological information, and the output is the notification to the veterinary facility. As a specific operation, the abnormal data is sent to the communication system and contact is made so that prompt medical treatment can be provided.

[0289] 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.

[0290] This invention is a system that enables deeper interaction by acquiring biometric information from both animals and users and analyzing their respective emotional states. Animals are fitted with wearable devices that acquire heart rate, body temperature, and movement data. Similarly, users collect biometric information such as heart rate and voice tone using smartwatches or smartphones.

[0291] The server receives this biometric information and uses an emotion engine to estimate the emotional states of the animals and users. The emotion engine analyzes the data using machine learning techniques based on an animal emotion analysis algorithm. The potential for mutual influence between the emotional states of the animals and users is considered, and this relationship is examined, especially in interaction scenarios.

[0292] The analyzed emotional states are transmitted to the terminal as integrated data, combining the emotions of both the animal and the user. The terminal processes this information and provides clear feedback to the user. The information is customized so that the user's emotions help to deepen the understanding of the animal's emotions. For example, if the animal is feeling anxious but the user is calm, the terminal can offer advice on how to improve the situation.

[0293] Users can review this feedback and have the option to improve the environment by operating the control device through their terminal. For example, they can play music to help animals relax or adjust the lighting based on the user's emotions.

[0294] For example, when a user is playing with their pet, if the device detects that the pet is excited and the user is feeling stressed at that time, it will suggest appropriate actions to help both of them relax. These suggestions may include moving to a favorite spot or playing specific music. In this way, mutual understanding between the animal and the user can be promoted, making it possible to build a better relationship.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The user attaches a wearable device to the animal and prepares a smartwatch or smartphone. This allows for the collection of biometric information from both the animal and the user. The connection process is automated via Bluetooth.

[0298] Step 2:

[0299] The server receives biometric information from the animals' wearable devices and biometric information from the users' devices. Animal data includes heart rate, body temperature, and movement information, while user data includes heart rate and voice tone.

[0300] Step 3:

[0301] The server activates an emotion engine to analyze both sets of biometric information individually. The emotion engine uses a machine learning model to estimate the emotional states of both the animal and the user. This process improves accuracy by comparing past and current data.

[0302] Step 4:

[0303] The server analyzes the relationship between the emotional states of the animals and the users and generates integrated emotional data. This data indicates whether the emotions of the animals and users are in harmony or whether improvement is needed.

[0304] Step 5:

[0305] The terminal receives the integrated emotion data and displays feedback through the user interface. The feedback includes a visual display of the emotional state and action suggestions. As an example, suggestions such as "The pet is a little excited. Let's play music to relax" are made.

[0306] Step 6:

[0307] The user operates the control device based on the feedback from the terminal to perform appropriate environmental adjustments. This is achieved through cooperation with smart devices such as music and lighting. The selected action is executed immediately.

[0308] Step 7:

[0309] The server records all interactions and emotion analysis results in the database, which is useful for improving the accuracy of future emotion estimation. In addition, it periodically generates a report on the health status of the animal and shares it with veterinary facilities as needed.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] There is a problem that it is difficult to simultaneously grasp the emotional states of the animal and the user and provide feedback based on the interaction. In the conventional technology, the analysis is mainly based on the biological information of only the animal or only the user, and it is difficult to realize an interaction considering the situation where the emotions of both affect each other.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0314] In this invention, the server includes a device for acquiring biometric information of animals and users, information processing means including an algorithm for analyzing emotional states based on the acquired biometric information of animals and users, and means for transmitting the analyzed emotional states as integrated data to a terminal and providing feedback to the user. This enables better mutual understanding by providing feedback that takes into account the emotional states of both the animal and the user.

[0315] "Biometric information" refers to data obtained from the body, such as the heart rate, body temperature, movements, and voice tone of animals or users.

[0316] "Information processing means" refers to an algorithm for analyzing collected biometric information and estimating emotional states, and the computer system that executes it.

[0317] "Integrated data" refers to data that summarizes the results of analyzing the emotional states of animals and users, showing how their emotions interact with each other.

[0318] "Means of providing feedback" refers to devices that have the function of presenting users with specific actions or information based on analyzed integrated data.

[0319] "Control means for adjusting the environment" refers to a system that has the function of changing the physical environment according to the feedback received, such as adjusting lighting or music playback.

[0320] The embodiments for carrying out this invention are shown below.

[0321] First, the user attaches a wearable device to the animal and collects biometric information using a smartwatch or smartphone. The animal's wearable device measures data such as heart rate, body temperature, and movement in real time, while the user's smart device also collects data such as heart rate and voice tone. This group of devices uses standard biosensor technology available on the market.

[0322] Next, the server receives this biometric information and analyzes the data using advanced information processing tools. Specific software examples include machine learning libraries and data analysis tools. The emotion analysis algorithm installed on the server analyzes the biometric information and infers the emotional state of the animal and the user. This analysis uses a generative AI model and compares it with past datasets.

[0323] The analyzed emotional state is sent to the device as integrated data, and the device uses this data to provide feedback to the user. The feedback suggests specific actions and is customized, for example, "Your pet is excited. Play their favorite music to help them relax." This feedback is provided to the user through visual or audio output.

[0324] For example, if the system detects that a user is excited while playing with their pet, and the user is also feeling stressed, the device could suggest, "Let's move to another room and take a short break." This would lead to better mutual understanding and improved relationships.

[0325] An example of a prompt message would be, "Please explain the procedure for sentiment analysis based on animal and user biometric data."

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The user attaches a wearable device to the animal and prepares their own smartwatch or smartphone. The animal's heart rate, body temperature, and activity data are acquired as input. This data is collected digitally through multiple biosensors and then initially processed.

[0329] Step 2:

[0330] The terminal transmits the collected biometric information to the server. This step includes biometric data of both the animal and the user as input. The data is transmitted securely, and its integrity is verified on the server. The output is the result that the data has successfully reached the server.

[0331] Step 3:

[0332] The server performs emotion analysis via a generative AI model based on the received biometric information. Inputs include the animal's and user's heart rate, body temperature, and movement data. Based on this, the data is fed into the algorithm for analysis. The output is an analysis result indicating the emotional state of the animal and user.

[0333] Step 4:

[0334] The server integrates the analysis results and prepares them for transmission to the terminal. In this step, data on emotional states is input, and an integrated emotional dataset is generated as output. This data is organized to take into account the effects of interactions.

[0335] Step 5:

[0336] The terminal receives integrated data from the server and generates feedback for the user. The input is an integrated dataset, and specific action suggestions are created based on the analysis results. The output is a feedback message presented to the user.

[0337] Step 6:

[0338] The user takes specific actions based on feedback from the device. The input is the feedback message, and the output is the actual action taken. For example, it is possible to play music to relax an animal.

[0339] (Application Example 2)

[0340] 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."

[0341] In environments where animals and humans live together, understanding each other's emotional states is crucial for reducing stress and promoting comfortable interaction. However, conventional technologies have limited means of simultaneously analyzing the emotional states of both animals and humans and providing easily understandable feedback. To address this problem, there is a need for a system that can comprehensively analyze the emotions of both animals and humans and promote mutual understanding.

[0342] 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.

[0343] In this invention, the server includes means for acquiring animal and human biometric information, means including an algorithm that analyzes the acquired biometric information to estimate the emotional states of animals and humans and generate integrated data, and means for displaying the estimated emotional states of animals and humans and providing feedback to the user. This makes it possible to understand the emotional states of animals and humans from a broad perspective and propose appropriate actions, thereby creating a comfortable interaction environment.

[0344] "Animal biometric information" refers to biological or behavioral data such as the animal's heart rate, body temperature, and movement data.

[0345] "Human biometric information" refers to biological or behavioral data such as a person's heart rate, voice tone, and body temperature.

[0346] A "device" refers to an electronic device attached to animals or humans to acquire biometric information.

[0347] A "data processing device" refers to a computing device that analyzes acquired biological information and estimates the emotional states of animals and humans.

[0348] An "algorithm" refers to the procedures and calculation methods that a data processing device uses to estimate the emotional states of animals and humans.

[0349] "Integrated data" refers to the results of an analysis that combines the emotional states of animals and humans.

[0350] "Terminal device" refers to an electronic device that provides users with visual or auditory feedback on the emotional states of animals and humans.

[0351] A "control device" refers to a device that adjusts the environment based on an estimated emotional state.

[0352] "Feedback" refers to information and suggestions provided to users based on their analyzed emotional state.

[0353] The system for realizing this invention collects and analyzes biometric information from animals and humans, and provides feedback based on that data. Animals are fitted with wearable devices that acquire biometric information such as heart rate, body temperature, and movement data. Human biometric information is collected via smartwatches and smartphones. This information is transmitted to a server where data processing takes place.

[0354] The server collects biometric information and analyzes the data using algorithms to estimate the emotional states of animals and humans. The analysis utilizes animal emotion analysis algorithms and leverages machine learning techniques. In this process, the server also considers the interrelationships between animal and human emotional states. The integrated data resulting from the analysis is transmitted to a terminal device, providing users with visual or auditory feedback.

[0355] Users can receive feedback through their devices and adjust the environment by operating the control system based on the estimated emotional state. For example, if a pet is stressed, specific music can be played or the lighting adjusted to encourage relaxation.

[0356] As a concrete example, when a pet and its owner visit a pet supply store, after a few minutes inside, biometric data transmitted from the pet's wearable device and data obtained from the owner's smartwatch reveal that the pet is stressed. The server analyzes this information and suggests via the terminal, "Please use the pet relaxation area." An example of a prompt message used is, "Please consider a method to analyze emotions based on animal and human biometric data and provide optimal feedback in a physical store."

[0357] In this way, the system promotes mutual understanding between animals and humans and helps build better relationships.

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The server receives biometric data from animal wearable devices and human smartwatches and smartphones. The input data includes animal heart rate, body temperature, and movement data, as well as human heart rate and voice tone. This data is stored as biometric information necessary for subsequent processing and analysis.

[0361] Step 2:

[0362] The server estimates the emotional states of animals and humans based on the received biometric information. The server uses emotion analysis algorithms and machine learning techniques to process and analyze the data. The output of the analysis converts the emotional states of animals and humans into numerical values ​​and categories.

[0363] Step 3:

[0364] The server integrates estimated animal and human emotional states and generates integrated data that considers the relevance in interactions. It uses the previously generated emotional state data as input and combines it to perform new emotional assessments. The output is a list or matrix of integrated emotional states.

[0365] Step 4:

[0366] The server sends this integrated data to the terminal, which then prepares to provide appropriate feedback to the user. The terminal receives the integrated data and generates visual or auditory feedback for the user. The output may be a feedback message or action suggestion.

[0367] Step 5:

[0368] The user reviews the feedback provided through the device and operates the control unit as needed. Based on the feedback, they perform operations such as adjusting music playback or lighting to optimize the environment. As a result of this series of operations, the environment for both the pet and the owner is optimized.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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".

[0385] This invention is a system that has a series of processes for acquiring biological information from an animal, analyzing that information, and providing feedback on the animal's emotional state to the user. Specifically, a wearable device attached to the animal acquires data such as heart rate, body temperature, and movement. This data is transmitted to a server via wireless communication.

[0386] The server receives this biometric information and uses advanced algorithms to analyze the animal's emotional state in real time. These algorithms are based on machine learning techniques and improve accuracy by comparing current data with past data. The analyzed emotional state is categorized into states such as joy, excitement, stress, and anxiety.

[0387] The analysis results are sent to a terminal. The terminal displays this information intuitively to the user via a dedicated application. For example, if an animal is stressed, the terminal will inform the user of the situation and suggest appropriate measures. These measures may include actions to improve the animal's comfort, such as changing the room temperature or playing calming music. The user can take the necessary actions by selecting options on the terminal.

[0388] Furthermore, the server has a mechanism that automatically notifies veterinary medical facilities when it detects abnormalities in an animal's health using the collected data. This system enables multi-sensory emotional feedback from animals, making it easier for users to maintain the animal's psychological and physical health.

[0389] As a concrete example, consider a scenario where a user attaches a device to their pet dog to collect behavioral data during walks. If the dog's heart rate becomes abnormally high, the server immediately analyzes the data and sends a notification to the device stating, "Your dog may be feeling anxious. We recommend taking a short break." The user can then adjust their behavior based on this advice, allowing them to spend more time with their dog with greater peace of mind.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The user attaches a wearable device to the animal and connects the device to the system. Since the device is configured to automatically begin pairing when powered on, the user can complete the connection without any special action.

[0393] Step 2:

[0394] The server receives animal biometric information in real time from connected devices. Specifically, it acquires heart rate, body temperature, and motion sensor data as data streams using a specific protocol. This information is temporarily stored within the system and immediately ready for analysis.

[0395] Step 3:

[0396] The server uses a machine learning model to analyze the received biometric information. This model is trained on historical datasets and estimates the animal's emotional state based on patterns in the biometric data. For example, a sudden increase in heart rate or specific behavioral patterns may be associated with anxiety or excitement.

[0397] Step 4:

[0398] The server organizes the analysis results and categorizes the animals' emotional states. This categorized information is then sent to the terminal in an easy-to-understand format. Examples of emotions include joy, surprise, anxiety, and relaxation.

[0399] Step 5:

[0400] The device provides feedback to the user via push notifications and a dedicated application, based on emotional data received from the server. The user interface is designed for quick understanding by displaying icons and messages that indicate emotions.

[0401] Step 6:

[0402] The user reviews the feedback presented by the device and selects an action. For example, if the data indicates that the pet is stressed, the user can select "Play music to help the pet relax" on the device.

[0403] Step 7:

[0404] The device controls smart home devices and robots based on user instructions. The system uses APIs to adjust the environment to be comfortable for animals, such as changing lighting or playing music from speakers.

[0405] Step 8:

[0406] The server stores long-term data, tracking the animals' health status and emotional tendencies. This information can be regularly shared with veterinarians to assist in health management.

[0407] (Example 1)

[0408] 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."

[0409] Traditional animal care systems have made it difficult to accurately understand and respond quickly to the emotional state of animals, and methods for detecting sudden changes in health or stressors in real time have been limited. As a result, there have been cases where animal health management has been inadequate, and there is a need for methods that provide accurate and immediate feedback on the physiological and psychological state of animals and effectively adjust the environment based on that feedback.

[0410] 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.

[0411] In this invention, the server includes a sensor device for acquiring biological information of animals, a data processing device for analyzing the acquired biological information using machine learning technology and classifying the animal's emotional state in real time, and an information display means for displaying the classified emotional state and providing visual feedback to the user. This enables real-time understanding of the animal's emotions and health status, and allows for rapid response and effective environmental adjustments based on that understanding.

[0412] "Animal biometric information" refers to data that indicates the physiological state of an animal, such as its heart rate, body temperature, and movements.

[0413] A "sensor device" refers to a device attached to an animal to acquire biological information.

[0414] "Machine learning technology" refers to techniques that find patterns and rules from large amounts of data and use them to make predictions and classifications about future data.

[0415] A "data processing device" refers to a computing device that analyzes collected biological information and determines the emotional state of an animal.

[0416] "Information display means" refers to devices or interfaces that visually show the emotional state of an analyzed animal to the user.

[0417] "Environmental adjustment" refers to appropriately changing the physical environment in which an animal interacts, based on the animal's emotional state.

[0418] "Communication means" refers to a system for sharing information with veterinary medical facilities when an anomaly is detected.

[0419] This invention is a system that provides a series of processes for acquiring and analyzing biological information of animals and providing feedback on the animal's emotional state to the user. Specific embodiments are shown below.

[0420] The user first attaches a sensor device to the animal. This sensor device is capable of continuously acquiring biometric information such as the animal's heart rate, body temperature, and movement. The acquired biometric information is transmitted to a server via wireless communication such as Bluetooth.

[0421] The server uses a data processing unit equipped with machine learning technology to process the received biometric information. This data processing unit utilizes a pre-trained generative AI model to analyze the animal's emotional state in real time. By comparing it with past data, the accuracy of emotion classification is improved. Through this process, the animal's emotional state is classified into categories such as "joy," "excitement," "stress," and "anxiety."

[0422] The analyzed data is sent to the terminal and displayed to the user as visual feedback via a dedicated application. For example, if the analysis indicates that the animal is experiencing stress, the terminal will suggest, "The current environment is causing stress. Please consider lowering the room temperature." Furthermore, it has a function to automatically notify veterinary medical facilities, enabling a quick response when an abnormality is detected.

[0423] As a concrete example, consider a scenario where a user attaches a sensor device to their pet dog and sends data to a server during walks. If the dog's heart rate becomes higher than normal, the server immediately analyzes this data and sends a notification to the device saying, "Your dog may be excited. Take a short break and observe its condition."

[0424] For example, a prompt message could be something like, "If my pet cat seems to be feeling anxious, what measures would you suggest?" This would allow the system to provide specific suggestions for action.

[0425] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0426] Step 1:

[0427] The user attaches a sensor device to the animal. This device acquires biometric information such as heart rate, body temperature, and movement in real time. The acquired data is transmitted to a server using Bluetooth. The input is biometric data from the sensor, and the output is data transmission to the server. After attachment, the user can continue living with their pet as usual.

[0428] Step 2:

[0429] The server preprocesses the received biometric data, including imputing missing data and detecting and removing outliers. The input is raw data transmitted from the sensor, and the output is data formatted into a clean format. Specifically, the server uses a program to check data consistency and filters out inappropriate data.

[0430] Step 3:

[0431] The server inputs pre-processed data into a machine learning algorithm to analyze the animals' emotional states. This algorithm utilizes a generative AI model to classify emotional states such as "joy," "excitement," "stress," and "anxiety" in real time based on the data. The output is the classification result of the emotional states. In this process, the server refers to past data to improve the accuracy of the model.

[0432] Step 4:

[0433] The server sends the analysis results to the terminal. The input is the classification result of the emotional state, and the output is data presented as visual feedback on the terminal. The server processes the analysis results immediately and notifies the terminal at the appropriate time.

[0434] Step 5:

[0435] The terminal displays the analysis results using a dedicated application. The application displays the animal's emotional state in an easy-to-understand graphical interface and shows the user specific actions to reduce stress. The input is the analysis results sent from the server, and the output is specific advice presented to the user.

[0436] Step 6:

[0437] The user adjusts the animal's environment based on information displayed on the device. For example, they might lower the room temperature or play quiet music. The input is the suggestions from the device, and the output is the animal's behavior and reaction after the adjustments. The user selects an option from the presented options and takes action to improve the animal's comfort.

[0438] (Application Example 1)

[0439] 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."

[0440] In commercial facilities such as pet shops and animal cafes, there is a need to reduce stress and anxiety in animals and create safer and more comfortable interactions. However, it is difficult to understand an animal's emotional state in real time, and there is a risk that customers may misinterpret the animal's condition and cause excessive stress. Improving this situation and optimizing the interaction between animals and people is a challenge.

[0441] 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.

[0442] In this invention, the server includes a device for acquiring biological information of an animal, means for analyzing the acquired biological information and estimating the animal's emotional state, and equipment for visually displaying the estimated emotional state and providing information to the user. This allows the user to understand the animal's emotional state in real time and make appropriate contact.

[0443] A "device for acquiring animal biological information" is a device designed to continuously collect physiological data such as heart rate, body temperature, and movement patterns of animals.

[0444] "Means for estimating emotional states" refer to algorithms and data processing systems that analyze acquired biological information to infer what emotions (joy, excitement, stress, anxiety, etc.) an animal is experiencing.

[0445] A "device for visually presenting and providing information to users" is a device that displays the analyzed emotional state of an animal on a display or mobile device, allowing users to understand it intuitively.

[0446] A "control device for managing animal interactions in commercial facilities" is a device or system that has management functions to optimize the environment settings within the facility and interactions with animals according to the emotional state of the animals.

[0447] "A means of communication for recording acquired biological information and transmitting the information to a designated specialized facility" refers to a communication device that has the function of securely storing the collected data and transferring the data to a specific veterinary medical facility or research institution when necessary.

[0448] This invention is a system for commercial facilities where customers can interact with animals, which helps them understand the emotional state of animals and facilitates appropriate interactions between customers and animals.

[0449] The server receives biometric data such as heart rate, body temperature, and movement information from wearable devices attached to animals within the facility. These wearable devices have the capability to transmit data in real time using Bluetooth or Wi-Fi. The server stores the received biometric data and analyzes the animals' emotional states using machine learning models based on TensorFlow and PyTorch. This analysis makes it possible to classify the animals' emotional states into categories such as joy, excitement, stress, and anxiety.

[0450] The terminal visualizes the analyzed results and provides an interface for facility visitors and staff to view. For example, by installing a dedicated application on a tablet or smartphone, visitors can easily check the current emotional state of the animals in the facility. This information is presented as specific messages such as "The cat is currently relaxed" or "The dog is excited, so caution is needed."

[0451] Based on this information, users can adjust their interactions with animals. For example, if they are notified that an animal is stressed, visitors can temporarily refrain from contacting that animal. Furthermore, if an abnormality is detected in an animal's health, the system automatically sends information to a designated veterinary facility, enabling a quick response.

[0452] As a concrete example, consider the case where a cat named Sakura at an animal cafe exhibits a higher-than-normal heart rate. In this case, based on the analysis results, the server notifies the store manager, "Sakura is a little agitated. Please provide a quiet environment." Based on this notification, the manager can reduce the cat's stress by adjusting the environment, such as changing the background music.

[0453] Possible prompts for using the generative AI model include requests such as, "Please suggest appropriate actions to take if the cat's heart rate exceeds the normal range."

[0454] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0455] Step 1:

[0456] The server receives biometric information from the animal's wearable device. Specifically, it acquires heart rate, body temperature, and activity information using Bluetooth or Wi-Fi. The input for this step is biometric data from the wearable device, and the output is a database on the server where that data is stored.

[0457] Step 2:

[0458] The server uses TensorFlow and PyTorch to analyze collected biometric data and estimate the emotional state of animals. The analysis involves cross-referencing with historical data and employing advanced algorithms. The input is the biometric data stored in step 1, and the output is the estimated emotional state. Specifically, the server supplies data to the analysis model and calculates results smoothly, taking response time into consideration.

[0459] Step 3:

[0460] The terminal visualizes the estimated emotional state results sent from the server and notifies the user. Here, the analysis results are converted into a visual message and displayed on the user's terminal display. The input is the estimated emotional state results from the server, and the output is the information provided on the user interface. The terminal appropriately lays out the message and displays it in an intuitively easy-to-understand format.

[0461] Step 4:

[0462] The user adjusts their interaction with the animal based on emotional state information provided by the device. For example, if the animal shows signs of stress, the user may reduce contact with the animal or adjust the environment. The input for this step is the emotional state information confirmed by the device, and the output is the user's physical actions. Specific actions include changing the music or making the environment around the animal quieter.

[0463] Step 5:

[0464] The server automatically transmits information to a designated veterinary facility if an abnormality in the animal's health is detected. The input is data on health abnormalities based on estimated emotional state and biometric information, and the output is a notification to the veterinary facility. Specifically, it sends abnormal data to a communication system to ensure prompt medical response.

[0465] 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.

[0466] This invention is a system that enables deeper interaction by acquiring biometric information from both animals and users and analyzing their respective emotional states. Animals are fitted with wearable devices that acquire heart rate, body temperature, and movement data. Similarly, users collect biometric information such as heart rate and voice tone using smartwatches or smartphones.

[0467] The server receives this biometric information and uses an emotion engine to estimate the emotional states of the animals and users. The emotion engine analyzes the data using machine learning techniques based on an animal emotion analysis algorithm. The potential for mutual influence between the emotional states of the animals and users is considered, and this relationship is examined, especially in interaction scenarios.

[0468] The analyzed emotional states are transmitted to the terminal as integrated data, combining the emotions of both the animal and the user. The terminal processes this information and provides clear feedback to the user. The information is customized so that the user's emotions help to deepen the understanding of the animal's emotions. For example, if the animal is feeling anxious but the user is calm, the terminal can offer advice on how to improve the situation.

[0469] Users can review this feedback and have the option to improve the environment by operating the control device through their terminal. For example, they can play music to help animals relax or adjust the lighting based on the user's emotions.

[0470] For example, when a user is playing with their pet, if the device detects that the pet is excited and the user is feeling stressed at that time, it will suggest appropriate actions to help both of them relax. These suggestions may include moving to a favorite spot or playing specific music. In this way, mutual understanding between the animal and the user can be promoted, making it possible to build a better relationship.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user attaches a wearable device to the animal and prepares a smartwatch or smartphone. This allows for the collection of biometric information from both the animal and the user. The connection process is automated via Bluetooth.

[0474] Step 2:

[0475] The server receives biometric information from the animals' wearable devices and biometric information from the users' devices. Animal data includes heart rate, body temperature, and movement information, while user data includes heart rate and voice tone.

[0476] Step 3:

[0477] The server activates an emotion engine to analyze both sets of biometric information individually. The emotion engine uses a machine learning model to estimate the emotional states of both the animal and the user. This process improves accuracy by comparing past and current data.

[0478] Step 4:

[0479] The server analyzes the relationship between the emotional states of the animals and the users and generates integrated emotional data. This data indicates whether the emotions of the animals and users are in harmony or whether improvement is needed.

[0480] Step 5:

[0481] The device receives integrated emotional data and displays feedback through the user interface. This feedback includes a visual representation of the emotional state and action suggestions. For example, a suggestion might be, "Your pet is a little agitated. Let's play some music to help it relax."

[0482] Step 6:

[0483] The user operates the control system based on feedback from their device to make appropriate environmental adjustments. This is achieved through integration with smart devices such as music and lighting. The selected action is executed immediately.

[0484] Step 7:

[0485] The server records all interactions and emotion analysis results in a database to help improve the accuracy of emotion estimation in the future. It also periodically generates reports on the animals' health status and shares them with veterinary facilities as needed.

[0486] (Example 2)

[0487] 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."

[0488] There is a challenge in simultaneously understanding the emotional states of both animals and users and providing feedback based on their interaction. Conventional technologies primarily analyze biometric information of either the animal or the user alone, making it difficult to realize interactions that take into account the mutual influence of their emotions.

[0489] 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.

[0490] In this invention, the server includes a device for acquiring biometric information of animals and users, information processing means including an algorithm for analyzing emotional states based on the acquired biometric information of animals and users, and means for transmitting the analyzed emotional states as integrated data to a terminal and providing feedback to the user. This enables better mutual understanding by providing feedback that takes into account the emotional states of both the animal and the user.

[0491] "Biometric information" refers to data obtained from the body, such as the heart rate, body temperature, movements, and voice tone of animals or users.

[0492] "Information processing means" refers to an algorithm for analyzing collected biometric information and estimating emotional states, and the computer system that executes it.

[0493] "Integrated data" refers to data that summarizes the results of analyzing the emotional states of animals and users, showing how their emotions interact with each other.

[0494] "Means of providing feedback" refers to devices that have the function of presenting users with specific actions or information based on analyzed integrated data.

[0495] "Control means for adjusting the environment" refers to a system that has the function of changing the physical environment according to the feedback received, such as adjusting lighting or music playback.

[0496] The embodiments for carrying out this invention are shown below.

[0497] First, the user attaches a wearable device to the animal and collects biometric information using a smartwatch or smartphone. The animal's wearable device measures data such as heart rate, body temperature, and movement in real time, while the user's smart device also collects data such as heart rate and voice tone. This group of devices uses standard biosensor technology available on the market.

[0498] Next, the server receives this biometric information and analyzes the data using advanced information processing tools. Specific software examples include machine learning libraries and data analysis tools. The emotion analysis algorithm installed on the server analyzes the biometric information and infers the emotional state of the animal and the user. This analysis uses a generative AI model and compares it with past datasets.

[0499] The analyzed emotional state is sent to the device as integrated data, and the device uses this data to provide feedback to the user. The feedback suggests specific actions and is customized, for example, "Your pet is excited. Play their favorite music to help them relax." This feedback is provided to the user through visual or audio output.

[0500] For example, if the system detects that a user is excited while playing with their pet, and the user is also feeling stressed, the device could suggest, "Let's move to another room and take a short break." This would lead to better mutual understanding and improved relationships.

[0501] An example of a prompt message would be, "Please explain the procedure for sentiment analysis based on animal and user biometric data."

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The user attaches a wearable device to the animal and prepares their own smartwatch or smartphone. The animal's heart rate, body temperature, and activity data are acquired as input. This data is collected digitally through multiple biosensors and then initially processed.

[0505] Step 2:

[0506] The terminal transmits the collected biometric information to the server. This step includes biometric data of both the animal and the user as input. The data is transmitted securely, and its integrity is verified on the server. The output is the result that the data has successfully reached the server.

[0507] Step 3:

[0508] The server performs emotion analysis via a generative AI model based on the received biometric information. Inputs include the animal's and user's heart rate, body temperature, and movement data. Based on this, the data is fed into the algorithm for analysis. The output is an analysis result indicating the emotional state of the animal and user.

[0509] Step 4:

[0510] The server integrates the analysis results and prepares them for transmission to the terminal. In this step, data on emotional states is input, and an integrated emotional dataset is generated as output. This data is organized to take into account the effects of interactions.

[0511] Step 5:

[0512] The terminal receives integrated data from the server and generates feedback for the user. The input is an integrated dataset, and specific action suggestions are created based on the analysis results. The output is a feedback message presented to the user.

[0513] Step 6:

[0514] The user takes specific actions based on feedback from the device. The input is the feedback message, and the output is the actual action taken. For example, it is possible to play music to relax an animal.

[0515] (Application Example 2)

[0516] 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."

[0517] In environments where animals and humans live together, understanding each other's emotional states is crucial for reducing stress and promoting comfortable interaction. However, conventional technologies have limited means of simultaneously analyzing the emotional states of both animals and humans and providing easily understandable feedback. To address this problem, there is a need for a system that can comprehensively analyze the emotions of both animals and humans and promote mutual understanding.

[0518] 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.

[0519] In this invention, the server includes means for acquiring animal and human biometric information, means including an algorithm that analyzes the acquired biometric information to estimate the emotional states of animals and humans and generate integrated data, and means for displaying the estimated emotional states of animals and humans and providing feedback to the user. This makes it possible to understand the emotional states of animals and humans from a broad perspective and propose appropriate actions, thereby creating a comfortable interaction environment.

[0520] "Animal biometric information" refers to biological or behavioral data such as the animal's heart rate, body temperature, and movement data.

[0521] "Human biometric information" refers to biological or behavioral data such as a person's heart rate, voice tone, and body temperature.

[0522] A "device" refers to an electronic device attached to animals or humans to acquire biometric information.

[0523] A "data processing device" refers to a computing device that analyzes acquired biological information and estimates the emotional states of animals and humans.

[0524] An "algorithm" refers to the procedures and calculation methods that a data processing device uses to estimate the emotional states of animals and humans.

[0525] "Integrated data" refers to the results of an analysis that combines the emotional states of animals and humans.

[0526] "Terminal device" refers to an electronic device that provides users with visual or auditory feedback on the emotional states of animals and humans.

[0527] A "control device" refers to a device that adjusts the environment based on an estimated emotional state.

[0528] "Feedback" refers to information and suggestions provided to users based on their analyzed emotional state.

[0529] The system for realizing this invention collects and analyzes biometric information from animals and humans, and provides feedback based on that data. Animals are fitted with wearable devices that acquire biometric information such as heart rate, body temperature, and movement data. Human biometric information is collected via smartwatches and smartphones. This information is transmitted to a server where data processing takes place.

[0530] The server collects biometric information and analyzes the data using algorithms to estimate the emotional states of animals and humans. The analysis utilizes animal emotion analysis algorithms and leverages machine learning techniques. In this process, the server also considers the interrelationships between animal and human emotional states. The integrated data resulting from the analysis is transmitted to a terminal device, providing users with visual or auditory feedback.

[0531] Users can receive feedback through their devices and adjust the environment by operating the control system based on the estimated emotional state. For example, if a pet is stressed, specific music can be played or the lighting adjusted to encourage relaxation.

[0532] As a concrete example, when a pet and its owner visit a pet supply store, after a few minutes inside, biometric data transmitted from the pet's wearable device and data obtained from the owner's smartwatch reveal that the pet is stressed. The server analyzes this information and suggests via the terminal, "Please use the pet relaxation area." An example of a prompt message used is, "Please consider a method to analyze emotions based on animal and human biometric data and provide optimal feedback in a physical store."

[0533] In this way, the system promotes mutual understanding between animals and humans and helps build better relationships.

[0534] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0535] Step 1:

[0536] The server receives biometric data from animal wearable devices and human smartwatches and smartphones. The input data includes animal heart rate, body temperature, and movement data, as well as human heart rate and voice tone. This data is stored as biometric information necessary for subsequent processing and analysis.

[0537] Step 2:

[0538] The server estimates the emotional states of animals and humans based on the received biometric information. The server uses emotion analysis algorithms and machine learning techniques to process and analyze the data. The output of the analysis converts the emotional states of animals and humans into numerical values ​​and categories.

[0539] Step 3:

[0540] The server integrates estimated animal and human emotional states and generates integrated data that considers the relevance in interactions. It uses the previously generated emotional state data as input and combines it to perform new emotional assessments. The output is a list or matrix of integrated emotional states.

[0541] Step 4:

[0542] The server sends this integrated data to the terminal, which then prepares to provide appropriate feedback to the user. The terminal receives the integrated data and generates visual or auditory feedback for the user. The output may be a feedback message or action suggestion.

[0543] Step 5:

[0544] The user reviews the feedback provided through the device and operates the control unit as needed. Based on the feedback, they perform operations such as adjusting music playback or lighting to optimize the environment. As a result of this series of operations, the environment for both the pet and the owner is optimized.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] [Fourth Embodiment]

[0549] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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 in 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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".

[0562] This invention is a system that has a series of processes for acquiring biological information from an animal, analyzing that information, and providing feedback on the animal's emotional state to the user. Specifically, a wearable device attached to the animal acquires data such as heart rate, body temperature, and movement. This data is transmitted to a server via wireless communication.

[0563] The server receives this biometric information and uses advanced algorithms to analyze the animal's emotional state in real time. These algorithms are based on machine learning techniques and improve accuracy by comparing current data with past data. The analyzed emotional state is categorized into states such as joy, excitement, stress, and anxiety.

[0564] The analysis results are sent to a terminal. The terminal displays this information intuitively to the user via a dedicated application. For example, if an animal is stressed, the terminal will inform the user of the situation and suggest appropriate measures. These measures may include actions to improve the animal's comfort, such as changing the room temperature or playing calming music. The user can take the necessary actions by selecting options on the terminal.

[0565] Furthermore, the server has a mechanism that automatically notifies veterinary medical facilities when it detects abnormalities in an animal's health using the collected data. This system enables multi-sensory emotional feedback from animals, making it easier for users to maintain the animal's psychological and physical health.

[0566] As a concrete example, consider a scenario where a user attaches a device to their pet dog to collect behavioral data during walks. If the dog's heart rate becomes abnormally high, the server immediately analyzes the data and sends a notification to the device stating, "Your dog may be feeling anxious. We recommend taking a short break." The user can then adjust their behavior based on this advice, allowing them to spend more time with their dog with greater peace of mind.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The user attaches a wearable device to the animal and connects the device to the system. Since the device is configured to automatically begin pairing when powered on, the user can complete the connection without any special action.

[0570] Step 2:

[0571] The server receives animal biometric information in real time from connected devices. Specifically, it acquires heart rate, body temperature, and motion sensor data as data streams using a specific protocol. This information is temporarily stored within the system and immediately ready for analysis.

[0572] Step 3:

[0573] The server uses a machine learning model to analyze the received biometric information. This model is trained on historical datasets and estimates the animal's emotional state based on patterns in the biometric data. For example, a sudden increase in heart rate or specific behavioral patterns may be associated with anxiety or excitement.

[0574] Step 4:

[0575] The server organizes the analysis results and categorizes the animals' emotional states. This categorized information is then sent to the terminal in an easy-to-understand format. Examples of emotions include joy, surprise, anxiety, and relaxation.

[0576] Step 5:

[0577] The device provides feedback to the user via push notifications and a dedicated application, based on emotional data received from the server. The user interface is designed for quick understanding by displaying icons and messages that indicate emotions.

[0578] Step 6:

[0579] The user reviews the feedback presented by the device and selects an action. For example, if the data indicates that the pet is stressed, the user can select "Play music to help the pet relax" on the device.

[0580] Step 7:

[0581] The device controls smart home devices and robots based on user instructions. The system uses APIs to adjust the environment to be comfortable for animals, such as changing lighting or playing music from speakers.

[0582] Step 8:

[0583] The server stores long-term data, tracking the animals' health status and emotional tendencies. This information can be regularly shared with veterinarians to assist in health management.

[0584] (Example 1)

[0585] 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".

[0586] Traditional animal care systems have made it difficult to accurately understand and respond quickly to the emotional state of animals, and methods for detecting sudden changes in health or stressors in real time have been limited. As a result, there have been cases where animal health management has been inadequate, and there is a need for methods that provide accurate and immediate feedback on the physiological and psychological state of animals and effectively adjust the environment based on that feedback.

[0587] 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.

[0588] In this invention, the server includes a sensor device for acquiring biological information of animals, a data processing device for analyzing the acquired biological information using machine learning technology and classifying the animal's emotional state in real time, and an information display means for displaying the classified emotional state and providing visual feedback to the user. This enables real-time understanding of the animal's emotions and health status, and allows for rapid response and effective environmental adjustments based on that understanding.

[0589] "Animal biometric information" refers to data that indicates the physiological state of an animal, such as its heart rate, body temperature, and movements.

[0590] A "sensor device" refers to a device attached to an animal to acquire biological information.

[0591] "Machine learning technology" refers to techniques that find patterns and rules from large amounts of data and use them to make predictions and classifications about future data.

[0592] A "data processing device" refers to a computing device that analyzes collected biological information and determines the emotional state of an animal.

[0593] "Information display means" refers to devices or interfaces that visually show the emotional state of an analyzed animal to the user.

[0594] "Environmental adjustment" refers to appropriately changing the physical environment in which an animal interacts, based on the animal's emotional state.

[0595] "Communication means" refers to a system for sharing information with veterinary medical facilities when an anomaly is detected.

[0596] This invention is a system that provides a series of processes for acquiring and analyzing biological information of animals and providing feedback on the animal's emotional state to the user. Specific embodiments are shown below.

[0597] The user first attaches a sensor device to the animal. This sensor device is capable of continuously acquiring biometric information such as the animal's heart rate, body temperature, and movement. The acquired biometric information is transmitted to a server via wireless communication such as Bluetooth.

[0598] The server uses a data processing unit equipped with machine learning technology to process the received biometric information. This data processing unit utilizes a pre-trained generative AI model to analyze the animal's emotional state in real time. By comparing it with past data, the accuracy of emotion classification is improved. Through this process, the animal's emotional state is classified into categories such as "joy," "excitement," "stress," and "anxiety."

[0599] The analyzed data is sent to the terminal and displayed to the user as visual feedback via a dedicated application. For example, if the analysis indicates that the animal is experiencing stress, the terminal will suggest, "The current environment is causing stress. Please consider lowering the room temperature." Furthermore, it has a function to automatically notify veterinary medical facilities, enabling a quick response when an abnormality is detected.

[0600] As a concrete example, consider a scenario where a user attaches a sensor device to their pet dog and sends data to a server during walks. If the dog's heart rate becomes higher than normal, the server immediately analyzes this data and sends a notification to the device saying, "Your dog may be excited. Take a short break and observe its condition."

[0601] For example, a prompt message could be something like, "If my pet cat seems to be feeling anxious, what measures would you suggest?" This would allow the system to provide specific suggestions for action.

[0602] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0603] Step 1:

[0604] The user attaches a sensor device to the animal. This device acquires biometric information such as heart rate, body temperature, and movement in real time. The acquired data is transmitted to a server using Bluetooth. The input is biometric data from the sensor, and the output is data transmission to the server. After attachment, the user can continue living with their pet as usual.

[0605] Step 2:

[0606] The server preprocesses the received biometric data, including imputing missing data and detecting and removing outliers. The input is raw data transmitted from the sensor, and the output is data formatted into a clean format. Specifically, the server uses a program to check data consistency and filters out inappropriate data.

[0607] Step 3:

[0608] The server inputs pre-processed data into a machine learning algorithm to analyze the animals' emotional states. This algorithm utilizes a generative AI model to classify emotional states such as "joy," "excitement," "stress," and "anxiety" in real time based on the data. The output is the classification result of the emotional states. In this process, the server refers to past data to improve the accuracy of the model.

[0609] Step 4:

[0610] The server sends the analysis results to the terminal. The input is the classification result of the emotional state, and the output is data presented as visual feedback on the terminal. The server processes the analysis results immediately and notifies the terminal at the appropriate time.

[0611] Step 5:

[0612] The terminal displays the analysis results using a dedicated application. The application displays the animal's emotional state in an easy-to-understand graphical interface and shows the user specific actions to reduce stress. The input is the analysis results sent from the server, and the output is specific advice presented to the user.

[0613] Step 6:

[0614] The user adjusts the animal's environment based on information displayed on the device. For example, they might lower the room temperature or play quiet music. The input is the suggestions from the device, and the output is the animal's behavior and reaction after the adjustments. The user selects an option from the presented options and takes action to improve the animal's comfort.

[0615] (Application Example 1)

[0616] 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".

[0617] In commercial facilities such as pet shops and animal cafes, there is a need to reduce stress and anxiety in animals and create safer and more comfortable interactions. However, it is difficult to understand an animal's emotional state in real time, and there is a risk that customers may misinterpret the animal's condition and cause excessive stress. Improving this situation and optimizing the interaction between animals and people is a challenge.

[0618] 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.

[0619] In this invention, the server includes a device for acquiring biological information of an animal, means for analyzing the acquired biological information and estimating the animal's emotional state, and equipment for visually displaying the estimated emotional state and providing information to the user. This allows the user to understand the animal's emotional state in real time and make appropriate contact.

[0620] A "device for acquiring animal biological information" is a device designed to continuously collect physiological data such as heart rate, body temperature, and movement patterns of animals.

[0621] "Means for estimating emotional states" refer to algorithms and data processing systems that analyze acquired biological information to infer what emotions (joy, excitement, stress, anxiety, etc.) an animal is experiencing.

[0622] A "device for visually presenting and providing information to users" is a device that displays the analyzed emotional state of an animal on a display or mobile device, allowing users to understand it intuitively.

[0623] A "control device for managing animal interactions in commercial facilities" is a device or system that has management functions to optimize the environment settings within the facility and interactions with animals according to the emotional state of the animals.

[0624] "A means of communication for recording acquired biological information and transmitting the information to a designated specialized facility" refers to a communication device that has the function of securely storing the collected data and transferring the data to a specific veterinary medical facility or research institution when necessary.

[0625] This invention is a system for commercial facilities where customers can interact with animals, which helps them understand the emotional state of animals and facilitates appropriate interactions between customers and animals.

[0626] The server receives biometric data such as heart rate, body temperature, and movement information from wearable devices attached to animals within the facility. These wearable devices have the capability to transmit data in real time using Bluetooth or Wi-Fi. The server stores the received biometric data and analyzes the animals' emotional states using machine learning models based on TensorFlow and PyTorch. This analysis makes it possible to classify the animals' emotional states into categories such as joy, excitement, stress, and anxiety.

[0627] The terminal visualizes the analyzed results and provides an interface for facility visitors and staff to view. For example, by installing a dedicated application on a tablet or smartphone, visitors can easily check the current emotional state of the animals in the facility. This information is presented as specific messages such as "The cat is currently relaxed" or "The dog is excited, so caution is needed."

[0628] Based on this information, users can adjust their interactions with animals. For example, if they are notified that an animal is stressed, visitors can temporarily refrain from contacting that animal. Furthermore, if an abnormality is detected in an animal's health, the system automatically sends information to a designated veterinary facility, enabling a quick response.

[0629] As a concrete example, consider the case where a cat named Sakura at an animal cafe exhibits a higher-than-normal heart rate. In this case, based on the analysis results, the server notifies the store manager, "Sakura is a little agitated. Please provide a quiet environment." Based on this notification, the manager can reduce the cat's stress by adjusting the environment, such as changing the background music.

[0630] Possible prompts for using the generative AI model include requests such as, "Please suggest appropriate actions to take if the cat's heart rate exceeds the normal range."

[0631] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0632] Step 1:

[0633] The server receives biometric information from the animal's wearable device. Specifically, it acquires heart rate, body temperature, and activity information using Bluetooth or Wi-Fi. The input for this step is biometric data from the wearable device, and the output is a database on the server where that data is stored.

[0634] Step 2:

[0635] The server uses TensorFlow and PyTorch to analyze collected biometric data and estimate the emotional state of animals. The analysis involves cross-referencing with historical data and employing advanced algorithms. The input is the biometric data stored in step 1, and the output is the estimated emotional state. Specifically, the server supplies data to the analysis model and calculates results smoothly, taking response time into consideration.

[0636] Step 3:

[0637] The terminal visualizes the estimated emotional state results sent from the server and notifies the user. Here, the analysis results are converted into a visual message and displayed on the user's terminal display. The input is the estimated emotional state results from the server, and the output is the information provided on the user interface. The terminal appropriately lays out the message and displays it in an intuitively easy-to-understand format.

[0638] Step 4:

[0639] The user adjusts their interaction with the animal based on emotional state information provided by the device. For example, if the animal shows signs of stress, the user may reduce contact with the animal or adjust the environment. The input for this step is the emotional state information confirmed by the device, and the output is the user's physical actions. Specific actions include changing the music or making the environment around the animal quieter.

[0640] Step 5:

[0641] The server automatically transmits information to a designated veterinary facility if an abnormality in the animal's health is detected. The input is data on health abnormalities based on estimated emotional state and biometric information, and the output is a notification to the veterinary facility. Specifically, it sends abnormal data to a communication system to ensure prompt medical response.

[0642] 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.

[0643] This invention is a system that enables deeper interaction by acquiring biometric information from both animals and users and analyzing their respective emotional states. Animals are fitted with wearable devices that acquire heart rate, body temperature, and movement data. Similarly, users collect biometric information such as heart rate and voice tone using smartwatches or smartphones.

[0644] The server receives this biometric information and uses an emotion engine to estimate the emotional states of the animals and users. The emotion engine analyzes the data using machine learning techniques based on an animal emotion analysis algorithm. The potential for mutual influence between the emotional states of the animals and users is considered, and this relationship is examined, especially in interaction scenarios.

[0645] The analyzed emotional states are transmitted to the terminal as integrated data, combining the emotions of both the animal and the user. The terminal processes this information and provides clear feedback to the user. The information is customized so that the user's emotions help to deepen the understanding of the animal's emotions. For example, if the animal is feeling anxious but the user is calm, the terminal can offer advice on how to improve the situation.

[0646] Users can review this feedback and have the option to improve the environment by operating the control device through their terminal. For example, they can play music to help animals relax or adjust the lighting based on the user's emotions.

[0647] For example, when a user is playing with their pet, if the device detects that the pet is excited and the user is feeling stressed at that time, it will suggest appropriate actions to help both of them relax. These suggestions may include moving to a favorite spot or playing specific music. In this way, mutual understanding between the animal and the user can be promoted, making it possible to build a better relationship.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The user attaches a wearable device to the animal and prepares a smartwatch or smartphone. This allows for the collection of biometric information from both the animal and the user. The connection process is automated via Bluetooth.

[0651] Step 2:

[0652] The server receives biometric information from the animals' wearable devices and biometric information from the users' devices. Animal data includes heart rate, body temperature, and movement information, while user data includes heart rate and voice tone.

[0653] Step 3:

[0654] The server activates an emotion engine to analyze both sets of biometric information individually. The emotion engine uses a machine learning model to estimate the emotional states of both the animal and the user. This process improves accuracy by comparing past and current data.

[0655] Step 4:

[0656] The server analyzes the relationship between the emotional states of the animals and the users and generates integrated emotional data. This data indicates whether the emotions of the animals and users are in harmony or whether improvement is needed.

[0657] Step 5:

[0658] The device receives integrated emotional data and displays feedback through the user interface. This feedback includes a visual representation of the emotional state and action suggestions. For example, a suggestion might be, "Your pet is a little agitated. Let's play some music to help it relax."

[0659] Step 6:

[0660] The user operates the control system based on feedback from their device to make appropriate environmental adjustments. This is achieved through integration with smart devices such as music and lighting. The selected action is executed immediately.

[0661] Step 7:

[0662] The server records all interactions and emotion analysis results in a database to help improve the accuracy of emotion estimation in the future. It also periodically generates reports on the animals' health status and shares them with veterinary facilities as needed.

[0663] (Example 2)

[0664] 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".

[0665] There is a challenge in simultaneously understanding the emotional states of both animals and users and providing feedback based on their interaction. Conventional technologies primarily analyze biometric information of either the animal or the user alone, making it difficult to realize interactions that take into account the mutual influence of their emotions.

[0666] 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.

[0667] In this invention, the server includes a device for acquiring biometric information of animals and users, information processing means including an algorithm for analyzing emotional states based on the acquired biometric information of animals and users, and means for transmitting the analyzed emotional states as integrated data to a terminal and providing feedback to the user. This enables better mutual understanding by providing feedback that takes into account the emotional states of both the animal and the user.

[0668] "Biometric information" refers to data obtained from the body, such as the heart rate, body temperature, movements, and voice tone of animals or users.

[0669] "Information processing means" refers to an algorithm for analyzing collected biometric information and estimating emotional states, and the computer system that executes it.

[0670] "Integrated data" refers to data that summarizes the results of analyzing the emotional states of animals and users, showing how their emotions interact with each other.

[0671] "Means of providing feedback" refers to devices that have the function of presenting users with specific actions or information based on analyzed integrated data.

[0672] "Control means for adjusting the environment" refers to a system that has the function of changing the physical environment according to the feedback received, such as adjusting lighting or music playback.

[0673] The embodiments for carrying out this invention are shown below.

[0674] First, the user attaches a wearable device to the animal and collects biometric information using a smartwatch or smartphone. The animal's wearable device measures data such as heart rate, body temperature, and movement in real time, while the user's smart device also collects data such as heart rate and voice tone. This group of devices uses standard biosensor technology available on the market.

[0675] Next, the server receives this biometric information and analyzes the data using advanced information processing tools. Specific software examples include machine learning libraries and data analysis tools. The emotion analysis algorithm installed on the server analyzes the biometric information and infers the emotional state of the animal and the user. This analysis uses a generative AI model and compares it with past datasets.

[0676] The analyzed emotional state is sent to the device as integrated data, and the device uses this data to provide feedback to the user. The feedback suggests specific actions and is customized, for example, "Your pet is excited. Play their favorite music to help them relax." This feedback is provided to the user through visual or audio output.

[0677] For example, if the system detects that a user is excited while playing with their pet, and the user is also feeling stressed, the device could suggest, "Let's move to another room and take a short break." This would lead to better mutual understanding and improved relationships.

[0678] An example of a prompt message would be, "Please explain the procedure for sentiment analysis based on animal and user biometric data."

[0679] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0680] Step 1:

[0681] The user attaches a wearable device to the animal and prepares their own smartwatch or smartphone. The animal's heart rate, body temperature, and activity data are acquired as input. This data is collected digitally through multiple biosensors and then initially processed.

[0682] Step 2:

[0683] The terminal transmits the collected biometric information to the server. This step includes biometric data of both the animal and the user as input. The data is transmitted securely, and its integrity is verified on the server. The output is the result that the data has successfully reached the server.

[0684] Step 3:

[0685] The server performs emotion analysis via a generative AI model based on the received biometric information. Inputs include the animal's and user's heart rate, body temperature, and movement data. Based on this, the data is fed into the algorithm for analysis. The output is an analysis result indicating the emotional state of the animal and user.

[0686] Step 4:

[0687] The server integrates the analysis results and prepares them for transmission to the terminal. In this step, data on emotional states is input, and an integrated emotional dataset is generated as output. This data is organized to take into account the effects of interactions.

[0688] Step 5:

[0689] The terminal receives integrated data from the server and generates feedback for the user. The input is an integrated dataset, and specific action suggestions are created based on the analysis results. The output is a feedback message presented to the user.

[0690] Step 6:

[0691] The user takes specific actions based on feedback from the device. The input is the feedback message, and the output is the actual action taken. For example, it is possible to play music to relax an animal.

[0692] (Application Example 2)

[0693] 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".

[0694] In environments where animals and humans live together, understanding each other's emotional states is crucial for reducing stress and promoting comfortable interaction. However, conventional technologies have limited means of simultaneously analyzing the emotional states of both animals and humans and providing easily understandable feedback. To address this problem, there is a need for a system that can comprehensively analyze the emotions of both animals and humans and promote mutual understanding.

[0695] 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.

[0696] In this invention, the server includes means for acquiring animal and human biometric information, means including an algorithm that analyzes the acquired biometric information to estimate the emotional states of animals and humans and generate integrated data, and means for displaying the estimated emotional states of animals and humans and providing feedback to the user. This makes it possible to understand the emotional states of animals and humans from a broad perspective and propose appropriate actions, thereby creating a comfortable interaction environment.

[0697] "Animal biometric information" refers to biological or behavioral data such as the animal's heart rate, body temperature, and movement data.

[0698] "Human biometric information" refers to biological or behavioral data such as a person's heart rate, voice tone, and body temperature.

[0699] A "device" refers to an electronic device attached to animals or humans to acquire biometric information.

[0700] A "data processing device" refers to a computing device that analyzes acquired biological information and estimates the emotional states of animals and humans.

[0701] An "algorithm" refers to the procedures and calculation methods that a data processing device uses to estimate the emotional states of animals and humans.

[0702] "Integrated data" refers to the results of an analysis that combines the emotional states of animals and humans.

[0703] "Terminal device" refers to an electronic device that provides users with visual or auditory feedback on the emotional states of animals and humans.

[0704] A "control device" refers to a device that adjusts the environment based on an estimated emotional state.

[0705] "Feedback" refers to information and suggestions provided to users based on their analyzed emotional state.

[0706] The system for realizing this invention collects and analyzes biometric information from animals and humans, and provides feedback based on that data. Animals are fitted with wearable devices that acquire biometric information such as heart rate, body temperature, and movement data. Human biometric information is collected via smartwatches and smartphones. This information is transmitted to a server where data processing takes place.

[0707] The server collects biometric information and analyzes the data using algorithms to estimate the emotional states of animals and humans. The analysis utilizes animal emotion analysis algorithms and leverages machine learning techniques. In this process, the server also considers the interrelationships between animal and human emotional states. The integrated data resulting from the analysis is transmitted to a terminal device, providing users with visual or auditory feedback.

[0708] Users can receive feedback through their devices and adjust the environment by operating the control system based on the estimated emotional state. For example, if a pet is stressed, specific music can be played or the lighting adjusted to encourage relaxation.

[0709] As a concrete example, when a pet and its owner visit a pet supply store, after a few minutes inside, biometric data transmitted from the pet's wearable device and data obtained from the owner's smartwatch reveal that the pet is stressed. The server analyzes this information and suggests via the terminal, "Please use the pet relaxation area." An example of a prompt message used is, "Please consider a method to analyze emotions based on animal and human biometric data and provide optimal feedback in a physical store."

[0710] In this way, the system promotes mutual understanding between animals and humans and helps build better relationships.

[0711] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0712] Step 1:

[0713] The server receives biometric data from animal wearable devices and human smartwatches and smartphones. The input data includes animal heart rate, body temperature, and movement data, as well as human heart rate and voice tone. This data is stored as biometric information necessary for subsequent processing and analysis.

[0714] Step 2:

[0715] The server estimates the emotional states of animals and humans based on the received biometric information. The server uses emotion analysis algorithms and machine learning techniques to process and analyze the data. The output of the analysis converts the emotional states of animals and humans into numerical values ​​and categories.

[0716] Step 3:

[0717] The server integrates estimated animal and human emotional states and generates integrated data that considers the relevance in interactions. It uses the previously generated emotional state data as input and combines it to perform new emotional assessments. The output is a list or matrix of integrated emotional states.

[0718] Step 4:

[0719] The server sends this integrated data to the terminal, which then prepares to provide appropriate feedback to the user. The terminal receives the integrated data and generates visual or auditory feedback for the user. The output may be a feedback message or action suggestion.

[0720] Step 5:

[0721] The user reviews the feedback provided through the device and operates the control unit as needed. Based on the feedback, they perform operations such as adjusting music playback or lighting to optimize the environment. As a result of this series of operations, the environment for both the pet and the owner is optimized.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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."

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] The following is further disclosed regarding the embodiments described above.

[0744] (Claim 1)

[0745] A device for acquiring animal biometric information,

[0746] A data processing device that includes an algorithm for estimating the emotional state of an animal by analyzing acquired biometric information,

[0747] A terminal device for displaying estimated emotional states and providing feedback to the user,

[0748] A system including a control device for adjusting the environment based on an estimated emotional state.

[0749] (Claim 2)

[0750] The system according to claim 1, further comprising a function to operate a control device based on an estimated emotional state according to the user's instructions.

[0751] (Claim 3)

[0752] The system according to claim 1, comprising communication means for storing acquired biological information and sharing it with veterinary medical facilities.

[0753] "Example 1"

[0754] (Claim 1)

[0755] A sensor device for acquiring biological information of animals,

[0756] A data processing device that analyzes acquired biometric information using machine learning technology and classifies the emotional state of animals in real time,

[0757] Information display means for displaying classified emotional states and providing visual feedback to the user,

[0758] A control means for adjusting the environment based on classified emotional states,

[0759] A system that includes communication means for notifying veterinary medical facilities of abnormalities.

[0760] (Claim 2)

[0761] The system according to claim 1, comprising a function to operate control means according to user instructions based on classified emotional states.

[0762] (Claim 3)

[0763] The system according to claim 1, further comprising an anomaly detection means for storing acquired biological information and sharing it with veterinary medical facilities.

[0764] "Application Example 1"

[0765] (Claim 1)

[0766] A device for acquiring biological information of animals,

[0767] A means of analyzing acquired biological information to estimate the emotional state of an animal,

[0768] A device that visually displays estimated emotional states and provides information to users,

[0769] A system including a control device for managing interactions with animals in commercial facilities based on estimated emotional states.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising an operational function for users to adjust their contact with animals within a commercial facility based on their estimated emotional state.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising communication means for recording acquired biometric information and transmitting the information to a designated specialized facility.

[0774] "Example 2 of combining an emotion engine"

[0775] (Claim 1)

[0776] A device for acquiring biometric information of animals and users,

[0777] Information processing means including an algorithm that analyzes emotional states based on acquired animal and user biometric information,

[0778] A means of transmitting the analyzed emotional state as integrated data to the terminal and providing feedback to the user,

[0779] Control means for adjusting the environment based on user feedback,

[0780] It has a function that analyzes situations where the emotions of animals and users mutually influence each other.

[0781] system.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising a function that allows the user to receive feedback based on emotion analysis and to operate environmental adjustment means.

[0784] (Claim 3)

[0785] The system according to claim 1, comprising communication means for storing analyzed biometric information and emotional state data and sharing them with appropriate facilities or specialists.

[0786] "Application example 2 when combining with an emotional engine"

[0787] (Claim 1)

[0788] A device for acquiring animal and human biometric information,

[0789] A data processing device including an algorithm that analyzes acquired biometric information to estimate the emotional states of animals and humans and generates integrated data,

[0790] A terminal device for displaying estimated animal and human emotional states and providing feedback to the user,

[0791] A system including a control device for adjusting the environment based on an estimated emotional state.

[0792] (Claim 2)

[0793] The system according to claim 1, having a feedback function that suggests specific actions to the user based on the estimated emotional states of animals and humans.

[0794] (Claim 3)

[0795] The system according to claim 1, further comprising communication means for storing acquired biometric information and emotion analysis data and sharing them with medical institutions. [Explanation of Symbols]

[0796] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for acquiring animal biometric information, A data processing device that includes an algorithm for estimating the emotional state of an animal by analyzing acquired biometric information, A terminal device for displaying estimated emotional states and providing feedback to the user, A system including a control device for adjusting the environment based on an estimated emotional state.

2. The system according to claim 1, further comprising a function to operate a control device based on the estimated emotional state and according to the user's instructions.

3. The system according to claim 1, further comprising communication means for storing acquired biological information and sharing it with veterinary medical facilities.

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