system

Abstract

We provide the system. [Solution] A wearable device for collecting biometric information, A communication means for transmitting data acquired from the wearable device to a server, A processing means having an algorithm for analyzing received data and detecting anomalies, A notification system that generates an alarm when an anomaly is detected and notifies the user's terminal, A means for analyzing a pet's behavior and emitting training sounds in response to that behavior, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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] Monitoring the health status and behavior of pets is important for pet owners, but it is difficult to continuously observe accurately in real time. Also, when a pet shows abnormal behavior, if there is no immediate means to respond, it may lead to deterioration of the pet's health. Furthermore, there is no system that can immediately execute an appropriate training method according to the pet's behavior. Therefore, a technology that can comprehensively solve these problems is required.

Means for Solving the Problems

[0005] This invention provides a system that uses a wearable device to collect biometric information from pets and a server to analyze that data. The data is transmitted to the server via communication means and, upon receipt, is analyzed by an algorithm on the server. If an abnormality is detected through this analysis, an alarm is generated and a notification is immediately sent to the user's terminal. Furthermore, by incorporating a voice output means that analyzes the pet's behavior and automatically outputs appropriate training voices, rapid response is possible. In addition, the system features health consultations using an AI-enabled dialogue means, promotes information sharing among pet owners through a community function, and provides region-specific information suggestions.

[0006] A "wearable device" is an electronic device that can be attached to a pet in the form of a collar or harness to collect biometric information from the pet.

[0007] "Communication means" refers to the means of transmitting data from a wearable device to a server, and typically utilizes wireless communication technologies such as Wi-Fi or Bluetooth.

[0008] "Processing means" refers to a program or device that has an algorithm for analyzing received data and determining the pet's health condition and behavioral patterns.

[0009] "Notification means" refers to a method of informing the pet owner's device of any abnormalities or warnings that arise based on the analysis results, and this is usually done through a mobile application.

[0010] "Voice output means" refers to a device or program that emits training sounds in response to a pet's behavior, particularly for controlling ambiguous behaviors.

[0011] An "AI-enabled dialogue system" is a dialogue system that utilizes artificial intelligence to receive inquiries about pet health conditions and provide appropriate answers and advice.

[0012] The "community function" is a platform where pet owners can exchange information about their pets and share experiences and advice. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] 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] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0016] In the following embodiments, a 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.

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

[0018] In the following embodiments, a 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, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention is a system for managing the health and behavior of pets, and consists of a wearable device, a server, and a terminal. The wearable device is attached to the pet's collar or harness and measures biometric information and behavioral data in real time. For example, it continuously collects data including heart rate, activity level, and location information.

[0035] The server receives data transmitted from the wearable device and analyzes it using AI algorithms. Based on the analyzed data, it detects abnormalities in the pet's health and behavior. If an abnormality is detected, the server generates an alarm and sends the alert to the device's mobile application.

[0036] The device notifies the pet owner based on information provided by the server. This notification may appear as a pop-up on the device screen, or be presented via sound or vibration. The user receives the notification and, if necessary, can use the AI ​​chatbot for health consultations. The chatbot is designed to provide immediate answers to common questions.

[0037] Users can also monitor their pets' behavior and set up training sounds through this system. The server analyzes the video data transmitted from the AI ​​camera, and if it determines that the pet is barking excessively, it sends a command to play a training sound from the wearable device.

[0038] Furthermore, the device provides users with local pet-related entertainment information and offers. Users can utilize community features within the application to share information and experiences with other pet owners. In this way, the system comprehensively supports pet health management and improves the quality of life for both pet owners and their pets.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires biometric information such as heart rate, body temperature, and activity level in real time through a wearable device attached to the pet's neck. It prepares to periodically send this data to a server.

[0042] Step 2:

[0043] The server receives data transmitted from the wearable device and prepares it for storage in the database. Once the data is received, the AI ​​algorithms within the system automatically begin analysis.

[0044] Step 3:

[0045] Within the server, an AI algorithm analyzes the data to determine if there are any abnormalities in the pet's health. For example, if it detects an activity level exceeding normal levels or a sudden change in heart rate, it flags it as an abnormality.

[0046] Step 4:

[0047] The server generates an alarm when an anomaly is detected. The alarm includes a description of the anomaly and recommended corrective actions. The generated alarm is immediately sent as a notification to the user's terminal.

[0048] Step 5:

[0049] The device receives alarm notifications sent from the server. Users can view these notifications in real time through a mobile application. The notifications display specific anomaly data and recommended actions.

[0050] Step 6:

[0051] Users take action based on the alerts they receive. If necessary, they can use the in-app AI chatbot for additional health consultations regarding their pets and get quick feedback.

[0052] Step 7:

[0053] The server analyzes video data monitored by the AI ​​camera to determine the pet's behavior. If it detects excessive barking or undesirable behavior, it sends appropriate training audio to the wearable device and plays the audio.

[0054] Step 8:

[0055] The device periodically suggests localized entertainment information and promotions related to pets to the user. This information is provided to improve user convenience and enrich life with pets.

[0056] (Example 1)

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

[0058] In modern times, monitoring animal health and behavior is a crucial issue, but conventional systems have struggled to collect detailed biometric information in real time, detect abnormalities, and provide rapid notification to users. Furthermore, there has been a lack of integrated systems that combine behavioral training guidance and region-specific information provision. Therefore, there is a need for a system that enables efficient and comprehensive animal health management.

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

[0060] In this invention, the server includes communication means for transmitting information acquired from a wearable device to a computing device, calculation means for analyzing the received information and detecting anomalies, and notification means for generating a warning when an anomaly is detected and notifying the user's electronic device. This makes it possible to manage the health and behavior of animals in real time and to quickly notify the user in the event of an anomaly.

[0061] A "wearable device" is a device attached to an animal to collect biological parameters.

[0062] "Communication means" refers to the technical function for transmitting information from a wearable device to a computing device.

[0063] The "computation means" refers to a function that analyzes received information and performs calculations to determine the animal's health status and behavioral abnormalities.

[0064] A "notification mechanism" is a function that generates a warning and notifies the user's electronic device when an abnormality is detected.

[0065] A "voice playback means" is a function for playing back voices, including training commands, in response to the animal's behavior.

[0066] This invention is a system for comprehensively managing animal health and behavior, consisting of wearable devices, a server, and terminals. The wearable devices are attached to the animal's neck or body and continuously collect biological parameters. Specifically, it is possible to acquire data such as heart rate, activity level, and location information in real time. This makes it possible to understand the health status and movements of pets in detail.

[0067] The server receives and analyzes data transmitted from wearable devices using communication methods. It utilizes AI algorithms to analyze various data, and if an anomaly is detected, it notifies the user's terminal via a notification system. This enables immediate health management and support for responding to abnormal situations.

[0068] For example, if a dog's activity level drops significantly below normal, the server detects the anomaly and sends a notification to the user's device via the application, such as, "Your pet's activity level is low. Please check." The device can then display this information and warn the user of the necessary action.

[0069] Furthermore, the device is equipped with a voice playback mechanism that analyzes animal behavior and emits training sounds. Based on the analysis of behavioral patterns, appropriate training sounds can be transmitted to the animal through the wearable device.

[0070] Furthermore, the devices provide region-specific information and have collaborative features that allow users to share information with other users. This promotes interaction within the community and allows for the accumulation of information related to animal health management.

[0071] A possible example of a prompt message would be: "Please describe an AI algorithm that detects abnormal patterns based on pet activity data. Please include specific use cases."

[0072] This system aims to improve the quality of life for animals by combining the methods described above.

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

[0074] Step 1:

[0075] The user attaches a wearable device to the animal's neck or body. The wearable device acquires bio-parameters (heart rate, activity level, location information, etc.) in real time. Inputs include data from various sensors, and this data is continuously sampled and stored in internal memory. Specifically, it acquires heart rate every second and calculates the average activity level every five seconds.

[0076] Step 2:

[0077] The wearable device collects data and transmits it to the server via a communication method. The input is biometric data from the wearable device, which is transmitted using a secure protocol. Specifically, data packets are constructed in batch processing once every minute and communicated to the server.

[0078] Step 3:

[0079] The server analyzes data received via communication channels. During this process, it uses AI algorithms to detect abnormalities in health status and behavior. The input consists of transmitted biometric data. Based on this data, an anomaly detection algorithm is executed, outputting an anomaly flag and its details. Specifically, it analyzes data from the past 24 hours and performs threshold determination to detect anomaly patterns.

[0080] Step 4:

[0081] If an anomaly is detected, the server will notify the user's device using a notification mechanism. The input consists of an anomaly flag and detailed information. Based on this, a warning message is generated, and an alert is sent to the user's device as output. Specifically, a notification containing specific information, such as "Anomaly detected: Decreased activity level," is generated and sent to the device using push notification technology.

[0082] Step 5:

[0083] The device displays notifications from the server and reports details of the anomaly to the user. The input is notification data sent from the server, which is displayed on the screen, and the user is alerted with sound and vibration. Specifically, this involves a message appearing in the smartphone's notification bar along with vibration.

[0084] Step 6:

[0085] The user controls their smartphone to monitor the animal's behavior in real time and set up training voice prompts as needed. Input is real-time feedback from the user via the device, and the system is configured to send commands to a wearable device as output. For example, a voice command such as "don't bark" is set within the app and associated with a trigger condition.

[0086] Step 7:

[0087] The device provides local information and displays information to facilitate interaction within the community through its sharing function. Input is local events and related information sent via the server, and output is useful information provided to the user. For example, notifications such as "A pet-related event is being held nearby" will be displayed within the app.

[0088] (Application Example 1)

[0089] 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."

[0090] To protect the health and safety of pets, early detection and rapid response to abnormal behavior are essential. However, current systems only detect and notify of abnormalities, and are insufficient in terms of security when the owner is absent. Furthermore, comprehensive security measures are needed that take into account not only abnormal pet behavior but also the surrounding environment.

[0091] 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.

[0092] In this invention, the server includes a wearable device, communication means, a computing system, and protection means for automatically detecting abnormal behavior and coordinating with external security services. This enables comprehensive security measures based on abnormal pet behavior, even when the owner is absent.

[0093] A "wearable device" is a device that is attached to a subject in order to collect biometric information.

[0094] "Communication means" refers to a method or device for transmitting data from a wearable device to an information processing device.

[0095] A "computational system" is a mechanism that analyzes received data and executes numerical processing and algorithms to detect anomalies.

[0096] "Information transmission means" refers to a method or device for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0097] "Speech generation means" refers to a device or method for outputting speech in response to analyzed behavior.

[0098] "Protective measures" refer to functions or devices that automatically detect abnormal behavior and coordinate with external security services.

[0099] To realize this invention, a system is configured in which a server, terminal, and wearable device function together. The wearable device collects biometric information and transmits the data to the server via communication means. The server analyzes the data using a computational system and detects abnormalities from normal values. If an abnormality is detected, an alarm or notification is sent to the user's information terminal via information transmission means. This notification allows the user to understand the health status of their pet in real time and take necessary measures.

[0100] Furthermore, the server can generate voice commands based on the pet's behavior using voice generation capabilities. This feature facilitates pet training and communication. It also includes protection mechanisms to automatically detect abnormal behavior and integrate with external security services, enabling more comprehensive pet safety management. The system uses AI models such as TENSORFLOW® for anomaly detection and behavioral analysis. The collected data can also be used to improve the accuracy of the generated AI models.

[0101] For example, if a pet suddenly starts exhibiting unusual behavior at night, the server quickly analyzes the behavioral pattern and sends an alert notification to the information terminal stating, "Your pet is moving around unusually. Is there a problem?" An example of a prompt in this situation would be, "Analyze the pet's behavioral data, identify the abnormal behavior, and send an alert." This feature allows users to enhance the safety of their homes while maintaining the health of their pets.

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

[0103] Step 1:

[0104] The wearable device collects biometric information from the pet. Inputs include heart rate, activity level, and location information; this data serves as fundamental information for the next step. The output is a dataset of collected biometric data.

[0105] Step 2:

[0106] The wearable device transmits the collected biometric data set to the server via a communication method. The input is the biometric data from the wearable device, and the output is the data received by the server. This allows the server to monitor the pet's condition in real time.

[0107] Step 3:

[0108] The server analyzes the received biometric data using an AI algorithm. The input is the data received by the server, and the output is the analysis result. The main purpose of the analysis is anomaly detection, and a generative AI model is in operation. In this process, data preprocessing is performed, abnormal behavior is identified according to prompt messages, and safety measures are considered as needed.

[0109] Step 4:

[0110] Based on the analysis results, the server sends alarms and notifications to the user's terminal via a communication system if an anomaly is detected. The input is the analysis results, and the output is notification data. Based on the analysis results, the server generates a warning message such as, "Your pet is moving around unusually. Is there a problem?"

[0111] Step 5:

[0112] The server uses a voice generation system to determine appropriate training voices based on behavioral data and transmits them to the wearable device. The input is the analysis results of the pet's behavioral data, and the output is training voice data. In this process, the system selects a voice to play when the pet performs a specific behavior and automatically issues commands.

[0113] Step 6:

[0114] If the server detects abnormal behavior, it will coordinate with external security services through protective measures. The input is the determination of an anomaly detection, and the output is the protocol for contacting external security. This step ensures that measures are taken to ensure the safety of pets and the living environment even when the user is absent.

[0115] 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.

[0116] This invention is a system for advanced pet health management and behavioral control, comprising a wearable device, a server, a terminal, and an emotion engine that recognizes the user's emotions. The wearable device is attached to a collar or harness to acquire biometric information such as the pet's heart rate, activity level, and location. This data is transmitted to the server in real time via communication means.

[0117] The server processes the received biometric information to detect anomalies. Using AI algorithms, it analyzes the data to detect abnormal behavioral patterns or changes in health status. Detected anomalies are immediately converted into alarms, and notifications are sent to the terminal. These notifications alert the user and prompt appropriate action.

[0118] The device also features an emotion engine that identifies the user's emotional state. This emotion engine analyzes the user's voice data and facial expressions to recognize their emotions. This recognized emotional information is then fed back into pet training and behavior improvement programs, enabling the provision of optimized interaction plans tailored to the user's emotional state.

[0119] For example, if the emotion engine determines that the user is stressed, the system will soften the pet training voice to reduce the burden. Conversely, if the user is relaxed, it will run a more assertive training program.

[0120] Furthermore, the device provides users with localized pet-related entertainment information, promotions, and special offers. Through the device's application, users can utilize features to share information with other pet owners and form communities. In this way, the system provides an integrated health management and entertainment solution that is valuable to both pets and their owners.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The device continuously records biometric information such as heart rate, activity level, and body temperature through a wearable device attached to the pet. It prepares to transmit this data to a server in real time.

[0124] Step 2:

[0125] The server receives biometric data transmitted from the device. After receiving the data, it stores it in a database and begins analysis.

[0126] Step 3:

[0127] The server uses AI algorithms to analyze data and detect anomalies. For example, it identifies and marks sudden changes in exercise volume or heart rate that deviate from normal activity patterns as anomalies.

[0128] Step 4:

[0129] The server generates an alarm based on the detected anomaly and sends a notification to the terminal. The notification includes the details of the anomaly and the recommended next action.

[0130] Step 5:

[0131] The device provides a means to inform the user of received alarm notifications. For example, it may display a pop-up message on the screen and attract the user's attention with sound or vibration.

[0132] Step 6:

[0133] The device analyzes the user's voice and facial expressions using an emotion engine to determine their emotional state. Based on this information, the system adjusts training plans and interactions with the pet.

[0134] Step 7:

[0135] When a user trains their pet, an appropriate training voice is selected based on an emotion engine and played back through a wearable device. This enables effective training tailored to the user's emotional state.

[0136] Step 8:

[0137] The device provides users with localized pet-related information, including local pet events and special offers. Users can share information with other pet owners within the app and utilize community features for further information exchange.

[0138] (Example 2)

[0139] 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".

[0140] In modern times, effectively managing pet health and behavior is crucial for many pet owners. However, traditional methods have limitations in real-time monitoring of pets' conditions and providing appropriate responses. Furthermore, they fail to address needs such as pet training that considers the owner's emotional state and the provision of localized information. Solving these problems and improving the quality of life for both pets and their owners is essential.

[0141] 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.

[0142] In this invention, the server includes a transmission means for transmitting information acquired from a device to a processing unit, an analysis means for analyzing the received information and detecting anomalies, and an emotion recognition means for recognizing the user's emotional state and adjusting the pet's training plan. This enables real-time monitoring of the pet's health and appropriate measures to be taken, as well as optimal pet behavior management tailored to the user's emotions. Furthermore, providing region-specific information further enhances convenience for pet owners.

[0143] "Biometric information" refers to data related to a pet's life activities, such as heart rate, activity level, and location information.

[0144] "Device" refers to the hardware and software used to collect and provide biometric information.

[0145] "Transmission means" refers to communication technologies and protocols used to transmit biometric information from a device to a processing unit or server.

[0146] "Analysis means" refers to technologies and algorithms used to process collected biological information and analyze whether there are any abnormalities or behavioral patterns.

[0147] A "notification method" refers to a method or tool for generating alarms or notifications and informing the user when an anomaly is detected based on the analysis results.

[0148] "Emotion recognition means" refers to technology that determines the user's emotions from their voice and facial expressions, and adjusts the pet's behavior based on that information.

[0149] An "information terminal" refers to a device or platform used by a user to receive and display notifications.

[0150] "Information exchange function" refers to online platforms and tools that allow users to share information with each other.

[0151] "Regionally specific suggested information" refers to pet-related events, services, or promotional information relevant to the user's residential area.

[0152] This invention is a system that comprehensively supports pet health management and behavioral control, and comprises a device, server, information terminal, and emotion recognition engine.

[0153] Users collect biometric information such as heart rate, activity level, and location data through devices attached to their pets. These devices utilize common wearable technologies and sensors and have the capability to acquire data in real time. The acquired data is transmitted to a server using common communication methods such as Bluetooth and Wi-Fi.

[0154] The server analyzes received biometric data using AI algorithms to detect anomalies. By performing data cleansing, detecting anomaly patterns, and conducting statistical analysis as needed, it can gain a detailed understanding of the pet's health and behavioral patterns. If an anomaly is detected, the server immediately generates an alarm and sends a notification to the information terminal.

[0155] The device receives alarm notifications and analyzes the user's voice and facial expression data using an emotion recognition engine. This emotion recognition engine evaluates the user's emotions (e.g., stress or relaxation) and dynamically adjusts the pet's training plan based on the results. In other words, if the user is feeling stressed, the system will gently adjust the voice commands for training.

[0156] Furthermore, the device can improve the quality of life for both pets and users by providing them with localized entertainment and promotional information. Users can also share information with other pet owners through this application, facilitating community building.

[0157] As a concrete example, when the emotion engine evaluates a user to be in a relaxed state, they will be notified of local events they can participate in with their pet on their days off. In this case, the following prompt can be used for the generative AI model: "Please suggest ways for the user to enjoy time with their pet when they are relaxed."

[0158] This configuration enables advanced management of pet health and behavior, while also improving the user experience.

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

[0160] Step 1:

[0161] The user attaches a wearable device to their pet. The device collects biometric information such as heart rate, activity level, and location in real time. This data is transmitted to a server via communication methods such as Bluetooth or Wi-Fi. The input is the pet's biometric information, which the device acquires and transmits. The output is the transfer of data to the server.

[0162] Step 2:

[0163] The server receives biometric data transmitted from a wearable device. This data is then analyzed using an AI algorithm to attempt to detect anomalies. Statistical methods are used in the analysis, and anomalies are identified by comparing the data to normal values. The input is biometric data from the wearable device, and the system analyzes this data to detect anomalies. The output is information regarding the presence or absence of an anomaly.

[0164] Step 3:

[0165] The server immediately generates an alarm if an anomaly is detected. The generated alarm is sent to the terminal as a notification. This process uses an algorithm to create a warning message and quickly notify the user. The input is the result of the anomaly detection, which is used to generate the alarm notification. The output is the alarm notification.

[0166] Step 4:

[0167] The terminal receives alarms transmitted from the server. Simultaneously, it collects user voice and facial expression data and analyzes it using an emotion recognition engine. The emotion engine determines whether the user is stressed or relaxed. The input consists of alarms from the server and the user's voice and facial expression data, and the system performs data calculations to analyze the emotional state. The output is information about the user's emotional state.

[0168] Step 5:

[0169] The device adjusts the pet's training plan based on information about the user's emotional state. Dynamic planning is employed; for example, if the user is stressed, the training voice commands are changed to a softer tone. The input is the user's emotional state information, which is used to optimize the training plan. The output is the adjusted training plan.

[0170] Step 6:

[0171] The device collects and provides users with localized entertainment and promotional information. In addition, it offers features for sharing information with other pet owners, supporting community building. Input consists of the user's geographical information and interests, and localized information is provided based on this. Output is personalized information suggestions for the user.

[0172] (Application Example 2)

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

[0174] In modern pet health management, it is difficult to monitor a pet's biological information in real time and to provide appropriate health management and behavioral control. Furthermore, there is a need to provide owners with dietary plans tailored to their pet's health condition and to respond flexibly to the owner's emotions. However, the challenge lies in the lack of an effective system to comprehensively achieve all of these goals.

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

[0176] In this invention, the server includes a portable device for collecting biometric data, communication means for transmitting information acquired from the portable device to an information processing device, calculation means for analyzing the received information and detecting anomalies, notification means for generating an alarm when an anomaly is detected and notifying the user's information terminal, acoustic means for analyzing the pet's behavior and outputting sound according to the behavior, suggestion means for suggesting a meal plan based on the pet's health data, and adjustment means for identifying the user's emotional state and adjusting the meal plan. This makes it possible to manage the pet's health and behavior in real time and provide the owner with the most appropriate response.

[0177] A "portable device" is a portable device attached to a pet to collect biometric data.

[0178] "Communication means" refers to technical means for transmitting information acquired from a portable device to an information processing device.

[0179] "Computation means" refers to a device or method that executes an algorithm for analyzing received information and detecting anomalies.

[0180] A "notification means" is a technical means for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0181] "Acoustic means" refers to a device or method for analyzing a pet's behavior and outputting sound corresponding to that behavior.

[0182] "Suggestion method" refers to a technical means for suggesting a diet plan to pet owners based on their pet's health data.

[0183] "Adjustment means" refers to a device or method for identifying the user's emotional state and adjusting the meal plan accordingly.

[0184] To implement this invention, a portable device is first attached to the pet to collect biometric data. The portable device acquires the pet's heart rate, activity level, and location information, and transmits this information to an information processing device using a communication means.

[0185] The server, acting as an information processing device, receives data and performs analysis using computing means. If abnormal data or behavioral patterns are detected, a notification means generates an alarm and notifies the user's information terminal. Upon receiving the notification, the user can immediately take appropriate action.

[0186] Furthermore, the server analyzes the pet's health data based on calculations and proposes a meal plan. The proposal system generates an optimal food plan, which is then presented to the user. At this time, an adjustment system identifies the user's emotional state and adjusts the proposal accordingly. Through this process, the pet's most appropriate meal plan is provided.

[0187] For example, if a pet is less active than usual, the server will recommend a low-calorie meal plan. However, if the server determines that the user is stressed, the suggestion will be adjusted to a simpler, more nutritionally balanced meal. Specifically, if there is little data on the dog's activity level and the owner is busy and stressed, the server will suggest a convenient meal option within the "Low-calorie diet" category.

[0188] As an example of a prompt in a generative AI model, it can provide text such as: "Please suggest an optimal meal plan for a pet that is inactive and whose owner is stressed. Please consider nutritional balance and provide a low-maintenance method."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server receives biometric data such as heart rate, activity level, and location information from the pet's portable device. This biometric data serves as input, and the output forms a dataset. During data reception, information is transmitted in real time using wireless communication.

[0192] Step 2:

[0193] The server executes an anomaly detection algorithm using the received biometric data. The input is the dataset from Step 1, and the output is an alarm generated when an anomaly pattern is detected. Data analysis is performed using a machine learning model to extract features for identifying abnormal behavior.

[0194] Step 3:

[0195] When an alarm is generated, the server sends a notification to the user's device. The input is the alarm information generated in step 2, and the output is the notification received on the user's device. This operation is performed quickly using push notification technology.

[0196] Step 4:

[0197] The server runs a food planning algorithm to suggest a meal plan based on the pet's health data. The input includes health data and the previously detected anomalies, and the output is a suggested meal plan. This plan uses an AI model to calculate a diet optimized for the pet's health condition.

[0198] Step 5:

[0199] The device performs emotion analysis to recognize the user's emotional state. Inputs are the user's voice data and facial expression information, and output is the detected emotional state. The emotion engine applies an emotion recognition algorithm to determine the user's stress level and relaxation level.

[0200] Step 6:

[0201] The server adjusts the suggested meal plan based on the user's emotional information. The input is the meal plan from step 4 and the emotional state from step 5, and the output is the adjusted meal plan. The adjustment uses prompts from a generative AI model to optimize the meal content by reflecting the results of the emotional analysis.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention is a system for managing the health and behavior of pets, and consists of a wearable device, a server, and a terminal. The wearable device is attached to the pet's collar or harness and measures biometric information and behavioral data in real time. For example, it continuously collects data including heart rate, activity level, and location information.

[0219] The server receives data transmitted from the wearable device and analyzes it using AI algorithms. Based on the analyzed data, it detects abnormalities in the pet's health and behavior. If an abnormality is detected, the server generates an alarm and sends the alert to the device's mobile application.

[0220] The device notifies the pet owner based on information provided by the server. This notification may appear as a pop-up on the device screen, or be presented via sound or vibration. The user receives the notification and, if necessary, can use the AI ​​chatbot for health consultations. The chatbot is designed to provide immediate answers to common questions.

[0221] Users can also monitor their pets' behavior and set up training sounds through this system. The server analyzes the video data transmitted from the AI ​​camera, and if it determines that the pet is barking excessively, it sends a command to play a training sound from the wearable device.

[0222] Furthermore, the device provides users with local pet-related entertainment information and offers. Users can utilize community features within the application to share information and experiences with other pet owners. In this way, the system comprehensively supports pet health management and improves the quality of life for both pet owners and their pets.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The device acquires biometric information such as heart rate, body temperature, and activity level in real time through a wearable device attached to the pet's neck. It prepares to periodically send this data to a server.

[0226] Step 2:

[0227] The server receives data transmitted from the wearable device and prepares it for storage in the database. Once the data is received, the AI ​​algorithms within the system automatically begin analysis.

[0228] Step 3:

[0229] Within the server, an AI algorithm analyzes the data to determine if there are any abnormalities in the pet's health. For example, if it detects an activity level exceeding normal levels or a sudden change in heart rate, it flags it as an abnormality.

[0230] Step 4:

[0231] The server generates an alarm when an anomaly is detected. The alarm includes a description of the anomaly and recommended corrective actions. The generated alarm is immediately sent as a notification to the user's terminal.

[0232] Step 5:

[0233] The device receives alarm notifications sent from the server. Users can view these notifications in real time through a mobile application. The notifications display specific anomaly data and recommended actions.

[0234] Step 6:

[0235] Users take action based on the alerts they receive. If necessary, they can use the in-app AI chatbot for additional health consultations regarding their pets and get quick feedback.

[0236] Step 7:

[0237] The server analyzes video data monitored by the AI ​​camera to determine the pet's behavior. If it detects excessive barking or undesirable behavior, it sends appropriate training audio to the wearable device and plays the audio.

[0238] Step 8:

[0239] The device periodically suggests localized entertainment information and promotions related to pets to the user. This information is provided to improve user convenience and enrich life with pets.

[0240] (Example 1)

[0241] 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."

[0242] In modern times, monitoring animal health and behavior is a crucial issue, but conventional systems have struggled to collect detailed biometric information in real time, detect abnormalities, and provide rapid notification to users. Furthermore, there has been a lack of integrated systems that combine behavioral training guidance and region-specific information provision. Therefore, there is a need for a system that enables efficient and comprehensive animal health management.

[0243] 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.

[0244] In this invention, the server includes communication means for transmitting information acquired from a wearable device to a computing device, calculation means for analyzing the received information and detecting anomalies, and notification means for generating a warning when an anomaly is detected and notifying the user's electronic device. This makes it possible to manage the health and behavior of animals in real time and to quickly notify the user in the event of an anomaly.

[0245] A "wearable device" is a device attached to an animal to collect biological parameters.

[0246] "Communication means" refers to the technical function for transmitting information from a wearable device to a computing device.

[0247] The "computation means" refers to a function that analyzes received information and performs calculations to determine the animal's health status and behavioral abnormalities.

[0248] A "notification mechanism" is a function that generates a warning and notifies the user's electronic device when an abnormality is detected.

[0249] A "voice playback means" is a function for playing back voices, including training commands, in response to the animal's behavior.

[0250] This invention is a system for comprehensively managing animal health and behavior, consisting of wearable devices, a server, and terminals. The wearable devices are attached to the animal's neck or body and continuously collect biological parameters. Specifically, it is possible to acquire data such as heart rate, activity level, and location information in real time. This makes it possible to understand the health status and movements of pets in detail.

[0251] The server receives and analyzes data transmitted from wearable devices using communication methods. It utilizes AI algorithms to analyze various data, and if an anomaly is detected, it notifies the user's terminal via a notification system. This enables immediate health management and support for responding to abnormal situations.

[0252] For example, if a dog's activity level drops significantly below normal, the server detects the anomaly and sends a notification to the user's device via the application, such as, "Your pet's activity level is low. Please check." The device can then display this information and warn the user of the necessary action.

[0253] Furthermore, the device is equipped with a voice playback mechanism that analyzes animal behavior and emits training sounds. Based on the analysis of behavioral patterns, appropriate training sounds can be transmitted to the animal through the wearable device.

[0254] Furthermore, the devices provide region-specific information and have collaborative features that allow users to share information with other users. This promotes interaction within the community and allows for the accumulation of information related to animal health management.

[0255] A possible example of a prompt message would be: "Please describe an AI algorithm that detects abnormal patterns based on pet activity data. Please include specific use cases."

[0256] This system aims to improve the quality of life for animals by combining the methods described above.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user attaches a wearable device to the animal's neck or body. The wearable device acquires bio-parameters (heart rate, activity level, location information, etc.) in real time. Inputs include data from various sensors, and this data is continuously sampled and stored in internal memory. Specifically, it acquires heart rate every second and calculates the average activity level every five seconds.

[0260] Step 2:

[0261] The wearable device collects data and transmits it to the server via a communication method. The input is biometric data from the wearable device, which is transmitted using a secure protocol. Specifically, data packets are constructed in batch processing once every minute and communicated to the server.

[0262] Step 3:

[0263] The server analyzes data received via communication channels. During this process, it uses AI algorithms to detect abnormalities in health status and behavior. The input consists of transmitted biometric data. Based on this data, an anomaly detection algorithm is executed, outputting an anomaly flag and its details. Specifically, it analyzes data from the past 24 hours and performs threshold determination to detect anomaly patterns.

[0264] Step 4:

[0265] If an anomaly is detected, the server will notify the user's device using a notification mechanism. The input consists of an anomaly flag and detailed information. Based on this, a warning message is generated, and an alert is sent to the user's device as output. Specifically, a notification containing specific information, such as "Anomaly detected: Decreased activity level," is generated and sent to the device using push notification technology.

[0266] Step 5:

[0267] The device displays notifications from the server and reports details of the anomaly to the user. The input is notification data sent from the server, which is displayed on the screen, and the user is alerted with sound and vibration. Specifically, this involves a message appearing in the smartphone's notification bar along with vibration.

[0268] Step 6:

[0269] The user controls their smartphone to monitor the animal's behavior in real time and set up training voice prompts as needed. Input is real-time feedback from the user via the device, and the system is configured to send commands to a wearable device as output. For example, a voice command such as "don't bark" is set within the app and associated with a trigger condition.

[0270] Step 7:

[0271] The device provides local information and displays information to facilitate interaction within the community through its sharing function. Input is local events and related information sent via the server, and output is useful information provided to the user. For example, notifications such as "A pet-related event is being held nearby" will be displayed within the app.

[0272] (Application Example 1)

[0273] 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."

[0274] To protect the health and safety of pets, early detection and rapid response to abnormal behavior are essential. However, current systems only detect and notify of abnormalities, and are insufficient in terms of security when the owner is absent. Furthermore, comprehensive security measures are needed that take into account not only abnormal pet behavior but also the surrounding environment.

[0275] 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.

[0276] In this invention, the server includes a wearable device, communication means, a computing system, and protection means for automatically detecting abnormal behavior and coordinating with external security services. This enables comprehensive security measures based on abnormal pet behavior, even when the owner is absent.

[0277] A "wearable device" is a device that is attached to a subject in order to collect biometric information.

[0278] "Communication means" refers to a method or device for transmitting data from a wearable device to an information processing device.

[0279] A "computational system" is a mechanism that analyzes received data and executes numerical processing and algorithms to detect anomalies.

[0280] "Information transmission means" refers to a method or device for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0281] "Speech generation means" refers to a device or method for outputting speech in response to analyzed behavior.

[0282] The "protection means" is a function or device that automatically senses abnormal behavior and cooperates with external security services.

[0283] To implement this invention, a system is configured in which a server, a terminal, and wearable devices function together. The wearable device collects biometric information and transmits data to the server through communication means. The server analyzes the data using a calculation system and detects abnormalities from normal values. At that time, if it is determined that there is an abnormality, an alarm or notification is transmitted to the user's information terminal through information transmission means. Through this notification, the user can grasp the health status of the pet in real time and take necessary measures.

[0284] Furthermore, the server can generate voice instructions based on the pet's behavior using voice generation means. This function promotes the training and communication of the pet. It also has protection means for automatically sensing abnormal behavior and cooperating with external security services, enabling more comprehensive safety management of the pet. In this system, an AI model such as TensorFlow is used for anomaly detection and behavior analysis. Also, the accuracy of the generated AI model can be improved using the collected data.

[0285] As a specific example, when the pet suddenly starts abnormal behavior at night, the server quickly analyzes the behavior pattern and sends an alarm notification to the information terminal saying "The pet is moving around abnormally. Is there a problem?" An example of a prompt sentence for this situation is "Analyze the pet's behavior data, identify abnormal behavior, and send an alert." This function enables the user to enhance the safety of their home while maintaining the health of the pet.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The wearable device collects biometric information from the pet. Inputs include heart rate, activity level, and location information; this data serves as fundamental information for the next step. The output is a dataset of collected biometric data.

[0289] Step 2:

[0290] The wearable device transmits the collected biometric data set to the server via a communication method. The input is the biometric data from the wearable device, and the output is the data received by the server. This allows the server to monitor the pet's condition in real time.

[0291] Step 3:

[0292] The server analyzes the received biometric data using an AI algorithm. The input is the data received by the server, and the output is the analysis result. The main purpose of the analysis is anomaly detection, and a generative AI model is in operation. In this process, data preprocessing is performed, abnormal behavior is identified according to prompt messages, and safety measures are considered as needed.

[0293] Step 4:

[0294] Based on the analysis results, the server sends alarms and notifications to the user's terminal via a communication system if an anomaly is detected. The input is the analysis results, and the output is notification data. Based on the analysis results, the server generates a warning message such as, "Your pet is moving around unusually. Is there a problem?"

[0295] Step 5:

[0296] The server uses a voice generation system to determine appropriate training voices based on behavioral data and transmits them to the wearable device. The input is the analysis results of the pet's behavioral data, and the output is training voice data. In this process, the system selects a voice to play when the pet performs a specific behavior and automatically issues commands.

[0297] Step 6:

[0298] If the server detects abnormal behavior, it will coordinate with external security services through protective measures. The input is the determination of an anomaly detection, and the output is the protocol for contacting external security. This step ensures that measures are taken to ensure the safety of pets and the living environment even when the user is absent.

[0299] 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.

[0300] This invention is a system for advanced pet health management and behavioral control, comprising a wearable device, a server, a terminal, and an emotion engine that recognizes the user's emotions. The wearable device is attached to a collar or harness to acquire biometric information such as the pet's heart rate, activity level, and location. This data is transmitted to the server in real time via communication means.

[0301] The server processes the received biometric information to detect anomalies. Using AI algorithms, it analyzes the data to detect abnormal behavioral patterns or changes in health status. Detected anomalies are immediately converted into alarms, and notifications are sent to the terminal. These notifications alert the user and prompt appropriate action.

[0302] The device also features an emotion engine that identifies the user's emotional state. This emotion engine analyzes the user's voice data and facial expressions to recognize their emotions. This recognized emotional information is then fed back into pet training and behavior improvement programs, enabling the provision of optimized interaction plans tailored to the user's emotional state.

[0303] For example, if the emotion engine determines that the user is feeling stressed, the system gently changes the pet training voice and encourages the user to reduce the burden. Conversely, if the user is relaxed, a more proactive training program is executed.

[0304] Furthermore, the terminal provides the user with region-limited entertainment information related to the pet, as well as promotions and privilege proposals. The user can share information with other pet owners through the terminal application and utilize the function to form a community. In this way, the system provides an integrated health management and entertainment solution that is valuable to both the pet and the owner.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The device continuously records biometric information such as heart rate, activity status, and body temperature through a wearable device worn on the pet. It prepares to transmit this data to the server in real time.

[0308] Step 2:

[0309] The server receives the biometric information data transmitted from the device. After receiving it, it saves the data in the database and starts analysis.

[0310] Step 3:

[0311] The server uses an AI algorithm to analyze the data and detect abnormalities. For example, it identifies a deviation in the amount of exercise or a sudden change in heart rate from the normal activity pattern and marks it as an abnormality.

[0312] Step 4:

[0313] The server generates an alarm based on the detected abnormality and sends a notification to the terminal. The notification includes the details of the abnormality and the recommended next action.

[0314] Step 5:

[0315] The device provides a means to inform the user of received alarm notifications. For example, it may display a pop-up message on the screen and attract the user's attention with sound or vibration.

[0316] Step 6:

[0317] The device analyzes the user's voice and facial expressions using an emotion engine to determine their emotional state. Based on this information, the system adjusts training plans and interactions with the pet.

[0318] Step 7:

[0319] When a user trains their pet, an appropriate training voice is selected based on an emotion engine and played back through a wearable device. This enables effective training tailored to the user's emotional state.

[0320] Step 8:

[0321] The device provides users with localized pet-related information, including local pet events and special offers. Users can share information with other pet owners within the app and utilize community features for further information exchange.

[0322] (Example 2)

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

[0324] In modern times, effectively managing pet health and behavior is crucial for many pet owners. However, traditional methods have limitations in real-time monitoring of pets' conditions and providing appropriate responses. Furthermore, they fail to address needs such as pet training that considers the owner's emotional state and the provision of localized information. Solving these problems and improving the quality of life for both pets and their owners is essential.

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

[0326] In this invention, the server includes a transmission means for transmitting information acquired from a device to a processing unit, an analysis means for analyzing the received information and detecting anomalies, and an emotion recognition means for recognizing the user's emotional state and adjusting the pet's training plan. This enables real-time monitoring of the pet's health and appropriate measures to be taken, as well as optimal pet behavior management tailored to the user's emotions. Furthermore, providing region-specific information further enhances convenience for pet owners.

[0327] "Biometric information" refers to data related to a pet's life activities, such as heart rate, activity level, and location information.

[0328] "Device" refers to the hardware and software used to collect and provide biometric information.

[0329] "Transmission means" refers to communication technologies and protocols used to transmit biometric information from a device to a processing unit or server.

[0330] "Analysis means" refers to technologies and algorithms used to process collected biological information and analyze whether there are any abnormalities or behavioral patterns.

[0331] A "notification method" refers to a method or tool for generating alarms or notifications and informing the user when an anomaly is detected based on the analysis results.

[0332] "Emotion recognition means" refers to technology that determines the user's emotions from their voice and facial expressions, and adjusts the pet's behavior based on that information.

[0333] An "information terminal" refers to a device or platform used by a user to receive and display notifications.

[0334] "Information exchange function" refers to online platforms and tools that allow users to share information with each other.

[0335] "Regionally specific suggested information" refers to pet-related events, services, or promotional information relevant to the user's residential area.

[0336] This invention is a system that comprehensively supports pet health management and behavioral control, and comprises a device, server, information terminal, and emotion recognition engine.

[0337] Users collect biometric information such as heart rate, activity level, and location data through devices attached to their pets. These devices utilize common wearable technologies and sensors and have the capability to acquire data in real time. The acquired data is transmitted to a server using common communication methods such as Bluetooth and Wi-Fi.

[0338] The server analyzes received biometric data using AI algorithms to detect anomalies. By performing data cleansing, detecting anomaly patterns, and conducting statistical analysis as needed, it can gain a detailed understanding of the pet's health and behavioral patterns. If an anomaly is detected, the server immediately generates an alarm and sends a notification to the information terminal.

[0339] The device receives alarm notifications and analyzes the user's voice and facial expression data using an emotion recognition engine. This emotion recognition engine evaluates the user's emotions (e.g., stress or relaxation) and dynamically adjusts the pet's training plan based on the results. In other words, if the user is feeling stressed, the system will gently adjust the voice commands for training.

[0340] Furthermore, the device can improve the quality of life for both pets and users by providing them with localized entertainment and promotional information. Users can also share information with other pet owners through this application, facilitating community building.

[0341] As a concrete example, when the emotion engine evaluates a user to be in a relaxed state, they will be notified of local events they can participate in with their pet on their days off. In this case, the following prompt can be used for the generative AI model: "Please suggest ways for the user to enjoy time with their pet when they are relaxed."

[0342] This configuration enables advanced management of pet health and behavior, while also improving the user experience.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] The user attaches a wearable device to their pet. The device collects biometric information such as heart rate, activity level, and location in real time. This data is transmitted to a server via communication methods such as Bluetooth or Wi-Fi. The input is the pet's biometric information, which the device acquires and transmits. The output is the transfer of data to the server.

[0346] Step 2:

[0347] The server receives biometric data transmitted from a wearable device. This data is then analyzed using an AI algorithm to attempt to detect anomalies. Statistical methods are used in the analysis, and anomalies are identified by comparing the data to normal values. The input is biometric data from the wearable device, and the system analyzes this data to detect anomalies. The output is information regarding the presence or absence of an anomaly.

[0348] Step 3:

[0349] The server immediately generates an alarm if an anomaly is detected. The generated alarm is sent to the terminal as a notification. This process uses an algorithm to create a warning message and quickly notify the user. The input is the result of the anomaly detection, which is used to generate the alarm notification. The output is the alarm notification.

[0350] Step 4:

[0351] The terminal receives alarms transmitted from the server. Simultaneously, it collects user voice and facial expression data and analyzes it using an emotion recognition engine. The emotion engine determines whether the user is stressed or relaxed. The input consists of alarms from the server and the user's voice and facial expression data, and the system performs data calculations to analyze the emotional state. The output is information about the user's emotional state.

[0352] Step 5:

[0353] The device adjusts the pet's training plan based on information about the user's emotional state. Dynamic planning is employed; for example, if the user is stressed, the training voice commands are changed to a softer tone. The input is the user's emotional state information, which is used to optimize the training plan. The output is the adjusted training plan.

[0354] Step 6:

[0355] The device collects and provides users with localized entertainment and promotional information. In addition, it offers features for sharing information with other pet owners, supporting community building. Input consists of the user's geographical information and interests, and localized information is provided based on this. Output is personalized information suggestions for the user.

[0356] (Application Example 2)

[0357] 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."

[0358] In modern pet health management, it is difficult to monitor a pet's biological information in real time and to provide appropriate health management and behavioral control. Furthermore, there is a need to provide owners with dietary plans tailored to their pet's health condition and to respond flexibly to the owner's emotions. However, the challenge lies in the lack of an effective system to comprehensively achieve all of these goals.

[0359] 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.

[0360] In this invention, the server includes a portable device for collecting biometric data, communication means for transmitting information acquired from the portable device to an information processing device, calculation means for analyzing the received information and detecting anomalies, notification means for generating an alarm when an anomaly is detected and notifying the user's information terminal, acoustic means for analyzing the pet's behavior and outputting sound according to the behavior, suggestion means for suggesting a meal plan based on the pet's health data, and adjustment means for identifying the user's emotional state and adjusting the meal plan. This makes it possible to manage the pet's health and behavior in real time and provide the owner with the most appropriate response.

[0361] A "portable device" is a portable device attached to a pet to collect biometric data.

[0362] "Communication means" refers to technical means for transmitting information acquired from a portable device to an information processing device.

[0363] "Computation means" refers to a device or method that executes an algorithm for analyzing received information and detecting anomalies.

[0364] A "notification means" is a technical means for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0365] "Acoustic means" refers to a device or method for analyzing a pet's behavior and outputting sound corresponding to that behavior.

[0366] "Suggestion method" refers to a technical means for suggesting a diet plan to pet owners based on their pet's health data.

[0367] "Adjustment means" refers to a device or method for identifying the user's emotional state and adjusting the meal plan accordingly.

[0368] To implement this invention, first, a portable device is attached to the pet to collect biometric data. The portable device acquires the pet's heart rate, activity level, and location information, and transmits it to an information processing device using a communication means.

[0369] The server, acting as an information processing device, receives data and performs analysis using computing means. If abnormal data or behavioral patterns are detected, a notification means generates an alarm and notifies the user's information terminal. Upon receiving the notification, the user can immediately take appropriate action.

[0370] Furthermore, the server analyzes the pet's health data based on calculations and proposes a meal plan. The proposal system generates an optimal food plan, which is then presented to the user. At this time, an adjustment system identifies the user's emotional state and adjusts the proposal accordingly. Through this process, the pet's most appropriate meal plan is provided.

[0371] For example, if a pet is less active than usual, the server will recommend a low-calorie meal plan. However, if the server determines that the user is stressed, the suggestion will be adjusted to a simpler, more nutritionally balanced meal. Specifically, if there is little data on the dog's activity level and the owner is busy and stressed, the server will suggest a convenient meal option within the "Low-calorie diet" category.

[0372] As an example of a prompt in a generative AI model, it can provide text such as: "Please suggest an optimal meal plan for a pet that is inactive and whose owner is stressed. Please consider nutritional balance and provide a low-maintenance method."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server receives biometric data such as heart rate, activity level, and location information from the pet's portable device. This biometric data serves as input, and the output forms a dataset. During data reception, information is transmitted in real time using wireless communication.

[0376] Step 2:

[0377] The server executes an anomaly detection algorithm using the received biometric data. The input is the dataset from Step 1, and the output is an alarm generated when an anomaly pattern is detected. Data analysis is performed using a machine learning model to extract features for identifying abnormal behavior.

[0378] Step 3:

[0379] When an alarm is generated, the server sends a notification to the user's device. The input is the alarm information generated in step 2, and the output is the notification received on the user's device. This operation is performed quickly using push notification technology.

[0380] Step 4:

[0381] The server runs a food planning algorithm to suggest a meal plan based on the pet's health data. The input includes health data and the previously detected anomalies, and the output is a suggested meal plan. This plan uses an AI model to calculate a diet optimized for the pet's health condition.

[0382] Step 5:

[0383] The device performs emotion analysis to recognize the user's emotional state. Inputs are the user's voice data and facial expression information, and output is the detected emotional state. The emotion engine applies an emotion recognition algorithm to determine the user's stress level and relaxation level.

[0384] Step 6:

[0385] The server adjusts the suggested meal plan based on the user's emotional information. The input is the meal plan from step 4 and the emotional state from step 5, and the output is the adjusted meal plan. The adjustment uses prompts from a generative AI model to optimize the meal content by reflecting the results of the emotional analysis.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] This invention is a system for managing the health and behavior of pets, and consists of a wearable device, a server, and a terminal. The wearable device is attached to the pet's collar or harness and measures biometric information and behavioral data in real time. For example, it continuously collects data including heart rate, activity level, and location information.

[0403] The server receives data transmitted from the wearable device and analyzes it using AI algorithms. Based on the analyzed data, it detects abnormalities in the pet's health and behavior. If an abnormality is detected, the server generates an alarm and sends the alert to the device's mobile application.

[0404] The device notifies the pet owner based on information provided by the server. This notification may appear as a pop-up on the device screen, or be presented via sound or vibration. The user receives the notification and, if necessary, can use the AI ​​chatbot for health consultations. The chatbot is designed to provide immediate answers to common questions.

[0405] Users can also monitor their pets' behavior and set up training sounds through this system. The server analyzes the video data transmitted from the AI ​​camera, and if it determines that the pet is barking excessively, it sends a command to play a training sound from the wearable device.

[0406] Furthermore, the device provides users with local pet-related entertainment information and offers. Users can utilize community features within the application to share information and experiences with other pet owners. In this way, the system comprehensively supports pet health management and improves the quality of life for both pet owners and their pets.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The device acquires biometric information such as heart rate, body temperature, and activity level in real time through a wearable device attached to the pet's neck. It prepares to periodically send this data to a server.

[0410] Step 2:

[0411] The server receives data transmitted from the wearable device and prepares it for storage in the database. Once the data is received, the AI ​​algorithms within the system automatically begin analysis.

[0412] Step 3:

[0413] Within the server, an AI algorithm analyzes the data to determine if there are any abnormalities in the pet's health. For example, if it detects an activity level exceeding normal levels or a sudden change in heart rate, it flags it as an abnormality.

[0414] Step 4:

[0415] The server generates an alarm when an anomaly is detected. The alarm includes a description of the anomaly and recommended corrective actions. The generated alarm is immediately sent as a notification to the user's terminal.

[0416] Step 5:

[0417] The device receives alarm notifications sent from the server. Users can view these notifications in real time through a mobile application. The notifications display specific anomaly data and recommended actions.

[0418] Step 6:

[0419] Users take action based on the alerts they receive. If necessary, they can use the in-app AI chatbot for additional health consultations regarding their pets and get quick feedback.

[0420] Step 7:

[0421] The server analyzes video data monitored by the AI ​​camera to determine the pet's behavior. If it detects excessive barking or undesirable behavior, it sends appropriate training audio to the wearable device and plays the audio.

[0422] Step 8:

[0423] The device periodically suggests localized entertainment information and promotions related to pets to the user. This information is provided to improve user convenience and enrich life with pets.

[0424] (Example 1)

[0425] 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."

[0426] In modern times, monitoring animal health and behavior is a crucial issue, but conventional systems have struggled to collect detailed biometric information in real time, detect abnormalities, and provide rapid notification to users. Furthermore, there has been a lack of integrated systems that combine behavioral training guidance and region-specific information provision. Therefore, there is a need for a system that enables efficient and comprehensive animal health management.

[0427] 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.

[0428] In this invention, the server includes communication means for transmitting information acquired from a wearable device to a computing device, calculation means for analyzing the received information and detecting anomalies, and notification means for generating a warning when an anomaly is detected and notifying the user's electronic device. This makes it possible to manage the health and behavior of animals in real time and to quickly notify the user in the event of an anomaly.

[0429] A "wearable device" is a device attached to an animal to collect biological parameters.

[0430] "Communication means" refers to the technical function for transmitting information from a wearable device to a computing device.

[0431] The "computation means" refers to a function that analyzes received information and performs calculations to determine the animal's health status and behavioral abnormalities.

[0432] A "notification mechanism" is a function that generates a warning and notifies the user's electronic device when an abnormality is detected.

[0433] A "voice playback means" is a function for playing back voices, including training commands, in response to the animal's behavior.

[0434] This invention is a system for comprehensively managing animal health and behavior, consisting of wearable devices, a server, and terminals. The wearable devices are attached to the animal's neck or body and continuously collect biological parameters. Specifically, it is possible to acquire data such as heart rate, activity level, and location information in real time. This makes it possible to understand the health status and movements of pets in detail.

[0435] The server receives and analyzes data transmitted from wearable devices using communication methods. It utilizes AI algorithms to analyze various data, and if an anomaly is detected, it notifies the user's terminal via a notification system. This enables immediate health management and support for responding to abnormal situations.

[0436] For example, if a dog's activity level drops significantly below normal, the server detects the anomaly and sends a notification to the user's device via the application, such as, "Your pet's activity level is low. Please check." The device can then display this information and warn the user of the necessary action.

[0437] Furthermore, the device is equipped with a voice playback mechanism that analyzes animal behavior and emits training sounds. Based on the analysis of behavioral patterns, appropriate training sounds can be transmitted to the animal through the wearable device.

[0438] Furthermore, the devices provide region-specific information and have collaborative features that allow users to share information with other users. This promotes interaction within the community and allows for the accumulation of information related to animal health management.

[0439] A possible example of a prompt message would be: "Please describe an AI algorithm that detects abnormal patterns based on pet activity data. Please include specific use cases."

[0440] This system aims to improve the quality of life for animals by combining the methods described above.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The user attaches a wearable device to the animal's neck or body. The wearable device acquires bio-parameters (heart rate, activity level, location information, etc.) in real time. Inputs include data from various sensors, and this data is continuously sampled and stored in internal memory. Specifically, it acquires heart rate every second and calculates the average activity level every five seconds.

[0444] Step 2:

[0445] The wearable device collects data and transmits it to the server via a communication method. The input is biometric data from the wearable device, which is transmitted using a secure protocol. Specifically, data packets are constructed in batch processing once every minute and communicated to the server.

[0446] Step 3:

[0447] The server analyzes data received via communication channels. During this process, it uses AI algorithms to detect abnormalities in health status and behavior. The input consists of transmitted biometric data. Based on this data, an anomaly detection algorithm is executed, outputting an anomaly flag and its details. Specifically, it analyzes data from the past 24 hours and performs threshold determination to detect anomaly patterns.

[0448] Step 4:

[0449] If an anomaly is detected, the server will notify the user's device using a notification mechanism. The input consists of an anomaly flag and detailed information. Based on this, a warning message is generated, and an alert is sent to the user's device as output. Specifically, a notification containing specific information, such as "Anomaly detected: Decreased activity level," is generated and sent to the device using push notification technology.

[0450] Step 5:

[0451] The device displays notifications from the server and reports details of the anomaly to the user. The input is notification data sent from the server, which is displayed on the screen, and the user is alerted with sound and vibration. Specifically, this involves a message appearing in the smartphone's notification bar along with vibration.

[0452] Step 6:

[0453] The user controls their smartphone to monitor the animal's behavior in real time and set up training voice prompts as needed. Input is real-time feedback from the user via the device, and the system is configured to send commands to a wearable device as output. For example, a voice command such as "don't bark" is set within the app and associated with a trigger condition.

[0454] Step 7:

[0455] The device provides local information and displays information to facilitate interaction within the community through its sharing function. Input is local events and related information sent via the server, and output is useful information provided to the user. For example, notifications such as "A pet-related event is being held nearby" will be displayed within the app.

[0456] (Application Example 1)

[0457] 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."

[0458] To protect the health and safety of pets, early detection and rapid response to abnormal behavior are essential. However, current systems only detect and notify of abnormalities, and are insufficient in terms of security when the owner is absent. Furthermore, comprehensive security measures are needed that take into account not only abnormal pet behavior but also the surrounding environment.

[0459] 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.

[0460] In this invention, the server includes a wearable device, communication means, a computing system, and protection means for automatically detecting abnormal behavior and coordinating with external security services. This enables comprehensive security measures based on abnormal pet behavior, even when the owner is absent.

[0461] A "wearable device" is a device that is attached to a subject in order to collect biometric information.

[0462] "Communication means" refers to a method or device for transmitting data from a wearable device to an information processing device.

[0463] A "computational system" is a mechanism that analyzes received data and executes numerical processing and algorithms to detect anomalies.

[0464] "Information transmission means" refers to a method or device for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0465] "Speech generation means" refers to a device or method for outputting speech in response to analyzed behavior.

[0466] "Protective measures" refer to functions or devices that automatically detect abnormal behavior and coordinate with external security services.

[0467] To realize this invention, a system is configured in which a server, terminal, and wearable device function together. The wearable device collects biometric information and transmits the data to the server via communication means. The server analyzes the data using a computational system and detects abnormalities from normal values. If an abnormality is detected, an alarm or notification is sent to the user's information terminal via information transmission means. This notification allows the user to understand the health status of their pet in real time and take necessary measures.

[0468] Furthermore, the server can generate voice commands based on the pet's behavior using voice generation capabilities. This feature facilitates pet training and communication. It also includes protection mechanisms to automatically detect abnormal behavior and integrate with external security services, enabling more comprehensive pet safety management. The system uses AI models such as TensorFlow for anomaly detection and behavioral analysis. The collected data can also be used to improve the accuracy of the generated AI models.

[0469] For example, if a pet suddenly starts exhibiting unusual behavior at night, the server quickly analyzes the behavioral pattern and sends an alert notification to the information terminal stating, "Your pet is moving around unusually. Is there a problem?" An example of a prompt in this situation would be, "Analyze the pet's behavioral data, identify the abnormal behavior, and send an alert." This feature allows users to enhance the safety of their homes while maintaining the health of their pets.

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The wearable device collects biometric information from the pet. Inputs include heart rate, activity level, and location information; this data serves as fundamental information for the next step. The output is a dataset of collected biometric data.

[0473] Step 2:

[0474] The wearable device transmits the collected biometric data set to the server via a communication method. The input is the biometric data from the wearable device, and the output is the data received by the server. This allows the server to monitor the pet's condition in real time.

[0475] Step 3:

[0476] The server analyzes the received biometric data using an AI algorithm. The input is the data received by the server, and the output is the analysis result. The main purpose of the analysis is anomaly detection, and a generative AI model is in operation. In this process, data preprocessing is performed, abnormal behavior is identified according to prompt messages, and safety measures are considered as needed.

[0477] Step 4:

[0478] Based on the analysis results, the server sends alarms and notifications to the user's terminal via a communication system if an anomaly is detected. The input is the analysis results, and the output is notification data. Based on the analysis results, the server generates a warning message such as, "Your pet is moving around unusually. Is there a problem?"

[0479] Step 5:

[0480] The server uses a voice generation system to determine appropriate training voices based on behavioral data and transmits them to the wearable device. The input is the analysis results of the pet's behavioral data, and the output is training voice data. In this process, the system selects a voice to play when the pet performs a specific behavior and automatically issues commands.

[0481] Step 6:

[0482] If the server detects abnormal behavior, it will coordinate with external security services through protective measures. The input is the determination of an anomaly detection, and the output is the protocol for contacting external security. This step ensures that measures are taken to ensure the safety of pets and the living environment even when the user is absent.

[0483] 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.

[0484] This invention is a system for advanced pet health management and behavioral control, comprising a wearable device, a server, a terminal, and an emotion engine that recognizes the user's emotions. The wearable device is attached to a collar or harness to acquire biometric information such as the pet's heart rate, activity level, and location. This data is transmitted to the server in real time via communication means.

[0485] The server processes the received biometric information to detect anomalies. Using AI algorithms, it analyzes the data to detect abnormal behavioral patterns or changes in health status. Detected anomalies are immediately converted into alarms, and notifications are sent to the terminal. These notifications alert the user and prompt appropriate action.

[0486] The device also features an emotion engine that identifies the user's emotional state. This emotion engine analyzes the user's voice data and facial expressions to recognize their emotions. This recognized emotional information is then fed back into pet training and behavior improvement programs, enabling the provision of optimized interaction plans tailored to the user's emotional state.

[0487] For example, if the emotion engine determines that the user is stressed, the system will soften the pet training voice to reduce the burden. Conversely, if the user is relaxed, it will run a more assertive training program.

[0488] Furthermore, the device provides users with localized pet-related entertainment information, promotions, and special offers. Through the device's application, users can utilize features to share information with other pet owners and form communities. In this way, the system provides an integrated health management and entertainment solution that is valuable to both pets and their owners.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The device continuously records biometric information such as heart rate, activity level, and body temperature through a wearable device attached to the pet. It prepares to transmit this data to a server in real time.

[0492] Step 2:

[0493] The server receives biometric data transmitted from the device. After receiving the data, it stores it in a database and begins analysis.

[0494] Step 3:

[0495] The server uses AI algorithms to analyze data and detect anomalies. For example, it identifies and marks sudden changes in exercise volume or heart rate that deviate from normal activity patterns as anomalies.

[0496] Step 4:

[0497] The server generates an alarm based on the detected anomaly and sends a notification to the terminal. The notification includes the details of the anomaly and the recommended next action.

[0498] Step 5:

[0499] The device provides a means to inform the user of received alarm notifications. For example, it may display a pop-up message on the screen and attract the user's attention with sound or vibration.

[0500] Step 6:

[0501] The device analyzes the user's voice and facial expressions using an emotion engine to determine their emotional state. Based on this information, the system adjusts training plans and interactions with the pet.

[0502] Step 7:

[0503] When a user trains their pet, an appropriate training voice is selected based on an emotion engine and played back through a wearable device. This enables effective training tailored to the user's emotional state.

[0504] Step 8:

[0505] The device provides users with localized pet-related information, including local pet events and special offers. Users can share information with other pet owners within the app and utilize community features for further information exchange.

[0506] (Example 2)

[0507] 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."

[0508] In modern times, effectively managing pet health and behavior is crucial for many pet owners. However, traditional methods have limitations in real-time monitoring of pets' conditions and providing appropriate responses. Furthermore, they fail to address needs such as pet training that considers the owner's emotional state and the provision of localized information. Solving these problems and improving the quality of life for both pets and their owners is essential.

[0509] 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.

[0510] In this invention, the server includes a transmission means for transmitting information acquired from a device to a processing unit, an analysis means for analyzing the received information and detecting anomalies, and an emotion recognition means for recognizing the user's emotional state and adjusting the pet's training plan. This enables real-time monitoring of the pet's health and appropriate measures to be taken, as well as optimal pet behavior management tailored to the user's emotions. Furthermore, providing region-specific information further enhances convenience for pet owners.

[0511] "Biometric information" refers to data related to a pet's life activities, such as heart rate, activity level, and location information.

[0512] "Device" refers to the hardware and software used to collect and provide biometric information.

[0513] "Transmission means" refers to communication technologies and protocols used to transmit biometric information from a device to a processing unit or server.

[0514] "Analysis means" refers to technologies and algorithms used to process collected biological information and analyze whether there are any abnormalities or behavioral patterns.

[0515] A "notification method" refers to a method or tool for generating alarms or notifications and informing the user when an anomaly is detected based on the analysis results.

[0516] "Emotion recognition means" refers to technology that determines the user's emotions from their voice and facial expressions, and adjusts the pet's behavior based on that information.

[0517] An "information terminal" refers to a device or platform used by a user to receive and display notifications.

[0518] "Information exchange function" refers to online platforms and tools that allow users to share information with each other.

[0519] "Regionally specific suggested information" refers to pet-related events, services, or promotional information relevant to the user's residential area.

[0520] This invention is a system that comprehensively supports pet health management and behavioral control, and comprises a device, server, information terminal, and emotion recognition engine.

[0521] Users collect biometric information such as heart rate, activity level, and location data through devices attached to their pets. These devices utilize common wearable technologies and sensors and have the capability to acquire data in real time. The acquired data is transmitted to a server using common communication methods such as Bluetooth and Wi-Fi.

[0522] The server analyzes received biometric data using AI algorithms to detect anomalies. By performing data cleansing, detecting anomaly patterns, and conducting statistical analysis as needed, it can gain a detailed understanding of the pet's health and behavioral patterns. If an anomaly is detected, the server immediately generates an alarm and sends a notification to the information terminal.

[0523] The device receives alarm notifications and analyzes the user's voice and facial expression data using an emotion recognition engine. This emotion recognition engine evaluates the user's emotions (e.g., stress or relaxation) and dynamically adjusts the pet's training plan based on the results. In other words, if the user is feeling stressed, the system will gently adjust the voice commands for training.

[0524] Furthermore, the device can improve the quality of life for both pets and users by providing them with localized entertainment and promotional information. Users can also share information with other pet owners through this application, facilitating community building.

[0525] As a concrete example, when the emotion engine evaluates a user to be in a relaxed state, they will be notified of local events they can participate in with their pet on their days off. In this case, the following prompt can be used for the generative AI model: "Please suggest ways for the user to enjoy time with their pet when they are relaxed."

[0526] This configuration enables advanced management of pet health and behavior, while also improving the user experience.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The user attaches a wearable device to their pet. The device collects biometric information such as heart rate, activity level, and location in real time. This data is transmitted to a server via communication methods such as Bluetooth or Wi-Fi. The input is the pet's biometric information, which the device acquires and transmits. The output is the transfer of data to the server.

[0530] Step 2:

[0531] The server receives biometric data transmitted from a wearable device. This data is then analyzed using an AI algorithm to attempt to detect anomalies. Statistical methods are used in the analysis, and anomalies are identified by comparing the data to normal values. The input is biometric data from the wearable device, and the system analyzes this data to detect anomalies. The output is information regarding the presence or absence of an anomaly.

[0532] Step 3:

[0533] The server immediately generates an alarm if an anomaly is detected. The generated alarm is sent to the terminal as a notification. This process uses an algorithm to create a warning message and quickly notify the user. The input is the result of the anomaly detection, which is used to generate the alarm notification. The output is the alarm notification.

[0534] Step 4:

[0535] The terminal receives alarms transmitted from the server. Simultaneously, it collects user voice and facial expression data and analyzes it using an emotion recognition engine. The emotion engine determines whether the user is stressed or relaxed. The input consists of alarms from the server and the user's voice and facial expression data, and the system performs data calculations to analyze the emotional state. The output is information about the user's emotional state.

[0536] Step 5:

[0537] The device adjusts the pet's training plan based on information about the user's emotional state. Dynamic planning is employed; for example, if the user is stressed, the training voice commands are changed to a softer tone. The input is the user's emotional state information, which is used to optimize the training plan. The output is the adjusted training plan.

[0538] Step 6:

[0539] The device collects and provides users with localized entertainment and promotional information. In addition, it offers features for sharing information with other pet owners, supporting community building. Input consists of the user's geographical information and interests, and localized information is provided based on this. Output is personalized information suggestions for the user.

[0540] (Application Example 2)

[0541] 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."

[0542] In modern pet health management, it is difficult to monitor a pet's biological information in real time and to provide appropriate health management and behavioral control. Furthermore, there is a need to provide owners with dietary plans tailored to their pet's health condition and to respond flexibly to the owner's emotions. However, the challenge lies in the lack of an effective system to comprehensively achieve all of these goals.

[0543] 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.

[0544] In this invention, the server includes a portable device for collecting biometric data, communication means for transmitting information acquired from the portable device to an information processing device, calculation means for analyzing the received information and detecting anomalies, notification means for generating an alarm when an anomaly is detected and notifying the user's information terminal, acoustic means for analyzing the pet's behavior and outputting sound according to the behavior, suggestion means for suggesting a meal plan based on the pet's health data, and adjustment means for identifying the user's emotional state and adjusting the meal plan. This makes it possible to manage the pet's health and behavior in real time and provide the owner with the most appropriate response.

[0545] A "portable device" is a portable device attached to a pet to collect biometric data.

[0546] "Communication means" refers to technical means for transmitting information acquired from a portable device to an information processing device.

[0547] "Computation means" refers to a device or method that executes an algorithm for analyzing received information and detecting anomalies.

[0548] A "notification means" is a technical means for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0549] "Acoustic means" refers to a device or method for analyzing a pet's behavior and outputting sound corresponding to that behavior.

[0550] "Suggestion method" refers to a technical means for suggesting a diet plan to pet owners based on their pet's health data.

[0551] "Adjustment means" refers to a device or method for identifying the user's emotional state and adjusting the meal plan accordingly.

[0552] To implement this invention, a portable device is first attached to the pet to collect biometric data. The portable device acquires the pet's heart rate, activity level, and location information, and transmits this information to an information processing device using a communication means.

[0553] The server, acting as an information processing device, receives data and performs analysis using computing means. If abnormal data or behavioral patterns are detected, a notification means generates an alarm and notifies the user's information terminal. Upon receiving the notification, the user can immediately take appropriate action.

[0554] Furthermore, the server analyzes the pet's health data based on calculations and proposes a meal plan. The proposal system generates an optimal food plan, which is then presented to the user. At this time, an adjustment system identifies the user's emotional state and adjusts the proposal accordingly. Through this process, the pet's most appropriate meal plan is provided.

[0555] For example, if a pet is less active than usual, the server will recommend a low-calorie meal plan. However, if the server determines that the user is stressed, the suggestion will be adjusted to a simpler, more nutritionally balanced meal. Specifically, if there is little data on the dog's activity level and the owner is busy and stressed, the server will suggest a convenient meal option within the "Low-calorie diet" category.

[0556] As an example of a prompt in a generative AI model, it can provide text such as: "Please suggest an optimal meal plan for a pet that is inactive and whose owner is stressed. Please consider nutritional balance and provide a low-maintenance method."

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The server receives biometric data such as heart rate, activity level, and location information from the pet's portable device. This biometric data serves as input, and the output forms a dataset. During data reception, information is transmitted in real time using wireless communication.

[0560] Step 2:

[0561] The server executes an anomaly detection algorithm using the received biometric data. The input is the dataset from Step 1, and the output is an alarm generated when an anomaly pattern is detected. Data analysis is performed using a machine learning model to extract features for identifying abnormal behavior.

[0562] Step 3:

[0563] When an alarm is generated, the server sends a notification to the user's device. The input is the alarm information generated in step 2, and the output is the notification received on the user's device. This operation is performed quickly using push notification technology.

[0564] Step 4:

[0565] The server runs a food planning algorithm to suggest a meal plan based on the pet's health data. The input includes health data and the previously detected anomalies, and the output is a suggested meal plan. This plan uses an AI model to calculate a diet optimized for the pet's health condition.

[0566] Step 5:

[0567] The device performs emotion analysis to recognize the user's emotional state. Inputs are the user's voice data and facial expression information, and output is the detected emotional state. The emotion engine applies an emotion recognition algorithm to determine the user's stress level and relaxation level.

[0568] Step 6:

[0569] The server adjusts the suggested meal plan based on the user's emotional information. The input is the meal plan from step 4 and the emotional state from step 5, and the output is the adjusted meal plan. The adjustment uses prompts from a generative AI model to optimize the meal content by reflecting the results of the emotional analysis.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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".

[0587] This invention is a system for managing the health and behavior of pets, and consists of a wearable device, a server, and a terminal. The wearable device is attached to the pet's collar or harness and measures biometric information and behavioral data in real time. For example, it continuously collects data including heart rate, activity level, and location information.

[0588] The server receives data transmitted from the wearable device and analyzes it using AI algorithms. Based on the analyzed data, it detects abnormalities in the pet's health and behavior. If an abnormality is detected, the server generates an alarm and sends the alert to the device's mobile application.

[0589] The device notifies the pet owner based on information provided by the server. This notification may appear as a pop-up on the device screen, or be presented via sound or vibration. The user receives the notification and, if necessary, can use the AI ​​chatbot for health consultations. The chatbot is designed to provide immediate answers to common questions.

[0590] Users can also monitor their pets' behavior and set up training sounds through this system. The server analyzes the video data transmitted from the AI ​​camera, and if it determines that the pet is barking excessively, it sends a command to play a training sound from the wearable device.

[0591] Furthermore, the device provides users with local pet-related entertainment information and offers. Users can utilize community features within the application to share information and experiences with other pet owners. In this way, the system comprehensively supports pet health management and improves the quality of life for both pet owners and their pets.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device acquires biometric information such as heart rate, body temperature, and activity level in real time through a wearable device attached to the pet's neck. It prepares to periodically send this data to a server.

[0595] Step 2:

[0596] The server receives data transmitted from the wearable device and prepares it for storage in the database. Once the data is received, the AI ​​algorithms within the system automatically begin analysis.

[0597] Step 3:

[0598] Within the server, an AI algorithm analyzes the data to determine if there are any abnormalities in the pet's health. For example, if it detects an activity level exceeding normal levels or a sudden change in heart rate, it flags it as an abnormality.

[0599] Step 4:

[0600] The server generates an alarm when an anomaly is detected. The alarm includes a description of the anomaly and recommended corrective actions. The generated alarm is immediately sent as a notification to the user's terminal.

[0601] Step 5:

[0602] The device receives alarm notifications sent from the server. Users can view these notifications in real time through a mobile application. The notifications display specific anomaly data and recommended actions.

[0603] Step 6:

[0604] Users take action based on the alerts they receive. If necessary, they can use the in-app AI chatbot for additional health consultations regarding their pets and get quick feedback.

[0605] Step 7:

[0606] The server analyzes video data monitored by the AI ​​camera to determine the pet's behavior. If it detects excessive barking or undesirable behavior, it sends appropriate training audio to the wearable device and plays the audio.

[0607] Step 8:

[0608] The device periodically suggests localized entertainment information and promotions related to pets to the user. This information is provided to improve user convenience and enrich life with pets.

[0609] (Example 1)

[0610] 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".

[0611] In modern times, monitoring animal health and behavior is a crucial issue, but conventional systems have struggled to collect detailed biometric information in real time, detect abnormalities, and provide rapid notification to users. Furthermore, there has been a lack of integrated systems that combine behavioral training guidance and region-specific information provision. Therefore, there is a need for a system that enables efficient and comprehensive animal health management.

[0612] 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.

[0613] In this invention, the server includes communication means for transmitting information acquired from a wearable device to a computing device, calculation means for analyzing the received information and detecting anomalies, and notification means for generating a warning when an anomaly is detected and notifying the user's electronic device. This makes it possible to manage the health and behavior of animals in real time and to quickly notify the user in the event of an anomaly.

[0614] A "wearable device" is a device attached to an animal to collect biological parameters.

[0615] "Communication means" refers to the technical function for transmitting information from a wearable device to a computing device.

[0616] The "computation means" refers to a function that analyzes received information and performs calculations to determine the animal's health status and behavioral abnormalities.

[0617] A "notification mechanism" is a function that generates a warning and notifies the user's electronic device when an abnormality is detected.

[0618] A "voice playback means" is a function for playing back voices, including training commands, in response to the animal's behavior.

[0619] This invention is a system for comprehensively managing animal health and behavior, consisting of wearable devices, a server, and terminals. The wearable devices are attached to the animal's neck or body and continuously collect biological parameters. Specifically, it is possible to acquire data such as heart rate, activity level, and location information in real time. This makes it possible to understand the health status and movements of pets in detail.

[0620] The server receives and analyzes data transmitted from wearable devices using communication methods. It utilizes AI algorithms to analyze various data, and if an anomaly is detected, it notifies the user's terminal via a notification system. This enables immediate health management and support for responding to abnormal situations.

[0621] For example, if a dog's activity level drops significantly below normal, the server detects the anomaly and sends a notification to the user's device via the application, such as, "Your pet's activity level is low. Please check." The device can then display this information and warn the user of the necessary action.

[0622] Furthermore, the device is equipped with a voice playback mechanism that analyzes animal behavior and emits training sounds. Based on the analysis of behavioral patterns, appropriate training sounds can be transmitted to the animal through the wearable device.

[0623] Furthermore, the devices provide region-specific information and have collaborative features that allow users to share information with other users. This promotes interaction within the community and allows for the accumulation of information related to animal health management.

[0624] A possible example of a prompt message would be: "Please describe an AI algorithm that detects abnormal patterns based on pet activity data. Please include specific use cases."

[0625] This system aims to improve the quality of life for animals by combining the methods described above.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The user attaches a wearable device to the animal's neck or body. The wearable device acquires bio-parameters (heart rate, activity level, location information, etc.) in real time. Inputs include data from various sensors, and this data is continuously sampled and stored in internal memory. Specifically, it acquires heart rate every second and calculates the average activity level every five seconds.

[0629] Step 2:

[0630] The wearable device collects data and transmits it to the server via a communication method. The input is biometric data from the wearable device, which is transmitted using a secure protocol. Specifically, data packets are constructed in batch processing once every minute and communicated to the server.

[0631] Step 3:

[0632] The server analyzes data received via communication channels. During this process, it uses AI algorithms to detect abnormalities in health status and behavior. The input consists of transmitted biometric data. Based on this data, an anomaly detection algorithm is executed, outputting an anomaly flag and its details. Specifically, it analyzes data from the past 24 hours and performs threshold determination to detect anomaly patterns.

[0633] Step 4:

[0634] If an anomaly is detected, the server will notify the user's device using a notification mechanism. The input consists of an anomaly flag and detailed information. Based on this, a warning message is generated, and an alert is sent to the user's device as output. Specifically, a notification containing specific information, such as "Anomaly detected: Decreased activity level," is generated and sent to the device using push notification technology.

[0635] Step 5:

[0636] The device displays notifications from the server and reports details of the anomaly to the user. The input is notification data sent from the server, which is displayed on the screen, and the user is alerted with sound and vibration. Specifically, this involves a message appearing in the smartphone's notification bar along with vibration.

[0637] Step 6:

[0638] The user controls their smartphone to monitor the animal's behavior in real time and set up training voice prompts as needed. Input is real-time feedback from the user via the device, and the system is configured to send commands to a wearable device as output. For example, a voice command such as "don't bark" is set within the app and associated with a trigger condition.

[0639] Step 7:

[0640] The device provides local information and displays information to facilitate interaction within the community through its sharing function. Input is local events and related information sent via the server, and output is useful information provided to the user. For example, notifications such as "A pet-related event is being held nearby" will be displayed within the app.

[0641] (Application Example 1)

[0642] 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".

[0643] To protect the health and safety of pets, early detection and rapid response to abnormal behavior are essential. However, current systems only detect and notify of abnormalities, and are insufficient in terms of security when the owner is absent. Furthermore, comprehensive security measures are needed that take into account not only abnormal pet behavior but also the surrounding environment.

[0644] 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.

[0645] In this invention, the server includes a wearable device, communication means, a computing system, and protection means for automatically detecting abnormal behavior and coordinating with external security services. This enables comprehensive security measures based on abnormal pet behavior, even when the owner is absent.

[0646] A "wearable device" is a device that is attached to a subject in order to collect biometric information.

[0647] "Communication means" refers to a method or device for transmitting data from a wearable device to an information processing device.

[0648] A "computational system" is a mechanism that analyzes received data and executes numerical processing and algorithms to detect anomalies.

[0649] "Information transmission means" refers to a method or device for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0650] "Speech generation means" refers to a device or method for outputting speech in response to analyzed behavior.

[0651] "Protective measures" refer to functions or devices that automatically detect abnormal behavior and coordinate with external security services.

[0652] To realize this invention, a system is configured in which a server, terminal, and wearable device function together. The wearable device collects biometric information and transmits the data to the server via communication means. The server analyzes the data using a computational system and detects abnormalities from normal values. If an abnormality is detected, an alarm or notification is sent to the user's information terminal via information transmission means. This notification allows the user to understand the health status of their pet in real time and take necessary measures.

[0653] Furthermore, the server can generate voice commands based on the pet's behavior using voice generation capabilities. This feature facilitates pet training and communication. It also includes protection mechanisms to automatically detect abnormal behavior and integrate with external security services, enabling more comprehensive pet safety management. The system uses AI models such as TensorFlow for anomaly detection and behavioral analysis. The collected data can also be used to improve the accuracy of the generated AI models.

[0654] For example, if a pet suddenly starts exhibiting unusual behavior at night, the server quickly analyzes the behavioral pattern and sends an alert notification to the information terminal stating, "Your pet is moving around unusually. Is there a problem?" An example of a prompt in this situation would be, "Analyze the pet's behavioral data, identify the abnormal behavior, and send an alert." This feature allows users to enhance the safety of their homes while maintaining the health of their pets.

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The wearable device collects biometric information from the pet. Inputs include heart rate, activity level, and location information; this data serves as fundamental information for the next step. The output is a dataset of collected biometric data.

[0658] Step 2:

[0659] The wearable device transmits the collected biometric data set to the server via a communication method. The input is the biometric data from the wearable device, and the output is the data received by the server. This allows the server to monitor the pet's condition in real time.

[0660] Step 3:

[0661] The server analyzes the received biometric data using an AI algorithm. The input is the data received by the server, and the output is the analysis result. The main purpose of the analysis is anomaly detection, and a generative AI model is in operation. In this process, data preprocessing is performed, abnormal behavior is identified according to prompt messages, and safety measures are considered as needed.

[0662] Step 4:

[0663] Based on the analysis results, the server sends alarms and notifications to the user's terminal via a communication system if an anomaly is detected. The input is the analysis results, and the output is notification data. Based on the analysis results, the server generates a warning message such as, "Your pet is moving around unusually. Is there a problem?"

[0664] Step 5:

[0665] The server uses a voice generation system to determine appropriate training voices based on behavioral data and transmits them to the wearable device. The input is the analysis results of the pet's behavioral data, and the output is training voice data. In this process, the system selects a voice to play when the pet performs a specific behavior and automatically issues commands.

[0666] Step 6:

[0667] If the server detects abnormal behavior, it will coordinate with external security services through protective measures. The input is the determination of an anomaly detection, and the output is the protocol for contacting external security. This step ensures that measures are taken to ensure the safety of pets and the living environment even when the user is absent.

[0668] 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.

[0669] This invention is a system for advanced pet health management and behavioral control, comprising a wearable device, a server, a terminal, and an emotion engine that recognizes the user's emotions. The wearable device is attached to a collar or harness to acquire biometric information such as the pet's heart rate, activity level, and location. This data is transmitted to the server in real time via communication means.

[0670] The server processes the received biometric information to detect anomalies. Using AI algorithms, it analyzes the data to detect abnormal behavioral patterns or changes in health status. Detected anomalies are immediately converted into alarms, and notifications are sent to the terminal. These notifications alert the user and prompt appropriate action.

[0671] The device also features an emotion engine that identifies the user's emotional state. This emotion engine analyzes the user's voice data and facial expressions to recognize their emotions. This recognized emotional information is then fed back into pet training and behavior improvement programs, enabling the provision of optimized interaction plans tailored to the user's emotional state.

[0672] For example, if the emotion engine determines that the user is stressed, the system will soften the pet training voice to reduce the burden. Conversely, if the user is relaxed, it will run a more assertive training program.

[0673] Furthermore, the device provides users with localized pet-related entertainment information, promotions, and special offers. Through the device's application, users can utilize features to share information with other pet owners and form communities. In this way, the system provides an integrated health management and entertainment solution that is valuable to both pets and their owners.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The device continuously records biometric information such as heart rate, activity level, and body temperature through a wearable device attached to the pet. It prepares to transmit this data to a server in real time.

[0677] Step 2:

[0678] The server receives biometric data transmitted from the device. After receiving the data, it stores it in a database and begins analysis.

[0679] Step 3:

[0680] The server uses AI algorithms to analyze data and detect anomalies. For example, it identifies and marks sudden changes in exercise volume or heart rate that deviate from normal activity patterns as anomalies.

[0681] Step 4:

[0682] The server generates an alarm based on the detected anomaly and sends a notification to the terminal. The notification includes the details of the anomaly and the recommended next action.

[0683] Step 5:

[0684] The device provides a means to inform the user of received alarm notifications. For example, it may display a pop-up message on the screen and attract the user's attention with sound or vibration.

[0685] Step 6:

[0686] The device analyzes the user's voice and facial expressions using an emotion engine to determine their emotional state. Based on this information, the system adjusts training plans and interactions with the pet.

[0687] Step 7:

[0688] When a user trains their pet, an appropriate training voice is selected based on an emotion engine and played back through a wearable device. This enables effective training tailored to the user's emotional state.

[0689] Step 8:

[0690] The device provides users with localized pet-related information, including local pet events and special offers. Users can share information with other pet owners within the app and utilize community features for further information exchange.

[0691] (Example 2)

[0692] 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".

[0693] In modern times, effectively managing pet health and behavior is crucial for many pet owners. However, traditional methods have limitations in real-time monitoring of pets' conditions and providing appropriate responses. Furthermore, they fail to address needs such as pet training that considers the owner's emotional state and the provision of localized information. Solving these problems and improving the quality of life for both pets and their owners is essential.

[0694] 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.

[0695] In this invention, the server includes a transmission means for transmitting information acquired from a device to a processing unit, an analysis means for analyzing the received information and detecting anomalies, and an emotion recognition means for recognizing the user's emotional state and adjusting the pet's training plan. This enables real-time monitoring of the pet's health and appropriate measures to be taken, as well as optimal pet behavior management tailored to the user's emotions. Furthermore, providing region-specific information further enhances convenience for pet owners.

[0696] "Biometric information" refers to data related to a pet's life activities, such as heart rate, activity level, and location information.

[0697] "Device" refers to the hardware and software used to collect and provide biometric information.

[0698] "Transmission means" refers to communication technologies and protocols used to transmit biometric information from a device to a processing unit or server.

[0699] "Analysis means" refers to technologies and algorithms used to process collected biological information and analyze whether there are any abnormalities or behavioral patterns.

[0700] A "notification method" refers to a method or tool for generating alarms or notifications and informing the user when an anomaly is detected based on the analysis results.

[0701] "Emotion recognition means" refers to technology that determines the user's emotions from their voice and facial expressions, and adjusts the pet's behavior based on that information.

[0702] An "information terminal" refers to a device or platform used by a user to receive and display notifications.

[0703] "Information exchange function" refers to online platforms and tools that allow users to share information with each other.

[0704] "Regionally specific suggested information" refers to pet-related events, services, or promotional information relevant to the user's residential area.

[0705] This invention is a system that comprehensively supports pet health management and behavioral control, and comprises a device, server, information terminal, and emotion recognition engine.

[0706] Users collect biometric information such as heart rate, activity level, and location data through devices attached to their pets. These devices utilize common wearable technologies and sensors and have the capability to acquire data in real time. The acquired data is transmitted to a server using common communication methods such as Bluetooth and Wi-Fi.

[0707] The server analyzes received biometric data using AI algorithms to detect anomalies. By performing data cleansing, detecting anomaly patterns, and conducting statistical analysis as needed, it can gain a detailed understanding of the pet's health and behavioral patterns. If an anomaly is detected, the server immediately generates an alarm and sends a notification to the information terminal.

[0708] The device receives alarm notifications and analyzes the user's voice and facial expression data using an emotion recognition engine. This emotion recognition engine evaluates the user's emotions (e.g., stress or relaxation) and dynamically adjusts the pet's training plan based on the results. In other words, if the user is feeling stressed, the system will gently adjust the voice commands for training.

[0709] Furthermore, the device can improve the quality of life for both pets and users by providing them with localized entertainment and promotional information. Users can also share information with other pet owners through this application, facilitating community building.

[0710] As a concrete example, when the emotion engine evaluates a user to be in a relaxed state, they will be notified of local events they can participate in with their pet on their days off. In this case, the following prompt can be used for the generative AI model: "Please suggest ways for the user to enjoy time with their pet when they are relaxed."

[0711] This configuration enables advanced management of pet health and behavior, while also improving the user experience.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The user attaches a wearable device to their pet. The device collects biometric information such as heart rate, activity level, and location in real time. This data is transmitted to a server via communication methods such as Bluetooth or Wi-Fi. The input is the pet's biometric information, which the device acquires and transmits. The output is the transfer of data to the server.

[0715] Step 2:

[0716] The server receives biometric data transmitted from a wearable device. This data is then analyzed using an AI algorithm to attempt to detect anomalies. Statistical methods are used in the analysis, and anomalies are identified by comparing the data to normal values. The input is biometric data from the wearable device, and the system analyzes this data to detect anomalies. The output is information regarding the presence or absence of an anomaly.

[0717] Step 3:

[0718] The server immediately generates an alarm if an anomaly is detected. The generated alarm is sent to the terminal as a notification. This process uses an algorithm to create a warning message and quickly notify the user. The input is the result of the anomaly detection, which is used to generate the alarm notification. The output is the alarm notification.

[0719] Step 4:

[0720] The terminal receives alarms transmitted from the server. Simultaneously, it collects user voice and facial expression data and analyzes it using an emotion recognition engine. The emotion engine determines whether the user is stressed or relaxed. The input consists of alarms from the server and the user's voice and facial expression data, and the system performs data calculations to analyze the emotional state. The output is information about the user's emotional state.

[0721] Step 5:

[0722] The device adjusts the pet's training plan based on information about the user's emotional state. Dynamic planning is employed; for example, if the user is stressed, the training voice commands are changed to a softer tone. The input is the user's emotional state information, which is used to optimize the training plan. The output is the adjusted training plan.

[0723] Step 6:

[0724] The device collects and provides users with localized entertainment and promotional information. In addition, it offers features for sharing information with other pet owners, supporting community building. Input consists of the user's geographical information and interests, and localized information is provided based on this. Output is personalized information suggestions for the user.

[0725] (Application Example 2)

[0726] 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".

[0727] In modern pet health management, it is difficult to monitor a pet's biological information in real time and to provide appropriate health management and behavioral control. Furthermore, there is a need to provide owners with dietary plans tailored to their pet's health condition and to respond flexibly to the owner's emotions. However, the challenge lies in the lack of an effective system to comprehensively achieve all of these goals.

[0728] 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.

[0729] In this invention, the server includes a portable device for collecting biometric data, communication means for transmitting information acquired from the portable device to an information processing device, calculation means for analyzing the received information and detecting anomalies, notification means for generating an alarm when an anomaly is detected and notifying the user's information terminal, acoustic means for analyzing the pet's behavior and outputting sound according to the behavior, suggestion means for suggesting a meal plan based on the pet's health data, and adjustment means for identifying the user's emotional state and adjusting the meal plan. This makes it possible to manage the pet's health and behavior in real time and provide the owner with the most appropriate response.

[0730] A "portable device" is a portable device attached to a pet to collect biometric data.

[0731] "Communication means" refers to technical means for transmitting information acquired from a portable device to an information processing device.

[0732] "Computation means" refers to a device or method that executes an algorithm for analyzing received information and detecting anomalies.

[0733] A "notification means" is a technical means for generating an alarm when an anomaly is detected and notifying the user's information terminal.

[0734] "Acoustic means" refers to a device or method for analyzing a pet's behavior and outputting sound corresponding to that behavior.

[0735] "Suggestion method" refers to a technical means for suggesting a diet plan to pet owners based on their pet's health data.

[0736] "Adjustment means" refers to a device or method for identifying the user's emotional state and adjusting the meal plan accordingly.

[0737] To implement this invention, a portable device is first attached to the pet to collect biometric data. The portable device acquires the pet's heart rate, activity level, and location information, and transmits this information to an information processing device using a communication means.

[0738] The server, acting as an information processing device, receives data and performs analysis using computing means. If abnormal data or behavioral patterns are detected, a notification means generates an alarm and notifies the user's information terminal. Upon receiving the notification, the user can immediately take appropriate action.

[0739] Furthermore, the server analyzes the pet's health data based on calculations and proposes a meal plan. The proposal system generates an optimal food plan, which is then presented to the user. At this time, an adjustment system identifies the user's emotional state and adjusts the proposal accordingly. Through this process, the pet's most appropriate meal plan is provided.

[0740] For example, if a pet is less active than usual, the server will recommend a low-calorie meal plan. However, if the server determines that the user is stressed, the suggestion will be adjusted to a simpler, more nutritionally balanced meal. Specifically, if there is little data on the dog's activity level and the owner is busy and stressed, the server will suggest a convenient meal option within the "Low-calorie diet" category.

[0741] As an example of a prompt in a generative AI model, it can provide text such as: "Please suggest an optimal meal plan for a pet that is inactive and whose owner is stressed. Please consider nutritional balance and provide a low-maintenance method."

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The server receives biometric data such as heart rate, activity level, and location information from the pet's portable device. This biometric data serves as input, and the output forms a dataset. During data reception, information is transmitted in real time using wireless communication.

[0745] Step 2:

[0746] The server executes an anomaly detection algorithm using the received biometric data. The input is the dataset from Step 1, and the output is an alarm generated when an anomaly pattern is detected. Data analysis is performed using a machine learning model to extract features for identifying abnormal behavior.

[0747] Step 3:

[0748] When an alarm is generated, the server sends a notification to the user's device. The input is the alarm information generated in step 2, and the output is the notification received on the user's device. This operation is performed quickly using push notification technology.

[0749] Step 4:

[0750] The server runs a food planning algorithm to suggest a meal plan based on the pet's health data. The input includes health data and the previously detected anomalies, and the output is a suggested meal plan. This plan uses an AI model to calculate a diet optimized for the pet's health condition.

[0751] Step 5:

[0752] The device performs emotion analysis to recognize the user's emotional state. Inputs are the user's voice data and facial expression information, and output is the detected emotional state. The emotion engine applies an emotion recognition algorithm to determine the user's stress level and relaxation level.

[0753] Step 6:

[0754] The server adjusts the suggested meal plan based on the user's emotional information. The input is the meal plan from step 4 and the emotional state from step 5, and the output is the adjusted meal plan. The adjustment uses prompts from a generative AI model to optimize the meal content by reflecting the results of the emotional analysis.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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."

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] A wearable device for collecting biometric information,

[0779] A communication means for transmitting data acquired from the wearable device to a server,

[0780] A processing means having an algorithm for analyzing received data and detecting anomalies,

[0781] A notification system that generates an alarm when an anomaly is detected and notifies the user's terminal,

[0782] A means for analyzing a pet's behavior and emitting training sounds in response to that behavior,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, comprising an artificial intelligence-enabled dialogue means for providing health consultations based on biometric information from a wearable device.

[0786] (Claim 3)

[0787] The system according to claim 1, which includes a community function for users to share information and provides region-specific suggestion information.

[0788] "Example 1"

[0789] (Claim 1)

[0790] Wearable devices for collecting biological parameters,

[0791] A communication means for transmitting information acquired from the wearable device to a computing device,

[0792] A calculation means for analyzing received information and detecting anomalies,

[0793] A notification means that generates a warning when an anomaly is detected and notifies the user's electronic device,

[0794] A means for analyzing animal behavior and emitting training sounds in response to that behavior,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, comprising an intelligent program-compatible dialogue means for providing health consultations based on biological parameters from a wearable device.

[0798] (Claim 3)

[0799] The system according to claim 1, which has a collaborative function for users to share information and provides region-specific suggestion information.

[0800] "Application Example 1"

[0801] (Claim 1)

[0802] A wearable device for collecting biometric information,

[0803] A communication means for transmitting data acquired from the wearable device to an information processing device,

[0804] A computational system for analyzing received data and detecting anomalies,

[0805] An information transmission means that generates an alarm when an anomaly is detected and notifies the user's information terminal,

[0806] A voice generation means for analyzing the behavior of a living organism and outputting sound in response to that behavior,

[0807] A protective mechanism to automatically detect abnormal behavior and cooperate with external security services,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, comprising intelligent dialogue means for providing health consultations based on biometric information from a wearable device.

[0811] (Claim 3)

[0812] The system according to claim 1, which includes a community function for users to share information and provides region-specific suggestion information.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] A device for collecting biological information,

[0816] A transmission means for transmitting information acquired from the device to a processing device,

[0817] An analytical means for analyzing received information and detecting anomalies,

[0818] A notification means that generates an alarm when an anomaly is detected and notifies the user's information terminal,

[0819] An emotion recognition means for recognizing the user's emotional state and adjusting the pet's training plan,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, comprising a dialogue means for providing health consultations based on biometric information from a wearable device.

[0823] (Claim 3)

[0824] The system according to claim 1, which includes an information exchange function for users to share information and provides region-specific suggested information.

[0825] "Application example 2 when combining with an emotional engine"

[0826] (Claim 1)

[0827] A portable device for collecting biometric data,

[0828] A communication means for transmitting information acquired from the portable device to an information processing device,

[0829] A computational means for analyzing received information and detecting anomalies,

[0830] A notification means that generates an alarm when an anomaly is detected and notifies the user's information terminal,

[0831] An acoustic means for analyzing pet behavior and outputting sound according to that behavior,

[0832] A method for suggesting a meal plan based on pet health data,

[0833] A means of adjusting the meal plan to identify the user's emotional state,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising an intelligent system-compatible dialogue means for providing health consultations based on biological information from a portable device.

[0837] (Claim 3)

[0838] The system according to claim 1, which includes a community function for users to share information and provides region-specific suggestion information. [Explanation of Symbols]

[0839] 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 wearable device for collecting biometric information, A communication means for transmitting data acquired from the wearable device to a server, A processing means having an algorithm for analyzing received data and detecting anomalies, A notification system that generates an alarm when an anomaly is detected and notifies the user's terminal, A means for analyzing a pet's behavior and emitting training sounds in response to that behavior, A system that includes this.

2. The system according to claim 1, comprising an artificial intelligence-enabled dialogue means for providing health consultations based on biometric information from a wearable device.

3. The system according to claim 1, which includes a community function for users to share information and provides region-specific suggestion information.

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