system

Abstract

We provide the system. [Solution] A wearable data acquisition means equipped with a sensor device for collecting biometric information of pets, A data communication means for transferring accumulated biological information to a remote data processing device in real time, A data processing device having analytical means for analyzing received biological information and detecting abnormalities, Based on the analysis results, a notification generation means for outputting the pet's health status, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to detect early changes and abnormalities in the physical condition of pets. Also, when a pet is sick or injured, the means for identifying the cause and dealing with it appropriately are often unclear. Such problems are factors that shorten the healthy lifespan of pets and increase the anxiety of their owners.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a wearable data acquisition means equipped with a sensor device for acquiring biological information of a pet, and a data communication means for transferring the accumulated biological information to a data processing device in real time. Furthermore, by incorporating an analysis means for analyzing the received biological information and detecting abnormalities into the data processing device, and providing a notification generation means for notifying the pet of its health status based on the analysis results, the present invention provides a system that enables real-time monitoring of the pet's health status and appropriate care.

[0006] A "sensor device" is a device used to measure and acquire data on a pet's biological information.

[0007] A "wearable data acquisition device" is a means of collecting biological information in real time by attaching it to a pet.

[0008] "Data communication means" refers to means for transferring collected biometric information to a data processing device located in a remote location.

[0009] A "data processing device" is a device that analyzes received biological information and evaluates the health status of a pet.

[0010] "Analysis means" refers to a method for detecting and analyzing abnormalities in biological information based on received data.

[0011] A "notification generation means" is a means for creating information about a pet's health status based on analysis results and providing it to users and relevant parties. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for monitoring a pet's health in real time and detecting abnormalities early. The system consists of a sensor device attached to the pet, a server for analyzing the data, and a terminal used by the user.

[0034] Terminal role

[0035] The device, when attached to a pet, continuously measures biometric information such as activity level, heart rate, and location. For example, a sensor measures heart rate every second, and a GPS module records location data. The device collects this data at regular intervals and temporarily stores it in buffer memory. Then, when the data meets the set conditions, it transmits it to a server via a secure protocol.

[0036] Server Processing

[0037] The server receives data transmitted from the terminal in real time. The received data is analyzed using AI algorithms to evaluate the pet's health and any abnormal behavior. For example, if an abnormal heart rate or activity pattern is detected, the server evaluates the cause and calculates the risk of stress or illness. Based on the assessed risk, alerts and reports are automatically generated.

[0038] User interaction

[0039] Users can view information analyzed by the server through a dedicated application. The application visually represents the pet's health status and has the function to send pop-up notifications or email notifications if an abnormality is detected. Users can receive notifications and consult a veterinarian as needed. In addition, users can gain insights to change their pet's daily lifestyle patterns. For example, if the pet's activity level is low, measures to increase exercise can be considered.

[0040] Thus, the present invention is a system that helps provide appropriate care by routinely monitoring the health of pets and detecting abnormalities early.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The measurement data is temporarily stored in buffer memory.

[0044] Step 2:

[0045] The terminal aggregates data stored in buffer memory at regular time intervals and sends it to the server using a data communication method. The data is transmitted securely via a secure protocol (e.g., HTTPS).

[0046] Step 3:

[0047] The server processes biometric information received from the terminal in real time via streaming and stores it in a designated database.

[0048] Step 4:

[0049] The server inputs the stored data into an AI analysis module to analyze the pet's health status. If any abnormal patterns or values ​​are detected, the AI ​​model immediately passes that information to a notification generation module.

[0050] Step 5:

[0051] The server generates alerts for users based on the analysis results. Specifically, it creates notifications that include the level of risk and recommended actions corresponding to the anomalies, and sends them to users and relevant parties based on the device's registration information.

[0052] Step 6:

[0053] Users can check received notifications within the application to stay informed about their pet's health. If necessary, they can use the provided information to consult with a veterinarian or adjust their pet's care plan.

[0054] (Example 1)

[0055] 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."

[0056] For pet owners, it is crucial to monitor their animals' health in real time and detect abnormalities early. However, conventional technologies have problems with accurately collecting biological information and rapidly evaluating abnormalities. Furthermore, there is a lack of systems that can instantly provide visualized information to users and enable immediate countermeasures. This invention was developed to solve these problems.

[0057] 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.

[0058] In this invention, the server includes activity monitoring means for continuously measuring and storing biological information of animals, data transmission means for transmitting the stored information to a processing device via secure communication means under certain conditions, and analysis device means for analyzing the received information and evaluating abnormal patterns using a machine learning model. This enables precise monitoring of the animal's biological state and immediate detection of abnormalities.

[0059] "Animal biometric information" is a general term for various types of data necessary to understand an animal's health status, such as heart rate, activity level, and location information.

[0060] "Activity monitoring means" refers to a device attached to an animal to continuously measure biological information and record its fluctuations.

[0061] "Data transmission means" refers to a mechanism that transmits collected biometric information to an external processing device using a secure protocol under certain conditions.

[0062] An "analysis device" refers to a processing system that evaluates received biological information and determines abnormal patterns using machine learning models or the like.

[0063] "Notification means" refers to a function that informs the user of alarms generated based on analysis results, either visually or electronically.

[0064] This invention relates to a system for continuously monitoring the health status of animals and detecting abnormalities early. This system consists of a device (terminal) attached to the animal, a computer system (server) that processes data, and an interface device (user interface) used by the user.

[0065] The device functions as an activity monitoring device attached to an animal, continuously measuring heart rate, activity level, and location information. This data is temporarily stored in an internal buffer at regular intervals. Hardware such as a heart rate sensor, accelerometer, and GPS module is used for data collection. Once data has accumulated in the buffer memory, the device sends the data to a server using a secure protocol (e.g., HTTPS).

[0066] The server receives biometric information transmitted from the terminal in real time and analyzes it using machine learning models and AI algorithms. For example, an anomaly detection algorithm can identify abnormal heart rate patterns and assess medical risks. Based on the analysis results, the server automatically generates alarms and provides the ability to detect invisible anomalies.

[0067] Users can access information analyzed on the server using a dedicated application. This information visually represents insights into the animal's health and provides notifications when abnormalities are detected. The smartphone or tablet application issues alerts via pop-up notifications and email. Based on this information, users can take swift action and consult a specialist if necessary.

[0068] For example, if the device detects an abnormality in an animal's heart rate, analysis on the server may suggest that the animal is experiencing stress. The user can then receive this information and take action to improve the animal's living environment.

[0069] Examples of prompts for generative AI models are as follows:

[0070] "Please describe the process of a system that analyzes data and notifies the user when a pet's heart rate is different from normal."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The device periodically acquires activity information, heart rate, and location information from sensors attached to the animal. Biometric data from the sensors is provided as input, which is converted into a digital format and temporarily stored. Specifically, the heart rate sensor captures the heart rate every second, the accelerometer assesses the activity level, and the GPS module records the location coordinates.

[0074] Step 2:

[0075] The terminal sends data to the server using a secure method (e.g., the HTTPS protocol) when certain conditions are met. The input is collected data in buffer memory, and the decision to send the data is made considering the data's timestamp and quantity. Specifically, the data is configured to be batched at regular intervals and securely sent to the endpoint.

[0076] Step 3:

[0077] The server receives data transmitted from the terminal and stores it in a database. The received biometric information becomes input and undergoes formatting processing for analysis. Specifically, the data is recorded and prepared for input into analysis models for anomaly and pattern detection.

[0078] Step 4:

[0079] The server analyzes data in real time using machine learning models. The input is biometric data that has been formatted, and the output is anomaly detection results and health risk assessment results. Specifically, it processes the received data through an analysis algorithm to identify, for example, heart rate patterns that are different from normal.

[0080] Step 5:

[0081] The server generates an alert when it detects an anomaly based on the analysis results. The analysis results are used as input, and the output is an alarm message. Specifically, when an anomaly is detected, the process automatically generates a warning message according to the risk level.

[0082] Step 6:

[0083] The server then initiates the process of sending the generated alert to the user's device. The input is the generated alert, and the output is the notification information sent to the user's device. Specifically, a push notification is triggered, and the alert is displayed on the user's smartphone or tablet.

[0084] Step 7:

[0085] Users can view received notifications within the application and take action as needed. Input is the alarm information displayed on the device, and output considers the user's actions and countermeasures. Specifically, users can check details within the app and consult a veterinarian if the abnormality persists.

[0086] (Application Example 1)

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

[0088] In addition to real-time monitoring of pets' health and early detection of abnormalities, there is a need to improve pet safety. Specifically, a system is required that, when an abnormality in a pet's health is detected, quickly adjusts the environment to reduce the pet's anxiety and stress, and provides an optimal living environment.

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

[0090] In this invention, the server includes an analysis means for analyzing received biometric information and detecting abnormalities, a notification generation means for outputting the pet's health status and safety based on the analysis results, and an external linkage means for coordinating with an environmental control system when an abnormality is detected. This makes it possible to notify the owner and automatically optimize the pet's living environment at the same time when the pet's health status shows an abnormality.

[0091] "Pet biometric information" refers to data such as heart rate, activity level, and location information necessary to assess a pet's health.

[0092] "Wearable data acquisition means" refers to sensor devices and communication equipment that are attached to pets to collect biological information.

[0093] "Data communication means" refers to technology for transmitting collected biometric information in real time to a data processing device located in a remote location.

[0094] "Analysis means" refers to functions or programs on a data processing device that detect abnormalities in a pet's health condition based on received biological information.

[0095] A "notification generation mechanism" is a system that generates reports and warnings to inform pet owners of their pet's health status and any abnormalities based on the analysis results.

[0096] "External interlocking means" refers to a technology that, when an abnormality is detected, works in cooperation with a pre-designated external environmental control system to adjust the pet's living environment.

[0097] A "predictive model" is a model that uses mathematical or statistical methods to predict future health risks for pets based on received biometric information.

[0098] A "user interface means" is an information display device that visually displays analysis results and linkage status from a data processing device for use by the user.

[0099] The embodiment for carrying out the invention describes a system for monitoring a pet's health and safety in real time, and for detecting and responding to abnormalities early. This system integrates a wearable data acquisition means, data communication means, analysis means, notification generation means, external linking means, and user interface means. The specific operation of each means is described below.

[0100] First, a wearable data acquisition device, which serves as the terminal, is attached to the pet. The terminal continuously measures biometric information such as activity level, heart rate, and location using its built-in sensors. This biometric information is transmitted to a server in real time via data communication. Communication technologies such as Bluetooth and Wi-Fi are used in this process.

[0101] Next, the server analyzes the received biometric information using AI algorithms. This analysis utilizes machine learning frameworks such as TENSORFLOW® and PyTorch, and if an anomaly is detected, the analysis results identify the cause of the anomaly. By using predictive models, the health risks are assessed, and adjustments to the external environment are proposed as needed.

[0102] The analyzed results are notified to the user via a notification generation mechanism. The information is presented in a visualized form on the user's smartphone or other display device, and a real-time pop-up notification is sent in case of anomalies.

[0103] Furthermore, the external connectivity system can interact with the smart home system in response to anomaly detection, enabling environmental adjustments to ensure the pet's comfort. For example, if a pet's heart rate is higher than normal, the system can dim the room lights or play music to calm the pet.

[0104] Examples of prompts for generative AI models:

[0105] "If a pet's heart rate is higher than normal, please suggest possible causes and countermeasures. Consider the stress level and create an appropriate notification for the owner."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. This data is temporarily stored in the device's internal memory. The input is biometric data, and the output is data stored in the device's memory.

[0109] Step 2:

[0110] The device communicates data via Bluetooth or Wi-Fi to send temporarily stored biometric information to the server at regular intervals. The input is the biometric information stored on the device, and the output is the biometric information sent to the server.

[0111] Step 3:

[0112] The server analyzes biometric information received from the terminal using AI algorithms. Here, TensorFlow and PyTorch are used to detect anomalies. The input is the biometric information received by the server, and the output is the analysis results, such as whether or not anomalies were detected.

[0113] Step 4:

[0114] The server uses a generative AI model based on the analysis results to assess health risks and analyze the causes of anomalies. Furthermore, it generates appropriate notification content. The input is the analysis results, and the output is the assessed risk level and notification content.

[0115] Step 5:

[0116] The server sends notifications based on the analysis results to the user's smartphone. The user interface displays the abnormal situation in real time and provides alerts to the user. The input is the notification content, and the output is the notification displayed on the user's smartphone.

[0117] Step 6:

[0118] When an anomaly is detected, the server communicates with the smart home system using an external linkage mechanism and automatically adjusts the pet's living environment. The input is the type and level of the anomaly, and the output is the adjusted environmental settings. This allows the system to respond in a way that ensures the pet is comfortable.

[0119] 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.

[0120] This invention is a system that collects and analyzes a pet's biological information to monitor its health, and further recognizes the user's emotions to optimize pet care. The system comprises a sensor device attached to the pet, a data processing device, an emotion engine that analyzes the user's emotions, and a terminal used by the user.

[0121] Terminal role

[0122] The device continuously collects biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The collected data is aggregated at regular intervals and transmitted to a server using data communication methods. The transmitted data is encrypted through a secure protocol to prevent leakage to external parties.

[0123] Server Processing

[0124] When the server receives biometric information transmitted from the terminal, an AI-powered data analysis module analyzes the pet's health status. If abnormal data patterns or health risks are detected, a notification is generated based on this. Furthermore, an emotion engine analyzes the user's emotions from the user terminal's sensors and historical data, and plans a response tailored to the user's psychological state. For example, if the system detects that the user's anxiety is increasing due to the pet's abnormal behavior, it will provide a reassuring message and useful information about the pet's health.

[0125] User interaction

[0126] Users receive information analyzed by the server through the application. The app graphically displays the pet's health status and indicates actions to take immediately as needed. The user's emotional state, analyzed by an emotion engine, is also fed back, providing advice to help with pet care activities. For example, if the user is stressed, the system will suggest ways to relax. The app also has a function that allows users to easily contact a veterinarian as needed based on this information.

[0127] In this way, the system supports pet health management by analyzing the condition of both the pet and the user in real time and providing appropriate care.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device detects activity levels, heart rate, and location information in real time through sensors attached to the pet. This data is temporarily stored in the built-in memory and sent to the server in batches at regular intervals (e.g., every 5 minutes).

[0131] Step 2:

[0132] The server receives batch data sent from the terminal. This data is stored in a cloud database and prepared to be passed to the AI ​​analysis module.

[0133] Step 3:

[0134] The server's AI analysis module analyzes the received biometric information. It detects abnormal heart rates and activity patterns and assesses the risk level. For example, if the heart rate deviates significantly from normal, it prepares to issue an alert.

[0135] Step 4:

[0136] The server uses an emotion engine to receive data for analyzing the user's current emotional state. Based on user input, historical data, and device sensor information (e.g., voice tone), it estimates the user's current emotion.

[0137] Step 5:

[0138] The server compares the analysis results with the user's emotional state to determine what notification to generate. For example, if an abnormal condition is detected in the pet and the user's emotional state is determined to be anxious, the notification will include specific suggestions for calming down and encouraging messages.

[0139] Step 6:

[0140] Users receive notifications through a dedicated application and take immediate action based on the information provided in those notifications. The notifications include details about the pet's health and recommendations for future care, allowing users to take appropriate action.

[0141] (Example 2)

[0142] 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".

[0143] Pet health management is a crucial issue for pet owners, but traditional methods make it difficult to monitor a pet's health in real time. Furthermore, care plans that take into account the user's emotional state are not provided, preventing optimal care for both pets and users. Therefore, there is a need for a system that collects pet biometric information in real time, monitors the user's emotional state, and supports appropriate health management.

[0144] 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.

[0145] In this invention, the server includes an analysis means that uses machine learning to analyze the pet's biometric information and detect abnormalities, an emotion analysis means that analyzes the user's emotional state and psychologically optimizes notifications regarding the pet's health status, and a notification generation means that outputs the pet's health status based on the analysis results and proposes appropriate responses to the user. This makes it possible to monitor the pet's health status in real time and provide a care plan that is appropriate to the user's emotional state.

[0146] A "sensor device" is a device attached to a pet to continuously acquire biological information, and its role is to measure activity level, heart rate, location information, and so on.

[0147] "Wearable data acquisition means" refers to a means for acquiring biological information through a sensor device attached to a pet and using it for subsequent data processing.

[0148] "Encrypted data communication means" refers to a method that uses data encryption technology to securely transfer biometric information collected from sensor devices to a server.

[0149] A "data processing device" is a computer system installed to analyze received biological information, and it is used to detect abnormalities in health conditions and predict risks.

[0150] "Analysis methods using machine learning" are methods installed in data processing devices that learn past data patterns and detect anomalies by comparing them with current biological information.

[0151] "Emotional analysis means" refers to a method that uses technology to acquire and analyze the user's emotional state and reflect the results in pet care.

[0152] A "notification generation mechanism" is a system function that generates and provides notifications to users based on analysis results, prompting them to provide appropriate information or take action.

[0153] "User interface means" refers to an interface that visually displays the analysis results of a data processing device, allowing the user to confirm the information.

[0154] A "predictive model" is an algorithm that uses received biometric information and user sentiment analysis results to predict future health risks and user behavior.

[0155] This invention uses multiple hardware and software components to monitor the health status of a pet in real time and to provide care that is tailored to the user's emotional state.

[0156] The device collects biometric information using a sensor device attached to the pet. The sensor device is equipped with sensors that measure heart rate and activity level, thereby continuously acquiring biometric data from the pet. The collected data is temporarily stored in the device and compiled at regular intervals.

[0157] The device uses the AES encryption algorithm to encrypt the collected data. This encryption ensures confidentiality during data transmission. The encrypted data is sent to the server via the HTTPS protocol. This process ensures data security.

[0158] The server decrypts the received data and begins analysis. The analysis utilizes machine learning, employing an AI data analysis module based on TensorFlow. It compares the current data with historical data to detect anomalies and predict health risks.

[0159] The server is also equipped with an emotion engine to analyze user emotions. This engine analyzes voice and input data received from the user's terminal to determine the user's emotional state. Natural language processing technology is used for this analysis, allowing for an accurate understanding of the user's psychological state.

[0160] The server generates and sends notifications to the user based on the analysis results. A generative AI model is used to automatically create messages appropriate to the user's emotional state. For example, by entering a question as a prompt, such as "My pet's heart rate is higher than normal, but is this normal because we are out for a walk?", the AI ​​helps to make an appropriate decision.

[0161] Users receive this information through the application. Developed with React Native, the app visually displays pet health status in graphs and alerts, allowing users to take quick action. It also includes a feature to contact a veterinarian with a single tap in emergencies.

[0162] In this way, the present invention realizes a system that provides optimal care for both pets and users and efficiently supports health management.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device receives biometric information as input from sensor devices attached to the pet. Here, data such as heart rate, activity level, and location information are acquired by the sensors. This information is sampled at regular intervals and temporarily stored in the device's internal memory. Specifically, for example, the heart rate sensor reads data every second and calculates the average value.

[0166] Step 2:

[0167] The device performs the process of encrypting the collected biometric information. The biometric information collected in step 1 is used as input. Data security is enhanced by encrypting the data using the AES encryption algorithm. The encrypted data is prepared as output and then ready for transmission. Specifically, an encryption module inside the device converts the data.

[0168] Step 3:

[0169] The terminal sends encrypted data to the server. The input here is encrypted biometric information. Data is securely transferred via the HTTPS protocol. The output is the receipt of data to the server. Specifically, the terminal's communication module becomes active and sends a request to the server over the internet.

[0170] Step 4:

[0171] The server decrypts the data received from the terminal and analyzes the biometric information. Using the decrypted input data, it performs calculations using machine learning to detect abnormal patterns and health risks. The output is the analysis results regarding the pet's health status. Specifically, the TensorFlow module runs on the server to identify abnormal data.

[0172] Step 5:

[0173] The server performs a process to analyze the user's emotions. Voice data and input speed from the user's terminal are used as input. Based on this data, the emotion analysis engine performs calculations to evaluate the psychological state and identify the emotional state. The output is the user's current emotional state. Specifically, a natural language processing algorithm analyzes the user input.

[0174] Step 6:

[0175] The server generates a notification based on the analysis results and sends it to the user. It then provides the generated notification message to the user as output. At this stage, a generation AI model is used to generate a message appropriate for the user. Specifically, the AI ​​selects and customizes a message template based on the input data.

[0176] Step 7:

[0177] The user receives notifications and analysis results from the server through the application. The input here is notification data from the server. The application visually displays this information and shows the user what specific action they should take. The output is an interface that allows the user to obtain information and take action. Specifically, the application, built with React Native, renders the data on the screen and allows the user to interact with UI elements as needed.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] There is a need to effectively manage pets' biometric information and provide an environment where pet owners can entrust their pets with peace of mind. However, conventional methods have not provided a comprehensive system that can monitor pets' conditions in real time and reduce the psychological burden on pet owners.

[0181] 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.

[0182] In this invention, the server includes a portable information acquisition device means equipped with a sensor mechanism for collecting biological information, a communication mechanism means for immediately transmitting the collected biological information to a remote information processing system, and an information processing tool means having an analysis system for analyzing the received biological information and detecting abnormalities. This makes it possible to monitor the health status of pets in real time and respond immediately if an abnormality is detected, thereby providing an environment where pets can be left with peace of mind.

[0183] "Biometric information" refers to data obtained from a pet's body, such as activity level, heart rate, and location information.

[0184] A "sensor mechanism" is a device used to physically measure a pet's biological information.

[0185] A "portable information acquisition device" is a device that can be attached to a pet and continuously acquires biological information even while it is in motion.

[0186] A "communication mechanism" is a technical means for instantly transmitting acquired biometric information to a remote location.

[0187] An "information processing system" is a computer system that analyzes received biological information and determines whether or not there are any abnormalities.

[0188] An "analysis system" is software or hardware used within an information processing system to analyze biological information and detect abnormal patterns.

[0189] "Information processing equipment" refers to devices and software used to analyze biological information and output the analysis results.

[0190] A "notification generation mechanism" is a means of informing users of important information based on analysis results.

[0191] "Emotional analysis function" is a technology used to understand the psychological state of a user.

[0192] A "user interface system" is an interface that visually displays data processing results and enables the exchange of information between the user and the system.

[0193] In this invention, the following system is configured to effectively manage the biometric information of pets. A terminal continuously collects biometric information such as activity level, heart rate, and location information using a sensor mechanism attached to the pet. This information is transmitted to a server in real time via a portable information acquisition device. The server receives the collected biometric information through a communication mechanism, analyzes it using an information processing system, and determines whether or not there are any abnormalities.

[0194] The information processing system incorporates an analysis system that analyzes biometric information to detect abnormal patterns. Based on these analysis results, a notification generation mechanism informs the user of important information. The emotion analysis function is used to understand the user's psychological state, enabling the suggestion of appropriate care.

[0195] The user interface system visually displays the analysis results, allowing users to check the animal's health status and take appropriate action. For example, if a pet's stress level rises while it is in the salon, the server can use the recognized data to notify the owner of recommended relaxation methods. An example of a prompt using the generative AI model is: "Generate a message suggesting ways to help the owner relax their pet based on its current stress level."

[0196] In this way, the invention becomes a system that manages the health status of pets in real time, contributing to pet owners being able to entrust their pets to care facilities with peace of mind.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The device acquires biometric information such as activity level, heart rate, and location from a sensor mechanism attached to the pet. This data is directly input from the sensor mechanism's sensors and sent to the device. The device then prepares to collect and store this data.

[0200] Step 2:

[0201] The device transmits collected biometric information to the server in real time. The input is biometric data, and the output is an encrypted data stream. Data is transferred using Bluetooth or Wi-Fi communication and delivered to the server via a secure protocol.

[0202] Step 3:

[0203] The server inputs biometric information received from the terminal into the analysis system. The information processing system uses machine learning algorithms to detect abnormal patterns. The input is the received biometric data, and the output is the analysis result indicating whether or not an abnormality is present. If an abnormality is detected, related detailed information is also generated.

[0204] Step 4:

[0205] The server generates a notification based on the analysis results and sends it to the user's terminal. In this process, the notification generation mechanism assembles information from the input analysis results and forms a notification message as output. The notification includes important information and recommended actions regarding the pet's health status.

[0206] Step 5:

[0207] The user views the analysis results through the terminal's user interface. Input is notification messages from the server, and output is visualized data. The user interface system displays this data in a viewable format, assisting the user in taking necessary actions.

[0208] Step 6:

[0209] The server uses sentiment analysis to determine the user's psychological state and optimize the notification content. Here, user feedback and historical data are used as input, and a generative AI model is used to create prompt sentences, resulting in the output of messages that include personalized advice and recommendations.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system for monitoring a pet's health in real time and detecting abnormalities early. The system consists of a sensor device attached to the pet, a server for analyzing the data, and a terminal used by the user.

[0227] Terminal role

[0228] The device, when attached to a pet, continuously measures biometric information such as activity level, heart rate, and location. For example, a sensor measures heart rate every second, and a GPS module records location data. The device collects this data at regular intervals and temporarily stores it in buffer memory. Then, when the data meets the set conditions, it transmits it to a server via a secure protocol.

[0229] Server Processing

[0230] The server receives data transmitted from the terminal in real time. The received data is analyzed using AI algorithms to evaluate the pet's health and any abnormal behavior. For example, if an abnormal heart rate or activity pattern is detected, the server evaluates the cause and calculates the risk of stress or illness. Based on the assessed risk, alerts and reports are automatically generated.

[0231] User interaction

[0232] Users can view information analyzed by the server through a dedicated application. The application visually represents the pet's health status and has the function to send pop-up notifications or email notifications if an abnormality is detected. Users can receive notifications and consult a veterinarian as needed. In addition, users can gain insights to change their pet's daily lifestyle patterns. For example, if the pet's activity level is low, measures to increase exercise can be considered.

[0233] Thus, the present invention is a system that helps provide appropriate care by routinely monitoring the health of pets and detecting abnormalities early.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The measurement data is temporarily stored in buffer memory.

[0237] Step 2:

[0238] The terminal aggregates data stored in buffer memory at regular time intervals and sends it to the server using a data communication method. The data is transmitted securely via a secure protocol (e.g., HTTPS).

[0239] Step 3:

[0240] The server processes biometric information received from the terminal in real time via streaming and stores it in a designated database.

[0241] Step 4:

[0242] The server inputs the stored data into an AI analysis module to analyze the pet's health status. If any abnormal patterns or values ​​are detected, the AI ​​model immediately passes that information to a notification generation module.

[0243] Step 5:

[0244] The server generates alerts for users based on the analysis results. Specifically, it creates notifications that include the level of risk and recommended actions corresponding to the anomalies, and sends them to users and relevant parties based on the device's registration information.

[0245] Step 6:

[0246] Users can check received notifications within the application to stay informed about their pet's health. If necessary, they can use the provided information to consult with a veterinarian or adjust their pet's care plan.

[0247] (Example 1)

[0248] 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."

[0249] For pet owners, it is crucial to monitor their animals' health in real time and detect abnormalities early. However, conventional technologies have problems with accurately collecting biological information and rapidly evaluating abnormalities. Furthermore, there is a lack of systems that can instantly provide visualized information to users and enable immediate countermeasures. This invention was developed to solve these problems.

[0250] 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.

[0251] In this invention, the server includes activity monitoring means for continuously measuring and storing biological information of animals, data transmission means for transmitting the stored information to a processing device via secure communication means under certain conditions, and analysis device means for analyzing the received information and evaluating abnormal patterns using a machine learning model. This enables precise monitoring of the animal's biological state and immediate detection of abnormalities.

[0252] "Animal biometric information" is a general term for various types of data necessary to understand an animal's health status, such as heart rate, activity level, and location information.

[0253] "Activity monitoring means" refers to a device attached to an animal to continuously measure biological information and record its fluctuations.

[0254] "Data transmission means" refers to a mechanism that transmits collected biometric information to an external processing device using a secure protocol under certain conditions.

[0255] An "analysis device" refers to a processing system that evaluates received biological information and determines abnormal patterns using machine learning models or the like.

[0256] "Notification means" refers to a function that informs the user of alarms generated based on analysis results, either visually or electronically.

[0257] This invention relates to a system for continuously monitoring the health status of animals and detecting abnormalities early. This system consists of a device (terminal) attached to the animal, a computer system (server) that processes data, and an interface device (user interface) used by the user.

[0258] The device functions as an activity monitoring device attached to an animal, continuously measuring heart rate, activity level, and location information. This data is temporarily stored in an internal buffer at regular intervals. Hardware such as a heart rate sensor, accelerometer, and GPS module is used for data collection. Once data has accumulated in the buffer memory, the device sends the data to a server using a secure protocol (e.g., HTTPS).

[0259] The server receives biometric information transmitted from the terminal in real time and analyzes it using machine learning models and AI algorithms. For example, an anomaly detection algorithm can identify abnormal heart rate patterns and assess medical risks. Based on the analysis results, the server automatically generates alarms and provides the ability to detect invisible anomalies.

[0260] Users can access information analyzed on the server using a dedicated application. This information visually represents insights into the animal's health and provides notifications when abnormalities are detected. The smartphone or tablet application issues alerts via pop-up notifications and email. Based on this information, users can take swift action and consult a specialist if necessary.

[0261] For example, if the device detects an abnormality in an animal's heart rate, analysis on the server may suggest that the animal is experiencing stress. The user can then receive this information and take action to improve the animal's living environment.

[0262] Examples of prompts for generative AI models are as follows:

[0263] "Please describe the process of a system that analyzes data and notifies the user when a pet's heart rate is different from normal."

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The device periodically acquires activity information, heart rate, and location information from sensors attached to the animal. Biometric data from the sensors is provided as input, which is converted into a digital format and temporarily stored. Specifically, the heart rate sensor captures the heart rate every second, the accelerometer assesses the activity level, and the GPS module records the location coordinates.

[0267] Step 2:

[0268] The terminal sends data to the server using a secure method (e.g., the HTTPS protocol) when certain conditions are met. The input is collected data in buffer memory, and the decision to send the data is made considering the data's timestamp and quantity. Specifically, the data is configured to be batched at regular intervals and securely sent to the endpoint.

[0269] Step 3:

[0270] The server receives data transmitted from the terminal and stores it in a database. The received biometric information becomes input and undergoes formatting processing for analysis. Specifically, the data is recorded and prepared for input into analysis models for anomaly and pattern detection.

[0271] Step 4:

[0272] The server analyzes data in real time using machine learning models. The input is biometric data that has been formatted, and the output is anomaly detection results and health risk assessment results. Specifically, it processes the received data through an analysis algorithm to identify, for example, heart rate patterns that are different from normal.

[0273] Step 5:

[0274] The server generates an alert when it detects an anomaly based on the analysis results. The analysis results are used as input, and the output is an alarm message. Specifically, when an anomaly is detected, the process automatically generates a warning message according to the risk level.

[0275] Step 6:

[0276] The server then initiates the process of sending the generated alert to the user's device. The input is the generated alert, and the output is the notification information sent to the user's device. Specifically, a push notification is triggered, and the alert is displayed on the user's smartphone or tablet.

[0277] Step 7:

[0278] Users can view received notifications within the application and take action as needed. Input is the alarm information displayed on the device, and output considers the user's actions and countermeasures. Specifically, users can check details within the app and consult a veterinarian if the abnormality persists.

[0279] (Application Example 1)

[0280] 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."

[0281] In addition to grasping the real-time health status of pets and detecting abnormalities at an early stage, it is necessary to improve the safety of pets. Specifically, when an abnormality in the health status is detected, a mechanism is required that can quickly adjust the environment to reduce the anxiety and stress of the pet and provide an optimal living environment.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes an analysis means for analyzing the received biological information and detecting abnormalities, a notification generation means for outputting the health status and safety of the pet based on the analysis result, and an external linkage means for linking with the environmental control system when an abnormality is detected. As a result, when the health status of the pet shows an abnormality, it is possible to notify the owner and at the same time automatically optimize the living environment of the pet.

[0284] "Pet biological information" refers to data such as heart rate, activity level, and position information required to evaluate the health status of the pet.

[0285] "Wearable data acquisition means" refers to a device such as a sensor device or a communication device that collects biological information by being worn on the pet.

[0286] "Data communication means" is a technology for transmitting the collected biological information to a data processing device located remotely in real time.

[0287] "Analysis means" refers to functions or programs on the data processing device for detecting abnormalities in the health status of the pet based on the received biological information.

[0288] "Notification generation means" is a mechanism for generating reports or warnings to inform the health status and abnormalities of the pet based on the analysis result.

[0289] "External interlocking means" refers to a technology that, when an abnormality is detected, works in cooperation with a pre-designated external environmental control system to adjust the pet's living environment.

[0290] A "predictive model" is a model that uses mathematical or statistical methods to predict future health risks for pets based on received biometric information.

[0291] A "user interface means" is an information display device that visually displays analysis results and linkage status from a data processing device for use by the user.

[0292] The embodiment for carrying out the invention describes a system for monitoring a pet's health and safety in real time, and for detecting and responding to abnormalities early. This system integrates a wearable data acquisition means, data communication means, analysis means, notification generation means, external linking means, and user interface means. The specific operation of each means is described below.

[0293] First, a wearable data acquisition device, which serves as the terminal, is attached to the pet. The terminal continuously measures biometric information such as activity level, heart rate, and location using its built-in sensors. This biometric information is transmitted to a server in real time via data communication. Communication technologies such as Bluetooth and Wi-Fi are used in this process.

[0294] Next, the server analyzes the received biometric information using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis, and if an anomaly is detected, the cause of the anomaly is identified as a result of the analysis. Using predictive models, health risks are assessed, and adjustments to the external environment are proposed as needed.

[0295] The analyzed results are notified to the user via a notification generation mechanism. The information is presented in a visualized form on the user's smartphone or other display device, and a real-time pop-up notification is sent in case of anomalies.

[0296] Furthermore, the external connectivity system can interact with the smart home system in response to anomaly detection, enabling environmental adjustments to ensure the pet's comfort. For example, if a pet's heart rate is higher than normal, the system can dim the room lights or play music to calm the pet.

[0297] Examples of prompts for generative AI models:

[0298] "If a pet's heart rate is higher than normal, please suggest possible causes and countermeasures. Consider the stress level and create an appropriate notification for the owner."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. This data is temporarily stored in the device's internal memory. The input is biometric data, and the output is data stored in the device's memory.

[0302] Step 2:

[0303] The device communicates data via Bluetooth or Wi-Fi to send temporarily stored biometric information to the server at regular intervals. The input is the biometric information stored on the device, and the output is the biometric information sent to the server.

[0304] Step 3:

[0305] The server analyzes biometric information received from the terminal using AI algorithms. Here, TensorFlow and PyTorch are used to detect anomalies. The input is the biometric information received by the server, and the output is the analysis results, such as whether or not anomalies were detected.

[0306] Step 4:

[0307] Based on the analysis results, the server uses the generated AI model to evaluate the health risks and analyze the causes of abnormalities. Furthermore, it generates appropriate notification content. The input is the analysis result, and the output is the evaluated risk level and notification content.

[0308] Step 5:

[0309] The server sends the notification content based on the analysis results to the user's smartphone. The user interface displays the abnormal situation in real time and provides an alert to the user. The input is the notification content, and the output is the notification displayed on the user's smartphone.

[0310] Step 6:

[0311] When an abnormality is detected, the server communicates with the smart home system using external linkage means and automatically adjusts the living environment of the pet. The input is the type and level of the abnormality, and the output is the adjusted environmental settings. Thus, the system responds so that the pet can live comfortably.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention is a system that monitors the health state by collecting and analyzing the biometric information of a pet, and further optimizes the care of the pet by recognizing the user's emotion. This system includes a sensor device worn on the pet, a data processing device, an emotion engine for analyzing the user's emotion, and a terminal used by the user.

[0314] Role of the Terminal

[0315] The device continuously collects biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The collected data is aggregated at regular intervals and transmitted to a server using data communication methods. The transmitted data is encrypted through a secure protocol to prevent leakage to external parties.

[0316] Server Processing

[0317] When the server receives biometric information transmitted from the terminal, an AI-powered data analysis module analyzes the pet's health status. If abnormal data patterns or health risks are detected, a notification is generated based on this. Furthermore, an emotion engine analyzes the user's emotions from the user terminal's sensors and historical data, and plans a response tailored to the user's psychological state. For example, if the system detects that the user's anxiety is increasing due to the pet's abnormal behavior, it will provide a reassuring message and useful information about the pet's health.

[0318] User interaction

[0319] Users receive information analyzed by the server through the application. The app graphically displays the pet's health status and indicates actions to take immediately as needed. The user's emotional state, analyzed by an emotion engine, is also fed back, providing advice to help with pet care activities. For example, if the user is stressed, the system will suggest ways to relax. The app also has a function that allows users to easily contact a veterinarian as needed based on this information.

[0320] In this way, the system supports pet health management by analyzing the condition of both the pet and the user in real time and providing appropriate care.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device detects activity levels, heart rate, and location information in real time through sensors attached to the pet. This data is temporarily stored in the built-in memory and sent to the server in batches at regular intervals (e.g., every 5 minutes).

[0324] Step 2:

[0325] The server receives batch data sent from the terminal. This data is stored in a cloud database and prepared to be passed to the AI ​​analysis module.

[0326] Step 3:

[0327] The server's AI analysis module analyzes the received biometric information. It detects abnormal heart rates and activity patterns and assesses the risk level. For example, if the heart rate deviates significantly from normal, it prepares to issue an alert.

[0328] Step 4:

[0329] The server uses an emotion engine to receive data for analyzing the user's current emotional state. Based on user input, historical data, and device sensor information (e.g., voice tone), it estimates the user's current emotion.

[0330] Step 5:

[0331] The server compares the analysis results with the user's emotional state to determine what notification to generate. For example, if an abnormal condition is detected in the pet and the user's emotional state is determined to be anxious, the notification will include specific suggestions for calming down and encouraging messages.

[0332] Step 6:

[0333] Users receive notifications through a dedicated application and take immediate action based on the information provided in those notifications. The notifications include details about the pet's health and recommendations for future care, allowing users to take appropriate action.

[0334] (Example 2)

[0335] 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".

[0336] Pet health management is a crucial issue for pet owners, but traditional methods make it difficult to monitor a pet's health in real time. Furthermore, care plans that take into account the user's emotional state are not provided, preventing optimal care for both pets and users. Therefore, there is a need for a system that collects pet biometric information in real time, monitors the user's emotional state, and supports appropriate health management.

[0337] 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.

[0338] In this invention, the server includes an analysis means that uses machine learning to analyze the pet's biometric information and detect abnormalities, an emotion analysis means that analyzes the user's emotional state and psychologically optimizes notifications regarding the pet's health status, and a notification generation means that outputs the pet's health status based on the analysis results and proposes appropriate responses to the user. This makes it possible to monitor the pet's health status in real time and provide a care plan that is appropriate to the user's emotional state.

[0339] A "sensor device" is a device attached to a pet to continuously acquire biological information, and its role is to measure activity level, heart rate, location information, and so on.

[0340] "Wearable data acquisition means" refers to a means for acquiring biological information through a sensor device attached to a pet and using it for subsequent data processing.

[0341] "Encrypted data communication means" refers to a method that uses data encryption technology to securely transfer biometric information collected from sensor devices to a server.

[0342] A "data processing device" is a computer system installed to analyze received biological information, and it is used to detect abnormalities in health conditions and predict risks.

[0343] "Analysis methods using machine learning" are methods installed in data processing devices that learn past data patterns and detect anomalies by comparing them with current biological information.

[0344] "Emotional analysis means" refers to a method that uses technology to acquire and analyze the user's emotional state and reflect the results in pet care.

[0345] A "notification generation mechanism" is a system function that generates and provides notifications to users based on analysis results, prompting them to provide appropriate information or take action.

[0346] "User interface means" refers to an interface that visually displays the analysis results of a data processing device, allowing the user to confirm the information.

[0347] A "predictive model" is an algorithm that uses received biometric information and user sentiment analysis results to predict future health risks and user behavior.

[0348] This invention uses multiple hardware and software components to monitor the health status of a pet in real time and to provide care that is tailored to the user's emotional state.

[0349] The device collects biometric information using a sensor device attached to the pet. The sensor device is equipped with sensors that measure heart rate and activity level, thereby continuously acquiring biometric data from the pet. The collected data is temporarily stored in the device and compiled at regular intervals.

[0350] The device uses the AES encryption algorithm to encrypt the collected data. This encryption ensures confidentiality during data transmission. The encrypted data is sent to the server via the HTTPS protocol. This process ensures data security.

[0351] The server decrypts the received data and begins analysis. The analysis utilizes machine learning, employing an AI data analysis module based on TensorFlow. It compares the current data with historical data to detect anomalies and predict health risks.

[0352] The server is also equipped with an emotion engine to analyze user emotions. This engine analyzes voice and input data received from the user's terminal to determine the user's emotional state. Natural language processing technology is used for this analysis, allowing for an accurate understanding of the user's psychological state.

[0353] The server generates and sends notifications to the user based on the analysis results. A generative AI model is used to automatically create messages appropriate to the user's emotional state. For example, by entering a question as a prompt, such as "My pet's heart rate is higher than normal, but is this normal because we are out for a walk?", the AI ​​helps to make an appropriate decision.

[0354] Users receive this information through the application. Developed with React Native, the app visually displays pet health status in graphs and alerts, allowing users to take quick action. It also includes a feature to contact a veterinarian with a single tap in emergencies.

[0355] In this way, the present invention realizes a system that provides optimal care for both pets and users and efficiently supports health management.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The device receives biometric information as input from sensor devices attached to the pet. Here, data such as heart rate, activity level, and location information are acquired by the sensors. This information is sampled at regular intervals and temporarily stored in the device's internal memory. Specifically, for example, the heart rate sensor reads data every second and calculates the average value.

[0359] Step 2:

[0360] The device performs the process of encrypting the collected biometric information. The biometric information collected in step 1 is used as input. Data security is enhanced by encrypting the data using the AES encryption algorithm. The encrypted data is prepared as output and then ready for transmission. Specifically, an encryption module inside the device converts the data.

[0361] Step 3:

[0362] The terminal sends encrypted data to the server. The input here is encrypted biometric information. Data is securely transferred via the HTTPS protocol. The output is the receipt of data to the server. Specifically, the terminal's communication module becomes active and sends a request to the server over the internet.

[0363] Step 4:

[0364] The server decrypts the data received from the terminal and analyzes the biometric information. Using the decrypted input data, it performs calculations using machine learning to detect abnormal patterns and health risks. The output is the analysis results regarding the pet's health status. Specifically, the TensorFlow module runs on the server to identify abnormal data.

[0365] Step 5:

[0366] The server performs a process to analyze the user's emotions. Voice data and input speed from the user's terminal are used as input. Based on this data, the emotion analysis engine performs calculations to evaluate the psychological state and identify the emotional state. The output is the user's current emotional state. Specifically, a natural language processing algorithm analyzes the user input.

[0367] Step 6:

[0368] The server generates a notification based on the analysis results and sends it to the user. It then provides the generated notification message to the user as output. At this stage, a generation AI model is used to generate a message appropriate for the user. Specifically, the AI ​​selects and customizes a message template based on the input data.

[0369] Step 7:

[0370] The user receives notifications and analysis results from the server through the application. The input here is notification data from the server. The application visually displays this information and shows the user what specific action they should take. The output is an interface that allows the user to obtain information and take action. Specifically, the application, built with React Native, renders the data on the screen and allows the user to interact with UI elements as needed.

[0371] (Application Example 2)

[0372] 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 as the "terminal".

[0373] There is a need to effectively manage pets' biometric information and provide an environment where pet owners can entrust their pets with peace of mind. However, conventional methods have not provided a comprehensive system that can monitor pets' conditions in real time and reduce the psychological burden on pet owners.

[0374] 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.

[0375] In this invention, the server includes a portable information acquisition device means equipped with a sensor mechanism for collecting biological information, a communication mechanism means for immediately transmitting the collected biological information to a remote information processing system, and an information processing tool means having an analysis system for analyzing the received biological information and detecting abnormalities. This makes it possible to monitor the health status of pets in real time and respond immediately if an abnormality is detected, thereby providing an environment where pets can be left with peace of mind.

[0376] "Biometric information" refers to data obtained from a pet's body, such as activity level, heart rate, and location information.

[0377] A "sensor mechanism" is a device used to physically measure a pet's biological information.

[0378] A "portable information acquisition device" is a device that can be attached to a pet and continuously acquires biological information even while it is in motion.

[0379] A "communication mechanism" is a technical means for instantly transmitting acquired biometric information to a remote location.

[0380] An "information processing system" is a computer system that analyzes received biological information and determines whether or not there are any abnormalities.

[0381] An "analysis system" is software or hardware used within an information processing system to analyze biological information and detect abnormal patterns.

[0382] "Information processing equipment" refers to devices and software used to analyze biological information and output the analysis results.

[0383] A "notification generation mechanism" is a means of informing users of important information based on analysis results.

[0384] "Emotional analysis function" is a technology used to understand the psychological state of a user.

[0385] A "user interface system" is an interface that visually displays data processing results and enables the exchange of information between the user and the system.

[0386] In this invention, the following system is configured to effectively manage the biometric information of pets. A terminal continuously collects biometric information such as activity level, heart rate, and location information using a sensor mechanism attached to the pet. This information is transmitted to a server in real time via a portable information acquisition device. The server receives the collected biometric information through a communication mechanism, analyzes it using an information processing system, and determines whether or not there are any abnormalities.

[0387] The information processing system incorporates an analysis system that analyzes biometric information to detect abnormal patterns. Based on these analysis results, a notification generation mechanism informs the user of important information. The emotion analysis function is used to understand the user's psychological state, enabling the suggestion of appropriate care.

[0388] The user interface system visually displays the analysis results, allowing users to check the animal's health status and take appropriate action. For example, if a pet's stress level rises while it is in the salon, the server can use the recognized data to notify the owner of recommended relaxation methods. An example of a prompt using the generative AI model is: "Generate a message suggesting ways to help the owner relax their pet based on its current stress level."

[0389] In this way, the invention becomes a system that manages the health status of pets in real time, contributing to pet owners being able to entrust their pets to care facilities with peace of mind.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The device acquires biometric information such as activity level, heart rate, and location from a sensor mechanism attached to the pet. This data is directly input from the sensor mechanism's sensors and sent to the device. The device then prepares to collect and store this data.

[0393] Step 2:

[0394] The device transmits collected biometric information to the server in real time. The input is biometric data, and the output is an encrypted data stream. Data is transferred using Bluetooth or Wi-Fi communication and delivered to the server via a secure protocol.

[0395] Step 3:

[0396] The server inputs biometric information received from the terminal into the analysis system. The information processing system uses machine learning algorithms to detect abnormal patterns. The input is the received biometric data, and the output is the analysis result indicating whether or not an abnormality is present. If an abnormality is detected, related detailed information is also generated.

[0397] Step 4:

[0398] The server generates a notification based on the analysis results and sends it to the user's terminal. In this process, the notification generation mechanism assembles information from the input analysis results and forms a notification message as output. The notification includes important information and recommended actions regarding the pet's health status.

[0399] Step 5:

[0400] The user views the analysis results through the terminal's user interface. Input is notification messages from the server, and output is visualized data. The user interface system displays this data in a viewable format, assisting the user in taking necessary actions.

[0401] Step 6:

[0402] The server uses sentiment analysis to determine the user's psychological state and optimize the notification content. Here, user feedback and historical data are used as input, and a generative AI model is used to create prompt sentences, resulting in the output of messages that include personalized advice and recommendations.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention is a system for monitoring a pet's health in real time and detecting abnormalities early. The system consists of a sensor device attached to the pet, a server for analyzing the data, and a terminal used by the user.

[0420] Terminal role

[0421] The device, when attached to a pet, continuously measures biometric information such as activity level, heart rate, and location. For example, a sensor measures heart rate every second, and a GPS module records location data. The device collects this data at regular intervals and temporarily stores it in buffer memory. Then, when the data meets the set conditions, it transmits it to a server via a secure protocol.

[0422] Server Processing

[0423] The server receives data transmitted from the terminal in real time. The received data is analyzed using AI algorithms to evaluate the pet's health and any abnormal behavior. For example, if an abnormal heart rate or activity pattern is detected, the server evaluates the cause and calculates the risk of stress or illness. Based on the assessed risk, alerts and reports are automatically generated.

[0424] User interaction

[0425] Users can view information analyzed by the server through a dedicated application. The application visually represents the pet's health status and has the function to send pop-up notifications or email notifications if an abnormality is detected. Users can receive notifications and consult a veterinarian as needed. In addition, users can gain insights to change their pet's daily lifestyle patterns. For example, if the pet's activity level is low, measures to increase exercise can be considered.

[0426] Thus, the present invention is a system that helps provide appropriate care by routinely monitoring the health of pets and detecting abnormalities early.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The measurement data is temporarily stored in buffer memory.

[0430] Step 2:

[0431] The terminal aggregates data stored in buffer memory at regular time intervals and sends it to the server using a data communication method. The data is transmitted securely via a secure protocol (e.g., HTTPS).

[0432] Step 3:

[0433] The server processes biometric information received from the terminal in real time via streaming and stores it in a designated database.

[0434] Step 4:

[0435] The server inputs the stored data into an AI analysis module to analyze the pet's health status. If any abnormal patterns or values ​​are detected, the AI ​​model immediately passes that information to a notification generation module.

[0436] Step 5:

[0437] The server generates alerts for users based on the analysis results. Specifically, it creates notifications that include the level of risk and recommended actions corresponding to the anomalies, and sends them to users and relevant parties based on the device's registration information.

[0438] Step 6:

[0439] Users can check received notifications within the application to stay informed about their pet's health. If necessary, they can use the provided information to consult with a veterinarian or adjust their pet's care plan.

[0440] (Example 1)

[0441] 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."

[0442] For pet owners, it is crucial to monitor their animals' health in real time and detect abnormalities early. However, conventional technologies have problems with accurately collecting biological information and rapidly evaluating abnormalities. Furthermore, there is a lack of systems that can instantly provide visualized information to users and enable immediate countermeasures. This invention was developed to solve these problems.

[0443] 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.

[0444] In this invention, the server includes activity monitoring means for continuously measuring and storing biological information of animals, data transmission means for transmitting the stored information to a processing device via secure communication means under certain conditions, and analysis device means for analyzing the received information and evaluating abnormal patterns using a machine learning model. This enables precise monitoring of the animal's biological state and immediate detection of abnormalities.

[0445] "Animal biometric information" is a general term for various types of data necessary to understand an animal's health status, such as heart rate, activity level, and location information.

[0446] "Activity monitoring means" refers to a device attached to an animal to continuously measure biological information and record its fluctuations.

[0447] "Data transmission means" refers to a mechanism that transmits collected biometric information to an external processing device using a secure protocol under certain conditions.

[0448] An "analysis device" refers to a processing system that evaluates received biological information and determines abnormal patterns using machine learning models or the like.

[0449] "Notification means" refers to a function that informs the user of alarms generated based on analysis results, either visually or electronically.

[0450] This invention relates to a system for continuously monitoring the health status of animals and detecting abnormalities early. This system consists of a device (terminal) attached to the animal, a computer system (server) that processes data, and an interface device (user interface) used by the user.

[0451] The device functions as an activity monitoring device attached to an animal, continuously measuring heart rate, activity level, and location information. This data is temporarily stored in an internal buffer at regular intervals. Hardware such as a heart rate sensor, accelerometer, and GPS module is used for data collection. Once data has accumulated in the buffer memory, the device sends the data to a server using a secure protocol (e.g., HTTPS).

[0452] The server receives biometric information transmitted from the terminal in real time and analyzes it using machine learning models and AI algorithms. For example, an anomaly detection algorithm can identify abnormal heart rate patterns and assess medical risks. Based on the analysis results, the server automatically generates alarms and provides the ability to detect invisible anomalies.

[0453] Users can access information analyzed on the server using a dedicated application. This information visually represents insights into the animal's health and provides notifications when abnormalities are detected. The smartphone or tablet application issues alerts via pop-up notifications and email. Based on this information, users can take swift action and consult a specialist if necessary.

[0454] For example, if the device detects an abnormality in an animal's heart rate, analysis on the server may suggest that the animal is experiencing stress. The user can then receive this information and take action to improve the animal's living environment.

[0455] Examples of prompts for generative AI models are as follows:

[0456] "Please describe the process of a system that analyzes data and notifies the user when a pet's heart rate is different from normal."

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The device periodically acquires activity information, heart rate, and location information from sensors attached to the animal. Biometric data from the sensors is provided as input, which is converted into a digital format and temporarily stored. Specifically, the heart rate sensor captures the heart rate every second, the accelerometer assesses the activity level, and the GPS module records the location coordinates.

[0460] Step 2:

[0461] The terminal sends data to the server using a secure method (e.g., the HTTPS protocol) when certain conditions are met. The input is collected data in buffer memory, and the decision to send the data is made considering the data's timestamp and quantity. Specifically, the data is configured to be batched at regular intervals and securely sent to the endpoint.

[0462] Step 3:

[0463] The server receives data transmitted from the terminal and stores it in a database. The received biometric information becomes input and undergoes formatting processing for analysis. Specifically, the data is recorded and prepared for input into analysis models for anomaly and pattern detection.

[0464] Step 4:

[0465] The server analyzes data in real time using machine learning models. The input is biometric data that has been formatted, and the output is anomaly detection results and health risk assessment results. Specifically, it processes the received data through an analysis algorithm to identify, for example, heart rate patterns that are different from normal.

[0466] Step 5:

[0467] The server generates an alert when it detects an anomaly based on the analysis results. The analysis results are used as input, and the output is an alarm message. Specifically, when an anomaly is detected, the process automatically generates a warning message according to the risk level.

[0468] Step 6:

[0469] The server then initiates the process of sending the generated alert to the user's device. The input is the generated alert, and the output is the notification information sent to the user's device. Specifically, a push notification is triggered, and the alert is displayed on the user's smartphone or tablet.

[0470] Step 7:

[0471] Users can view received notifications within the application and take action as needed. Input is the alarm information displayed on the device, and output considers the user's actions and countermeasures. Specifically, users can check details within the app and consult a veterinarian if the abnormality persists.

[0472] (Application Example 1)

[0473] 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."

[0474] In addition to real-time monitoring of pets' health and early detection of abnormalities, there is a need to improve pet safety. Specifically, a system is required that, when an abnormality in a pet's health is detected, quickly adjusts the environment to reduce the pet's anxiety and stress, and provides an optimal living environment.

[0475] 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.

[0476] In this invention, the server includes an analysis means for analyzing received biometric information and detecting abnormalities, a notification generation means for outputting the pet's health status and safety based on the analysis results, and an external linkage means for coordinating with an environmental control system when an abnormality is detected. This makes it possible to notify the owner and automatically optimize the pet's living environment at the same time when the pet's health status shows an abnormality.

[0477] "Pet biometric information" refers to data such as heart rate, activity level, and location information necessary to assess a pet's health.

[0478] "Wearable data acquisition means" refers to sensor devices and communication equipment that are attached to pets to collect biological information.

[0479] "Data communication means" refers to technology for transmitting collected biometric information in real time to a data processing device located in a remote location.

[0480] "Analysis means" refers to functions or programs on a data processing device that detect abnormalities in a pet's health condition based on received biological information.

[0481] A "notification generation mechanism" is a system that generates reports and warnings to inform pet owners of their pet's health status and any abnormalities based on the analysis results.

[0482] "External interlocking means" refers to a technology that, when an abnormality is detected, works in cooperation with a pre-designated external environmental control system to adjust the pet's living environment.

[0483] A "predictive model" is a model that uses mathematical or statistical methods to predict future health risks for pets based on received biometric information.

[0484] A "user interface means" is an information display device that visually displays analysis results and linkage status from a data processing device for use by the user.

[0485] The embodiment for carrying out the invention describes a system for monitoring a pet's health and safety in real time, and for detecting and responding to abnormalities early. This system integrates a wearable data acquisition means, data communication means, analysis means, notification generation means, external linking means, and user interface means. The specific operation of each means is described below.

[0486] First, a wearable data acquisition device, which serves as the terminal, is attached to the pet. The terminal continuously measures biometric information such as activity level, heart rate, and location using its built-in sensors. This biometric information is transmitted to a server in real time via data communication. Communication technologies such as Bluetooth and Wi-Fi are used in this process.

[0487] Next, the server analyzes the received biometric information using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis, and if an anomaly is detected, the cause of the anomaly is identified as a result of the analysis. Using predictive models, health risks are assessed, and adjustments to the external environment are proposed as needed.

[0488] The analyzed results are notified to the user via a notification generation mechanism. The information is presented in a visualized form on the user's smartphone or other display device, and a real-time pop-up notification is sent in case of anomalies.

[0489] Furthermore, the external connectivity system can interact with the smart home system in response to anomaly detection, enabling environmental adjustments to ensure the pet's comfort. For example, if a pet's heart rate is higher than normal, the system can dim the room lights or play music to calm the pet.

[0490] Examples of prompts for generative AI models:

[0491] "If a pet's heart rate is higher than normal, please suggest possible causes and countermeasures. Consider the stress level and create an appropriate notification for the owner."

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. This data is temporarily stored in the device's internal memory. The input is biometric data, and the output is data stored in the device's memory.

[0495] Step 2:

[0496] The device communicates data via Bluetooth or Wi-Fi to send temporarily stored biometric information to the server at regular intervals. The input is the biometric information stored on the device, and the output is the biometric information sent to the server.

[0497] Step 3:

[0498] The server analyzes biometric information received from the terminal using AI algorithms. Here, TensorFlow and PyTorch are used to detect anomalies. The input is the biometric information received by the server, and the output is the analysis results, such as whether or not anomalies were detected.

[0499] Step 4:

[0500] The server uses a generative AI model based on the analysis results to assess health risks and analyze the causes of anomalies. Furthermore, it generates appropriate notification content. The input is the analysis results, and the output is the assessed risk level and notification content.

[0501] Step 5:

[0502] The server sends notifications based on the analysis results to the user's smartphone. The user interface displays the abnormal situation in real time and provides alerts to the user. The input is the notification content, and the output is the notification displayed on the user's smartphone.

[0503] Step 6:

[0504] When an anomaly is detected, the server communicates with the smart home system using an external linkage mechanism and automatically adjusts the pet's living environment. The input is the type and level of the anomaly, and the output is the adjusted environmental settings. This allows the system to respond in a way that ensures the pet is comfortable.

[0505] 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.

[0506] This invention is a system that collects and analyzes a pet's biological information to monitor its health, and further recognizes the user's emotions to optimize pet care. The system comprises a sensor device attached to the pet, a data processing device, an emotion engine that analyzes the user's emotions, and a terminal used by the user.

[0507] Terminal role

[0508] The device continuously collects biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The collected data is aggregated at regular intervals and transmitted to a server using data communication methods. The transmitted data is encrypted through a secure protocol to prevent leakage to external parties.

[0509] Server Processing

[0510] When the server receives biometric information transmitted from the terminal, an AI-powered data analysis module analyzes the pet's health status. If abnormal data patterns or health risks are detected, a notification is generated based on this. Furthermore, an emotion engine analyzes the user's emotions from the user terminal's sensors and historical data, and plans a response tailored to the user's psychological state. For example, if the system detects that the user's anxiety is increasing due to the pet's abnormal behavior, it will provide a reassuring message and useful information about the pet's health.

[0511] User interaction

[0512] Users receive information analyzed by the server through the application. The app graphically displays the pet's health status and indicates actions to take immediately as needed. The user's emotional state, analyzed by an emotion engine, is also fed back, providing advice to help with pet care activities. For example, if the user is stressed, the system will suggest ways to relax. The app also has a function that allows users to easily contact a veterinarian as needed based on this information.

[0513] In this way, the system supports pet health management by analyzing the condition of both the pet and the user in real time and providing appropriate care.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The device detects activity levels, heart rate, and location information in real time through sensors attached to the pet. This data is temporarily stored in the built-in memory and sent to the server in batches at regular intervals (e.g., every 5 minutes).

[0517] Step 2:

[0518] The server receives batch data sent from the terminal. This data is stored in a cloud database and prepared to be passed to the AI ​​analysis module.

[0519] Step 3:

[0520] The server's AI analysis module analyzes the received biometric information. It detects abnormal heart rates and activity patterns and assesses the risk level. For example, if the heart rate deviates significantly from normal, it prepares to issue an alert.

[0521] Step 4:

[0522] The server uses an emotion engine to receive data for analyzing the user's current emotional state. Based on user input, historical data, and device sensor information (e.g., voice tone), it estimates the user's current emotion.

[0523] Step 5:

[0524] The server compares the analysis results with the user's emotional state to determine what notification to generate. For example, if an abnormal condition is detected in the pet and the user's emotional state is determined to be anxious, the notification will include specific suggestions for calming down and encouraging messages.

[0525] Step 6:

[0526] Users receive notifications through a dedicated application and take immediate action based on the information provided in those notifications. The notifications include details about the pet's health and recommendations for future care, allowing users to take appropriate action.

[0527] (Example 2)

[0528] 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."

[0529] Pet health management is a crucial issue for pet owners, but traditional methods make it difficult to monitor a pet's health in real time. Furthermore, care plans that take into account the user's emotional state are not provided, preventing optimal care for both pets and users. Therefore, there is a need for a system that collects pet biometric information in real time, monitors the user's emotional state, and supports appropriate health management.

[0530] 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.

[0531] In this invention, the server includes an analysis means that uses machine learning to analyze the pet's biometric information and detect abnormalities, an emotion analysis means that analyzes the user's emotional state and psychologically optimizes notifications regarding the pet's health status, and a notification generation means that outputs the pet's health status based on the analysis results and proposes appropriate responses to the user. This makes it possible to monitor the pet's health status in real time and provide a care plan that is appropriate to the user's emotional state.

[0532] A "sensor device" is a device attached to a pet to continuously acquire biological information, and its role is to measure activity level, heart rate, location information, and so on.

[0533] "Wearable data acquisition means" refers to a means for acquiring biological information through a sensor device attached to a pet and using it for subsequent data processing.

[0534] "Encrypted data communication means" refers to a method that uses data encryption technology to securely transfer biometric information collected from sensor devices to a server.

[0535] A "data processing device" is a computer system installed to analyze received biological information, and it is used to detect abnormalities in health conditions and predict risks.

[0536] "Analysis methods using machine learning" are methods installed in data processing devices that learn past data patterns and detect anomalies by comparing them with current biological information.

[0537] "Emotional analysis means" refers to a method that uses technology to acquire and analyze the user's emotional state and reflect the results in pet care.

[0538] A "notification generation mechanism" is a system function that generates and provides notifications to users based on analysis results, prompting them to provide appropriate information or take action.

[0539] "User interface means" refers to an interface that visually displays the analysis results of a data processing device, allowing the user to confirm the information.

[0540] A "predictive model" is an algorithm that uses received biometric information and user sentiment analysis results to predict future health risks and user behavior.

[0541] This invention uses multiple hardware and software components to monitor the health status of a pet in real time and to provide care that is tailored to the user's emotional state.

[0542] The device collects biometric information using a sensor device attached to the pet. The sensor device is equipped with sensors that measure heart rate and activity level, thereby continuously acquiring biometric data from the pet. The collected data is temporarily stored in the device and compiled at regular intervals.

[0543] The device uses the AES encryption algorithm to encrypt the collected data. This encryption ensures confidentiality during data transmission. The encrypted data is sent to the server via the HTTPS protocol. This process ensures data security.

[0544] The server decrypts the received data and begins analysis. The analysis utilizes machine learning, employing an AI data analysis module based on TensorFlow. It compares the current data with historical data to detect anomalies and predict health risks.

[0545] The server is also equipped with an emotion engine to analyze user emotions. This engine analyzes voice and input data received from the user's terminal to determine the user's emotional state. Natural language processing technology is used for this analysis, allowing for an accurate understanding of the user's psychological state.

[0546] The server generates and sends notifications to the user based on the analysis results. A generative AI model is used to automatically create messages appropriate to the user's emotional state. For example, by entering a question as a prompt, such as "My pet's heart rate is higher than normal, but is this normal because we are out for a walk?", the AI ​​helps to make an appropriate decision.

[0547] Users receive this information through the application. Developed with React Native, the app visually displays pet health status in graphs and alerts, allowing users to take quick action. It also includes a feature to contact a veterinarian with a single tap in emergencies.

[0548] In this way, the present invention realizes a system that provides optimal care for both pets and users and efficiently supports health management.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The device receives biometric information as input from sensor devices attached to the pet. Here, data such as heart rate, activity level, and location information are acquired by the sensors. This information is sampled at regular intervals and temporarily stored in the device's internal memory. Specifically, for example, the heart rate sensor reads data every second and calculates the average value.

[0552] Step 2:

[0553] The device performs the process of encrypting the collected biometric information. The biometric information collected in step 1 is used as input. Data security is enhanced by encrypting the data using the AES encryption algorithm. The encrypted data is prepared as output and then ready for transmission. Specifically, an encryption module inside the device converts the data.

[0554] Step 3:

[0555] The terminal sends encrypted data to the server. The input here is encrypted biometric information. Data is securely transferred via the HTTPS protocol. The output is the receipt of data to the server. Specifically, the terminal's communication module becomes active and sends a request to the server over the internet.

[0556] Step 4:

[0557] The server decrypts the data received from the terminal and analyzes the biometric information. Using the decrypted input data, it performs calculations using machine learning to detect abnormal patterns and health risks. The output is the analysis results regarding the pet's health status. Specifically, the TensorFlow module runs on the server to identify abnormal data.

[0558] Step 5:

[0559] The server performs a process to analyze the user's emotions. Voice data and input speed from the user's terminal are used as input. Based on this data, the emotion analysis engine performs calculations to evaluate the psychological state and identify the emotional state. The output is the user's current emotional state. Specifically, a natural language processing algorithm analyzes the user input.

[0560] Step 6:

[0561] The server generates a notification based on the analysis results and sends it to the user. It then provides the generated notification message to the user as output. At this stage, a generation AI model is used to generate a message appropriate for the user. Specifically, the AI ​​selects and customizes a message template based on the input data.

[0562] Step 7:

[0563] The user receives notifications and analysis results from the server through the application. The input here is notification data from the server. The application visually displays this information and shows the user what specific action they should take. The output is an interface that allows the user to obtain information and take action. Specifically, the application, built with React Native, renders the data on the screen and allows the user to interact with UI elements as needed.

[0564] (Application Example 2)

[0565] 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."

[0566] There is a need to effectively manage pets' biometric information and provide an environment where pet owners can entrust their pets with peace of mind. However, conventional methods have not provided a comprehensive system that can monitor pets' conditions in real time and reduce the psychological burden on pet owners.

[0567] 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.

[0568] In this invention, the server includes a portable information acquisition device means equipped with a sensor mechanism for collecting biological information, a communication mechanism means for immediately transmitting the collected biological information to a remote information processing system, and an information processing tool means having an analysis system for analyzing the received biological information and detecting abnormalities. This makes it possible to monitor the health status of pets in real time and respond immediately if an abnormality is detected, thereby providing an environment where pets can be left with peace of mind.

[0569] "Biometric information" refers to data obtained from a pet's body, such as activity level, heart rate, and location information.

[0570] A "sensor mechanism" is a device used to physically measure a pet's biological information.

[0571] A "portable information acquisition device" is a device that can be attached to a pet and continuously acquires biological information even while it is in motion.

[0572] A "communication mechanism" is a technical means for instantly transmitting acquired biometric information to a remote location.

[0573] An "information processing system" is a computer system that analyzes received biological information and determines whether or not there are any abnormalities.

[0574] An "analysis system" is software or hardware used within an information processing system to analyze biological information and detect abnormal patterns.

[0575] "Information processing equipment" refers to devices and software used to analyze biological information and output the analysis results.

[0576] A "notification generation mechanism" is a means of informing users of important information based on analysis results.

[0577] "Emotional analysis function" is a technology used to understand the psychological state of a user.

[0578] A "user interface system" is an interface that visually displays data processing results and enables the exchange of information between the user and the system.

[0579] In this invention, the following system is configured to effectively manage the biometric information of pets. A terminal continuously collects biometric information such as activity level, heart rate, and location information using a sensor mechanism attached to the pet. This information is transmitted to a server in real time via a portable information acquisition device. The server receives the collected biometric information through a communication mechanism, analyzes it using an information processing system, and determines whether or not there are any abnormalities.

[0580] The information processing system incorporates an analysis system that analyzes biometric information to detect abnormal patterns. Based on these analysis results, a notification generation mechanism informs the user of important information. The emotion analysis function is used to understand the user's psychological state, enabling the suggestion of appropriate care.

[0581] The user interface system visually displays the analysis results, allowing users to check the animal's health status and take appropriate action. For example, if a pet's stress level rises while it is in the salon, the server can use the recognized data to notify the owner of recommended relaxation methods. An example of a prompt using the generative AI model is: "Generate a message suggesting ways to help the owner relax their pet based on its current stress level."

[0582] In this way, the invention becomes a system that manages the health status of pets in real time, contributing to pet owners being able to entrust their pets to care facilities with peace of mind.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The device acquires biometric information such as activity level, heart rate, and location from a sensor mechanism attached to the pet. This data is directly input from the sensor mechanism's sensors and sent to the device. The device then prepares to collect and store this data.

[0586] Step 2:

[0587] The device transmits collected biometric information to the server in real time. The input is biometric data, and the output is an encrypted data stream. Data is transferred using Bluetooth or Wi-Fi communication and delivered to the server via a secure protocol.

[0588] Step 3:

[0589] The server inputs biometric information received from the terminal into the analysis system. The information processing system uses machine learning algorithms to detect abnormal patterns. The input is the received biometric data, and the output is the analysis result indicating whether or not an abnormality is present. If an abnormality is detected, related detailed information is also generated.

[0590] Step 4:

[0591] The server generates a notification based on the analysis results and sends it to the user's terminal. In this process, the notification generation mechanism assembles information from the input analysis results and forms a notification message as output. The notification includes important information and recommended actions regarding the pet's health status.

[0592] Step 5:

[0593] The user views the analysis results through the terminal's user interface. Input is notification messages from the server, and output is visualized data. The user interface system displays this data in a viewable format, assisting the user in taking necessary actions.

[0594] Step 6:

[0595] The server uses sentiment analysis to determine the user's psychological state and optimize the notification content. Here, user feedback and historical data are used as input, and a generative AI model is used to create prompt sentences, resulting in the output of messages that include personalized advice and recommendations.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This invention is a system for monitoring a pet's health in real time and detecting abnormalities early. The system consists of a sensor device attached to the pet, a server for analyzing the data, and a terminal used by the user.

[0614] Terminal role

[0615] The device, when attached to a pet, continuously measures biometric information such as activity level, heart rate, and location. For example, a sensor measures heart rate every second, and a GPS module records location data. The device collects this data at regular intervals and temporarily stores it in buffer memory. Then, when the data meets the set conditions, it transmits it to a server via a secure protocol.

[0616] Server Processing

[0617] The server receives data transmitted from the terminal in real time. The received data is analyzed using AI algorithms to evaluate the pet's health and any abnormal behavior. For example, if an abnormal heart rate or activity pattern is detected, the server evaluates the cause and calculates the risk of stress or illness. Based on the assessed risk, alerts and reports are automatically generated.

[0618] User interaction

[0619] Users can view information analyzed by the server through a dedicated application. The application visually represents the pet's health status and has the function to send pop-up notifications or email notifications if an abnormality is detected. Users can receive notifications and consult a veterinarian as needed. In addition, users can gain insights to change their pet's daily lifestyle patterns. For example, if the pet's activity level is low, measures to increase exercise can be considered.

[0620] Thus, the present invention is a system that helps provide appropriate care by routinely monitoring the health of pets and detecting abnormalities early.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The measurement data is temporarily stored in buffer memory.

[0624] Step 2:

[0625] The terminal aggregates data stored in buffer memory at regular time intervals and sends it to the server using a data communication method. The data is transmitted securely via a secure protocol (e.g., HTTPS).

[0626] Step 3:

[0627] The server processes biometric information received from the terminal in real time via streaming and stores it in a designated database.

[0628] Step 4:

[0629] The server inputs the stored data into an AI analysis module to analyze the pet's health status. If any abnormal patterns or values ​​are detected, the AI ​​model immediately passes that information to a notification generation module.

[0630] Step 5:

[0631] The server generates alerts for users based on the analysis results. Specifically, it creates notifications that include the level of risk and recommended actions corresponding to the anomalies, and sends them to users and relevant parties based on the device's registration information.

[0632] Step 6:

[0633] Users can check received notifications within the application to stay informed about their pet's health. If necessary, they can use the provided information to consult with a veterinarian or adjust their pet's care plan.

[0634] (Example 1)

[0635] 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".

[0636] For pet owners, it is crucial to monitor their animals' health in real time and detect abnormalities early. However, conventional technologies have problems with accurately collecting biological information and rapidly evaluating abnormalities. Furthermore, there is a lack of systems that can instantly provide visualized information to users and enable immediate countermeasures. This invention was developed to solve these problems.

[0637] 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.

[0638] In this invention, the server includes activity monitoring means for continuously measuring and storing biological information of animals, data transmission means for transmitting the stored information to a processing device via secure communication means under certain conditions, and analysis device means for analyzing the received information and evaluating abnormal patterns using a machine learning model. This enables precise monitoring of the animal's biological state and immediate detection of abnormalities.

[0639] "Animal biometric information" is a general term for various types of data necessary to understand an animal's health status, such as heart rate, activity level, and location information.

[0640] "Activity monitoring means" refers to a device attached to an animal to continuously measure biological information and record its fluctuations.

[0641] "Data transmission means" refers to a mechanism that transmits collected biometric information to an external processing device using a secure protocol under certain conditions.

[0642] An "analysis device" refers to a processing system that evaluates received biological information and determines abnormal patterns using machine learning models or the like.

[0643] "Notification means" refers to a function that informs the user of alarms generated based on analysis results, either visually or electronically.

[0644] This invention relates to a system for continuously monitoring the health status of animals and detecting abnormalities early. This system consists of a device (terminal) attached to the animal, a computer system (server) that processes data, and an interface device (user interface) used by the user.

[0645] The device functions as an activity monitoring device attached to an animal, continuously measuring heart rate, activity level, and location information. This data is temporarily stored in an internal buffer at regular intervals. Hardware such as a heart rate sensor, accelerometer, and GPS module is used for data collection. Once data has accumulated in the buffer memory, the device sends the data to a server using a secure protocol (e.g., HTTPS).

[0646] The server receives biometric information transmitted from the terminal in real time and analyzes it using machine learning models and AI algorithms. For example, an anomaly detection algorithm can identify abnormal heart rate patterns and assess medical risks. Based on the analysis results, the server automatically generates alarms and provides the ability to detect invisible anomalies.

[0647] Users can access information analyzed on the server using a dedicated application. This information visually represents insights into the animal's health and provides notifications when abnormalities are detected. The smartphone or tablet application issues alerts via pop-up notifications and email. Based on this information, users can take swift action and consult a specialist if necessary.

[0648] For example, if the device detects an abnormality in an animal's heart rate, analysis on the server may suggest that the animal is experiencing stress. The user can then receive this information and take action to improve the animal's living environment.

[0649] Examples of prompts for generative AI models are as follows:

[0650] "Please describe the process of a system that analyzes data and notifies the user when a pet's heart rate is different from normal."

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The device periodically acquires activity information, heart rate, and location information from sensors attached to the animal. Biometric data from the sensors is provided as input, which is converted into a digital format and temporarily stored. Specifically, the heart rate sensor captures the heart rate every second, the accelerometer assesses the activity level, and the GPS module records the location coordinates.

[0654] Step 2:

[0655] The terminal sends data to the server using a secure method (e.g., the HTTPS protocol) when certain conditions are met. The input is collected data in buffer memory, and the decision to send the data is made considering the data's timestamp and quantity. Specifically, the data is configured to be batched at regular intervals and securely sent to the endpoint.

[0656] Step 3:

[0657] The server receives data transmitted from the terminal and stores it in a database. The received biometric information becomes input and undergoes formatting processing for analysis. Specifically, the data is recorded and prepared for input into analysis models for anomaly and pattern detection.

[0658] Step 4:

[0659] The server analyzes data in real time using machine learning models. The input is biometric data that has been formatted, and the output is anomaly detection results and health risk assessment results. Specifically, it processes the received data through an analysis algorithm to identify, for example, heart rate patterns that are different from normal.

[0660] Step 5:

[0661] The server generates an alert when it detects an anomaly based on the analysis results. The analysis results are used as input, and the output is an alarm message. Specifically, when an anomaly is detected, the process automatically generates a warning message according to the risk level.

[0662] Step 6:

[0663] The server then initiates the process of sending the generated alert to the user's device. The input is the generated alert, and the output is the notification information sent to the user's device. Specifically, a push notification is triggered, and the alert is displayed on the user's smartphone or tablet.

[0664] Step 7:

[0665] Users can view received notifications within the application and take action as needed. Input is the alarm information displayed on the device, and output considers the user's actions and countermeasures. Specifically, users can check details within the app and consult a veterinarian if the abnormality persists.

[0666] (Application Example 1)

[0667] 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".

[0668] In addition to real-time monitoring of pets' health and early detection of abnormalities, there is a need to improve pet safety. Specifically, a system is required that, when an abnormality in a pet's health is detected, quickly adjusts the environment to reduce the pet's anxiety and stress, and provides an optimal living environment.

[0669] 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.

[0670] In this invention, the server includes an analysis means for analyzing received biometric information and detecting abnormalities, a notification generation means for outputting the pet's health status and safety based on the analysis results, and an external linkage means for coordinating with an environmental control system when an abnormality is detected. This makes it possible to notify the owner and automatically optimize the pet's living environment at the same time when the pet's health status shows an abnormality.

[0671] "Pet biometric information" refers to data such as heart rate, activity level, and location information necessary to assess a pet's health.

[0672] "Wearable data acquisition means" refers to sensor devices and communication equipment that are attached to pets to collect biological information.

[0673] "Data communication means" refers to technology for transmitting collected biometric information in real time to a data processing device located in a remote location.

[0674] "Analysis means" refers to functions or programs on a data processing device that detect abnormalities in a pet's health condition based on received biological information.

[0675] A "notification generation mechanism" is a system that generates reports and warnings to inform pet owners of their pet's health status and any abnormalities based on the analysis results.

[0676] "External interlocking means" refers to a technology that, when an abnormality is detected, works in cooperation with a pre-designated external environmental control system to adjust the pet's living environment.

[0677] A "predictive model" is a model that uses mathematical or statistical methods to predict future health risks for pets based on received biometric information.

[0678] A "user interface means" is an information display device that visually displays analysis results and linkage status from a data processing device for use by the user.

[0679] The embodiment for carrying out the invention describes a system for monitoring a pet's health and safety in real time, and for detecting and responding to abnormalities early. This system integrates a wearable data acquisition means, data communication means, analysis means, notification generation means, external linking means, and user interface means. The specific operation of each means is described below.

[0680] First, a wearable data acquisition device, which serves as the terminal, is attached to the pet. The terminal continuously measures biometric information such as activity level, heart rate, and location using its built-in sensors. This biometric information is transmitted to a server in real time via data communication. Communication technologies such as Bluetooth and Wi-Fi are used in this process.

[0681] Next, the server analyzes the received biometric information using AI algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis, and if an anomaly is detected, the cause of the anomaly is identified as a result of the analysis. Using predictive models, health risks are assessed, and adjustments to the external environment are proposed as needed.

[0682] The analyzed results are notified to the user via a notification generation mechanism. The information is presented in a visualized form on the user's smartphone or other display device, and a real-time pop-up notification is sent in case of anomalies.

[0683] Furthermore, the external connectivity system can interact with the smart home system in response to anomaly detection, enabling environmental adjustments to ensure the pet's comfort. For example, if a pet's heart rate is higher than normal, the system can dim the room lights or play music to calm the pet.

[0684] Examples of prompts for generative AI models:

[0685] "If a pet's heart rate is higher than normal, please suggest possible causes and countermeasures. Consider the stress level and create an appropriate notification for the owner."

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The device continuously measures biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. This data is temporarily stored in the device's internal memory. The input is biometric data, and the output is data stored in the device's memory.

[0689] Step 2:

[0690] The device communicates data via Bluetooth or Wi-Fi to send temporarily stored biometric information to the server at regular intervals. The input is the biometric information stored on the device, and the output is the biometric information sent to the server.

[0691] Step 3:

[0692] The server analyzes biometric information received from the terminal using AI algorithms. Here, TensorFlow and PyTorch are used to detect anomalies. The input is the biometric information received by the server, and the output is the analysis results, such as whether or not anomalies were detected.

[0693] Step 4:

[0694] The server uses a generative AI model based on the analysis results to assess health risks and analyze the causes of anomalies. Furthermore, it generates appropriate notification content. The input is the analysis results, and the output is the assessed risk level and notification content.

[0695] Step 5:

[0696] The server sends notifications based on the analysis results to the user's smartphone. The user interface displays the abnormal situation in real time and provides alerts to the user. The input is the notification content, and the output is the notification displayed on the user's smartphone.

[0697] Step 6:

[0698] When an anomaly is detected, the server communicates with the smart home system using an external linkage mechanism and automatically adjusts the pet's living environment. The input is the type and level of the anomaly, and the output is the adjusted environmental settings. This allows the system to respond in a way that ensures the pet is comfortable.

[0699] 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.

[0700] This invention is a system that collects and analyzes a pet's biological information to monitor its health, and further recognizes the user's emotions to optimize pet care. The system comprises a sensor device attached to the pet, a data processing device, an emotion engine that analyzes the user's emotions, and a terminal used by the user.

[0701] Terminal role

[0702] The device continuously collects biometric information such as activity level, heart rate, and location using sensor devices attached to the pet. The collected data is aggregated at regular intervals and transmitted to a server using data communication methods. The transmitted data is encrypted through a secure protocol to prevent leakage to external parties.

[0703] Server Processing

[0704] When the server receives biometric information transmitted from the terminal, an AI-powered data analysis module analyzes the pet's health status. If abnormal data patterns or health risks are detected, a notification is generated based on this. Furthermore, an emotion engine analyzes the user's emotions from the user terminal's sensors and historical data, and plans a response tailored to the user's psychological state. For example, if the system detects that the user's anxiety is increasing due to the pet's abnormal behavior, it will provide a reassuring message and useful information about the pet's health.

[0705] User interaction

[0706] Users receive information analyzed by the server through the application. The app graphically displays the pet's health status and indicates actions to take immediately as needed. The user's emotional state, analyzed by an emotion engine, is also fed back, providing advice to help with pet care activities. For example, if the user is stressed, the system will suggest ways to relax. The app also has a function that allows users to easily contact a veterinarian as needed based on this information.

[0707] In this way, the system supports pet health management by analyzing the condition of both the pet and the user in real time and providing appropriate care.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The device detects activity levels, heart rate, and location information in real time through sensors attached to the pet. This data is temporarily stored in the built-in memory and sent to the server in batches at regular intervals (e.g., every 5 minutes).

[0711] Step 2:

[0712] The server receives batch data sent from the terminal. This data is stored in a cloud database and prepared to be passed to the AI ​​analysis module.

[0713] Step 3:

[0714] The server's AI analysis module analyzes the received biometric information. It detects abnormal heart rates and activity patterns and assesses the risk level. For example, if the heart rate deviates significantly from normal, it prepares to issue an alert.

[0715] Step 4:

[0716] The server uses an emotion engine to receive data for analyzing the user's current emotional state. Based on user input, historical data, and device sensor information (e.g., voice tone), it estimates the user's current emotion.

[0717] Step 5:

[0718] The server compares the analysis results with the user's emotional state to determine what notification to generate. For example, if an abnormal condition is detected in the pet and the user's emotional state is determined to be anxious, the notification will include specific suggestions for calming down and encouraging messages.

[0719] Step 6:

[0720] Users receive notifications through a dedicated application and take immediate action based on the information provided in those notifications. The notifications include details about the pet's health and recommendations for future care, allowing users to take appropriate action.

[0721] (Example 2)

[0722] 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".

[0723] Pet health management is a crucial issue for pet owners, but traditional methods make it difficult to monitor a pet's health in real time. Furthermore, care plans that take into account the user's emotional state are not provided, preventing optimal care for both pets and users. Therefore, there is a need for a system that collects pet biometric information in real time, monitors the user's emotional state, and supports appropriate health management.

[0724] 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.

[0725] In this invention, the server includes an analysis means that uses machine learning to analyze the pet's biometric information and detect abnormalities, an emotion analysis means that analyzes the user's emotional state and psychologically optimizes notifications regarding the pet's health status, and a notification generation means that outputs the pet's health status based on the analysis results and proposes appropriate responses to the user. This makes it possible to monitor the pet's health status in real time and provide a care plan that is appropriate to the user's emotional state.

[0726] A "sensor device" is a device attached to a pet to continuously acquire biological information, and its role is to measure activity level, heart rate, location information, and so on.

[0727] "Wearable data acquisition means" refers to a means for acquiring biological information through a sensor device attached to a pet and using it for subsequent data processing.

[0728] "Encrypted data communication means" refers to a method that uses data encryption technology to securely transfer biometric information collected from sensor devices to a server.

[0729] A "data processing device" is a computer system installed to analyze received biological information, and it is used to detect abnormalities in health conditions and predict risks.

[0730] "Analysis methods using machine learning" are methods installed in data processing devices that learn past data patterns and detect anomalies by comparing them with current biological information.

[0731] "Emotional analysis means" refers to a method that uses technology to acquire and analyze the user's emotional state and reflect the results in pet care.

[0732] A "notification generation mechanism" is a system function that generates and provides notifications to users based on analysis results, prompting them to provide appropriate information or take action.

[0733] "User interface means" refers to an interface that visually displays the analysis results of a data processing device, allowing the user to confirm the information.

[0734] A "predictive model" is an algorithm that uses received biometric information and user sentiment analysis results to predict future health risks and user behavior.

[0735] This invention uses multiple hardware and software components to monitor the health status of a pet in real time and to provide care that is tailored to the user's emotional state.

[0736] The device collects biometric information using a sensor device attached to the pet. The sensor device is equipped with sensors that measure heart rate and activity level, thereby continuously acquiring biometric data from the pet. The collected data is temporarily stored in the device and compiled at regular intervals.

[0737] The device uses the AES encryption algorithm to encrypt the collected data. This encryption ensures confidentiality during data transmission. The encrypted data is sent to the server via the HTTPS protocol. This process ensures data security.

[0738] The server decrypts the received data and begins analysis. The analysis utilizes machine learning, employing an AI data analysis module based on TensorFlow. It compares the current data with historical data to detect anomalies and predict health risks.

[0739] The server is also equipped with an emotion engine to analyze user emotions. This engine analyzes voice and input data received from the user's terminal to determine the user's emotional state. Natural language processing technology is used for this analysis, allowing for an accurate understanding of the user's psychological state.

[0740] The server generates and sends notifications to the user based on the analysis results. A generative AI model is used to automatically create messages appropriate to the user's emotional state. For example, by entering a question as a prompt, such as "My pet's heart rate is higher than normal, but is this normal because we are out for a walk?", the AI ​​helps to make an appropriate decision.

[0741] Users receive this information through the application. Developed with React Native, the app visually displays pet health status in graphs and alerts, allowing users to take quick action. It also includes a feature to contact a veterinarian with a single tap in emergencies.

[0742] In this way, the present invention realizes a system that provides optimal care for both pets and users and efficiently supports health management.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The device receives biometric information as input from sensor devices attached to the pet. Here, data such as heart rate, activity level, and location information are acquired by the sensors. This information is sampled at regular intervals and temporarily stored in the device's internal memory. Specifically, for example, the heart rate sensor reads data every second and calculates the average value.

[0746] Step 2:

[0747] The device performs the process of encrypting the collected biometric information. The biometric information collected in step 1 is used as input. Data security is enhanced by encrypting the data using the AES encryption algorithm. The encrypted data is prepared as output and then ready for transmission. Specifically, an encryption module inside the device converts the data.

[0748] Step 3:

[0749] The terminal sends encrypted data to the server. The input here is encrypted biometric information. Data is securely transferred via the HTTPS protocol. The output is the receipt of data to the server. Specifically, the terminal's communication module becomes active and sends a request to the server over the internet.

[0750] Step 4:

[0751] The server decrypts the data received from the terminal and analyzes the biometric information. Using the decrypted input data, it performs calculations using machine learning to detect abnormal patterns and health risks. The output is the analysis results regarding the pet's health status. Specifically, the TensorFlow module runs on the server to identify abnormal data.

[0752] Step 5:

[0753] The server performs a process to analyze the user's emotions. Voice data and input speed from the user's terminal are used as input. Based on this data, the emotion analysis engine performs calculations to evaluate the psychological state and identify the emotional state. The output is the user's current emotional state. Specifically, a natural language processing algorithm analyzes the user input.

[0754] Step 6:

[0755] The server generates a notification based on the analysis results and sends it to the user. It then provides the generated notification message to the user as output. At this stage, a generation AI model is used to generate a message appropriate for the user. Specifically, the AI ​​selects and customizes a message template based on the input data.

[0756] Step 7:

[0757] The user receives notifications and analysis results from the server through the application. The input here is notification data from the server. The application visually displays this information and shows the user what specific action they should take. The output is an interface that allows the user to obtain information and take action. Specifically, the application, built with React Native, renders the data on the screen and allows the user to interact with UI elements as needed.

[0758] (Application Example 2)

[0759] 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".

[0760] There is a need to effectively manage pets' biometric information and provide an environment where pet owners can entrust their pets with peace of mind. However, conventional methods have not provided a comprehensive system that can monitor pets' conditions in real time and reduce the psychological burden on pet owners.

[0761] 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.

[0762] In this invention, the server includes a portable information acquisition device means equipped with a sensor mechanism for collecting biological information, a communication mechanism means for immediately transmitting the collected biological information to a remote information processing system, and an information processing tool means having an analysis system for analyzing the received biological information and detecting abnormalities. This makes it possible to monitor the health status of pets in real time and respond immediately if an abnormality is detected, thereby providing an environment where pets can be left with peace of mind.

[0763] "Biometric information" refers to data obtained from a pet's body, such as activity level, heart rate, and location information.

[0764] A "sensor mechanism" is a device used to physically measure a pet's biological information.

[0765] A "portable information acquisition device" is a device that can be attached to a pet and continuously acquires biological information even while it is in motion.

[0766] A "communication mechanism" is a technical means for instantly transmitting acquired biometric information to a remote location.

[0767] An "information processing system" is a computer system that analyzes received biological information and determines whether or not there are any abnormalities.

[0768] An "analysis system" is software or hardware used within an information processing system to analyze biological information and detect abnormal patterns.

[0769] "Information processing equipment" refers to devices and software used to analyze biological information and output the analysis results.

[0770] A "notification generation mechanism" is a means of informing users of important information based on analysis results.

[0771] "Emotional analysis function" is a technology used to understand the psychological state of a user.

[0772] A "user interface system" is an interface that visually displays data processing results and enables the exchange of information between the user and the system.

[0773] In this invention, the following system is configured to effectively manage the biometric information of pets. A terminal continuously collects biometric information such as activity level, heart rate, and location information using a sensor mechanism attached to the pet. This information is transmitted to a server in real time via a portable information acquisition device. The server receives the collected biometric information through a communication mechanism, analyzes it using an information processing system, and determines whether or not there are any abnormalities.

[0774] The information processing system incorporates an analysis system that analyzes biometric information to detect abnormal patterns. Based on these analysis results, a notification generation mechanism informs the user of important information. The emotion analysis function is used to understand the user's psychological state, enabling the suggestion of appropriate care.

[0775] The user interface system visually displays the analysis results, allowing users to check the animal's health status and take appropriate action. For example, if a pet's stress level rises while it is in the salon, the server can use the recognized data to notify the owner of recommended relaxation methods. An example of a prompt using the generative AI model is: "Generate a message suggesting ways to help the owner relax their pet based on its current stress level."

[0776] In this way, the invention becomes a system that manages the health status of pets in real time, contributing to pet owners being able to entrust their pets to care facilities with peace of mind.

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The device acquires biometric information such as activity level, heart rate, and location from a sensor mechanism attached to the pet. This data is directly input from the sensor mechanism's sensors and sent to the device. The device then prepares to collect and store this data.

[0780] Step 2:

[0781] The device transmits collected biometric information to the server in real time. The input is biometric data, and the output is an encrypted data stream. Data is transferred using Bluetooth or Wi-Fi communication and delivered to the server via a secure protocol.

[0782] Step 3:

[0783] The server inputs biometric information received from the terminal into the analysis system. The information processing system uses machine learning algorithms to detect abnormal patterns. The input is the received biometric data, and the output is the analysis result indicating whether or not an abnormality is present. If an abnormality is detected, related detailed information is also generated.

[0784] Step 4:

[0785] The server generates a notification based on the analysis results and sends it to the user's terminal. In this process, the notification generation mechanism assembles information from the input analysis results and forms a notification message as output. The notification includes important information and recommended actions regarding the pet's health status.

[0786] Step 5:

[0787] The user views the analysis results through the terminal's user interface. Input is notification messages from the server, and output is visualized data. The user interface system displays this data in a viewable format, assisting the user in taking necessary actions.

[0788] Step 6:

[0789] The server uses sentiment analysis to determine the user's psychological state and optimize the notification content. Here, user feedback and historical data are used as input, and a generative AI model is used to create prompt sentences, resulting in the output of messages that include personalized advice and recommendations.

[0790] 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.

[0791] 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.

[0792] 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 robot 414.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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 as being incorporated by reference.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] A wearable data acquisition means equipped with a sensor device for collecting biometric information of pets,

[0814] A data communication means for transferring accumulated biological information to a remote data processing device in real time,

[0815] A data processing device having analytical means for analyzing received biological information and detecting abnormalities,

[0816] Based on the analysis results, a notification generation means for outputting the pet's health status,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, which includes a predictive model for predicting the health risks of a pet based on received biometric information in a data processing device.

[0820] (Claim 3)

[0821] The system according to claim 1, having a user interface means for visualizing the analysis results of a data processing device, including a display terminal.

[0822] "Example 1"

[0823] (Claim 1)

[0824] Activity monitoring means for continuously measuring and storing biological information of animals,

[0825] A data transmission means that transmits stored information to a processing device via secure communication means under certain conditions,

[0826] An analysis device means that analyzes the received information and evaluates abnormal patterns using a machine learning model,

[0827] A notification system for generating alarms based on the risks obtained from the analysis and for providing visual and electronic notifications,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising a processing unit containing a predictive algorithm for estimating the health risks of animals based on analyzed data and evaluating the results.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising a human interaction means having a display device for visualizing and displaying analysis results from a processing device to a user.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A wearable data acquisition means equipped with a sensor device for collecting biometric information of pets,

[0836] A data communication means for transferring accumulated biological information to a remote data processing device in real time,

[0837] A data processing device having analytical means for analyzing received biological information and detecting abnormalities,

[0838] A notification generation means for outputting the health status and safety of the pet based on the analysis results,

[0839] External interlocking means for coordinating with the environmental control system when an anomaly is detected,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which includes a predictive model in a data processing device for predicting the health risks of a pet based on received biometric information and for proposing adjustments to the external environment.

[0843] (Claim 3)

[0844] The system according to claim 1, having a user interface means for visualizing the analysis results of a data processing device and the status of its interaction with the external environment, including a display terminal.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] A wearable data acquisition means equipped with a sensor device for collecting biometric information of pets,

[0848] An encrypted data communication means for transferring collected biometric information to a remote data processing device in real time,

[0849] A data processing device having an analysis means that uses machine learning to analyze received biological information and detect abnormalities,

[0850] An emotional analysis method for analyzing the user's emotional state and psychologically optimizing notifications regarding the pet's health status,

[0851] A notification generation means for outputting the pet's health status based on the analysis results and suggesting appropriate responses to the user,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, which includes a predictive model in the data processing device for predicting the health risks of a pet and providing advice appropriate to the user's psychological state, based on the received biometric information and the results of user emotion analysis.

[0855] (Claim 3)

[0856] The system according to claim 1, having a user interface means for visualizing information generated based on the analysis results of a data processing device and sentiment analysis, with a display terminal for visualizing the information.

[0857] "Application example 2 of combining emotional engines"

[0858] (Claim 1)

[0859] A portable information acquisition device equipped with a sensor mechanism for collecting biological information,

[0860] A communication mechanism means for immediately transmitting collected biological information to a remote information processing system,

[0861] An information processing device having an analysis system for analyzing received biological information and detecting abnormalities,

[0862] Based on the analysis results, a notification generation mechanism means for outputting the health status of the living organism,

[0863] A means for optimizing output information using a system that has an emotion analysis function and analyzes the user's psychological state,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which includes a predictive algorithm for predicting the health risks of a living organism based on received biological information in an information processing device.

[0867] (Claim 3)

[0868] The system according to claim 1, a user interface system having a display device for visualizing the analysis results of an information processing device. [Explanation of Symbols]

[0869] 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 data acquisition means equipped with a sensor device for collecting biometric information of pets, A data communication means for transferring accumulated biological information to a remote data processing device in real time, A data processing device having analytical means for analyzing received biological information and detecting abnormalities, Based on the analysis results, a notification generation means for outputting the pet's health status, A system that includes this.

2. The system according to claim 1, which includes a predictive model for predicting the health risks of a pet based on received biometric information in a data processing device.

3. The system according to claim 1, having a user interface means for visualizing the analysis results of a data processing device, including a display terminal.

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